hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ccde24498317c6fcb97fefd7969f39d4dd246514
| 165
|
py
|
Python
|
06/01/LocaleHTMLCalendar/calendar.LocaleHTMLCalendar.py
|
pylangstudy/201709
|
53d868786d7327a83bfa7f4149549c6f9855a6c6
|
[
"CC0-1.0"
] | null | null | null |
06/01/LocaleHTMLCalendar/calendar.LocaleHTMLCalendar.py
|
pylangstudy/201709
|
53d868786d7327a83bfa7f4149549c6f9855a6c6
|
[
"CC0-1.0"
] | 32
|
2017-09-01T00:52:17.000Z
|
2017-10-01T00:30:02.000Z
|
06/01/LocaleHTMLCalendar/calendar.LocaleHTMLCalendar.py
|
pylangstudy/201709
|
53d868786d7327a83bfa7f4149549c6f9855a6c6
|
[
"CC0-1.0"
] | null | null | null |
import calendar
print(calendar.LocaleHTMLCalendar())
print(calendar.LocaleHTMLCalendar(firstweekday=0))#月曜日
print(calendar.LocaleHTMLCalendar(firstweekday=6))#日曜日
| 23.571429
| 54
| 0.842424
| 17
| 165
| 8.176471
| 0.529412
| 0.280576
| 0.669065
| 0.618705
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012658
| 0.042424
| 165
| 6
| 55
| 27.5
| 0.867089
| 0.036364
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.25
| 0
| 0.25
| 0.75
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
690f416a0aa88169f4795760415e6a9d4bff8a93
| 193,542
|
py
|
Python
|
google/ads/google_ads/v6/proto/errors/errors_pb2.py
|
jphanwebstaurant/google-ads-python
|
600812b2afcc4d57f00b47dfe436620ce50bfe9b
|
[
"Apache-2.0"
] | 1
|
2021-04-09T04:28:47.000Z
|
2021-04-09T04:28:47.000Z
|
google/ads/google_ads/v6/proto/errors/errors_pb2.py
|
jphanwebstaurant/google-ads-python
|
600812b2afcc4d57f00b47dfe436620ce50bfe9b
|
[
"Apache-2.0"
] | null | null | null |
google/ads/google_ads/v6/proto/errors/errors_pb2.py
|
jphanwebstaurant/google-ads-python
|
600812b2afcc4d57f00b47dfe436620ce50bfe9b
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/ads/googleads_v6/proto/errors/errors.proto
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from google.ads.google_ads.v6.proto.common import policy_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_policy__pb2
from google.ads.google_ads.v6.proto.common import value_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_value__pb2
from google.ads.google_ads.v6.proto.errors import access_invitation_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_access__invitation__error__pb2
from google.ads.google_ads.v6.proto.errors import account_budget_proposal_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_account__budget__proposal__error__pb2
from google.ads.google_ads.v6.proto.errors import account_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_account__link__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_customizer_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__customizer__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_group_ad_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__ad__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_group_bid_modifier_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__bid__modifier__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_group_criterion_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__criterion__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_group_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_group_feed_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__feed__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_parameter_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__parameter__error__pb2
from google.ads.google_ads.v6.proto.errors import ad_sharing_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__sharing__error__pb2
from google.ads.google_ads.v6.proto.errors import adx_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_adx__error__pb2
from google.ads.google_ads.v6.proto.errors import asset_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_asset__error__pb2
from google.ads.google_ads.v6.proto.errors import asset_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_asset__link__error__pb2
from google.ads.google_ads.v6.proto.errors import authentication_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_authentication__error__pb2
from google.ads.google_ads.v6.proto.errors import authorization_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_authorization__error__pb2
from google.ads.google_ads.v6.proto.errors import batch_job_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_batch__job__error__pb2
from google.ads.google_ads.v6.proto.errors import bidding_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_bidding__error__pb2
from google.ads.google_ads.v6.proto.errors import bidding_strategy_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_bidding__strategy__error__pb2
from google.ads.google_ads.v6.proto.errors import billing_setup_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_billing__setup__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_budget_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__budget__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_criterion_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__criterion__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_draft_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__draft__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_experiment_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__experiment__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_feed_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__feed__error__pb2
from google.ads.google_ads.v6.proto.errors import campaign_shared_set_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__shared__set__error__pb2
from google.ads.google_ads.v6.proto.errors import change_event_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_change__event__error__pb2
from google.ads.google_ads.v6.proto.errors import change_status_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_change__status__error__pb2
from google.ads.google_ads.v6.proto.errors import collection_size_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_collection__size__error__pb2
from google.ads.google_ads.v6.proto.errors import context_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_context__error__pb2
from google.ads.google_ads.v6.proto.errors import conversion_action_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__action__error__pb2
from google.ads.google_ads.v6.proto.errors import conversion_adjustment_upload_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__adjustment__upload__error__pb2
from google.ads.google_ads.v6.proto.errors import conversion_upload_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__upload__error__pb2
from google.ads.google_ads.v6.proto.errors import country_code_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_country__code__error__pb2
from google.ads.google_ads.v6.proto.errors import criterion_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_criterion__error__pb2
from google.ads.google_ads.v6.proto.errors import currency_code_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_currency__code__error__pb2
from google.ads.google_ads.v6.proto.errors import custom_audience_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_custom__audience__error__pb2
from google.ads.google_ads.v6.proto.errors import custom_interest_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_custom__interest__error__pb2
from google.ads.google_ads.v6.proto.errors import customer_client_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__client__link__error__pb2
from google.ads.google_ads.v6.proto.errors import customer_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__error__pb2
from google.ads.google_ads.v6.proto.errors import customer_feed_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__feed__error__pb2
from google.ads.google_ads.v6.proto.errors import customer_manager_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__manager__link__error__pb2
from google.ads.google_ads.v6.proto.errors import customer_user_access_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__user__access__error__pb2
from google.ads.google_ads.v6.proto.errors import database_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_database__error__pb2
from google.ads.google_ads.v6.proto.errors import date_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_date__error__pb2
from google.ads.google_ads.v6.proto.errors import date_range_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_date__range__error__pb2
from google.ads.google_ads.v6.proto.errors import distinct_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_distinct__error__pb2
from google.ads.google_ads.v6.proto.errors import enum_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_enum__error__pb2
from google.ads.google_ads.v6.proto.errors import extension_feed_item_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_extension__feed__item__error__pb2
from google.ads.google_ads.v6.proto.errors import extension_setting_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_extension__setting__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_attribute_reference_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__attribute__reference__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_item_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_item_set_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__set__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_item_set_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__set__link__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_item_target_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__target__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_item_validation_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__validation__error__pb2
from google.ads.google_ads.v6.proto.errors import feed_mapping_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__mapping__error__pb2
from google.ads.google_ads.v6.proto.errors import field_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_field__error__pb2
from google.ads.google_ads.v6.proto.errors import field_mask_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_field__mask__error__pb2
from google.ads.google_ads.v6.proto.errors import function_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_function__error__pb2
from google.ads.google_ads.v6.proto.errors import function_parsing_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_function__parsing__error__pb2
from google.ads.google_ads.v6.proto.errors import geo_target_constant_suggestion_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_geo__target__constant__suggestion__error__pb2
from google.ads.google_ads.v6.proto.errors import header_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_header__error__pb2
from google.ads.google_ads.v6.proto.errors import id_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_id__error__pb2
from google.ads.google_ads.v6.proto.errors import image_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_image__error__pb2
from google.ads.google_ads.v6.proto.errors import internal_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_internal__error__pb2
from google.ads.google_ads.v6.proto.errors import invoice_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_invoice__error__pb2
from google.ads.google_ads.v6.proto.errors import keyword_plan_ad_group_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__ad__group__error__pb2
from google.ads.google_ads.v6.proto.errors import keyword_plan_ad_group_keyword_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__ad__group__keyword__error__pb2
from google.ads.google_ads.v6.proto.errors import keyword_plan_campaign_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__campaign__error__pb2
from google.ads.google_ads.v6.proto.errors import keyword_plan_campaign_keyword_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__campaign__keyword__error__pb2
from google.ads.google_ads.v6.proto.errors import keyword_plan_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__error__pb2
from google.ads.google_ads.v6.proto.errors import keyword_plan_idea_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__idea__error__pb2
from google.ads.google_ads.v6.proto.errors import label_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_label__error__pb2
from google.ads.google_ads.v6.proto.errors import language_code_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_language__code__error__pb2
from google.ads.google_ads.v6.proto.errors import list_operation_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_list__operation__error__pb2
from google.ads.google_ads.v6.proto.errors import manager_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_manager__link__error__pb2
from google.ads.google_ads.v6.proto.errors import media_bundle_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_media__bundle__error__pb2
from google.ads.google_ads.v6.proto.errors import media_file_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_media__file__error__pb2
from google.ads.google_ads.v6.proto.errors import media_upload_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_media__upload__error__pb2
from google.ads.google_ads.v6.proto.errors import multiplier_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_multiplier__error__pb2
from google.ads.google_ads.v6.proto.errors import mutate_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_mutate__error__pb2
from google.ads.google_ads.v6.proto.errors import new_resource_creation_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_new__resource__creation__error__pb2
from google.ads.google_ads.v6.proto.errors import not_allowlisted_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_not__allowlisted__error__pb2
from google.ads.google_ads.v6.proto.errors import not_empty_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_not__empty__error__pb2
from google.ads.google_ads.v6.proto.errors import null_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_null__error__pb2
from google.ads.google_ads.v6.proto.errors import offline_user_data_job_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_offline__user__data__job__error__pb2
from google.ads.google_ads.v6.proto.errors import operation_access_denied_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_operation__access__denied__error__pb2
from google.ads.google_ads.v6.proto.errors import operator_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_operator__error__pb2
from google.ads.google_ads.v6.proto.errors import partial_failure_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_partial__failure__error__pb2
from google.ads.google_ads.v6.proto.errors import payments_account_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_payments__account__error__pb2
from google.ads.google_ads.v6.proto.errors import policy_finding_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_policy__finding__error__pb2
from google.ads.google_ads.v6.proto.errors import policy_validation_parameter_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_policy__validation__parameter__error__pb2
from google.ads.google_ads.v6.proto.errors import policy_violation_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_policy__violation__error__pb2
from google.ads.google_ads.v6.proto.errors import query_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_query__error__pb2
from google.ads.google_ads.v6.proto.errors import quota_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_quota__error__pb2
from google.ads.google_ads.v6.proto.errors import range_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_range__error__pb2
from google.ads.google_ads.v6.proto.errors import reach_plan_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_reach__plan__error__pb2
from google.ads.google_ads.v6.proto.errors import recommendation_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_recommendation__error__pb2
from google.ads.google_ads.v6.proto.errors import region_code_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_region__code__error__pb2
from google.ads.google_ads.v6.proto.errors import request_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_request__error__pb2
from google.ads.google_ads.v6.proto.errors import resource_access_denied_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_resource__access__denied__error__pb2
from google.ads.google_ads.v6.proto.errors import resource_count_limit_exceeded_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_resource__count__limit__exceeded__error__pb2
from google.ads.google_ads.v6.proto.errors import setting_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_setting__error__pb2
from google.ads.google_ads.v6.proto.errors import shared_criterion_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_shared__criterion__error__pb2
from google.ads.google_ads.v6.proto.errors import shared_set_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_shared__set__error__pb2
from google.ads.google_ads.v6.proto.errors import size_limit_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_size__limit__error__pb2
from google.ads.google_ads.v6.proto.errors import string_format_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_string__format__error__pb2
from google.ads.google_ads.v6.proto.errors import string_length_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_string__length__error__pb2
from google.ads.google_ads.v6.proto.errors import third_party_app_analytics_link_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_third__party__app__analytics__link__error__pb2
from google.ads.google_ads.v6.proto.errors import time_zone_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_time__zone__error__pb2
from google.ads.google_ads.v6.proto.errors import url_field_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_url__field__error__pb2
from google.ads.google_ads.v6.proto.errors import user_data_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_user__data__error__pb2
from google.ads.google_ads.v6.proto.errors import user_list_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_user__list__error__pb2
from google.ads.google_ads.v6.proto.errors import youtube_video_registration_error_pb2 as google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_youtube__video__registration__error__pb2
from google.protobuf import duration_pb2 as google_dot_protobuf_dot_duration__pb2
from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='google/ads/googleads_v6/proto/errors/errors.proto',
package='google.ads.googleads.v6.errors',
syntax='proto3',
serialized_options=b'\n\"com.google.ads.googleads.v6.errorsB\013ErrorsProtoP\001ZDgoogle.golang.org/genproto/googleapis/ads/googleads/v6/errors;errors\242\002\003GAA\252\002\036Google.Ads.GoogleAds.V6.Errors\312\002\036Google\\Ads\\GoogleAds\\V6\\Errors\352\002\"Google::Ads::GoogleAds::V6::Errors',
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n1google/ads/googleads_v6/proto/errors/errors.proto\x12\x1egoogle.ads.googleads.v6.errors\x1a\x31google/ads/googleads_v6/proto/common/policy.proto\x1a\x30google/ads/googleads_v6/proto/common/value.proto\x1a\x42google/ads/googleads_v6/proto/errors/access_invitation_error.proto\x1aHgoogle/ads/googleads_v6/proto/errors/account_budget_proposal_error.proto\x1a=google/ads/googleads_v6/proto/errors/account_link_error.proto\x1a>google/ads/googleads_v6/proto/errors/ad_customizer_error.proto\x1a\x33google/ads/googleads_v6/proto/errors/ad_error.proto\x1a<google/ads/googleads_v6/proto/errors/ad_group_ad_error.proto\x1a\x46google/ads/googleads_v6/proto/errors/ad_group_bid_modifier_error.proto\x1a\x43google/ads/googleads_v6/proto/errors/ad_group_criterion_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/ad_group_error.proto\x1a>google/ads/googleads_v6/proto/errors/ad_group_feed_error.proto\x1a=google/ads/googleads_v6/proto/errors/ad_parameter_error.proto\x1a;google/ads/googleads_v6/proto/errors/ad_sharing_error.proto\x1a\x34google/ads/googleads_v6/proto/errors/adx_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/asset_error.proto\x1a;google/ads/googleads_v6/proto/errors/asset_link_error.proto\x1a?google/ads/googleads_v6/proto/errors/authentication_error.proto\x1a>google/ads/googleads_v6/proto/errors/authorization_error.proto\x1a:google/ads/googleads_v6/proto/errors/batch_job_error.proto\x1a\x38google/ads/googleads_v6/proto/errors/bidding_error.proto\x1a\x41google/ads/googleads_v6/proto/errors/bidding_strategy_error.proto\x1a>google/ads/googleads_v6/proto/errors/billing_setup_error.proto\x1a@google/ads/googleads_v6/proto/errors/campaign_budget_error.proto\x1a\x43google/ads/googleads_v6/proto/errors/campaign_criterion_error.proto\x1a?google/ads/googleads_v6/proto/errors/campaign_draft_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/campaign_error.proto\x1a\x44google/ads/googleads_v6/proto/errors/campaign_experiment_error.proto\x1a>google/ads/googleads_v6/proto/errors/campaign_feed_error.proto\x1a\x44google/ads/googleads_v6/proto/errors/campaign_shared_set_error.proto\x1a=google/ads/googleads_v6/proto/errors/change_event_error.proto\x1a>google/ads/googleads_v6/proto/errors/change_status_error.proto\x1a@google/ads/googleads_v6/proto/errors/collection_size_error.proto\x1a\x38google/ads/googleads_v6/proto/errors/context_error.proto\x1a\x42google/ads/googleads_v6/proto/errors/conversion_action_error.proto\x1aMgoogle/ads/googleads_v6/proto/errors/conversion_adjustment_upload_error.proto\x1a\x42google/ads/googleads_v6/proto/errors/conversion_upload_error.proto\x1a=google/ads/googleads_v6/proto/errors/country_code_error.proto\x1a:google/ads/googleads_v6/proto/errors/criterion_error.proto\x1a>google/ads/googleads_v6/proto/errors/currency_code_error.proto\x1a@google/ads/googleads_v6/proto/errors/custom_audience_error.proto\x1a@google/ads/googleads_v6/proto/errors/custom_interest_error.proto\x1a\x45google/ads/googleads_v6/proto/errors/customer_client_link_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/customer_error.proto\x1a>google/ads/googleads_v6/proto/errors/customer_feed_error.proto\x1a\x46google/ads/googleads_v6/proto/errors/customer_manager_link_error.proto\x1a\x45google/ads/googleads_v6/proto/errors/customer_user_access_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/database_error.proto\x1a\x35google/ads/googleads_v6/proto/errors/date_error.proto\x1a;google/ads/googleads_v6/proto/errors/date_range_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/distinct_error.proto\x1a\x35google/ads/googleads_v6/proto/errors/enum_error.proto\x1a\x44google/ads/googleads_v6/proto/errors/extension_feed_item_error.proto\x1a\x42google/ads/googleads_v6/proto/errors/extension_setting_error.proto\x1aIgoogle/ads/googleads_v6/proto/errors/feed_attribute_reference_error.proto\x1a\x35google/ads/googleads_v6/proto/errors/feed_error.proto\x1a:google/ads/googleads_v6/proto/errors/feed_item_error.proto\x1a>google/ads/googleads_v6/proto/errors/feed_item_set_error.proto\x1a\x43google/ads/googleads_v6/proto/errors/feed_item_set_link_error.proto\x1a\x41google/ads/googleads_v6/proto/errors/feed_item_target_error.proto\x1a\x45google/ads/googleads_v6/proto/errors/feed_item_validation_error.proto\x1a=google/ads/googleads_v6/proto/errors/feed_mapping_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/field_error.proto\x1a;google/ads/googleads_v6/proto/errors/field_mask_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/function_error.proto\x1a\x41google/ads/googleads_v6/proto/errors/function_parsing_error.proto\x1aOgoogle/ads/googleads_v6/proto/errors/geo_target_constant_suggestion_error.proto\x1a\x37google/ads/googleads_v6/proto/errors/header_error.proto\x1a\x33google/ads/googleads_v6/proto/errors/id_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/image_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/internal_error.proto\x1a\x38google/ads/googleads_v6/proto/errors/invoice_error.proto\x1a\x46google/ads/googleads_v6/proto/errors/keyword_plan_ad_group_error.proto\x1aNgoogle/ads/googleads_v6/proto/errors/keyword_plan_ad_group_keyword_error.proto\x1a\x46google/ads/googleads_v6/proto/errors/keyword_plan_campaign_error.proto\x1aNgoogle/ads/googleads_v6/proto/errors/keyword_plan_campaign_keyword_error.proto\x1a=google/ads/googleads_v6/proto/errors/keyword_plan_error.proto\x1a\x42google/ads/googleads_v6/proto/errors/keyword_plan_idea_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/label_error.proto\x1a>google/ads/googleads_v6/proto/errors/language_code_error.proto\x1a?google/ads/googleads_v6/proto/errors/list_operation_error.proto\x1a=google/ads/googleads_v6/proto/errors/manager_link_error.proto\x1a=google/ads/googleads_v6/proto/errors/media_bundle_error.proto\x1a;google/ads/googleads_v6/proto/errors/media_file_error.proto\x1a=google/ads/googleads_v6/proto/errors/media_upload_error.proto\x1a;google/ads/googleads_v6/proto/errors/multiplier_error.proto\x1a\x37google/ads/googleads_v6/proto/errors/mutate_error.proto\x1a\x46google/ads/googleads_v6/proto/errors/new_resource_creation_error.proto\x1a@google/ads/googleads_v6/proto/errors/not_allowlisted_error.proto\x1a:google/ads/googleads_v6/proto/errors/not_empty_error.proto\x1a\x35google/ads/googleads_v6/proto/errors/null_error.proto\x1a\x46google/ads/googleads_v6/proto/errors/offline_user_data_job_error.proto\x1aHgoogle/ads/googleads_v6/proto/errors/operation_access_denied_error.proto\x1a\x39google/ads/googleads_v6/proto/errors/operator_error.proto\x1a@google/ads/googleads_v6/proto/errors/partial_failure_error.proto\x1a\x41google/ads/googleads_v6/proto/errors/payments_account_error.proto\x1a?google/ads/googleads_v6/proto/errors/policy_finding_error.proto\x1aLgoogle/ads/googleads_v6/proto/errors/policy_validation_parameter_error.proto\x1a\x41google/ads/googleads_v6/proto/errors/policy_violation_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/query_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/quota_error.proto\x1a\x36google/ads/googleads_v6/proto/errors/range_error.proto\x1a;google/ads/googleads_v6/proto/errors/reach_plan_error.proto\x1a?google/ads/googleads_v6/proto/errors/recommendation_error.proto\x1a<google/ads/googleads_v6/proto/errors/region_code_error.proto\x1a\x38google/ads/googleads_v6/proto/errors/request_error.proto\x1aGgoogle/ads/googleads_v6/proto/errors/resource_access_denied_error.proto\x1aNgoogle/ads/googleads_v6/proto/errors/resource_count_limit_exceeded_error.proto\x1a\x38google/ads/googleads_v6/proto/errors/setting_error.proto\x1a\x41google/ads/googleads_v6/proto/errors/shared_criterion_error.proto\x1a;google/ads/googleads_v6/proto/errors/shared_set_error.proto\x1a;google/ads/googleads_v6/proto/errors/size_limit_error.proto\x1a>google/ads/googleads_v6/proto/errors/string_format_error.proto\x1a>google/ads/googleads_v6/proto/errors/string_length_error.proto\x1aOgoogle/ads/googleads_v6/proto/errors/third_party_app_analytics_link_error.proto\x1a:google/ads/googleads_v6/proto/errors/time_zone_error.proto\x1a:google/ads/googleads_v6/proto/errors/url_field_error.proto\x1a:google/ads/googleads_v6/proto/errors/user_data_error.proto\x1a:google/ads/googleads_v6/proto/errors/user_list_error.proto\x1aKgoogle/ads/googleads_v6/proto/errors/youtube_video_registration_error.proto\x1a\x1egoogle/protobuf/duration.proto\x1a\x1cgoogle/api/annotations.proto\"R\n\x10GoogleAdsFailure\x12>\n\x06\x65rrors\x18\x01 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dependencies=[google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_policy__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_value__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_access__invitation__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_account__budget__proposal__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_account__link__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__customizer__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__ad__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__bid__modifier__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__criterion__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__feed__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__parameter__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__sharing__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_adx__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_asset__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_asset__link__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_authentication__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_authorization__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_batch__job__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_bidding__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_bidding__strategy__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_billing__setup__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__budget__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__criterion__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__draft__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__experiment__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__feed__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__shared__set__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_change__event__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_change__status__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_collection__size__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_context__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__action__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__adjustment__upload__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__upload__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_country__code__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_criterion__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_currency__code__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_custom__audience__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_custom__interest__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__client__link__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__feed__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__manager__link__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__user__access__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_database__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_date__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_date__range__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_distinct__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_enum__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_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ads_dot_googleads__v6_dot_proto_dot_errors_dot_region__code__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_request__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_resource__access__denied__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_resource__count__limit__exceeded__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_setting__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_shared__criterion__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_shared__set__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_size__limit__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_string__format__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_string__length__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_third__party__app__analytics__link__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_time__zone__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_url__field__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_user__data__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_user__list__error__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_youtube__video__registration__error__pb2.DESCRIPTOR,google_dot_protobuf_dot_duration__pb2.DESCRIPTOR,google_dot_api_dot_annotations__pb2.DESCRIPTOR,])
_QUOTAERRORDETAILS_QUOTARATESCOPE = _descriptor.EnumDescriptor(
name='QuotaRateScope',
full_name='google.ads.googleads.v6.errors.QuotaErrorDetails.QuotaRateScope',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='UNSPECIFIED', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='UNKNOWN', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='ACCOUNT', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='DEVELOPER', index=3, number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=21587,
serialized_end=21661,
)
_sym_db.RegisterEnumDescriptor(_QUOTAERRORDETAILS_QUOTARATESCOPE)
_GOOGLEADSFAILURE = _descriptor.Descriptor(
name='GoogleAdsFailure',
full_name='google.ads.googleads.v6.errors.GoogleAdsFailure',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='errors', full_name='google.ads.googleads.v6.errors.GoogleAdsFailure.errors', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=7841,
serialized_end=7923,
)
_GOOGLEADSERROR = _descriptor.Descriptor(
name='GoogleAdsError',
full_name='google.ads.googleads.v6.errors.GoogleAdsError',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='error_code', full_name='google.ads.googleads.v6.errors.GoogleAdsError.error_code', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='message', full_name='google.ads.googleads.v6.errors.GoogleAdsError.message', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger', full_name='google.ads.googleads.v6.errors.GoogleAdsError.trigger', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='location', full_name='google.ads.googleads.v6.errors.GoogleAdsError.location', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='details', full_name='google.ads.googleads.v6.errors.GoogleAdsError.details', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=7926,
serialized_end=8206,
)
_ERRORCODE = _descriptor.Descriptor(
name='ErrorCode',
full_name='google.ads.googleads.v6.errors.ErrorCode',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='request_error', full_name='google.ads.googleads.v6.errors.ErrorCode.request_error', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='bidding_strategy_error', full_name='google.ads.googleads.v6.errors.ErrorCode.bidding_strategy_error', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='url_field_error', full_name='google.ads.googleads.v6.errors.ErrorCode.url_field_error', index=2,
number=3, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='list_operation_error', full_name='google.ads.googleads.v6.errors.ErrorCode.list_operation_error', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='query_error', full_name='google.ads.googleads.v6.errors.ErrorCode.query_error', index=4,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='mutate_error', full_name='google.ads.googleads.v6.errors.ErrorCode.mutate_error', index=5,
number=7, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='field_mask_error', full_name='google.ads.googleads.v6.errors.ErrorCode.field_mask_error', index=6,
number=8, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='authorization_error', full_name='google.ads.googleads.v6.errors.ErrorCode.authorization_error', index=7,
number=9, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='internal_error', full_name='google.ads.googleads.v6.errors.ErrorCode.internal_error', index=8,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='quota_error', full_name='google.ads.googleads.v6.errors.ErrorCode.quota_error', index=9,
number=11, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_error', index=10,
number=12, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_group_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_group_error', index=11,
number=13, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_budget_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_budget_error', index=12,
number=14, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_error', index=13,
number=15, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='authentication_error', full_name='google.ads.googleads.v6.errors.ErrorCode.authentication_error', index=14,
number=17, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_group_criterion_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_group_criterion_error', index=15,
number=18, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_customizer_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_customizer_error', index=16,
number=19, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_group_ad_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_group_ad_error', index=17,
number=21, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_sharing_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_sharing_error', index=18,
number=24, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='adx_error', full_name='google.ads.googleads.v6.errors.ErrorCode.adx_error', index=19,
number=25, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='asset_error', full_name='google.ads.googleads.v6.errors.ErrorCode.asset_error', index=20,
number=107, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='bidding_error', full_name='google.ads.googleads.v6.errors.ErrorCode.bidding_error', index=21,
number=26, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_criterion_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_criterion_error', index=22,
number=29, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='collection_size_error', full_name='google.ads.googleads.v6.errors.ErrorCode.collection_size_error', index=23,
number=31, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='country_code_error', full_name='google.ads.googleads.v6.errors.ErrorCode.country_code_error', index=24,
number=109, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='criterion_error', full_name='google.ads.googleads.v6.errors.ErrorCode.criterion_error', index=25,
number=32, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='customer_error', full_name='google.ads.googleads.v6.errors.ErrorCode.customer_error', index=26,
number=90, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='date_error', full_name='google.ads.googleads.v6.errors.ErrorCode.date_error', index=27,
number=33, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='date_range_error', full_name='google.ads.googleads.v6.errors.ErrorCode.date_range_error', index=28,
number=34, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='distinct_error', full_name='google.ads.googleads.v6.errors.ErrorCode.distinct_error', index=29,
number=35, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_attribute_reference_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_attribute_reference_error', index=30,
number=36, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='function_error', full_name='google.ads.googleads.v6.errors.ErrorCode.function_error', index=31,
number=37, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='function_parsing_error', full_name='google.ads.googleads.v6.errors.ErrorCode.function_parsing_error', index=32,
number=38, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='id_error', full_name='google.ads.googleads.v6.errors.ErrorCode.id_error', index=33,
number=39, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='image_error', full_name='google.ads.googleads.v6.errors.ErrorCode.image_error', index=34,
number=40, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='language_code_error', full_name='google.ads.googleads.v6.errors.ErrorCode.language_code_error', index=35,
number=110, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='media_bundle_error', full_name='google.ads.googleads.v6.errors.ErrorCode.media_bundle_error', index=36,
number=42, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='media_upload_error', full_name='google.ads.googleads.v6.errors.ErrorCode.media_upload_error', index=37,
number=116, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='media_file_error', full_name='google.ads.googleads.v6.errors.ErrorCode.media_file_error', index=38,
number=86, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='multiplier_error', full_name='google.ads.googleads.v6.errors.ErrorCode.multiplier_error', index=39,
number=44, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='new_resource_creation_error', full_name='google.ads.googleads.v6.errors.ErrorCode.new_resource_creation_error', index=40,
number=45, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='not_empty_error', full_name='google.ads.googleads.v6.errors.ErrorCode.not_empty_error', index=41,
number=46, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='null_error', full_name='google.ads.googleads.v6.errors.ErrorCode.null_error', index=42,
number=47, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='operator_error', full_name='google.ads.googleads.v6.errors.ErrorCode.operator_error', index=43,
number=48, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='range_error', full_name='google.ads.googleads.v6.errors.ErrorCode.range_error', index=44,
number=49, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='recommendation_error', full_name='google.ads.googleads.v6.errors.ErrorCode.recommendation_error', index=45,
number=58, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='region_code_error', full_name='google.ads.googleads.v6.errors.ErrorCode.region_code_error', index=46,
number=51, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='setting_error', full_name='google.ads.googleads.v6.errors.ErrorCode.setting_error', index=47,
number=52, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='string_format_error', full_name='google.ads.googleads.v6.errors.ErrorCode.string_format_error', index=48,
number=53, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='string_length_error', full_name='google.ads.googleads.v6.errors.ErrorCode.string_length_error', index=49,
number=54, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='operation_access_denied_error', full_name='google.ads.googleads.v6.errors.ErrorCode.operation_access_denied_error', index=50,
number=55, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='resource_access_denied_error', full_name='google.ads.googleads.v6.errors.ErrorCode.resource_access_denied_error', index=51,
number=56, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='resource_count_limit_exceeded_error', full_name='google.ads.googleads.v6.errors.ErrorCode.resource_count_limit_exceeded_error', index=52,
number=57, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='youtube_video_registration_error', full_name='google.ads.googleads.v6.errors.ErrorCode.youtube_video_registration_error', index=53,
number=117, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_group_bid_modifier_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_group_bid_modifier_error', index=54,
number=59, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='context_error', full_name='google.ads.googleads.v6.errors.ErrorCode.context_error', index=55,
number=60, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='field_error', full_name='google.ads.googleads.v6.errors.ErrorCode.field_error', index=56,
number=61, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='shared_set_error', full_name='google.ads.googleads.v6.errors.ErrorCode.shared_set_error', index=57,
number=62, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='shared_criterion_error', full_name='google.ads.googleads.v6.errors.ErrorCode.shared_criterion_error', index=58,
number=63, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_shared_set_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_shared_set_error', index=59,
number=64, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='conversion_action_error', full_name='google.ads.googleads.v6.errors.ErrorCode.conversion_action_error', index=60,
number=65, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='conversion_adjustment_upload_error', full_name='google.ads.googleads.v6.errors.ErrorCode.conversion_adjustment_upload_error', index=61,
number=115, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='conversion_upload_error', full_name='google.ads.googleads.v6.errors.ErrorCode.conversion_upload_error', index=62,
number=111, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='header_error', full_name='google.ads.googleads.v6.errors.ErrorCode.header_error', index=63,
number=66, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database_error', full_name='google.ads.googleads.v6.errors.ErrorCode.database_error', index=64,
number=67, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='policy_finding_error', full_name='google.ads.googleads.v6.errors.ErrorCode.policy_finding_error', index=65,
number=68, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='enum_error', full_name='google.ads.googleads.v6.errors.ErrorCode.enum_error', index=66,
number=70, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keyword_plan_error', full_name='google.ads.googleads.v6.errors.ErrorCode.keyword_plan_error', index=67,
number=71, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keyword_plan_campaign_error', full_name='google.ads.googleads.v6.errors.ErrorCode.keyword_plan_campaign_error', index=68,
number=72, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keyword_plan_campaign_keyword_error', full_name='google.ads.googleads.v6.errors.ErrorCode.keyword_plan_campaign_keyword_error', index=69,
number=132, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keyword_plan_ad_group_error', full_name='google.ads.googleads.v6.errors.ErrorCode.keyword_plan_ad_group_error', index=70,
number=74, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keyword_plan_ad_group_keyword_error', full_name='google.ads.googleads.v6.errors.ErrorCode.keyword_plan_ad_group_keyword_error', index=71,
number=133, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keyword_plan_idea_error', full_name='google.ads.googleads.v6.errors.ErrorCode.keyword_plan_idea_error', index=72,
number=76, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='account_budget_proposal_error', full_name='google.ads.googleads.v6.errors.ErrorCode.account_budget_proposal_error', index=73,
number=77, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='user_list_error', full_name='google.ads.googleads.v6.errors.ErrorCode.user_list_error', index=74,
number=78, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='change_event_error', full_name='google.ads.googleads.v6.errors.ErrorCode.change_event_error', index=75,
number=136, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='change_status_error', full_name='google.ads.googleads.v6.errors.ErrorCode.change_status_error', index=76,
number=79, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_error', index=77,
number=80, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='geo_target_constant_suggestion_error', full_name='google.ads.googleads.v6.errors.ErrorCode.geo_target_constant_suggestion_error', index=78,
number=81, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_draft_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_draft_error', index=79,
number=82, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_item_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_item_error', index=80,
number=83, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='label_error', full_name='google.ads.googleads.v6.errors.ErrorCode.label_error', index=81,
number=84, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='billing_setup_error', full_name='google.ads.googleads.v6.errors.ErrorCode.billing_setup_error', index=82,
number=87, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='customer_client_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.customer_client_link_error', index=83,
number=88, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='customer_manager_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.customer_manager_link_error', index=84,
number=91, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_mapping_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_mapping_error', index=85,
number=92, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='customer_feed_error', full_name='google.ads.googleads.v6.errors.ErrorCode.customer_feed_error', index=86,
number=93, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_group_feed_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_group_feed_error', index=87,
number=94, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_feed_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_feed_error', index=88,
number=96, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='custom_interest_error', full_name='google.ads.googleads.v6.errors.ErrorCode.custom_interest_error', index=89,
number=97, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='campaign_experiment_error', full_name='google.ads.googleads.v6.errors.ErrorCode.campaign_experiment_error', index=90,
number=98, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='extension_feed_item_error', full_name='google.ads.googleads.v6.errors.ErrorCode.extension_feed_item_error', index=91,
number=100, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ad_parameter_error', full_name='google.ads.googleads.v6.errors.ErrorCode.ad_parameter_error', index=92,
number=101, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_item_validation_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_item_validation_error', index=93,
number=102, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='extension_setting_error', full_name='google.ads.googleads.v6.errors.ErrorCode.extension_setting_error', index=94,
number=103, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_item_set_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_item_set_error', index=95,
number=140, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_item_set_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_item_set_link_error', index=96,
number=141, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='feed_item_target_error', full_name='google.ads.googleads.v6.errors.ErrorCode.feed_item_target_error', index=97,
number=104, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='policy_violation_error', full_name='google.ads.googleads.v6.errors.ErrorCode.policy_violation_error', index=98,
number=105, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='partial_failure_error', full_name='google.ads.googleads.v6.errors.ErrorCode.partial_failure_error', index=99,
number=112, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='policy_validation_parameter_error', full_name='google.ads.googleads.v6.errors.ErrorCode.policy_validation_parameter_error', index=100,
number=114, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='size_limit_error', full_name='google.ads.googleads.v6.errors.ErrorCode.size_limit_error', index=101,
number=118, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='offline_user_data_job_error', full_name='google.ads.googleads.v6.errors.ErrorCode.offline_user_data_job_error', index=102,
number=119, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='not_allowlisted_error', full_name='google.ads.googleads.v6.errors.ErrorCode.not_allowlisted_error', index=103,
number=137, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='manager_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.manager_link_error', index=104,
number=121, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='currency_code_error', full_name='google.ads.googleads.v6.errors.ErrorCode.currency_code_error', index=105,
number=122, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_invitation_error', full_name='google.ads.googleads.v6.errors.ErrorCode.access_invitation_error', index=106,
number=124, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='reach_plan_error', full_name='google.ads.googleads.v6.errors.ErrorCode.reach_plan_error', index=107,
number=125, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='invoice_error', full_name='google.ads.googleads.v6.errors.ErrorCode.invoice_error', index=108,
number=126, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='payments_account_error', full_name='google.ads.googleads.v6.errors.ErrorCode.payments_account_error', index=109,
number=127, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='time_zone_error', full_name='google.ads.googleads.v6.errors.ErrorCode.time_zone_error', index=110,
number=128, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='asset_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.asset_link_error', index=111,
number=129, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='user_data_error', full_name='google.ads.googleads.v6.errors.ErrorCode.user_data_error', index=112,
number=130, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='batch_job_error', full_name='google.ads.googleads.v6.errors.ErrorCode.batch_job_error', index=113,
number=131, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='account_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.account_link_error', index=114,
number=134, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='third_party_app_analytics_link_error', full_name='google.ads.googleads.v6.errors.ErrorCode.third_party_app_analytics_link_error', index=115,
number=135, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='customer_user_access_error', full_name='google.ads.googleads.v6.errors.ErrorCode.customer_user_access_error', index=116,
number=138, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='custom_audience_error', full_name='google.ads.googleads.v6.errors.ErrorCode.custom_audience_error', index=117,
number=139, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='error_code', full_name='google.ads.googleads.v6.errors.ErrorCode.error_code',
index=0, containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[]),
],
serialized_start=8209,
serialized_end=20638,
)
_ERRORLOCATION_FIELDPATHELEMENT = _descriptor.Descriptor(
name='FieldPathElement',
full_name='google.ads.googleads.v6.errors.ErrorLocation.FieldPathElement',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='field_name', full_name='google.ads.googleads.v6.errors.ErrorLocation.FieldPathElement.field_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='index', full_name='google.ads.googleads.v6.errors.ErrorLocation.FieldPathElement.index', index=1,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='_index', full_name='google.ads.googleads.v6.errors.ErrorLocation.FieldPathElement._index',
index=0, containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[]),
],
serialized_start=20751,
serialized_end=20819,
)
_ERRORLOCATION = _descriptor.Descriptor(
name='ErrorLocation',
full_name='google.ads.googleads.v6.errors.ErrorLocation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='field_path_elements', full_name='google.ads.googleads.v6.errors.ErrorLocation.field_path_elements', index=0,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[_ERRORLOCATION_FIELDPATHELEMENT, ],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=20641,
serialized_end=20819,
)
_ERRORDETAILS = _descriptor.Descriptor(
name='ErrorDetails',
full_name='google.ads.googleads.v6.errors.ErrorDetails',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='unpublished_error_code', full_name='google.ads.googleads.v6.errors.ErrorDetails.unpublished_error_code', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='policy_violation_details', full_name='google.ads.googleads.v6.errors.ErrorDetails.policy_violation_details', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='policy_finding_details', full_name='google.ads.googleads.v6.errors.ErrorDetails.policy_finding_details', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='quota_error_details', full_name='google.ads.googleads.v6.errors.ErrorDetails.quota_error_details', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=20822,
serialized_end=21124,
)
_POLICYVIOLATIONDETAILS = _descriptor.Descriptor(
name='PolicyViolationDetails',
full_name='google.ads.googleads.v6.errors.PolicyViolationDetails',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='external_policy_description', full_name='google.ads.googleads.v6.errors.PolicyViolationDetails.external_policy_description', index=0,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='key', full_name='google.ads.googleads.v6.errors.PolicyViolationDetails.key', index=1,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='external_policy_name', full_name='google.ads.googleads.v6.errors.PolicyViolationDetails.external_policy_name', index=2,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='is_exemptible', full_name='google.ads.googleads.v6.errors.PolicyViolationDetails.is_exemptible', index=3,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=21127,
serialized_end=21306,
)
_POLICYFINDINGDETAILS = _descriptor.Descriptor(
name='PolicyFindingDetails',
full_name='google.ads.googleads.v6.errors.PolicyFindingDetails',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='policy_topic_entries', full_name='google.ads.googleads.v6.errors.PolicyFindingDetails.policy_topic_entries', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=21308,
serialized_end=21410,
)
_QUOTAERRORDETAILS = _descriptor.Descriptor(
name='QuotaErrorDetails',
full_name='google.ads.googleads.v6.errors.QuotaErrorDetails',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='rate_scope', full_name='google.ads.googleads.v6.errors.QuotaErrorDetails.rate_scope', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='rate_name', full_name='google.ads.googleads.v6.errors.QuotaErrorDetails.rate_name', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='retry_delay', full_name='google.ads.googleads.v6.errors.QuotaErrorDetails.retry_delay', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
_QUOTAERRORDETAILS_QUOTARATESCOPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=21413,
serialized_end=21661,
)
_GOOGLEADSFAILURE.fields_by_name['errors'].message_type = _GOOGLEADSERROR
_GOOGLEADSERROR.fields_by_name['error_code'].message_type = _ERRORCODE
_GOOGLEADSERROR.fields_by_name['trigger'].message_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_value__pb2._VALUE
_GOOGLEADSERROR.fields_by_name['location'].message_type = _ERRORLOCATION
_GOOGLEADSERROR.fields_by_name['details'].message_type = _ERRORDETAILS
_ERRORCODE.fields_by_name['request_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_request__error__pb2._REQUESTERRORENUM_REQUESTERROR
_ERRORCODE.fields_by_name['bidding_strategy_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_bidding__strategy__error__pb2._BIDDINGSTRATEGYERRORENUM_BIDDINGSTRATEGYERROR
_ERRORCODE.fields_by_name['url_field_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_url__field__error__pb2._URLFIELDERRORENUM_URLFIELDERROR
_ERRORCODE.fields_by_name['list_operation_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_list__operation__error__pb2._LISTOPERATIONERRORENUM_LISTOPERATIONERROR
_ERRORCODE.fields_by_name['query_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_query__error__pb2._QUERYERRORENUM_QUERYERROR
_ERRORCODE.fields_by_name['mutate_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_mutate__error__pb2._MUTATEERRORENUM_MUTATEERROR
_ERRORCODE.fields_by_name['field_mask_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_field__mask__error__pb2._FIELDMASKERRORENUM_FIELDMASKERROR
_ERRORCODE.fields_by_name['authorization_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_authorization__error__pb2._AUTHORIZATIONERRORENUM_AUTHORIZATIONERROR
_ERRORCODE.fields_by_name['internal_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_internal__error__pb2._INTERNALERRORENUM_INTERNALERROR
_ERRORCODE.fields_by_name['quota_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_quota__error__pb2._QUOTAERRORENUM_QUOTAERROR
_ERRORCODE.fields_by_name['ad_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__error__pb2._ADERRORENUM_ADERROR
_ERRORCODE.fields_by_name['ad_group_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__error__pb2._ADGROUPERRORENUM_ADGROUPERROR
_ERRORCODE.fields_by_name['campaign_budget_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__budget__error__pb2._CAMPAIGNBUDGETERRORENUM_CAMPAIGNBUDGETERROR
_ERRORCODE.fields_by_name['campaign_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__error__pb2._CAMPAIGNERRORENUM_CAMPAIGNERROR
_ERRORCODE.fields_by_name['authentication_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_authentication__error__pb2._AUTHENTICATIONERRORENUM_AUTHENTICATIONERROR
_ERRORCODE.fields_by_name['ad_group_criterion_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__criterion__error__pb2._ADGROUPCRITERIONERRORENUM_ADGROUPCRITERIONERROR
_ERRORCODE.fields_by_name['ad_customizer_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__customizer__error__pb2._ADCUSTOMIZERERRORENUM_ADCUSTOMIZERERROR
_ERRORCODE.fields_by_name['ad_group_ad_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__ad__error__pb2._ADGROUPADERRORENUM_ADGROUPADERROR
_ERRORCODE.fields_by_name['ad_sharing_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__sharing__error__pb2._ADSHARINGERRORENUM_ADSHARINGERROR
_ERRORCODE.fields_by_name['adx_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_adx__error__pb2._ADXERRORENUM_ADXERROR
_ERRORCODE.fields_by_name['asset_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_asset__error__pb2._ASSETERRORENUM_ASSETERROR
_ERRORCODE.fields_by_name['bidding_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_bidding__error__pb2._BIDDINGERRORENUM_BIDDINGERROR
_ERRORCODE.fields_by_name['campaign_criterion_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__criterion__error__pb2._CAMPAIGNCRITERIONERRORENUM_CAMPAIGNCRITERIONERROR
_ERRORCODE.fields_by_name['collection_size_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_collection__size__error__pb2._COLLECTIONSIZEERRORENUM_COLLECTIONSIZEERROR
_ERRORCODE.fields_by_name['country_code_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_country__code__error__pb2._COUNTRYCODEERRORENUM_COUNTRYCODEERROR
_ERRORCODE.fields_by_name['criterion_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_criterion__error__pb2._CRITERIONERRORENUM_CRITERIONERROR
_ERRORCODE.fields_by_name['customer_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__error__pb2._CUSTOMERERRORENUM_CUSTOMERERROR
_ERRORCODE.fields_by_name['date_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_date__error__pb2._DATEERRORENUM_DATEERROR
_ERRORCODE.fields_by_name['date_range_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_date__range__error__pb2._DATERANGEERRORENUM_DATERANGEERROR
_ERRORCODE.fields_by_name['distinct_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_distinct__error__pb2._DISTINCTERRORENUM_DISTINCTERROR
_ERRORCODE.fields_by_name['feed_attribute_reference_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__attribute__reference__error__pb2._FEEDATTRIBUTEREFERENCEERRORENUM_FEEDATTRIBUTEREFERENCEERROR
_ERRORCODE.fields_by_name['function_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_function__error__pb2._FUNCTIONERRORENUM_FUNCTIONERROR
_ERRORCODE.fields_by_name['function_parsing_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_function__parsing__error__pb2._FUNCTIONPARSINGERRORENUM_FUNCTIONPARSINGERROR
_ERRORCODE.fields_by_name['id_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_id__error__pb2._IDERRORENUM_IDERROR
_ERRORCODE.fields_by_name['image_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_image__error__pb2._IMAGEERRORENUM_IMAGEERROR
_ERRORCODE.fields_by_name['language_code_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_language__code__error__pb2._LANGUAGECODEERRORENUM_LANGUAGECODEERROR
_ERRORCODE.fields_by_name['media_bundle_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_media__bundle__error__pb2._MEDIABUNDLEERRORENUM_MEDIABUNDLEERROR
_ERRORCODE.fields_by_name['media_upload_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_media__upload__error__pb2._MEDIAUPLOADERRORENUM_MEDIAUPLOADERROR
_ERRORCODE.fields_by_name['media_file_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_media__file__error__pb2._MEDIAFILEERRORENUM_MEDIAFILEERROR
_ERRORCODE.fields_by_name['multiplier_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_multiplier__error__pb2._MULTIPLIERERRORENUM_MULTIPLIERERROR
_ERRORCODE.fields_by_name['new_resource_creation_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_new__resource__creation__error__pb2._NEWRESOURCECREATIONERRORENUM_NEWRESOURCECREATIONERROR
_ERRORCODE.fields_by_name['not_empty_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_not__empty__error__pb2._NOTEMPTYERRORENUM_NOTEMPTYERROR
_ERRORCODE.fields_by_name['null_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_null__error__pb2._NULLERRORENUM_NULLERROR
_ERRORCODE.fields_by_name['operator_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_operator__error__pb2._OPERATORERRORENUM_OPERATORERROR
_ERRORCODE.fields_by_name['range_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_range__error__pb2._RANGEERRORENUM_RANGEERROR
_ERRORCODE.fields_by_name['recommendation_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_recommendation__error__pb2._RECOMMENDATIONERRORENUM_RECOMMENDATIONERROR
_ERRORCODE.fields_by_name['region_code_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_region__code__error__pb2._REGIONCODEERRORENUM_REGIONCODEERROR
_ERRORCODE.fields_by_name['setting_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_setting__error__pb2._SETTINGERRORENUM_SETTINGERROR
_ERRORCODE.fields_by_name['string_format_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_string__format__error__pb2._STRINGFORMATERRORENUM_STRINGFORMATERROR
_ERRORCODE.fields_by_name['string_length_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_string__length__error__pb2._STRINGLENGTHERRORENUM_STRINGLENGTHERROR
_ERRORCODE.fields_by_name['operation_access_denied_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_operation__access__denied__error__pb2._OPERATIONACCESSDENIEDERRORENUM_OPERATIONACCESSDENIEDERROR
_ERRORCODE.fields_by_name['resource_access_denied_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_resource__access__denied__error__pb2._RESOURCEACCESSDENIEDERRORENUM_RESOURCEACCESSDENIEDERROR
_ERRORCODE.fields_by_name['resource_count_limit_exceeded_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_resource__count__limit__exceeded__error__pb2._RESOURCECOUNTLIMITEXCEEDEDERRORENUM_RESOURCECOUNTLIMITEXCEEDEDERROR
_ERRORCODE.fields_by_name['youtube_video_registration_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_youtube__video__registration__error__pb2._YOUTUBEVIDEOREGISTRATIONERRORENUM_YOUTUBEVIDEOREGISTRATIONERROR
_ERRORCODE.fields_by_name['ad_group_bid_modifier_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__bid__modifier__error__pb2._ADGROUPBIDMODIFIERERRORENUM_ADGROUPBIDMODIFIERERROR
_ERRORCODE.fields_by_name['context_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_context__error__pb2._CONTEXTERRORENUM_CONTEXTERROR
_ERRORCODE.fields_by_name['field_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_field__error__pb2._FIELDERRORENUM_FIELDERROR
_ERRORCODE.fields_by_name['shared_set_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_shared__set__error__pb2._SHAREDSETERRORENUM_SHAREDSETERROR
_ERRORCODE.fields_by_name['shared_criterion_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_shared__criterion__error__pb2._SHAREDCRITERIONERRORENUM_SHAREDCRITERIONERROR
_ERRORCODE.fields_by_name['campaign_shared_set_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__shared__set__error__pb2._CAMPAIGNSHAREDSETERRORENUM_CAMPAIGNSHAREDSETERROR
_ERRORCODE.fields_by_name['conversion_action_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__action__error__pb2._CONVERSIONACTIONERRORENUM_CONVERSIONACTIONERROR
_ERRORCODE.fields_by_name['conversion_adjustment_upload_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__adjustment__upload__error__pb2._CONVERSIONADJUSTMENTUPLOADERRORENUM_CONVERSIONADJUSTMENTUPLOADERROR
_ERRORCODE.fields_by_name['conversion_upload_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_conversion__upload__error__pb2._CONVERSIONUPLOADERRORENUM_CONVERSIONUPLOADERROR
_ERRORCODE.fields_by_name['header_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_header__error__pb2._HEADERERRORENUM_HEADERERROR
_ERRORCODE.fields_by_name['database_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_database__error__pb2._DATABASEERRORENUM_DATABASEERROR
_ERRORCODE.fields_by_name['policy_finding_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_policy__finding__error__pb2._POLICYFINDINGERRORENUM_POLICYFINDINGERROR
_ERRORCODE.fields_by_name['enum_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_enum__error__pb2._ENUMERRORENUM_ENUMERROR
_ERRORCODE.fields_by_name['keyword_plan_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__error__pb2._KEYWORDPLANERRORENUM_KEYWORDPLANERROR
_ERRORCODE.fields_by_name['keyword_plan_campaign_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__campaign__error__pb2._KEYWORDPLANCAMPAIGNERRORENUM_KEYWORDPLANCAMPAIGNERROR
_ERRORCODE.fields_by_name['keyword_plan_campaign_keyword_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__campaign__keyword__error__pb2._KEYWORDPLANCAMPAIGNKEYWORDERRORENUM_KEYWORDPLANCAMPAIGNKEYWORDERROR
_ERRORCODE.fields_by_name['keyword_plan_ad_group_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__ad__group__error__pb2._KEYWORDPLANADGROUPERRORENUM_KEYWORDPLANADGROUPERROR
_ERRORCODE.fields_by_name['keyword_plan_ad_group_keyword_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__ad__group__keyword__error__pb2._KEYWORDPLANADGROUPKEYWORDERRORENUM_KEYWORDPLANADGROUPKEYWORDERROR
_ERRORCODE.fields_by_name['keyword_plan_idea_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_keyword__plan__idea__error__pb2._KEYWORDPLANIDEAERRORENUM_KEYWORDPLANIDEAERROR
_ERRORCODE.fields_by_name['account_budget_proposal_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_account__budget__proposal__error__pb2._ACCOUNTBUDGETPROPOSALERRORENUM_ACCOUNTBUDGETPROPOSALERROR
_ERRORCODE.fields_by_name['user_list_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_user__list__error__pb2._USERLISTERRORENUM_USERLISTERROR
_ERRORCODE.fields_by_name['change_event_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_change__event__error__pb2._CHANGEEVENTERRORENUM_CHANGEEVENTERROR
_ERRORCODE.fields_by_name['change_status_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_change__status__error__pb2._CHANGESTATUSERRORENUM_CHANGESTATUSERROR
_ERRORCODE.fields_by_name['feed_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__error__pb2._FEEDERRORENUM_FEEDERROR
_ERRORCODE.fields_by_name['geo_target_constant_suggestion_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_geo__target__constant__suggestion__error__pb2._GEOTARGETCONSTANTSUGGESTIONERRORENUM_GEOTARGETCONSTANTSUGGESTIONERROR
_ERRORCODE.fields_by_name['campaign_draft_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__draft__error__pb2._CAMPAIGNDRAFTERRORENUM_CAMPAIGNDRAFTERROR
_ERRORCODE.fields_by_name['feed_item_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__error__pb2._FEEDITEMERRORENUM_FEEDITEMERROR
_ERRORCODE.fields_by_name['label_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_label__error__pb2._LABELERRORENUM_LABELERROR
_ERRORCODE.fields_by_name['billing_setup_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_billing__setup__error__pb2._BILLINGSETUPERRORENUM_BILLINGSETUPERROR
_ERRORCODE.fields_by_name['customer_client_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__client__link__error__pb2._CUSTOMERCLIENTLINKERRORENUM_CUSTOMERCLIENTLINKERROR
_ERRORCODE.fields_by_name['customer_manager_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__manager__link__error__pb2._CUSTOMERMANAGERLINKERRORENUM_CUSTOMERMANAGERLINKERROR
_ERRORCODE.fields_by_name['feed_mapping_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__mapping__error__pb2._FEEDMAPPINGERRORENUM_FEEDMAPPINGERROR
_ERRORCODE.fields_by_name['customer_feed_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__feed__error__pb2._CUSTOMERFEEDERRORENUM_CUSTOMERFEEDERROR
_ERRORCODE.fields_by_name['ad_group_feed_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__group__feed__error__pb2._ADGROUPFEEDERRORENUM_ADGROUPFEEDERROR
_ERRORCODE.fields_by_name['campaign_feed_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__feed__error__pb2._CAMPAIGNFEEDERRORENUM_CAMPAIGNFEEDERROR
_ERRORCODE.fields_by_name['custom_interest_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_custom__interest__error__pb2._CUSTOMINTERESTERRORENUM_CUSTOMINTERESTERROR
_ERRORCODE.fields_by_name['campaign_experiment_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_campaign__experiment__error__pb2._CAMPAIGNEXPERIMENTERRORENUM_CAMPAIGNEXPERIMENTERROR
_ERRORCODE.fields_by_name['extension_feed_item_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_extension__feed__item__error__pb2._EXTENSIONFEEDITEMERRORENUM_EXTENSIONFEEDITEMERROR
_ERRORCODE.fields_by_name['ad_parameter_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_ad__parameter__error__pb2._ADPARAMETERERRORENUM_ADPARAMETERERROR
_ERRORCODE.fields_by_name['feed_item_validation_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__validation__error__pb2._FEEDITEMVALIDATIONERRORENUM_FEEDITEMVALIDATIONERROR
_ERRORCODE.fields_by_name['extension_setting_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_extension__setting__error__pb2._EXTENSIONSETTINGERRORENUM_EXTENSIONSETTINGERROR
_ERRORCODE.fields_by_name['feed_item_set_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__set__error__pb2._FEEDITEMSETERRORENUM_FEEDITEMSETERROR
_ERRORCODE.fields_by_name['feed_item_set_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__set__link__error__pb2._FEEDITEMSETLINKERRORENUM_FEEDITEMSETLINKERROR
_ERRORCODE.fields_by_name['feed_item_target_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_feed__item__target__error__pb2._FEEDITEMTARGETERRORENUM_FEEDITEMTARGETERROR
_ERRORCODE.fields_by_name['policy_violation_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_policy__violation__error__pb2._POLICYVIOLATIONERRORENUM_POLICYVIOLATIONERROR
_ERRORCODE.fields_by_name['partial_failure_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_partial__failure__error__pb2._PARTIALFAILUREERRORENUM_PARTIALFAILUREERROR
_ERRORCODE.fields_by_name['policy_validation_parameter_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_policy__validation__parameter__error__pb2._POLICYVALIDATIONPARAMETERERRORENUM_POLICYVALIDATIONPARAMETERERROR
_ERRORCODE.fields_by_name['size_limit_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_size__limit__error__pb2._SIZELIMITERRORENUM_SIZELIMITERROR
_ERRORCODE.fields_by_name['offline_user_data_job_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_offline__user__data__job__error__pb2._OFFLINEUSERDATAJOBERRORENUM_OFFLINEUSERDATAJOBERROR
_ERRORCODE.fields_by_name['not_allowlisted_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_not__allowlisted__error__pb2._NOTALLOWLISTEDERRORENUM_NOTALLOWLISTEDERROR
_ERRORCODE.fields_by_name['manager_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_manager__link__error__pb2._MANAGERLINKERRORENUM_MANAGERLINKERROR
_ERRORCODE.fields_by_name['currency_code_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_currency__code__error__pb2._CURRENCYCODEERRORENUM_CURRENCYCODEERROR
_ERRORCODE.fields_by_name['access_invitation_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_access__invitation__error__pb2._ACCESSINVITATIONERRORENUM_ACCESSINVITATIONERROR
_ERRORCODE.fields_by_name['reach_plan_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_reach__plan__error__pb2._REACHPLANERRORENUM_REACHPLANERROR
_ERRORCODE.fields_by_name['invoice_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_invoice__error__pb2._INVOICEERRORENUM_INVOICEERROR
_ERRORCODE.fields_by_name['payments_account_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_payments__account__error__pb2._PAYMENTSACCOUNTERRORENUM_PAYMENTSACCOUNTERROR
_ERRORCODE.fields_by_name['time_zone_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_time__zone__error__pb2._TIMEZONEERRORENUM_TIMEZONEERROR
_ERRORCODE.fields_by_name['asset_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_asset__link__error__pb2._ASSETLINKERRORENUM_ASSETLINKERROR
_ERRORCODE.fields_by_name['user_data_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_user__data__error__pb2._USERDATAERRORENUM_USERDATAERROR
_ERRORCODE.fields_by_name['batch_job_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_batch__job__error__pb2._BATCHJOBERRORENUM_BATCHJOBERROR
_ERRORCODE.fields_by_name['account_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_account__link__error__pb2._ACCOUNTLINKERRORENUM_ACCOUNTLINKERROR
_ERRORCODE.fields_by_name['third_party_app_analytics_link_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_third__party__app__analytics__link__error__pb2._THIRDPARTYAPPANALYTICSLINKERRORENUM_THIRDPARTYAPPANALYTICSLINKERROR
_ERRORCODE.fields_by_name['customer_user_access_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_customer__user__access__error__pb2._CUSTOMERUSERACCESSERRORENUM_CUSTOMERUSERACCESSERROR
_ERRORCODE.fields_by_name['custom_audience_error'].enum_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_errors_dot_custom__audience__error__pb2._CUSTOMAUDIENCEERRORENUM_CUSTOMAUDIENCEERROR
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['request_error'])
_ERRORCODE.fields_by_name['request_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['bidding_strategy_error'])
_ERRORCODE.fields_by_name['bidding_strategy_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['url_field_error'])
_ERRORCODE.fields_by_name['url_field_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['list_operation_error'])
_ERRORCODE.fields_by_name['list_operation_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['query_error'])
_ERRORCODE.fields_by_name['query_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['mutate_error'])
_ERRORCODE.fields_by_name['mutate_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['field_mask_error'])
_ERRORCODE.fields_by_name['field_mask_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['authorization_error'])
_ERRORCODE.fields_by_name['authorization_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['internal_error'])
_ERRORCODE.fields_by_name['internal_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['quota_error'])
_ERRORCODE.fields_by_name['quota_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_error'])
_ERRORCODE.fields_by_name['ad_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_group_error'])
_ERRORCODE.fields_by_name['ad_group_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_budget_error'])
_ERRORCODE.fields_by_name['campaign_budget_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_error'])
_ERRORCODE.fields_by_name['campaign_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['authentication_error'])
_ERRORCODE.fields_by_name['authentication_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_group_criterion_error'])
_ERRORCODE.fields_by_name['ad_group_criterion_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_customizer_error'])
_ERRORCODE.fields_by_name['ad_customizer_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_group_ad_error'])
_ERRORCODE.fields_by_name['ad_group_ad_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_sharing_error'])
_ERRORCODE.fields_by_name['ad_sharing_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['adx_error'])
_ERRORCODE.fields_by_name['adx_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['asset_error'])
_ERRORCODE.fields_by_name['asset_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['bidding_error'])
_ERRORCODE.fields_by_name['bidding_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_criterion_error'])
_ERRORCODE.fields_by_name['campaign_criterion_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['collection_size_error'])
_ERRORCODE.fields_by_name['collection_size_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['country_code_error'])
_ERRORCODE.fields_by_name['country_code_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['criterion_error'])
_ERRORCODE.fields_by_name['criterion_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['customer_error'])
_ERRORCODE.fields_by_name['customer_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['date_error'])
_ERRORCODE.fields_by_name['date_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['date_range_error'])
_ERRORCODE.fields_by_name['date_range_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['distinct_error'])
_ERRORCODE.fields_by_name['distinct_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_attribute_reference_error'])
_ERRORCODE.fields_by_name['feed_attribute_reference_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['function_error'])
_ERRORCODE.fields_by_name['function_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['function_parsing_error'])
_ERRORCODE.fields_by_name['function_parsing_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['id_error'])
_ERRORCODE.fields_by_name['id_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['image_error'])
_ERRORCODE.fields_by_name['image_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['language_code_error'])
_ERRORCODE.fields_by_name['language_code_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['media_bundle_error'])
_ERRORCODE.fields_by_name['media_bundle_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['media_upload_error'])
_ERRORCODE.fields_by_name['media_upload_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['media_file_error'])
_ERRORCODE.fields_by_name['media_file_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['multiplier_error'])
_ERRORCODE.fields_by_name['multiplier_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['new_resource_creation_error'])
_ERRORCODE.fields_by_name['new_resource_creation_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['not_empty_error'])
_ERRORCODE.fields_by_name['not_empty_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['null_error'])
_ERRORCODE.fields_by_name['null_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['operator_error'])
_ERRORCODE.fields_by_name['operator_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['range_error'])
_ERRORCODE.fields_by_name['range_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['recommendation_error'])
_ERRORCODE.fields_by_name['recommendation_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['region_code_error'])
_ERRORCODE.fields_by_name['region_code_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['setting_error'])
_ERRORCODE.fields_by_name['setting_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['string_format_error'])
_ERRORCODE.fields_by_name['string_format_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['string_length_error'])
_ERRORCODE.fields_by_name['string_length_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['operation_access_denied_error'])
_ERRORCODE.fields_by_name['operation_access_denied_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['resource_access_denied_error'])
_ERRORCODE.fields_by_name['resource_access_denied_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['resource_count_limit_exceeded_error'])
_ERRORCODE.fields_by_name['resource_count_limit_exceeded_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['youtube_video_registration_error'])
_ERRORCODE.fields_by_name['youtube_video_registration_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_group_bid_modifier_error'])
_ERRORCODE.fields_by_name['ad_group_bid_modifier_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['context_error'])
_ERRORCODE.fields_by_name['context_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['field_error'])
_ERRORCODE.fields_by_name['field_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['shared_set_error'])
_ERRORCODE.fields_by_name['shared_set_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['shared_criterion_error'])
_ERRORCODE.fields_by_name['shared_criterion_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_shared_set_error'])
_ERRORCODE.fields_by_name['campaign_shared_set_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['conversion_action_error'])
_ERRORCODE.fields_by_name['conversion_action_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['conversion_adjustment_upload_error'])
_ERRORCODE.fields_by_name['conversion_adjustment_upload_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['conversion_upload_error'])
_ERRORCODE.fields_by_name['conversion_upload_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['header_error'])
_ERRORCODE.fields_by_name['header_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['database_error'])
_ERRORCODE.fields_by_name['database_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['policy_finding_error'])
_ERRORCODE.fields_by_name['policy_finding_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['enum_error'])
_ERRORCODE.fields_by_name['enum_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['keyword_plan_error'])
_ERRORCODE.fields_by_name['keyword_plan_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['keyword_plan_campaign_error'])
_ERRORCODE.fields_by_name['keyword_plan_campaign_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['keyword_plan_campaign_keyword_error'])
_ERRORCODE.fields_by_name['keyword_plan_campaign_keyword_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['keyword_plan_ad_group_error'])
_ERRORCODE.fields_by_name['keyword_plan_ad_group_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['keyword_plan_ad_group_keyword_error'])
_ERRORCODE.fields_by_name['keyword_plan_ad_group_keyword_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['keyword_plan_idea_error'])
_ERRORCODE.fields_by_name['keyword_plan_idea_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['account_budget_proposal_error'])
_ERRORCODE.fields_by_name['account_budget_proposal_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['user_list_error'])
_ERRORCODE.fields_by_name['user_list_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['change_event_error'])
_ERRORCODE.fields_by_name['change_event_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['change_status_error'])
_ERRORCODE.fields_by_name['change_status_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_error'])
_ERRORCODE.fields_by_name['feed_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['geo_target_constant_suggestion_error'])
_ERRORCODE.fields_by_name['geo_target_constant_suggestion_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_draft_error'])
_ERRORCODE.fields_by_name['campaign_draft_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_item_error'])
_ERRORCODE.fields_by_name['feed_item_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['label_error'])
_ERRORCODE.fields_by_name['label_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['billing_setup_error'])
_ERRORCODE.fields_by_name['billing_setup_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['customer_client_link_error'])
_ERRORCODE.fields_by_name['customer_client_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['customer_manager_link_error'])
_ERRORCODE.fields_by_name['customer_manager_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_mapping_error'])
_ERRORCODE.fields_by_name['feed_mapping_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['customer_feed_error'])
_ERRORCODE.fields_by_name['customer_feed_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_group_feed_error'])
_ERRORCODE.fields_by_name['ad_group_feed_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_feed_error'])
_ERRORCODE.fields_by_name['campaign_feed_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['custom_interest_error'])
_ERRORCODE.fields_by_name['custom_interest_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['campaign_experiment_error'])
_ERRORCODE.fields_by_name['campaign_experiment_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['extension_feed_item_error'])
_ERRORCODE.fields_by_name['extension_feed_item_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['ad_parameter_error'])
_ERRORCODE.fields_by_name['ad_parameter_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_item_validation_error'])
_ERRORCODE.fields_by_name['feed_item_validation_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['extension_setting_error'])
_ERRORCODE.fields_by_name['extension_setting_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_item_set_error'])
_ERRORCODE.fields_by_name['feed_item_set_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_item_set_link_error'])
_ERRORCODE.fields_by_name['feed_item_set_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['feed_item_target_error'])
_ERRORCODE.fields_by_name['feed_item_target_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['policy_violation_error'])
_ERRORCODE.fields_by_name['policy_violation_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['partial_failure_error'])
_ERRORCODE.fields_by_name['partial_failure_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['policy_validation_parameter_error'])
_ERRORCODE.fields_by_name['policy_validation_parameter_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['size_limit_error'])
_ERRORCODE.fields_by_name['size_limit_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['offline_user_data_job_error'])
_ERRORCODE.fields_by_name['offline_user_data_job_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['not_allowlisted_error'])
_ERRORCODE.fields_by_name['not_allowlisted_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['manager_link_error'])
_ERRORCODE.fields_by_name['manager_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['currency_code_error'])
_ERRORCODE.fields_by_name['currency_code_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['access_invitation_error'])
_ERRORCODE.fields_by_name['access_invitation_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['reach_plan_error'])
_ERRORCODE.fields_by_name['reach_plan_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['invoice_error'])
_ERRORCODE.fields_by_name['invoice_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['payments_account_error'])
_ERRORCODE.fields_by_name['payments_account_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['time_zone_error'])
_ERRORCODE.fields_by_name['time_zone_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['asset_link_error'])
_ERRORCODE.fields_by_name['asset_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['user_data_error'])
_ERRORCODE.fields_by_name['user_data_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['batch_job_error'])
_ERRORCODE.fields_by_name['batch_job_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['account_link_error'])
_ERRORCODE.fields_by_name['account_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['third_party_app_analytics_link_error'])
_ERRORCODE.fields_by_name['third_party_app_analytics_link_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['customer_user_access_error'])
_ERRORCODE.fields_by_name['customer_user_access_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORCODE.oneofs_by_name['error_code'].fields.append(
_ERRORCODE.fields_by_name['custom_audience_error'])
_ERRORCODE.fields_by_name['custom_audience_error'].containing_oneof = _ERRORCODE.oneofs_by_name['error_code']
_ERRORLOCATION_FIELDPATHELEMENT.containing_type = _ERRORLOCATION
_ERRORLOCATION_FIELDPATHELEMENT.oneofs_by_name['_index'].fields.append(
_ERRORLOCATION_FIELDPATHELEMENT.fields_by_name['index'])
_ERRORLOCATION_FIELDPATHELEMENT.fields_by_name['index'].containing_oneof = _ERRORLOCATION_FIELDPATHELEMENT.oneofs_by_name['_index']
_ERRORLOCATION.fields_by_name['field_path_elements'].message_type = _ERRORLOCATION_FIELDPATHELEMENT
_ERRORDETAILS.fields_by_name['policy_violation_details'].message_type = _POLICYVIOLATIONDETAILS
_ERRORDETAILS.fields_by_name['policy_finding_details'].message_type = _POLICYFINDINGDETAILS
_ERRORDETAILS.fields_by_name['quota_error_details'].message_type = _QUOTAERRORDETAILS
_POLICYVIOLATIONDETAILS.fields_by_name['key'].message_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_policy__pb2._POLICYVIOLATIONKEY
_POLICYFINDINGDETAILS.fields_by_name['policy_topic_entries'].message_type = google_dot_ads_dot_googleads__v6_dot_proto_dot_common_dot_policy__pb2._POLICYTOPICENTRY
_QUOTAERRORDETAILS.fields_by_name['rate_scope'].enum_type = _QUOTAERRORDETAILS_QUOTARATESCOPE
_QUOTAERRORDETAILS.fields_by_name['retry_delay'].message_type = google_dot_protobuf_dot_duration__pb2._DURATION
_QUOTAERRORDETAILS_QUOTARATESCOPE.containing_type = _QUOTAERRORDETAILS
DESCRIPTOR.message_types_by_name['GoogleAdsFailure'] = _GOOGLEADSFAILURE
DESCRIPTOR.message_types_by_name['GoogleAdsError'] = _GOOGLEADSERROR
DESCRIPTOR.message_types_by_name['ErrorCode'] = _ERRORCODE
DESCRIPTOR.message_types_by_name['ErrorLocation'] = _ERRORLOCATION
DESCRIPTOR.message_types_by_name['ErrorDetails'] = _ERRORDETAILS
DESCRIPTOR.message_types_by_name['PolicyViolationDetails'] = _POLICYVIOLATIONDETAILS
DESCRIPTOR.message_types_by_name['PolicyFindingDetails'] = _POLICYFINDINGDETAILS
DESCRIPTOR.message_types_by_name['QuotaErrorDetails'] = _QUOTAERRORDETAILS
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
GoogleAdsFailure = _reflection.GeneratedProtocolMessageType('GoogleAdsFailure', (_message.Message,), {
'DESCRIPTOR' : _GOOGLEADSFAILURE,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """Describes how a GoogleAds API call failed. It's returned inside
google.rpc.Status.details when a call fails.
Attributes:
errors:
The list of errors that occurred.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.GoogleAdsFailure)
})
_sym_db.RegisterMessage(GoogleAdsFailure)
GoogleAdsError = _reflection.GeneratedProtocolMessageType('GoogleAdsError', (_message.Message,), {
'DESCRIPTOR' : _GOOGLEADSERROR,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """GoogleAds-specific error.
Attributes:
error_code:
An enum value that indicates which error occurred.
message:
A human-readable description of the error.
trigger:
The value that triggered the error.
location:
Describes the part of the request proto that caused the error.
details:
Additional error details, which are returned by certain error
codes. Most error codes do not include details.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.GoogleAdsError)
})
_sym_db.RegisterMessage(GoogleAdsError)
ErrorCode = _reflection.GeneratedProtocolMessageType('ErrorCode', (_message.Message,), {
'DESCRIPTOR' : _ERRORCODE,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """The error reason represented by type and enum.
Attributes:
error_code:
The list of error enums
request_error:
An error caused by the request
bidding_strategy_error:
An error with a Bidding Strategy mutate.
url_field_error:
An error with a URL field mutate.
list_operation_error:
An error with a list operation.
query_error:
An error with an AWQL query
mutate_error:
An error with a mutate
field_mask_error:
An error with a field mask
authorization_error:
An error encountered when trying to authorize a user.
internal_error:
An unexpected server-side error.
quota_error:
An error with the amonut of quota remaining.
ad_error:
An error with an Ad Group Ad mutate.
ad_group_error:
An error with an Ad Group mutate.
campaign_budget_error:
An error with a Campaign Budget mutate.
campaign_error:
An error with a Campaign mutate.
authentication_error:
Indicates failure to properly authenticate user.
ad_group_criterion_error:
Indicates failure to properly authenticate user.
ad_customizer_error:
The reasons for the ad customizer error
ad_group_ad_error:
The reasons for the ad group ad error
ad_sharing_error:
The reasons for the ad sharing error
adx_error:
The reasons for the adx error
asset_error:
The reasons for the asset error
bidding_error:
The reasons for the bidding errors
campaign_criterion_error:
The reasons for the campaign criterion error
collection_size_error:
The reasons for the collection size error
country_code_error:
The reasons for the country code error
criterion_error:
The reasons for the criterion error
customer_error:
The reasons for the customer error
date_error:
The reasons for the date error
date_range_error:
The reasons for the date range error
distinct_error:
The reasons for the distinct error
feed_attribute_reference_error:
The reasons for the feed attribute reference error
function_error:
The reasons for the function error
function_parsing_error:
The reasons for the function parsing error
id_error:
The reasons for the id error
image_error:
The reasons for the image error
language_code_error:
The reasons for the language code error
media_bundle_error:
The reasons for the media bundle error
media_upload_error:
The reasons for media uploading errors.
media_file_error:
The reasons for the media file error
multiplier_error:
The reasons for the multiplier error
new_resource_creation_error:
The reasons for the new resource creation error
not_empty_error:
The reasons for the not empty error
null_error:
The reasons for the null error
operator_error:
The reasons for the operator error
range_error:
The reasons for the range error
recommendation_error:
The reasons for error in applying a recommendation
region_code_error:
The reasons for the region code error
setting_error:
The reasons for the setting error
string_format_error:
The reasons for the string format error
string_length_error:
The reasons for the string length error
operation_access_denied_error:
The reasons for the operation access denied error
resource_access_denied_error:
The reasons for the resource access denied error
resource_count_limit_exceeded_error:
The reasons for the resource count limit exceeded error
youtube_video_registration_error:
The reasons for YouTube video registration errors.
ad_group_bid_modifier_error:
The reasons for the ad group bid modifier error
context_error:
The reasons for the context error
field_error:
The reasons for the field error
shared_set_error:
The reasons for the shared set error
shared_criterion_error:
The reasons for the shared criterion error
campaign_shared_set_error:
The reasons for the campaign shared set error
conversion_action_error:
The reasons for the conversion action error
conversion_adjustment_upload_error:
The reasons for the conversion adjustment upload error
conversion_upload_error:
The reasons for the conversion upload error
header_error:
The reasons for the header error.
database_error:
The reasons for the database error.
policy_finding_error:
The reasons for the policy finding error.
enum_error:
The reason for enum error.
keyword_plan_error:
The reason for keyword plan error.
keyword_plan_campaign_error:
The reason for keyword plan campaign error.
keyword_plan_campaign_keyword_error:
The reason for keyword plan campaign keyword error.
keyword_plan_ad_group_error:
The reason for keyword plan ad group error.
keyword_plan_ad_group_keyword_error:
The reason for keyword plan ad group keyword error.
keyword_plan_idea_error:
The reason for keyword idea error.
account_budget_proposal_error:
The reasons for account budget proposal errors.
user_list_error:
The reasons for the user list error
change_event_error:
The reasons for the change event error
change_status_error:
The reasons for the change status error
feed_error:
The reasons for the feed error
geo_target_constant_suggestion_error:
The reasons for the geo target constant suggestion error.
campaign_draft_error:
The reasons for the campaign draft error
feed_item_error:
The reasons for the feed item error
label_error:
The reason for the label error.
billing_setup_error:
The reasons for the billing setup error
customer_client_link_error:
The reasons for the customer client link error
customer_manager_link_error:
The reasons for the customer manager link error
feed_mapping_error:
The reasons for the feed mapping error
customer_feed_error:
The reasons for the customer feed error
ad_group_feed_error:
The reasons for the ad group feed error
campaign_feed_error:
The reasons for the campaign feed error
custom_interest_error:
The reasons for the custom interest error
campaign_experiment_error:
The reasons for the campaign experiment error
extension_feed_item_error:
The reasons for the extension feed item error
ad_parameter_error:
The reasons for the ad parameter error
feed_item_validation_error:
The reasons for the feed item validation error
extension_setting_error:
The reasons for the extension setting error
feed_item_set_error:
The reasons for the feed item set error
feed_item_set_link_error:
The reasons for the feed item set link error
feed_item_target_error:
The reasons for the feed item target error
policy_violation_error:
The reasons for the policy violation error
partial_failure_error:
The reasons for the mutate job error
policy_validation_parameter_error:
The reasons for the policy validation parameter error
size_limit_error:
The reasons for the size limit error
offline_user_data_job_error:
The reasons for the offline user data job error.
not_allowlisted_error:
The reasons for the not allowlisted error
manager_link_error:
The reasons for the manager link error
currency_code_error:
The reasons for the currency code error
access_invitation_error:
The reasons for the access invitation error
reach_plan_error:
The reasons for the reach plan error
invoice_error:
The reasons for the invoice error
payments_account_error:
The reasons for errors in payments accounts service
time_zone_error:
The reasons for the time zone error
asset_link_error:
The reasons for the asset link error
user_data_error:
The reasons for the user data error.
batch_job_error:
The reasons for the batch job error
account_link_error:
The reasons for the account link status change error
third_party_app_analytics_link_error:
The reasons for the third party app analytics link mutate
error
customer_user_access_error:
The reasons for the customer user access mutate error
custom_audience_error:
The reasons for the custom audience error
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.ErrorCode)
})
_sym_db.RegisterMessage(ErrorCode)
ErrorLocation = _reflection.GeneratedProtocolMessageType('ErrorLocation', (_message.Message,), {
'FieldPathElement' : _reflection.GeneratedProtocolMessageType('FieldPathElement', (_message.Message,), {
'DESCRIPTOR' : _ERRORLOCATION_FIELDPATHELEMENT,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """A part of a field path.
Attributes:
field_name:
The name of a field or a oneof
index:
If field\_name is a repeated field, this is the element that
failed
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.ErrorLocation.FieldPathElement)
})
,
'DESCRIPTOR' : _ERRORLOCATION,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """Describes the part of the request proto that caused the error.
Attributes:
field_path_elements:
A field path that indicates which field was invalid in the
request.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.ErrorLocation)
})
_sym_db.RegisterMessage(ErrorLocation)
_sym_db.RegisterMessage(ErrorLocation.FieldPathElement)
ErrorDetails = _reflection.GeneratedProtocolMessageType('ErrorDetails', (_message.Message,), {
'DESCRIPTOR' : _ERRORDETAILS,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """Additional error details.
Attributes:
unpublished_error_code:
The error code that should have been returned, but wasn't.
This is used when the error code is not published in the
client specified version.
policy_violation_details:
Describes an ad policy violation.
policy_finding_details:
Describes policy violation findings.
quota_error_details:
Details on the quota error, including the scope (account or
developer), the rate bucket name and the retry delay.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.ErrorDetails)
})
_sym_db.RegisterMessage(ErrorDetails)
PolicyViolationDetails = _reflection.GeneratedProtocolMessageType('PolicyViolationDetails', (_message.Message,), {
'DESCRIPTOR' : _POLICYVIOLATIONDETAILS,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """Error returned as part of a mutate response. This error indicates
single policy violation by some text in one of the fields.
Attributes:
external_policy_description:
Human readable description of policy violation.
key:
Unique identifier for this violation. If policy is exemptible,
this key may be used to request exemption.
external_policy_name:
Human readable name of the policy.
is_exemptible:
Whether user can file an exemption request for this violation.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.PolicyViolationDetails)
})
_sym_db.RegisterMessage(PolicyViolationDetails)
PolicyFindingDetails = _reflection.GeneratedProtocolMessageType('PolicyFindingDetails', (_message.Message,), {
'DESCRIPTOR' : _POLICYFINDINGDETAILS,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """Error returned as part of a mutate response. This error indicates one
or more policy findings in the fields of a resource.
Attributes:
policy_topic_entries:
The list of policy topics for the resource. Contains the
PROHIBITED or FULLY\_LIMITED policy topic entries that
prevented the resource from being saved (among any other
entries the resource may also have).
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.PolicyFindingDetails)
})
_sym_db.RegisterMessage(PolicyFindingDetails)
QuotaErrorDetails = _reflection.GeneratedProtocolMessageType('QuotaErrorDetails', (_message.Message,), {
'DESCRIPTOR' : _QUOTAERRORDETAILS,
'__module__' : 'google.ads.googleads_v6.proto.errors.errors_pb2'
,
'__doc__': """Additional quota error details when there is QuotaError.
Attributes:
rate_scope:
The rate scope of the quota limit.
rate_name:
The high level description of the quota bucket. Examples are
"Get requests for standard access" or "Requests per account".
retry_delay:
Backoff period that customers should wait before sending next
request.
""",
# @@protoc_insertion_point(class_scope:google.ads.googleads.v6.errors.QuotaErrorDetails)
})
_sym_db.RegisterMessage(QuotaErrorDetails)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| 84.112125
| 26,537
| 0.832848
| 27,510
| 193,542
| 5.329117
| 0.041985
| 0.05965
| 0.041445
| 0.036936
| 0.812631
| 0.793307
| 0.759154
| 0.724339
| 0.686025
| 0.639873
| 0
| 0.030342
| 0.079419
| 193,542
| 2,300
| 26,538
| 84.148696
| 0.792486
| 0.005126
| 0
| 0.472086
| 1
| 0.065654
| 0.294533
| 0.196245
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.056275
| 0
| 0.056275
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
69251a47601cb3e59d9b74a4654509644b90a020
| 121
|
py
|
Python
|
src/cinnamon/drift/tree_ensemble_drift_explainer.py
|
zelros/cinnamon
|
9fbac281ed3ab2a1ce61fb48d845f3a6e97d1221
|
[
"MIT"
] | 51
|
2021-11-18T10:52:30.000Z
|
2022-03-17T10:23:09.000Z
|
src/cinnamon/drift/tree_ensemble_drift_explainer.py
|
YohannLeFaou/cinnamon
|
0e1592f3c01a9e907f7467a2928a57ce1c2f6625
|
[
"MIT"
] | 3
|
2021-11-28T01:09:01.000Z
|
2022-03-16T10:32:13.000Z
|
src/cinnamon/drift/tree_ensemble_drift_explainer.py
|
YohannLeFaou/cinnamon
|
0e1592f3c01a9e907f7467a2928a57ce1c2f6625
|
[
"MIT"
] | 4
|
2021-11-22T14:11:45.000Z
|
2022-03-08T09:06:11.000Z
|
from .model_drift_explainer import ModelDriftExplainer
class TreeEnsembleDriftExplainer(ModelDriftExplainer):
pass
| 20.166667
| 54
| 0.859504
| 10
| 121
| 10.2
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107438
| 121
| 5
| 55
| 24.2
| 0.944444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
15f88a2e4be247863023a0229a85b907f87597cf
| 265
|
py
|
Python
|
utils/__init__.py
|
archongum/Tensorflow-quickstart-offline
|
3772710cc794271cf361195925b145c1e0c47671
|
[
"Apache-2.0"
] | 2
|
2019-07-04T02:57:31.000Z
|
2021-09-03T08:20:06.000Z
|
utils/__init__.py
|
archongum/Tensorflow-quickstart-offline
|
3772710cc794271cf361195925b145c1e0c47671
|
[
"Apache-2.0"
] | null | null | null |
utils/__init__.py
|
archongum/Tensorflow-quickstart-offline
|
3772710cc794271cf361195925b145c1e0c47671
|
[
"Apache-2.0"
] | 2
|
2019-01-21T03:01:49.000Z
|
2019-07-04T02:57:52.000Z
|
from utils.local_datasets import get_word_index
from utils.local_datasets import load_data_boston_housing
from utils.local_datasets import load_data_fashion_mnist
from utils.local_datasets import load_data_imdb
from utils.local_datasets import load_data_mnist
| 44.166667
| 58
| 0.886792
| 42
| 265
| 5.190476
| 0.357143
| 0.206422
| 0.321101
| 0.504587
| 0.788991
| 0.66055
| 0.66055
| 0
| 0
| 0
| 0
| 0
| 0.09434
| 265
| 5
| 59
| 53
| 0.908333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c611a1bd77ec9be8b11d69dcbb2da0706155dd74
| 84
|
py
|
Python
|
deeptile/algorithms/__init__.py
|
zjniu/DeepTile
|
fd5d9b8410915db83851ff8a1d2e44d39f06a492
|
[
"MIT"
] | null | null | null |
deeptile/algorithms/__init__.py
|
zjniu/DeepTile
|
fd5d9b8410915db83851ff8a1d2e44d39f06a492
|
[
"MIT"
] | null | null | null |
deeptile/algorithms/__init__.py
|
zjniu/DeepTile
|
fd5d9b8410915db83851ff8a1d2e44d39f06a492
|
[
"MIT"
] | null | null | null |
from deeptile.algorithms import segmentation
from deeptile.algorithms import stitch
| 28
| 44
| 0.880952
| 10
| 84
| 7.4
| 0.6
| 0.324324
| 0.594595
| 0.756757
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 84
| 2
| 45
| 42
| 0.973684
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
c626f23f73f04c0f832c31ea48034040d92b4754
| 73
|
py
|
Python
|
elm-finder/apps/feeds/tests/data.py
|
martin-jahn/elm-finder
|
7510e38d52eebaf462ae5c5ce961e4884b1709bd
|
[
"MIT"
] | 2
|
2019-04-28T21:32:46.000Z
|
2019-05-13T05:27:09.000Z
|
elm-finder/apps/package/tests/initial_data.py
|
martin-jahn/elm-finder
|
7510e38d52eebaf462ae5c5ce961e4884b1709bd
|
[
"MIT"
] | null | null | null |
elm-finder/apps/package/tests/initial_data.py
|
martin-jahn/elm-finder
|
7510e38d52eebaf462ae5c5ce961e4884b1709bd
|
[
"MIT"
] | null | null | null |
from libs.tests import load as utils_load
def load():
utils_load()
| 12.166667
| 41
| 0.712329
| 12
| 73
| 4.166667
| 0.666667
| 0.36
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205479
| 73
| 5
| 42
| 14.6
| 0.862069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c635bf8a8a6fb381b2394613291bb1709173b289
| 95
|
py
|
Python
|
lunchmenu/sql/__init__.py
|
janeuzil/lunchmenu
|
717404561198618e2da5f435f15f20088f811f6b
|
[
"MIT"
] | 1
|
2018-04-18T07:14:36.000Z
|
2018-04-18T07:14:36.000Z
|
lunchmenu/sql/__init__.py
|
janeuzil/lunchmenu
|
717404561198618e2da5f435f15f20088f811f6b
|
[
"MIT"
] | null | null | null |
lunchmenu/sql/__init__.py
|
janeuzil/lunchmenu
|
717404561198618e2da5f435f15f20088f811f6b
|
[
"MIT"
] | null | null | null |
from sql import Room
from sql import User
from sql import Restaurant
from sql import Database
| 15.833333
| 26
| 0.821053
| 16
| 95
| 4.875
| 0.4375
| 0.358974
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178947
| 95
| 5
| 27
| 19
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c6467cffa90876981b8fa4f5a4171f3ab12cc46f
| 1,741
|
py
|
Python
|
award/tests.py
|
eyern/awards
|
8893adf15ff869d67746521606bfc3e7ba68446b
|
[
"MIT"
] | null | null | null |
award/tests.py
|
eyern/awards
|
8893adf15ff869d67746521606bfc3e7ba68446b
|
[
"MIT"
] | 4
|
2020-06-05T22:45:26.000Z
|
2021-09-08T01:16:40.000Z
|
award/tests.py
|
eyern/awards
|
8893adf15ff869d67746521606bfc3e7ba68446b
|
[
"MIT"
] | null | null | null |
from django.test import TestCase
from django.contrib.auth.models import User
from .models import Project,Profile,Vote
# Create your tests here.
class ProjectTestClass(TestCase):
# Set up method
def setUp(self):
self.user = User.objects.create_user(username='testuser', password='12345')
self.profile = Profile(user = self.user)
self.profile.save()
self.project = Project(id=1, image = 'path/to/image',title='test',description='test caption',url='path/to/project',screenshot='path/to/screenshot',user=self.user,profile=self.profile)
#Testing instance
def test_instance(self):
self.assertTrue(isinstance(self.project,Project))
class ProfileTestClass(TestCase):
# Set up method
def setUp(self):
self.user = User.objects.create_user(username='testuser', password='12345')
self.profile = Profile(id=1,profile_photo='path/to/photo',user = self.user,bio='test bio',contacts='test contact')
#Testing instance
def test_instance(self):
self.assertTrue(isinstance(self.profile,Profile))
class VoteTestClass(TestCase):
# Set up method
def setUp(self):
self.user = User.objects.create_user(username='testuser', password='12345')
self.profile = Profile(user = self.user)
self.profile.save()
self.project = Project(id=1, image = 'path/to/image',title='test',description='test caption',url='path/to/project',screenshot='path/to/screenshot',user=self.user,profile=self.profile)
self.project.save()
self.vote = Vote(id=1,project=self.project,design=10,usability=10,content=10,profile=self.profile)
#Testing instance
def test_instance(self):
self.assertTrue(isinstance(self.vote,Vote))
| 42.463415
| 191
| 0.70247
| 228
| 1,741
| 5.333333
| 0.236842
| 0.081414
| 0.049342
| 0.046875
| 0.706414
| 0.706414
| 0.706414
| 0.706414
| 0.706414
| 0.706414
| 0
| 0.0171
| 0.160253
| 1,741
| 40
| 192
| 43.525
| 0.814637
| 0.064905
| 0
| 0.555556
| 0
| 0
| 0.120988
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 1
| 0.222222
| false
| 0.111111
| 0.111111
| 0
| 0.444444
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
d6cf8aaf311adaafcda761b4e551ab69fc760acb
| 1,047
|
py
|
Python
|
prompts/dialKG.py
|
andreamad8/FSB
|
a81593590189fa5ad1cc37c5857f974effd9750a
|
[
"MIT"
] | 53
|
2021-10-11T03:24:14.000Z
|
2022-03-30T15:17:23.000Z
|
prompts/dialKG.py
|
andreamad8/FSB
|
a81593590189fa5ad1cc37c5857f974effd9750a
|
[
"MIT"
] | 1
|
2021-12-26T22:48:38.000Z
|
2022-01-15T18:05:32.000Z
|
prompts/dialKG.py
|
andreamad8/FSB
|
a81593590189fa5ad1cc37c5857f974effd9750a
|
[
"MIT"
] | 5
|
2022-01-27T09:07:39.000Z
|
2022-03-04T08:58:23.000Z
|
def convert_sample_to_shot_dialKG(sample,with_knowledge):
prefix = "Dialogue:\n"
assert len(sample["dialogue"]) == len(sample["KG"])
for turn, meta in zip(sample["dialogue"],sample["KG"]):
prefix += f"User: {turn[0]}" +"\n"
if with_knowledge and len(meta)>0:
prefix += f"KG: {meta[0]}" +"\n"
if turn[1] == "":
prefix += f"Assistant:"
return prefix
else:
prefix += f"Assistant: {turn[1]}" +"\n"
return prefix
def convert_sample_to_shot_dialKG_interact(sample,with_knowledge):
prefix = "Dialogue:\n"
assert len(sample["dialogue"]) == len(sample["KG"])
for turn, meta in zip(sample["dialogue"],sample["KG"]):
prefix += f"User: {turn[0]}" +"\n"
if with_knowledge and len(meta)>0:
prefix += f"KG: {meta[0]}" +"\n"
if turn[1] == "":
prefix += f"Assistant:"
return prefix
else:
prefix += f"Assistant: {turn[1]}" +"\n"
return prefix
| 33.774194
| 66
| 0.526266
| 129
| 1,047
| 4.170543
| 0.224806
| 0.104089
| 0.02974
| 0.066915
| 0.98513
| 0.98513
| 0.881041
| 0.881041
| 0.881041
| 0.881041
| 0
| 0.013605
| 0.297994
| 1,047
| 30
| 67
| 34.9
| 0.718367
| 0
| 0
| 0.923077
| 0
| 0
| 0.181471
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 1
| 0.076923
| false
| 0
| 0
| 0
| 0.230769
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d6e35cac4ee97f75912a9ca16355f587f45a1f61
| 37
|
py
|
Python
|
src/xbrief/deco/deco_vector/__init__.py
|
pydget/xbrief
|
9e91927a98754b0fca1fa55eae9a785b15e963f9
|
[
"MIT"
] | null | null | null |
src/xbrief/deco/deco_vector/__init__.py
|
pydget/xbrief
|
9e91927a98754b0fca1fa55eae9a785b15e963f9
|
[
"MIT"
] | null | null | null |
src/xbrief/deco/deco_vector/__init__.py
|
pydget/xbrief
|
9e91927a98754b0fca1fa55eae9a785b15e963f9
|
[
"MIT"
] | null | null | null |
from .deco_vector import deco_vector
| 18.5
| 36
| 0.864865
| 6
| 37
| 5
| 0.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 37
| 1
| 37
| 37
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
d6e53b405bc2fb938dd7a484031593d25a5aa5f9
| 4,644
|
py
|
Python
|
tests/test_kit/test_dbm.py
|
koehlma/momba
|
68d6431d2732570696d3c67a9e23006e6e3a7740
|
[
"MIT"
] | 12
|
2021-01-18T14:38:32.000Z
|
2022-01-17T09:16:52.000Z
|
tests/test_kit/test_dbm.py
|
koehlma/momba
|
68d6431d2732570696d3c67a9e23006e6e3a7740
|
[
"MIT"
] | 3
|
2021-05-16T15:26:34.000Z
|
2022-02-21T20:46:55.000Z
|
tests/test_kit/test_dbm.py
|
koehlma/momba
|
68d6431d2732570696d3c67a9e23006e6e3a7740
|
[
"MIT"
] | null | null | null |
# -*- coding:utf-8 -*-
#
# Copyright (C) 2019-2020, Maximilian Köhl <koehl@cs.uni-saarland.de>
from __future__ import annotations
from momba.kit import dbm
import pytest
def test_basic_operations() -> None:
x, y, z = dbm.create_clocks("x", "y", "z")
valuations = dbm.DBM.create_unconstrained({x, y, z})
assert valuations.get_interval(x).infimum == 0
assert valuations.get_interval(x).infimum_included is True
assert valuations.get_interval(x).supremum == float("inf")
assert valuations.get_interval(x).supremum_included is False
assert not valuations.is_empty
valuations.constrain(
dbm.difference(x, dbm.ZERO_CLOCK).less_or_equal(5),
dbm.difference(dbm.ZERO_CLOCK, x).less_than(-2),
)
assert valuations.get_interval(x).infimum == 2
assert valuations.get_interval(x).infimum_included is False
assert valuations.get_interval(x).supremum == 5
assert valuations.get_interval(x).supremum_included is True
assert not valuations.is_empty
valuations.constrain(dbm.difference(dbm.ZERO_CLOCK, x).less_than(-5))
assert valuations.is_empty
def test_unknown_clocks() -> None:
x, y, z = dbm.create_clocks("x", "y", "z")
valuations = dbm.DBM.create_unconstrained({x, y})
with pytest.raises(dbm.InvalidClockError):
valuations.constrain(dbm.difference(x, z).less_or_equal(3))
def test_clock_reset() -> None:
x, y, z = dbm.create_clocks("x", "y", "z")
valuations = dbm.DBM.create_unconstrained({x, y, z})
valuations.reset(x, 5)
assert valuations.get_interval(x).infimum == 5
assert valuations.get_interval(x).infimum_included is True
assert valuations.get_interval(x).supremum == 5
assert valuations.get_interval(x).supremum_included is True
valuations.constrain(dbm.difference(x, y).less_or_equal(2))
assert valuations.get_interval(y).infimum == 3
assert valuations.get_interval(y).infimum_included is True
assert valuations.get_interval(y).supremum == float("inf")
assert valuations.get_interval(y).supremum_included is False
valuations.reset(y, 10)
assert valuations.get_interval(y).infimum == 10
assert valuations.get_interval(y).infimum_included is True
assert valuations.get_interval(y).supremum == 10
assert valuations.get_interval(y).supremum_included is True
assert valuations.get_interval(x).infimum == 5
assert valuations.get_interval(x).infimum_included is True
assert valuations.get_interval(x).supremum == 5
assert valuations.get_interval(x).supremum_included is True
assert valuations.get_bound(x, y) == dbm.Bound.less_or_equal(-5)
assert not valuations.is_empty
def test_intersection() -> None:
x, y, z = dbm.create_clocks("x", "y", "z")
valuations = dbm.DBM.create_zero({x, y, z})
valuations.advance_upper_bounds(5)
valuations.advance_lower_bounds(2)
# constrain x and z to have the same value
valuations.constrain(
dbm.difference(x, z).less_or_equal(0), dbm.difference(z, x).less_or_equal(0)
)
invariant = dbm.DBM.create_unconstrained({x, y})
invariant.constrain(dbm.difference(x, dbm.ZERO_CLOCK).less_than(3)) # x < 3
valuations.intersect(invariant)
assert valuations.get_interval(x).infimum == 2
assert valuations.get_interval(x).infimum_included is True
assert valuations.get_interval(x).supremum == 3
assert valuations.get_interval(x).supremum_included is False
assert valuations.get_interval(z).infimum == 2
assert valuations.get_interval(z).infimum_included is True
assert valuations.get_interval(z).supremum == 3
assert valuations.get_interval(z).supremum_included is False
assert valuations.get_interval(y).infimum == 2
assert valuations.get_interval(y).infimum_included is True
assert valuations.get_interval(y).supremum == 3
assert valuations.get_interval(y).supremum_included is False
valuations.reset(z, 42)
assert valuations.get_interval(x).infimum == 2
assert valuations.get_interval(x).infimum_included is True
assert valuations.get_interval(x).supremum == 3
assert valuations.get_interval(x).supremum_included is False
assert valuations.get_interval(z).infimum == 42
assert valuations.get_interval(z).infimum_included is True
assert valuations.get_interval(z).supremum == 42
assert valuations.get_interval(z).supremum_included is True
assert valuations.get_interval(y).infimum == 2
assert valuations.get_interval(y).infimum_included is True
assert valuations.get_interval(y).supremum == 3
assert valuations.get_interval(y).supremum_included is False
| 36
| 84
| 0.73385
| 657
| 4,644
| 5.004566
| 0.121766
| 0.243309
| 0.283151
| 0.394161
| 0.855839
| 0.827859
| 0.806265
| 0.772506
| 0.692822
| 0.63382
| 0
| 0.013252
| 0.155039
| 4,644
| 128
| 85
| 36.28125
| 0.824669
| 0.02907
| 0
| 0.517241
| 0
| 0
| 0.003997
| 0
| 0
| 0
| 0
| 0
| 0.609195
| 1
| 0.045977
| false
| 0
| 0.034483
| 0
| 0.08046
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
ba7aa2f5481145e8b8a78f3acb61fe8396a347b6
| 7,005
|
py
|
Python
|
lifetables.py
|
marcysweber/hamadryas-social-sim
|
95b4e51f9b1ca116df7eb2f6d0205511eba780c4
|
[
"MIT"
] | null | null | null |
lifetables.py
|
marcysweber/hamadryas-social-sim
|
95b4e51f9b1ca116df7eb2f6d0205511eba780c4
|
[
"MIT"
] | null | null | null |
lifetables.py
|
marcysweber/hamadryas-social-sim
|
95b4e51f9b1ca116df7eb2f6d0205511eba780c4
|
[
"MIT"
] | null | null | null |
# key is age, chance is contents
def getdeathchance(agent):
deathchance = 0.0
if agent.taxon == "savannah":
if agent.sex == 'm':
deathchance = SavannahLifeTable.male_death_chance[agent.age]
else:
deathchance = SavannahLifeTable.female_death_chance[agent.age]
if agent.taxon == "hamadryas":
if agent.sex == 'm':
deathchance = HamadryasLifeTable.male_death_chance[agent.age]
else:
deathchance = HamadryasLifeTable.female_death_chance[agent.age]
# print deathchance
return deathchance
def getbirthchance(agent):
birthchance = 0.0
if agent.taxon == "savannah":
birthchance = SavannahLifeTable.birth_chance[agent.age]
if agent.taxon == "hamadryas":
birthchance = HamadryasLifeTable.birth_chance[agent.age]
return birthchance
class SavannahLifeTable:
male_death_chance = {
0: 0,
0.5: 0.10875,
1: 0.10875,
1.5: 0.0439,
2: 0.0439,
2.5: 0.03315,
3: 0.03315,
3.5: 0.04165,
4: 0.04165,
4.5: 0.0206,
5: 0.0206,
5.5: 0.02865,
6: 0.02865,
6.5: 0.0346375,
7: 0.0346375,
7.5: 0.0346375,
8: 0.0346375,
8.5: 0.0346375,
9: 0.0346375,
9.5: 0.0346375,
10: 0.0346375,
10.5: 0.0685625,
11: 0.0685625,
11.5: 0.0685625,
12: 0.0685625,
12.5: 0.0685625,
13: 0.0685625,
13.5: 0.0685625,
14: 0.0685625,
14.5: 0.140825,
15: 0.140825,
15.5: 0.140825,
16: 0.140825,
16.5: 0.140825,
17: 0.140825,
17.5: 0.140825,
18: 0.140825,
18.5: 0.125,
19: 0.125,
19.5: 0.335,
20: 0.335,
20.5: 1,
21: 1
}
female_death_chance = {
0: 0,
0.5: 0.1031,
1: 0.1031,
1.5: 0.0558,
2: 0.0558,
2.5: 0.0317,
3: 0.0317,
3.5: 0.0156,
4: 0.0156,
4.5: 0.02355,
5: 0.02355,
5.5: 0.027125,
6: 0.027125,
6.5: 0.027125,
7: 0.027125,
7.5: 0.027125,
8: 0.027125,
8.5: 0.027125,
9: 0.027125,
9.5: 0.0436875,
10: 0.0436875,
10.5: 0.0436875,
11: 0.0436875,
11.5: 0.0436875,
12: 0.0436875,
12.5: 0.0436875,
13: 0.0436875,
13.5: 0.0691,
14: 0.0691,
14.5: 0.0691,
15: 0.0691,
15.5: 0.0691,
16: 0.0691,
16.5: 0.0691,
17: 0.0691,
17.5: 0.141125,
18: 0.141125,
18.5: 0.141125,
19: 0.141125,
19.5: 0.141125,
20: 0.141125,
20.5: 0.141125,
21: 0.141125,
21.5: 0.2552875,
22: 0.2552875,
22.5: 0.2552875,
23: 0.2552875,
23.5: 0.2552875,
24: 0.2552875,
24.5: 0.2552875,
25: 1,
25.5: 1
}
birth_chance = {
0: 0,
0.5: 0,
1: 0,
1.5: 0,
2: 0,
2.5: 0,
3: 0,
3.5: 0,
4: 0,
4.5: 0,
5: 0.85,
5.5: 0.85,
6: 0.85,
6.5: 0.85,
7: 0.85,
7.5: 0.9,
8: 0.9,
8.5: 0.9,
9: 0.9,
9.5: 0.9,
10: 0.9,
10.5: 0.85,
11: 0.85,
11.5: 0.85,
12: 0.85,
12.5: 0.8,
13: 0.8,
13.5: 0.8,
14: 0.8,
14.5: 0.8,
15: 0.8,
15.5: 0.75,
16: 0.75,
16.5: 0.75,
17: 0.75,
17.5: 0.75,
18: 0.75,
18.5: 0.75,
19: 0.75,
19.5: 0.75,
20: 0.75,
20.5: 0.6,
21: 0.6,
21.5: 0.6,
22: 0.6,
22.5: 0.6,
23: 0.6,
23.5: 0.6,
24: 0.6,
24.5: 0.6,
25: 0
}
class HamadryasLifeTable:
male_death_chance = {
0: 0,
0.5: 0.10875,
1: 0.10875,
1.5: 0.0439,
2: 0.0439,
2.5: 0.03315,
3: 0.03315,
3.5: 0.04165,
4: 0.04165,
4.5: 0.0206,
5: 0.0206,
5.5: 0.02865,
6: 0.02865,
6.5: 0.0346375,
7: 0.0346375,
7.5: 0.0346375,
8: 0.0346375,
8.5: 0.0346375,
9: 0.0346375,
9.5: 0.0346375,
10: 0.0346375,
10.5: 0.0685625,
11: 0.0685625,
11.5: 0.0685625,
12: 0.0685625,
12.5: 0.0685625,
13: 0.0685625,
13.5: 0.0685625,
14: 0.0685625,
14.5: 0.140825,
15: 0.140825,
15.5: 0.140825,
16: 0.140825,
16.5: 0.140825,
17: 0.140825,
17.5: 0.140825,
18: 0.140825,
18.5: 0.125,
19: 0.125,
19.5: 0.335,
20: 0.335,
20.5: 1
}
female_death_chance = {
0: 0,
0.5: 0.1031,
1: 0.1031,
1.5: 0.0558,
2: 0.0558,
2.5: 0.0317,
3: 0.0317,
3.5: 0.0156,
4: 0.0156,
4.5: 0.02355,
5: 0.02355,
5.5: 0.027125,
6: 0.027125,
6.5: 0.027125,
7: 0.027125,
7.5: 0.027125,
8: 0.027125,
8.5: 0.027125,
9: 0.027125,
9.5: 0.0436875,
10: 0.0436875,
10.5: 0.0436875,
11: 0.0436875,
11.5: 0.0436875,
12: 0.0436875,
12.5: 0.0436875,
13: 0.0436875,
13.5: 0.0691,
14: 0.0691,
14.5: 0.0691,
15: 0.0691,
15.5: 0.0691,
16: 0.0691,
16.5: 0.0691,
17: 0.0691,
17.5: 0.141125,
18: 0.141125,
18.5: 0.141125,
19: 0.141125,
19.5: 0.141125,
20: 0.141125,
20.5: 0.141125,
21: 0.141125,
21.5: 0.2552875,
22: 0.2552875,
22.5: 0.2552875,
23: 0.2552875,
23.5: 0.2552875,
24: 0.2552875,
24.5: 0.2552875,
25: 1
}
birth_chance = {
0: 0,
0.5: 0,
1: 0,
1.5: 0,
2: 0,
2.5: 0,
3: 0,
3.5: 0,
4: 0,
4.5: 0,
5: 0.85,
5.5: 0.85,
6: 0.85,
6.5: 0.85,
7: 0.85,
7.5: 0.9,
8: 0.9,
8.5: 0.9,
9: 0.9,
9.5: 0.9,
10: 0.9,
10.5: 0.85,
11: 0.85,
11.5: 0.85,
12: 0.85,
12.5: 0.8,
13: 0.8,
13.5: 0.8,
14: 0.8,
14.5: 0.8,
15: 0.8,
15.5: 0.75,
16: 0.75,
16.5: 0.75,
17: 0.75,
17.5: 0.75,
18: 0.75,
18.5: 0.75,
19: 0.75,
19.5: 0.75,
20: 0.75,
20.5: 0.6,
21: 0.6,
21.5: 0.6,
22: 0.6,
22.5: 0.6,
23: 0.6,
23.5: 0.6,
24: 0.6,
24.5: 0.6,
25: 0
}
| 20.482456
| 75
| 0.389579
| 1,091
| 7,005
| 2.483043
| 0.069661
| 0.107789
| 0.014766
| 0.019934
| 0.874123
| 0.844592
| 0.82835
| 0.774456
| 0.774456
| 0.774456
| 0
| 0.531143
| 0.456817
| 7,005
| 341
| 76
| 20.542522
| 0.180815
| 0.006852
| 0
| 0.919753
| 0
| 0
| 0.005178
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.006173
| false
| 0
| 0
| 0
| 0.037037
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
246aefd0f3e5032d145af51aefb5548545bafadf
| 1,078
|
py
|
Python
|
lexer_test.py
|
drew-loukusa/ID-CAS
|
c758968dbc84ac56bc5385e433763aa534259a54
|
[
"MIT"
] | 1
|
2020-10-05T07:12:59.000Z
|
2020-10-05T07:12:59.000Z
|
lexer_test.py
|
drew-loukusa/ID-CAS
|
c758968dbc84ac56bc5385e433763aa534259a54
|
[
"MIT"
] | null | null | null |
lexer_test.py
|
drew-loukusa/ID-CAS
|
c758968dbc84ac56bc5385e433763aa534259a54
|
[
"MIT"
] | null | null | null |
from lexer import Lexer
#============================== Lexer Tests ===============================#
def test_method_lex_1():
assert Lexer()._split_text("2x") == ['2','*','x','$']
def test_method_lex_2():
assert Lexer()._split_text("x2") == ['x','*','2','$']
def test_method_lex_3():
assert Lexer()._split_text("x2x5") == ['x','*','2','*','x','*','5','$']
def test_method_lex_4():
assert Lexer()._split_text("x20") == ['x','*','20','$']
def test_method_lex_5():
assert Lexer()._split_text("20x") == ['20','*','x','$']
def test_method_lex_6():
assert Lexer()._split_text("20*x5*300+45") == ['20','*','x','*','5','*','300','+','45','$']
def test_method_lex_7():
assert Lexer()._split_text("sinx") == ['sin','x','$']
def test_method_lex_8():
assert Lexer()._split_text("sin(x)") == ['sin','(','x',')','$']
def test_method_lex_9():
assert Lexer()._split_text("sin(x)x5") == ['sin','(','x',')','*','x','*','5','$']
def test_method_lex_10():
assert Lexer()._split_text("sin(20*x5*300+45)") == ['sin','(','20','*','x','*','5','*','300','+','45',')','$']
| 31.705882
| 111
| 0.509276
| 147
| 1,078
| 3.394558
| 0.22449
| 0.140281
| 0.260521
| 0.320641
| 0.398798
| 0.248497
| 0
| 0
| 0
| 0
| 0
| 0.062308
| 0.091837
| 1,078
| 34
| 111
| 31.705882
| 0.447395
| 0.068646
| 0
| 0
| 0
| 0
| 0.142715
| 0
| 0
| 0
| 0
| 0
| 0.47619
| 1
| 0.47619
| true
| 0
| 0.047619
| 0
| 0.52381
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
79f4adfd28c7d97951d5016cb052abce74a803f6
| 112
|
py
|
Python
|
notebooks/ProFET/ProFET/feat_extract/__init__.py
|
cactuskid/Fourier2Struct
|
da1e2382c779487e7d0726ec4a5acad050db2ce7
|
[
"MIT"
] | 1
|
2020-11-05T18:48:12.000Z
|
2020-11-05T18:48:12.000Z
|
notebooks/ProFET/ProFET/feat_extract/__init__.py
|
cactuskid/Fourier2Struct
|
da1e2382c779487e7d0726ec4a5acad050db2ce7
|
[
"MIT"
] | null | null | null |
notebooks/ProFET/ProFET/feat_extract/__init__.py
|
cactuskid/Fourier2Struct
|
da1e2382c779487e7d0726ec4a5acad050db2ce7
|
[
"MIT"
] | null | null | null |
__all__ = ['FeatureGen', 'ProtFeat']
from Feature_Extract import FeatureGen
from Feature_Extract import ProtFeat
| 37.333333
| 38
| 0.830357
| 13
| 112
| 6.692308
| 0.538462
| 0.252874
| 0.413793
| 0.551724
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098214
| 112
| 3
| 39
| 37.333333
| 0.861386
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0
| 7
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0354866e16a371185e7e1aaf7c31525a318ff016
| 9,533
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py
|
Python
|
workspace/module/python-2.7/LxGui/qt/guiQtWidgets/_guiQtWgtTabview.py
|
no7hings/Lynxi
|
43c745198a714c2e5aca86c6d7a014adeeb9abf7
|
[
"MIT"
] | 2
|
2018-03-06T03:33:55.000Z
|
2019-03-26T03:25:11.000Z
|
workspace/module/python-2.7/LxGui/qt/guiQtWidgets/_guiQtWgtTabview.py
|
no7hings/lynxi
|
43c745198a714c2e5aca86c6d7a014adeeb9abf7
|
[
"MIT"
] | null | null | null |
workspace/module/python-2.7/LxGui/qt/guiQtWidgets/_guiQtWgtTabview.py
|
no7hings/lynxi
|
43c745198a714c2e5aca86c6d7a014adeeb9abf7
|
[
"MIT"
] | null | null | null |
# coding:utf-8
from LxGui.qt import qtCore, guiQtWgtAbs
#
from LxGui.qt.guiQtModels import _guiQtMdlTabview
#
from ..guiQtWidgets import _guiQtWgtBasic, _guiQtWgtValueitem, _guiQtWgtTabitem, _guiQtWgtItem
class _QtButtonTabbar(guiQtWgtAbs.Abs_GuiQtTabbarWgt):
CLS_gui_qt__tabbar_wgt_model = _guiQtMdlTabview.QtButtonTabbarModel
def __init__(self, *args, **kwargs):
if qtCore.LOAD_INDEX is 0:
self._clsSuper = super(qtCore.QWidget, self)
self._clsSuper.__init__(*args, **kwargs)
else:
self._clsSuper = super(_QtButtonTabbar, self)
self._clsSuper.__init__(*args, **kwargs)
#
self._initAbsGuiQtTabbarWgt()
#
self.setupUi()
#
def setupUi(self):
self._viewModel = self.CLS_gui_qt__tabbar_wgt_model(self)
#
class QtButtonTabgroup(guiQtWgtAbs.Abs_GuiQtTabgroupWgt):
CLS_gui_qt__tabgroup_wgt__model = _guiQtMdlTabview.QtButtonTabGroupModel
CLS_gui_qt__tabgroup_wgt__tabbutton = _guiQtWgtTabitem.QtTabbutton
def __init__(self, *args, **kwargs):
if qtCore.LOAD_INDEX is 0:
self._clsSuper = super(qtCore.QWidget, self)
self._clsSuper.__init__(*args, **kwargs)
else:
self._clsSuper = super(QtButtonTabgroup, self)
self._clsSuper.__init__(*args, **kwargs)
#
self._initAbsGuiQtTabgroupWgt()
#
self.setupUi()
#
self.viewModel().setTabPosition(qtCore.West)
self.viewModel().setTabSize(128, 24)
#
def setupUi(self):
self._tabBar = _QtButtonTabbar(self)
self._tabBar.currentChanged.connect(self._currentChangedEmit)
#
self._viewModel = self.CLS_gui_qt__tabgroup_wgt__model(self)
self._viewModel.setItemClass(
self.CLS_gui_qt__tabgroup_wgt__tabbutton
)
#
self._tabBar.valueChanged.connect(self.viewModel()._updateScrollButtonState)
# Shelf Tab Bar
class _QtShelfTabbar(guiQtWgtAbs.Abs_GuiQtTabbarWgt):
CLS_gui_qt__tabbar_wgt_model = _guiQtMdlTabview.QtShelfTabbarModel
def __init__(self, *args, **kwargs):
if qtCore.LOAD_INDEX is 0:
self._clsSuper = super(qtCore.QWidget, self)
self._clsSuper.__init__(*args, **kwargs)
else:
self._clsSuper = super(_QtShelfTabbar, self)
self._clsSuper.__init__(*args, **kwargs)
#
self._initAbsGuiQtTabbarWgt()
#
def paintEvent(self, event):
painter = qtCore.QPainter_(self)
# painter.begin(self) # for pyside2
#
if self.viewModel()._uiTabBarPath:
painter.setBackgroundRgba(self._wgt__background_rgba)
painter.setBorderRgba(self._wgt__border_rgba)
painter.drawPath(self.viewModel()._uiTabBarPath)
if self.viewModel()._uiTabPathLis:
for seq, i in enumerate(self.viewModel()._uiTabPathLis):
if not seq == self.viewModel().currentItemIndex() and seq == self.viewModel().hoverItemIndex():
if self.viewModel().tabPosition() == qtCore.South or self.viewModel().tabPosition() == qtCore.North:
gradient = qtCore.QtGui.QLinearGradient(self.viewModel().basicRect().topLeft(), self.viewModel().basicRect().bottomLeft())
else:
gradient = qtCore.QtGui.QLinearGradient(self.viewModel().basicRect().topLeft(), self.viewModel().basicRect().topRight())
#
gradient.setColorAt(0, qtCore.CLS_color(*self._uiTabHoverBackgroundRgba))
gradient.setColorAt(1, qtCore.CLS_color(0, 0, 0, 0))
brush = qtCore.CLS_brush(gradient)
painter.setBrush(brush)
painter.setBorderRgba((0, 0, 0, 0))
#
painter.drawPath(i)
# painter.end() # for pyside2
#
def setupUi(self):
self._viewModel = self.CLS_gui_qt__tabbar_wgt_model(self)
# Shelf Tab Group
class QtVShelfTabgroup(guiQtWgtAbs.Abs_GuiQtTabgroupWgt):
CLS_gui_qt__tabgroup_wgt__model = _guiQtMdlTabview.QtShelfTabGroupModel
CLS_gui_qt__tabgroup_wgt__iconbutton = _guiQtWgtBasic.QtIconbutton
CLS_gui_qt__tabgroup_wgt__action_iconbutton = _guiQtWgtBasic.QtActionIconbutton
CLS_gui_qt__tabgroup_wgt__tabbar = _QtShelfTabbar
CLS_gui_qt__tabgroup_wgt__tabbutton = _guiQtWgtTabitem.QtShelfTabbutton
def __init__(self, *args, **kwargs):
if qtCore.LOAD_INDEX is 0:
self._clsSuper = super(qtCore.QWidget, self)
self._clsSuper.__init__(*args, **kwargs)
else:
self._clsSuper = super(QtVShelfTabgroup, self)
self._clsSuper.__init__(*args, **kwargs)
#
self._initAbsGuiQtTabgroupWgt()
#
self.setupUi()
#
self.viewModel().setTabPosition(qtCore.West)
self.viewModel().setTabSize(64, 32)
#
def paintEvent(self, event):
painter = qtCore.QPainter_(self)
# painter.begin(self) # for pyside2
# Background
painter.setBackgroundRgba(self._wgt__background_rgba)
painter.setBorderRgba(self._wgt__border_rgba)
#
painter.drawRect(self.viewModel().scrollRect())
# painter.end() # for pyside2
#
def setupUi(self):
self._tabBar = self.CLS_gui_qt__tabgroup_wgt__tabbar(self)
self._tabBar.currentChanged.connect(self._currentChangedEmit)
#
self._addButton = self.CLS_gui_qt__tabgroup_wgt__action_iconbutton('svg_basic/addtab', self)
#
self._subScrollButton, self._addScrollButton = (
self.CLS_gui_qt__tabgroup_wgt__iconbutton('svg_basic/vscrollsub', self),
self.CLS_gui_qt__tabgroup_wgt__iconbutton('svg_basic/vscrolladd', self)
)
#
self._viewModel = self.CLS_gui_qt__tabgroup_wgt__model(self)
self._viewModel.setItemClass(self.CLS_gui_qt__tabgroup_wgt__tabbutton)
#
self._tabBar.valueChanged.connect(self.viewModel()._updateScrollButtonState)
self._subScrollButton.clicked.connect(self._tabBar.viewModel()._hScrollSubAction)
self._subScrollButton.clicked.connect(self._tabBar.viewModel()._vScrollSubAction)
self._subScrollButton.clicked.connect(self.viewModel()._updateScrollButtonState)
self._addScrollButton.clicked.connect(self._tabBar.viewModel()._hScrollAddAction)
self._addScrollButton.clicked.connect(self._tabBar.viewModel()._vScrollAddAction)
self._addScrollButton.clicked.connect(self.viewModel()._updateScrollButtonState)
# Shelf Tab Group
class QtHShelfTabgroup(guiQtWgtAbs.Abs_GuiQtTabgroupWgt):
CLS_gui_qt__tabgroup_wgt__model = _guiQtMdlTabview.QtShelfTabGroupModel
CLS_gui_qt__tabgroup_wgt__iconbutton = _guiQtWgtBasic.QtIconbutton
CLS_gui_qt__tabgroup_wgt__action_iconbutton = _guiQtWgtBasic.QtActionIconbutton
CLS_gui_qt__tabgroup_wgt__tabbar = _QtShelfTabbar
CLS_gui_qt__tabgroup_wgt__tabbutton = _guiQtWgtTabitem.QtShelfTabbutton
CLS_gui_qt__tabgroup_wgt__choose_tabbutton = _guiQtWgtValueitem.QtChooseTabbutton
def __init__(self, *args, **kwargs):
if qtCore.LOAD_INDEX is 0:
self._clsSuper = super(qtCore.QWidget, self)
self._clsSuper.__init__(*args, **kwargs)
else:
self._clsSuper = super(QtHShelfTabgroup, self)
self._clsSuper.__init__(*args, **kwargs)
#
self._initAbsGuiQtTabgroupWgt()
#
self.setupUi()
#
self.viewModel().setTabPosition(qtCore.North)
self.viewModel().setTabSize(192, 24)
#
def paintEvent(self, event):
painter = qtCore.QPainter_(self)
# painter.begin(self) # for pyside2
# Background
painter.setBackgroundRgba(self._wgt__background_rgba)
painter.setBorderRgba(self._wgt__border_rgba)
#
painter.drawRect(self.viewModel().scrollRect())
# painter.end() # for pyside2
#
def chooseTab(self):
return self._chooseTabbuttonWgtObj
#
def setupUi(self):
self._tabBar = self.CLS_gui_qt__tabgroup_wgt__tabbar(self)
self._tabBar.currentChanged.connect(self._currentChangedEmit)
#
self._addButton = self.CLS_gui_qt__tabgroup_wgt__action_iconbutton('svg_basic/addtab', self)
#
self._subScrollButton, self._addScrollButton = (
self.CLS_gui_qt__tabgroup_wgt__iconbutton('svg_basic/vscrollsub', self),
self.CLS_gui_qt__tabgroup_wgt__iconbutton('svg_basic/vscrolladd', self)
)
#
self._viewModel = self.CLS_gui_qt__tabgroup_wgt__model(self)
self._viewModel.setItemClass(self.CLS_gui_qt__tabgroup_wgt__tabbutton)
#
self._tabBar.valueChanged.connect(self.viewModel()._updateScrollButtonState)
self._subScrollButton.clicked.connect(self._tabBar.viewModel()._hScrollSubAction)
self._subScrollButton.clicked.connect(self._tabBar.viewModel()._vScrollSubAction)
self._subScrollButton.clicked.connect(self.viewModel()._updateScrollButtonState)
self._addScrollButton.clicked.connect(self._tabBar.viewModel()._hScrollAddAction)
self._addScrollButton.clicked.connect(self._tabBar.viewModel()._vScrollAddAction)
self._addScrollButton.clicked.connect(self.viewModel()._updateScrollButtonState)
self._chooseTabbuttonWgtObj = self.CLS_gui_qt__tabgroup_wgt__choose_tabbutton(self)
| 41.811404
| 146
| 0.684779
| 940
| 9,533
| 6.465957
| 0.147872
| 0.07486
| 0.042119
| 0.073708
| 0.821981
| 0.818032
| 0.814084
| 0.795821
| 0.765054
| 0.765054
| 0
| 0.004843
| 0.220287
| 9,533
| 227
| 147
| 41.995595
| 0.812862
| 0.027903
| 0
| 0.701987
| 0
| 0
| 0.012162
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.092715
| false
| 0
| 0.019868
| 0.006623
| 0.251656
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3030c34e23af681257502d12b7d814458d49f90b
| 128
|
py
|
Python
|
python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_0/_pkg0_1_0_0_0/_mod0_1_0_0_0_0.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2018-12-29T09:53:39.000Z
|
2018-12-29T09:53:42.000Z
|
python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_0/_pkg0_1_0_0_0/_mod0_1_0_0_0_0.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_0/_pkg0_1_0_0_0/_mod0_1_0_0_0_0.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
name0_1_0_0_0_0_0 = None
name0_1_0_0_0_0_1 = None
name0_1_0_0_0_0_2 = None
name0_1_0_0_0_0_3 = None
name0_1_0_0_0_0_4 = None
| 14.222222
| 24
| 0.820313
| 40
| 128
| 1.875
| 0.175
| 0.426667
| 0.44
| 0.32
| 0.88
| 0.88
| 0.746667
| 0
| 0
| 0
| 0
| 0.318182
| 0.140625
| 128
| 9
| 25
| 14.222222
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
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| 0
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| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
|
0
| 10
|
0650f80378a6243543e6da450bd74fa24d68b0ed
| 29,238
|
py
|
Python
|
test/test_iam_api.py
|
sdnit-se/intersight-python
|
551f7685c0f76bb8af60ec83ffb6f9672d49a4ae
|
[
"Apache-2.0"
] | 21
|
2018-03-29T14:20:35.000Z
|
2021-10-13T05:11:41.000Z
|
test/test_iam_api.py
|
sdnit-se/intersight-python
|
551f7685c0f76bb8af60ec83ffb6f9672d49a4ae
|
[
"Apache-2.0"
] | 14
|
2018-01-30T15:45:46.000Z
|
2022-02-23T14:23:21.000Z
|
test/test_iam_api.py
|
sdnit-se/intersight-python
|
551f7685c0f76bb8af60ec83ffb6f9672d49a4ae
|
[
"Apache-2.0"
] | 18
|
2018-01-03T15:09:56.000Z
|
2021-07-16T02:21:54.000Z
|
# coding: utf-8
"""
Cisco Intersight
Cisco Intersight is a management platform delivered as a service with embedded analytics for your Cisco and 3rd party IT infrastructure. This platform offers an intelligent level of management that enables IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools. Cisco Intersight provides an integrated and intuitive management experience for resources in the traditional data center as well as at the edge. With flexible deployment options to address complex security needs, getting started with Intersight is quick and easy. Cisco Intersight has deep integration with Cisco UCS and HyperFlex systems allowing for remote deployment, configuration, and ongoing maintenance. The model-based deployment works for a single system in a remote location or hundreds of systems in a data center and enables rapid, standardized configuration and deployment. It also streamlines maintaining those systems whether you are working with small or very large configurations. # noqa: E501
The version of the OpenAPI document: 1.0.9-1295
Contact: intersight@cisco.com
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import unittest
import intersight
from intersight.api.iam_api import IamApi # noqa: E501
from intersight.rest import ApiException
class TestIamApi(unittest.TestCase):
"""IamApi unit test stubs"""
def setUp(self):
self.api = intersight.api.iam_api.IamApi() # noqa: E501
def tearDown(self):
pass
def test_create_iam_account(self):
"""Test case for create_iam_account
Create a 'iam.Account' resource. # noqa: E501
"""
pass
def test_create_iam_api_key(self):
"""Test case for create_iam_api_key
Create a 'iam.ApiKey' resource. # noqa: E501
"""
pass
def test_create_iam_app_registration(self):
"""Test case for create_iam_app_registration
Create a 'iam.AppRegistration' resource. # noqa: E501
"""
pass
def test_create_iam_certificate(self):
"""Test case for create_iam_certificate
Create a 'iam.Certificate' resource. # noqa: E501
"""
pass
def test_create_iam_certificate_request(self):
"""Test case for create_iam_certificate_request
Create a 'iam.CertificateRequest' resource. # noqa: E501
"""
pass
def test_create_iam_end_point_user(self):
"""Test case for create_iam_end_point_user
Create a 'iam.EndPointUser' resource. # noqa: E501
"""
pass
def test_create_iam_end_point_user_policy(self):
"""Test case for create_iam_end_point_user_policy
Create a 'iam.EndPointUserPolicy' resource. # noqa: E501
"""
pass
def test_create_iam_end_point_user_role(self):
"""Test case for create_iam_end_point_user_role
Create a 'iam.EndPointUserRole' resource. # noqa: E501
"""
pass
def test_create_iam_idp(self):
"""Test case for create_iam_idp
Create a 'iam.Idp' resource. # noqa: E501
"""
pass
def test_create_iam_ldap_group(self):
"""Test case for create_iam_ldap_group
Create a 'iam.LdapGroup' resource. # noqa: E501
"""
pass
def test_create_iam_ldap_policy(self):
"""Test case for create_iam_ldap_policy
Create a 'iam.LdapPolicy' resource. # noqa: E501
"""
pass
def test_create_iam_ldap_provider(self):
"""Test case for create_iam_ldap_provider
Create a 'iam.LdapProvider' resource. # noqa: E501
"""
pass
def test_create_iam_permission(self):
"""Test case for create_iam_permission
Create a 'iam.Permission' resource. # noqa: E501
"""
pass
def test_create_iam_private_key_spec(self):
"""Test case for create_iam_private_key_spec
Create a 'iam.PrivateKeySpec' resource. # noqa: E501
"""
pass
def test_create_iam_qualifier(self):
"""Test case for create_iam_qualifier
Create a 'iam.Qualifier' resource. # noqa: E501
"""
pass
def test_create_iam_resource_roles(self):
"""Test case for create_iam_resource_roles
Create a 'iam.ResourceRoles' resource. # noqa: E501
"""
pass
def test_create_iam_trust_point(self):
"""Test case for create_iam_trust_point
Create a 'iam.TrustPoint' resource. # noqa: E501
"""
pass
def test_create_iam_user(self):
"""Test case for create_iam_user
Create a 'iam.User' resource. # noqa: E501
"""
pass
def test_create_iam_user_group(self):
"""Test case for create_iam_user_group
Create a 'iam.UserGroup' resource. # noqa: E501
"""
pass
def test_delete_iam_account(self):
"""Test case for delete_iam_account
Delete a 'iam.Account' resource. # noqa: E501
"""
pass
def test_delete_iam_api_key(self):
"""Test case for delete_iam_api_key
Delete a 'iam.ApiKey' resource. # noqa: E501
"""
pass
def test_delete_iam_app_registration(self):
"""Test case for delete_iam_app_registration
Delete a 'iam.AppRegistration' resource. # noqa: E501
"""
pass
def test_delete_iam_certificate(self):
"""Test case for delete_iam_certificate
Delete a 'iam.Certificate' resource. # noqa: E501
"""
pass
def test_delete_iam_certificate_request(self):
"""Test case for delete_iam_certificate_request
Delete a 'iam.CertificateRequest' resource. # noqa: E501
"""
pass
def test_delete_iam_end_point_user(self):
"""Test case for delete_iam_end_point_user
Delete a 'iam.EndPointUser' resource. # noqa: E501
"""
pass
def test_delete_iam_end_point_user_policy(self):
"""Test case for delete_iam_end_point_user_policy
Delete a 'iam.EndPointUserPolicy' resource. # noqa: E501
"""
pass
def test_delete_iam_end_point_user_role(self):
"""Test case for delete_iam_end_point_user_role
Delete a 'iam.EndPointUserRole' resource. # noqa: E501
"""
pass
def test_delete_iam_idp(self):
"""Test case for delete_iam_idp
Delete a 'iam.Idp' resource. # noqa: E501
"""
pass
def test_delete_iam_ldap_group(self):
"""Test case for delete_iam_ldap_group
Delete a 'iam.LdapGroup' resource. # noqa: E501
"""
pass
def test_delete_iam_ldap_policy(self):
"""Test case for delete_iam_ldap_policy
Delete a 'iam.LdapPolicy' resource. # noqa: E501
"""
pass
def test_delete_iam_ldap_provider(self):
"""Test case for delete_iam_ldap_provider
Delete a 'iam.LdapProvider' resource. # noqa: E501
"""
pass
def test_delete_iam_o_auth_token(self):
"""Test case for delete_iam_o_auth_token
Delete a 'iam.OAuthToken' resource. # noqa: E501
"""
pass
def test_delete_iam_permission(self):
"""Test case for delete_iam_permission
Delete a 'iam.Permission' resource. # noqa: E501
"""
pass
def test_delete_iam_private_key_spec(self):
"""Test case for delete_iam_private_key_spec
Delete a 'iam.PrivateKeySpec' resource. # noqa: E501
"""
pass
def test_delete_iam_qualifier(self):
"""Test case for delete_iam_qualifier
Delete a 'iam.Qualifier' resource. # noqa: E501
"""
pass
def test_delete_iam_resource_roles(self):
"""Test case for delete_iam_resource_roles
Delete a 'iam.ResourceRoles' resource. # noqa: E501
"""
pass
def test_delete_iam_session(self):
"""Test case for delete_iam_session
Delete a 'iam.Session' resource. # noqa: E501
"""
pass
def test_delete_iam_trust_point(self):
"""Test case for delete_iam_trust_point
Delete a 'iam.TrustPoint' resource. # noqa: E501
"""
pass
def test_delete_iam_user(self):
"""Test case for delete_iam_user
Delete a 'iam.User' resource. # noqa: E501
"""
pass
def test_delete_iam_user_group(self):
"""Test case for delete_iam_user_group
Delete a 'iam.UserGroup' resource. # noqa: E501
"""
pass
def test_get_iam_account_by_moid(self):
"""Test case for get_iam_account_by_moid
Read a 'iam.Account' resource. # noqa: E501
"""
pass
def test_get_iam_account_list(self):
"""Test case for get_iam_account_list
Read a 'iam.Account' resource. # noqa: E501
"""
pass
def test_get_iam_api_key_by_moid(self):
"""Test case for get_iam_api_key_by_moid
Read a 'iam.ApiKey' resource. # noqa: E501
"""
pass
def test_get_iam_api_key_list(self):
"""Test case for get_iam_api_key_list
Read a 'iam.ApiKey' resource. # noqa: E501
"""
pass
def test_get_iam_app_registration_by_moid(self):
"""Test case for get_iam_app_registration_by_moid
Read a 'iam.AppRegistration' resource. # noqa: E501
"""
pass
def test_get_iam_app_registration_list(self):
"""Test case for get_iam_app_registration_list
Read a 'iam.AppRegistration' resource. # noqa: E501
"""
pass
def test_get_iam_certificate_by_moid(self):
"""Test case for get_iam_certificate_by_moid
Read a 'iam.Certificate' resource. # noqa: E501
"""
pass
def test_get_iam_certificate_list(self):
"""Test case for get_iam_certificate_list
Read a 'iam.Certificate' resource. # noqa: E501
"""
pass
def test_get_iam_certificate_request_by_moid(self):
"""Test case for get_iam_certificate_request_by_moid
Read a 'iam.CertificateRequest' resource. # noqa: E501
"""
pass
def test_get_iam_certificate_request_list(self):
"""Test case for get_iam_certificate_request_list
Read a 'iam.CertificateRequest' resource. # noqa: E501
"""
pass
def test_get_iam_domain_group_by_moid(self):
"""Test case for get_iam_domain_group_by_moid
Read a 'iam.DomainGroup' resource. # noqa: E501
"""
pass
def test_get_iam_domain_group_list(self):
"""Test case for get_iam_domain_group_list
Read a 'iam.DomainGroup' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_privilege_by_moid(self):
"""Test case for get_iam_end_point_privilege_by_moid
Read a 'iam.EndPointPrivilege' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_privilege_list(self):
"""Test case for get_iam_end_point_privilege_list
Read a 'iam.EndPointPrivilege' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_role_by_moid(self):
"""Test case for get_iam_end_point_role_by_moid
Read a 'iam.EndPointRole' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_role_list(self):
"""Test case for get_iam_end_point_role_list
Read a 'iam.EndPointRole' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_user_by_moid(self):
"""Test case for get_iam_end_point_user_by_moid
Read a 'iam.EndPointUser' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_user_list(self):
"""Test case for get_iam_end_point_user_list
Read a 'iam.EndPointUser' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_user_policy_by_moid(self):
"""Test case for get_iam_end_point_user_policy_by_moid
Read a 'iam.EndPointUserPolicy' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_user_policy_list(self):
"""Test case for get_iam_end_point_user_policy_list
Read a 'iam.EndPointUserPolicy' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_user_role_by_moid(self):
"""Test case for get_iam_end_point_user_role_by_moid
Read a 'iam.EndPointUserRole' resource. # noqa: E501
"""
pass
def test_get_iam_end_point_user_role_list(self):
"""Test case for get_iam_end_point_user_role_list
Read a 'iam.EndPointUserRole' resource. # noqa: E501
"""
pass
def test_get_iam_idp_by_moid(self):
"""Test case for get_iam_idp_by_moid
Read a 'iam.Idp' resource. # noqa: E501
"""
pass
def test_get_iam_idp_list(self):
"""Test case for get_iam_idp_list
Read a 'iam.Idp' resource. # noqa: E501
"""
pass
def test_get_iam_idp_reference_by_moid(self):
"""Test case for get_iam_idp_reference_by_moid
Read a 'iam.IdpReference' resource. # noqa: E501
"""
pass
def test_get_iam_idp_reference_list(self):
"""Test case for get_iam_idp_reference_list
Read a 'iam.IdpReference' resource. # noqa: E501
"""
pass
def test_get_iam_ldap_group_by_moid(self):
"""Test case for get_iam_ldap_group_by_moid
Read a 'iam.LdapGroup' resource. # noqa: E501
"""
pass
def test_get_iam_ldap_group_list(self):
"""Test case for get_iam_ldap_group_list
Read a 'iam.LdapGroup' resource. # noqa: E501
"""
pass
def test_get_iam_ldap_policy_by_moid(self):
"""Test case for get_iam_ldap_policy_by_moid
Read a 'iam.LdapPolicy' resource. # noqa: E501
"""
pass
def test_get_iam_ldap_policy_list(self):
"""Test case for get_iam_ldap_policy_list
Read a 'iam.LdapPolicy' resource. # noqa: E501
"""
pass
def test_get_iam_ldap_provider_by_moid(self):
"""Test case for get_iam_ldap_provider_by_moid
Read a 'iam.LdapProvider' resource. # noqa: E501
"""
pass
def test_get_iam_ldap_provider_list(self):
"""Test case for get_iam_ldap_provider_list
Read a 'iam.LdapProvider' resource. # noqa: E501
"""
pass
def test_get_iam_o_auth_token_by_moid(self):
"""Test case for get_iam_o_auth_token_by_moid
Read a 'iam.OAuthToken' resource. # noqa: E501
"""
pass
def test_get_iam_o_auth_token_list(self):
"""Test case for get_iam_o_auth_token_list
Read a 'iam.OAuthToken' resource. # noqa: E501
"""
pass
def test_get_iam_permission_by_moid(self):
"""Test case for get_iam_permission_by_moid
Read a 'iam.Permission' resource. # noqa: E501
"""
pass
def test_get_iam_permission_list(self):
"""Test case for get_iam_permission_list
Read a 'iam.Permission' resource. # noqa: E501
"""
pass
def test_get_iam_private_key_spec_by_moid(self):
"""Test case for get_iam_private_key_spec_by_moid
Read a 'iam.PrivateKeySpec' resource. # noqa: E501
"""
pass
def test_get_iam_private_key_spec_list(self):
"""Test case for get_iam_private_key_spec_list
Read a 'iam.PrivateKeySpec' resource. # noqa: E501
"""
pass
def test_get_iam_privilege_by_moid(self):
"""Test case for get_iam_privilege_by_moid
Read a 'iam.Privilege' resource. # noqa: E501
"""
pass
def test_get_iam_privilege_list(self):
"""Test case for get_iam_privilege_list
Read a 'iam.Privilege' resource. # noqa: E501
"""
pass
def test_get_iam_privilege_set_by_moid(self):
"""Test case for get_iam_privilege_set_by_moid
Read a 'iam.PrivilegeSet' resource. # noqa: E501
"""
pass
def test_get_iam_privilege_set_list(self):
"""Test case for get_iam_privilege_set_list
Read a 'iam.PrivilegeSet' resource. # noqa: E501
"""
pass
def test_get_iam_qualifier_by_moid(self):
"""Test case for get_iam_qualifier_by_moid
Read a 'iam.Qualifier' resource. # noqa: E501
"""
pass
def test_get_iam_qualifier_list(self):
"""Test case for get_iam_qualifier_list
Read a 'iam.Qualifier' resource. # noqa: E501
"""
pass
def test_get_iam_resource_limits_by_moid(self):
"""Test case for get_iam_resource_limits_by_moid
Read a 'iam.ResourceLimits' resource. # noqa: E501
"""
pass
def test_get_iam_resource_limits_list(self):
"""Test case for get_iam_resource_limits_list
Read a 'iam.ResourceLimits' resource. # noqa: E501
"""
pass
def test_get_iam_resource_permission_by_moid(self):
"""Test case for get_iam_resource_permission_by_moid
Read a 'iam.ResourcePermission' resource. # noqa: E501
"""
pass
def test_get_iam_resource_permission_list(self):
"""Test case for get_iam_resource_permission_list
Read a 'iam.ResourcePermission' resource. # noqa: E501
"""
pass
def test_get_iam_resource_roles_by_moid(self):
"""Test case for get_iam_resource_roles_by_moid
Read a 'iam.ResourceRoles' resource. # noqa: E501
"""
pass
def test_get_iam_resource_roles_list(self):
"""Test case for get_iam_resource_roles_list
Read a 'iam.ResourceRoles' resource. # noqa: E501
"""
pass
def test_get_iam_role_by_moid(self):
"""Test case for get_iam_role_by_moid
Read a 'iam.Role' resource. # noqa: E501
"""
pass
def test_get_iam_role_list(self):
"""Test case for get_iam_role_list
Read a 'iam.Role' resource. # noqa: E501
"""
pass
def test_get_iam_security_holder_by_moid(self):
"""Test case for get_iam_security_holder_by_moid
Read a 'iam.SecurityHolder' resource. # noqa: E501
"""
pass
def test_get_iam_security_holder_list(self):
"""Test case for get_iam_security_holder_list
Read a 'iam.SecurityHolder' resource. # noqa: E501
"""
pass
def test_get_iam_service_provider_by_moid(self):
"""Test case for get_iam_service_provider_by_moid
Read a 'iam.ServiceProvider' resource. # noqa: E501
"""
pass
def test_get_iam_service_provider_list(self):
"""Test case for get_iam_service_provider_list
Read a 'iam.ServiceProvider' resource. # noqa: E501
"""
pass
def test_get_iam_session_by_moid(self):
"""Test case for get_iam_session_by_moid
Read a 'iam.Session' resource. # noqa: E501
"""
pass
def test_get_iam_session_limits_by_moid(self):
"""Test case for get_iam_session_limits_by_moid
Read a 'iam.SessionLimits' resource. # noqa: E501
"""
pass
def test_get_iam_session_limits_list(self):
"""Test case for get_iam_session_limits_list
Read a 'iam.SessionLimits' resource. # noqa: E501
"""
pass
def test_get_iam_session_list(self):
"""Test case for get_iam_session_list
Read a 'iam.Session' resource. # noqa: E501
"""
pass
def test_get_iam_system_by_moid(self):
"""Test case for get_iam_system_by_moid
Read a 'iam.System' resource. # noqa: E501
"""
pass
def test_get_iam_system_list(self):
"""Test case for get_iam_system_list
Read a 'iam.System' resource. # noqa: E501
"""
pass
def test_get_iam_trust_point_by_moid(self):
"""Test case for get_iam_trust_point_by_moid
Read a 'iam.TrustPoint' resource. # noqa: E501
"""
pass
def test_get_iam_trust_point_list(self):
"""Test case for get_iam_trust_point_list
Read a 'iam.TrustPoint' resource. # noqa: E501
"""
pass
def test_get_iam_user_by_moid(self):
"""Test case for get_iam_user_by_moid
Read a 'iam.User' resource. # noqa: E501
"""
pass
def test_get_iam_user_group_by_moid(self):
"""Test case for get_iam_user_group_by_moid
Read a 'iam.UserGroup' resource. # noqa: E501
"""
pass
def test_get_iam_user_group_list(self):
"""Test case for get_iam_user_group_list
Read a 'iam.UserGroup' resource. # noqa: E501
"""
pass
def test_get_iam_user_list(self):
"""Test case for get_iam_user_list
Read a 'iam.User' resource. # noqa: E501
"""
pass
def test_get_iam_user_preference_by_moid(self):
"""Test case for get_iam_user_preference_by_moid
Read a 'iam.UserPreference' resource. # noqa: E501
"""
pass
def test_get_iam_user_preference_list(self):
"""Test case for get_iam_user_preference_list
Read a 'iam.UserPreference' resource. # noqa: E501
"""
pass
def test_patch_iam_account(self):
"""Test case for patch_iam_account
Update a 'iam.Account' resource. # noqa: E501
"""
pass
def test_patch_iam_api_key(self):
"""Test case for patch_iam_api_key
Update a 'iam.ApiKey' resource. # noqa: E501
"""
pass
def test_patch_iam_app_registration(self):
"""Test case for patch_iam_app_registration
Update a 'iam.AppRegistration' resource. # noqa: E501
"""
pass
def test_patch_iam_certificate(self):
"""Test case for patch_iam_certificate
Update a 'iam.Certificate' resource. # noqa: E501
"""
pass
def test_patch_iam_certificate_request(self):
"""Test case for patch_iam_certificate_request
Update a 'iam.CertificateRequest' resource. # noqa: E501
"""
pass
def test_patch_iam_end_point_user(self):
"""Test case for patch_iam_end_point_user
Update a 'iam.EndPointUser' resource. # noqa: E501
"""
pass
def test_patch_iam_end_point_user_policy(self):
"""Test case for patch_iam_end_point_user_policy
Update a 'iam.EndPointUserPolicy' resource. # noqa: E501
"""
pass
def test_patch_iam_end_point_user_role(self):
"""Test case for patch_iam_end_point_user_role
Update a 'iam.EndPointUserRole' resource. # noqa: E501
"""
pass
def test_patch_iam_idp(self):
"""Test case for patch_iam_idp
Update a 'iam.Idp' resource. # noqa: E501
"""
pass
def test_patch_iam_idp_reference(self):
"""Test case for patch_iam_idp_reference
Update a 'iam.IdpReference' resource. # noqa: E501
"""
pass
def test_patch_iam_ldap_group(self):
"""Test case for patch_iam_ldap_group
Update a 'iam.LdapGroup' resource. # noqa: E501
"""
pass
def test_patch_iam_ldap_policy(self):
"""Test case for patch_iam_ldap_policy
Update a 'iam.LdapPolicy' resource. # noqa: E501
"""
pass
def test_patch_iam_ldap_provider(self):
"""Test case for patch_iam_ldap_provider
Update a 'iam.LdapProvider' resource. # noqa: E501
"""
pass
def test_patch_iam_local_user_password(self):
"""Test case for patch_iam_local_user_password
Update a 'iam.LocalUserPassword' resource. # noqa: E501
"""
pass
def test_patch_iam_permission(self):
"""Test case for patch_iam_permission
Update a 'iam.Permission' resource. # noqa: E501
"""
pass
def test_patch_iam_private_key_spec(self):
"""Test case for patch_iam_private_key_spec
Update a 'iam.PrivateKeySpec' resource. # noqa: E501
"""
pass
def test_patch_iam_qualifier(self):
"""Test case for patch_iam_qualifier
Update a 'iam.Qualifier' resource. # noqa: E501
"""
pass
def test_patch_iam_resource_roles(self):
"""Test case for patch_iam_resource_roles
Update a 'iam.ResourceRoles' resource. # noqa: E501
"""
pass
def test_patch_iam_user(self):
"""Test case for patch_iam_user
Update a 'iam.User' resource. # noqa: E501
"""
pass
def test_patch_iam_user_group(self):
"""Test case for patch_iam_user_group
Update a 'iam.UserGroup' resource. # noqa: E501
"""
pass
def test_patch_iam_user_preference(self):
"""Test case for patch_iam_user_preference
Update a 'iam.UserPreference' resource. # noqa: E501
"""
pass
def test_update_iam_account(self):
"""Test case for update_iam_account
Update a 'iam.Account' resource. # noqa: E501
"""
pass
def test_update_iam_api_key(self):
"""Test case for update_iam_api_key
Update a 'iam.ApiKey' resource. # noqa: E501
"""
pass
def test_update_iam_app_registration(self):
"""Test case for update_iam_app_registration
Update a 'iam.AppRegistration' resource. # noqa: E501
"""
pass
def test_update_iam_certificate(self):
"""Test case for update_iam_certificate
Update a 'iam.Certificate' resource. # noqa: E501
"""
pass
def test_update_iam_certificate_request(self):
"""Test case for update_iam_certificate_request
Update a 'iam.CertificateRequest' resource. # noqa: E501
"""
pass
def test_update_iam_end_point_user(self):
"""Test case for update_iam_end_point_user
Update a 'iam.EndPointUser' resource. # noqa: E501
"""
pass
def test_update_iam_end_point_user_policy(self):
"""Test case for update_iam_end_point_user_policy
Update a 'iam.EndPointUserPolicy' resource. # noqa: E501
"""
pass
def test_update_iam_end_point_user_role(self):
"""Test case for update_iam_end_point_user_role
Update a 'iam.EndPointUserRole' resource. # noqa: E501
"""
pass
def test_update_iam_idp(self):
"""Test case for update_iam_idp
Update a 'iam.Idp' resource. # noqa: E501
"""
pass
def test_update_iam_idp_reference(self):
"""Test case for update_iam_idp_reference
Update a 'iam.IdpReference' resource. # noqa: E501
"""
pass
def test_update_iam_ldap_group(self):
"""Test case for update_iam_ldap_group
Update a 'iam.LdapGroup' resource. # noqa: E501
"""
pass
def test_update_iam_ldap_policy(self):
"""Test case for update_iam_ldap_policy
Update a 'iam.LdapPolicy' resource. # noqa: E501
"""
pass
def test_update_iam_ldap_provider(self):
"""Test case for update_iam_ldap_provider
Update a 'iam.LdapProvider' resource. # noqa: E501
"""
pass
def test_update_iam_local_user_password(self):
"""Test case for update_iam_local_user_password
Update a 'iam.LocalUserPassword' resource. # noqa: E501
"""
pass
def test_update_iam_permission(self):
"""Test case for update_iam_permission
Update a 'iam.Permission' resource. # noqa: E501
"""
pass
def test_update_iam_private_key_spec(self):
"""Test case for update_iam_private_key_spec
Update a 'iam.PrivateKeySpec' resource. # noqa: E501
"""
pass
def test_update_iam_qualifier(self):
"""Test case for update_iam_qualifier
Update a 'iam.Qualifier' resource. # noqa: E501
"""
pass
def test_update_iam_resource_roles(self):
"""Test case for update_iam_resource_roles
Update a 'iam.ResourceRoles' resource. # noqa: E501
"""
pass
def test_update_iam_user(self):
"""Test case for update_iam_user
Update a 'iam.User' resource. # noqa: E501
"""
pass
def test_update_iam_user_group(self):
"""Test case for update_iam_user_group
Update a 'iam.UserGroup' resource. # noqa: E501
"""
pass
def test_update_iam_user_preference(self):
"""Test case for update_iam_user_preference
Update a 'iam.UserPreference' resource. # noqa: E501
"""
pass
if __name__ == '__main__':
unittest.main()
| 26.677007
| 1,052
| 0.629113
| 3,785
| 29,238
| 4.519155
| 0.055746
| 0.072493
| 0.097749
| 0.133294
| 0.896872
| 0.885823
| 0.839462
| 0.747852
| 0.647355
| 0.453902
| 0
| 0.022843
| 0.290307
| 29,238
| 1,095
| 1,053
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| 0.000741
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| 1
| 0.487342
| false
| 0.490506
| 0.015823
| 0
| 0.506329
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| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
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|
0
| 8
|
066f73274d1d3a58d474c2b63b0f637932c6902f
| 20,077
|
py
|
Python
|
lexed/editor/moves.py
|
lexycore/lexed
|
fc2037a8f4e48a8ddc420d7021532d1cc444bd31
|
[
"MIT"
] | null | null | null |
lexed/editor/moves.py
|
lexycore/lexed
|
fc2037a8f4e48a8ddc420d7021532d1cc444bd31
|
[
"MIT"
] | 1
|
2019-05-03T08:17:31.000Z
|
2019-06-06T11:32:09.000Z
|
lexed/editor/moves.py
|
lexycore/lexed
|
fc2037a8f4e48a8ddc420d7021532d1cc444bd31
|
[
"MIT"
] | null | null | null |
from lexed.editor.meta import EditorMeta, BareException
class EditorMoves(EditorMeta):
def __init__(self):
super().__init__()
def add_character(self, char):
"""program specific function that adds character to line"""
# global current_line, text_entered, program_message, saved_since_edit, continue_down, continue_up
self.continue_down = 0
self.continue_up = 0
# if len(current_line.text) > 4: saved_since_edit = False # Updated so 'new', 'run', 'save', or 'load' won't count as an edit.
self.program_message = ""
if not self.text_entered:
self.text_entered = True
old_number_of_rows = self.current_line.number_of_rows
old_x = self.current_line.x
temp_list = self.current_line.listing
if self.current_line.y == 0 and self.current_line.x == self.current_line.end_x:
temp_list.append(char)
else:
position = self.row_size * (
self.current_line.number_of_rows - 1 - abs(self.current_line.y)) + self.current_line.x - 6
temp_list.insert(position, char)
temp_string = ""
for item in temp_list:
temp_string += item
self.current_line.text = temp_string
self.current_line.x += 1
if self.config["live_syntax"] and \
self.current_line.number_of_rows < (self.window.height - 4):
self.current_line.add_syntax() # added 'live' check to speed up program
if old_number_of_rows != self.current_line.number_of_rows:
if self.current_line.y != 0:
self.current_line.y -= 1
if self.current_line.y == 0:
self.current_line.y -= 1
self.current_line.x = old_x + 1
def key_backspace(self):
"""This function determines what happens when delete/backspace key pressed"""
# global current_line, current_num, saved_since_edit, text_entered, continue_up, continue_down
self.continue_down = 0
self.continue_up = 0
self.saved_since_edit = False
if not self.text_entered and len(self.current_line.text) > 4:
self.text_entered = True
if not self.current_line.text and self.current_line.number == self.lines.total:
self.lines.add() # create emtpy line
if not self.lines.db[self.current_num].text: # delete line if empty
self.delete(self.current_num)
self.text_entered = True
return
if (self.current_num - 1) in self.lines.db and \
self.lines.db[self.current_num].text and self.current_line.x == 6 and \
self.current_line.y == self.current_line.end_y: # end_y added to fix bug
part1 = self.lines.db[self.current_num - 1].text
part2 = self.lines.db[self.current_num].text
self.combine_lines(self.current_num, part1, part2)
self.text_entered = True
return
old_number_of_rows = self.current_line.number_of_rows
temp_list = self.current_line.listing
if self.current_line.y == 0 and self.current_line.x == self.current_line.end_x: # delete last character on line
del temp_list[-1]
else:
position = self.row_size * (
self.current_line.number_of_rows - 1 - abs(self.current_line.y)) + self.current_line.x - 6
try:
if position <= self.current_line.indentation and \
self.current_line.text[position - 3:position + 1] and \
self.current_line.indentation / 4.0 == int(self.current_line.indentation / 4.0): # delete tab
del temp_list[position - 4:position]
self.current_line.x -= 3 # move cursor position 3 spaces, final one below
else:
del temp_list[position - 1] # delete position
except BareException:
del temp_list[position - 1] # delete position
temp_string = ""
for item in temp_list:
temp_string += item
self.current_line.text = temp_string
self.current_line.x -= 1
if self.config["syntax_highlighting"]:
self.current_line.add_syntax()
if old_number_of_rows != self.current_line.number_of_rows:
self.current_line.y += 1
if self.current_line.number_of_rows == 1 and self.current_line.x == 6:
self.current_line.x = self.current_line.end_x
def return_key(self):
"""Function that handles return/enter key"""
# global current_num, text_entered, program_message, saved_since_edit
self.program_message = ''
self.saved_since_edit = False
# new section to deal with undo
if self.text_entered:
self.update_undo()
self.update_que('text entry')
if self.config['syntax_highlighting']:
self.syntax_visible()
if self.current_line.number == self.lines.total and self.current_line.x != 6:
self.lines.add('')
self.current_num += 1
elif self.current_line.text and self.current_line.number_of_rows == 1 and \
6 < self.current_line.x < self.current_line.end_x: # split line in two
part1 = self.current_line.text[:self.current_line.x - 6]
part2 = self.current_line.text[self.current_line.x - 6:]
self.split_line(self.current_num, part1, part2)
elif self.current_line.text and self.current_line.number_of_rows > 1 and \
self.current_line.y > -(self.current_line.number_of_rows - 1) or \
self.current_line.x > 6: # split line in two
prev_part = ''
after_part = ''
current_line1 = self.current_line.row[
self.current_line.y + self.current_line.number_of_rows - 1][:self.current_line.x - 6]
current_line2 = self.current_line.row[
self.current_line.y + self.current_line.number_of_rows - 1][self.current_line.x - 6:]
for i in range(0, -self.current_line.number_of_rows, -1):
r = i + self.current_line.number_of_rows - 1
if self.current_line.y > i:
prev_part = self.current_line.row[r] + prev_part
elif self.current_line.y < i:
after_part = self.current_line.row[r] + after_part
part1 = prev_part + current_line1
part2 = current_line2 + after_part
self.split_line(self.current_num, part1, part2)
elif not self.current_line.text:
self.insert(self.current_line.number) # new bit, inserts line
self.current_num += 1
elif self.current_line.x == self.current_line.end_x:
self.current_num += 1
self.lines.db[self.current_num].x = 6
self.lines.db[self.current_num].y = self.lines.db[self.current_num].end_y
elif self.current_line.x == 6:
self.insert(self.current_line.number) # new bit, inserts line
self.current_num += 1
else:
pass
self.debug_visible()
def tab_key(self):
"""program specific function that handles 'tab'"""
char = ' '
# global current_line, continue_down, continue_up
self.continue_down = 0
self.continue_up = 0
for i in range(0, 4):
old_number_of_rows = self.current_line.number_of_rows
old_x = self.current_line.x
temp_list = self.current_line.listing
if self.current_line.y == 0 and self.current_line.x == self.current_line.end_x:
temp_list.append(char)
else:
position = self.row_size * (
self.current_line.number_of_rows - 1 - abs(self.current_line.y)) + self.current_line.x - 6
temp_list.insert(position, char)
temp_string = ''
for item in temp_list:
temp_string += item
self.current_line.text = temp_string
self.current_line.x += 1
if old_number_of_rows != self.current_line.number_of_rows:
if self.current_line.y != 0:
self.current_line.y -= 1
if self.current_line.y == 0:
self.current_line.y -= 1
self.current_line.x = old_x + 1
def prev(self):
"""Goto previous line"""
# global program_message, prev_line, current_num
self.reset_line()
try:
self.current_num, self.prev_line = self.prev_line, self.current_num
self.lines.db[self.current_num].x = self.lines.db[self.current_num].end_x # update cursor position
self.program_message = f' Moved from line {self.prev_line:d} to {self.current_num:d} '
if self.config['syntax_highlighting']:
self.syntax_visible()
except BareException:
self.program_message = ' Prev failed! '
def move_up(self):
"""program specific function that moves up one line"""
# global current_num, program_message, saved_since_edit, continue_down, continue_up
self.program_message = ''
self.continue_down = 0
self.continue_left = 0
self.continue_right = 0
if self.config['syntax_highlighting']:
self.lines.db[self.current_num].add_syntax() # update syntax BEFORE leaving line
if self.current_line.text and self.current_line.number == self.lines.total:
self.lines.add() # create emtpy line
if self.text_entered:
self.update_undo()
self.update_que('text entry')
self.saved_since_edit = False
if self.current_line.number_of_rows > 1 and self.current_line.y == 0 and \
self.current_line.x == self.current_line.end_x and not self.lines.locked:
self.current_num -= 1
if self.current_num < 1:
self.current_num = 1
self.lines.db[self.current_num].y = 0
self.lines.db[self.current_num].x = self.lines.db[self.current_num].end_x
elif self.current_line.number_of_rows > 1 and \
self.current_line.y > self.current_line.end_y: # deal with large lines
prev_y = self.current_line.y
if self.current_line.x >= 6:
self.current_line.y -= 1
if prev_y == 0 and self.current_line.x == self.current_line.end_x:
self.current_line.x = self.window.width - 1
else: # deal with normal lines
if self.config['cursor_acceleration']:
move_rate = min(self.config['cursor_max_vertical_speed'], int(self.continue_up / 10.0) + 1)
else:
move_rate = 1
self.current_num -= move_rate
self.continue_up += 1
if self.current_num < 1:
self.current_num = 1
self.lines.db[self.current_num].y = 0
self.lines.db[self.current_num].x = self.lines.db[self.current_num].end_x
if self.config['syntax_highlighting']:
self.lines.db[self.current_num].add_syntax() # added to speed up program
if self.config['debug']:
self.debug_visible()
def move_down(self):
"""program specific function that moves down one line"""
# global current_num, program_message, saved_since_edit, continue_down, continue_up
self.program_message = ''
self.continue_up = 0
self.continue_left = 0
self.continue_right = 0
if self.config['syntax_highlighting']:
self.lines.db[self.current_num].add_syntax() # update syntax BEFORE leaving line
if self.current_line.text and self.current_line.number == self.lines.total:
self.lines.add() # create emtpy line
if self.text_entered:
self.update_undo()
self.update_que('text entry')
self.saved_since_edit = False
if self.current_line.number_of_rows > 1 and self.current_line.y != 0: # deal with large lines
prev_y = self.current_line.y
prev_x = self.current_line.x
self.current_line.y += 1
if self.current_line.y == 0 and prev_x == self.window.height - 1:
self.current_line.x = self.current_line.end_x
elif self.current_line.y == 0 and prev_x > self.current_line.end_x:
self.current_line.x = self.current_line.end_x
elif prev_y == self.current_line.end_y and self.current_line.x == self.window.width - 1:
self.current_line.x = self.window.width - 1
else: # deal with normal lines
if self.config['cursor_acceleration']:
move_rate = min(self.config['cursor_max_vertical_speed'], int(self.continue_down / 10.0) + 1)
else:
move_rate = 1
self.current_num += move_rate
self.continue_down += 1
if self.current_num > self.lines.total:
self.current_num = self.lines.total
if self.lines.db[self.current_num].number_of_rows > (self.window.height - 4) and self.lines.locked:
self.lines.db[self.current_num].y = self.lines.db[self.current_num].end_y + (self.window.height - 5)
elif self.current_line.y != 0:
self.lines.db[self.current_num].y = self.lines.db[self.current_num].end_y # changed
self.lines.db[self.current_num].x = self.window.width - 1
else:
self.lines.db[self.current_num].x = self.lines.db[self.current_num].end_x
self.lines.db[self.current_num].y = 0
if self.config['syntax_highlighting']:
self.lines.db[self.current_num].add_syntax() # added to speed up program
if self.config['debug']:
self.debug_visible()
def move_left(self):
"""program specific function that moves left one space"""
# global continue_up, continue_down, continue_right, continue_left
self.continue_up = 0
self.continue_down = 0
self.continue_right = 0
if self.current_line.text and self.current_line.number == self.lines.total:
self.lines.add() # create emtpy line
try: # if tab, move 4 spaces
if self.current_line.x - 6 <= self.current_line.indentation and \
self.current_line.text[self.current_line.x - 6 - 4:self.current_line.x - 6] == ' ' and \
self.current_line.y == self.current_line.end_y:
self.current_line.x -= 4
return
except BareException:
pass
if self.config['cursor_acceleration']:
move_rate = min(self.config['cursor_max_horizontal_speed'], int(self.continue_left / 10.0) + 1)
else:
move_rate = 1
self.continue_left += 1
self.current_line.x -= move_rate
def move_right(self):
"""program specific function that moves right one space"""
# global continue_up, continue_down, continue_right, continue_left
self.continue_up = 0
self.continue_down = 0
self.continue_left = 0
if self.current_line.text and self.current_line.number == self.lines.total:
self.lines.add() # create emtpy line
try: # if tab, move 4 spaces
if self.current_line.x - 6 < self.current_line.indentation and \
self.current_line.text[self.current_line.x - 6:self.current_line.x - 6 + 4] == ' ' and \
self.current_line.y == self.current_line.end_y:
self.current_line.x += 4
return
except BareException:
pass
if self.config['cursor_acceleration']:
move_rate = min(self.config['cursor_max_horizontal_speed'], int(self.continue_right / 10.0) + 1)
else:
move_rate = 1
self.continue_right += 1
self.current_line.x += move_rate
def page_up(self):
"""program specific function that moves up one page"""
# global current_num, program_message, saved_since_edit, continue_down, continue_up, continue_left, continue_right
self.program_message = ''
self.continue_down = 0
self.continue_left = 0
self.continue_right = 0
self.continue_up = 0
if self.config['syntax_highlighting']:
self.lines.db[self.current_num].add_syntax() # update syntax BEFORE leaving line
self.current_num = max((self.current_num - (self.window.height - 1)), 1)
def page_down(self):
"""program specific function that moves down one page"""
# global current_num, program_message, saved_since_edit, continue_down, continue_up, continue_left, continue_right
self.program_message = ''
self.continue_down = 0
self.continue_left = 0
self.continue_right = 0
self.continue_up = 0
if self.config['syntax_highlighting']:
self.lines.db[self.current_num].add_syntax() # update syntax BEFORE leaving line
self.current_num = min((self.current_num + (self.window.height - 1)), self.lines.total)
def goto(self, text):
"""program specific function which moves to given line number"""
# global current_num, program_message, prev_line
self.prev_line = self.current_num
temp_string = text[5:]
self.reset_line()
try:
if not temp_string.isdigit(): # Find function or class
find_function = 'def ' + temp_string + '('
find_class = 'class ' + temp_string + '('
for i in range(1, len(self.lines.db) + 1):
item = self.lines.db[i]
if item.text.strip().startswith(find_function) or item.text.strip().startswith(find_class):
# if item.text.strip().startswith('def'):
# item_found = 'function'
# elif item.text.strip().startswith('class'):
# item_found = 'class'
temp_string = i
break
if temp_string == text[5:]:
if temp_string == 'start':
temp_string = 1
elif temp_string == 'end':
temp_string = self.lines.total
else:
for i in range(1, len(self.lines.db) + 1):
item = self.lines.db[i]
if item.text.strip().startswith('def %s' % temp_string) or item.text.strip().startswith(
'class %s' % temp_string):
# if item.text.strip().startswith('def'):
# item_found = 'function'
# elif item.text.strip().startswith('class'):
# item_found = 'class'
temp_string = i
break
if temp_string == text[5:]:
self.program_message = ' Specified function/class not found! '
return
self.current_num = max(min(int(temp_string), self.lines.total), 1)
self.lines.db[self.current_num].x = self.lines.db[self.current_num].end_x # update cursor position
if self.lines.db[self.current_num].collapsed:
self.program_message = f' Moved to line {self.current_num} (collapsed) '
else:
self.program_message = f' Moved from line {self.prev_line} to {self.current_num} '
if self.config['syntax_highlighting']:
self.syntax_visible()
except BareException:
self.program_message = ' Goto failed! '
| 45.629545
| 134
| 0.587588
| 2,608
| 20,077
| 4.296396
| 0.071702
| 0.212048
| 0.206158
| 0.064257
| 0.835966
| 0.786345
| 0.74913
| 0.70299
| 0.673092
| 0.629897
| 0
| 0.01331
| 0.315187
| 20,077
| 439
| 135
| 45.733485
| 0.801658
| 0.13513
| 0
| 0.631884
| 0
| 0
| 0.040063
| 0.00603
| 0
| 0
| 0
| 0
| 0
| 1
| 0.037681
| false
| 0.008696
| 0.002899
| 0
| 0.057971
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
88f2f8aa7eaeb8fcec5fcbeb09340b4c85b9feb6
| 2,792
|
py
|
Python
|
day_1.py
|
bastoche/adventofcode2017
|
a93ecff1de78376b03d4c922c82dff96574f2466
|
[
"MIT"
] | null | null | null |
day_1.py
|
bastoche/adventofcode2017
|
a93ecff1de78376b03d4c922c82dff96574f2466
|
[
"MIT"
] | null | null | null |
day_1.py
|
bastoche/adventofcode2017
|
a93ecff1de78376b03d4c922c82dff96574f2466
|
[
"MIT"
] | null | null | null |
def rotate(list, n):
return list[n:] + list[:n]
def filter_equal_values(lhs, rhs):
return [a for a, b in zip(lhs, rhs) if a == b]
def sum_as_integers(list_of_strings):
return sum(map(int, list_of_strings))
def part_one(input):
lhs = list(input)
rhs = rotate(lhs, 1)
values = filter_equal_values(lhs, rhs)
return sum_as_integers(values)
def part_two(input):
lhs = list(input)
rhs = rotate(lhs, len(lhs) // 2)
values = filter_equal_values(lhs, rhs)
return sum_as_integers(values)
if __name__ == "__main__":
input = '9513446799636685297929646689682997114316733445451534532351778534251427172168183621874641711534917291674333857423799375512628489423332297538215855176592633692631974822259161766238385922277893623911332569448978771948316155868781496698895492971356383996932885518732997624253678694279666572149831616312497994856288871586777793459926952491318336997159553714584541897294117487641872629796825583725975692264125865827534677223541484795877371955124463989228886498682421539667224963783616245646832154384756663251487668681425754536722827563651327524674183443696227523828832466473538347472991998913211857749878157579176457395375632995576569388455888156465451723693767887681392547189273391948632726499868313747261828186732986628365773728583387184112323696592536446536231376615949825166773536471531487969852535699774113163667286537193767515119362865141925612849443983484245268194842563154567638354645735331855896155142741664246715666899824364722914296492444672653852387389477634257768229772399416521198625393426443499223611843766134883441223328256883497423324753229392393974622181429913535973327323952241674979677481518733692544535323219895684629719868384266425386835539719237716339198485163916562434854579365958111931354576991558771236977242668756782139961638347251644828724786827751748399123668854393894787851872256667336215726674348886747128237416273154988619267824361227888751562445622387695218161341884756795223464751862965655559143779425283154533252573949165492138175581615176611845489857169132936848668646319955661492488428427435269169173654812114842568381636982389224236455633316898178163297452453296667661849622174541778669494388167451186352488555379581934999276412919598411422973399319799937518713422398874326665375216437246445791623283898584648278989674418242112957668397484671119761553847275799873495363759266296477844157237423239163559391553961176475377151369399646747881452252547741718734949967752564774161341784833521492494243662658471121369649641815562327698395293573991648351369767162642763475561544795982183714447737149239846151871434656618825566387329765118727515699213962477996399781652131918996434125559698427945714572488376342126989157872118279163127742349'
print(part_one(input))
print(part_two(input))
| 90.064516
| 2,178
| 0.922278
| 100
| 2,792
| 25.47
| 0.34
| 0.009423
| 0.020024
| 0.023557
| 0.076561
| 0.076561
| 0.065175
| 0.042403
| 0.042403
| 0.042403
| 0
| 0.814898
| 0.047994
| 2,792
| 30
| 2,179
| 93.066667
| 0.143341
| 0
| 0
| 0.3
| 0
| 0
| 0.777937
| 0.775072
| 0
| 1
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.15
| 0.5
| 0.1
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 9
|
cc59f4b729a80fd0902f4937fd3d30536fd489fa
| 1,239
|
py
|
Python
|
catalog/shoes/tests/conftest.py
|
eduleones/catalog-backend-example
|
8938662d391daad94e73152f7291e800a360e689
|
[
"MIT"
] | null | null | null |
catalog/shoes/tests/conftest.py
|
eduleones/catalog-backend-example
|
8938662d391daad94e73152f7291e800a360e689
|
[
"MIT"
] | 6
|
2019-12-04T23:52:51.000Z
|
2022-02-10T12:30:40.000Z
|
catalog/shoes/tests/conftest.py
|
eduleones/catalog-backend-example
|
8938662d391daad94e73152f7291e800a360e689
|
[
"MIT"
] | null | null | null |
import pytest
@pytest.fixture
def shoes_payload():
return {
"sku": "3487-1230912",
"brand": "adidas",
"model": "airflow",
"price": "190.00",
"description": "Lançamento mundial da Adidas",
"main_color": "black",
"style": "casual",
"size": 38,
"external_material": "sintetico",
"internal_material": "sintetico",
"sole_material": "borracha",
"weight": "1.000",
"height": "2.000",
"width": "1.000",
"length": "1.000",
"stock": 33
}
@pytest.fixture
def shoes_payload_invalid():
return {
"sku": "3487-1230912",
"brand": "adidas",
"model": "airflow",
"price": "190.00",
"description": "Lançamento mundial da Adidas",
"main_color": "black",
"style": "casual",
"size": 88,
"external_material": "sintetico",
"internal_material": "sintetico",
"sole_material": "borracha",
"weight": "1.000",
"height": "2.000",
"width": "1.000",
"length": "1.000",
"stock": 33
}
@pytest.fixture
def shoes_payload_partial():
return {
"price": "190.00",
"stock": 33
}
| 22.944444
| 54
| 0.498789
| 117
| 1,239
| 5.17094
| 0.393162
| 0.039669
| 0.079339
| 0.104132
| 0.912397
| 0.866116
| 0.866116
| 0.866116
| 0.866116
| 0.866116
| 0
| 0.093713
| 0.319613
| 1,239
| 53
| 55
| 23.377358
| 0.623962
| 0
| 0
| 0.808511
| 0
| 0
| 0.398709
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.06383
| true
| 0
| 0.021277
| 0.06383
| 0.148936
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ccc286e7bede438c260d0856e19717af2d324384
| 202
|
py
|
Python
|
src/buvar/components/__init__.py
|
diefans/buvar
|
5c47cef0a1a9792d6b2aee0d32f724f83834715c
|
[
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 1
|
2020-02-28T22:59:19.000Z
|
2020-02-28T22:59:19.000Z
|
src/buvar/components/__init__.py
|
diefans/buvar
|
5c47cef0a1a9792d6b2aee0d32f724f83834715c
|
[
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | null | null | null |
src/buvar/components/__init__.py
|
diefans/buvar
|
5c47cef0a1a9792d6b2aee0d32f724f83834715c
|
[
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | null | null | null |
class ComponentLookupError(Exception):
pass
try:
# gains over 100% speed up
from .c_components import Components
except ImportError:
from .py_components import Components # noqa: F40
| 20.2
| 54
| 0.737624
| 24
| 202
| 6.125
| 0.791667
| 0.217687
| 0.353742
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.03125
| 0.207921
| 202
| 9
| 55
| 22.444444
| 0.8875
| 0.168317
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.5
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 8
|
aeb38e92eeb618b2032a63efe5677a293f2329f9
| 55,507
|
py
|
Python
|
DQMOffline/Trigger/python/HiggsMonitoring_Client_cff.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 852
|
2015-01-11T21:03:51.000Z
|
2022-03-25T21:14:00.000Z
|
DQMOffline/Trigger/python/HiggsMonitoring_Client_cff.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 30,371
|
2015-01-02T00:14:40.000Z
|
2022-03-31T23:26:05.000Z
|
DQMOffline/Trigger/python/HiggsMonitoring_Client_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 DQMServices.Core.DQMEDHarvester import DQMEDHarvester
from DQMOffline.Trigger.VBFMETMonitor_Client_cff import *
from DQMOffline.Trigger.HMesonGammaMonitor_Client_cff import *
from DQMOffline.Trigger.VBFTauMonitor_Client_cff import *
from DQMOffline.Trigger.MssmHbbBtagTriggerMonitor_Client_cfi import *
from DQMOffline.Trigger.MssmHbbMonitoring_Client_cfi import *
from DQMOffline.Trigger.PhotonMonitor_cff import *
metbtagEfficiency_met = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/*"),
subDirs = cms.untracked.vstring("HLT/HIG/*"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_met 'MET turnON; PF MET [GeV]; efficiency' met_numerator met_denominator",
"effic_met_variable 'MET turnON; PF MET [GeV]; efficiency' met_variable_numerator met_variable_denominator",
"effic_metPhi 'MET efficiency vs phi; PF MET phi [rad]; efficiency' metPhi_numerator metPhi_denominator",
"effic_ht 'HT turnON; PF HT [GeV]; efficiency' ht_numerator ht_denominator",
"effic_ht_variable 'HT turnON; PF HT [GeV]; efficiency' ht_variable_numerator ht_variable_denominator",
"effic_deltaphimetj1 'DELTAPHI turnON; DELTA PHI (PFMET, PFJET1); efficiency' deltaphimetj1_numerator deltaphimetj1_denominator",
"effic_deltaphij1j2 'DELTAPHI turnON; DELTA PHI (PFJET1, PFJET2); efficiency' deltaphij1j2_numerator deltaphij1j2_denominator"
),
efficiencyProfile = cms.untracked.vstring(
"effic_met_vs_LS 'MET efficiency vs LS; LS; PF MET efficiency' metVsLS_numerator metVsLS_denominator",
"effic_ht_vs_LS 'HT efficiency vs LS; LS; PF HT efficiency' htVsLS_numerator htVsLS_denominator"
),
)
metbtagEfficiency_btag = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/*"),
subDirs = cms.untracked.vstring("HLT/HIG/*"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_jetPt_1 'efficiency vs 1st jet pt; jet pt [GeV]; efficiency' jetPt_1_numerator jetPt_1_denominator",
#
"effic_jetEta_1 'efficiency vs 1st jet eta; jet eta ; efficiency' jetEta_1_numerator jetEta_1_denominator",
#
"effic_jetPhi_1 'efficiency vs 1st jet phi; jet phi ; efficiency' jetPhi_1_numerator jetPhi_1_denominator",
#
"effic_bjetPt_1 'efficiency vs 1st b-jet pt; bjet pt [GeV]; efficiency' bjetPt_1_numerator bjetPt_1_denominator",
"effic_bjetEta_1 'efficiency vs 1st b-jet eta; bjet eta ; efficiency' bjetEta_1_numerator bjetEta_1_denominator",
"effic_bjetPhi_1 'efficiency vs 1st b-jet phi; bjet phi ; efficiency' bjetPhi_1_numerator bjetPhi_1_denominator",
"effic_bjetCSV_1 'efficiency vs 1st b-jet csv; bjet CSV; efficiency' bjetCSV_1_numerator bjetCSV_1_denominator",
#
"effic_eventHT 'efficiency vs event HT; event HT [GeV]; efficiency' eventHT_numerator eventHT_denominator",
"effic_jetEtaPhi_HEP17 'efficiency vs jet #eta-#phi; jet #eta; jet #phi' jetEtaPhi_HEP17_numerator jetEtaPhi_HEP17_denominator",
#
"effic_jetPt_1_variableBinning 'efficiency vs 1st jet pt; jet pt [GeV]; efficiency' jetPt_1_variableBinning_numerator jetPt_1_variableBinning_denominator",
#
"effic_jetEta_1_variableBinning 'efficiency vs 1st jet eta; jet eta ; efficiency' jetEta_1_variableBinning_numerator jetEta_1_variableBinning_denominator",
#
"effic_bjetPt_1_variableBinning 'efficiency vs 1st b-jet pt; bjet pt [GeV]; efficiency' bjetPt_1_variableBinning_numerator bjetPt_1_variableBinning_denominator",
#
"effic_eventHT_variableBinning 'efficiency vs event HT; event HT [GeV]; efficiency' eventHT_variableBinning_numerator eventHT_variableBinning_denominator",
#
"effic_jetMulti 'efficiency vs jet multiplicity; jet multiplicity; efficiency' jetMulti_numerator jetMulti_denominator",
"effic_bjetMulti 'efficiency vs b-jet multiplicity; bjet multiplicity; efficiency' bjetMulti_numerator bjetMulti_denominator",
#
"effic_jetPtEta_1 'efficiency vs 1st jet pt-#eta; jet pt [GeV]; jet #eta' jetPtEta_1_numerator jetPtEta_1_denominator",
#
"effic_jetEtaPhi_1 'efficiency vs 1st jet #eta-#phi; jet #eta ; jet #phi' jetEtaPhi_1_numerator jetEtaPhi_1_denominator",
#
"effic_bjetPtEta_1 'efficiency vs 1st b-jet pt-#eta; jet pt [GeV]; bjet #eta' bjetPtEta_1_numerator bjetPtEta_1_denominator",
#
"effic_bjetEtaPhi_1 'efficiency vs 1st b-jet #eta-#phi; bjet #eta ; bjet #phi' bjetEtaPhi_1_numerator bjetEtaPhi_1_denominator",
#
"effic_bjetCSVHT_1 'efficiency vs 1st b-jet csv - event HT; bjet csv ; event HT [GeV]' bjetCSVHT_1_numerator bjetCSVHT_1_denominator"
),
)
###############Same flavour dilepton with dz cuts#######################
ele23Ele12CaloIdLTrackIdLIsoVL_effdz = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/DiLepton/HLT_Ele23_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/"),
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Ele23_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs lead electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs lead electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs lead electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs lead electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs lead electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs lead electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs lead electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
"effic_elePt_2 'efficiency vs sub-lead electron pt; electron pt [GeV]; efficiency' elePt_2_numerator elePt_2_denominator",
"effic_eleEta_2 'efficiency vs sub-lead electron eta; electron eta ; efficiency' eleEta_2_numerator eleEta_2_denominator",
"effic_elePhi_2 'efficiency vs sub-lead electron phi; electron phi ; efficiency' elePhi_2_numerator elePhi_2_denominator",
"effic_elePt_2_variableBinning 'efficiency vs sub-lead electron pt; electron pt [GeV]; efficiency' elePt_2_variableBinning_numerator elePt_2_variableBinning_denominator",
"effic_eleEta_2_variableBinning 'efficiency vs sub-lead electron eta; electron eta ; efficiency' eleEta_2_variableBinning_numerator eleEta_2_variableBinning_denominator",
"effic_elePtEta_2 'efficiency vs sub-lead electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_2_numerator elePtEta_2_denominator",
"effic_eleEtaPhi_2 'efficiency vs sub-lead electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_2_numerator eleEtaPhi_2_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_ElectronPt_vs_LS 'Lead electron p_T efficiency vs LS; LS; Electron p_T efficiency' eleVsLS_numerator eleVsLS_denominator"
),
)
################################MuEG cross triggers###################################
muEleDz_effele = DQMEDHarvester("DQMGenericClient",
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/eleLeg/",
"HLT/HIG/DiLepton/HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ/eleLeg/"
),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_ElectronPt_vs_LS 'Electron p_T efficiency vs LS; LS; Electron p_T efficiency' eleVsLS_numerator eleVsLS_denominator"
),
)
mu23TrkIsoVVLEle12CaloIdLTrackIdLIsoVLDZ_effele = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/DiLepton/HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/eleLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/eleLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_ElectronPt_vs_LS 'Electron p_T efficiency vs LS; LS; Electron p_T efficiency' eleVsLS_numerator eleVsLS_denominator"
),
)
muEleDz_effmu = DQMEDHarvester("DQMGenericClient",
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/muLeg/",
"HLT/HIG/DiLepton/HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ/muLeg/"
),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_MuonPt_vs_LS 'Muon p_T efficiency vs LS; LS; Muon p_T efficiency' muVsLS_numerator muVsLS_denominator"
),
)
mu23TrkIsoVVLEle12CaloIdLTrackIdLIsoVLDZ_effmu = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/DiLepton/HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/muLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ/muLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_MuonPt_vs_LS 'Muon p_T efficiency vs LS; LS; Muon p_T efficiency' muVsLS_numerator muVsLS_denominator"
),
)
mu12TrkIsoVVLEle23CaloIdLTrackIdLIsoVLDZ_effele = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/DiLepton/HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ/eleLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ/eleLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_ElectronPt_vs_LS 'Electron p_T efficiency vs LS; LS; Electron p_T efficiency' eleVsLS_numerator eleVsLS_denominator"
),
)
mu12TrkIsoVVLEle23CaloIdLTrackIdLIsoVLDZ_effmu = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/DiLepton/HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ/muLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/DiLepton/HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ/muLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_MuonPt_vs_LS 'Muon p_T efficiency vs LS; LS; Muon p_T efficiency' muVsLS_numerator muVsLS_denominator"
),
)
##########################Triple Electron################################3##
ele16ele12ele8caloIdLTrackIdL = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_Ele16_Ele12_Ele8_CaloIdL_TrackIdL/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_Ele16_Ele12_Ele8_CaloIdL_TrackIdL/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs lead electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs lead electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs lead electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs lead electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs lead electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs lead electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs lead electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
"effic_elePt_2 'efficiency vs sub-leading electron pt; electron pt [GeV]; efficiency' elePt_2_numerator elePt_2_denominator",
"effic_eleEta_2 'efficiency vs sub-leading electron eta; electron eta ; efficiency' eleEta_2_numerator eleEta_2_denominator",
"effic_elePhi_2 'efficiency vs sub-leading electron phi; electron phi ; efficiency' elePhi_2_numerator elePhi_2_denominator",
"effic_elePt_2_variableBinning 'efficiency vs sub-leading electron pt; electron pt [GeV]; efficiency' elePt_2_variableBinning_numerator elePt_2_variableBinning_denominator",
"effic_eleEta_2_variableBinning 'efficiency vs sub-leading electron eta; electron eta ; efficiency' eleEta_2_variableBinning_numerator eleEta_2_variableBinning_denominator",
"effic_elePtEta_2 'efficiency vs sub-leading electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_2_numerator elePtEta_2_denominator",
"effic_eleEtaPhi_2 'efficiency vs sub-leading electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_2_numerator eleEtaPhi_2_denominator",
"effic_elePt_3 'efficiency vs trailing electron pt; electron pt [GeV]; efficiency' elePt_3_numerator elePt_3_denominator",
"effic_eleEta_3 'efficiency vs trailing electron eta; electron eta ; efficiency' eleEta_3_numerator eleEta_3_denominator",
"effic_elePhi_3 'efficiency vs trailing electron phi; electron phi ; efficiency' elePhi_3_numerator elePhi_3_denominator",
"effic_elePt_3_variableBinning 'efficiency vs trailing electron pt; electron pt [GeV]; efficiency' elePt_3_variableBinning_numerator elePt_3_variableBinning_denominator",
"effic_eleEta_3_variableBinning 'efficiency vs trailing electron eta; electron eta ; efficiency' eleEta_3_variableBinning_numerator eleEta_3_variableBinning_denominator",
"effic_elePtEta_3 'efficiency vs trailing electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_3_numerator elePtEta_3_denominator",
"effic_eleEtaPhi_3 'efficiency vs trailing electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_3_numerator eleEtaPhi_3_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_LeadElectronPt_vs_LS 'Electron p_T efficiency vs LS; LS; Electron p_T efficiency' eleVsLS_numerator eleVsLS_denominator"
),
)
################################Triple Muon##########################
triplemu12mu10mu5 = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_TripleMu_12_10_5/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_TripleMu_12_10_5/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs leading muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
"effic_muPt_2 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_numerator muPt_2_denominator",
"effic_muEta_2 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_numerator muEta_2_denominator",
"effic_muPhi_2 'efficiency vs sub-leading muon phi; muon phi ; efficiency' muPhi_2_numerator muPhi_2_denominator",
"effic_muPt_2_variableBinning 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_variableBinning_numerator muPt_2_variableBinning_denominator",
"effic_muEta_2_variableBinning 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_variableBinning_numerator muEta_2_variableBinning_denominator",
"effic_muPtEta_2 'efficiency vs sub-leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_2_numerator muPtEta_2_denominator",
"effic_muEtaPhi_2 'efficiency vs sub-leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_2_numerator muEtaPhi_2_denominator",
"effic_muPt_3 'efficiency vs trailing muon pt; muon pt [GeV]; efficiency' muPt_3_numerator muPt_3_denominator",
"effic_muEta_3 'efficiency vs trailing muon eta; muon eta ; efficiency' muEta_3_numerator muEta_3_denominator",
"effic_muPhi_3 'efficiency vs trailing muon phi; muon phi ; efficiency' muPhi_3_numerator muPhi_3_denominator",
"effic_muPt_3_variableBinning 'efficiency vs trailing muon pt; muon pt [GeV]; efficiency' muPt_3_variableBinning_numerator muPt_3_variableBinning_denominator",
"effic_muEta_3_variableBinning 'efficiency vs trailing muon eta; muon eta ; efficiency' muEta_3_variableBinning_numerator muEta_3_variableBinning_denominator",
"effic_muPtEta_3 'efficiency vs trailing muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_3_numerator muPtEta_3_denominator",
"effic_muEtaPhi_3 'efficiency vs trailing muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_3_numerator muEtaPhi_3_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_LeadMuonPt_vs_LS 'Muon p_T efficiency vs LS; LS; Muon p_T efficiency' muVsLS_numerator muVsLS_denominator"
),
)
triplemu10mu5mu5DZ = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_TripleM_10_5_5_DZ/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_TripleM_10_5_5_DZ/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs leading muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
"effic_muPt_2 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_numerator muPt_2_denominator",
"effic_muEta_2 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_numerator muEta_2_denominator",
"effic_muPhi_2 'efficiency vs sub-leading muon phi; muon phi ; efficiency' muPhi_2_numerator muPhi_2_denominator",
"effic_muPt_2_variableBinning 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_variableBinning_numerator muPt_2_variableBinning_denominator",
"effic_muEta_2_variableBinning 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_variableBinning_numerator muEta_2_variableBinning_denominator",
"effic_muPtEta_2 'efficiency vs sub-leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_2_numerator muPtEta_2_denominator",
"effic_muEtaPhi_2 'efficiency vs sub-leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_2_numerator muEtaPhi_2_denominator",
"effic_muPt_3 'efficiency vs trailing muon pt; muon pt [GeV]; efficiency' muPt_3_numerator muPt_3_denominator",
"effic_muEta_3 'efficiency vs trailing muon eta; muon eta ; efficiency' muEta_3_numerator muEta_3_denominator",
"effic_muPhi_3 'efficiency vs trailing muon phi; muon phi ; efficiency' muPhi_3_numerator muPhi_3_denominator",
"effic_muPt_3_variableBinning 'efficiency vs trailing muon pt; muon pt [GeV]; efficiency' muPt_3_variableBinning_numerator muPt_3_variableBinning_denominator",
"effic_muEta_3_variableBinning 'efficiency vs trailing muon eta; muon eta ; efficiency' muEta_3_variableBinning_numerator muEta_3_variableBinning_denominator",
"effic_muPtEta_3 'efficiency vs trailing muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_3_numerator muPtEta_3_denominator",
"effic_muEtaPhi_3 'efficiency vs trailing muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_3_numerator muEtaPhi_3_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"effic_LeadMuonPt_vs_LS 'Muon p_T efficiency vs LS; LS; Muon p_T efficiency' muVsLS_numerator muVsLS_denominator"
),
)
#############################Double Mu + Single Ele######################################
dimu9ele9caloIdLTrackIdLdz_effmu = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_DiMu9_Ele9_CaloIdL_TrackIdL/muLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_DiMu9_Ele9_CaloIdL_TrackIdL/muLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs leading muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
"effic_muPt_2 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_numerator muPt_2_denominator",
"effic_muEta_2 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_numerator muEta_2_denominator",
"effic_muPhi_2 'efficiency vs sub-leading muon phi; muon phi ; efficiency' muPhi_2_numerator muPhi_2_denominator",
"effic_muPt_2_variableBinning 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_variableBinning_numerator muPt_2_variableBinning_denominator",
"effic_muEta_2_variableBinning 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_variableBinning_numerator muEta_2_variableBinning_denominator",
"effic_muPtEta_2 'efficiency vs sub-leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_2_numerator muPtEta_2_denominator",
"effic_muEtaPhi_2 'efficiency vs sub-leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_2_numerator muEtaPhi_2_denominator",
),
)
dimu9ele9caloIdLTrackIdLdz_effele = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_DiMu9_Ele9_CaloIdL_TrackIdL/eleLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_DiMu9_Ele9_CaloIdL_TrackIdL/eleLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
),
)
dimu9ele9caloIdLTrackIdLdz_effdz = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_DiMu9_Ele9_CaloIdL_TrackIdL/dzMon/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_DiMu9_Ele9_CaloIdL_TrackIdL/dzMon/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs leading muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs leading muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs leading muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
"effic_muPt_2 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_numerator muPt_2_denominator",
"effic_muEta_2 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_numerator muEta_2_denominator",
"effic_muPhi_2 'efficiency vs sub-leading muon phi; muon phi ; efficiency' muPhi_2_numerator muPhi_2_denominator",
"effic_muPt_2_variableBinning 'efficiency vs sub-leading muon pt; muon pt [GeV]; efficiency' muPt_2_variableBinning_numerator muPt_2_variableBinning_denominator",
"effic_muEta_2_variableBinning 'efficiency vs sub-leading muon eta; muon eta ; efficiency' muEta_2_variableBinning_numerator muEta_2_variableBinning_denominator",
"effic_muPtEta_2 'efficiency vs sub-leading muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_2_numerator muPtEta_2_denominator",
"effic_muEtaPhi_2 'efficiency vs sub-leading muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_2_numerator muEtaPhi_2_denominator",
"effic_elePt_1 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
),
)
######Double Electron + Single Muon######
mu8diEle12CaloIdLTrackIdL_effele = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_Mu8_DiEle12_CaloIdL_TrackIdL/eleLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_Mu8_DiEle12_CaloIdL_TrackIdL/eleLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs leading electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs leading electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs leading electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs leading electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs leading electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs leading electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs leading electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
"effic_elePt_2 'efficiency vs sub-leading electron pt; electron pt [GeV]; efficiency' elePt_2_numerator elePt_2_denominator",
"effic_eleEta_2 'efficiency vs sub-leading electron eta; electron eta ; efficiency' eleEta_2_numerator eleEta_2_denominator",
"effic_elePhi_2 'efficiency vs sub-leading electron phi; electron phi ; efficiency' elePhi_2_numerator elePhi_2_denominator",
"effic_elePt_2_variableBinning 'efficiency vs sub-leading electron pt; electron pt [GeV]; efficiency' elePt_2_variableBinning_numerator elePt_2_variableBinning_denominator",
"effic_eleEta_2_variableBinning 'efficiency vs sub-leading electron eta; electron eta ; efficiency' eleEta_2_variableBinning_numerator eleEta_2_variableBinning_denominator",
"effic_elePtEta_2 'efficiency vs sub-leading electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_2_numerator elePtEta_2_denominator",
"effic_eleEtaPhi_2 'efficiency vs sub-leading electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_2_numerator eleEtaPhi_2_denominator",
),
)
mu8diEle12CaloIdLTrackIdL_effmu = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_Mu8_DiEle12_CaloIdL_TrackIdL/muLeg/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_Mu8_DiEle12_CaloIdL_TrackIdL/muLeg/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_muPt_1 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
),
)
mu8diEle12CaloIdLTrackIdL_effdz = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/TriLepton/HLT_Mu8_DiEle12_CaloIdL_TrackIdL/dzMon/"),
subDirs = cms.untracked.vstring("HLT/HIG/TriLepton/HLT_Mu8_DiEle12_CaloIdL_TrackIdL/dzMon/"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_elePt_1 'efficiency vs leading electron pt; electron pt [GeV]; efficiency' elePt_1_numerator elePt_1_denominator",
"effic_eleEta_1 'efficiency vs leading electron eta; electron eta ; efficiency' eleEta_1_numerator eleEta_1_denominator",
"effic_elePhi_1 'efficiency vs leading electron phi; electron phi ; efficiency' elePhi_1_numerator elePhi_1_denominator",
"effic_elePt_1_variableBinning 'efficiency vs leading electron pt; electron pt [GeV]; efficiency' elePt_1_variableBinning_numerator elePt_1_variableBinning_denominator",
"effic_eleEta_1_variableBinning 'efficiency vs leading electron eta; electron eta ; efficiency' eleEta_1_variableBinning_numerator eleEta_1_variableBinning_denominator",
"effic_elePtEta_1 'efficiency vs leading electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_1_numerator elePtEta_1_denominator",
"effic_eleEtaPhi_1 'efficiency vs leading electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_1_numerator eleEtaPhi_1_denominator",
"effic_elePt_2 'efficiency vs sub-leading electron pt; electron pt [GeV]; efficiency' elePt_2_numerator elePt_2_denominator",
"effic_eleEta_2 'efficiency vs sub-leading electron eta; electron eta ; efficiency' eleEta_2_numerator eleEta_2_denominator",
"effic_elePhi_2 'efficiency vs sub-leading electron phi; electron phi ; efficiency' elePhi_2_numerator elePhi_2_denominator",
"effic_elePt_2_variableBinning 'efficiency vs sub-leading electron pt; electron pt [GeV]; efficiency' elePt_2_variableBinning_numerator elePt_2_variableBinning_denominator",
"effic_eleEta_2_variableBinning 'efficiency vs sub-leading electron eta; electron eta ; efficiency' eleEta_2_variableBinning_numerator eleEta_2_variableBinning_denominator",
"effic_elePtEta_2 'efficiency vs sub-leading electron pt-#eta; electron pt [GeV]; electron #eta' elePtEta_2_numerator elePtEta_2_denominator",
"effic_eleEtaPhi_2 'efficiency vs sub-leading electron #eta-#phi; electron #eta ; electron #phi' eleEtaPhi_2_numerator eleEtaPhi_2_denominator",
"effic_muPt_1 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_numerator muPt_1_denominator",
"effic_muEta_1 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_numerator muEta_1_denominator",
"effic_muPhi_1 'efficiency vs muon phi; muon phi ; efficiency' muPhi_1_numerator muPhi_1_denominator",
"effic_muPt_1_variableBinning 'efficiency vs muon pt; muon pt [GeV]; efficiency' muPt_1_variableBinning_numerator muPt_1_variableBinning_denominator",
"effic_muEta_1_variableBinning 'efficiency vs muon eta; muon eta ; efficiency' muEta_1_variableBinning_numerator muEta_1_variableBinning_denominator",
"effic_muPtEta_1 'efficiency vs muon pt-#eta; muon pt [GeV]; muon #eta' muPtEta_1_numerator muPtEta_1_denominator",
"effic_muEtaPhi_1 'efficiency vs muon #eta-#phi; muon #eta ; muon #phi' muEtaPhi_1_numerator muEtaPhi_1_denominator",
),
)
### mia: FOCA D'OVATTA !
diphotonEfficiency = DQMEDHarvester("DQMGenericClient",
subDirs = cms.untracked.vstring("HLT/photon/HLT_Diphoton30_22_R9Id_OR_IsoCaloId_AND_HE_R9Id_Mass90_v",
"HLT/photon/HLT_Diphoton30_22_R9Id_OR_IsoCaloId_AND_HE_R9Id_Mass95_v",
"HLT_Diphoton30PV_18PV_R9Id_AND_IsoCaloId_AND_HE_R9Id_PixelVeto_Mass55_v",
"HLT_Diphoton30PV_18PV_R9Id_AND_IsoCaloId_AND_HE_R9Id_NoPixelVeto_Mass55_v",
"HLT_Diphoton30EB_18EB_R9Id_OR_IsoCaloId_AND_HE_R9Id_NoPixelVeto_Mass55_v",
"HLT_Diphoton30EB_18EB_R9Id_OR_IsoCaloId_AND_HE_R9Id_PixelVeto_Mass55_v",
"HLT_Diphoton30_18_PVrealAND_R9Id_AND_IsoCaloId_AND_HE_R9Id_PixelVeto_Mass55_v",
"HLT_Diphoton30_18_PVrealAND_R9Id_AND_IsoCaloId_AND_HE_R9Id_NoPixelVeto_Mass55_v"),
#subDirs = cms.untracked.vstring("HLT/Higgs/*"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"eff_diphoton_pt 'efficiency vs lead pt; Photon pt [GeV]; efficiency' photon_pt_numerator photon_pt_denominator",
"eff_diphoton_variable 'efficiency vs lead pt; Photon pt [GeV]; efficiency' photon_pt_variable_numerator photon_pt_variable_denominator",
"eff_diphoton_eta 'efficiency vs lead eta; Photon eta; efficiency' photon_eta_numerator photon_eta_denominator",
"eff_diphoton_subpt 'efficiency vs sublead pt; Photon subpt [GeV]; efficiency' subphoton_pt_numerator subphoton_pt_denominator",
"eff_diphoton_subeta 'efficiency vs sublead eta; Photon subeta; efficiency' subphoton_eta_numerator subphoton_eta_denominator",
"eff_diphoton_mass 'efficiency vs diphoton mass; Diphoton mass; efficiency' diphoton_mass_numerator diphoton_mass_denominator",
"eff_photon_phi 'efficiency vs lead phi; Photon phi [rad]; efficiency' photon_phi_numerator photon_phi_denominator",
"eff_photon_subphi 'efficiency vs sublead phi; Photon subphi [rad]; efficiency' subphoton_phi_numerator subphoton_phi_denominator",
"eff_photonr9 'efficiency vs r9; Photon r9; efficiency' photon_r9_numerator photon_r9_denominator",
"eff_photonhoE 'efficiency vs hoE; Photon hoE; efficiency' photon_hoE_numerator photon_hoE_denominator",
"eff_photonEtaPhi 'Photon phi; Photon eta; efficiency' photon_etaphi_numerator photon_etaphi_denominator",
"eff_photon_subr9 'efficiency vs sublead r9; Photon subr9; efficiency' subphoton_r9_numerator subphoton_r9_denominator",
"eff_photon_subhoE 'efficiency vs sublead hoE; Photon subhoE; efficiency' subphoton_hoE_numerator subphoton_hoE_denominator",
"eff_photon_subEtaPhi 'Photon sublead phi; Photon sublead eta; efficiency' subphoton_etaphi_numerator subphoton_etaphi_denominator",
),
efficiencyProfile = cms.untracked.vstring(
"eff_photon_vs_LS 'Photon pt efficiency vs LS; LS' photonVsLS_numerator photonVsLS_denominator"
),
)
VBFEfficiency = DQMEDHarvester("DQMGenericClient",
# subDirs = cms.untracked.vstring("HLT/Higgs/VBFHbb/*"),
subDirs = cms.untracked.vstring("HLT/HIG/VBFHbb/*"),
verbose = cms.untracked.uint32(0), # Set to 2 for all messages
resolution = cms.vstring(),
efficiency = cms.vstring(
"effic_jetPhi_1 'efficiency vs 1st jet phi; jet phi ; efficiency' jetPhi_1_numerator jetPhi_1_denominator",
"effic_jetPhi_2 'efficiency vs 2nd jet phi; jet phi ; efficiency' jetPhi_2_numerator jetPhi_2_denominator",
"effic_jetPhi_3 'efficiency vs 3rd jet phi; jet phi ; efficiency' jetPhi_3_numerator jetPhi_3_denominator",
"effic_jetPhi_4 'efficiency vs 4th jet phi; jet phi ; efficiency' jetPhi_4_numerator jetPhi_4_denominator",
#
"effic_bjetPhi_1 'efficiency vs 1st b-jet phi; bjet phi ; efficiency' bjetPhi_1_numerator bjetPhi_1_denominator",
"effic_bjetCSV_1 'efficiency vs 1st b-jet csv; bjet CSV; efficiency' bjetCSV_1_numerator bjetCSV_1_denominator",
#
"effic_bjetPhi_2 'efficiency vs 2nd b-jet phi; bjet phi ; efficiency' bjetPhi_2_numerator bjetPhi_2_denominator",
"effic_bjetCSV_2 'efficiency vs 2nd b-jet csv; bjet CSV; efficiency' bjetCSV_2_numerator bjetCSV_2_denominator",
#
"effic_jetPt_1_variableBinning 'efficiency vs 1st jet pt; jet pt [GeV]; efficiency' jetPt_1_variableBinning_numerator jetPt_1_variableBinning_denominator",
"effic_jetPt_2_variableBinning 'efficiency vs 2nd jet pt; jet pt [GeV]; efficiency' jetPt_2_variableBinning_numerator jetPt_2_variableBinning_denominator",
"effic_jetPt_3_variableBinning 'efficiency vs 3rd jet pt; jet pt [GeV]; efficiency' jetPt_3_variableBinning_numerator jetPt_3_variableBinning_denominator",
"effic_jetPt_4_variableBinning 'efficiency vs 4th jet pt; jet pt [GeV]; efficiency' jetPt_4_variableBinning_numerator jetPt_4_variableBinning_denominator",
#
"effic_jetEta_1_variableBinning 'efficiency vs 1st jet eta; jet eta ; efficiency' jetEta_1_variableBinning_numerator jetEta_1_variableBinning_denominator",
"effic_jetEta_2_variableBinning 'efficiency vs 2nd jet eta; jet eta ; efficiency' jetEta_2_variableBinning_numerator jetEta_2_variableBinning_denominator",
"effic_jetEta_3_variableBinning 'efficiency vs 3rd jet eta; jet eta ; efficiency' jetEta_3_variableBinning_numerator jetEta_3_variableBinning_denominator",
"effic_jetEta_4_variableBinning 'efficiency vs 4th jet eta; jet eta ; efficiency' jetEta_4_variableBinning_numerator jetEta_4_variableBinning_denominator",
#
"effic_bjetPt_1_variableBinning 'efficiency vs 1st b-jet pt; bjet pt [GeV]; efficiency' bjetPt_1_variableBinning_numerator bjetPt_1_variableBinning_denominator",
"effic_bjetEta_1_variableBinning 'efficiency vs 1st b-jet eta; bjet eta ; efficiency' bjetEta_1_variableBinning_numerator bjetEta_1_variableBinning_denominator",
#
"effic_bjetPt_2_variableBinning 'efficiency vs 2nd b-jet pt; bjet pt [GeV]; efficiency' bjetPt_2_variableBinning_numerator bjetPt_2_variableBinning_denominator",
"effic_bjetEta_2_variableBinning 'efficiency vs 2nd b-jet eta; bjet eta ; efficiency' bjetEta_2_variableBinning_numerator bjetEta_2_variableBinning_denominator",
#
"effic_jetMulti 'efficiency vs jet multiplicity; jet multiplicity; efficiency' jetMulti_numerator jetMulti_denominator",
"effic_bjetMulti 'efficiency vs b-jet multiplicity; bjet multiplicity; efficiency' bjetMulti_numerator bjetMulti_denominator",
#
"effic_jetPtEta_1 'efficiency vs 1st jet pt-#eta; jet pt [GeV]; jet #eta' jetPtEta_1_numerator jetPtEta_1_denominator",
"effic_jetPtEta_2 'efficiency vs 2nd jet pt-#eta; jet pt [GeV]; jet #eta' jetPtEta_2_numerator jetPtEta_2_denominator",
"effic_jetPtEta_3 'efficiency vs 3rd jet pt-#eta; jet pt [GeV]; jet #eta' jetPtEta_3_numerator jetPtEta_3_denominator",
"effic_jetPtEta_4 'efficiency vs 4th jet pt-#eta; jet pt [GeV]; jet #eta' jetPtEta_4_numerator jetPtEta_4_denominator",
"effic_jetEtaPhi_1 'efficiency vs 1st jet #eta-#phi; jet #eta ; jet #phi' jetEtaPhi_1_numerator jetEtaPhi_1_denominator",
"effic_jetEtaPhi_2 'efficiency vs 2nd jet #eta-#phi; jet #eta ; jet #phi' jetEtaPhi_2_numerator jetEtaPhi_2_denominator",
"effic_jetEtaPhi_3 'efficiency vs 3rd jet #eta-#phi; jet #eta ; jet #phi' jetEtaPhi_3_numerator jetEtaPhi_3_denominator",
"effic_jetEtaPhi_4 'efficiency vs 4th jet #eta-#phi; jet #eta ; jet #phi' jetEtaPhi_4_numerator jetEtaPhi_4_denominator",
#
"effic_bjetPtEta_1 'efficiency vs 1st b-jet pt-#eta; jet pt [GeV]; bjet #eta' bjetPtEta_1_numerator bjetPtEta_1_denominator",
#
"effic_bjetEtaPhi_1 'efficiency vs 1st b-jet #eta-#phi; bjet #eta ; bjet #phi' bjetEtaPhi_1_numerator bjetEtaPhi_1_denominator",
#
"effic_bjetPtEta_2 'efficiency vs 2nd b-jet pt-#eta; jet pt [GeV]; bjet #eta' bjetPtEta_2_numerator bjetPtEta_2_denominator",
#
"effic_bjetEtaPhi_2 'efficiency vs 2nd b-jet #eta-#phi; bjet #eta ; bjet #phi' bjetEtaPhi_2_numerator bjetEtaPhi_2_denominator",
),
)
higgsClient = cms.Sequence(
diphotonEfficiency
+ vbfmetClient
+ vbftauClient
+ ele23Ele12CaloIdLTrackIdLIsoVL_effdz
+ dimu9ele9caloIdLTrackIdLdz_effmu
+ dimu9ele9caloIdLTrackIdLdz_effele
+ dimu9ele9caloIdLTrackIdLdz_effdz
+ mu8diEle12CaloIdLTrackIdL_effmu
+ mu8diEle12CaloIdLTrackIdL_effele
+ mu8diEle12CaloIdLTrackIdL_effdz
+ ele16ele12ele8caloIdLTrackIdL
+ triplemu12mu10mu5
+ triplemu10mu5mu5DZ
+ muEleDz_effmu
+ muEleDz_effele
# + mu12TrkIsoVVLEle23CaloIdLTrackIdLIsoVLDZ_effele
# + mu12TrkIsoVVLEle23CaloIdLTrackIdLIsoVLDZ_effmu
# + mu23TrkIsoVVLEle12CaloIdLTrackIdLIsoVLDZ_effele
# + mu23TrkIsoVVLEle12CaloIdLTrackIdLIsoVLDZ_effmu
+ metbtagEfficiency_met
+ metbtagEfficiency_btag
+ VBFEfficiency
+ mssmHbbBtagTriggerEfficiency
+ mssmHbbHLTEfficiency
+ hmesongammaEfficiency
)
| 96.533913
| 193
| 0.717333
| 6,567
| 55,507
| 5.721943
| 0.033958
| 0.090377
| 0.03771
| 0.028689
| 0.864302
| 0.856531
| 0.833697
| 0.818022
| 0.804503
| 0.795375
| 0
| 0.026918
| 0.208911
| 55,507
| 574
| 194
| 96.702091
| 0.828813
| 0.047273
| 0
| 0.657948
| 0
| 0.110664
| 0.799261
| 0.234811
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016097
| 0
| 0.016097
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
aef01b116b20cd31285cee7d14dfe6339af45672
| 646,263
|
py
|
Python
|
UFO_models/SMEFTsim_MFV_MwScheme_UFO/vertices.py
|
matthewfeickert/SMEFTsim
|
db7d4a80bdcff424eee27dde71f1eb09ac894039
|
[
"MIT"
] | 4
|
2020-12-29T03:42:43.000Z
|
2021-09-22T09:57:37.000Z
|
UFO_models/SMEFTsim_MFV_MwScheme_UFO/vertices.py
|
matthewfeickert/SMEFTsim
|
db7d4a80bdcff424eee27dde71f1eb09ac894039
|
[
"MIT"
] | 3
|
2021-05-19T11:06:59.000Z
|
2021-12-11T00:12:02.000Z
|
UFO_models/SMEFTsim_MFV_MwScheme_UFO/vertices.py
|
matthewfeickert/SMEFTsim
|
db7d4a80bdcff424eee27dde71f1eb09ac894039
|
[
"MIT"
] | 4
|
2021-09-22T09:57:39.000Z
|
2022-03-29T16:09:36.000Z
|
# This file was automatically created by FeynRules 2.3.35
# Mathematica version: 12.1.0 for Linux x86 (64-bit) (March 18, 2020)
# Date: Thu 7 Jan 2021 11:40:42
from object_library import all_vertices, Vertex
import particles as P
import couplings as C
import lorentz as L
V_1 = Vertex(name = 'V_1',
particles = [ P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2, L.VVV3, L.VVV4 ],
couplings = {(0,2):C.GC_178,(0,0):C.GC_3,(0,1):C.GC_365})
V_2 = Vertex(name = 'V_2',
particles = [ P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_285})
V_3 = Vertex(name = 'V_3',
particles = [ P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_292})
V_4 = Vertex(name = 'V_4',
particles = [ P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_303})
V_5 = Vertex(name = 'V_5',
particles = [ P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV1, L.VVV2, L.VVV4 ],
couplings = {(0,2):C.GC_55,(0,1):C.GC_119,(0,0):C.GC_288})
V_6 = Vertex(name = 'V_6',
particles = [ P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_361})
V_7 = Vertex(name = 'V_7',
particles = [ P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_362})
V_8 = Vertex(name = 'V_8',
particles = [ P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_366})
V_9 = Vertex(name = 'V_9',
particles = [ P.g, P.g, P.g ],
color = [ 'f(1,2,3)' ],
lorentz = [ L.VVV2, L.VVV4 ],
couplings = {(0,1):C.GC_15,(0,0):C.GC_7})
V_10 = Vertex(name = 'V_10',
particles = [ P.g, P.g, P.g, P.g ],
color = [ 'f(-1,1,2)*f(3,4,-1)', 'f(-1,1,3)*f(2,4,-1)', 'f(-1,1,4)*f(2,3,-1)' ],
lorentz = [ L.VVVV1, L.VVVV2, L.VVVV4, L.VVVV5, L.VVVV7, L.VVVV8 ],
couplings = {(0,1):C.GC_61,(1,5):C.GC_61,(2,4):C.GC_61,(1,2):C.GC_8,(0,0):C.GC_8,(2,3):C.GC_8})
V_11 = Vertex(name = 'V_11',
particles = [ P.g, P.g, P.g, P.g, P.g ],
color = [ 'f(-2,1,2)*f(-1,-2,3)*f(4,5,-1)', 'f(-2,1,2)*f(-1,-2,4)*f(3,5,-1)', 'f(-2,1,2)*f(-1,-2,5)*f(3,4,-1)', 'f(-2,1,3)*f(-1,-2,2)*f(4,5,-1)', 'f(-2,1,3)*f(-1,-2,4)*f(2,5,-1)', 'f(-2,1,3)*f(-1,-2,5)*f(2,4,-1)', 'f(-2,1,4)*f(-1,-2,2)*f(3,5,-1)', 'f(-2,1,4)*f(-1,-2,3)*f(2,5,-1)', 'f(-2,1,4)*f(-1,-2,5)*f(2,3,-1)', 'f(-2,1,5)*f(-1,-2,2)*f(3,4,-1)', 'f(-2,1,5)*f(-1,-2,3)*f(2,4,-1)', 'f(-2,1,5)*f(-1,-2,4)*f(2,3,-1)', 'f(-2,2,3)*f(-1,-2,1)*f(4,5,-1)', 'f(-2,2,3)*f(-1,-2,4)*f(1,5,-1)', 'f(-2,2,3)*f(-1,-2,5)*f(1,4,-1)', 'f(-2,2,4)*f(-1,-2,1)*f(3,5,-1)', 'f(-2,2,4)*f(-1,-2,3)*f(1,5,-1)', 'f(-2,2,4)*f(-1,-2,5)*f(1,3,-1)', 'f(-2,2,5)*f(-1,-2,1)*f(3,4,-1)', 'f(-2,2,5)*f(-1,-2,3)*f(1,4,-1)', 'f(-2,2,5)*f(-1,-2,4)*f(1,3,-1)', 'f(-2,3,4)*f(-1,-2,1)*f(2,5,-1)', 'f(-2,3,4)*f(-1,-2,2)*f(1,5,-1)', 'f(-2,3,4)*f(-1,-2,5)*f(1,2,-1)', 'f(-2,3,5)*f(-1,-2,1)*f(2,4,-1)', 'f(-2,3,5)*f(-1,-2,2)*f(1,4,-1)', 'f(-2,3,5)*f(-1,-2,4)*f(1,2,-1)', 'f(-2,4,5)*f(-1,-2,1)*f(2,3,-1)', 'f(-2,4,5)*f(-1,-2,2)*f(1,3,-1)', 'f(-2,4,5)*f(-1,-2,3)*f(1,2,-1)' ],
lorentz = [ L.VVVVV1, L.VVVVV10, L.VVVVV11, L.VVVVV12, L.VVVVV13, L.VVVVV14, L.VVVVV15, L.VVVVV17, L.VVVVV18, L.VVVVV2, L.VVVVV4, L.VVVVV5, L.VVVVV6, L.VVVVV7, L.VVVVV8 ],
couplings = {(24,11):C.GC_64,(21,12):C.GC_63,(18,12):C.GC_64,(15,11):C.GC_63,(28,9):C.GC_64,(22,2):C.GC_64,(9,2):C.GC_63,(3,9):C.GC_63,(29,10):C.GC_64,(16,3):C.GC_64,(10,3):C.GC_63,(0,10):C.GC_63,(26,6):C.GC_63,(20,5):C.GC_63,(4,5):C.GC_64,(1,6):C.GC_64,(25,1):C.GC_64,(6,1):C.GC_63,(19,4):C.GC_64,(7,4):C.GC_63,(23,8):C.GC_63,(17,7):C.GC_63,(5,7):C.GC_64,(2,8):C.GC_64,(27,0):C.GC_64,(12,0):C.GC_63,(13,13):C.GC_64,(11,13):C.GC_63,(14,14):C.GC_63,(8,14):C.GC_64})
V_12 = Vertex(name = 'V_12',
particles = [ P.g, P.g, P.g, P.g, P.g, P.g ],
color = [ 'f(-3,1,2)*f(-2,3,4)*f(-1,-2,-3)*f(5,6,-1)', 'f(-3,1,2)*f(-2,3,5)*f(-1,-2,-3)*f(4,6,-1)', 'f(-3,1,2)*f(-2,3,6)*f(-1,-2,-3)*f(4,5,-1)', 'f(-3,1,2)*f(-2,4,5)*f(-1,-2,-3)*f(3,6,-1)', 'f(-3,1,2)*f(-2,4,6)*f(-1,-2,-3)*f(3,5,-1)', 'f(-3,1,2)*f(-2,5,6)*f(-1,-2,-3)*f(3,4,-1)', 'f(-3,1,3)*f(-2,2,4)*f(-1,-2,-3)*f(5,6,-1)', 'f(-3,1,3)*f(-2,2,5)*f(-1,-2,-3)*f(4,6,-1)', 'f(-3,1,3)*f(-2,2,6)*f(-1,-2,-3)*f(4,5,-1)', 'f(-3,1,3)*f(-2,4,5)*f(-1,-2,-3)*f(2,6,-1)', 'f(-3,1,3)*f(-2,4,6)*f(-1,-2,-3)*f(2,5,-1)', 'f(-3,1,3)*f(-2,5,6)*f(-1,-2,-3)*f(2,4,-1)', 'f(-3,1,4)*f(-2,2,3)*f(-1,-2,-3)*f(5,6,-1)', 'f(-3,1,4)*f(-2,2,5)*f(-1,-2,-3)*f(3,6,-1)', 'f(-3,1,4)*f(-2,2,6)*f(-1,-2,-3)*f(3,5,-1)', 'f(-3,1,4)*f(-2,3,5)*f(-1,-2,-3)*f(2,6,-1)', 'f(-3,1,4)*f(-2,3,6)*f(-1,-2,-3)*f(2,5,-1)', 'f(-3,1,4)*f(-2,5,6)*f(-1,-2,-3)*f(2,3,-1)', 'f(-3,1,5)*f(-2,2,3)*f(-1,-2,-3)*f(4,6,-1)', 'f(-3,1,5)*f(-2,2,4)*f(-1,-2,-3)*f(3,6,-1)', 'f(-3,1,5)*f(-2,2,6)*f(-1,-2,-3)*f(3,4,-1)', 'f(-3,1,5)*f(-2,3,4)*f(-1,-2,-3)*f(2,6,-1)', 'f(-3,1,5)*f(-2,3,6)*f(-1,-2,-3)*f(2,4,-1)', 'f(-3,1,5)*f(-2,4,6)*f(-1,-2,-3)*f(2,3,-1)', 'f(-3,1,6)*f(-2,2,3)*f(-1,-2,-3)*f(4,5,-1)', 'f(-3,1,6)*f(-2,2,4)*f(-1,-2,-3)*f(3,5,-1)', 'f(-3,1,6)*f(-2,2,5)*f(-1,-2,-3)*f(3,4,-1)', 'f(-3,1,6)*f(-2,3,4)*f(-1,-2,-3)*f(2,5,-1)', 'f(-3,1,6)*f(-2,3,5)*f(-1,-2,-3)*f(2,4,-1)', 'f(-3,1,6)*f(-2,4,5)*f(-1,-2,-3)*f(2,3,-1)', 'f(-3,2,3)*f(-2,1,4)*f(-1,-2,-3)*f(5,6,-1)', 'f(-3,2,3)*f(-2,1,5)*f(-1,-2,-3)*f(4,6,-1)', 'f(-3,2,3)*f(-2,1,6)*f(-1,-2,-3)*f(4,5,-1)', 'f(-3,2,3)*f(-2,4,5)*f(-1,-2,-3)*f(1,6,-1)', 'f(-3,2,3)*f(-2,4,6)*f(-1,-2,-3)*f(1,5,-1)', 'f(-3,2,3)*f(-2,5,6)*f(-1,-2,-3)*f(1,4,-1)', 'f(-3,2,4)*f(-2,1,3)*f(-1,-2,-3)*f(5,6,-1)', 'f(-3,2,4)*f(-2,1,5)*f(-1,-2,-3)*f(3,6,-1)', 'f(-3,2,4)*f(-2,1,6)*f(-1,-2,-3)*f(3,5,-1)', 'f(-3,2,4)*f(-2,3,5)*f(-1,-2,-3)*f(1,6,-1)', 'f(-3,2,4)*f(-2,3,6)*f(-1,-2,-3)*f(1,5,-1)', 'f(-3,2,4)*f(-2,5,6)*f(-1,-2,-3)*f(1,3,-1)', 'f(-3,2,5)*f(-2,1,3)*f(-1,-2,-3)*f(4,6,-1)', 'f(-3,2,5)*f(-2,1,4)*f(-1,-2,-3)*f(3,6,-1)', 'f(-3,2,5)*f(-2,1,6)*f(-1,-2,-3)*f(3,4,-1)', 'f(-3,2,5)*f(-2,3,4)*f(-1,-2,-3)*f(1,6,-1)', 'f(-3,2,5)*f(-2,3,6)*f(-1,-2,-3)*f(1,4,-1)', 'f(-3,2,5)*f(-2,4,6)*f(-1,-2,-3)*f(1,3,-1)', 'f(-3,2,6)*f(-2,1,3)*f(-1,-2,-3)*f(4,5,-1)', 'f(-3,2,6)*f(-2,1,4)*f(-1,-2,-3)*f(3,5,-1)', 'f(-3,2,6)*f(-2,1,5)*f(-1,-2,-3)*f(3,4,-1)', 'f(-3,2,6)*f(-2,3,4)*f(-1,-2,-3)*f(1,5,-1)', 'f(-3,2,6)*f(-2,3,5)*f(-1,-2,-3)*f(1,4,-1)', 'f(-3,2,6)*f(-2,4,5)*f(-1,-2,-3)*f(1,3,-1)', 'f(-3,3,4)*f(-2,1,2)*f(-1,-2,-3)*f(5,6,-1)', 'f(-3,3,4)*f(-2,1,5)*f(-1,-2,-3)*f(2,6,-1)', 'f(-3,3,4)*f(-2,1,6)*f(-1,-2,-3)*f(2,5,-1)', 'f(-3,3,4)*f(-2,2,5)*f(-1,-2,-3)*f(1,6,-1)', 'f(-3,3,4)*f(-2,2,6)*f(-1,-2,-3)*f(1,5,-1)', 'f(-3,3,4)*f(-2,5,6)*f(-1,-2,-3)*f(1,2,-1)', 'f(-3,3,5)*f(-2,1,2)*f(-1,-2,-3)*f(4,6,-1)', 'f(-3,3,5)*f(-2,1,4)*f(-1,-2,-3)*f(2,6,-1)', 'f(-3,3,5)*f(-2,1,6)*f(-1,-2,-3)*f(2,4,-1)', 'f(-3,3,5)*f(-2,2,4)*f(-1,-2,-3)*f(1,6,-1)', 'f(-3,3,5)*f(-2,2,6)*f(-1,-2,-3)*f(1,4,-1)', 'f(-3,3,5)*f(-2,4,6)*f(-1,-2,-3)*f(1,2,-1)', 'f(-3,3,6)*f(-2,1,2)*f(-1,-2,-3)*f(4,5,-1)', 'f(-3,3,6)*f(-2,1,4)*f(-1,-2,-3)*f(2,5,-1)', 'f(-3,3,6)*f(-2,1,5)*f(-1,-2,-3)*f(2,4,-1)', 'f(-3,3,6)*f(-2,2,4)*f(-1,-2,-3)*f(1,5,-1)', 'f(-3,3,6)*f(-2,2,5)*f(-1,-2,-3)*f(1,4,-1)', 'f(-3,3,6)*f(-2,4,5)*f(-1,-2,-3)*f(1,2,-1)', 'f(-3,4,5)*f(-2,1,2)*f(-1,-2,-3)*f(3,6,-1)', 'f(-3,4,5)*f(-2,1,3)*f(-1,-2,-3)*f(2,6,-1)', 'f(-3,4,5)*f(-2,1,6)*f(-1,-2,-3)*f(2,3,-1)', 'f(-3,4,5)*f(-2,2,3)*f(-1,-2,-3)*f(1,6,-1)', 'f(-3,4,5)*f(-2,2,6)*f(-1,-2,-3)*f(1,3,-1)', 'f(-3,4,5)*f(-2,3,6)*f(-1,-2,-3)*f(1,2,-1)', 'f(-3,4,6)*f(-2,1,2)*f(-1,-2,-3)*f(3,5,-1)', 'f(-3,4,6)*f(-2,1,3)*f(-1,-2,-3)*f(2,5,-1)', 'f(-3,4,6)*f(-2,1,5)*f(-1,-2,-3)*f(2,3,-1)', 'f(-3,4,6)*f(-2,2,3)*f(-1,-2,-3)*f(1,5,-1)', 'f(-3,4,6)*f(-2,2,5)*f(-1,-2,-3)*f(1,3,-1)', 'f(-3,4,6)*f(-2,3,5)*f(-1,-2,-3)*f(1,2,-1)', 'f(-3,5,6)*f(-2,1,2)*f(-1,-2,-3)*f(3,4,-1)', 'f(-3,5,6)*f(-2,1,3)*f(-1,-2,-3)*f(2,4,-1)', 'f(-3,5,6)*f(-2,1,4)*f(-1,-2,-3)*f(2,3,-1)', 'f(-3,5,6)*f(-2,2,3)*f(-1,-2,-3)*f(1,4,-1)', 'f(-3,5,6)*f(-2,2,4)*f(-1,-2,-3)*f(1,3,-1)', 'f(-3,5,6)*f(-2,3,4)*f(-1,-2,-3)*f(1,2,-1)' ],
lorentz = [ L.VVVVVV1, L.VVVVVV10, L.VVVVVV11, L.VVVVVV12, L.VVVVVV13, L.VVVVVV14, L.VVVVVV15, L.VVVVVV16, L.VVVVVV2, L.VVVVVV3, L.VVVVVV4, L.VVVVVV5, L.VVVVVV6, L.VVVVVV7, L.VVVVVV9 ],
couplings = {(65,10):C.GC_66,(71,12):C.GC_67,(77,12):C.GC_66,(83,10):C.GC_67,(41,8):C.GC_66,(53,2):C.GC_66,(76,2):C.GC_67,(88,8):C.GC_67,(35,9):C.GC_66,(52,5):C.GC_66,(64,5):C.GC_67,(87,9):C.GC_67,(34,4):C.GC_67,(40,3):C.GC_67,(69,3):C.GC_66,(81,4):C.GC_66,(17,9):C.GC_67,(23,4):C.GC_66,(80,4):C.GC_67,(86,9):C.GC_66,(11,8):C.GC_67,(22,3):C.GC_66,(68,3):C.GC_67,(85,8):C.GC_66,(9,2):C.GC_67,(15,5):C.GC_67,(61,5):C.GC_66,(73,2):C.GC_66,(4,10):C.GC_67,(14,5):C.GC_66,(49,5):C.GC_67,(78,10):C.GC_66,(3,12):C.GC_66,(19,3):C.GC_67,(37,3):C.GC_66,(72,12):C.GC_67,(2,12):C.GC_67,(8,2):C.GC_66,(48,2):C.GC_67,(66,12):C.GC_66,(1,10):C.GC_66,(18,4):C.GC_67,(31,4):C.GC_66,(60,10):C.GC_67,(6,8):C.GC_66,(12,9):C.GC_66,(30,9):C.GC_67,(36,8):C.GC_67,(47,14):C.GC_66,(82,14):C.GC_67,(46,6):C.GC_66,(70,6):C.GC_67,(33,7):C.GC_67,(39,1):C.GC_67,(63,1):C.GC_66,(75,7):C.GC_66,(29,7):C.GC_66,(74,7):C.GC_67,(28,1):C.GC_66,(62,1):C.GC_67,(10,14):C.GC_67,(16,6):C.GC_67,(67,6):C.GC_66,(79,14):C.GC_66,(25,1):C.GC_67,(38,1):C.GC_66,(13,6):C.GC_66,(43,6):C.GC_67,(24,7):C.GC_67,(32,7):C.GC_66,(7,14):C.GC_66,(42,14):C.GC_67,(59,0):C.GC_66,(89,0):C.GC_67,(51,11):C.GC_66,(58,11):C.GC_67,(21,11):C.GC_67,(55,11):C.GC_66,(5,0):C.GC_67,(20,11):C.GC_66,(50,11):C.GC_67,(84,0):C.GC_66,(0,0):C.GC_66,(54,0):C.GC_67,(45,13):C.GC_67,(57,13):C.GC_66,(27,13):C.GC_66,(56,13):C.GC_67,(26,13):C.GC_67,(44,13):C.GC_66})
V_13 = Vertex(name = 'V_13',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV10, L.VVVV3 ],
couplings = {(0,0):C.GC_179,(0,1):C.GC_5})
V_14 = Vertex(name = 'V_14',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_294})
V_15 = Vertex(name = 'V_15',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_295})
V_16 = Vertex(name = 'V_16',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_314})
V_17 = Vertex(name = 'V_17',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_368})
V_18 = Vertex(name = 'V_18',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6, L.VVVV9 ],
couplings = {(0,1):C.GC_58,(0,0):C.GC_120})
V_19 = Vertex(name = 'V_19',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_394})
V_20 = Vertex(name = 'V_20',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_395})
V_21 = Vertex(name = 'V_21',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_367})
V_22 = Vertex(name = 'V_22',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_370})
V_23 = Vertex(name = 'V_23',
particles = [ P.a, P.a, P.a, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVVV3 ],
couplings = {(0,0):C.GC_180})
V_24 = Vertex(name = 'V_24',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV10, L.VVVV3 ],
couplings = {(0,0):C.GC_142,(0,1):C.GC_82})
V_25 = Vertex(name = 'V_25',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_307})
V_26 = Vertex(name = 'V_26',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_309})
V_27 = Vertex(name = 'V_27',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVVV19 ],
couplings = {(0,0):C.GC_60})
V_28 = Vertex(name = 'V_28',
particles = [ P.a, P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVVV21 ],
couplings = {(0,0):C.GC_145})
V_29 = Vertex(name = 'V_29',
particles = [ P.a, P.a, P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVVVV8 ],
couplings = {(0,0):C.GC_147})
V_30 = Vertex(name = 'V_30',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV10, L.VVVV3 ],
couplings = {(0,0):C.GC_143,(0,1):C.GC_84})
V_31 = Vertex(name = 'V_31',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_315})
V_32 = Vertex(name = 'V_32',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_316})
V_33 = Vertex(name = 'V_33',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_317})
V_34 = Vertex(name = 'V_34',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_369})
V_35 = Vertex(name = 'V_35',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVVV9 ],
couplings = {(0,0):C.GC_146})
V_36 = Vertex(name = 'V_36',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVVV16 ],
couplings = {(0,0):C.GC_88})
V_37 = Vertex(name = 'V_37',
particles = [ P.a, P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVVVV17 ],
couplings = {(0,0):C.GC_90})
V_38 = Vertex(name = 'V_38',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVVVV8 ],
couplings = {(0,0):C.GC_80})
V_39 = Vertex(name = 'V_39',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVVV20 ],
couplings = {(0,0):C.GC_89})
V_40 = Vertex(name = 'V_40',
particles = [ P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSS1, L.SSSS2, L.SSSS3 ],
couplings = {(0,0):C.GC_9,(0,2):C.GC_17,(0,1):C.GC_18})
V_41 = Vertex(name = 'V_41',
particles = [ P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_281})
V_42 = Vertex(name = 'V_42',
particles = [ P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_296})
V_43 = Vertex(name = 'V_43',
particles = [ P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_297})
V_44 = Vertex(name = 'V_44',
particles = [ P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_298})
V_45 = Vertex(name = 'V_45',
particles = [ P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_299})
V_46 = Vertex(name = 'V_46',
particles = [ P.H, P.H, P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSSSS1 ],
couplings = {(0,0):C.GC_16})
V_47 = Vertex(name = 'V_47',
particles = [ P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSS1, L.SSS2, L.SSS3 ],
couplings = {(0,0):C.GC_226,(0,2):C.GC_228,(0,1):C.GC_229})
V_48 = Vertex(name = 'V_48',
particles = [ P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_380})
V_49 = Vertex(name = 'V_49',
particles = [ P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_381})
V_50 = Vertex(name = 'V_50',
particles = [ P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_382})
V_51 = Vertex(name = 'V_51',
particles = [ P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_383})
V_52 = Vertex(name = 'V_52',
particles = [ P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_384})
V_53 = Vertex(name = 'V_53',
particles = [ P.H, P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.SSSSS1 ],
couplings = {(0,0):C.GC_227})
V_54 = Vertex(name = 'V_54',
particles = [ P.a, P.a, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_49})
V_55 = Vertex(name = 'V_55',
particles = [ P.a, P.a, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_176})
V_56 = Vertex(name = 'V_56',
particles = [ P.a, P.a, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_187})
V_57 = Vertex(name = 'V_57',
particles = [ P.a, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_189})
V_58 = Vertex(name = 'V_58',
particles = [ P.a, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_232})
V_59 = Vertex(name = 'V_59',
particles = [ P.a, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_277})
V_60 = Vertex(name = 'V_60',
particles = [ P.a, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_280})
V_61 = Vertex(name = 'V_61',
particles = [ P.g, P.g, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_19})
V_62 = Vertex(name = 'V_62',
particles = [ P.g, P.g, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.VVS2, L.VVS3, L.VVS4, L.VVS5 ],
couplings = {(0,0):C.GC_190,(0,2):C.GC_203,(0,1):C.GC_199,(0,3):C.GC_194})
V_63 = Vertex(name = 'V_63',
particles = [ P.g, P.g, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_230})
V_64 = Vertex(name = 'V_64',
particles = [ P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1, L.VVSS2 ],
couplings = {(0,1):C.GC_20,(0,0):C.GC_81})
V_65 = Vertex(name = 'V_65',
particles = [ P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_304})
V_66 = Vertex(name = 'V_66',
particles = [ P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_305})
V_67 = Vertex(name = 'V_67',
particles = [ P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_306})
V_68 = Vertex(name = 'V_68',
particles = [ P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_308})
V_69 = Vertex(name = 'V_69',
particles = [ P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1, L.VVS2 ],
couplings = {(0,1):C.GC_231,(0,0):C.GC_242})
V_70 = Vertex(name = 'V_70',
particles = [ P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_385})
V_71 = Vertex(name = 'V_71',
particles = [ P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_386})
V_72 = Vertex(name = 'V_72',
particles = [ P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_387})
V_73 = Vertex(name = 'V_73',
particles = [ P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_388})
V_74 = Vertex(name = 'V_74',
particles = [ P.a, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_188})
V_75 = Vertex(name = 'V_75',
particles = [ P.a, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_174})
V_76 = Vertex(name = 'V_76',
particles = [ P.a, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS2 ],
couplings = {(0,0):C.GC_175})
V_77 = Vertex(name = 'V_77',
particles = [ P.a, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_193})
V_78 = Vertex(name = 'V_78',
particles = [ P.a, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_393})
V_79 = Vertex(name = 'V_79',
particles = [ P.a, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_275})
V_80 = Vertex(name = 'V_80',
particles = [ P.a, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_276})
V_81 = Vertex(name = 'V_81',
particles = [ P.Z, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1, L.VVSS2 ],
couplings = {(0,1):C.GC_50,(0,0):C.GC_83})
V_82 = Vertex(name = 'V_82',
particles = [ P.Z, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1, L.VVSS2 ],
couplings = {(0,1):C.GC_177,(0,0):C.GC_310})
V_83 = Vertex(name = 'V_83',
particles = [ P.Z, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1, L.VVSS2 ],
couplings = {(0,1):C.GC_186,(0,0):C.GC_311})
V_84 = Vertex(name = 'V_84',
particles = [ P.Z, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_312})
V_85 = Vertex(name = 'V_85',
particles = [ P.Z, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_313})
V_86 = Vertex(name = 'V_86',
particles = [ P.Z, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1, L.VVS2 ],
couplings = {(0,1):C.GC_233,(0,0):C.GC_243})
V_87 = Vertex(name = 'V_87',
particles = [ P.Z, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1, L.VVS2 ],
couplings = {(0,1):C.GC_278,(0,0):C.GC_389})
V_88 = Vertex(name = 'V_88',
particles = [ P.Z, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1, L.VVS2 ],
couplings = {(0,1):C.GC_279,(0,0):C.GC_390})
V_89 = Vertex(name = 'V_89',
particles = [ P.Z, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_391})
V_90 = Vertex(name = 'V_90',
particles = [ P.Z, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_392})
V_91 = Vertex(name = 'V_91',
particles = [ P.g, P.g, P.g, P.H, P.H ],
color = [ 'f(1,2,3)' ],
lorentz = [ L.VVVSS3 ],
couplings = {(0,0):C.GC_62})
V_92 = Vertex(name = 'V_92',
particles = [ P.g, P.g, P.g, P.H ],
color = [ 'f(1,2,3)' ],
lorentz = [ L.VVVS3, L.VVVS4, L.VVVS5, L.VVVS6, L.VVVS7 ],
couplings = {(0,2):C.GC_195,(0,4):C.GC_204,(0,3):C.GC_200,(0,1):C.GC_197,(0,0):C.GC_191})
V_93 = Vertex(name = 'V_93',
particles = [ P.g, P.g, P.g, P.H ],
color = [ 'f(1,2,3)' ],
lorentz = [ L.VVVS3 ],
couplings = {(0,0):C.GC_237})
V_94 = Vertex(name = 'V_94',
particles = [ P.g, P.g, P.g, P.g, P.H, P.H ],
color = [ 'f(-1,1,2)*f(3,4,-1)', 'f(-1,1,3)*f(2,4,-1)', 'f(-1,1,4)*f(2,3,-1)' ],
lorentz = [ L.VVVVSS1, L.VVVVSS3, L.VVVVSS4 ],
couplings = {(1,1):C.GC_65,(0,0):C.GC_65,(2,2):C.GC_65})
V_95 = Vertex(name = 'V_95',
particles = [ P.g, P.g, P.g, P.g, P.H ],
color = [ 'f(-1,1,2)*f(3,4,-1)', 'f(-1,1,3)*f(2,4,-1)', 'f(-1,1,4)*f(2,3,-1)' ],
lorentz = [ L.VVVVS1, L.VVVVS10, L.VVVVS11, L.VVVVS12, L.VVVVS13, L.VVVVS14, L.VVVVS15, L.VVVVS16, L.VVVVS17, L.VVVVS19, L.VVVVS2, L.VVVVS3, L.VVVVS4, L.VVVVS7, L.VVVVS8 ],
couplings = {(2,5):C.GC_196,(2,8):C.GC_205,(1,4):C.GC_196,(1,9):C.GC_205,(2,6):C.GC_202,(0,11):C.GC_198,(0,12):C.GC_206,(1,7):C.GC_202,(0,3):C.GC_201,(1,2):C.GC_198,(2,1):C.GC_198,(0,10):C.GC_196,(1,13):C.GC_192,(0,0):C.GC_192,(2,14):C.GC_192})
V_96 = Vertex(name = 'V_96',
particles = [ P.g, P.g, P.g, P.g, P.H ],
color = [ 'f(-1,1,2)*f(3,4,-1)', 'f(-1,1,3)*f(2,4,-1)', 'f(-1,1,4)*f(2,3,-1)' ],
lorentz = [ L.VVVVS1, L.VVVVS7, L.VVVVS8 ],
couplings = {(1,1):C.GC_238,(0,0):C.GC_238,(2,2):C.GC_238})
V_97 = Vertex(name = 'V_97',
particles = [ P.a, P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVVSS1, L.VVVSS3 ],
couplings = {(0,1):C.GC_56,(0,0):C.GC_141})
V_98 = Vertex(name = 'V_98',
particles = [ P.a, P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVVS1, L.VVVS3 ],
couplings = {(0,1):C.GC_234,(0,0):C.GC_273})
V_99 = Vertex(name = 'V_99',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVSS2 ],
couplings = {(0,0):C.GC_59})
V_100 = Vertex(name = 'V_100',
particles = [ P.a, P.a, P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVS6 ],
couplings = {(0,0):C.GC_236})
V_101 = Vertex(name = 'V_101',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVVSS2, L.VVVSS3 ],
couplings = {(0,1):C.GC_140,(0,0):C.GC_57})
V_102 = Vertex(name = 'V_102',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVVS2, L.VVVS3 ],
couplings = {(0,1):C.GC_272,(0,0):C.GC_235})
V_103 = Vertex(name = 'V_103',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVSS2 ],
couplings = {(0,0):C.GC_85})
V_104 = Vertex(name = 'V_104',
particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVS6 ],
couplings = {(0,0):C.GC_244})
V_105 = Vertex(name = 'V_105',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVSS5 ],
couplings = {(0,0):C.GC_144})
V_106 = Vertex(name = 'V_106',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVS9 ],
couplings = {(0,0):C.GC_274})
V_107 = Vertex(name = 'V_107',
particles = [ P.Z, P.Z, P.H, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSSSS1 ],
couplings = {(0,0):C.GC_86})
V_108 = Vertex(name = 'V_108',
particles = [ P.Z, P.Z, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSSS1 ],
couplings = {(0,0):C.GC_245})
V_109 = Vertex(name = 'V_109',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVSS2 ],
couplings = {(0,0):C.GC_87})
V_110 = Vertex(name = 'V_110',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.VVVVS6 ],
couplings = {(0,0):C.GC_246})
V_111 = Vertex(name = 'V_111',
particles = [ P.H, P.H, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_72})
V_112 = Vertex(name = 'V_112',
particles = [ P.H, P.H, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_77})
V_113 = Vertex(name = 'V_113',
particles = [ P.H, P.H1, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_78})
V_114 = Vertex(name = 'V_114',
particles = [ P.H1, P.H1, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSSS1 ],
couplings = {(0,0):C.GC_79})
V_115 = Vertex(name = 'V_115',
particles = [ P.H, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_239})
V_116 = Vertex(name = 'V_116',
particles = [ P.H, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_240})
V_117 = Vertex(name = 'V_117',
particles = [ P.H1, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.SSS1 ],
couplings = {(0,0):C.GC_241})
V_118 = Vertex(name = 'V_118',
particles = [ P.a, P.W__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_69})
V_119 = Vertex(name = 'V_119',
particles = [ P.a, P.W1__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_74})
V_120 = Vertex(name = 'V_120',
particles = [ P.a, P.W1__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_69})
V_121 = Vertex(name = 'V_121',
particles = [ P.W__minus__, P.W1__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_91})
V_122 = Vertex(name = 'V_122',
particles = [ P.W__minus__, P.W1__plus__, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_95})
V_123 = Vertex(name = 'V_123',
particles = [ P.W__minus__, P.W1__plus__, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_99})
V_124 = Vertex(name = 'V_124',
particles = [ P.W__minus__, P.W1__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_247})
V_125 = Vertex(name = 'V_125',
particles = [ P.W__minus__, P.W1__plus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_249})
V_126 = Vertex(name = 'V_126',
particles = [ P.a, P.a, P.W__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_70})
V_127 = Vertex(name = 'V_127',
particles = [ P.W__minus__, P.W1__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_160})
V_128 = Vertex(name = 'V_128',
particles = [ P.W__minus__, P.W1__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_166})
V_129 = Vertex(name = 'V_129',
particles = [ P.W1__minus__, P.W1__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_95})
V_130 = Vertex(name = 'V_130',
particles = [ P.W1__minus__, P.W1__plus__, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_99})
V_131 = Vertex(name = 'V_131',
particles = [ P.W1__minus__, P.W1__plus__, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_103})
V_132 = Vertex(name = 'V_132',
particles = [ P.W1__minus__, P.W1__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_249})
V_133 = Vertex(name = 'V_133',
particles = [ P.W1__minus__, P.W1__plus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_251})
V_134 = Vertex(name = 'V_134',
particles = [ P.a, P.a, P.W1__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_75})
V_135 = Vertex(name = 'V_135',
particles = [ P.W1__minus__, P.W1__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_166})
V_136 = Vertex(name = 'V_136',
particles = [ P.W1__minus__, P.W1__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_169})
V_137 = Vertex(name = 'V_137',
particles = [ P.W__minus__, P.W__minus__, P.W1__plus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_96})
V_138 = Vertex(name = 'V_138',
particles = [ P.W__minus__, P.W1__minus__, P.W1__plus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_100})
V_139 = Vertex(name = 'V_139',
particles = [ P.W1__minus__, P.W1__minus__, P.W1__plus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_104})
V_140 = Vertex(name = 'V_140',
particles = [ P.W__minus__, P.W__plus__, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_91})
V_141 = Vertex(name = 'V_141',
particles = [ P.W__minus__, P.W__plus__, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_95})
V_142 = Vertex(name = 'V_142',
particles = [ P.W__minus__, P.W__plus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_247})
V_143 = Vertex(name = 'V_143',
particles = [ P.W__minus__, P.W__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_160})
V_144 = Vertex(name = 'V_144',
particles = [ P.W1__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_91})
V_145 = Vertex(name = 'V_145',
particles = [ P.W1__minus__, P.W__plus__, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_95})
V_146 = Vertex(name = 'V_146',
particles = [ P.W1__minus__, P.W__plus__, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_99})
V_147 = Vertex(name = 'V_147',
particles = [ P.W1__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_247})
V_148 = Vertex(name = 'V_148',
particles = [ P.W1__minus__, P.W__plus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_249})
V_149 = Vertex(name = 'V_149',
particles = [ P.a, P.a, P.W1__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_70})
V_150 = Vertex(name = 'V_150',
particles = [ P.W1__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_160})
V_151 = Vertex(name = 'V_151',
particles = [ P.W1__minus__, P.W__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVV2 ],
couplings = {(0,0):C.GC_166})
V_152 = Vertex(name = 'V_152',
particles = [ P.W__minus__, P.W__minus__, P.W1__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_92})
V_153 = Vertex(name = 'V_153',
particles = [ P.W__minus__, P.W1__minus__, P.W1__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_96})
V_154 = Vertex(name = 'V_154',
particles = [ P.W1__minus__, P.W1__minus__, P.W1__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_100})
V_155 = Vertex(name = 'V_155',
particles = [ P.W__minus__, P.W1__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_92})
V_156 = Vertex(name = 'V_156',
particles = [ P.W1__minus__, P.W1__minus__, P.W__plus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_96})
V_157 = Vertex(name = 'V_157',
particles = [ P.a, P.W__minus__, P.W1__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_161})
V_158 = Vertex(name = 'V_158',
particles = [ P.a, P.W1__minus__, P.W1__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_167})
V_159 = Vertex(name = 'V_159',
particles = [ P.a, P.W1__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_161})
V_160 = Vertex(name = 'V_160',
particles = [ P.Z, P.Z, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_93})
V_161 = Vertex(name = 'V_161',
particles = [ P.Z, P.Z, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_97})
V_162 = Vertex(name = 'V_162',
particles = [ P.Z, P.Z, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_248})
V_163 = Vertex(name = 'V_163',
particles = [ P.W__minus__, P.W1__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_94})
V_164 = Vertex(name = 'V_164',
particles = [ P.W1__minus__, P.W1__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_98})
V_165 = Vertex(name = 'V_165',
particles = [ P.W1__minus__, P.W__plus__, P.Z, P.Z ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_94})
V_166 = Vertex(name = 'V_166',
particles = [ P.a, P.W__minus__, P.W1__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_167})
V_167 = Vertex(name = 'V_167',
particles = [ P.a, P.W1__minus__, P.W1__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_170})
V_168 = Vertex(name = 'V_168',
particles = [ P.a, P.W__minus__, P.W__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_161})
V_169 = Vertex(name = 'V_169',
particles = [ P.a, P.W1__minus__, P.W__plus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV6 ],
couplings = {(0,0):C.GC_167})
V_170 = Vertex(name = 'V_170',
particles = [ P.Z, P.Z1, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_93})
V_171 = Vertex(name = 'V_171',
particles = [ P.Z, P.Z1, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_97})
V_172 = Vertex(name = 'V_172',
particles = [ P.Z, P.Z1, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_101})
V_173 = Vertex(name = 'V_173',
particles = [ P.Z, P.Z1, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_248})
V_174 = Vertex(name = 'V_174',
particles = [ P.Z, P.Z1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_250})
V_175 = Vertex(name = 'V_175',
particles = [ P.W__minus__, P.W1__plus__, P.Z, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_98})
V_176 = Vertex(name = 'V_176',
particles = [ P.W1__minus__, P.W1__plus__, P.Z, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_102})
V_177 = Vertex(name = 'V_177',
particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_94})
V_178 = Vertex(name = 'V_178',
particles = [ P.W1__minus__, P.W__plus__, P.Z, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_98})
V_179 = Vertex(name = 'V_179',
particles = [ P.Z1, P.Z1, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_97})
V_180 = Vertex(name = 'V_180',
particles = [ P.Z1, P.Z1, P.H, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_101})
V_181 = Vertex(name = 'V_181',
particles = [ P.Z1, P.Z1, P.H1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVSS1 ],
couplings = {(0,0):C.GC_105})
V_182 = Vertex(name = 'V_182',
particles = [ P.Z1, P.Z1, P.H ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_250})
V_183 = Vertex(name = 'V_183',
particles = [ P.Z1, P.Z1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS1 ],
couplings = {(0,0):C.GC_252})
V_184 = Vertex(name = 'V_184',
particles = [ P.W__minus__, P.W1__plus__, P.Z1, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_102})
V_185 = Vertex(name = 'V_185',
particles = [ P.W1__minus__, P.W1__plus__, P.Z1, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_106})
V_186 = Vertex(name = 'V_186',
particles = [ P.W__minus__, P.W__plus__, P.Z1, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_98})
V_187 = Vertex(name = 'V_187',
particles = [ P.W1__minus__, P.W__plus__, P.Z1, P.Z1 ],
color = [ '1' ],
lorentz = [ L.VVVV3 ],
couplings = {(0,0):C.GC_102})
V_188 = Vertex(name = 'V_188',
particles = [ P.e__plus__, P.e__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_4,(0,1):C.GC_567})
V_189 = Vertex(name = 'V_189',
particles = [ P.e__plus__, P.e__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_284,(0,1):C.GC_575})
V_190 = Vertex(name = 'V_190',
particles = [ P.e__plus__, P.e__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_293})
V_191 = Vertex(name = 'V_191',
particles = [ P.e__plus__, P.e__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_302})
V_192 = Vertex(name = 'V_192',
particles = [ P.e__plus__, P.e__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_365})
V_193 = Vertex(name = 'V_193',
particles = [ P.mu__plus__, P.mu__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_4,(0,1):C.GC_624})
V_194 = Vertex(name = 'V_194',
particles = [ P.mu__plus__, P.mu__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_284,(0,1):C.GC_632})
V_195 = Vertex(name = 'V_195',
particles = [ P.mu__plus__, P.mu__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_293})
V_196 = Vertex(name = 'V_196',
particles = [ P.mu__plus__, P.mu__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_302})
V_197 = Vertex(name = 'V_197',
particles = [ P.mu__plus__, P.mu__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_365})
V_198 = Vertex(name = 'V_198',
particles = [ P.ta__plus__, P.ta__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_4,(0,1):C.GC_895})
V_199 = Vertex(name = 'V_199',
particles = [ P.ta__plus__, P.ta__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_284,(0,1):C.GC_903})
V_200 = Vertex(name = 'V_200',
particles = [ P.ta__plus__, P.ta__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_293})
V_201 = Vertex(name = 'V_201',
particles = [ P.ta__plus__, P.ta__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_302})
V_202 = Vertex(name = 'V_202',
particles = [ P.ta__plus__, P.ta__minus__, P.a ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_365})
V_203 = Vertex(name = 'V_203',
particles = [ P.e__plus__, P.e__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV5, L.FFV8 ],
couplings = {(0,0):C.GC_173,(0,1):C.GC_118,(0,3):C.GC_350,(0,2):C.GC_351,(0,4):C.GC_568})
V_204 = Vertex(name = 'V_204',
particles = [ P.e__plus__, P.e__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_289,(0,1):C.GC_352,(0,2):C.GC_574})
V_205 = Vertex(name = 'V_205',
particles = [ P.e__plus__, P.e__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_373,(0,1):C.GC_360})
V_206 = Vertex(name = 'V_206',
particles = [ P.e__plus__, P.e__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_376})
V_207 = Vertex(name = 'V_207',
particles = [ P.e__plus__, P.e__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_379})
V_208 = Vertex(name = 'V_208',
particles = [ P.mu__plus__, P.mu__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV5, L.FFV8 ],
couplings = {(0,0):C.GC_173,(0,1):C.GC_118,(0,3):C.GC_350,(0,2):C.GC_351,(0,4):C.GC_625})
V_209 = Vertex(name = 'V_209',
particles = [ P.mu__plus__, P.mu__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_289,(0,1):C.GC_352,(0,2):C.GC_631})
V_210 = Vertex(name = 'V_210',
particles = [ P.mu__plus__, P.mu__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_373,(0,1):C.GC_360})
V_211 = Vertex(name = 'V_211',
particles = [ P.mu__plus__, P.mu__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_376})
V_212 = Vertex(name = 'V_212',
particles = [ P.mu__plus__, P.mu__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_379})
V_213 = Vertex(name = 'V_213',
particles = [ P.ta__plus__, P.ta__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV5, L.FFV8 ],
couplings = {(0,0):C.GC_173,(0,1):C.GC_118,(0,3):C.GC_350,(0,2):C.GC_351,(0,4):C.GC_896})
V_214 = Vertex(name = 'V_214',
particles = [ P.ta__plus__, P.ta__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_289,(0,1):C.GC_352,(0,2):C.GC_902})
V_215 = Vertex(name = 'V_215',
particles = [ P.ta__plus__, P.ta__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_373,(0,1):C.GC_360})
V_216 = Vertex(name = 'V_216',
particles = [ P.ta__plus__, P.ta__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_376})
V_217 = Vertex(name = 'V_217',
particles = [ P.ta__plus__, P.ta__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_379})
V_218 = Vertex(name = 'V_218',
particles = [ P.d__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_1,(0,1):C.GC_511})
V_219 = Vertex(name = 'V_219',
particles = [ P.d__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_282,(0,1):C.GC_520})
V_220 = Vertex(name = 'V_220',
particles = [ P.d__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_290,(0,1):C.GC_2702})
V_221 = Vertex(name = 'V_221',
particles = [ P.d__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_300,(0,1):C.GC_2715})
V_222 = Vertex(name = 'V_222',
particles = [ P.d__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_363})
V_223 = Vertex(name = 'V_223',
particles = [ P.s__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_1,(0,1):C.GC_684})
V_224 = Vertex(name = 'V_224',
particles = [ P.s__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_282,(0,1):C.GC_693})
V_225 = Vertex(name = 'V_225',
particles = [ P.s__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_290,(0,1):C.GC_3450})
V_226 = Vertex(name = 'V_226',
particles = [ P.s__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_300,(0,1):C.GC_3482})
V_227 = Vertex(name = 'V_227',
particles = [ P.s__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_363})
V_228 = Vertex(name = 'V_228',
particles = [ P.b__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_1,(0,1):C.GC_414})
V_229 = Vertex(name = 'V_229',
particles = [ P.b__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_282,(0,1):C.GC_423})
V_230 = Vertex(name = 'V_230',
particles = [ P.b__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_290,(0,1):C.GC_4166})
V_231 = Vertex(name = 'V_231',
particles = [ P.b__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_300,(0,1):C.GC_4198})
V_232 = Vertex(name = 'V_232',
particles = [ P.b__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_363})
V_233 = Vertex(name = 'V_233',
particles = [ P.u__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_2,(0,1):C.GC_984})
V_234 = Vertex(name = 'V_234',
particles = [ P.u__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_283,(0,1):C.GC_996})
V_235 = Vertex(name = 'V_235',
particles = [ P.u__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_291,(0,1):C.GC_3998})
V_236 = Vertex(name = 'V_236',
particles = [ P.u__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_301,(0,1):C.GC_4020})
V_237 = Vertex(name = 'V_237',
particles = [ P.u__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_364})
V_238 = Vertex(name = 'V_238',
particles = [ P.c__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_2,(0,1):C.GC_453})
V_239 = Vertex(name = 'V_239',
particles = [ P.c__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_283,(0,1):C.GC_465})
V_240 = Vertex(name = 'V_240',
particles = [ P.c__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_291,(0,1):C.GC_2486})
V_241 = Vertex(name = 'V_241',
particles = [ P.c__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_301,(0,1):C.GC_2518})
V_242 = Vertex(name = 'V_242',
particles = [ P.c__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_364})
V_243 = Vertex(name = 'V_243',
particles = [ P.t__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_2,(0,1):C.GC_762})
V_244 = Vertex(name = 'V_244',
particles = [ P.t__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_283,(0,1):C.GC_774})
V_245 = Vertex(name = 'V_245',
particles = [ P.t__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_291,(0,1):C.GC_3845})
V_246 = Vertex(name = 'V_246',
particles = [ P.t__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_301,(0,1):C.GC_3877})
V_247 = Vertex(name = 'V_247',
particles = [ P.t__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_364})
V_248 = Vertex(name = 'V_248',
particles = [ P.d__tilde__, P.d, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_6,(0,1):C.GC_508})
V_249 = Vertex(name = 'V_249',
particles = [ P.d__tilde__, P.d, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV8 ],
couplings = {(0,0):C.GC_2703})
V_250 = Vertex(name = 'V_250',
particles = [ P.s__tilde__, P.s, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_6,(0,1):C.GC_681})
V_251 = Vertex(name = 'V_251',
particles = [ P.s__tilde__, P.s, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV8 ],
couplings = {(0,0):C.GC_3453})
V_252 = Vertex(name = 'V_252',
particles = [ P.b__tilde__, P.b, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_6,(0,1):C.GC_411})
V_253 = Vertex(name = 'V_253',
particles = [ P.b__tilde__, P.b, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV8 ],
couplings = {(0,0):C.GC_4169})
V_254 = Vertex(name = 'V_254',
particles = [ P.u__tilde__, P.u, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_6,(0,1):C.GC_985})
V_255 = Vertex(name = 'V_255',
particles = [ P.u__tilde__, P.u, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV8 ],
couplings = {(0,0):C.GC_4000})
V_256 = Vertex(name = 'V_256',
particles = [ P.c__tilde__, P.c, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_6,(0,1):C.GC_454})
V_257 = Vertex(name = 'V_257',
particles = [ P.c__tilde__, P.c, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV8 ],
couplings = {(0,0):C.GC_2489})
V_258 = Vertex(name = 'V_258',
particles = [ P.t__tilde__, P.t, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1, L.FFV8 ],
couplings = {(0,0):C.GC_6,(0,1):C.GC_763})
V_259 = Vertex(name = 'V_259',
particles = [ P.t__tilde__, P.t, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV8 ],
couplings = {(0,0):C.GC_3848})
V_260 = Vertex(name = 'V_260',
particles = [ P.d__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV10, L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,2):C.GC_987,(0,4):C.GC_510,(0,0):C.GC_1134,(0,1):C.GC_108,(0,3):C.GC_1017})
V_261 = Vertex(name = 'V_261',
particles = [ P.d__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,1):C.GC_4004,(0,2):C.GC_2705,(0,0):C.GC_318})
V_262 = Vertex(name = 'V_262',
particles = [ P.d__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_319})
V_263 = Vertex(name = 'V_263',
particles = [ P.d__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_337})
V_264 = Vertex(name = 'V_264',
particles = [ P.d__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1091})
V_265 = Vertex(name = 'V_265',
particles = [ P.s__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_2631,(0,3):C.GC_3053,(0,0):C.GC_109,(0,2):C.GC_1048})
V_266 = Vertex(name = 'V_266',
particles = [ P.s__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_320})
V_267 = Vertex(name = 'V_267',
particles = [ P.s__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_321})
V_268 = Vertex(name = 'V_268',
particles = [ P.s__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_338})
V_269 = Vertex(name = 'V_269',
particles = [ P.s__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1092})
V_270 = Vertex(name = 'V_270',
particles = [ P.b__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_4003,(0,3):C.GC_2978,(0,0):C.GC_110,(0,2):C.GC_1008})
V_271 = Vertex(name = 'V_271',
particles = [ P.b__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_322})
V_272 = Vertex(name = 'V_272',
particles = [ P.b__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_323})
V_273 = Vertex(name = 'V_273',
particles = [ P.b__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_339})
V_274 = Vertex(name = 'V_274',
particles = [ P.b__tilde__, P.u, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1093})
V_275 = Vertex(name = 'V_275',
particles = [ P.d__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_1665,(0,3):C.GC_3401,(0,0):C.GC_111,(0,2):C.GC_534})
V_276 = Vertex(name = 'V_276',
particles = [ P.d__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_324})
V_277 = Vertex(name = 'V_277',
particles = [ P.d__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_325})
V_278 = Vertex(name = 'V_278',
particles = [ P.d__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_340})
V_279 = Vertex(name = 'V_279',
particles = [ P.d__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_551})
V_280 = Vertex(name = 'V_280',
particles = [ P.s__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_456,(0,3):C.GC_683,(0,0):C.GC_112,(0,2):C.GC_707})
V_281 = Vertex(name = 'V_281',
particles = [ P.s__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,1):C.GC_2495,(0,2):C.GC_3459,(0,0):C.GC_326})
V_282 = Vertex(name = 'V_282',
particles = [ P.s__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_327})
V_283 = Vertex(name = 'V_283',
particles = [ P.s__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_341})
V_284 = Vertex(name = 'V_284',
particles = [ P.s__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_736})
V_285 = Vertex(name = 'V_285',
particles = [ P.b__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_3782,(0,3):C.GC_3378,(0,0):C.GC_113,(0,2):C.GC_477})
V_286 = Vertex(name = 'V_286',
particles = [ P.b__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_328})
V_287 = Vertex(name = 'V_287',
particles = [ P.b__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_329})
V_288 = Vertex(name = 'V_288',
particles = [ P.b__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_342})
V_289 = Vertex(name = 'V_289',
particles = [ P.b__tilde__, P.c, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_492})
V_290 = Vertex(name = 'V_290',
particles = [ P.d__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_1698,(0,3):C.GC_4216,(0,0):C.GC_114,(0,2):C.GC_795})
V_291 = Vertex(name = 'V_291',
particles = [ P.d__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_330})
V_292 = Vertex(name = 'V_292',
particles = [ P.d__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_331})
V_293 = Vertex(name = 'V_293',
particles = [ P.d__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_343})
V_294 = Vertex(name = 'V_294',
particles = [ P.d__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_853})
V_295 = Vertex(name = 'V_295',
particles = [ P.s__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_2570,(0,3):C.GC_4247,(0,0):C.GC_115,(0,2):C.GC_826})
V_296 = Vertex(name = 'V_296',
particles = [ P.s__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_332})
V_297 = Vertex(name = 'V_297',
particles = [ P.s__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_333})
V_298 = Vertex(name = 'V_298',
particles = [ P.s__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_344})
V_299 = Vertex(name = 'V_299',
particles = [ P.s__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_854})
V_300 = Vertex(name = 'V_300',
particles = [ P.b__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_765,(0,3):C.GC_413,(0,0):C.GC_116,(0,2):C.GC_786})
V_301 = Vertex(name = 'V_301',
particles = [ P.b__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,1):C.GC_3854,(0,2):C.GC_4175,(0,0):C.GC_334})
V_302 = Vertex(name = 'V_302',
particles = [ P.b__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_335})
V_303 = Vertex(name = 'V_303',
particles = [ P.b__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_345})
V_304 = Vertex(name = 'V_304',
particles = [ P.b__tilde__, P.t, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_855})
V_305 = Vertex(name = 'V_305',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1015,(0,0):C.GC_122})
V_306 = Vertex(name = 'V_306',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1085})
V_307 = Vertex(name = 'V_307',
particles = [ P.s__tilde__, P.u, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1046,(0,0):C.GC_123})
V_308 = Vertex(name = 'V_308',
particles = [ P.s__tilde__, P.u, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1086})
V_309 = Vertex(name = 'V_309',
particles = [ P.b__tilde__, P.u, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1006,(0,0):C.GC_124})
V_310 = Vertex(name = 'V_310',
particles = [ P.b__tilde__, P.u, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1087})
V_311 = Vertex(name = 'V_311',
particles = [ P.d__tilde__, P.c, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_532,(0,0):C.GC_125})
V_312 = Vertex(name = 'V_312',
particles = [ P.d__tilde__, P.c, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_549})
V_313 = Vertex(name = 'V_313',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_705,(0,0):C.GC_126})
V_314 = Vertex(name = 'V_314',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_734})
V_315 = Vertex(name = 'V_315',
particles = [ P.b__tilde__, P.c, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_475,(0,0):C.GC_127})
V_316 = Vertex(name = 'V_316',
particles = [ P.b__tilde__, P.c, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_490})
V_317 = Vertex(name = 'V_317',
particles = [ P.d__tilde__, P.t, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_793,(0,0):C.GC_128})
V_318 = Vertex(name = 'V_318',
particles = [ P.d__tilde__, P.t, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_847})
V_319 = Vertex(name = 'V_319',
particles = [ P.s__tilde__, P.t, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_824,(0,0):C.GC_129})
V_320 = Vertex(name = 'V_320',
particles = [ P.s__tilde__, P.t, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_848})
V_321 = Vertex(name = 'V_321',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_784,(0,0):C.GC_130})
V_322 = Vertex(name = 'V_322',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_849})
V_323 = Vertex(name = 'V_323',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_973,(0,3):C.GC_496,(0,2):C.GC_1016,(0,0):C.GC_254})
V_324 = Vertex(name = 'V_324',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,1):C.GC_3980,(0,2):C.GC_2692,(0,0):C.GC_1088})
V_325 = Vertex(name = 'V_325',
particles = [ P.s__tilde__, P.u, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_2620,(0,3):C.GC_3042,(0,2):C.GC_1047,(0,0):C.GC_255})
V_326 = Vertex(name = 'V_326',
particles = [ P.s__tilde__, P.u, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_1089})
V_327 = Vertex(name = 'V_327',
particles = [ P.b__tilde__, P.u, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_3979,(0,3):C.GC_2967,(0,2):C.GC_1007,(0,0):C.GC_256})
V_328 = Vertex(name = 'V_328',
particles = [ P.b__tilde__, P.u, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_1090})
V_329 = Vertex(name = 'V_329',
particles = [ P.d__tilde__, P.c, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_1654,(0,3):C.GC_3390,(0,2):C.GC_533,(0,0):C.GC_257})
V_330 = Vertex(name = 'V_330',
particles = [ P.d__tilde__, P.c, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_550})
V_331 = Vertex(name = 'V_331',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_442,(0,3):C.GC_669,(0,2):C.GC_706,(0,0):C.GC_258})
V_332 = Vertex(name = 'V_332',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,1):C.GC_2460,(0,2):C.GC_3424,(0,0):C.GC_735})
V_333 = Vertex(name = 'V_333',
particles = [ P.b__tilde__, P.c, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_3771,(0,3):C.GC_3367,(0,2):C.GC_476,(0,0):C.GC_259})
V_334 = Vertex(name = 'V_334',
particles = [ P.b__tilde__, P.c, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_491})
V_335 = Vertex(name = 'V_335',
particles = [ P.d__tilde__, P.t, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_1687,(0,3):C.GC_4205,(0,2):C.GC_794,(0,0):C.GC_260})
V_336 = Vertex(name = 'V_336',
particles = [ P.d__tilde__, P.t, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_850})
V_337 = Vertex(name = 'V_337',
particles = [ P.s__tilde__, P.t, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_2559,(0,3):C.GC_4236,(0,2):C.GC_825,(0,0):C.GC_261})
V_338 = Vertex(name = 'V_338',
particles = [ P.s__tilde__, P.t, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_851})
V_339 = Vertex(name = 'V_339',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_749,(0,3):C.GC_399,(0,2):C.GC_785,(0,0):C.GC_262})
V_340 = Vertex(name = 'V_340',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,1):C.GC_3819,(0,2):C.GC_4140,(0,0):C.GC_852})
V_341 = Vertex(name = 'V_341',
particles = [ P.e__plus__, P.ve, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2, L.FFV9 ],
couplings = {(0,1):C.GC_566,(0,0):C.GC_107})
V_342 = Vertex(name = 'V_342',
particles = [ P.e__plus__, P.ve, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_336})
V_343 = Vertex(name = 'V_343',
particles = [ P.mu__plus__, P.vm, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2, L.FFV9 ],
couplings = {(0,1):C.GC_623,(0,0):C.GC_107})
V_344 = Vertex(name = 'V_344',
particles = [ P.mu__plus__, P.vm, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_336})
V_345 = Vertex(name = 'V_345',
particles = [ P.ta__plus__, P.vt, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2, L.FFV9 ],
couplings = {(0,1):C.GC_894,(0,0):C.GC_107})
V_346 = Vertex(name = 'V_346',
particles = [ P.ta__plus__, P.vt, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_336})
V_347 = Vertex(name = 'V_347',
particles = [ P.e__plus__, P.ve, P.W__minus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_121})
V_348 = Vertex(name = 'V_348',
particles = [ P.mu__plus__, P.vm, P.W__minus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_121})
V_349 = Vertex(name = 'V_349',
particles = [ P.ta__plus__, P.vt, P.W__minus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_121})
V_350 = Vertex(name = 'V_350',
particles = [ P.e__plus__, P.ve, P.W__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS5 ],
couplings = {(0,1):C.GC_554,(0,0):C.GC_253})
V_351 = Vertex(name = 'V_351',
particles = [ P.mu__plus__, P.vm, P.W__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS5 ],
couplings = {(0,1):C.GC_611,(0,0):C.GC_253})
V_352 = Vertex(name = 'V_352',
particles = [ P.ta__plus__, P.vt, P.W__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS5 ],
couplings = {(0,1):C.GC_882,(0,0):C.GC_253})
V_353 = Vertex(name = 'V_353',
particles = [ P.u__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_510,(0,3):C.GC_987,(0,0):C.GC_1171,(0,2):C.GC_1231})
V_354 = Vertex(name = 'V_354',
particles = [ P.u__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,1):C.GC_2706,(0,2):C.GC_4004,(0,0):C.GC_1175})
V_355 = Vertex(name = 'V_355',
particles = [ P.u__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1176})
V_356 = Vertex(name = 'V_356',
particles = [ P.u__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1177})
V_357 = Vertex(name = 'V_357',
particles = [ P.u__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1279})
V_358 = Vertex(name = 'V_358',
particles = [ P.c__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_3015,(0,3):C.GC_2494,(0,0):C.GC_1862,(0,2):C.GC_1899})
V_359 = Vertex(name = 'V_359',
particles = [ P.c__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1866})
V_360 = Vertex(name = 'V_360',
particles = [ P.c__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1867})
V_361 = Vertex(name = 'V_361',
particles = [ P.c__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1868})
V_362 = Vertex(name = 'V_362',
particles = [ P.c__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1980})
V_363 = Vertex(name = 'V_363',
particles = [ P.t__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_3016,(0,3):C.GC_3852,(0,0):C.GC_2748,(0,2):C.GC_2814})
V_364 = Vertex(name = 'V_364',
particles = [ P.t__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2753})
V_365 = Vertex(name = 'V_365',
particles = [ P.t__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2754})
V_366 = Vertex(name = 'V_366',
particles = [ P.t__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2755})
V_367 = Vertex(name = 'V_367',
particles = [ P.t__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2891})
V_368 = Vertex(name = 'V_368',
particles = [ P.u__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_3458,(0,3):C.GC_1755,(0,0):C.GC_1320,(0,2):C.GC_1412})
V_369 = Vertex(name = 'V_369',
particles = [ P.u__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1324})
V_370 = Vertex(name = 'V_370',
particles = [ P.u__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1325})
V_371 = Vertex(name = 'V_371',
particles = [ P.u__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1326})
V_372 = Vertex(name = 'V_372',
particles = [ P.u__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1452})
V_373 = Vertex(name = 'V_373',
particles = [ P.c__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_683,(0,3):C.GC_456,(0,0):C.GC_2036,(0,2):C.GC_2107})
V_374 = Vertex(name = 'V_374',
particles = [ P.c__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,1):C.GC_3459,(0,2):C.GC_2495,(0,0):C.GC_2040})
V_375 = Vertex(name = 'V_375',
particles = [ P.c__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2041})
V_376 = Vertex(name = 'V_376',
particles = [ P.c__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2042})
V_377 = Vertex(name = 'V_377',
particles = [ P.c__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2174})
V_378 = Vertex(name = 'V_378',
particles = [ P.t__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_3460,(0,3):C.GC_3853,(0,0):C.GC_3096,(0,2):C.GC_3194})
V_379 = Vertex(name = 'V_379',
particles = [ P.t__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3101})
V_380 = Vertex(name = 'V_380',
particles = [ P.t__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3102})
V_381 = Vertex(name = 'V_381',
particles = [ P.t__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3103})
V_382 = Vertex(name = 'V_382',
particles = [ P.t__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3258})
V_383 = Vertex(name = 'V_383',
particles = [ P.u__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_4173,(0,3):C.GC_1756,(0,0):C.GC_1493,(0,2):C.GC_1557})
V_384 = Vertex(name = 'V_384',
particles = [ P.u__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1497})
V_385 = Vertex(name = 'V_385',
particles = [ P.u__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1498})
V_386 = Vertex(name = 'V_386',
particles = [ P.u__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1499})
V_387 = Vertex(name = 'V_387',
particles = [ P.u__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1821})
V_388 = Vertex(name = 'V_388',
particles = [ P.c__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_4174,(0,3):C.GC_2496,(0,0):C.GC_2233,(0,2):C.GC_2262})
V_389 = Vertex(name = 'V_389',
particles = [ P.c__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2237})
V_390 = Vertex(name = 'V_390',
particles = [ P.c__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2238})
V_391 = Vertex(name = 'V_391',
particles = [ P.c__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2239})
V_392 = Vertex(name = 'V_392',
particles = [ P.c__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2372})
V_393 = Vertex(name = 'V_393',
particles = [ P.t__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV4, L.FFV9 ],
couplings = {(0,1):C.GC_413,(0,3):C.GC_765,(0,0):C.GC_3526,(0,2):C.GC_3588})
V_394 = Vertex(name = 'V_394',
particles = [ P.t__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,1):C.GC_4175,(0,2):C.GC_3854,(0,0):C.GC_3531})
V_395 = Vertex(name = 'V_395',
particles = [ P.t__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3532})
V_396 = Vertex(name = 'V_396',
particles = [ P.t__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3533})
V_397 = Vertex(name = 'V_397',
particles = [ P.t__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3932})
V_398 = Vertex(name = 'V_398',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1229,(0,0):C.GC_1172})
V_399 = Vertex(name = 'V_399',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1277})
V_400 = Vertex(name = 'V_400',
particles = [ P.c__tilde__, P.d, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1897,(0,0):C.GC_1863})
V_401 = Vertex(name = 'V_401',
particles = [ P.c__tilde__, P.d, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1978})
V_402 = Vertex(name = 'V_402',
particles = [ P.t__tilde__, P.d, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_2812,(0,0):C.GC_2749})
V_403 = Vertex(name = 'V_403',
particles = [ P.t__tilde__, P.d, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2889})
V_404 = Vertex(name = 'V_404',
particles = [ P.u__tilde__, P.s, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1410,(0,0):C.GC_1321})
V_405 = Vertex(name = 'V_405',
particles = [ P.u__tilde__, P.s, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1450})
V_406 = Vertex(name = 'V_406',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_2105,(0,0):C.GC_2037})
V_407 = Vertex(name = 'V_407',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2172})
V_408 = Vertex(name = 'V_408',
particles = [ P.t__tilde__, P.s, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_3192,(0,0):C.GC_3097})
V_409 = Vertex(name = 'V_409',
particles = [ P.t__tilde__, P.s, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3256})
V_410 = Vertex(name = 'V_410',
particles = [ P.u__tilde__, P.b, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_1555,(0,0):C.GC_1494})
V_411 = Vertex(name = 'V_411',
particles = [ P.u__tilde__, P.b, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1819})
V_412 = Vertex(name = 'V_412',
particles = [ P.c__tilde__, P.b, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_2260,(0,0):C.GC_2234})
V_413 = Vertex(name = 'V_413',
particles = [ P.c__tilde__, P.b, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2370})
V_414 = Vertex(name = 'V_414',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,1):C.GC_3586,(0,0):C.GC_3527})
V_415 = Vertex(name = 'V_415',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3930})
V_416 = Vertex(name = 'V_416',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_496,(0,3):C.GC_973,(0,2):C.GC_1230,(0,0):C.GC_1174})
V_417 = Vertex(name = 'V_417',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,1):C.GC_2692,(0,2):C.GC_3980,(0,0):C.GC_1278})
V_418 = Vertex(name = 'V_418',
particles = [ P.c__tilde__, P.d, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_2993,(0,3):C.GC_2459,(0,2):C.GC_1898,(0,0):C.GC_1865})
V_419 = Vertex(name = 'V_419',
particles = [ P.c__tilde__, P.d, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_1979})
V_420 = Vertex(name = 'V_420',
particles = [ P.t__tilde__, P.d, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_2994,(0,3):C.GC_3817,(0,2):C.GC_2813,(0,0):C.GC_2752})
V_421 = Vertex(name = 'V_421',
particles = [ P.t__tilde__, P.d, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_2890})
V_422 = Vertex(name = 'V_422',
particles = [ P.u__tilde__, P.s, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_3423,(0,3):C.GC_1733,(0,2):C.GC_1411,(0,0):C.GC_1323})
V_423 = Vertex(name = 'V_423',
particles = [ P.u__tilde__, P.s, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_1451})
V_424 = Vertex(name = 'V_424',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_669,(0,3):C.GC_442,(0,2):C.GC_2106,(0,0):C.GC_2039})
V_425 = Vertex(name = 'V_425',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,1):C.GC_3424,(0,2):C.GC_2460,(0,0):C.GC_2173})
V_426 = Vertex(name = 'V_426',
particles = [ P.t__tilde__, P.s, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_3425,(0,3):C.GC_3818,(0,2):C.GC_3193,(0,0):C.GC_3100})
V_427 = Vertex(name = 'V_427',
particles = [ P.t__tilde__, P.s, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_3257})
V_428 = Vertex(name = 'V_428',
particles = [ P.u__tilde__, P.b, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_4138,(0,3):C.GC_1734,(0,2):C.GC_1556,(0,0):C.GC_1496})
V_429 = Vertex(name = 'V_429',
particles = [ P.u__tilde__, P.b, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_1820})
V_430 = Vertex(name = 'V_430',
particles = [ P.c__tilde__, P.b, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_4139,(0,3):C.GC_2461,(0,2):C.GC_2261,(0,0):C.GC_2236})
V_431 = Vertex(name = 'V_431',
particles = [ P.c__tilde__, P.b, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_2371})
V_432 = Vertex(name = 'V_432',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS3, L.FFVS5 ],
couplings = {(0,1):C.GC_399,(0,3):C.GC_749,(0,2):C.GC_3587,(0,0):C.GC_3530})
V_433 = Vertex(name = 'V_433',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,1):C.GC_4140,(0,2):C.GC_3819,(0,0):C.GC_3931})
V_434 = Vertex(name = 'V_434',
particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2, L.FFV3 ],
couplings = {(0,1):C.GC_566,(0,0):C.GC_107})
V_435 = Vertex(name = 'V_435',
particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_336})
V_436 = Vertex(name = 'V_436',
particles = [ P.vm__tilde__, P.mu__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2, L.FFV3 ],
couplings = {(0,1):C.GC_623,(0,0):C.GC_107})
V_437 = Vertex(name = 'V_437',
particles = [ P.vm__tilde__, P.mu__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_336})
V_438 = Vertex(name = 'V_438',
particles = [ P.vt__tilde__, P.ta__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2, L.FFV3 ],
couplings = {(0,1):C.GC_894,(0,0):C.GC_107})
V_439 = Vertex(name = 'V_439',
particles = [ P.vt__tilde__, P.ta__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_336})
V_440 = Vertex(name = 'V_440',
particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_121})
V_441 = Vertex(name = 'V_441',
particles = [ P.vm__tilde__, P.mu__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_121})
V_442 = Vertex(name = 'V_442',
particles = [ P.vt__tilde__, P.ta__minus__, P.W__plus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_121})
V_443 = Vertex(name = 'V_443',
particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS2 ],
couplings = {(0,1):C.GC_554,(0,0):C.GC_253})
V_444 = Vertex(name = 'V_444',
particles = [ P.vm__tilde__, P.mu__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS2 ],
couplings = {(0,1):C.GC_611,(0,0):C.GC_253})
V_445 = Vertex(name = 'V_445',
particles = [ P.vt__tilde__, P.ta__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS2 ],
couplings = {(0,1):C.GC_882,(0,0):C.GC_253})
V_446 = Vertex(name = 'V_446',
particles = [ P.d__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_136,(0,1):C.GC_131})
V_447 = Vertex(name = 'V_447',
particles = [ P.d__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_138,(0,1):C.GC_541})
V_448 = Vertex(name = 'V_448',
particles = [ P.d__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2683})
V_449 = Vertex(name = 'V_449',
particles = [ P.d__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2684})
V_450 = Vertex(name = 'V_450',
particles = [ P.s__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2952})
V_451 = Vertex(name = 'V_451',
particles = [ P.s__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2954})
V_452 = Vertex(name = 'V_452',
particles = [ P.b__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2953})
V_453 = Vertex(name = 'V_453',
particles = [ P.b__tilde__, P.d, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2955})
V_454 = Vertex(name = 'V_454',
particles = [ P.d__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3346})
V_455 = Vertex(name = 'V_455',
particles = [ P.d__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3349})
V_456 = Vertex(name = 'V_456',
particles = [ P.s__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_136,(0,1):C.GC_131})
V_457 = Vertex(name = 'V_457',
particles = [ P.s__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_138,(0,1):C.GC_724})
V_458 = Vertex(name = 'V_458',
particles = [ P.s__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3347})
V_459 = Vertex(name = 'V_459',
particles = [ P.s__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3350})
V_460 = Vertex(name = 'V_460',
particles = [ P.b__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3348})
V_461 = Vertex(name = 'V_461',
particles = [ P.b__tilde__, P.s, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3351})
V_462 = Vertex(name = 'V_462',
particles = [ P.d__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_4111})
V_463 = Vertex(name = 'V_463',
particles = [ P.d__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_4114})
V_464 = Vertex(name = 'V_464',
particles = [ P.s__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_4112})
V_465 = Vertex(name = 'V_465',
particles = [ P.s__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_4115})
V_466 = Vertex(name = 'V_466',
particles = [ P.b__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_136,(0,1):C.GC_131})
V_467 = Vertex(name = 'V_467',
particles = [ P.b__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_138,(0,1):C.GC_435})
V_468 = Vertex(name = 'V_468',
particles = [ P.b__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_4113})
V_469 = Vertex(name = 'V_469',
particles = [ P.b__tilde__, P.b, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_4116})
V_470 = Vertex(name = 'V_470',
particles = [ P.d__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_268,(0,1):C.GC_263,(0,2):C.GC_498})
V_471 = Vertex(name = 'V_471',
particles = [ P.d__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_270,(0,1):C.GC_542,(0,2):C.GC_506})
V_472 = Vertex(name = 'V_472',
particles = [ P.d__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_2685,(0,1):C.GC_2693})
V_473 = Vertex(name = 'V_473',
particles = [ P.d__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_2686,(0,1):C.GC_2700})
V_474 = Vertex(name = 'V_474',
particles = [ P.s__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2956,(0,1):C.GC_2995,(0,2):C.GC_3043})
V_475 = Vertex(name = 'V_475',
particles = [ P.s__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2958,(0,1):C.GC_3005,(0,2):C.GC_3048})
V_476 = Vertex(name = 'V_476',
particles = [ P.b__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2957,(0,1):C.GC_2996,(0,2):C.GC_2968})
V_477 = Vertex(name = 'V_477',
particles = [ P.b__tilde__, P.d, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2959,(0,1):C.GC_3006,(0,2):C.GC_2973})
V_478 = Vertex(name = 'V_478',
particles = [ P.d__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3352,(0,1):C.GC_3426,(0,2):C.GC_3391})
V_479 = Vertex(name = 'V_479',
particles = [ P.d__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3355,(0,1):C.GC_3443,(0,2):C.GC_3396})
V_480 = Vertex(name = 'V_480',
particles = [ P.s__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_268,(0,1):C.GC_263,(0,2):C.GC_671})
V_481 = Vertex(name = 'V_481',
particles = [ P.s__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_270,(0,1):C.GC_725,(0,2):C.GC_679})
V_482 = Vertex(name = 'V_482',
particles = [ P.s__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_3353,(0,1):C.GC_3427})
V_483 = Vertex(name = 'V_483',
particles = [ P.s__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_3356,(0,1):C.GC_3444})
V_484 = Vertex(name = 'V_484',
particles = [ P.b__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3354,(0,1):C.GC_3428,(0,2):C.GC_3368})
V_485 = Vertex(name = 'V_485',
particles = [ P.b__tilde__, P.s, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3357,(0,1):C.GC_3445,(0,2):C.GC_3373})
V_486 = Vertex(name = 'V_486',
particles = [ P.d__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4117,(0,1):C.GC_4141,(0,2):C.GC_4206})
V_487 = Vertex(name = 'V_487',
particles = [ P.d__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4120,(0,1):C.GC_4158,(0,2):C.GC_4211})
V_488 = Vertex(name = 'V_488',
particles = [ P.s__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4118,(0,1):C.GC_4142,(0,2):C.GC_4237})
V_489 = Vertex(name = 'V_489',
particles = [ P.s__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4121,(0,1):C.GC_4159,(0,2):C.GC_4242})
V_490 = Vertex(name = 'V_490',
particles = [ P.b__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_268,(0,1):C.GC_263,(0,2):C.GC_401})
V_491 = Vertex(name = 'V_491',
particles = [ P.b__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_270,(0,1):C.GC_436,(0,2):C.GC_409})
V_492 = Vertex(name = 'V_492',
particles = [ P.b__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_4119,(0,1):C.GC_4143})
V_493 = Vertex(name = 'V_493',
particles = [ P.b__tilde__, P.b, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_4122,(0,1):C.GC_4160})
V_494 = Vertex(name = 'V_494',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV7, L.FFV8 ],
couplings = {(0,0):C.GC_171,(0,1):C.GC_118,(0,3):C.GC_347,(0,2):C.GC_346,(0,4):C.GC_512})
V_495 = Vertex(name = 'V_495',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV8 ],
couplings = {(0,0):C.GC_286,(0,1):C.GC_353,(0,2):C.GC_543,(0,3):C.GC_519})
V_496 = Vertex(name = 'V_496',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_371,(0,1):C.GC_355,(0,2):C.GC_2707})
V_497 = Vertex(name = 'V_497',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_374,(0,1):C.GC_357,(0,2):C.GC_2714})
V_498 = Vertex(name = 'V_498',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_377,(0,1):C.GC_360})
V_499 = Vertex(name = 'V_499',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2687})
V_500 = Vertex(name = 'V_500',
particles = [ P.d__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2688})
V_501 = Vertex(name = 'V_501',
particles = [ P.s__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2960,(0,1):C.GC_3017,(0,2):C.GC_3054})
V_502 = Vertex(name = 'V_502',
particles = [ P.s__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2962,(0,1):C.GC_3027,(0,2):C.GC_3059})
V_503 = Vertex(name = 'V_503',
particles = [ P.b__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2961,(0,1):C.GC_3018,(0,2):C.GC_2979})
V_504 = Vertex(name = 'V_504',
particles = [ P.b__tilde__, P.d, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2963,(0,1):C.GC_3028,(0,2):C.GC_2984})
V_505 = Vertex(name = 'V_505',
particles = [ P.d__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3358,(0,1):C.GC_3461,(0,2):C.GC_3402})
V_506 = Vertex(name = 'V_506',
particles = [ P.d__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3361,(0,1):C.GC_3478,(0,2):C.GC_3407})
V_507 = Vertex(name = 'V_507',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV7, L.FFV8 ],
couplings = {(0,0):C.GC_171,(0,1):C.GC_118,(0,3):C.GC_347,(0,2):C.GC_346,(0,4):C.GC_685})
V_508 = Vertex(name = 'V_508',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV8 ],
couplings = {(0,0):C.GC_286,(0,1):C.GC_353,(0,2):C.GC_726,(0,3):C.GC_692})
V_509 = Vertex(name = 'V_509',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_371,(0,1):C.GC_355,(0,2):C.GC_3462})
V_510 = Vertex(name = 'V_510',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_374,(0,1):C.GC_357,(0,2):C.GC_3479})
V_511 = Vertex(name = 'V_511',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_377,(0,1):C.GC_360})
V_512 = Vertex(name = 'V_512',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3359})
V_513 = Vertex(name = 'V_513',
particles = [ P.s__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3362})
V_514 = Vertex(name = 'V_514',
particles = [ P.b__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3360,(0,1):C.GC_3463,(0,2):C.GC_3379})
V_515 = Vertex(name = 'V_515',
particles = [ P.b__tilde__, P.s, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3363,(0,1):C.GC_3480,(0,2):C.GC_3384})
V_516 = Vertex(name = 'V_516',
particles = [ P.d__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4123,(0,1):C.GC_4176,(0,2):C.GC_4217})
V_517 = Vertex(name = 'V_517',
particles = [ P.d__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4126,(0,1):C.GC_4193,(0,2):C.GC_4222})
V_518 = Vertex(name = 'V_518',
particles = [ P.s__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4124,(0,1):C.GC_4177,(0,2):C.GC_4248})
V_519 = Vertex(name = 'V_519',
particles = [ P.s__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4127,(0,1):C.GC_4194,(0,2):C.GC_4253})
V_520 = Vertex(name = 'V_520',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV7, L.FFV8 ],
couplings = {(0,0):C.GC_171,(0,1):C.GC_118,(0,3):C.GC_347,(0,2):C.GC_346,(0,4):C.GC_415})
V_521 = Vertex(name = 'V_521',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV8 ],
couplings = {(0,0):C.GC_286,(0,1):C.GC_353,(0,2):C.GC_437,(0,3):C.GC_422})
V_522 = Vertex(name = 'V_522',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_371,(0,1):C.GC_355,(0,2):C.GC_4178})
V_523 = Vertex(name = 'V_523',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_374,(0,1):C.GC_357,(0,2):C.GC_4195})
V_524 = Vertex(name = 'V_524',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_377,(0,1):C.GC_360})
V_525 = Vertex(name = 'V_525',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_4125})
V_526 = Vertex(name = 'V_526',
particles = [ P.b__tilde__, P.b, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_4128})
V_527 = Vertex(name = 'V_527',
particles = [ P.e__plus__, P.e__minus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_133,(0,1):C.GC_132})
V_528 = Vertex(name = 'V_528',
particles = [ P.e__plus__, P.e__minus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_135})
V_529 = Vertex(name = 'V_529',
particles = [ P.mu__plus__, P.mu__minus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_133,(0,1):C.GC_132})
V_530 = Vertex(name = 'V_530',
particles = [ P.mu__plus__, P.mu__minus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_135})
V_531 = Vertex(name = 'V_531',
particles = [ P.ta__plus__, P.ta__minus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_133,(0,1):C.GC_132})
V_532 = Vertex(name = 'V_532',
particles = [ P.ta__plus__, P.ta__minus__, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_135})
V_533 = Vertex(name = 'V_533',
particles = [ P.e__plus__, P.e__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_265,(0,1):C.GC_264,(0,2):C.GC_556})
V_534 = Vertex(name = 'V_534',
particles = [ P.e__plus__, P.e__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_267,(0,1):C.GC_563})
V_535 = Vertex(name = 'V_535',
particles = [ P.mu__plus__, P.mu__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_265,(0,1):C.GC_264,(0,2):C.GC_613})
V_536 = Vertex(name = 'V_536',
particles = [ P.mu__plus__, P.mu__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_267,(0,1):C.GC_620})
V_537 = Vertex(name = 'V_537',
particles = [ P.ta__plus__, P.ta__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_265,(0,1):C.GC_264,(0,2):C.GC_884})
V_538 = Vertex(name = 'V_538',
particles = [ P.ta__plus__, P.ta__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_267,(0,1):C.GC_891})
V_539 = Vertex(name = 'V_539',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV6, L.FFV8 ],
couplings = {(0,0):C.GC_172,(0,1):C.GC_117,(0,3):C.GC_349,(0,2):C.GC_358,(0,4):C.GC_988})
V_540 = Vertex(name = 'V_540',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV8 ],
couplings = {(0,0):C.GC_287,(0,1):C.GC_354,(0,2):C.GC_1071,(0,3):C.GC_995})
V_541 = Vertex(name = 'V_541',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_372,(0,1):C.GC_355,(0,2):C.GC_4006})
V_542 = Vertex(name = 'V_542',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_375,(0,1):C.GC_356,(0,2):C.GC_4018})
V_543 = Vertex(name = 'V_543',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_378,(0,1):C.GC_359})
V_544 = Vertex(name = 'V_544',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3763})
V_545 = Vertex(name = 'V_545',
particles = [ P.u__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3766})
V_546 = Vertex(name = 'V_546',
particles = [ P.c__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2444,(0,1):C.GC_2632,(0,2):C.GC_2497})
V_547 = Vertex(name = 'V_547',
particles = [ P.c__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2447,(0,1):C.GC_2637,(0,2):C.GC_2514})
V_548 = Vertex(name = 'V_548',
particles = [ P.t__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3760,(0,1):C.GC_4005,(0,2):C.GC_3855})
V_549 = Vertex(name = 'V_549',
particles = [ P.t__tilde__, P.u, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3764,(0,1):C.GC_4017,(0,2):C.GC_3872})
V_550 = Vertex(name = 'V_550',
particles = [ P.u__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1647,(0,1):C.GC_1666,(0,2):C.GC_1757})
V_551 = Vertex(name = 'V_551',
particles = [ P.u__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1649,(0,1):C.GC_1671,(0,2):C.GC_1767})
V_552 = Vertex(name = 'V_552',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV6, L.FFV8 ],
couplings = {(0,0):C.GC_172,(0,1):C.GC_117,(0,3):C.GC_349,(0,2):C.GC_358,(0,4):C.GC_457})
V_553 = Vertex(name = 'V_553',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV8 ],
couplings = {(0,0):C.GC_287,(0,1):C.GC_354,(0,2):C.GC_486,(0,3):C.GC_464})
V_554 = Vertex(name = 'V_554',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_372,(0,1):C.GC_355,(0,2):C.GC_2498})
V_555 = Vertex(name = 'V_555',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_375,(0,1):C.GC_356,(0,2):C.GC_2515})
V_556 = Vertex(name = 'V_556',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_378,(0,1):C.GC_359})
V_557 = Vertex(name = 'V_557',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2445})
V_558 = Vertex(name = 'V_558',
particles = [ P.c__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2448})
V_559 = Vertex(name = 'V_559',
particles = [ P.t__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3761,(0,1):C.GC_3783,(0,2):C.GC_3856})
V_560 = Vertex(name = 'V_560',
particles = [ P.t__tilde__, P.c, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3765,(0,1):C.GC_3788,(0,2):C.GC_3873})
V_561 = Vertex(name = 'V_561',
particles = [ P.u__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1648,(0,1):C.GC_1699,(0,2):C.GC_1758})
V_562 = Vertex(name = 'V_562',
particles = [ P.u__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1650,(0,1):C.GC_1704,(0,2):C.GC_1768})
V_563 = Vertex(name = 'V_563',
particles = [ P.c__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2446,(0,1):C.GC_2571,(0,2):C.GC_2499})
V_564 = Vertex(name = 'V_564',
particles = [ P.c__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2, L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2449,(0,1):C.GC_2576,(0,2):C.GC_2516})
V_565 = Vertex(name = 'V_565',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV6, L.FFV8 ],
couplings = {(0,0):C.GC_172,(0,1):C.GC_117,(0,3):C.GC_349,(0,2):C.GC_358,(0,4):C.GC_766})
V_566 = Vertex(name = 'V_566',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV4, L.FFV8 ],
couplings = {(0,0):C.GC_287,(0,1):C.GC_354,(0,2):C.GC_835,(0,3):C.GC_773})
V_567 = Vertex(name = 'V_567',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_372,(0,1):C.GC_355,(0,2):C.GC_3857})
V_568 = Vertex(name = 'V_568',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2, L.FFV8 ],
couplings = {(0,0):C.GC_375,(0,1):C.GC_356,(0,2):C.GC_3874})
V_569 = Vertex(name = 'V_569',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_378,(0,1):C.GC_359})
V_570 = Vertex(name = 'V_570',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3762})
V_571 = Vertex(name = 'V_571',
particles = [ P.t__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3767})
V_572 = Vertex(name = 'V_572',
particles = [ P.u__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_136,(0,1):C.GC_139})
V_573 = Vertex(name = 'V_573',
particles = [ P.u__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_137,(0,1):C.GC_1069})
V_574 = Vertex(name = 'V_574',
particles = [ P.u__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3747})
V_575 = Vertex(name = 'V_575',
particles = [ P.u__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3750})
V_576 = Vertex(name = 'V_576',
particles = [ P.c__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2432})
V_577 = Vertex(name = 'V_577',
particles = [ P.c__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2435})
V_578 = Vertex(name = 'V_578',
particles = [ P.t__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3744})
V_579 = Vertex(name = 'V_579',
particles = [ P.t__tilde__, P.u, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3748})
V_580 = Vertex(name = 'V_580',
particles = [ P.u__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1639})
V_581 = Vertex(name = 'V_581',
particles = [ P.u__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1641})
V_582 = Vertex(name = 'V_582',
particles = [ P.c__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_136,(0,1):C.GC_139})
V_583 = Vertex(name = 'V_583',
particles = [ P.c__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_137,(0,1):C.GC_484})
V_584 = Vertex(name = 'V_584',
particles = [ P.c__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2433})
V_585 = Vertex(name = 'V_585',
particles = [ P.c__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2436})
V_586 = Vertex(name = 'V_586',
particles = [ P.t__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3745})
V_587 = Vertex(name = 'V_587',
particles = [ P.t__tilde__, P.c, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3749})
V_588 = Vertex(name = 'V_588',
particles = [ P.u__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1640})
V_589 = Vertex(name = 'V_589',
particles = [ P.u__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_1642})
V_590 = Vertex(name = 'V_590',
particles = [ P.c__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2434})
V_591 = Vertex(name = 'V_591',
particles = [ P.c__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_2437})
V_592 = Vertex(name = 'V_592',
particles = [ P.t__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_136,(0,1):C.GC_139})
V_593 = Vertex(name = 'V_593',
particles = [ P.t__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1, L.FFVSS2 ],
couplings = {(0,0):C.GC_137,(0,1):C.GC_833})
V_594 = Vertex(name = 'V_594',
particles = [ P.t__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3746})
V_595 = Vertex(name = 'V_595',
particles = [ P.t__tilde__, P.t, P.Z, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_3751})
V_596 = Vertex(name = 'V_596',
particles = [ P.u__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_268,(0,1):C.GC_271,(0,2):C.GC_974})
V_597 = Vertex(name = 'V_597',
particles = [ P.u__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_269,(0,1):C.GC_1070,(0,2):C.GC_982})
V_598 = Vertex(name = 'V_598',
particles = [ P.u__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_3755,(0,1):C.GC_3982})
V_599 = Vertex(name = 'V_599',
particles = [ P.u__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_3758,(0,1):C.GC_3994})
V_600 = Vertex(name = 'V_600',
particles = [ P.c__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2438,(0,1):C.GC_2621,(0,2):C.GC_2462})
V_601 = Vertex(name = 'V_601',
particles = [ P.c__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2441,(0,1):C.GC_2626,(0,2):C.GC_2479})
V_602 = Vertex(name = 'V_602',
particles = [ P.t__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3752,(0,1):C.GC_3981,(0,2):C.GC_3820})
V_603 = Vertex(name = 'V_603',
particles = [ P.t__tilde__, P.u, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3756,(0,1):C.GC_3993,(0,2):C.GC_3837})
V_604 = Vertex(name = 'V_604',
particles = [ P.u__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1643,(0,1):C.GC_1655,(0,2):C.GC_1735})
V_605 = Vertex(name = 'V_605',
particles = [ P.u__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1645,(0,1):C.GC_1660,(0,2):C.GC_1745})
V_606 = Vertex(name = 'V_606',
particles = [ P.c__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_268,(0,1):C.GC_271,(0,2):C.GC_443})
V_607 = Vertex(name = 'V_607',
particles = [ P.c__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_269,(0,1):C.GC_485,(0,2):C.GC_451})
V_608 = Vertex(name = 'V_608',
particles = [ P.c__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_2439,(0,1):C.GC_2463})
V_609 = Vertex(name = 'V_609',
particles = [ P.c__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_2442,(0,1):C.GC_2480})
V_610 = Vertex(name = 'V_610',
particles = [ P.t__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3753,(0,1):C.GC_3772,(0,2):C.GC_3821})
V_611 = Vertex(name = 'V_611',
particles = [ P.t__tilde__, P.c, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3757,(0,1):C.GC_3777,(0,2):C.GC_3838})
V_612 = Vertex(name = 'V_612',
particles = [ P.u__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1644,(0,1):C.GC_1688,(0,2):C.GC_1736})
V_613 = Vertex(name = 'V_613',
particles = [ P.u__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1646,(0,1):C.GC_1693,(0,2):C.GC_1746})
V_614 = Vertex(name = 'V_614',
particles = [ P.c__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2440,(0,1):C.GC_2560,(0,2):C.GC_2464})
V_615 = Vertex(name = 'V_615',
particles = [ P.c__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2443,(0,1):C.GC_2565,(0,2):C.GC_2481})
V_616 = Vertex(name = 'V_616',
particles = [ P.t__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_268,(0,1):C.GC_271,(0,2):C.GC_750})
V_617 = Vertex(name = 'V_617',
particles = [ P.t__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS3, L.FFVS4 ],
couplings = {(0,0):C.GC_269,(0,1):C.GC_834,(0,2):C.GC_760})
V_618 = Vertex(name = 'V_618',
particles = [ P.t__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_3754,(0,1):C.GC_3822})
V_619 = Vertex(name = 'V_619',
particles = [ P.t__tilde__, P.t, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS1, L.FFVS4 ],
couplings = {(0,0):C.GC_3759,(0,1):C.GC_3839})
V_620 = Vertex(name = 'V_620',
particles = [ P.ve__tilde__, P.ve, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_117})
V_621 = Vertex(name = 'V_621',
particles = [ P.ve__tilde__, P.ve, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_348})
V_622 = Vertex(name = 'V_622',
particles = [ P.ve__tilde__, P.ve, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_352})
V_623 = Vertex(name = 'V_623',
particles = [ P.ve__tilde__, P.ve, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_359})
V_624 = Vertex(name = 'V_624',
particles = [ P.vm__tilde__, P.vm, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_117})
V_625 = Vertex(name = 'V_625',
particles = [ P.vm__tilde__, P.vm, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_348})
V_626 = Vertex(name = 'V_626',
particles = [ P.vm__tilde__, P.vm, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_352})
V_627 = Vertex(name = 'V_627',
particles = [ P.vm__tilde__, P.vm, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_359})
V_628 = Vertex(name = 'V_628',
particles = [ P.vt__tilde__, P.vt, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_117})
V_629 = Vertex(name = 'V_629',
particles = [ P.vt__tilde__, P.vt, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_348})
V_630 = Vertex(name = 'V_630',
particles = [ P.vt__tilde__, P.vt, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_352})
V_631 = Vertex(name = 'V_631',
particles = [ P.vt__tilde__, P.vt, P.Z ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_359})
V_632 = Vertex(name = 'V_632',
particles = [ P.ve__tilde__, P.ve, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_133})
V_633 = Vertex(name = 'V_633',
particles = [ P.ve__tilde__, P.ve, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_134})
V_634 = Vertex(name = 'V_634',
particles = [ P.vm__tilde__, P.vm, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_133})
V_635 = Vertex(name = 'V_635',
particles = [ P.vm__tilde__, P.vm, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_134})
V_636 = Vertex(name = 'V_636',
particles = [ P.vt__tilde__, P.vt, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_133})
V_637 = Vertex(name = 'V_637',
particles = [ P.vt__tilde__, P.vt, P.Z, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFVSS1 ],
couplings = {(0,0):C.GC_134})
V_638 = Vertex(name = 'V_638',
particles = [ P.ve__tilde__, P.ve, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_265})
V_639 = Vertex(name = 'V_639',
particles = [ P.ve__tilde__, P.ve, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_266})
V_640 = Vertex(name = 'V_640',
particles = [ P.vm__tilde__, P.vm, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_265})
V_641 = Vertex(name = 'V_641',
particles = [ P.vm__tilde__, P.vm, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_266})
V_642 = Vertex(name = 'V_642',
particles = [ P.vt__tilde__, P.vt, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_265})
V_643 = Vertex(name = 'V_643',
particles = [ P.vt__tilde__, P.vt, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS1 ],
couplings = {(0,0):C.GC_266})
V_644 = Vertex(name = 'V_644',
particles = [ P.t__tilde__, P.t1, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_68})
V_645 = Vertex(name = 'V_645',
particles = [ P.t1__tilde__, P.t1, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_73})
V_646 = Vertex(name = 'V_646',
particles = [ P.t1__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_68})
V_647 = Vertex(name = 'V_647',
particles = [ P.t__tilde__, P.t1, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_71})
V_648 = Vertex(name = 'V_648',
particles = [ P.t1__tilde__, P.t1, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_76})
V_649 = Vertex(name = 'V_649',
particles = [ P.t1__tilde__, P.t, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV1 ],
couplings = {(0,0):C.GC_71})
V_650 = Vertex(name = 'V_650',
particles = [ P.d__tilde__, P.t1, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_155})
V_651 = Vertex(name = 'V_651',
particles = [ P.s__tilde__, P.t1, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_156})
V_652 = Vertex(name = 'V_652',
particles = [ P.b__tilde__, P.t1, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_157})
V_653 = Vertex(name = 'V_653',
particles = [ P.d__tilde__, P.t1, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_162})
V_654 = Vertex(name = 'V_654',
particles = [ P.s__tilde__, P.t1, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_163})
V_655 = Vertex(name = 'V_655',
particles = [ P.b__tilde__, P.t1, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_164})
V_656 = Vertex(name = 'V_656',
particles = [ P.d__tilde__, P.u, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_149})
V_657 = Vertex(name = 'V_657',
particles = [ P.s__tilde__, P.u, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_150})
V_658 = Vertex(name = 'V_658',
particles = [ P.b__tilde__, P.u, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_151})
V_659 = Vertex(name = 'V_659',
particles = [ P.d__tilde__, P.c, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_152})
V_660 = Vertex(name = 'V_660',
particles = [ P.s__tilde__, P.c, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_153})
V_661 = Vertex(name = 'V_661',
particles = [ P.b__tilde__, P.c, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_154})
V_662 = Vertex(name = 'V_662',
particles = [ P.d__tilde__, P.t, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_155})
V_663 = Vertex(name = 'V_663',
particles = [ P.s__tilde__, P.t, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_156})
V_664 = Vertex(name = 'V_664',
particles = [ P.b__tilde__, P.t, P.W1__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_157})
V_665 = Vertex(name = 'V_665',
particles = [ P.e__plus__, P.ve, P.W1__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_148})
V_666 = Vertex(name = 'V_666',
particles = [ P.mu__plus__, P.vm, P.W1__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_148})
V_667 = Vertex(name = 'V_667',
particles = [ P.ta__plus__, P.vt, P.W1__minus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_148})
V_668 = Vertex(name = 'V_668',
particles = [ P.t1__tilde__, P.d, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2751})
V_669 = Vertex(name = 'V_669',
particles = [ P.t1__tilde__, P.s, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3099})
V_670 = Vertex(name = 'V_670',
particles = [ P.t1__tilde__, P.b, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3529})
V_671 = Vertex(name = 'V_671',
particles = [ P.u__tilde__, P.d, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1173})
V_672 = Vertex(name = 'V_672',
particles = [ P.c__tilde__, P.d, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1864})
V_673 = Vertex(name = 'V_673',
particles = [ P.t__tilde__, P.d, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2750})
V_674 = Vertex(name = 'V_674',
particles = [ P.u__tilde__, P.s, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1322})
V_675 = Vertex(name = 'V_675',
particles = [ P.c__tilde__, P.s, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2038})
V_676 = Vertex(name = 'V_676',
particles = [ P.t__tilde__, P.s, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3098})
V_677 = Vertex(name = 'V_677',
particles = [ P.u__tilde__, P.b, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_1495})
V_678 = Vertex(name = 'V_678',
particles = [ P.c__tilde__, P.b, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2235})
V_679 = Vertex(name = 'V_679',
particles = [ P.t__tilde__, P.b, P.W1__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3528})
V_680 = Vertex(name = 'V_680',
particles = [ P.ve__tilde__, P.e__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_148})
V_681 = Vertex(name = 'V_681',
particles = [ P.vm__tilde__, P.mu__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_148})
V_682 = Vertex(name = 'V_682',
particles = [ P.vt__tilde__, P.ta__minus__, P.W1__plus__ ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_148})
V_683 = Vertex(name = 'V_683',
particles = [ P.t1__tilde__, P.d, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_2750})
V_684 = Vertex(name = 'V_684',
particles = [ P.t1__tilde__, P.s, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3098})
V_685 = Vertex(name = 'V_685',
particles = [ P.t1__tilde__, P.b, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_3528})
V_686 = Vertex(name = 'V_686',
particles = [ P.t__tilde__, P.t1, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_182,(0,1):C.GC_158})
V_687 = Vertex(name = 'V_687',
particles = [ P.t1__tilde__, P.t1, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_184,(0,1):C.GC_165})
V_688 = Vertex(name = 'V_688',
particles = [ P.t1__tilde__, P.t, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_182,(0,1):C.GC_158})
V_689 = Vertex(name = 'V_689',
particles = [ P.d__tilde__, P.d, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_181,(0,1):C.GC_159})
V_690 = Vertex(name = 'V_690',
particles = [ P.s__tilde__, P.s, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_181,(0,1):C.GC_159})
V_691 = Vertex(name = 'V_691',
particles = [ P.b__tilde__, P.b, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_181,(0,1):C.GC_159})
V_692 = Vertex(name = 'V_692',
particles = [ P.e__plus__, P.e__minus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_183,(0,1):C.GC_159})
V_693 = Vertex(name = 'V_693',
particles = [ P.mu__plus__, P.mu__minus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_183,(0,1):C.GC_159})
V_694 = Vertex(name = 'V_694',
particles = [ P.ta__plus__, P.ta__minus__, P.Z1 ],
color = [ '1' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_183,(0,1):C.GC_159})
V_695 = Vertex(name = 'V_695',
particles = [ P.t__tilde__, P.t1, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_184,(0,1):C.GC_165})
V_696 = Vertex(name = 'V_696',
particles = [ P.t1__tilde__, P.t1, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_185,(0,1):C.GC_168})
V_697 = Vertex(name = 'V_697',
particles = [ P.t1__tilde__, P.t, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_184,(0,1):C.GC_165})
V_698 = Vertex(name = 'V_698',
particles = [ P.u__tilde__, P.u, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_182,(0,1):C.GC_158})
V_699 = Vertex(name = 'V_699',
particles = [ P.c__tilde__, P.c, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_182,(0,1):C.GC_158})
V_700 = Vertex(name = 'V_700',
particles = [ P.t__tilde__, P.t, P.Z1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV1, L.FFV2 ],
couplings = {(0,0):C.GC_182,(0,1):C.GC_158})
V_701 = Vertex(name = 'V_701',
particles = [ P.ve__tilde__, P.ve, P.Z1 ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_158})
V_702 = Vertex(name = 'V_702',
particles = [ P.vm__tilde__, P.vm, P.Z1 ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_158})
V_703 = Vertex(name = 'V_703',
particles = [ P.vt__tilde__, P.vt, P.Z1 ],
color = [ '1' ],
lorentz = [ L.FFV2 ],
couplings = {(0,0):C.GC_158})
V_704 = Vertex(name = 'V_704',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_493})
V_705 = Vertex(name = 'V_705',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_521})
V_706 = Vertex(name = 'V_706',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_522})
V_707 = Vertex(name = 'V_707',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_523})
V_708 = Vertex(name = 'V_708',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_524})
V_709 = Vertex(name = 'V_709',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_525})
V_710 = Vertex(name = 'V_710',
particles = [ P.d__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_2716})
V_711 = Vertex(name = 'V_711',
particles = [ P.s__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_3031,(0,1):C.GC_3061})
V_712 = Vertex(name = 'V_712',
particles = [ P.b__tilde__, P.d, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_3032,(0,1):C.GC_2986})
V_713 = Vertex(name = 'V_713',
particles = [ P.d__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_3484,(0,1):C.GC_3409})
V_714 = Vertex(name = 'V_714',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_666})
V_715 = Vertex(name = 'V_715',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_694})
V_716 = Vertex(name = 'V_716',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_695})
V_717 = Vertex(name = 'V_717',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_696})
V_718 = Vertex(name = 'V_718',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_697})
V_719 = Vertex(name = 'V_719',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_698})
V_720 = Vertex(name = 'V_720',
particles = [ P.s__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_3485})
V_721 = Vertex(name = 'V_721',
particles = [ P.b__tilde__, P.s, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_3486,(0,1):C.GC_3386})
V_722 = Vertex(name = 'V_722',
particles = [ P.d__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_4199,(0,1):C.GC_4224})
V_723 = Vertex(name = 'V_723',
particles = [ P.s__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_4200,(0,1):C.GC_4255})
V_724 = Vertex(name = 'V_724',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_396})
V_725 = Vertex(name = 'V_725',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_424})
V_726 = Vertex(name = 'V_726',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_425})
V_727 = Vertex(name = 'V_727',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_426})
V_728 = Vertex(name = 'V_728',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_427})
V_729 = Vertex(name = 'V_729',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_428})
V_730 = Vertex(name = 'V_730',
particles = [ P.b__tilde__, P.b, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_4201})
V_731 = Vertex(name = 'V_731',
particles = [ P.d__tilde__, P.d, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_495})
V_732 = Vertex(name = 'V_732',
particles = [ P.d__tilde__, P.d, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_2691})
V_733 = Vertex(name = 'V_733',
particles = [ P.s__tilde__, P.d, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_2991,(0,1):C.GC_3041})
V_734 = Vertex(name = 'V_734',
particles = [ P.b__tilde__, P.d, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_2992,(0,1):C.GC_2966})
V_735 = Vertex(name = 'V_735',
particles = [ P.d__tilde__, P.s, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_3420,(0,1):C.GC_3389})
V_736 = Vertex(name = 'V_736',
particles = [ P.s__tilde__, P.s, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_668})
V_737 = Vertex(name = 'V_737',
particles = [ P.s__tilde__, P.s, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_3421})
V_738 = Vertex(name = 'V_738',
particles = [ P.b__tilde__, P.s, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_3422,(0,1):C.GC_3366})
V_739 = Vertex(name = 'V_739',
particles = [ P.d__tilde__, P.b, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_4135,(0,1):C.GC_4204})
V_740 = Vertex(name = 'V_740',
particles = [ P.s__tilde__, P.b, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_4136,(0,1):C.GC_4235})
V_741 = Vertex(name = 'V_741',
particles = [ P.b__tilde__, P.b, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_398})
V_742 = Vertex(name = 'V_742',
particles = [ P.b__tilde__, P.b, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_4137})
V_743 = Vertex(name = 'V_743',
particles = [ P.d__tilde__, P.d, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_509})
V_744 = Vertex(name = 'V_744',
particles = [ P.d__tilde__, P.d, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_2704})
V_745 = Vertex(name = 'V_745',
particles = [ P.s__tilde__, P.d, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_3013,(0,1):C.GC_3052})
V_746 = Vertex(name = 'V_746',
particles = [ P.b__tilde__, P.d, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_3014,(0,1):C.GC_2977})
V_747 = Vertex(name = 'V_747',
particles = [ P.d__tilde__, P.s, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_3455,(0,1):C.GC_3400})
V_748 = Vertex(name = 'V_748',
particles = [ P.s__tilde__, P.s, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_682})
V_749 = Vertex(name = 'V_749',
particles = [ P.s__tilde__, P.s, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_3456})
V_750 = Vertex(name = 'V_750',
particles = [ P.b__tilde__, P.s, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_3457,(0,1):C.GC_3377})
V_751 = Vertex(name = 'V_751',
particles = [ P.d__tilde__, P.b, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_4170,(0,1):C.GC_4215})
V_752 = Vertex(name = 'V_752',
particles = [ P.s__tilde__, P.b, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_4171,(0,1):C.GC_4246})
V_753 = Vertex(name = 'V_753',
particles = [ P.b__tilde__, P.b, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_412})
V_754 = Vertex(name = 'V_754',
particles = [ P.b__tilde__, P.b, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_4172})
V_755 = Vertex(name = 'V_755',
particles = [ P.e__plus__, P.e__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_552})
V_756 = Vertex(name = 'V_756',
particles = [ P.e__plus__, P.e__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_576})
V_757 = Vertex(name = 'V_757',
particles = [ P.e__plus__, P.e__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_577})
V_758 = Vertex(name = 'V_758',
particles = [ P.e__plus__, P.e__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_578})
V_759 = Vertex(name = 'V_759',
particles = [ P.e__plus__, P.e__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_579})
V_760 = Vertex(name = 'V_760',
particles = [ P.e__plus__, P.e__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_580})
V_761 = Vertex(name = 'V_761',
particles = [ P.mu__plus__, P.mu__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_609})
V_762 = Vertex(name = 'V_762',
particles = [ P.mu__plus__, P.mu__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_633})
V_763 = Vertex(name = 'V_763',
particles = [ P.mu__plus__, P.mu__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_634})
V_764 = Vertex(name = 'V_764',
particles = [ P.mu__plus__, P.mu__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_635})
V_765 = Vertex(name = 'V_765',
particles = [ P.mu__plus__, P.mu__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_636})
V_766 = Vertex(name = 'V_766',
particles = [ P.mu__plus__, P.mu__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_637})
V_767 = Vertex(name = 'V_767',
particles = [ P.ta__plus__, P.ta__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_880})
V_768 = Vertex(name = 'V_768',
particles = [ P.ta__plus__, P.ta__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_904})
V_769 = Vertex(name = 'V_769',
particles = [ P.ta__plus__, P.ta__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_905})
V_770 = Vertex(name = 'V_770',
particles = [ P.ta__plus__, P.ta__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_906})
V_771 = Vertex(name = 'V_771',
particles = [ P.ta__plus__, P.ta__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_907})
V_772 = Vertex(name = 'V_772',
particles = [ P.ta__plus__, P.ta__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_908})
V_773 = Vertex(name = 'V_773',
particles = [ P.e__plus__, P.e__minus__, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_553})
V_774 = Vertex(name = 'V_774',
particles = [ P.mu__plus__, P.mu__minus__, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_610})
V_775 = Vertex(name = 'V_775',
particles = [ P.ta__plus__, P.ta__minus__, P.H, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_881})
V_776 = Vertex(name = 'V_776',
particles = [ P.e__plus__, P.e__minus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_565})
V_777 = Vertex(name = 'V_777',
particles = [ P.mu__plus__, P.mu__minus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_622})
V_778 = Vertex(name = 'V_778',
particles = [ P.ta__plus__, P.ta__minus__, P.H, P.H ],
color = [ '1' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_893})
V_779 = Vertex(name = 'V_779',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_969})
V_780 = Vertex(name = 'V_780',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_997})
V_781 = Vertex(name = 'V_781',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_998})
V_782 = Vertex(name = 'V_782',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_999})
V_783 = Vertex(name = 'V_783',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_1000})
V_784 = Vertex(name = 'V_784',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_1001})
V_785 = Vertex(name = 'V_785',
particles = [ P.u__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_4022})
V_786 = Vertex(name = 'V_786',
particles = [ P.c__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_2639,(0,1):C.GC_2520})
V_787 = Vertex(name = 'V_787',
particles = [ P.t__tilde__, P.u, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_4021,(0,1):C.GC_3878})
V_788 = Vertex(name = 'V_788',
particles = [ P.u__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_1673,(0,1):C.GC_1771})
V_789 = Vertex(name = 'V_789',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_438})
V_790 = Vertex(name = 'V_790',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_466})
V_791 = Vertex(name = 'V_791',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_467})
V_792 = Vertex(name = 'V_792',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_468})
V_793 = Vertex(name = 'V_793',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_469})
V_794 = Vertex(name = 'V_794',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_470})
V_795 = Vertex(name = 'V_795',
particles = [ P.c__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_2521})
V_796 = Vertex(name = 'V_796',
particles = [ P.t__tilde__, P.c, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_3790,(0,1):C.GC_3879})
V_797 = Vertex(name = 'V_797',
particles = [ P.u__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_1706,(0,1):C.GC_1772})
V_798 = Vertex(name = 'V_798',
particles = [ P.c__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS2, L.FFS3 ],
couplings = {(0,0):C.GC_2578,(0,1):C.GC_2522})
V_799 = Vertex(name = 'V_799',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_745})
V_800 = Vertex(name = 'V_800',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_775})
V_801 = Vertex(name = 'V_801',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_776})
V_802 = Vertex(name = 'V_802',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_777})
V_803 = Vertex(name = 'V_803',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_778})
V_804 = Vertex(name = 'V_804',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_779})
V_805 = Vertex(name = 'V_805',
particles = [ P.t__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_3880})
V_806 = Vertex(name = 'V_806',
particles = [ P.u__tilde__, P.u, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_972})
V_807 = Vertex(name = 'V_807',
particles = [ P.u__tilde__, P.u, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_3978})
V_808 = Vertex(name = 'V_808',
particles = [ P.c__tilde__, P.u, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_2619,(0,1):C.GC_2456})
V_809 = Vertex(name = 'V_809',
particles = [ P.t__tilde__, P.u, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_3977,(0,1):C.GC_3814})
V_810 = Vertex(name = 'V_810',
particles = [ P.u__tilde__, P.c, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_1653,(0,1):C.GC_1731})
V_811 = Vertex(name = 'V_811',
particles = [ P.c__tilde__, P.c, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_441})
V_812 = Vertex(name = 'V_812',
particles = [ P.c__tilde__, P.c, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_2457})
V_813 = Vertex(name = 'V_813',
particles = [ P.t__tilde__, P.c, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_3770,(0,1):C.GC_3815})
V_814 = Vertex(name = 'V_814',
particles = [ P.u__tilde__, P.t, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_1686,(0,1):C.GC_1732})
V_815 = Vertex(name = 'V_815',
particles = [ P.c__tilde__, P.t, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS2, L.FFSSS3 ],
couplings = {(0,0):C.GC_2558,(0,1):C.GC_2458})
V_816 = Vertex(name = 'V_816',
particles = [ P.t__tilde__, P.t, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_748})
V_817 = Vertex(name = 'V_817',
particles = [ P.t__tilde__, P.t, P.H, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSSS1 ],
couplings = {(0,0):C.GC_3816})
V_818 = Vertex(name = 'V_818',
particles = [ P.u__tilde__, P.u, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_986})
V_819 = Vertex(name = 'V_819',
particles = [ P.u__tilde__, P.u, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_4002})
V_820 = Vertex(name = 'V_820',
particles = [ P.c__tilde__, P.u, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_2630,(0,1):C.GC_2491})
V_821 = Vertex(name = 'V_821',
particles = [ P.t__tilde__, P.u, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_4001,(0,1):C.GC_3849})
V_822 = Vertex(name = 'V_822',
particles = [ P.u__tilde__, P.c, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_1664,(0,1):C.GC_1753})
V_823 = Vertex(name = 'V_823',
particles = [ P.c__tilde__, P.c, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_455})
V_824 = Vertex(name = 'V_824',
particles = [ P.c__tilde__, P.c, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_2492})
V_825 = Vertex(name = 'V_825',
particles = [ P.t__tilde__, P.c, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_3781,(0,1):C.GC_3850})
V_826 = Vertex(name = 'V_826',
particles = [ P.u__tilde__, P.t, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_1697,(0,1):C.GC_1754})
V_827 = Vertex(name = 'V_827',
particles = [ P.c__tilde__, P.t, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS2, L.FFSS3 ],
couplings = {(0,0):C.GC_2569,(0,1):C.GC_2493})
V_828 = Vertex(name = 'V_828',
particles = [ P.t__tilde__, P.t, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_764})
V_829 = Vertex(name = 'V_829',
particles = [ P.t__tilde__, P.t, P.H, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFSS1 ],
couplings = {(0,0):C.GC_3851})
V_830 = Vertex(name = 'V_830',
particles = [ P.d__tilde__, P.d, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_502})
V_831 = Vertex(name = 'V_831',
particles = [ P.s__tilde__, P.s, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_675})
V_832 = Vertex(name = 'V_832',
particles = [ P.b__tilde__, P.b, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_405})
V_833 = Vertex(name = 'V_833',
particles = [ P.e__plus__, P.e__minus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_559})
V_834 = Vertex(name = 'V_834',
particles = [ P.mu__plus__, P.mu__minus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_616})
V_835 = Vertex(name = 'V_835',
particles = [ P.ta__plus__, P.ta__minus__, P.H1 ],
color = [ '1' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_887})
V_836 = Vertex(name = 'V_836',
particles = [ P.t__tilde__, P.t1, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_754})
V_837 = Vertex(name = 'V_837',
particles = [ P.t__tilde__, P.t1, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_755})
V_838 = Vertex(name = 'V_838',
particles = [ P.t1__tilde__, P.t1, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_755})
V_839 = Vertex(name = 'V_839',
particles = [ P.t1__tilde__, P.t1, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_756})
V_840 = Vertex(name = 'V_840',
particles = [ P.t1__tilde__, P.t, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_754})
V_841 = Vertex(name = 'V_841',
particles = [ P.t1__tilde__, P.t, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_755})
V_842 = Vertex(name = 'V_842',
particles = [ P.u__tilde__, P.u, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_978})
V_843 = Vertex(name = 'V_843',
particles = [ P.c__tilde__, P.c, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_447})
V_844 = Vertex(name = 'V_844',
particles = [ P.t__tilde__, P.t, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFS1 ],
couplings = {(0,0):C.GC_754})
V_845 = Vertex(name = 'V_845',
particles = [ P.d__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_497})
V_846 = Vertex(name = 'V_846',
particles = [ P.d__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_507})
V_847 = Vertex(name = 'V_847',
particles = [ P.d__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_2689})
V_848 = Vertex(name = 'V_848',
particles = [ P.d__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_2701})
V_849 = Vertex(name = 'V_849',
particles = [ P.s__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2987,(0,1):C.GC_3039})
V_850 = Vertex(name = 'V_850',
particles = [ P.s__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3007,(0,1):C.GC_3049})
V_851 = Vertex(name = 'V_851',
particles = [ P.b__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2988,(0,1):C.GC_2964})
V_852 = Vertex(name = 'V_852',
particles = [ P.b__tilde__, P.d, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3008,(0,1):C.GC_2974})
V_853 = Vertex(name = 'V_853',
particles = [ P.d__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3414,(0,1):C.GC_3387})
V_854 = Vertex(name = 'V_854',
particles = [ P.d__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3446,(0,1):C.GC_3397})
V_855 = Vertex(name = 'V_855',
particles = [ P.s__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_670})
V_856 = Vertex(name = 'V_856',
particles = [ P.s__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_680})
V_857 = Vertex(name = 'V_857',
particles = [ P.s__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3415})
V_858 = Vertex(name = 'V_858',
particles = [ P.s__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3447})
V_859 = Vertex(name = 'V_859',
particles = [ P.b__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3416,(0,1):C.GC_3364})
V_860 = Vertex(name = 'V_860',
particles = [ P.b__tilde__, P.s, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3448,(0,1):C.GC_3374})
V_861 = Vertex(name = 'V_861',
particles = [ P.d__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4129,(0,1):C.GC_4202})
V_862 = Vertex(name = 'V_862',
particles = [ P.d__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4161,(0,1):C.GC_4212})
V_863 = Vertex(name = 'V_863',
particles = [ P.s__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4130,(0,1):C.GC_4233})
V_864 = Vertex(name = 'V_864',
particles = [ P.s__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4162,(0,1):C.GC_4243})
V_865 = Vertex(name = 'V_865',
particles = [ P.b__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_400})
V_866 = Vertex(name = 'V_866',
particles = [ P.b__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_410})
V_867 = Vertex(name = 'V_867',
particles = [ P.b__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_4131})
V_868 = Vertex(name = 'V_868',
particles = [ P.b__tilde__, P.b, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_4163})
V_869 = Vertex(name = 'V_869',
particles = [ P.s__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3009,(0,1):C.GC_3050})
V_870 = Vertex(name = 'V_870',
particles = [ P.s__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3029,(0,1):C.GC_3060})
V_871 = Vertex(name = 'V_871',
particles = [ P.b__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3010,(0,1):C.GC_2975})
V_872 = Vertex(name = 'V_872',
particles = [ P.b__tilde__, P.d, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3030,(0,1):C.GC_2985})
V_873 = Vertex(name = 'V_873',
particles = [ P.d__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3449,(0,1):C.GC_3398})
V_874 = Vertex(name = 'V_874',
particles = [ P.d__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3481,(0,1):C.GC_3408})
V_875 = Vertex(name = 'V_875',
particles = [ P.b__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3451,(0,1):C.GC_3375})
V_876 = Vertex(name = 'V_876',
particles = [ P.b__tilde__, P.s, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3483,(0,1):C.GC_3385})
V_877 = Vertex(name = 'V_877',
particles = [ P.d__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4164,(0,1):C.GC_4213})
V_878 = Vertex(name = 'V_878',
particles = [ P.d__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4196,(0,1):C.GC_4223})
V_879 = Vertex(name = 'V_879',
particles = [ P.s__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4165,(0,1):C.GC_4244})
V_880 = Vertex(name = 'V_880',
particles = [ P.s__tilde__, P.b, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4197,(0,1):C.GC_4254})
V_881 = Vertex(name = 'V_881',
particles = [ P.d__tilde__, P.d, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_494})
V_882 = Vertex(name = 'V_882',
particles = [ P.d__tilde__, P.d, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_2690})
V_883 = Vertex(name = 'V_883',
particles = [ P.s__tilde__, P.d, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2989,(0,1):C.GC_3040})
V_884 = Vertex(name = 'V_884',
particles = [ P.b__tilde__, P.d, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2990,(0,1):C.GC_2965})
V_885 = Vertex(name = 'V_885',
particles = [ P.d__tilde__, P.s, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3417,(0,1):C.GC_3388})
V_886 = Vertex(name = 'V_886',
particles = [ P.s__tilde__, P.s, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_667})
V_887 = Vertex(name = 'V_887',
particles = [ P.s__tilde__, P.s, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3418})
V_888 = Vertex(name = 'V_888',
particles = [ P.b__tilde__, P.s, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3419,(0,1):C.GC_3365})
V_889 = Vertex(name = 'V_889',
particles = [ P.d__tilde__, P.b, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4132,(0,1):C.GC_4203})
V_890 = Vertex(name = 'V_890',
particles = [ P.s__tilde__, P.b, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_4133,(0,1):C.GC_4234})
V_891 = Vertex(name = 'V_891',
particles = [ P.b__tilde__, P.b, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_397})
V_892 = Vertex(name = 'V_892',
particles = [ P.b__tilde__, P.b, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_4134})
V_893 = Vertex(name = 'V_893',
particles = [ P.s__tilde__, P.d, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3011,(0,1):C.GC_3051})
V_894 = Vertex(name = 'V_894',
particles = [ P.b__tilde__, P.d, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3012,(0,1):C.GC_2976})
V_895 = Vertex(name = 'V_895',
particles = [ P.d__tilde__, P.s, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3452,(0,1):C.GC_3399})
V_896 = Vertex(name = 'V_896',
particles = [ P.b__tilde__, P.s, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3454,(0,1):C.GC_3376})
V_897 = Vertex(name = 'V_897',
particles = [ P.d__tilde__, P.b, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4167,(0,1):C.GC_4214})
V_898 = Vertex(name = 'V_898',
particles = [ P.s__tilde__, P.b, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4168,(0,1):C.GC_4245})
V_899 = Vertex(name = 'V_899',
particles = [ P.d__tilde__, P.d, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_501})
V_900 = Vertex(name = 'V_900',
particles = [ P.d__tilde__, P.d, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_2696})
V_901 = Vertex(name = 'V_901',
particles = [ P.s__tilde__, P.d, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2999,(0,1):C.GC_3045})
V_902 = Vertex(name = 'V_902',
particles = [ P.b__tilde__, P.d, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3000,(0,1):C.GC_2970})
V_903 = Vertex(name = 'V_903',
particles = [ P.d__tilde__, P.s, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3433,(0,1):C.GC_3393})
V_904 = Vertex(name = 'V_904',
particles = [ P.s__tilde__, P.s, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_674})
V_905 = Vertex(name = 'V_905',
particles = [ P.s__tilde__, P.s, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_3434})
V_906 = Vertex(name = 'V_906',
particles = [ P.b__tilde__, P.s, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3435,(0,1):C.GC_3370})
V_907 = Vertex(name = 'V_907',
particles = [ P.d__tilde__, P.b, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4148,(0,1):C.GC_4208})
V_908 = Vertex(name = 'V_908',
particles = [ P.s__tilde__, P.b, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4149,(0,1):C.GC_4239})
V_909 = Vertex(name = 'V_909',
particles = [ P.b__tilde__, P.b, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_404})
V_910 = Vertex(name = 'V_910',
particles = [ P.b__tilde__, P.b, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_4150})
V_911 = Vertex(name = 'V_911',
particles = [ P.d__tilde__, P.d, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_515})
V_912 = Vertex(name = 'V_912',
particles = [ P.d__tilde__, P.d, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_2710})
V_913 = Vertex(name = 'V_913',
particles = [ P.s__tilde__, P.d, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3021,(0,1):C.GC_3056})
V_914 = Vertex(name = 'V_914',
particles = [ P.b__tilde__, P.d, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3022,(0,1):C.GC_2981})
V_915 = Vertex(name = 'V_915',
particles = [ P.d__tilde__, P.s, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3468,(0,1):C.GC_3404})
V_916 = Vertex(name = 'V_916',
particles = [ P.s__tilde__, P.s, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_688})
V_917 = Vertex(name = 'V_917',
particles = [ P.s__tilde__, P.s, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_3469})
V_918 = Vertex(name = 'V_918',
particles = [ P.b__tilde__, P.s, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3470,(0,1):C.GC_3381})
V_919 = Vertex(name = 'V_919',
particles = [ P.d__tilde__, P.b, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4183,(0,1):C.GC_4219})
V_920 = Vertex(name = 'V_920',
particles = [ P.s__tilde__, P.b, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4184,(0,1):C.GC_4250})
V_921 = Vertex(name = 'V_921',
particles = [ P.b__tilde__, P.b, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_418})
V_922 = Vertex(name = 'V_922',
particles = [ P.b__tilde__, P.b, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_4185})
V_923 = Vertex(name = 'V_923',
particles = [ P.u__tilde__, P.d, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_500,(0,1):C.GC_976})
V_924 = Vertex(name = 'V_924',
particles = [ P.u__tilde__, P.d, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2695,(0,1):C.GC_3985})
V_925 = Vertex(name = 'V_925',
particles = [ P.c__tilde__, P.d, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2997,(0,1):C.GC_2465})
V_926 = Vertex(name = 'V_926',
particles = [ P.t__tilde__, P.d, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2998,(0,1):C.GC_3823})
V_927 = Vertex(name = 'V_927',
particles = [ P.u__tilde__, P.s, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3429,(0,1):C.GC_1737})
V_928 = Vertex(name = 'V_928',
particles = [ P.c__tilde__, P.s, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_673,(0,1):C.GC_445})
V_929 = Vertex(name = 'V_929',
particles = [ P.c__tilde__, P.s, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3430,(0,1):C.GC_2466})
V_930 = Vertex(name = 'V_930',
particles = [ P.t__tilde__, P.s, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3432,(0,1):C.GC_3824})
V_931 = Vertex(name = 'V_931',
particles = [ P.u__tilde__, P.b, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4144,(0,1):C.GC_1738})
V_932 = Vertex(name = 'V_932',
particles = [ P.c__tilde__, P.b, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4145,(0,1):C.GC_2468})
V_933 = Vertex(name = 'V_933',
particles = [ P.t__tilde__, P.b, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_403,(0,1):C.GC_752})
V_934 = Vertex(name = 'V_934',
particles = [ P.t__tilde__, P.b, P.a, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4146,(0,1):C.GC_3825})
V_935 = Vertex(name = 'V_935',
particles = [ P.u__tilde__, P.d, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_514,(0,1):C.GC_990})
V_936 = Vertex(name = 'V_936',
particles = [ P.u__tilde__, P.d, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2709,(0,1):C.GC_4009})
V_937 = Vertex(name = 'V_937',
particles = [ P.c__tilde__, P.d, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3019,(0,1):C.GC_2500})
V_938 = Vertex(name = 'V_938',
particles = [ P.t__tilde__, P.d, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3020,(0,1):C.GC_3858})
V_939 = Vertex(name = 'V_939',
particles = [ P.u__tilde__, P.s, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3464,(0,1):C.GC_1759})
V_940 = Vertex(name = 'V_940',
particles = [ P.c__tilde__, P.s, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_687,(0,1):C.GC_459})
V_941 = Vertex(name = 'V_941',
particles = [ P.c__tilde__, P.s, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3465,(0,1):C.GC_2501})
V_942 = Vertex(name = 'V_942',
particles = [ P.t__tilde__, P.s, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3467,(0,1):C.GC_3859})
V_943 = Vertex(name = 'V_943',
particles = [ P.u__tilde__, P.b, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4179,(0,1):C.GC_1760})
V_944 = Vertex(name = 'V_944',
particles = [ P.c__tilde__, P.b, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4180,(0,1):C.GC_2503})
V_945 = Vertex(name = 'V_945',
particles = [ P.t__tilde__, P.b, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_417,(0,1):C.GC_768})
V_946 = Vertex(name = 'V_946',
particles = [ P.t__tilde__, P.b, P.a, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4181,(0,1):C.GC_3860})
V_947 = Vertex(name = 'V_947',
particles = [ P.d__tilde__, P.d, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_503})
V_948 = Vertex(name = 'V_948',
particles = [ P.d__tilde__, P.d, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_2697})
V_949 = Vertex(name = 'V_949',
particles = [ P.s__tilde__, P.d, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3001,(0,1):C.GC_3046})
V_950 = Vertex(name = 'V_950',
particles = [ P.b__tilde__, P.d, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3002,(0,1):C.GC_2971})
V_951 = Vertex(name = 'V_951',
particles = [ P.d__tilde__, P.s, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3436,(0,1):C.GC_3394})
V_952 = Vertex(name = 'V_952',
particles = [ P.s__tilde__, P.s, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_676})
V_953 = Vertex(name = 'V_953',
particles = [ P.s__tilde__, P.s, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_3437})
V_954 = Vertex(name = 'V_954',
particles = [ P.b__tilde__, P.s, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3438,(0,1):C.GC_3371})
V_955 = Vertex(name = 'V_955',
particles = [ P.d__tilde__, P.b, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4151,(0,1):C.GC_4209})
V_956 = Vertex(name = 'V_956',
particles = [ P.s__tilde__, P.b, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4152,(0,1):C.GC_4240})
V_957 = Vertex(name = 'V_957',
particles = [ P.b__tilde__, P.b, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_406})
V_958 = Vertex(name = 'V_958',
particles = [ P.b__tilde__, P.b, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_4153})
V_959 = Vertex(name = 'V_959',
particles = [ P.d__tilde__, P.d, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_516})
V_960 = Vertex(name = 'V_960',
particles = [ P.d__tilde__, P.d, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_2711})
V_961 = Vertex(name = 'V_961',
particles = [ P.s__tilde__, P.d, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV3, L.FFVV5 ],
couplings = {(0,0):C.GC_3023,(0,1):C.GC_3057,(0,2):C.GC_1926})
V_962 = Vertex(name = 'V_962',
particles = [ P.b__tilde__, P.d, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3024,(0,1):C.GC_2982})
V_963 = Vertex(name = 'V_963',
particles = [ P.d__tilde__, P.s, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3471,(0,1):C.GC_3405})
V_964 = Vertex(name = 'V_964',
particles = [ P.s__tilde__, P.s, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_689})
V_965 = Vertex(name = 'V_965',
particles = [ P.s__tilde__, P.s, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_3472})
V_966 = Vertex(name = 'V_966',
particles = [ P.b__tilde__, P.s, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3473,(0,1):C.GC_3382})
V_967 = Vertex(name = 'V_967',
particles = [ P.d__tilde__, P.b, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4186,(0,1):C.GC_4220})
V_968 = Vertex(name = 'V_968',
particles = [ P.s__tilde__, P.b, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4187,(0,1):C.GC_4251})
V_969 = Vertex(name = 'V_969',
particles = [ P.b__tilde__, P.b, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_419})
V_970 = Vertex(name = 'V_970',
particles = [ P.b__tilde__, P.b, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_4188})
V_971 = Vertex(name = 'V_971',
particles = [ P.e__plus__, P.e__minus__, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_555})
V_972 = Vertex(name = 'V_972',
particles = [ P.e__plus__, P.e__minus__, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_564})
V_973 = Vertex(name = 'V_973',
particles = [ P.mu__plus__, P.mu__minus__, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_612})
V_974 = Vertex(name = 'V_974',
particles = [ P.mu__plus__, P.mu__minus__, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_621})
V_975 = Vertex(name = 'V_975',
particles = [ P.ta__plus__, P.ta__minus__, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_883})
V_976 = Vertex(name = 'V_976',
particles = [ P.ta__plus__, P.ta__minus__, P.a, P.H ],
color = [ '1' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_892})
V_977 = Vertex(name = 'V_977',
particles = [ P.ve__tilde__, P.e__minus__, P.a, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS1 ],
couplings = {(0,0):C.GC_558})
V_978 = Vertex(name = 'V_978',
particles = [ P.vm__tilde__, P.mu__minus__, P.a, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS1 ],
couplings = {(0,0):C.GC_615})
V_979 = Vertex(name = 'V_979',
particles = [ P.vt__tilde__, P.ta__minus__, P.a, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS1 ],
couplings = {(0,0):C.GC_886})
V_980 = Vertex(name = 'V_980',
particles = [ P.ve__tilde__, P.e__minus__, P.a, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFVV1 ],
couplings = {(0,0):C.GC_570})
V_981 = Vertex(name = 'V_981',
particles = [ P.vm__tilde__, P.mu__minus__, P.a, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFVV1 ],
couplings = {(0,0):C.GC_627})
V_982 = Vertex(name = 'V_982',
particles = [ P.vt__tilde__, P.ta__minus__, P.a, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFVV1 ],
couplings = {(0,0):C.GC_898})
V_983 = Vertex(name = 'V_983',
particles = [ P.e__plus__, P.e__minus__, P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_560})
V_984 = Vertex(name = 'V_984',
particles = [ P.mu__plus__, P.mu__minus__, P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_617})
V_985 = Vertex(name = 'V_985',
particles = [ P.ta__plus__, P.ta__minus__, P.W__minus__, P.W__plus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_888})
V_986 = Vertex(name = 'V_986',
particles = [ P.e__plus__, P.e__minus__, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_571})
V_987 = Vertex(name = 'V_987',
particles = [ P.mu__plus__, P.mu__minus__, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_628})
V_988 = Vertex(name = 'V_988',
particles = [ P.ta__plus__, P.ta__minus__, P.W__minus__, P.W__plus__ ],
color = [ '1' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_899})
V_989 = Vertex(name = 'V_989',
particles = [ P.u__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_970})
V_990 = Vertex(name = 'V_990',
particles = [ P.u__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_983})
V_991 = Vertex(name = 'V_991',
particles = [ P.u__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3974})
V_992 = Vertex(name = 'V_992',
particles = [ P.u__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3996})
V_993 = Vertex(name = 'V_993',
particles = [ P.c__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2617,(0,1):C.GC_2450})
V_994 = Vertex(name = 'V_994',
particles = [ P.c__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2627,(0,1):C.GC_2482})
V_995 = Vertex(name = 'V_995',
particles = [ P.t__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3973,(0,1):C.GC_3808})
V_996 = Vertex(name = 'V_996',
particles = [ P.t__tilde__, P.u, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3995,(0,1):C.GC_3840})
V_997 = Vertex(name = 'V_997',
particles = [ P.u__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1651,(0,1):C.GC_1727})
V_998 = Vertex(name = 'V_998',
particles = [ P.u__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1661,(0,1):C.GC_1747})
V_999 = Vertex(name = 'V_999',
particles = [ P.c__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_439})
V_1000 = Vertex(name = 'V_1000',
particles = [ P.c__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_452})
V_1001 = Vertex(name = 'V_1001',
particles = [ P.c__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_2451})
V_1002 = Vertex(name = 'V_1002',
particles = [ P.c__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_2483})
V_1003 = Vertex(name = 'V_1003',
particles = [ P.t__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3768,(0,1):C.GC_3809})
V_1004 = Vertex(name = 'V_1004',
particles = [ P.t__tilde__, P.c, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3778,(0,1):C.GC_3841})
V_1005 = Vertex(name = 'V_1005',
particles = [ P.u__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1684,(0,1):C.GC_1728})
V_1006 = Vertex(name = 'V_1006',
particles = [ P.u__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1694,(0,1):C.GC_1748})
V_1007 = Vertex(name = 'V_1007',
particles = [ P.c__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5, L.FFVS6 ],
couplings = {(0,0):C.GC_2556,(0,1):C.GC_2452,(0,2):C.GC_2263})
V_1008 = Vertex(name = 'V_1008',
particles = [ P.c__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2566,(0,1):C.GC_2484})
V_1009 = Vertex(name = 'V_1009',
particles = [ P.t__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_746})
V_1010 = Vertex(name = 'V_1010',
particles = [ P.t__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_761})
V_1011 = Vertex(name = 'V_1011',
particles = [ P.t__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3810})
V_1012 = Vertex(name = 'V_1012',
particles = [ P.t__tilde__, P.t, P.a, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3842})
V_1013 = Vertex(name = 'V_1013',
particles = [ P.c__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2628,(0,1):C.GC_2485})
V_1014 = Vertex(name = 'V_1014',
particles = [ P.c__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2638,(0,1):C.GC_2517})
V_1015 = Vertex(name = 'V_1015',
particles = [ P.t__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3997,(0,1):C.GC_3843})
V_1016 = Vertex(name = 'V_1016',
particles = [ P.t__tilde__, P.u, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_4019,(0,1):C.GC_3875})
V_1017 = Vertex(name = 'V_1017',
particles = [ P.u__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1662,(0,1):C.GC_1749})
V_1018 = Vertex(name = 'V_1018',
particles = [ P.u__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1672,(0,1):C.GC_1769})
V_1019 = Vertex(name = 'V_1019',
particles = [ P.t__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3779,(0,1):C.GC_3844})
V_1020 = Vertex(name = 'V_1020',
particles = [ P.t__tilde__, P.c, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3789,(0,1):C.GC_3876})
V_1021 = Vertex(name = 'V_1021',
particles = [ P.u__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1695,(0,1):C.GC_1750})
V_1022 = Vertex(name = 'V_1022',
particles = [ P.u__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1705,(0,1):C.GC_1770})
V_1023 = Vertex(name = 'V_1023',
particles = [ P.c__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2567,(0,1):C.GC_2487})
V_1024 = Vertex(name = 'V_1024',
particles = [ P.c__tilde__, P.t, P.a ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2577,(0,1):C.GC_2519})
V_1025 = Vertex(name = 'V_1025',
particles = [ P.u__tilde__, P.u, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_971})
V_1026 = Vertex(name = 'V_1026',
particles = [ P.u__tilde__, P.u, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3976})
V_1027 = Vertex(name = 'V_1027',
particles = [ P.c__tilde__, P.u, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2618,(0,1):C.GC_2453})
V_1028 = Vertex(name = 'V_1028',
particles = [ P.t__tilde__, P.u, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3975,(0,1):C.GC_3811})
V_1029 = Vertex(name = 'V_1029',
particles = [ P.u__tilde__, P.c, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1652,(0,1):C.GC_1729})
V_1030 = Vertex(name = 'V_1030',
particles = [ P.c__tilde__, P.c, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_440})
V_1031 = Vertex(name = 'V_1031',
particles = [ P.c__tilde__, P.c, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_2454})
V_1032 = Vertex(name = 'V_1032',
particles = [ P.t__tilde__, P.c, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_3769,(0,1):C.GC_3812})
V_1033 = Vertex(name = 'V_1033',
particles = [ P.u__tilde__, P.t, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_1685,(0,1):C.GC_1730})
V_1034 = Vertex(name = 'V_1034',
particles = [ P.c__tilde__, P.t, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS2, L.FFVS5 ],
couplings = {(0,0):C.GC_2557,(0,1):C.GC_2455})
V_1035 = Vertex(name = 'V_1035',
particles = [ P.t__tilde__, P.t, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_747})
V_1036 = Vertex(name = 'V_1036',
particles = [ P.t__tilde__, P.t, P.g, P.H ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFVS4 ],
couplings = {(0,0):C.GC_3813})
V_1037 = Vertex(name = 'V_1037',
particles = [ P.c__tilde__, P.u, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2629,(0,1):C.GC_2488})
V_1038 = Vertex(name = 'V_1038',
particles = [ P.t__tilde__, P.u, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3999,(0,1):C.GC_3846})
V_1039 = Vertex(name = 'V_1039',
particles = [ P.u__tilde__, P.c, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1663,(0,1):C.GC_1751})
V_1040 = Vertex(name = 'V_1040',
particles = [ P.t__tilde__, P.c, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_3780,(0,1):C.GC_3847})
V_1041 = Vertex(name = 'V_1041',
particles = [ P.u__tilde__, P.t, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_1696,(0,1):C.GC_1752})
V_1042 = Vertex(name = 'V_1042',
particles = [ P.c__tilde__, P.t, P.g ],
color = [ 'T(3,2,1)' ],
lorentz = [ L.FFV3, L.FFV9 ],
couplings = {(0,0):C.GC_2568,(0,1):C.GC_2490})
V_1043 = Vertex(name = 'V_1043',
particles = [ P.u__tilde__, P.u, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_977})
V_1044 = Vertex(name = 'V_1044',
particles = [ P.u__tilde__, P.u, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_3987})
V_1045 = Vertex(name = 'V_1045',
particles = [ P.c__tilde__, P.u, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2623,(0,1):C.GC_2469})
V_1046 = Vertex(name = 'V_1046',
particles = [ P.t__tilde__, P.u, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3986,(0,1):C.GC_3827})
V_1047 = Vertex(name = 'V_1047',
particles = [ P.u__tilde__, P.c, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1657,(0,1):C.GC_1739})
V_1048 = Vertex(name = 'V_1048',
particles = [ P.c__tilde__, P.c, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_446})
V_1049 = Vertex(name = 'V_1049',
particles = [ P.c__tilde__, P.c, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_2470})
V_1050 = Vertex(name = 'V_1050',
particles = [ P.t__tilde__, P.c, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3774,(0,1):C.GC_3828})
V_1051 = Vertex(name = 'V_1051',
particles = [ P.u__tilde__, P.t, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1690,(0,1):C.GC_1740})
V_1052 = Vertex(name = 'V_1052',
particles = [ P.c__tilde__, P.t, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2562,(0,1):C.GC_2471})
V_1053 = Vertex(name = 'V_1053',
particles = [ P.t__tilde__, P.t, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_753})
V_1054 = Vertex(name = 'V_1054',
particles = [ P.t__tilde__, P.t, P.g, P.g, P.H ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_3829})
V_1055 = Vertex(name = 'V_1055',
particles = [ P.u__tilde__, P.u, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_991})
V_1056 = Vertex(name = 'V_1056',
particles = [ P.u__tilde__, P.u, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_4011})
V_1057 = Vertex(name = 'V_1057',
particles = [ P.c__tilde__, P.u, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2634,(0,1):C.GC_2504})
V_1058 = Vertex(name = 'V_1058',
particles = [ P.t__tilde__, P.u, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4010,(0,1):C.GC_3862})
V_1059 = Vertex(name = 'V_1059',
particles = [ P.u__tilde__, P.c, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1668,(0,1):C.GC_1761})
V_1060 = Vertex(name = 'V_1060',
particles = [ P.c__tilde__, P.c, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_460})
V_1061 = Vertex(name = 'V_1061',
particles = [ P.c__tilde__, P.c, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_2505})
V_1062 = Vertex(name = 'V_1062',
particles = [ P.t__tilde__, P.c, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3785,(0,1):C.GC_3863})
V_1063 = Vertex(name = 'V_1063',
particles = [ P.u__tilde__, P.t, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1701,(0,1):C.GC_1762})
V_1064 = Vertex(name = 'V_1064',
particles = [ P.c__tilde__, P.t, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2573,(0,1):C.GC_2506})
V_1065 = Vertex(name = 'V_1065',
particles = [ P.t__tilde__, P.t, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_769})
V_1066 = Vertex(name = 'V_1066',
particles = [ P.t__tilde__, P.t, P.g, P.g ],
color = [ 'f(-1,3,4)*T(-1,2,1)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_3864})
V_1067 = Vertex(name = 'V_1067',
particles = [ P.d__tilde__, P.u, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_975,(0,1):C.GC_499})
V_1068 = Vertex(name = 'V_1068',
particles = [ P.d__tilde__, P.u, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3984,(0,1):C.GC_2694})
V_1069 = Vertex(name = 'V_1069',
particles = [ P.s__tilde__, P.u, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2622,(0,1):C.GC_3044})
V_1070 = Vertex(name = 'V_1070',
particles = [ P.b__tilde__, P.u, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3983,(0,1):C.GC_2969})
V_1071 = Vertex(name = 'V_1071',
particles = [ P.d__tilde__, P.c, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1656,(0,1):C.GC_3392})
V_1072 = Vertex(name = 'V_1072',
particles = [ P.s__tilde__, P.c, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_444,(0,1):C.GC_672})
V_1073 = Vertex(name = 'V_1073',
particles = [ P.s__tilde__, P.c, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2467,(0,1):C.GC_3431})
V_1074 = Vertex(name = 'V_1074',
particles = [ P.b__tilde__, P.c, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3773,(0,1):C.GC_3369})
V_1075 = Vertex(name = 'V_1075',
particles = [ P.d__tilde__, P.t, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1689,(0,1):C.GC_4207})
V_1076 = Vertex(name = 'V_1076',
particles = [ P.s__tilde__, P.t, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2561,(0,1):C.GC_4238})
V_1077 = Vertex(name = 'V_1077',
particles = [ P.b__tilde__, P.t, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_751,(0,1):C.GC_402})
V_1078 = Vertex(name = 'V_1078',
particles = [ P.b__tilde__, P.t, P.a, P.W__minus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3826,(0,1):C.GC_4147})
V_1079 = Vertex(name = 'V_1079',
particles = [ P.d__tilde__, P.u, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_989,(0,1):C.GC_513})
V_1080 = Vertex(name = 'V_1080',
particles = [ P.d__tilde__, P.u, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4008,(0,1):C.GC_2708})
V_1081 = Vertex(name = 'V_1081',
particles = [ P.s__tilde__, P.u, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2633,(0,1):C.GC_3055})
V_1082 = Vertex(name = 'V_1082',
particles = [ P.b__tilde__, P.u, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4007,(0,1):C.GC_2980})
V_1083 = Vertex(name = 'V_1083',
particles = [ P.d__tilde__, P.c, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1667,(0,1):C.GC_3403})
V_1084 = Vertex(name = 'V_1084',
particles = [ P.s__tilde__, P.c, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_458,(0,1):C.GC_686})
V_1085 = Vertex(name = 'V_1085',
particles = [ P.s__tilde__, P.c, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2502,(0,1):C.GC_3466})
V_1086 = Vertex(name = 'V_1086',
particles = [ P.b__tilde__, P.c, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3784,(0,1):C.GC_3380})
V_1087 = Vertex(name = 'V_1087',
particles = [ P.d__tilde__, P.t, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1700,(0,1):C.GC_4218})
V_1088 = Vertex(name = 'V_1088',
particles = [ P.s__tilde__, P.t, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2572,(0,1):C.GC_4249})
V_1089 = Vertex(name = 'V_1089',
particles = [ P.b__tilde__, P.t, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_767,(0,1):C.GC_416})
V_1090 = Vertex(name = 'V_1090',
particles = [ P.b__tilde__, P.t, P.a, P.W__minus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3861,(0,1):C.GC_4182})
V_1091 = Vertex(name = 'V_1091',
particles = [ P.u__tilde__, P.u, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_979})
V_1092 = Vertex(name = 'V_1092',
particles = [ P.u__tilde__, P.u, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_3989})
V_1093 = Vertex(name = 'V_1093',
particles = [ P.c__tilde__, P.u, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2624,(0,1):C.GC_2472})
V_1094 = Vertex(name = 'V_1094',
particles = [ P.t__tilde__, P.u, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3988,(0,1):C.GC_3830})
V_1095 = Vertex(name = 'V_1095',
particles = [ P.u__tilde__, P.c, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1658,(0,1):C.GC_1741})
V_1096 = Vertex(name = 'V_1096',
particles = [ P.c__tilde__, P.c, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_448})
V_1097 = Vertex(name = 'V_1097',
particles = [ P.c__tilde__, P.c, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_2473})
V_1098 = Vertex(name = 'V_1098',
particles = [ P.t__tilde__, P.c, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3775,(0,1):C.GC_3831})
V_1099 = Vertex(name = 'V_1099',
particles = [ P.u__tilde__, P.t, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1691,(0,1):C.GC_1742})
V_1100 = Vertex(name = 'V_1100',
particles = [ P.c__tilde__, P.t, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2563,(0,1):C.GC_2474})
V_1101 = Vertex(name = 'V_1101',
particles = [ P.t__tilde__, P.t, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_757})
V_1102 = Vertex(name = 'V_1102',
particles = [ P.t__tilde__, P.t, P.W__minus__, P.W__plus__, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS2 ],
couplings = {(0,0):C.GC_3832})
V_1103 = Vertex(name = 'V_1103',
particles = [ P.u__tilde__, P.u, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_992})
V_1104 = Vertex(name = 'V_1104',
particles = [ P.u__tilde__, P.u, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_4013})
V_1105 = Vertex(name = 'V_1105',
particles = [ P.c__tilde__, P.u, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2635,(0,1):C.GC_2507})
V_1106 = Vertex(name = 'V_1106',
particles = [ P.t__tilde__, P.u, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4012,(0,1):C.GC_3865})
V_1107 = Vertex(name = 'V_1107',
particles = [ P.u__tilde__, P.c, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1669,(0,1):C.GC_1763})
V_1108 = Vertex(name = 'V_1108',
particles = [ P.c__tilde__, P.c, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_461})
V_1109 = Vertex(name = 'V_1109',
particles = [ P.c__tilde__, P.c, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_2508})
V_1110 = Vertex(name = 'V_1110',
particles = [ P.t__tilde__, P.c, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3786,(0,1):C.GC_3866})
V_1111 = Vertex(name = 'V_1111',
particles = [ P.u__tilde__, P.t, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1702,(0,1):C.GC_1764})
V_1112 = Vertex(name = 'V_1112',
particles = [ P.c__tilde__, P.t, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2574,(0,1):C.GC_2509})
V_1113 = Vertex(name = 'V_1113',
particles = [ P.t__tilde__, P.t, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_770})
V_1114 = Vertex(name = 'V_1114',
particles = [ P.t__tilde__, P.t, P.W__minus__, P.W__plus__ ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV2 ],
couplings = {(0,0):C.GC_3867})
V_1115 = Vertex(name = 'V_1115',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_504,(0,1):C.GC_980})
V_1116 = Vertex(name = 'V_1116',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2698,(0,1):C.GC_3991})
V_1117 = Vertex(name = 'V_1117',
particles = [ P.c__tilde__, P.d, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3003,(0,1):C.GC_2475})
V_1118 = Vertex(name = 'V_1118',
particles = [ P.t__tilde__, P.d, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3004,(0,1):C.GC_3833})
V_1119 = Vertex(name = 'V_1119',
particles = [ P.u__tilde__, P.s, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3439,(0,1):C.GC_1743})
V_1120 = Vertex(name = 'V_1120',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_677,(0,1):C.GC_449})
V_1121 = Vertex(name = 'V_1121',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3441,(0,1):C.GC_2477})
V_1122 = Vertex(name = 'V_1122',
particles = [ P.t__tilde__, P.s, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3442,(0,1):C.GC_3834})
V_1123 = Vertex(name = 'V_1123',
particles = [ P.u__tilde__, P.b, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4154,(0,1):C.GC_1744})
V_1124 = Vertex(name = 'V_1124',
particles = [ P.c__tilde__, P.b, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4155,(0,1):C.GC_2478})
V_1125 = Vertex(name = 'V_1125',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_407,(0,1):C.GC_758})
V_1126 = Vertex(name = 'V_1126',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_4157,(0,1):C.GC_3836})
V_1127 = Vertex(name = 'V_1127',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_517,(0,1):C.GC_993})
V_1128 = Vertex(name = 'V_1128',
particles = [ P.u__tilde__, P.d, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2712,(0,1):C.GC_4015})
V_1129 = Vertex(name = 'V_1129',
particles = [ P.c__tilde__, P.d, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3025,(0,1):C.GC_2510})
V_1130 = Vertex(name = 'V_1130',
particles = [ P.t__tilde__, P.d, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3026,(0,1):C.GC_3868})
V_1131 = Vertex(name = 'V_1131',
particles = [ P.u__tilde__, P.s, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3474,(0,1):C.GC_1765})
V_1132 = Vertex(name = 'V_1132',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_690,(0,1):C.GC_462})
V_1133 = Vertex(name = 'V_1133',
particles = [ P.c__tilde__, P.s, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3476,(0,1):C.GC_2512})
V_1134 = Vertex(name = 'V_1134',
particles = [ P.t__tilde__, P.s, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3477,(0,1):C.GC_3869})
V_1135 = Vertex(name = 'V_1135',
particles = [ P.u__tilde__, P.b, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4189,(0,1):C.GC_1766})
V_1136 = Vertex(name = 'V_1136',
particles = [ P.c__tilde__, P.b, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4190,(0,1):C.GC_2513})
V_1137 = Vertex(name = 'V_1137',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_420,(0,1):C.GC_771})
V_1138 = Vertex(name = 'V_1138',
particles = [ P.t__tilde__, P.b, P.W__plus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4192,(0,1):C.GC_3871})
V_1139 = Vertex(name = 'V_1139',
particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS1 ],
couplings = {(0,0):C.GC_561})
V_1140 = Vertex(name = 'V_1140',
particles = [ P.vm__tilde__, P.mu__minus__, P.W__plus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS1 ],
couplings = {(0,0):C.GC_618})
V_1141 = Vertex(name = 'V_1141',
particles = [ P.vt__tilde__, P.ta__minus__, P.W__plus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS1 ],
couplings = {(0,0):C.GC_889})
V_1142 = Vertex(name = 'V_1142',
particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFVV1 ],
couplings = {(0,0):C.GC_572})
V_1143 = Vertex(name = 'V_1143',
particles = [ P.vm__tilde__, P.mu__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFVV1 ],
couplings = {(0,0):C.GC_629})
V_1144 = Vertex(name = 'V_1144',
particles = [ P.vt__tilde__, P.ta__minus__, P.W__plus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFVV1 ],
couplings = {(0,0):C.GC_900})
V_1145 = Vertex(name = 'V_1145',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_981,(0,1):C.GC_505})
V_1146 = Vertex(name = 'V_1146',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3992,(0,1):C.GC_2699})
V_1147 = Vertex(name = 'V_1147',
particles = [ P.s__tilde__, P.u, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2625,(0,1):C.GC_3047})
V_1148 = Vertex(name = 'V_1148',
particles = [ P.b__tilde__, P.u, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3990,(0,1):C.GC_2972})
V_1149 = Vertex(name = 'V_1149',
particles = [ P.d__tilde__, P.c, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1659,(0,1):C.GC_3395})
V_1150 = Vertex(name = 'V_1150',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_450,(0,1):C.GC_678})
V_1151 = Vertex(name = 'V_1151',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2476,(0,1):C.GC_3440})
V_1152 = Vertex(name = 'V_1152',
particles = [ P.b__tilde__, P.c, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3776,(0,1):C.GC_3372})
V_1153 = Vertex(name = 'V_1153',
particles = [ P.d__tilde__, P.t, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_1692,(0,1):C.GC_4210})
V_1154 = Vertex(name = 'V_1154',
particles = [ P.s__tilde__, P.t, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_2564,(0,1):C.GC_4241})
V_1155 = Vertex(name = 'V_1155',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_759,(0,1):C.GC_408})
V_1156 = Vertex(name = 'V_1156',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.Z, P.H ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVVS1, L.FFVVS3 ],
couplings = {(0,0):C.GC_3835,(0,1):C.GC_4156})
V_1157 = Vertex(name = 'V_1157',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_994,(0,1):C.GC_518})
V_1158 = Vertex(name = 'V_1158',
particles = [ P.d__tilde__, P.u, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4016,(0,1):C.GC_2713})
V_1159 = Vertex(name = 'V_1159',
particles = [ P.s__tilde__, P.u, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2636,(0,1):C.GC_3058})
V_1160 = Vertex(name = 'V_1160',
particles = [ P.b__tilde__, P.u, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_4014,(0,1):C.GC_2983})
V_1161 = Vertex(name = 'V_1161',
particles = [ P.d__tilde__, P.c, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1670,(0,1):C.GC_3406})
V_1162 = Vertex(name = 'V_1162',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_463,(0,1):C.GC_691})
V_1163 = Vertex(name = 'V_1163',
particles = [ P.s__tilde__, P.c, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2511,(0,1):C.GC_3475})
V_1164 = Vertex(name = 'V_1164',
particles = [ P.b__tilde__, P.c, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3787,(0,1):C.GC_3383})
V_1165 = Vertex(name = 'V_1165',
particles = [ P.d__tilde__, P.t, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_1703,(0,1):C.GC_4221})
V_1166 = Vertex(name = 'V_1166',
particles = [ P.s__tilde__, P.t, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_2575,(0,1):C.GC_4252})
V_1167 = Vertex(name = 'V_1167',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_772,(0,1):C.GC_421})
V_1168 = Vertex(name = 'V_1168',
particles = [ P.b__tilde__, P.t, P.W__minus__, P.Z ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFVV1, L.FFVV4 ],
couplings = {(0,0):C.GC_3870,(0,1):C.GC_4191})
V_1169 = Vertex(name = 'V_1169',
particles = [ P.e__plus__, P.ve, P.a, P.W__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS3 ],
couplings = {(0,0):C.GC_557})
V_1170 = Vertex(name = 'V_1170',
particles = [ P.mu__plus__, P.vm, P.a, P.W__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS3 ],
couplings = {(0,0):C.GC_614})
V_1171 = Vertex(name = 'V_1171',
particles = [ P.ta__plus__, P.vt, P.a, P.W__minus__, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS3 ],
couplings = {(0,0):C.GC_885})
V_1172 = Vertex(name = 'V_1172',
particles = [ P.e__plus__, P.ve, P.a, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFVV4 ],
couplings = {(0,0):C.GC_569})
V_1173 = Vertex(name = 'V_1173',
particles = [ P.mu__plus__, P.vm, P.a, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFVV4 ],
couplings = {(0,0):C.GC_626})
V_1174 = Vertex(name = 'V_1174',
particles = [ P.ta__plus__, P.vt, P.a, P.W__minus__ ],
color = [ '1' ],
lorentz = [ L.FFVV4 ],
couplings = {(0,0):C.GC_897})
V_1175 = Vertex(name = 'V_1175',
particles = [ P.e__plus__, P.ve, P.W__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS3 ],
couplings = {(0,0):C.GC_562})
V_1176 = Vertex(name = 'V_1176',
particles = [ P.mu__plus__, P.vm, P.W__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS3 ],
couplings = {(0,0):C.GC_619})
V_1177 = Vertex(name = 'V_1177',
particles = [ P.ta__plus__, P.vt, P.W__minus__, P.Z, P.H ],
color = [ '1' ],
lorentz = [ L.FFVVS3 ],
couplings = {(0,0):C.GC_890})
V_1178 = Vertex(name = 'V_1178',
particles = [ P.e__plus__, P.ve, P.W__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFVV4 ],
couplings = {(0,0):C.GC_573})
V_1179 = Vertex(name = 'V_1179',
particles = [ P.mu__plus__, P.vm, P.W__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFVV4 ],
couplings = {(0,0):C.GC_630})
V_1180 = Vertex(name = 'V_1180',
particles = [ P.ta__plus__, P.vt, P.W__minus__, P.Z ],
color = [ '1' ],
lorentz = [ L.FFVV4 ],
couplings = {(0,0):C.GC_901})
V_1181 = Vertex(name = 'V_1181',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_41,(0,0):C.GC_38,(2,0):C.GC_39,(1,3):C.GC_38,(3,3):C.GC_39,(1,1):C.GC_38,(3,1):C.GC_39,(1,2):C.GC_10,(0,4):C.GC_38,(2,4):C.GC_39,(0,5):C.GC_10})
V_1182 = Vertex(name = 'V_1182',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_42,(0,7):C.GC_42,(0,0):C.GC_2649,(2,0):C.GC_2650,(1,3):C.GC_2649,(3,3):C.GC_2650,(1,1):C.GC_2649,(3,1):C.GC_2650,(1,2):C.GC_11,(0,4):C.GC_2649,(2,4):C.GC_2650,(0,5):C.GC_11})
V_1183 = Vertex(name = 'V_1183',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_44,(0,7):C.GC_44,(0,0):C.GC_1135,(2,0):C.GC_1136,(1,3):C.GC_1135,(3,3):C.GC_1136,(1,1):C.GC_1135,(3,1):C.GC_1136,(1,2):C.GC_537,(0,4):C.GC_1135,(2,4):C.GC_1136,(0,5):C.GC_537})
V_1184 = Vertex(name = 'V_1184',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF14, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_45,(0,3):C.GC_45,(1,0):C.GC_538,(0,1):C.GC_538})
V_1185 = Vertex(name = 'V_1185',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2663,(0,1):C.GC_2663})
V_1186 = Vertex(name = 'V_1186',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2669,(0,1):C.GC_2669})
V_1187 = Vertex(name = 'V_1187',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2675,(0,1):C.GC_2675})
V_1188 = Vertex(name = 'V_1188',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2681,(0,1):C.GC_2681})
V_1189 = Vertex(name = 'V_1189',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_2936,(0,5):C.GC_2936,(0,0):C.GC_1927,(2,0):C.GC_1930,(1,2):C.GC_1192,(3,2):C.GC_1194,(1,1):C.GC_1927,(3,1):C.GC_1930,(0,3):C.GC_1192,(2,3):C.GC_1194})
V_1190 = Vertex(name = 'V_1190',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2940,(0,3):C.GC_2940,(1,0):C.GC_2920,(3,0):C.GC_2922,(0,1):C.GC_2920,(2,1):C.GC_2922})
V_1191 = Vertex(name = 'V_1191',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2944,(0,1):C.GC_2944})
V_1192 = Vertex(name = 'V_1192',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2950,(0,1):C.GC_2950})
V_1193 = Vertex(name = 'V_1193',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_2937,(0,5):C.GC_2937,(0,0):C.GC_2762,(2,0):C.GC_2765,(1,2):C.GC_1193,(3,2):C.GC_1195,(1,1):C.GC_2762,(3,1):C.GC_2765,(0,3):C.GC_1193,(2,3):C.GC_1195})
V_1194 = Vertex(name = 'V_1194',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2941,(0,3):C.GC_2941,(1,0):C.GC_2921,(3,0):C.GC_2923,(0,1):C.GC_2921,(2,1):C.GC_2923})
V_1195 = Vertex(name = 'V_1195',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2946,(0,1):C.GC_2946})
V_1196 = Vertex(name = 'V_1196',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2951,(0,1):C.GC_2951})
V_1197 = Vertex(name = 'V_1197',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_3317,(0,5):C.GC_3317,(0,0):C.GC_1333,(2,0):C.GC_1336,(1,2):C.GC_1333,(3,2):C.GC_1336,(1,1):C.GC_1198,(3,1):C.GC_1201,(0,3):C.GC_1198,(2,3):C.GC_1201})
V_1198 = Vertex(name = 'V_1198',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3325,(0,3):C.GC_3325,(0,0):C.GC_3287,(2,0):C.GC_3291,(1,1):C.GC_3287,(3,1):C.GC_3291})
V_1199 = Vertex(name = 'V_1199',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3332,(0,1):C.GC_3332})
V_1200 = Vertex(name = 'V_1200',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3341,(0,1):C.GC_3341})
V_1201 = Vertex(name = 'V_1201',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_42,(0,0):C.GC_2108,(2,0):C.GC_2111,(1,3):C.GC_38,(3,3):C.GC_39,(1,1):C.GC_38,(3,1):C.GC_39,(1,2):C.GC_10,(0,4):C.GC_1199,(2,4):C.GC_1202,(0,5):C.GC_11})
V_1202 = Vertex(name = 'V_1202',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_44,(0,5):C.GC_45,(1,2):C.GC_3229,(2,2):C.GC_3230,(1,0):C.GC_2864,(2,0):C.GC_2866,(1,1):C.GC_728,(0,3):C.GC_730})
V_1203 = Vertex(name = 'V_1203',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_4080,(0,3):C.GC_4088,(1,1):C.GC_1334,(2,1):C.GC_1337,(1,0):C.GC_1933,(2,0):C.GC_1936})
V_1204 = Vertex(name = 'V_1204',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4098,(0,1):C.GC_4106})
V_1205 = Vertex(name = 'V_1205',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_3321,(0,5):C.GC_3328,(0,0):C.GC_3110,(2,0):C.GC_3113,(1,2):C.GC_1335,(3,2):C.GC_1338,(1,1):C.GC_2778,(3,1):C.GC_2781,(0,3):C.GC_1200,(2,3):C.GC_1203})
V_1206 = Vertex(name = 'V_1206',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3336,(0,2):C.GC_3345,(1,0):C.GC_3290,(2,0):C.GC_3294})
V_1207 = Vertex(name = 'V_1207',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_4075,(0,5):C.GC_4075,(0,0):C.GC_1518,(2,0):C.GC_1521,(1,2):C.GC_1518,(3,2):C.GC_1521,(1,1):C.GC_1178,(3,1):C.GC_1181,(0,3):C.GC_1178,(2,3):C.GC_1181})
V_1208 = Vertex(name = 'V_1208',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_4086,(0,3):C.GC_4086,(0,0):C.GC_4045,(2,0):C.GC_4049,(1,1):C.GC_4045,(3,1):C.GC_4049})
V_1209 = Vertex(name = 'V_1209',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4094,(0,1):C.GC_4094})
V_1210 = Vertex(name = 'V_1210',
particles = [ P.d__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4104,(0,1):C.GC_4104})
V_1211 = Vertex(name = 'V_1211',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_4076,(0,5):C.GC_4087,(0,0):C.GC_2308,(2,0):C.GC_2311,(1,2):C.GC_1519,(3,2):C.GC_1522,(1,1):C.GC_1908,(3,1):C.GC_1911,(0,3):C.GC_1179,(2,3):C.GC_1182})
V_1212 = Vertex(name = 'V_1212',
particles = [ P.s__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_4096,(0,2):C.GC_4105,(1,0):C.GC_4046,(2,0):C.GC_4050})
V_1213 = Vertex(name = 'V_1213',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_42,(0,0):C.GC_3552,(2,0):C.GC_3555,(1,3):C.GC_38,(3,3):C.GC_39,(1,1):C.GC_38,(3,1):C.GC_39,(1,2):C.GC_10,(0,4):C.GC_1180,(2,4):C.GC_1183,(0,5):C.GC_11})
V_1214 = Vertex(name = 'V_1214',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_44,(0,5):C.GC_45,(1,2):C.GC_3899,(2,2):C.GC_3900,(1,0):C.GC_2863,(2,0):C.GC_2865,(1,1):C.GC_544,(0,3):C.GC_545})
V_1215 = Vertex(name = 'V_1215',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3319,(0,3):C.GC_3326,(1,1):C.GC_1520,(2,1):C.GC_1523,(1,0):C.GC_2756,(2,0):C.GC_2759})
V_1216 = Vertex(name = 'V_1216',
particles = [ P.b__tilde__, P.d, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3334,(0,1):C.GC_3342})
V_1217 = Vertex(name = 'V_1217',
particles = [ P.s__tilde__, P.d, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_2936,(0,5):C.GC_2936,(0,0):C.GC_2109,(2,0):C.GC_2112,(1,2):C.GC_2109,(3,2):C.GC_2112,(1,1):C.GC_2920,(3,1):C.GC_2922,(0,3):C.GC_2920,(2,3):C.GC_2922})
V_1218 = Vertex(name = 'V_1218',
particles = [ P.s__tilde__, P.d, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2940,(0,3):C.GC_2940,(1,0):C.GC_1934,(3,0):C.GC_1937,(0,1):C.GC_1934,(2,1):C.GC_1937})
V_1219 = Vertex(name = 'V_1219',
particles = [ P.s__tilde__, P.d, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2944,(0,1):C.GC_2944})
V_1220 = Vertex(name = 'V_1220',
particles = [ P.s__tilde__, P.d, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2950,(0,1):C.GC_2950})
V_1221 = Vertex(name = 'V_1221',
particles = [ P.b__tilde__, P.d, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_2941,(0,5):C.GC_2937,(0,0):C.GC_3111,(2,0):C.GC_3114,(1,2):C.GC_2110,(3,2):C.GC_2113,(1,1):C.GC_2779,(3,1):C.GC_2782,(0,3):C.GC_2921,(2,3):C.GC_2923})
V_1222 = Vertex(name = 'V_1222',
particles = [ P.b__tilde__, P.d, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)' ],
lorentz = [ L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2951,(0,2):C.GC_2946,(0,0):C.GC_1935,(2,0):C.GC_1938})
V_1223 = Vertex(name = 'V_1223',
particles = [ P.b__tilde__, P.d, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_2936,(0,5):C.GC_2940,(0,0):C.GC_3553,(2,0):C.GC_3556,(1,2):C.GC_2310,(3,2):C.GC_2313,(1,1):C.GC_2920,(3,1):C.GC_2922,(0,3):C.GC_1910,(2,3):C.GC_1913})
V_1224 = Vertex(name = 'V_1224',
particles = [ P.b__tilde__, P.d, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2944,(0,2):C.GC_2950,(1,0):C.GC_2757,(2,0):C.GC_2760})
V_1225 = Vertex(name = 'V_1225',
particles = [ P.b__tilde__, P.d, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_2937,(0,5):C.GC_2937,(0,0):C.GC_3554,(2,0):C.GC_3557,(1,2):C.GC_3554,(3,2):C.GC_3557,(1,1):C.GC_2921,(3,1):C.GC_2923,(0,3):C.GC_2921,(2,3):C.GC_2923})
V_1226 = Vertex(name = 'V_1226',
particles = [ P.b__tilde__, P.d, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2941,(0,3):C.GC_2941,(1,0):C.GC_2758,(3,0):C.GC_2761,(0,1):C.GC_2758,(2,1):C.GC_2761})
V_1227 = Vertex(name = 'V_1227',
particles = [ P.b__tilde__, P.d, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2946,(0,1):C.GC_2946})
V_1228 = Vertex(name = 'V_1228',
particles = [ P.b__tilde__, P.d, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2951,(0,1):C.GC_2951})
V_1229 = Vertex(name = 'V_1229',
particles = [ P.s__tilde__, P.s, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_3317,(0,5):C.GC_3317,(0,0):C.GC_3287,(2,0):C.GC_3291,(1,2):C.GC_1352,(3,2):C.GC_1355,(1,1):C.GC_3287,(3,1):C.GC_3291,(0,3):C.GC_1352,(2,3):C.GC_1355})
V_1230 = Vertex(name = 'V_1230',
particles = [ P.s__tilde__, P.s, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3325,(0,3):C.GC_3325,(0,0):C.GC_2116,(2,0):C.GC_2119,(1,1):C.GC_2116,(3,1):C.GC_2119})
V_1231 = Vertex(name = 'V_1231',
particles = [ P.s__tilde__, P.s, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3332,(0,1):C.GC_3332})
V_1232 = Vertex(name = 'V_1232',
particles = [ P.s__tilde__, P.s, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3341,(0,1):C.GC_3341})
V_1233 = Vertex(name = 'V_1233',
particles = [ P.s__tilde__, P.s, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_4086,(0,5):C.GC_4075,(0,0):C.GC_4045,(2,0):C.GC_4049,(1,2):C.GC_1527,(3,2):C.GC_1530,(1,1):C.GC_2079,(3,1):C.GC_2082,(0,3):C.GC_1328,(2,3):C.GC_1331})
V_1234 = Vertex(name = 'V_1234',
particles = [ P.s__tilde__, P.s, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)' ],
lorentz = [ L.FFFF12, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_4104,(0,2):C.GC_4094,(0,0):C.GC_2314,(2,0):C.GC_2317})
V_1235 = Vertex(name = 'V_1235',
particles = [ P.b__tilde__, P.s, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_3317,(0,5):C.GC_3325,(0,0):C.GC_3560,(2,0):C.GC_3563,(1,2):C.GC_1528,(3,2):C.GC_1531,(1,1):C.GC_3287,(3,1):C.GC_3291,(0,3):C.GC_1329,(2,3):C.GC_1332})
V_1236 = Vertex(name = 'V_1236',
particles = [ P.b__tilde__, P.s, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3332,(0,2):C.GC_3341,(1,0):C.GC_3104,(2,0):C.GC_3107})
V_1237 = Vertex(name = 'V_1237',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_41,(0,0):C.GC_38,(2,0):C.GC_39,(1,3):C.GC_38,(3,3):C.GC_39,(1,1):C.GC_38,(3,1):C.GC_39,(1,2):C.GC_10,(0,4):C.GC_38,(2,4):C.GC_39,(0,5):C.GC_10})
V_1238 = Vertex(name = 'V_1238',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_42,(0,7):C.GC_42,(0,0):C.GC_3289,(2,0):C.GC_3293,(1,3):C.GC_3289,(3,3):C.GC_3293,(1,1):C.GC_3289,(3,1):C.GC_3293,(1,2):C.GC_11,(0,4):C.GC_3289,(2,4):C.GC_3293,(0,5):C.GC_11})
V_1239 = Vertex(name = 'V_1239',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_44,(0,7):C.GC_44,(0,0):C.GC_2117,(2,0):C.GC_2120,(1,3):C.GC_2117,(3,3):C.GC_2120,(1,1):C.GC_2117,(3,1):C.GC_2120,(1,2):C.GC_720,(0,4):C.GC_2117,(2,4):C.GC_2120,(0,5):C.GC_720})
V_1240 = Vertex(name = 'V_1240',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF14, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_45,(0,3):C.GC_45,(1,0):C.GC_721,(0,1):C.GC_721})
V_1241 = Vertex(name = 'V_1241',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3320,(0,1):C.GC_3320})
V_1242 = Vertex(name = 'V_1242',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3327,(0,1):C.GC_3327})
V_1243 = Vertex(name = 'V_1243',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3335,(0,1):C.GC_3335})
V_1244 = Vertex(name = 'V_1244',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3343,(0,1):C.GC_3343})
V_1245 = Vertex(name = 'V_1245',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_3321,(0,5):C.GC_3321,(0,0):C.GC_3117,(2,0):C.GC_3120,(1,2):C.GC_3290,(3,2):C.GC_3294,(1,1):C.GC_3117,(3,1):C.GC_3120,(0,3):C.GC_3290,(2,3):C.GC_3294})
V_1246 = Vertex(name = 'V_1246',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3328,(0,3):C.GC_3328,(1,0):C.GC_2118,(3,0):C.GC_2121,(0,1):C.GC_2118,(2,1):C.GC_2121})
V_1247 = Vertex(name = 'V_1247',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3336,(0,1):C.GC_3336})
V_1248 = Vertex(name = 'V_1248',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3345,(0,1):C.GC_3345})
V_1249 = Vertex(name = 'V_1249',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_4076,(0,5):C.GC_4076,(0,0):C.GC_4046,(2,0):C.GC_4050,(1,2):C.GC_4046,(3,2):C.GC_4050,(1,1):C.GC_2080,(3,1):C.GC_2083,(0,3):C.GC_2080,(2,3):C.GC_2083})
V_1250 = Vertex(name = 'V_1250',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_4087,(0,3):C.GC_4087,(0,0):C.GC_2315,(2,0):C.GC_2318,(1,1):C.GC_2315,(3,1):C.GC_2318})
V_1251 = Vertex(name = 'V_1251',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4096,(0,1):C.GC_4096})
V_1252 = Vertex(name = 'V_1252',
particles = [ P.s__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4105,(0,1):C.GC_4105})
V_1253 = Vertex(name = 'V_1253',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_42,(0,0):C.GC_3561,(2,0):C.GC_3564,(1,3):C.GC_38,(3,3):C.GC_39,(1,1):C.GC_38,(3,1):C.GC_39,(1,2):C.GC_10,(0,4):C.GC_2081,(2,4):C.GC_2084,(0,5):C.GC_11})
V_1254 = Vertex(name = 'V_1254',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_44,(0,5):C.GC_45,(1,2):C.GC_4048,(2,2):C.GC_4052,(1,0):C.GC_3288,(2,0):C.GC_3292,(1,1):C.GC_727,(0,3):C.GC_729})
V_1255 = Vertex(name = 'V_1255',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2661,(0,3):C.GC_2668,(1,1):C.GC_2316,(2,1):C.GC_2319,(1,0):C.GC_3105,(2,0):C.GC_3108})
V_1256 = Vertex(name = 'V_1256',
particles = [ P.b__tilde__, P.s, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2674,(0,1):C.GC_2680})
V_1257 = Vertex(name = 'V_1257',
particles = [ P.b__tilde__, P.s, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_3321,(0,5):C.GC_3321,(0,0):C.GC_3562,(2,0):C.GC_3565,(1,2):C.GC_3562,(3,2):C.GC_3565,(1,1):C.GC_3290,(3,1):C.GC_3294,(0,3):C.GC_3290,(2,3):C.GC_3294})
V_1258 = Vertex(name = 'V_1258',
particles = [ P.b__tilde__, P.s, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3328,(0,3):C.GC_3328,(1,0):C.GC_3106,(3,0):C.GC_3109,(0,1):C.GC_3106,(2,1):C.GC_3109})
V_1259 = Vertex(name = 'V_1259',
particles = [ P.b__tilde__, P.s, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3336,(0,1):C.GC_3336})
V_1260 = Vertex(name = 'V_1260',
particles = [ P.b__tilde__, P.s, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3345,(0,1):C.GC_3345})
V_1261 = Vertex(name = 'V_1261',
particles = [ P.b__tilde__, P.b, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_4075,(0,5):C.GC_4075,(0,0):C.GC_4045,(2,0):C.GC_4049,(1,2):C.GC_1514,(3,2):C.GC_1517,(1,1):C.GC_4045,(3,1):C.GC_4049,(0,3):C.GC_1514,(2,3):C.GC_1517})
V_1262 = Vertex(name = 'V_1262',
particles = [ P.b__tilde__, P.b, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_4086,(0,3):C.GC_4086,(0,0):C.GC_3534,(2,0):C.GC_3537,(1,1):C.GC_3534,(3,1):C.GC_3537})
V_1263 = Vertex(name = 'V_1263',
particles = [ P.b__tilde__, P.b, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4094,(0,1):C.GC_4094})
V_1264 = Vertex(name = 'V_1264',
particles = [ P.b__tilde__, P.b, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4104,(0,1):C.GC_4104})
V_1265 = Vertex(name = 'V_1265',
particles = [ P.b__tilde__, P.b, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_4076,(0,5):C.GC_4076,(0,0):C.GC_4046,(2,0):C.GC_4050,(1,2):C.GC_2292,(3,2):C.GC_2295,(1,1):C.GC_4046,(3,1):C.GC_4050,(0,3):C.GC_2292,(2,3):C.GC_2295})
V_1266 = Vertex(name = 'V_1266',
particles = [ P.b__tilde__, P.b, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_4087,(0,3):C.GC_4087,(0,0):C.GC_3535,(2,0):C.GC_3538,(1,1):C.GC_3535,(3,1):C.GC_3538})
V_1267 = Vertex(name = 'V_1267',
particles = [ P.b__tilde__, P.b, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4096,(0,1):C.GC_4096})
V_1268 = Vertex(name = 'V_1268',
particles = [ P.b__tilde__, P.b, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4105,(0,1):C.GC_4105})
V_1269 = Vertex(name = 'V_1269',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_41,(0,0):C.GC_38,(2,0):C.GC_39,(1,3):C.GC_38,(3,3):C.GC_39,(1,1):C.GC_38,(3,1):C.GC_39,(1,2):C.GC_10,(0,4):C.GC_38,(2,4):C.GC_39,(0,5):C.GC_10})
V_1270 = Vertex(name = 'V_1270',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_42,(0,7):C.GC_42,(0,0):C.GC_4047,(2,0):C.GC_4051,(1,3):C.GC_4047,(3,3):C.GC_4051,(1,1):C.GC_4047,(3,1):C.GC_4051,(1,2):C.GC_11,(0,4):C.GC_4047,(2,4):C.GC_4051,(0,5):C.GC_11})
V_1271 = Vertex(name = 'V_1271',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_44,(0,7):C.GC_44,(0,0):C.GC_3536,(2,0):C.GC_3539,(1,3):C.GC_3536,(3,3):C.GC_3539,(1,1):C.GC_3536,(3,1):C.GC_3539,(1,2):C.GC_431,(0,4):C.GC_3536,(2,4):C.GC_3539,(0,5):C.GC_431})
V_1272 = Vertex(name = 'V_1272',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF14, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_45,(0,3):C.GC_45,(1,0):C.GC_432,(0,1):C.GC_432})
V_1273 = Vertex(name = 'V_1273',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4081,(0,1):C.GC_4081})
V_1274 = Vertex(name = 'V_1274',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4089,(0,1):C.GC_4089})
V_1275 = Vertex(name = 'V_1275',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4099,(0,1):C.GC_4099})
V_1276 = Vertex(name = 'V_1276',
particles = [ P.b__tilde__, P.b, P.b__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4107,(0,1):C.GC_4107})
V_1277 = Vertex(name = 'V_1277',
particles = [ P.e__plus__, P.e__minus__, P.e__plus__, P.e__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(0,6):C.GC_23,(0,7):C.GC_23,(0,0):C.GC_22,(0,3):C.GC_22,(0,1):C.GC_22,(0,2):C.GC_13,(0,4):C.GC_22,(0,5):C.GC_13})
V_1278 = Vertex(name = 'V_1278',
particles = [ P.e__plus__, P.e__minus__, P.e__plus__, P.e__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_24,(0,1):C.GC_24})
V_1279 = Vertex(name = 'V_1279',
particles = [ P.mu__plus__, P.e__minus__, P.e__plus__, P.mu__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(0,3):C.GC_23,(0,4):C.GC_24,(0,2):C.GC_22,(0,0):C.GC_22,(0,1):C.GC_13})
V_1280 = Vertex(name = 'V_1280',
particles = [ P.ta__plus__, P.e__minus__, P.e__plus__, P.ta__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(0,3):C.GC_23,(0,4):C.GC_24,(0,2):C.GC_22,(0,0):C.GC_22,(0,1):C.GC_13})
V_1281 = Vertex(name = 'V_1281',
particles = [ P.mu__plus__, P.mu__minus__, P.mu__plus__, P.mu__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(0,6):C.GC_23,(0,7):C.GC_23,(0,0):C.GC_22,(0,3):C.GC_22,(0,1):C.GC_22,(0,2):C.GC_13,(0,4):C.GC_22,(0,5):C.GC_13})
V_1282 = Vertex(name = 'V_1282',
particles = [ P.mu__plus__, P.mu__minus__, P.mu__plus__, P.mu__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_24,(0,1):C.GC_24})
V_1283 = Vertex(name = 'V_1283',
particles = [ P.ta__plus__, P.mu__minus__, P.mu__plus__, P.ta__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(0,3):C.GC_23,(0,4):C.GC_24,(0,2):C.GC_22,(0,0):C.GC_22,(0,1):C.GC_13})
V_1284 = Vertex(name = 'V_1284',
particles = [ P.ta__plus__, P.ta__minus__, P.ta__plus__, P.ta__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(0,6):C.GC_23,(0,7):C.GC_23,(0,0):C.GC_22,(0,3):C.GC_22,(0,1):C.GC_22,(0,2):C.GC_13,(0,4):C.GC_22,(0,5):C.GC_13})
V_1285 = Vertex(name = 'V_1285',
particles = [ P.ta__plus__, P.ta__minus__, P.ta__plus__, P.ta__minus__ ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_24,(0,1):C.GC_24})
V_1286 = Vertex(name = 'V_1286',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_597,(0,0):C.GC_597})
V_1287 = Vertex(name = 'V_1287',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_2660,(0,2):C.GC_540,(0,3):C.GC_539,(0,5):C.GC_2717,(0,0):C.GC_2717})
V_1288 = Vertex(name = 'V_1288',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2657})
V_1289 = Vertex(name = 'V_1289',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2659})
V_1290 = Vertex(name = 'V_1290',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_654,(0,0):C.GC_654})
V_1291 = Vertex(name = 'V_1291',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_2660,(0,2):C.GC_540,(0,3):C.GC_539,(0,5):C.GC_2718,(0,0):C.GC_2718})
V_1292 = Vertex(name = 'V_1292',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2657})
V_1293 = Vertex(name = 'V_1293',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2659})
V_1294 = Vertex(name = 'V_1294',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_925,(0,0):C.GC_925})
V_1295 = Vertex(name = 'V_1295',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_2660,(0,2):C.GC_540,(0,3):C.GC_539,(0,5):C.GC_2719,(0,0):C.GC_2719})
V_1296 = Vertex(name = 'V_1296',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2657})
V_1297 = Vertex(name = 'V_1297',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2659})
V_1298 = Vertex(name = 'V_1298',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_2928,(0,1):C.GC_2934,(0,3):C.GC_3034,(0,0):C.GC_3062})
V_1299 = Vertex(name = 'V_1299',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2930})
V_1300 = Vertex(name = 'V_1300',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_2928,(0,1):C.GC_2934,(0,3):C.GC_3037,(0,0):C.GC_3063})
V_1301 = Vertex(name = 'V_1301',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2930})
V_1302 = Vertex(name = 'V_1302',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_2928,(0,1):C.GC_2934,(0,3):C.GC_3065,(0,0):C.GC_3067})
V_1303 = Vertex(name = 'V_1303',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2930})
V_1304 = Vertex(name = 'V_1304',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_2929,(0,1):C.GC_2935,(0,3):C.GC_3035,(0,0):C.GC_3033})
V_1305 = Vertex(name = 'V_1305',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2932})
V_1306 = Vertex(name = 'V_1306',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_2929,(0,1):C.GC_2935,(0,3):C.GC_3038,(0,0):C.GC_3036})
V_1307 = Vertex(name = 'V_1307',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2932})
V_1308 = Vertex(name = 'V_1308',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_2929,(0,1):C.GC_2935,(0,3):C.GC_3066,(0,0):C.GC_3064})
V_1309 = Vertex(name = 'V_1309',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.d ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2932})
V_1310 = Vertex(name = 'V_1310',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_3305,(0,1):C.GC_3314,(0,3):C.GC_3487,(0,0):C.GC_3411})
V_1311 = Vertex(name = 'V_1311',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3308})
V_1312 = Vertex(name = 'V_1312',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_3305,(0,1):C.GC_3314,(0,3):C.GC_3490,(0,0):C.GC_3413})
V_1313 = Vertex(name = 'V_1313',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3308})
V_1314 = Vertex(name = 'V_1314',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_3305,(0,1):C.GC_3314,(0,3):C.GC_3495,(0,0):C.GC_3494})
V_1315 = Vertex(name = 'V_1315',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3308})
V_1316 = Vertex(name = 'V_1316',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_710,(0,0):C.GC_710})
V_1317 = Vertex(name = 'V_1317',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_3315,(0,2):C.GC_723,(0,3):C.GC_722,(0,5):C.GC_3488,(0,0):C.GC_3488})
V_1318 = Vertex(name = 'V_1318',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3306})
V_1319 = Vertex(name = 'V_1319',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3310})
V_1320 = Vertex(name = 'V_1320',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_714,(0,0):C.GC_714})
V_1321 = Vertex(name = 'V_1321',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_3315,(0,2):C.GC_723,(0,3):C.GC_722,(0,5):C.GC_3491,(0,0):C.GC_3491})
V_1322 = Vertex(name = 'V_1322',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3306})
V_1323 = Vertex(name = 'V_1323',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3310})
V_1324 = Vertex(name = 'V_1324',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_929,(0,0):C.GC_929})
V_1325 = Vertex(name = 'V_1325',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_3315,(0,2):C.GC_723,(0,3):C.GC_722,(0,5):C.GC_3496,(0,0):C.GC_3496})
V_1326 = Vertex(name = 'V_1326',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3306})
V_1327 = Vertex(name = 'V_1327',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3310})
V_1328 = Vertex(name = 'V_1328',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_3307,(0,1):C.GC_3316,(0,3):C.GC_3489,(0,0):C.GC_3410})
V_1329 = Vertex(name = 'V_1329',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3312})
V_1330 = Vertex(name = 'V_1330',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_3307,(0,1):C.GC_3316,(0,3):C.GC_3492,(0,0):C.GC_3412})
V_1331 = Vertex(name = 'V_1331',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3312})
V_1332 = Vertex(name = 'V_1332',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_3307,(0,1):C.GC_3316,(0,3):C.GC_3497,(0,0):C.GC_3493})
V_1333 = Vertex(name = 'V_1333',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.s ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3312})
V_1334 = Vertex(name = 'V_1334',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_4063,(0,1):C.GC_4072,(0,3):C.GC_4225,(0,0):C.GC_4228})
V_1335 = Vertex(name = 'V_1335',
particles = [ P.e__plus__, P.e__minus__, P.d__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4066})
V_1336 = Vertex(name = 'V_1336',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_4063,(0,1):C.GC_4072,(0,3):C.GC_4229,(0,0):C.GC_4232})
V_1337 = Vertex(name = 'V_1337',
particles = [ P.mu__plus__, P.mu__minus__, P.d__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4066})
V_1338 = Vertex(name = 'V_1338',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_4063,(0,1):C.GC_4072,(0,3):C.GC_4258,(0,0):C.GC_4261})
V_1339 = Vertex(name = 'V_1339',
particles = [ P.ta__plus__, P.ta__minus__, P.d__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4066})
V_1340 = Vertex(name = 'V_1340',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_4064,(0,1):C.GC_4073,(0,3):C.GC_4226,(0,0):C.GC_4256})
V_1341 = Vertex(name = 'V_1341',
particles = [ P.e__plus__, P.e__minus__, P.s__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4068})
V_1342 = Vertex(name = 'V_1342',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_4064,(0,1):C.GC_4073,(0,3):C.GC_4230,(0,0):C.GC_4257})
V_1343 = Vertex(name = 'V_1343',
particles = [ P.mu__plus__, P.mu__minus__, P.s__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4068})
V_1344 = Vertex(name = 'V_1344',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF4, L.FFFF8 ],
couplings = {(0,2):C.GC_4064,(0,1):C.GC_4073,(0,3):C.GC_4259,(0,0):C.GC_4262})
V_1345 = Vertex(name = 'V_1345',
particles = [ P.ta__plus__, P.ta__minus__, P.s__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4068})
V_1346 = Vertex(name = 'V_1346',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_581,(0,0):C.GC_581})
V_1347 = Vertex(name = 'V_1347',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_4074,(0,2):C.GC_434,(0,3):C.GC_433,(0,5):C.GC_4227,(0,0):C.GC_4227})
V_1348 = Vertex(name = 'V_1348',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4065})
V_1349 = Vertex(name = 'V_1349',
particles = [ P.e__plus__, P.e__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4070})
V_1350 = Vertex(name = 'V_1350',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_638,(0,0):C.GC_638})
V_1351 = Vertex(name = 'V_1351',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_4074,(0,2):C.GC_434,(0,3):C.GC_433,(0,5):C.GC_4231,(0,0):C.GC_4231})
V_1352 = Vertex(name = 'V_1352',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4065})
V_1353 = Vertex(name = 'V_1353',
particles = [ P.mu__plus__, P.mu__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4070})
V_1354 = Vertex(name = 'V_1354',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_25,(0,1):C.GC_40,(0,2):C.GC_21,(0,3):C.GC_12,(0,5):C.GC_909,(0,0):C.GC_909})
V_1355 = Vertex(name = 'V_1355',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF4, L.FFFF8 ],
couplings = {(0,4):C.GC_27,(0,1):C.GC_4074,(0,2):C.GC_434,(0,3):C.GC_433,(0,5):C.GC_4260,(0,0):C.GC_4260})
V_1356 = Vertex(name = 'V_1356',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4065})
V_1357 = Vertex(name = 'V_1357',
particles = [ P.ta__plus__, P.ta__minus__, P.b__tilde__, P.b ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4070})
V_1358 = Vertex(name = 'V_1358',
particles = [ P.ve__tilde__, P.e__minus__, P.d__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_28,(0,5):C.GC_1025,(0,3):C.GC_1024,(0,4):C.GC_1024,(0,1):C.GC_1019,(0,0):C.GC_598})
V_1359 = Vertex(name = 'V_1359',
particles = [ P.ve__tilde__, P.e__minus__, P.d__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1076,(0,5):C.GC_1107,(0,3):C.GC_1106,(0,4):C.GC_1106,(0,1):C.GC_1103,(0,0):C.GC_1095})
V_1360 = Vertex(name = 'V_1360',
particles = [ P.ve__tilde__, P.e__minus__, P.d__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_31,(0,5):C.GC_592,(0,3):C.GC_591,(0,4):C.GC_591,(0,1):C.GC_586,(0,0):C.GC_599})
V_1361 = Vertex(name = 'V_1361',
particles = [ P.ve__tilde__, P.e__minus__, P.d__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_546,(0,5):C.GC_607,(0,3):C.GC_606,(0,4):C.GC_606,(0,1):C.GC_605,(0,0):C.GC_608})
V_1362 = Vertex(name = 'V_1362',
particles = [ P.ve__tilde__, P.e__minus__, P.d__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_34,(0,5):C.GC_803,(0,3):C.GC_802,(0,4):C.GC_802,(0,1):C.GC_797,(0,0):C.GC_600})
V_1363 = Vertex(name = 'V_1363',
particles = [ P.ve__tilde__, P.e__minus__, P.d__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_838,(0,5):C.GC_866,(0,3):C.GC_865,(0,4):C.GC_865,(0,1):C.GC_862,(0,0):C.GC_857})
V_1364 = Vertex(name = 'V_1364',
particles = [ P.ve__tilde__, P.e__minus__, P.s__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_29,(0,5):C.GC_1027,(0,3):C.GC_1026,(0,4):C.GC_1026,(0,1):C.GC_1020,(0,0):C.GC_711})
V_1365 = Vertex(name = 'V_1365',
particles = [ P.ve__tilde__, P.e__minus__, P.s__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1077,(0,5):C.GC_1109,(0,3):C.GC_1108,(0,4):C.GC_1108,(0,1):C.GC_1104,(0,0):C.GC_1098})
V_1366 = Vertex(name = 'V_1366',
particles = [ P.ve__tilde__, P.e__minus__, P.s__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_32,(0,5):C.GC_594,(0,3):C.GC_593,(0,4):C.GC_593,(0,1):C.GC_587,(0,0):C.GC_712})
V_1367 = Vertex(name = 'V_1367',
particles = [ P.ve__tilde__, P.e__minus__, P.s__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_731,(0,5):C.GC_739,(0,3):C.GC_738,(0,4):C.GC_738,(0,1):C.GC_737,(0,0):C.GC_743})
V_1368 = Vertex(name = 'V_1368',
particles = [ P.ve__tilde__, P.e__minus__, P.s__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_35,(0,5):C.GC_805,(0,3):C.GC_804,(0,4):C.GC_804,(0,1):C.GC_798,(0,0):C.GC_713})
V_1369 = Vertex(name = 'V_1369',
particles = [ P.ve__tilde__, P.e__minus__, P.s__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_839,(0,5):C.GC_868,(0,3):C.GC_867,(0,4):C.GC_867,(0,1):C.GC_863,(0,0):C.GC_860})
V_1370 = Vertex(name = 'V_1370',
particles = [ P.ve__tilde__, P.e__minus__, P.b__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_30,(0,5):C.GC_1029,(0,3):C.GC_1028,(0,4):C.GC_1028,(0,1):C.GC_1021,(0,0):C.GC_582})
V_1371 = Vertex(name = 'V_1371',
particles = [ P.ve__tilde__, P.e__minus__, P.b__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1078,(0,5):C.GC_1111,(0,3):C.GC_1110,(0,4):C.GC_1110,(0,1):C.GC_1105,(0,0):C.GC_1094})
V_1372 = Vertex(name = 'V_1372',
particles = [ P.ve__tilde__, P.e__minus__, P.b__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_33,(0,5):C.GC_596,(0,3):C.GC_595,(0,4):C.GC_595,(0,1):C.GC_588,(0,0):C.GC_583})
V_1373 = Vertex(name = 'V_1373',
particles = [ P.ve__tilde__, P.e__minus__, P.b__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_487,(0,5):C.GC_604,(0,3):C.GC_603,(0,4):C.GC_603,(0,1):C.GC_602,(0,0):C.GC_601})
V_1374 = Vertex(name = 'V_1374',
particles = [ P.ve__tilde__, P.e__minus__, P.b__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_36,(0,5):C.GC_807,(0,3):C.GC_806,(0,4):C.GC_806,(0,1):C.GC_799,(0,0):C.GC_584})
V_1375 = Vertex(name = 'V_1375',
particles = [ P.ve__tilde__, P.e__minus__, P.b__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_840,(0,5):C.GC_870,(0,3):C.GC_869,(0,4):C.GC_869,(0,1):C.GC_864,(0,0):C.GC_856})
V_1376 = Vertex(name = 'V_1376',
particles = [ P.vm__tilde__, P.mu__minus__, P.d__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_28,(0,5):C.GC_1037,(0,3):C.GC_1036,(0,4):C.GC_1036,(0,1):C.GC_1031,(0,0):C.GC_655})
V_1377 = Vertex(name = 'V_1377',
particles = [ P.vm__tilde__, P.mu__minus__, P.d__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1076,(0,5):C.GC_1116,(0,3):C.GC_1115,(0,4):C.GC_1115,(0,1):C.GC_1112,(0,0):C.GC_1097})
V_1378 = Vertex(name = 'V_1378',
particles = [ P.vm__tilde__, P.mu__minus__, P.d__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_31,(0,5):C.GC_649,(0,3):C.GC_648,(0,4):C.GC_648,(0,1):C.GC_643,(0,0):C.GC_656})
V_1379 = Vertex(name = 'V_1379',
particles = [ P.vm__tilde__, P.mu__minus__, P.d__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_546,(0,5):C.GC_664,(0,3):C.GC_663,(0,4):C.GC_663,(0,1):C.GC_662,(0,0):C.GC_665})
V_1380 = Vertex(name = 'V_1380',
particles = [ P.vm__tilde__, P.mu__minus__, P.d__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_34,(0,5):C.GC_815,(0,3):C.GC_814,(0,4):C.GC_814,(0,1):C.GC_809,(0,0):C.GC_657})
V_1381 = Vertex(name = 'V_1381',
particles = [ P.vm__tilde__, P.mu__minus__, P.d__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_838,(0,5):C.GC_875,(0,3):C.GC_874,(0,4):C.GC_874,(0,1):C.GC_871,(0,0):C.GC_859})
V_1382 = Vertex(name = 'V_1382',
particles = [ P.vm__tilde__, P.mu__minus__, P.s__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_29,(0,5):C.GC_1039,(0,3):C.GC_1038,(0,4):C.GC_1038,(0,1):C.GC_1032,(0,0):C.GC_715})
V_1383 = Vertex(name = 'V_1383',
particles = [ P.vm__tilde__, P.mu__minus__, P.s__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1077,(0,5):C.GC_1118,(0,3):C.GC_1117,(0,4):C.GC_1117,(0,1):C.GC_1113,(0,0):C.GC_1099})
V_1384 = Vertex(name = 'V_1384',
particles = [ P.vm__tilde__, P.mu__minus__, P.s__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_32,(0,5):C.GC_651,(0,3):C.GC_650,(0,4):C.GC_650,(0,1):C.GC_644,(0,0):C.GC_716})
V_1385 = Vertex(name = 'V_1385',
particles = [ P.vm__tilde__, P.mu__minus__, P.s__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_731,(0,5):C.GC_742,(0,3):C.GC_741,(0,4):C.GC_741,(0,1):C.GC_740,(0,0):C.GC_744})
V_1386 = Vertex(name = 'V_1386',
particles = [ P.vm__tilde__, P.mu__minus__, P.s__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_35,(0,5):C.GC_817,(0,3):C.GC_816,(0,4):C.GC_816,(0,1):C.GC_810,(0,0):C.GC_717})
V_1387 = Vertex(name = 'V_1387',
particles = [ P.vm__tilde__, P.mu__minus__, P.s__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_839,(0,5):C.GC_877,(0,3):C.GC_876,(0,4):C.GC_876,(0,1):C.GC_872,(0,0):C.GC_861})
V_1388 = Vertex(name = 'V_1388',
particles = [ P.vm__tilde__, P.mu__minus__, P.b__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_30,(0,5):C.GC_1041,(0,3):C.GC_1040,(0,4):C.GC_1040,(0,1):C.GC_1033,(0,0):C.GC_639})
V_1389 = Vertex(name = 'V_1389',
particles = [ P.vm__tilde__, P.mu__minus__, P.b__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1078,(0,5):C.GC_1120,(0,3):C.GC_1119,(0,4):C.GC_1119,(0,1):C.GC_1114,(0,0):C.GC_1096})
V_1390 = Vertex(name = 'V_1390',
particles = [ P.vm__tilde__, P.mu__minus__, P.b__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_33,(0,5):C.GC_653,(0,3):C.GC_652,(0,4):C.GC_652,(0,1):C.GC_645,(0,0):C.GC_640})
V_1391 = Vertex(name = 'V_1391',
particles = [ P.vm__tilde__, P.mu__minus__, P.b__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_487,(0,5):C.GC_661,(0,3):C.GC_660,(0,4):C.GC_660,(0,1):C.GC_659,(0,0):C.GC_658})
V_1392 = Vertex(name = 'V_1392',
particles = [ P.vm__tilde__, P.mu__minus__, P.b__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_36,(0,5):C.GC_819,(0,3):C.GC_818,(0,4):C.GC_818,(0,1):C.GC_811,(0,0):C.GC_641})
V_1393 = Vertex(name = 'V_1393',
particles = [ P.vm__tilde__, P.mu__minus__, P.b__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_840,(0,5):C.GC_879,(0,3):C.GC_878,(0,4):C.GC_878,(0,1):C.GC_873,(0,0):C.GC_858})
V_1394 = Vertex(name = 'V_1394',
particles = [ P.vt__tilde__, P.ta__minus__, P.d__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_28,(0,5):C.GC_1058,(0,3):C.GC_1057,(0,4):C.GC_1057,(0,1):C.GC_1052,(0,0):C.GC_926})
V_1395 = Vertex(name = 'V_1395',
particles = [ P.vt__tilde__, P.ta__minus__, P.d__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1076,(0,5):C.GC_1125,(0,3):C.GC_1124,(0,4):C.GC_1124,(0,1):C.GC_1121,(0,0):C.GC_1101})
V_1396 = Vertex(name = 'V_1396',
particles = [ P.vt__tilde__, P.ta__minus__, P.d__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_31,(0,5):C.GC_920,(0,3):C.GC_919,(0,4):C.GC_919,(0,1):C.GC_914,(0,0):C.GC_927})
V_1397 = Vertex(name = 'V_1397',
particles = [ P.vt__tilde__, P.ta__minus__, P.d__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_546,(0,5):C.GC_951,(0,3):C.GC_950,(0,4):C.GC_950,(0,1):C.GC_949,(0,0):C.GC_952})
V_1398 = Vertex(name = 'V_1398',
particles = [ P.vt__tilde__, P.ta__minus__, P.d__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_34,(0,5):C.GC_940,(0,3):C.GC_939,(0,4):C.GC_939,(0,1):C.GC_934,(0,0):C.GC_928})
V_1399 = Vertex(name = 'V_1399',
particles = [ P.vt__tilde__, P.ta__minus__, P.d__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_838,(0,5):C.GC_964,(0,3):C.GC_963,(0,4):C.GC_963,(0,1):C.GC_960,(0,0):C.GC_958})
V_1400 = Vertex(name = 'V_1400',
particles = [ P.vt__tilde__, P.ta__minus__, P.s__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_29,(0,5):C.GC_1060,(0,3):C.GC_1059,(0,4):C.GC_1059,(0,1):C.GC_1053,(0,0):C.GC_930})
V_1401 = Vertex(name = 'V_1401',
particles = [ P.vt__tilde__, P.ta__minus__, P.s__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1077,(0,5):C.GC_1127,(0,3):C.GC_1126,(0,4):C.GC_1126,(0,1):C.GC_1122,(0,0):C.GC_1102})
V_1402 = Vertex(name = 'V_1402',
particles = [ P.vt__tilde__, P.ta__minus__, P.s__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_32,(0,5):C.GC_922,(0,3):C.GC_921,(0,4):C.GC_921,(0,1):C.GC_915,(0,0):C.GC_931})
V_1403 = Vertex(name = 'V_1403',
particles = [ P.vt__tilde__, P.ta__minus__, P.s__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_731,(0,5):C.GC_955,(0,3):C.GC_954,(0,4):C.GC_954,(0,1):C.GC_953,(0,0):C.GC_956})
V_1404 = Vertex(name = 'V_1404',
particles = [ P.vt__tilde__, P.ta__minus__, P.s__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_35,(0,5):C.GC_942,(0,3):C.GC_941,(0,4):C.GC_941,(0,1):C.GC_935,(0,0):C.GC_932})
V_1405 = Vertex(name = 'V_1405',
particles = [ P.vt__tilde__, P.ta__minus__, P.s__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_839,(0,5):C.GC_966,(0,3):C.GC_965,(0,4):C.GC_965,(0,1):C.GC_961,(0,0):C.GC_959})
V_1406 = Vertex(name = 'V_1406',
particles = [ P.vt__tilde__, P.ta__minus__, P.b__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_30,(0,5):C.GC_1062,(0,3):C.GC_1061,(0,4):C.GC_1061,(0,1):C.GC_1054,(0,0):C.GC_910})
V_1407 = Vertex(name = 'V_1407',
particles = [ P.vt__tilde__, P.ta__minus__, P.b__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_1078,(0,5):C.GC_1129,(0,3):C.GC_1128,(0,4):C.GC_1128,(0,1):C.GC_1123,(0,0):C.GC_1100})
V_1408 = Vertex(name = 'V_1408',
particles = [ P.vt__tilde__, P.ta__minus__, P.b__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_33,(0,5):C.GC_924,(0,3):C.GC_923,(0,4):C.GC_923,(0,1):C.GC_916,(0,0):C.GC_911})
V_1409 = Vertex(name = 'V_1409',
particles = [ P.vt__tilde__, P.ta__minus__, P.b__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_487,(0,5):C.GC_948,(0,3):C.GC_947,(0,4):C.GC_947,(0,1):C.GC_946,(0,0):C.GC_945})
V_1410 = Vertex(name = 'V_1410',
particles = [ P.vt__tilde__, P.ta__minus__, P.b__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_36,(0,5):C.GC_944,(0,3):C.GC_943,(0,4):C.GC_943,(0,1):C.GC_936,(0,0):C.GC_912})
V_1411 = Vertex(name = 'V_1411',
particles = [ P.vt__tilde__, P.ta__minus__, P.b__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF10, L.FFFF2, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,2):C.GC_840,(0,5):C.GC_968,(0,3):C.GC_967,(0,4):C.GC_967,(0,1):C.GC_962,(0,0):C.GC_957})
V_1412 = Vertex(name = 'V_1412',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_1011,(3,0):C.GC_1013,(0,5):C.GC_1141,(2,5):C.GC_1142,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_1011,(3,8):C.GC_1013,(0,1):C.GC_1141,(2,1):C.GC_1142})
V_1413 = Vertex(name = 'V_1413',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_3742,(1,0):C.GC_1227,(3,0):C.GC_1228,(0,5):C.GC_1012,(2,5):C.GC_1014,(1,4):C.GC_3671,(3,4):C.GC_3679,(1,2):C.GC_2653,(3,2):C.GC_2656,(1,3):C.GC_1080,(3,3):C.GC_1083,(1,8):C.GC_1227,(3,8):C.GC_1228,(0,1):C.GC_1012,(2,1):C.GC_1014})
V_1414 = Vertex(name = 'V_1414',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3713,(0,3):C.GC_1153,(1,1):C.GC_535,(2,1):C.GC_536,(1,0):C.GC_1255,(2,0):C.GC_1258})
V_1415 = Vertex(name = 'V_1415',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2875,(0,1):C.GC_1267})
V_1416 = Vertex(name = 'V_1416',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1144})
V_1417 = Vertex(name = 'V_1417',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1162})
V_1418 = Vertex(name = 'V_1418',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1262})
V_1419 = Vertex(name = 'V_1419',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1272})
V_1420 = Vertex(name = 'V_1420',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1835,(0,6):C.GC_1844,(1,0):C.GC_2614,(3,0):C.GC_2616,(0,4):C.GC_2613,(2,4):C.GC_2615,(1,3):C.GC_2381,(3,3):C.GC_2386,(1,2):C.GC_1957,(3,2):C.GC_1960,(1,7):C.GC_2375,(3,7):C.GC_2379,(0,1):C.GC_2373,(2,1):C.GC_2377})
V_1421 = Vertex(name = 'V_1421',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1853,(0,1):C.GC_2672})
V_1422 = Vertex(name = 'V_1422',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2666,(0,1):C.GC_2364})
V_1423 = Vertex(name = 'V_1423',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2362})
V_1424 = Vertex(name = 'V_1424',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2679})
V_1425 = Vertex(name = 'V_1425',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2411})
V_1426 = Vertex(name = 'V_1426',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2721,(0,6):C.GC_2730,(1,0):C.GC_3967,(3,0):C.GC_3971,(0,4):C.GC_3965,(2,4):C.GC_3969,(1,3):C.GC_3665,(3,3):C.GC_3673,(1,2):C.GC_2851,(3,2):C.GC_2854,(1,7):C.GC_3802,(3,7):C.GC_3806,(0,1):C.GC_3800,(2,1):C.GC_3804})
V_1427 = Vertex(name = 'V_1427',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2739,(0,1):C.GC_4092})
V_1428 = Vertex(name = 'V_1428',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_4084,(0,1):C.GC_3659})
V_1429 = Vertex(name = 'V_1429',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3655})
V_1430 = Vertex(name = 'V_1430',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4103})
V_1431 = Vertex(name = 'V_1431',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3707})
V_1432 = Vertex(name = 'V_1432',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1147,(0,6):C.GC_1156,(1,0):C.GC_1610,(3,0):C.GC_1612,(0,4):C.GC_1609,(2,4):C.GC_1611,(1,3):C.GC_1613,(3,3):C.GC_1615,(1,2):C.GC_1221,(3,2):C.GC_1224,(1,7):C.GC_1721,(3,7):C.GC_1725,(0,1):C.GC_1719,(2,1):C.GC_1723})
V_1433 = Vertex(name = 'V_1433',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1165,(0,1):C.GC_1804})
V_1434 = Vertex(name = 'V_1434',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1795,(0,1):C.GC_1607})
V_1435 = Vertex(name = 'V_1435',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1605})
V_1436 = Vertex(name = 'V_1436',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1814})
V_1437 = Vertex(name = 'V_1437',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1629})
V_1438 = Vertex(name = 'V_1438',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(0,7):C.GC_46,(1,6):C.GC_41,(1,0):C.GC_528,(3,0):C.GC_530,(0,5):C.GC_1130,(2,5):C.GC_1131,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_528,(3,8):C.GC_530,(0,1):C.GC_1130,(2,1):C.GC_1131})
V_1439 = Vertex(name = 'V_1439',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(0,7):C.GC_2682,(1,6):C.GC_43,(1,0):C.GC_1889,(3,0):C.GC_1894,(0,5):C.GC_529,(2,5):C.GC_531,(1,4):C.GC_2383,(3,4):C.GC_2388,(1,2):C.GC_2651,(3,2):C.GC_2654,(1,3):C.GC_547,(3,3):C.GC_548,(1,8):C.GC_1889,(3,8):C.GC_1894,(0,1):C.GC_529,(2,1):C.GC_531})
V_1440 = Vertex(name = 'V_1440',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(0,2):C.GC_1847,(1,1):C.GC_2662,(1,0):C.GC_1881,(2,0):C.GC_1884})
V_1441 = Vertex(name = 'V_1441',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,1):C.GC_1974,(1,0):C.GC_2876})
V_1442 = Vertex(name = 'V_1442',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1838})
V_1443 = Vertex(name = 'V_1443',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1856})
V_1444 = Vertex(name = 'V_1444',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1972})
V_1445 = Vertex(name = 'V_1445',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1976})
V_1446 = Vertex(name = 'V_1446',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2724,(0,6):C.GC_2733,(1,0):C.GC_3662,(3,0):C.GC_3664,(0,4):C.GC_3661,(2,4):C.GC_3663,(1,3):C.GC_3666,(3,3):C.GC_3674,(1,2):C.GC_2796,(3,2):C.GC_2799,(1,7):C.GC_3803,(3,7):C.GC_3807,(0,1):C.GC_3801,(2,1):C.GC_3805})
V_1447 = Vertex(name = 'V_1447',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2742,(0,1):C.GC_3911})
V_1448 = Vertex(name = 'V_1448',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3904,(0,1):C.GC_3660})
V_1449 = Vertex(name = 'V_1449',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3656})
V_1450 = Vertex(name = 'V_1450',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3924})
V_1451 = Vertex(name = 'V_1451',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3708})
V_1452 = Vertex(name = 'V_1452',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1150,(0,6):C.GC_1159,(1,0):C.GC_1681,(3,0):C.GC_1683,(0,4):C.GC_1680,(2,4):C.GC_1682,(1,3):C.GC_1614,(3,3):C.GC_1616,(1,2):C.GC_1246,(3,2):C.GC_1249,(1,7):C.GC_1722,(3,7):C.GC_1726,(0,1):C.GC_1720,(2,1):C.GC_1724})
V_1453 = Vertex(name = 'V_1453',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1168,(0,1):C.GC_1808})
V_1454 = Vertex(name = 'V_1454',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1799,(0,1):C.GC_1608})
V_1455 = Vertex(name = 'V_1455',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1606})
V_1456 = Vertex(name = 'V_1456',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1818})
V_1457 = Vertex(name = 'V_1457',
particles = [ P.u__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1630})
V_1458 = Vertex(name = 'V_1458',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1841,(0,6):C.GC_1850,(1,0):C.GC_2553,(3,0):C.GC_2555,(0,4):C.GC_2552,(2,4):C.GC_2554,(1,3):C.GC_2385,(3,3):C.GC_2390,(1,2):C.GC_1939,(3,2):C.GC_1942,(1,7):C.GC_2376,(3,7):C.GC_2380,(0,1):C.GC_2374,(2,1):C.GC_2378})
V_1459 = Vertex(name = 'V_1459',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1859,(0,1):C.GC_2591})
V_1460 = Vertex(name = 'V_1460',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2587,(0,1):C.GC_2365})
V_1461 = Vertex(name = 'V_1461',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2363})
V_1462 = Vertex(name = 'V_1462',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2596})
V_1463 = Vertex(name = 'V_1463',
particles = [ P.c__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2414})
V_1464 = Vertex(name = 'V_1464',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_789,(3,0):C.GC_791,(0,5):C.GC_1137,(2,5):C.GC_1138,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_789,(3,8):C.GC_791,(0,1):C.GC_1137,(2,1):C.GC_1138})
V_1465 = Vertex(name = 'V_1465',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_3739,(1,0):C.GC_2804,(3,0):C.GC_2809,(0,5):C.GC_790,(2,5):C.GC_792,(1,4):C.GC_3668,(3,4):C.GC_3676,(1,2):C.GC_2652,(3,2):C.GC_2655,(1,3):C.GC_842,(3,3):C.GC_845,(1,8):C.GC_2804,(3,8):C.GC_2809,(0,1):C.GC_790,(2,1):C.GC_792})
V_1466 = Vertex(name = 'V_1466',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3710,(0,2):C.GC_2736,(1,0):C.GC_2833,(2,0):C.GC_2836})
V_1467 = Vertex(name = 'V_1467',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3657,(0,1):C.GC_2877})
V_1468 = Vertex(name = 'V_1468',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2727})
V_1469 = Vertex(name = 'V_1469',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2745})
V_1470 = Vertex(name = 'V_1470',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2870})
V_1471 = Vertex(name = 'V_1471',
particles = [ P.t__tilde__, P.d, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2884})
V_1472 = Vertex(name = 'V_1472',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1145,(0,5):C.GC_1154,(1,0):C.GC_2910,(3,0):C.GC_2914,(0,3):C.GC_2908,(2,3):C.GC_2912,(1,2):C.GC_1256,(3,2):C.GC_1259,(1,6):C.GC_2917,(3,6):C.GC_2919,(0,1):C.GC_2916,(2,1):C.GC_2918})
V_1473 = Vertex(name = 'V_1473',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1163,(0,2):C.GC_2861,(1,0):C.GC_2924,(2,0):C.GC_2926})
V_1474 = Vertex(name = 'V_1474',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2860,(0,1):C.GC_2886})
V_1475 = Vertex(name = 'V_1475',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2878})
V_1476 = Vertex(name = 'V_1476',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2862})
V_1477 = Vertex(name = 'V_1477',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2936})
V_1478 = Vertex(name = 'V_1478',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1836,(0,5):C.GC_1845,(1,0):C.GC_1965,(3,0):C.GC_1969,(0,3):C.GC_1963,(2,3):C.GC_1967,(1,2):C.GC_1958,(3,2):C.GC_1961,(1,6):C.GC_1917,(3,6):C.GC_1923,(0,1):C.GC_1914,(2,1):C.GC_1920})
V_1479 = Vertex(name = 'V_1479',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1854,(0,1):C.GC_2004})
V_1480 = Vertex(name = 'V_1480',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2002})
V_1481 = Vertex(name = 'V_1481',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2006})
V_1482 = Vertex(name = 'V_1482',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2722,(0,5):C.GC_2731,(1,0):C.GC_2845,(3,0):C.GC_2849,(0,3):C.GC_2843,(2,3):C.GC_2847,(1,2):C.GC_2852,(3,2):C.GC_2855,(1,6):C.GC_2824,(3,6):C.GC_2830,(0,1):C.GC_2821,(2,1):C.GC_2827})
V_1483 = Vertex(name = 'V_1483',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2740,(0,1):C.GC_2942})
V_1484 = Vertex(name = 'V_1484',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2938})
V_1485 = Vertex(name = 'V_1485',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2948})
V_1486 = Vertex(name = 'V_1486',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1148,(0,6):C.GC_1157,(1,0):C.GC_1186,(3,0):C.GC_1190,(0,4):C.GC_1184,(2,4):C.GC_1188,(1,3):C.GC_708,(3,3):C.GC_709,(1,2):C.GC_1222,(3,2):C.GC_1225,(1,7):C.GC_1240,(3,7):C.GC_1244,(0,1):C.GC_1238,(2,1):C.GC_1242})
V_1487 = Vertex(name = 'V_1487',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1166,(0,1):C.GC_1268})
V_1488 = Vertex(name = 'V_1488',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1263})
V_1489 = Vertex(name = 'V_1489',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1273})
V_1490 = Vertex(name = 'V_1490',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1839,(0,5):C.GC_2886,(1,0):C.GC_1890,(3,0):C.GC_1895,(0,3):C.GC_1887,(2,3):C.GC_1892,(1,2):C.GC_1882,(3,2):C.GC_1885,(1,6):C.GC_1918,(3,6):C.GC_1924,(0,1):C.GC_1915,(2,1):C.GC_1921})
V_1491 = Vertex(name = 'V_1491',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1857,(0,2):C.GC_1848,(1,0):C.GC_2924,(2,0):C.GC_2926})
V_1492 = Vertex(name = 'V_1492',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2936,(0,1):C.GC_1990})
V_1493 = Vertex(name = 'V_1493',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1989})
V_1494 = Vertex(name = 'V_1494',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2945})
V_1495 = Vertex(name = 'V_1495',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1991})
V_1496 = Vertex(name = 'V_1496',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2725,(0,5):C.GC_2734,(1,0):C.GC_2770,(3,0):C.GC_2774,(0,3):C.GC_2768,(2,3):C.GC_2772,(1,2):C.GC_2797,(3,2):C.GC_2800,(1,6):C.GC_2825,(3,6):C.GC_2831,(0,1):C.GC_2822,(2,1):C.GC_2828})
V_1497 = Vertex(name = 'V_1497',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2743,(0,1):C.GC_2873})
V_1498 = Vertex(name = 'V_1498',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2868})
V_1499 = Vertex(name = 'V_1499',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2882})
V_1500 = Vertex(name = 'V_1500',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1151,(0,5):C.GC_1160,(1,0):C.GC_1206,(3,0):C.GC_1210,(0,3):C.GC_1204,(2,3):C.GC_1208,(1,2):C.GC_1247,(3,2):C.GC_1250,(1,6):C.GC_1241,(3,6):C.GC_1245,(0,1):C.GC_1239,(2,1):C.GC_1243})
V_1501 = Vertex(name = 'V_1501',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1169,(0,1):C.GC_1270})
V_1502 = Vertex(name = 'V_1502',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1265})
V_1503 = Vertex(name = 'V_1503',
particles = [ P.u__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1275})
V_1504 = Vertex(name = 'V_1504',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1842,(0,5):C.GC_1851,(1,0):C.GC_1947,(3,0):C.GC_1951,(0,3):C.GC_1945,(2,3):C.GC_1949,(1,2):C.GC_1940,(3,2):C.GC_1943,(1,6):C.GC_1919,(3,6):C.GC_1925,(0,1):C.GC_1916,(2,1):C.GC_1922})
V_1505 = Vertex(name = 'V_1505',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1860,(0,1):C.GC_1994})
V_1506 = Vertex(name = 'V_1506',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1992})
V_1507 = Vertex(name = 'V_1507',
particles = [ P.c__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1996})
V_1508 = Vertex(name = 'V_1508',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2936,(0,5):C.GC_2886,(1,0):C.GC_2805,(3,0):C.GC_2810,(0,3):C.GC_2802,(2,3):C.GC_2807,(1,2):C.GC_2924,(3,2):C.GC_2926,(1,6):C.GC_2826,(3,6):C.GC_2832,(0,1):C.GC_2823,(2,1):C.GC_2829})
V_1509 = Vertex(name = 'V_1509',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2945,(0,2):C.GC_2737,(1,0):C.GC_2834,(2,0):C.GC_2837})
V_1510 = Vertex(name = 'V_1510',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2728,(0,1):C.GC_2879})
V_1511 = Vertex(name = 'V_1511',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2746})
V_1512 = Vertex(name = 'V_1512',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2871})
V_1513 = Vertex(name = 'V_1513',
particles = [ P.t__tilde__, P.d, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2885})
V_1514 = Vertex(name = 'V_1514',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1146,(0,5):C.GC_1155,(1,0):C.GC_2911,(3,0):C.GC_2915,(0,3):C.GC_2909,(2,3):C.GC_2913,(1,2):C.GC_1257,(3,2):C.GC_1260,(1,6):C.GC_2905,(3,6):C.GC_2907,(0,1):C.GC_2904,(2,1):C.GC_2906})
V_1515 = Vertex(name = 'V_1515',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1164,(0,2):C.GC_2858,(1,0):C.GC_2925,(2,0):C.GC_2927})
V_1516 = Vertex(name = 'V_1516',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2857,(0,1):C.GC_2888})
V_1517 = Vertex(name = 'V_1517',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2880})
V_1518 = Vertex(name = 'V_1518',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2859})
V_1519 = Vertex(name = 'V_1519',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2937})
V_1520 = Vertex(name = 'V_1520',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1837,(0,5):C.GC_1846,(1,0):C.GC_1966,(3,0):C.GC_1970,(0,3):C.GC_1964,(2,3):C.GC_1968,(1,2):C.GC_1959,(3,2):C.GC_1962,(1,6):C.GC_1872,(3,6):C.GC_1878,(0,1):C.GC_1869,(2,1):C.GC_1875})
V_1521 = Vertex(name = 'V_1521',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1855,(0,1):C.GC_2005})
V_1522 = Vertex(name = 'V_1522',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2003})
V_1523 = Vertex(name = 'V_1523',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2007})
V_1524 = Vertex(name = 'V_1524',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2723,(0,5):C.GC_2732,(1,0):C.GC_2846,(3,0):C.GC_2850,(0,3):C.GC_2844,(2,3):C.GC_2848,(1,2):C.GC_2853,(3,2):C.GC_2856,(1,6):C.GC_2787,(3,6):C.GC_2793,(0,1):C.GC_2784,(2,1):C.GC_2790})
V_1525 = Vertex(name = 'V_1525',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2741,(0,1):C.GC_2943})
V_1526 = Vertex(name = 'V_1526',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2939})
V_1527 = Vertex(name = 'V_1527',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2949})
V_1528 = Vertex(name = 'V_1528',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1149,(0,5):C.GC_1158,(1,0):C.GC_1187,(3,0):C.GC_1191,(0,3):C.GC_1185,(2,3):C.GC_1189,(1,2):C.GC_1223,(3,2):C.GC_1226,(1,6):C.GC_1215,(3,6):C.GC_1219,(0,1):C.GC_1213,(2,1):C.GC_1217})
V_1529 = Vertex(name = 'V_1529',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1167,(0,1):C.GC_1269})
V_1530 = Vertex(name = 'V_1530',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1264})
V_1531 = Vertex(name = 'V_1531',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1274})
V_1532 = Vertex(name = 'V_1532',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1840,(0,5):C.GC_2888,(1,0):C.GC_1891,(3,0):C.GC_1896,(0,3):C.GC_1888,(2,3):C.GC_1893,(1,2):C.GC_1883,(3,2):C.GC_1886,(1,6):C.GC_1873,(3,6):C.GC_1879,(0,1):C.GC_1870,(2,1):C.GC_1876})
V_1533 = Vertex(name = 'V_1533',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1858,(0,2):C.GC_1849,(1,0):C.GC_2925,(2,0):C.GC_2927})
V_1534 = Vertex(name = 'V_1534',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1973,(0,1):C.GC_1975})
V_1535 = Vertex(name = 'V_1535',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1977})
V_1536 = Vertex(name = 'V_1536',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2937})
V_1537 = Vertex(name = 'V_1537',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2947})
V_1538 = Vertex(name = 'V_1538',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2726,(0,5):C.GC_2735,(1,0):C.GC_2771,(3,0):C.GC_2775,(0,3):C.GC_2769,(2,3):C.GC_2773,(1,2):C.GC_2798,(3,2):C.GC_2801,(1,6):C.GC_2788,(3,6):C.GC_2794,(0,1):C.GC_2785,(2,1):C.GC_2791})
V_1539 = Vertex(name = 'V_1539',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2744,(0,1):C.GC_2874})
V_1540 = Vertex(name = 'V_1540',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2869})
V_1541 = Vertex(name = 'V_1541',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2883})
V_1542 = Vertex(name = 'V_1542',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1152,(0,6):C.GC_1161,(1,0):C.GC_1207,(3,0):C.GC_1211,(0,4):C.GC_1205,(2,4):C.GC_1209,(1,3):C.GC_526,(3,3):C.GC_527,(1,2):C.GC_1248,(3,2):C.GC_1251,(1,7):C.GC_1216,(3,7):C.GC_1220,(0,1):C.GC_1214,(2,1):C.GC_1218})
V_1543 = Vertex(name = 'V_1543',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1170,(0,1):C.GC_1271})
V_1544 = Vertex(name = 'V_1544',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1266})
V_1545 = Vertex(name = 'V_1545',
particles = [ P.u__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1276})
V_1546 = Vertex(name = 'V_1546',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1843,(0,5):C.GC_1852,(1,0):C.GC_1948,(3,0):C.GC_1952,(0,3):C.GC_1946,(2,3):C.GC_1950,(1,2):C.GC_1941,(3,2):C.GC_1944,(1,6):C.GC_1874,(3,6):C.GC_1880,(0,1):C.GC_1871,(2,1):C.GC_1877})
V_1547 = Vertex(name = 'V_1547',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1861,(0,1):C.GC_1995})
V_1548 = Vertex(name = 'V_1548',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1993})
V_1549 = Vertex(name = 'V_1549',
particles = [ P.c__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1997})
V_1550 = Vertex(name = 'V_1550',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2937,(0,5):C.GC_2888,(1,0):C.GC_2806,(3,0):C.GC_2811,(0,3):C.GC_2803,(2,3):C.GC_2808,(1,2):C.GC_2925,(3,2):C.GC_2927,(1,6):C.GC_2789,(3,6):C.GC_2795,(0,1):C.GC_2786,(2,1):C.GC_2792})
V_1551 = Vertex(name = 'V_1551',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2947,(0,2):C.GC_2738,(1,0):C.GC_2835,(2,0):C.GC_2838})
V_1552 = Vertex(name = 'V_1552',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2729,(0,1):C.GC_2881})
V_1553 = Vertex(name = 'V_1553',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2747})
V_1554 = Vertex(name = 'V_1554',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2872})
V_1555 = Vertex(name = 'V_1555',
particles = [ P.t__tilde__, P.d, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2887})
V_1556 = Vertex(name = 'V_1556',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1293,(0,5):C.GC_1302,(1,0):C.GC_3281,(3,0):C.GC_3285,(0,3):C.GC_3279,(2,3):C.GC_3283,(1,2):C.GC_1422,(3,2):C.GC_1425,(1,6):C.GC_3276,(3,6):C.GC_3278,(0,1):C.GC_3275,(2,1):C.GC_3277})
V_1557 = Vertex(name = 'V_1557',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1311,(0,2):C.GC_3225,(1,0):C.GC_3295,(2,0):C.GC_3300})
V_1558 = Vertex(name = 'V_1558',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3223,(0,1):C.GC_3252})
V_1559 = Vertex(name = 'V_1559',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3241})
V_1560 = Vertex(name = 'V_1560',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3227})
V_1561 = Vertex(name = 'V_1561',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3317})
V_1562 = Vertex(name = 'V_1562',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2009,(0,6):C.GC_2018,(1,0):C.GC_2153,(3,0):C.GC_2159,(0,4):C.GC_2150,(2,4):C.GC_2156,(1,3):C.GC_708,(3,3):C.GC_709,(1,2):C.GC_2144,(3,2):C.GC_2147,(1,7):C.GC_2064,(3,7):C.GC_2070,(0,1):C.GC_2061,(2,1):C.GC_2067})
V_1563 = Vertex(name = 'V_1563',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2027,(0,1):C.GC_2199})
V_1564 = Vertex(name = 'V_1564',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2196})
V_1565 = Vertex(name = 'V_1565',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2202})
V_1566 = Vertex(name = 'V_1566',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3069,(0,5):C.GC_3078,(1,0):C.GC_3208,(3,0):C.GC_3214,(0,3):C.GC_3205,(2,3):C.GC_3211,(1,2):C.GC_3217,(3,2):C.GC_3220,(1,6):C.GC_3157,(3,6):C.GC_3163,(0,1):C.GC_3154,(2,1):C.GC_3160})
V_1567 = Vertex(name = 'V_1567',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3087,(0,1):C.GC_3329})
V_1568 = Vertex(name = 'V_1568',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3322})
V_1569 = Vertex(name = 'V_1569',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3338})
V_1570 = Vertex(name = 'V_1570',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1296,(0,5):C.GC_1305,(1,0):C.GC_1342,(3,0):C.GC_1348,(0,3):C.GC_1339,(2,3):C.GC_1345,(1,2):C.GC_1380,(3,2):C.GC_1383,(1,6):C.GC_1388,(3,6):C.GC_1392,(0,1):C.GC_1386,(2,1):C.GC_1390})
V_1571 = Vertex(name = 'V_1571',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1314,(0,1):C.GC_1437})
V_1572 = Vertex(name = 'V_1572',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1430})
V_1573 = Vertex(name = 'V_1573',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1444})
V_1574 = Vertex(name = 'V_1574',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2012,(0,5):C.GC_3252,(1,0):C.GC_2091,(3,0):C.GC_2101,(0,3):C.GC_2086,(2,3):C.GC_2096,(1,2):C.GC_2055,(3,2):C.GC_2058,(1,6):C.GC_2065,(3,6):C.GC_2071,(0,1):C.GC_2062,(2,1):C.GC_2068})
V_1575 = Vertex(name = 'V_1575',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2030,(0,2):C.GC_2021,(1,0):C.GC_3295,(2,0):C.GC_3300})
V_1576 = Vertex(name = 'V_1576',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3317,(0,1):C.GC_2166})
V_1577 = Vertex(name = 'V_1577',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2163})
V_1578 = Vertex(name = 'V_1578',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3333})
V_1579 = Vertex(name = 'V_1579',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2169})
V_1580 = Vertex(name = 'V_1580',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3072,(0,5):C.GC_3081,(1,0):C.GC_3125,(3,0):C.GC_3131,(0,3):C.GC_3122,(2,3):C.GC_3128,(1,2):C.GC_3148,(3,2):C.GC_3151,(1,6):C.GC_3158,(3,6):C.GC_3164,(0,1):C.GC_3155,(2,1):C.GC_3161})
V_1581 = Vertex(name = 'V_1581',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3090,(0,1):C.GC_3238})
V_1582 = Vertex(name = 'V_1582',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3232})
V_1583 = Vertex(name = 'V_1583',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3248})
V_1584 = Vertex(name = 'V_1584',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1299,(0,5):C.GC_1308,(1,0):C.GC_1362,(3,0):C.GC_1368,(0,3):C.GC_1359,(2,3):C.GC_1365,(1,2):C.GC_1413,(3,2):C.GC_1416,(1,6):C.GC_1389,(3,6):C.GC_1393,(0,1):C.GC_1387,(2,1):C.GC_1391})
V_1585 = Vertex(name = 'V_1585',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1317,(0,1):C.GC_1440})
V_1586 = Vertex(name = 'V_1586',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1433})
V_1587 = Vertex(name = 'V_1587',
particles = [ P.u__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1447})
V_1588 = Vertex(name = 'V_1588',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2015,(0,5):C.GC_2024,(1,0):C.GC_2131,(3,0):C.GC_2137,(0,3):C.GC_2128,(2,3):C.GC_2134,(1,2):C.GC_2122,(3,2):C.GC_2125,(1,6):C.GC_2066,(3,6):C.GC_2072,(0,1):C.GC_2063,(2,1):C.GC_2069})
V_1589 = Vertex(name = 'V_1589',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2033,(0,1):C.GC_2186})
V_1590 = Vertex(name = 'V_1590',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2183})
V_1591 = Vertex(name = 'V_1591',
particles = [ P.c__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2189})
V_1592 = Vertex(name = 'V_1592',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3317,(0,5):C.GC_3252,(1,0):C.GC_3179,(3,0):C.GC_3189,(0,3):C.GC_3174,(2,3):C.GC_3184,(1,2):C.GC_3295,(3,2):C.GC_3300,(1,6):C.GC_3159,(3,6):C.GC_3165,(0,1):C.GC_3156,(2,1):C.GC_3162})
V_1593 = Vertex(name = 'V_1593',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3333,(0,2):C.GC_3084,(1,0):C.GC_3195,(2,0):C.GC_3198})
V_1594 = Vertex(name = 'V_1594',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3075,(0,1):C.GC_3242})
V_1595 = Vertex(name = 'V_1595',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3093})
V_1596 = Vertex(name = 'V_1596',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3235})
V_1597 = Vertex(name = 'V_1597',
particles = [ P.t__tilde__, P.s, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3251})
V_1598 = Vertex(name = 'V_1598',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_1042,(3,0):C.GC_1044,(0,5):C.GC_1132,(2,5):C.GC_1133,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_1042,(3,8):C.GC_1044,(0,1):C.GC_1132,(2,1):C.GC_1133})
V_1599 = Vertex(name = 'V_1599',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_3743,(1,0):C.GC_1402,(3,0):C.GC_1407,(0,5):C.GC_1043,(2,5):C.GC_1045,(1,4):C.GC_3672,(3,4):C.GC_3680,(1,2):C.GC_3298,(3,2):C.GC_3303,(1,3):C.GC_1081,(3,3):C.GC_1084,(1,8):C.GC_1402,(3,8):C.GC_1407,(0,1):C.GC_1043,(2,1):C.GC_1045})
V_1600 = Vertex(name = 'V_1600',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3714,(0,2):C.GC_1303,(1,0):C.GC_1423,(2,0):C.GC_1426})
V_1601 = Vertex(name = 'V_1601',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3243,(0,1):C.GC_1436})
V_1602 = Vertex(name = 'V_1602',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1294})
V_1603 = Vertex(name = 'V_1603',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1312})
V_1604 = Vertex(name = 'V_1604',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1429})
V_1605 = Vertex(name = 'V_1605',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1443})
V_1606 = Vertex(name = 'V_1606',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2010,(0,6):C.GC_2364,(1,0):C.GC_2154,(3,0):C.GC_2160,(0,4):C.GC_2151,(2,4):C.GC_2157,(1,3):C.GC_2381,(3,3):C.GC_2386,(1,2):C.GC_2145,(3,2):C.GC_2148,(1,7):C.GC_2090,(3,7):C.GC_2100,(0,1):C.GC_2085,(2,1):C.GC_2095})
V_1607 = Vertex(name = 'V_1607',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2028,(0,1):C.GC_2019})
V_1608 = Vertex(name = 'V_1608',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2197,(0,1):C.GC_2200})
V_1609 = Vertex(name = 'V_1609',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2203})
V_1610 = Vertex(name = 'V_1610',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2411})
V_1611 = Vertex(name = 'V_1611',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2421})
V_1612 = Vertex(name = 'V_1612',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_3070,(0,6):C.GC_3659,(1,0):C.GC_3209,(3,0):C.GC_3215,(0,4):C.GC_3206,(2,4):C.GC_3212,(1,3):C.GC_3665,(3,3):C.GC_3673,(1,2):C.GC_3218,(3,2):C.GC_3221,(1,7):C.GC_3177,(3,7):C.GC_3187,(0,1):C.GC_3172,(2,1):C.GC_3182})
V_1613 = Vertex(name = 'V_1613',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3088,(0,1):C.GC_3079})
V_1614 = Vertex(name = 'V_1614',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3707,(0,1):C.GC_3330})
V_1615 = Vertex(name = 'V_1615',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3725})
V_1616 = Vertex(name = 'V_1616',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3323})
V_1617 = Vertex(name = 'V_1617',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3339})
V_1618 = Vertex(name = 'V_1618',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1297,(0,6):C.GC_1607,(1,0):C.GC_1343,(3,0):C.GC_1349,(0,4):C.GC_1340,(2,4):C.GC_1346,(1,3):C.GC_1613,(3,3):C.GC_1615,(1,2):C.GC_1381,(3,2):C.GC_1384,(1,7):C.GC_1403,(3,7):C.GC_1408,(0,1):C.GC_1400,(2,1):C.GC_1405})
V_1619 = Vertex(name = 'V_1619',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1315,(0,1):C.GC_1306})
V_1620 = Vertex(name = 'V_1620',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1431,(0,1):C.GC_1438})
V_1621 = Vertex(name = 'V_1621',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1445})
V_1622 = Vertex(name = 'V_1622',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1629})
V_1623 = Vertex(name = 'V_1623',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1634})
V_1624 = Vertex(name = 'V_1624',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_701,(3,0):C.GC_703,(0,5):C.GC_702,(2,5):C.GC_704,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_701,(3,8):C.GC_703,(0,1):C.GC_702,(2,1):C.GC_704})
V_1625 = Vertex(name = 'V_1625',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_3344,(1,0):C.GC_2092,(3,0):C.GC_2102,(0,5):C.GC_2087,(2,5):C.GC_2097,(1,4):C.GC_2384,(3,4):C.GC_2389,(1,2):C.GC_3296,(3,2):C.GC_3301,(1,3):C.GC_732,(3,3):C.GC_733,(1,8):C.GC_2092,(3,8):C.GC_2102,(0,1):C.GC_2087,(2,1):C.GC_2097})
V_1626 = Vertex(name = 'V_1626',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3318,(0,3):C.GC_2022,(1,1):C.GC_718,(2,1):C.GC_719,(1,0):C.GC_2056,(2,0):C.GC_2059})
V_1627 = Vertex(name = 'V_1627',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3244,(0,1):C.GC_2167})
V_1628 = Vertex(name = 'V_1628',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2013})
V_1629 = Vertex(name = 'V_1629',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2031})
V_1630 = Vertex(name = 'V_1630',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2164})
V_1631 = Vertex(name = 'V_1631',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2170})
V_1632 = Vertex(name = 'V_1632',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_3073,(0,6):C.GC_3660,(1,0):C.GC_3126,(3,0):C.GC_3132,(0,4):C.GC_3123,(2,4):C.GC_3129,(1,3):C.GC_3666,(3,3):C.GC_3674,(1,2):C.GC_3149,(3,2):C.GC_3152,(1,7):C.GC_3178,(3,7):C.GC_3188,(0,1):C.GC_3173,(2,1):C.GC_3183})
V_1633 = Vertex(name = 'V_1633',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3091,(0,1):C.GC_3082})
V_1634 = Vertex(name = 'V_1634',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3233,(0,1):C.GC_3239})
V_1635 = Vertex(name = 'V_1635',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3249})
V_1636 = Vertex(name = 'V_1636',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3708})
V_1637 = Vertex(name = 'V_1637',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3727})
V_1638 = Vertex(name = 'V_1638',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1300,(0,6):C.GC_1608,(1,0):C.GC_1363,(3,0):C.GC_1369,(0,4):C.GC_1360,(2,4):C.GC_1366,(1,3):C.GC_1614,(3,3):C.GC_1616,(1,2):C.GC_1414,(3,2):C.GC_1417,(1,7):C.GC_1404,(3,7):C.GC_1409,(0,1):C.GC_1401,(2,1):C.GC_1406})
V_1639 = Vertex(name = 'V_1639',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1318,(0,1):C.GC_1309})
V_1640 = Vertex(name = 'V_1640',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1630,(0,1):C.GC_1441})
V_1641 = Vertex(name = 'V_1641',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1636})
V_1642 = Vertex(name = 'V_1642',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1434})
V_1643 = Vertex(name = 'V_1643',
particles = [ P.u__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1448})
V_1644 = Vertex(name = 'V_1644',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2016,(0,6):C.GC_2365,(1,0):C.GC_2132,(3,0):C.GC_2138,(0,4):C.GC_2129,(2,4):C.GC_2135,(1,3):C.GC_2385,(3,3):C.GC_2390,(1,2):C.GC_2123,(3,2):C.GC_2126,(1,7):C.GC_2094,(3,7):C.GC_2104,(0,1):C.GC_2089,(2,1):C.GC_2099})
V_1645 = Vertex(name = 'V_1645',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2034,(0,1):C.GC_2025})
V_1646 = Vertex(name = 'V_1646',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2184,(0,1):C.GC_2187})
V_1647 = Vertex(name = 'V_1647',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2190})
V_1648 = Vertex(name = 'V_1648',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2414})
V_1649 = Vertex(name = 'V_1649',
particles = [ P.c__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2426})
V_1650 = Vertex(name = 'V_1650',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_820,(3,0):C.GC_822,(0,5):C.GC_821,(2,5):C.GC_823,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_820,(3,8):C.GC_822,(0,1):C.GC_821,(2,1):C.GC_823})
V_1651 = Vertex(name = 'V_1651',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_3740,(1,0):C.GC_3180,(3,0):C.GC_3190,(0,5):C.GC_3175,(2,5):C.GC_3185,(1,4):C.GC_3669,(3,4):C.GC_3677,(1,2):C.GC_3297,(3,2):C.GC_3302,(1,3):C.GC_843,(3,3):C.GC_846,(1,8):C.GC_3180,(3,8):C.GC_3190,(0,1):C.GC_3175,(2,1):C.GC_3185})
V_1652 = Vertex(name = 'V_1652',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3711,(0,2):C.GC_3085,(1,0):C.GC_3196,(2,0):C.GC_3199})
V_1653 = Vertex(name = 'V_1653',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3658,(0,1):C.GC_3245})
V_1654 = Vertex(name = 'V_1654',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3076})
V_1655 = Vertex(name = 'V_1655',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3094})
V_1656 = Vertex(name = 'V_1656',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3236})
V_1657 = Vertex(name = 'V_1657',
particles = [ P.t__tilde__, P.s, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3253})
V_1658 = Vertex(name = 'V_1658',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1295,(0,5):C.GC_1304,(1,0):C.GC_3282,(3,0):C.GC_3286,(0,3):C.GC_3280,(2,3):C.GC_3284,(1,2):C.GC_1424,(3,2):C.GC_1427,(1,6):C.GC_3272,(3,6):C.GC_3274,(0,1):C.GC_3271,(2,1):C.GC_3273})
V_1659 = Vertex(name = 'V_1659',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1313,(0,2):C.GC_3226,(1,0):C.GC_3299,(2,0):C.GC_3304})
V_1660 = Vertex(name = 'V_1660',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3224,(0,1):C.GC_3255})
V_1661 = Vertex(name = 'V_1661',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3246})
V_1662 = Vertex(name = 'V_1662',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3228})
V_1663 = Vertex(name = 'V_1663',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3321})
V_1664 = Vertex(name = 'V_1664',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2011,(0,5):C.GC_2020,(1,0):C.GC_2155,(3,0):C.GC_2161,(0,3):C.GC_2152,(2,3):C.GC_2158,(1,2):C.GC_2146,(3,2):C.GC_2149,(1,6):C.GC_2046,(3,6):C.GC_2052,(0,1):C.GC_2043,(2,1):C.GC_2049})
V_1665 = Vertex(name = 'V_1665',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2029,(0,1):C.GC_2201})
V_1666 = Vertex(name = 'V_1666',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2198})
V_1667 = Vertex(name = 'V_1667',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2204})
V_1668 = Vertex(name = 'V_1668',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3071,(0,5):C.GC_3080,(1,0):C.GC_3210,(3,0):C.GC_3216,(0,3):C.GC_3207,(2,3):C.GC_3213,(1,2):C.GC_3219,(3,2):C.GC_3222,(1,6):C.GC_3139,(3,6):C.GC_3145,(0,1):C.GC_3136,(2,1):C.GC_3142})
V_1669 = Vertex(name = 'V_1669',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3089,(0,1):C.GC_3331})
V_1670 = Vertex(name = 'V_1670',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3324})
V_1671 = Vertex(name = 'V_1671',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3340})
V_1672 = Vertex(name = 'V_1672',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1298,(0,5):C.GC_1307,(1,0):C.GC_1344,(3,0):C.GC_1350,(0,3):C.GC_1341,(2,3):C.GC_1347,(1,2):C.GC_1382,(3,2):C.GC_1385,(1,6):C.GC_1374,(3,6):C.GC_1378,(0,1):C.GC_1372,(2,1):C.GC_1376})
V_1673 = Vertex(name = 'V_1673',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1316,(0,1):C.GC_1439})
V_1674 = Vertex(name = 'V_1674',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1432})
V_1675 = Vertex(name = 'V_1675',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1446})
V_1676 = Vertex(name = 'V_1676',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2014,(0,5):C.GC_3255,(1,0):C.GC_2093,(3,0):C.GC_2103,(0,3):C.GC_2088,(2,3):C.GC_2098,(1,2):C.GC_2057,(3,2):C.GC_2060,(1,6):C.GC_2047,(3,6):C.GC_2053,(0,1):C.GC_2044,(2,1):C.GC_2050})
V_1677 = Vertex(name = 'V_1677',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2032,(0,2):C.GC_2023,(1,0):C.GC_3299,(2,0):C.GC_3304})
V_1678 = Vertex(name = 'V_1678',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2165,(0,1):C.GC_2168})
V_1679 = Vertex(name = 'V_1679',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2171})
V_1680 = Vertex(name = 'V_1680',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3321})
V_1681 = Vertex(name = 'V_1681',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3337})
V_1682 = Vertex(name = 'V_1682',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3074,(0,5):C.GC_3083,(1,0):C.GC_3127,(3,0):C.GC_3133,(0,3):C.GC_3124,(2,3):C.GC_3130,(1,2):C.GC_3150,(3,2):C.GC_3153,(1,6):C.GC_3140,(3,6):C.GC_3146,(0,1):C.GC_3137,(2,1):C.GC_3143})
V_1683 = Vertex(name = 'V_1683',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3092,(0,1):C.GC_3240})
V_1684 = Vertex(name = 'V_1684',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3234})
V_1685 = Vertex(name = 'V_1685',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3250})
V_1686 = Vertex(name = 'V_1686',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1301,(0,5):C.GC_1310,(1,0):C.GC_1364,(3,0):C.GC_1370,(0,3):C.GC_1361,(2,3):C.GC_1367,(1,2):C.GC_1415,(3,2):C.GC_1418,(1,6):C.GC_1375,(3,6):C.GC_1379,(0,1):C.GC_1373,(2,1):C.GC_1377})
V_1687 = Vertex(name = 'V_1687',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1319,(0,1):C.GC_1442})
V_1688 = Vertex(name = 'V_1688',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1435})
V_1689 = Vertex(name = 'V_1689',
particles = [ P.u__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1449})
V_1690 = Vertex(name = 'V_1690',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2017,(0,6):C.GC_2026,(1,0):C.GC_2133,(3,0):C.GC_2139,(0,4):C.GC_2130,(2,4):C.GC_2136,(1,3):C.GC_699,(3,3):C.GC_700,(1,2):C.GC_2124,(3,2):C.GC_2127,(1,7):C.GC_2048,(3,7):C.GC_2054,(0,1):C.GC_2045,(2,1):C.GC_2051})
V_1691 = Vertex(name = 'V_1691',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2035,(0,1):C.GC_2188})
V_1692 = Vertex(name = 'V_1692',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2185})
V_1693 = Vertex(name = 'V_1693',
particles = [ P.c__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2191})
V_1694 = Vertex(name = 'V_1694',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3321,(0,5):C.GC_3255,(1,0):C.GC_3181,(3,0):C.GC_3191,(0,3):C.GC_3176,(2,3):C.GC_3186,(1,2):C.GC_3299,(3,2):C.GC_3304,(1,6):C.GC_3141,(3,6):C.GC_3147,(0,1):C.GC_3138,(2,1):C.GC_3144})
V_1695 = Vertex(name = 'V_1695',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3337,(0,2):C.GC_3086,(1,0):C.GC_3197,(2,0):C.GC_3200})
V_1696 = Vertex(name = 'V_1696',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3077,(0,1):C.GC_3247})
V_1697 = Vertex(name = 'V_1697',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3095})
V_1698 = Vertex(name = 'V_1698',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3237})
V_1699 = Vertex(name = 'V_1699',
particles = [ P.t__tilde__, P.s, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3254})
V_1700 = Vertex(name = 'V_1700',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1466,(0,5):C.GC_1475,(1,0):C.GC_3959,(3,0):C.GC_3963,(0,3):C.GC_3957,(2,3):C.GC_3961,(1,2):C.GC_1595,(3,2):C.GC_1598,(1,6):C.GC_3968,(3,6):C.GC_3972,(0,1):C.GC_3966,(2,1):C.GC_3970})
V_1701 = Vertex(name = 'V_1701',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1484,(0,2):C.GC_3728,(1,0):C.GC_4053,(2,0):C.GC_4058})
V_1702 = Vertex(name = 'V_1702',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3719,(0,1):C.GC_3926})
V_1703 = Vertex(name = 'V_1703',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3913})
V_1704 = Vertex(name = 'V_1704',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3735})
V_1705 = Vertex(name = 'V_1705',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4075})
V_1706 = Vertex(name = 'V_1706',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2206,(0,5):C.GC_2215,(1,0):C.GC_2345,(3,0):C.GC_2351,(0,3):C.GC_2342,(2,3):C.GC_2348,(1,2):C.GC_2354,(3,2):C.GC_2357,(1,6):C.GC_2273,(3,6):C.GC_2279,(0,1):C.GC_2270,(2,1):C.GC_2276})
V_1707 = Vertex(name = 'V_1707',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2224,(0,1):C.GC_2670})
V_1708 = Vertex(name = 'V_1708',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2664})
V_1709 = Vertex(name = 'V_1709',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2676})
V_1710 = Vertex(name = 'V_1710',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_3499,(0,6):C.GC_3508,(1,0):C.GC_3638,(3,0):C.GC_3644,(0,4):C.GC_3635,(2,4):C.GC_3641,(1,3):C.GC_526,(3,3):C.GC_527,(1,2):C.GC_3647,(3,2):C.GC_3650,(1,7):C.GC_3598,(3,7):C.GC_3604,(0,1):C.GC_3595,(2,1):C.GC_3601})
V_1711 = Vertex(name = 'V_1711',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3517,(0,1):C.GC_4090})
V_1712 = Vertex(name = 'V_1712',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4082})
V_1713 = Vertex(name = 'V_1713',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4100})
V_1714 = Vertex(name = 'V_1714',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1469,(0,5):C.GC_1478,(1,0):C.GC_1503,(3,0):C.GC_1509,(0,3):C.GC_1500,(2,3):C.GC_1506,(1,2):C.GC_1558,(3,2):C.GC_1561,(1,6):C.GC_1566,(3,6):C.GC_1570,(0,1):C.GC_1564,(2,1):C.GC_1568})
V_1715 = Vertex(name = 'V_1715',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1487,(0,1):C.GC_1802})
V_1716 = Vertex(name = 'V_1716',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1793})
V_1717 = Vertex(name = 'V_1717',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1811})
V_1718 = Vertex(name = 'V_1718',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2209,(0,5):C.GC_3926,(1,0):C.GC_2246,(3,0):C.GC_2256,(0,3):C.GC_2241,(2,3):C.GC_2251,(1,2):C.GC_2264,(3,2):C.GC_2267,(1,6):C.GC_2274,(3,6):C.GC_2280,(0,1):C.GC_2271,(2,1):C.GC_2277})
V_1719 = Vertex(name = 'V_1719',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2227,(0,2):C.GC_2218,(1,0):C.GC_4053,(2,0):C.GC_4058})
V_1720 = Vertex(name = 'V_1720',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2416,(0,1):C.GC_2422})
V_1721 = Vertex(name = 'V_1721',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2428})
V_1722 = Vertex(name = 'V_1722',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4075})
V_1723 = Vertex(name = 'V_1723',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4095})
V_1724 = Vertex(name = 'V_1724',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3502,(0,5):C.GC_3511,(1,0):C.GC_3543,(3,0):C.GC_3549,(0,3):C.GC_3540,(2,3):C.GC_3546,(1,2):C.GC_3589,(3,2):C.GC_3592,(1,6):C.GC_3599,(3,6):C.GC_3605,(0,1):C.GC_3596,(2,1):C.GC_3602})
V_1725 = Vertex(name = 'V_1725',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3520,(0,1):C.GC_3909})
V_1726 = Vertex(name = 'V_1726',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3902})
V_1727 = Vertex(name = 'V_1727',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3921})
V_1728 = Vertex(name = 'V_1728',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1472,(0,5):C.GC_1481,(1,0):C.GC_1535,(3,0):C.GC_1541,(0,3):C.GC_1532,(2,3):C.GC_1538,(1,2):C.GC_1586,(3,2):C.GC_1589,(1,6):C.GC_1567,(3,6):C.GC_1571,(0,1):C.GC_1565,(2,1):C.GC_1569})
V_1729 = Vertex(name = 'V_1729',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1490,(0,1):C.GC_1806})
V_1730 = Vertex(name = 'V_1730',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1797})
V_1731 = Vertex(name = 'V_1731',
particles = [ P.u__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1815})
V_1732 = Vertex(name = 'V_1732',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2212,(0,5):C.GC_2221,(1,0):C.GC_2323,(3,0):C.GC_2329,(0,3):C.GC_2320,(2,3):C.GC_2326,(1,2):C.GC_2332,(3,2):C.GC_2335,(1,6):C.GC_2275,(3,6):C.GC_2281,(0,1):C.GC_2272,(2,1):C.GC_2278})
V_1733 = Vertex(name = 'V_1733',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2230,(0,1):C.GC_2589})
V_1734 = Vertex(name = 'V_1734',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2585})
V_1735 = Vertex(name = 'V_1735',
particles = [ P.c__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2593})
V_1736 = Vertex(name = 'V_1736',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_4075,(0,5):C.GC_3926,(1,0):C.GC_3573,(3,0):C.GC_3583,(0,3):C.GC_3568,(2,3):C.GC_3578,(1,2):C.GC_4053,(3,2):C.GC_4058,(1,6):C.GC_3600,(3,6):C.GC_3606,(0,1):C.GC_3597,(2,1):C.GC_3603})
V_1737 = Vertex(name = 'V_1737',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_4095,(0,2):C.GC_3514,(1,0):C.GC_3625,(2,0):C.GC_3628})
V_1738 = Vertex(name = 'V_1738',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3505,(0,1):C.GC_3914})
V_1739 = Vertex(name = 'V_1739',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3523})
V_1740 = Vertex(name = 'V_1740',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3906})
V_1741 = Vertex(name = 'V_1741',
particles = [ P.t__tilde__, P.b, P.d__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3925})
V_1742 = Vertex(name = 'V_1742',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1467,(0,5):C.GC_1476,(1,0):C.GC_3960,(3,0):C.GC_3964,(0,3):C.GC_3958,(2,3):C.GC_3962,(1,2):C.GC_1596,(3,2):C.GC_1599,(1,6):C.GC_4036,(3,6):C.GC_4038,(0,1):C.GC_4035,(2,1):C.GC_4037})
V_1743 = Vertex(name = 'V_1743',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1485,(0,2):C.GC_3798,(1,0):C.GC_4054,(2,0):C.GC_4059})
V_1744 = Vertex(name = 'V_1744',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3797,(0,1):C.GC_3928})
V_1745 = Vertex(name = 'V_1745',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3915})
V_1746 = Vertex(name = 'V_1746',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3799})
V_1747 = Vertex(name = 'V_1747',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4076})
V_1748 = Vertex(name = 'V_1748',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2207,(0,5):C.GC_2216,(1,0):C.GC_2346,(3,0):C.GC_2352,(0,3):C.GC_2343,(2,3):C.GC_2349,(1,2):C.GC_2355,(3,2):C.GC_2358,(1,6):C.GC_2299,(3,6):C.GC_2305,(0,1):C.GC_2296,(2,1):C.GC_2302})
V_1749 = Vertex(name = 'V_1749',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2225,(0,1):C.GC_2671})
V_1750 = Vertex(name = 'V_1750',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2665})
V_1751 = Vertex(name = 'V_1751',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2677})
V_1752 = Vertex(name = 'V_1752',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_3500,(0,5):C.GC_3509,(1,0):C.GC_3639,(3,0):C.GC_3645,(0,3):C.GC_3636,(2,3):C.GC_3642,(1,2):C.GC_3648,(3,2):C.GC_3651,(1,6):C.GC_3616,(3,6):C.GC_3622,(0,1):C.GC_3613,(2,1):C.GC_3619})
V_1753 = Vertex(name = 'V_1753',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3518,(0,1):C.GC_4091})
V_1754 = Vertex(name = 'V_1754',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4083})
V_1755 = Vertex(name = 'V_1755',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4101})
V_1756 = Vertex(name = 'V_1756',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1470,(0,5):C.GC_1479,(1,0):C.GC_1504,(3,0):C.GC_1510,(0,3):C.GC_1501,(2,3):C.GC_1507,(1,2):C.GC_1559,(3,2):C.GC_1562,(1,6):C.GC_1580,(3,6):C.GC_1584,(0,1):C.GC_1578,(2,1):C.GC_1582})
V_1757 = Vertex(name = 'V_1757',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1488,(0,1):C.GC_1803})
V_1758 = Vertex(name = 'V_1758',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1794})
V_1759 = Vertex(name = 'V_1759',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1812})
V_1760 = Vertex(name = 'V_1760',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2210,(0,5):C.GC_3928,(1,0):C.GC_2247,(3,0):C.GC_2257,(0,3):C.GC_2242,(2,3):C.GC_2252,(1,2):C.GC_2265,(3,2):C.GC_2268,(1,6):C.GC_2300,(3,6):C.GC_2306,(0,1):C.GC_2297,(2,1):C.GC_2303})
V_1761 = Vertex(name = 'V_1761',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2228,(0,2):C.GC_2219,(1,0):C.GC_4054,(2,0):C.GC_4059})
V_1762 = Vertex(name = 'V_1762',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2549,(0,1):C.GC_2550})
V_1763 = Vertex(name = 'V_1763',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2551})
V_1764 = Vertex(name = 'V_1764',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4076})
V_1765 = Vertex(name = 'V_1765',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4097})
V_1766 = Vertex(name = 'V_1766',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_3503,(0,6):C.GC_3512,(1,0):C.GC_3544,(3,0):C.GC_3550,(0,4):C.GC_3541,(2,4):C.GC_3547,(1,3):C.GC_699,(3,3):C.GC_700,(1,2):C.GC_3590,(3,2):C.GC_3593,(1,7):C.GC_3617,(3,7):C.GC_3623,(0,1):C.GC_3614,(2,1):C.GC_3620})
V_1767 = Vertex(name = 'V_1767',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3521,(0,1):C.GC_3910})
V_1768 = Vertex(name = 'V_1768',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3903})
V_1769 = Vertex(name = 'V_1769',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3922})
V_1770 = Vertex(name = 'V_1770',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_1473,(0,5):C.GC_1482,(1,0):C.GC_1536,(3,0):C.GC_1542,(0,3):C.GC_1533,(2,3):C.GC_1539,(1,2):C.GC_1587,(3,2):C.GC_1590,(1,6):C.GC_1581,(3,6):C.GC_1585,(0,1):C.GC_1579,(2,1):C.GC_1583})
V_1771 = Vertex(name = 'V_1771',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1491,(0,1):C.GC_1807})
V_1772 = Vertex(name = 'V_1772',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1798})
V_1773 = Vertex(name = 'V_1773',
particles = [ P.u__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1816})
V_1774 = Vertex(name = 'V_1774',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_2213,(0,5):C.GC_2222,(1,0):C.GC_2324,(3,0):C.GC_2330,(0,3):C.GC_2321,(2,3):C.GC_2327,(1,2):C.GC_2333,(3,2):C.GC_2336,(1,6):C.GC_2301,(3,6):C.GC_2307,(0,1):C.GC_2298,(2,1):C.GC_2304})
V_1775 = Vertex(name = 'V_1775',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2231,(0,1):C.GC_2590})
V_1776 = Vertex(name = 'V_1776',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2586})
V_1777 = Vertex(name = 'V_1777',
particles = [ P.c__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2594})
V_1778 = Vertex(name = 'V_1778',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,4):C.GC_4076,(0,5):C.GC_3928,(1,0):C.GC_3574,(3,0):C.GC_3584,(0,3):C.GC_3569,(2,3):C.GC_3579,(1,2):C.GC_4054,(3,2):C.GC_4059,(1,6):C.GC_3618,(3,6):C.GC_3624,(0,1):C.GC_3615,(2,1):C.GC_3621})
V_1779 = Vertex(name = 'V_1779',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_4097,(0,2):C.GC_3515,(1,0):C.GC_3626,(2,0):C.GC_3629})
V_1780 = Vertex(name = 'V_1780',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3506,(0,1):C.GC_3916})
V_1781 = Vertex(name = 'V_1781',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3524})
V_1782 = Vertex(name = 'V_1782',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3907})
V_1783 = Vertex(name = 'V_1783',
particles = [ P.t__tilde__, P.b, P.s__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3927})
V_1784 = Vertex(name = 'V_1784',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_1002,(3,0):C.GC_1004,(0,5):C.GC_1139,(2,5):C.GC_1140,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_1002,(3,8):C.GC_1004,(0,1):C.GC_1139,(2,1):C.GC_1140})
V_1785 = Vertex(name = 'V_1785',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_4110,(1,0):C.GC_1547,(3,0):C.GC_1552,(0,5):C.GC_1003,(2,5):C.GC_1005,(1,4):C.GC_3670,(3,4):C.GC_3678,(1,2):C.GC_4057,(3,2):C.GC_4062,(1,3):C.GC_1079,(3,3):C.GC_1082,(1,8):C.GC_1547,(3,8):C.GC_1552,(0,1):C.GC_1003,(2,1):C.GC_1005})
V_1786 = Vertex(name = 'V_1786',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_4079,(0,2):C.GC_1477,(1,0):C.GC_1597,(2,0):C.GC_1600})
V_1787 = Vertex(name = 'V_1787',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3917,(0,1):C.GC_1801})
V_1788 = Vertex(name = 'V_1788',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1468})
V_1789 = Vertex(name = 'V_1789',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1486})
V_1790 = Vertex(name = 'V_1790',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1792})
V_1791 = Vertex(name = 'V_1791',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1810})
V_1792 = Vertex(name = 'V_1792',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2411,(0,6):C.GC_2364,(1,0):C.GC_2347,(3,0):C.GC_2353,(0,4):C.GC_2344,(2,4):C.GC_2350,(1,3):C.GC_2381,(3,3):C.GC_2386,(1,2):C.GC_2356,(3,2):C.GC_2359,(1,7):C.GC_2245,(3,7):C.GC_2255,(0,1):C.GC_2240,(2,1):C.GC_2250})
V_1793 = Vertex(name = 'V_1793',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2421,(0,1):C.GC_2217})
V_1794 = Vertex(name = 'V_1794',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2208,(0,1):C.GC_2673})
V_1795 = Vertex(name = 'V_1795',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2226})
V_1796 = Vertex(name = 'V_1796',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2667})
V_1797 = Vertex(name = 'V_1797',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2678})
V_1798 = Vertex(name = 'V_1798',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_3707,(0,6):C.GC_3659,(1,0):C.GC_3640,(3,0):C.GC_3646,(0,4):C.GC_3637,(2,4):C.GC_3643,(1,3):C.GC_3665,(3,3):C.GC_3673,(1,2):C.GC_3649,(3,2):C.GC_3652,(1,7):C.GC_3571,(3,7):C.GC_3581,(0,1):C.GC_3566,(2,1):C.GC_3576})
V_1799 = Vertex(name = 'V_1799',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3725,(0,1):C.GC_3510})
V_1800 = Vertex(name = 'V_1800',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3501,(0,1):C.GC_4093})
V_1801 = Vertex(name = 'V_1801',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3519})
V_1802 = Vertex(name = 'V_1802',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4085})
V_1803 = Vertex(name = 'V_1803',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.u ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_4102})
V_1804 = Vertex(name = 'V_1804',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1629,(0,6):C.GC_1607,(1,0):C.GC_1505,(3,0):C.GC_1511,(0,4):C.GC_1502,(2,4):C.GC_1508,(1,3):C.GC_1613,(3,3):C.GC_1615,(1,2):C.GC_1560,(3,2):C.GC_1563,(1,7):C.GC_1548,(3,7):C.GC_1553,(0,1):C.GC_1545,(2,1):C.GC_1550})
V_1805 = Vertex(name = 'V_1805',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1634,(0,1):C.GC_1480})
V_1806 = Vertex(name = 'V_1806',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1471,(0,1):C.GC_1805})
V_1807 = Vertex(name = 'V_1807',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1489})
V_1808 = Vertex(name = 'V_1808',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1796})
V_1809 = Vertex(name = 'V_1809',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1813})
V_1810 = Vertex(name = 'V_1810',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_471,(3,0):C.GC_473,(0,5):C.GC_472,(2,5):C.GC_474,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_471,(3,8):C.GC_473,(0,1):C.GC_472,(2,1):C.GC_474})
V_1811 = Vertex(name = 'V_1811',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_4108,(1,0):C.GC_2248,(3,0):C.GC_2258,(0,5):C.GC_2243,(2,5):C.GC_2253,(1,4):C.GC_2382,(3,4):C.GC_2387,(1,2):C.GC_4055,(3,2):C.GC_4060,(1,3):C.GC_488,(3,3):C.GC_489,(1,8):C.GC_2248,(3,8):C.GC_2258,(0,1):C.GC_2243,(2,1):C.GC_2253})
V_1812 = Vertex(name = 'V_1812',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_4077,(0,2):C.GC_2220,(1,0):C.GC_2266,(2,0):C.GC_2269})
V_1813 = Vertex(name = 'V_1813',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3918,(0,1):C.GC_2368})
V_1814 = Vertex(name = 'V_1814',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2211})
V_1815 = Vertex(name = 'V_1815',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2229})
V_1816 = Vertex(name = 'V_1816',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2367})
V_1817 = Vertex(name = 'V_1817',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2369})
V_1818 = Vertex(name = 'V_1818',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_3708,(0,6):C.GC_3660,(1,0):C.GC_3545,(3,0):C.GC_3551,(0,4):C.GC_3542,(2,4):C.GC_3548,(1,3):C.GC_3666,(3,3):C.GC_3674,(1,2):C.GC_3591,(3,2):C.GC_3594,(1,7):C.GC_3572,(3,7):C.GC_3582,(0,1):C.GC_3567,(2,1):C.GC_3577})
V_1819 = Vertex(name = 'V_1819',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3727,(0,1):C.GC_3513})
V_1820 = Vertex(name = 'V_1820',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3504,(0,1):C.GC_3912})
V_1821 = Vertex(name = 'V_1821',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3522})
V_1822 = Vertex(name = 'V_1822',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3905})
V_1823 = Vertex(name = 'V_1823',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.c ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3923})
V_1824 = Vertex(name = 'V_1824',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_1630,(0,6):C.GC_1608,(1,0):C.GC_1537,(3,0):C.GC_1543,(0,4):C.GC_1534,(2,4):C.GC_1540,(1,3):C.GC_1614,(3,3):C.GC_1616,(1,2):C.GC_1588,(3,2):C.GC_1591,(1,7):C.GC_1549,(3,7):C.GC_1554,(0,1):C.GC_1546,(2,1):C.GC_1551})
V_1825 = Vertex(name = 'V_1825',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1636,(0,1):C.GC_1483})
V_1826 = Vertex(name = 'V_1826',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1474,(0,1):C.GC_1809})
V_1827 = Vertex(name = 'V_1827',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1492})
V_1828 = Vertex(name = 'V_1828',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1800})
V_1829 = Vertex(name = 'V_1829',
particles = [ P.u__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_1817})
V_1830 = Vertex(name = 'V_1830',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,5):C.GC_2414,(0,6):C.GC_2365,(1,0):C.GC_2325,(3,0):C.GC_2331,(0,4):C.GC_2322,(2,4):C.GC_2328,(1,3):C.GC_2385,(3,3):C.GC_2390,(1,2):C.GC_2334,(3,2):C.GC_2337,(1,7):C.GC_2249,(3,7):C.GC_2259,(0,1):C.GC_2244,(2,1):C.GC_2254})
V_1831 = Vertex(name = 'V_1831',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2426,(0,1):C.GC_2223})
V_1832 = Vertex(name = 'V_1832',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2214,(0,1):C.GC_2592})
V_1833 = Vertex(name = 'V_1833',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2232})
V_1834 = Vertex(name = 'V_1834',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2588})
V_1835 = Vertex(name = 'V_1835',
particles = [ P.c__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_2595})
V_1836 = Vertex(name = 'V_1836',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_46,(1,0):C.GC_780,(3,0):C.GC_782,(0,5):C.GC_781,(2,5):C.GC_783,(1,4):C.GC_38,(3,4):C.GC_39,(1,2):C.GC_47,(3,2):C.GC_48,(1,3):C.GC_51,(3,3):C.GC_52,(1,8):C.GC_780,(3,8):C.GC_782,(0,1):C.GC_781,(2,1):C.GC_783})
V_1837 = Vertex(name = 'V_1837',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF1, L.FFFF11, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF2, L.FFFF3, L.FFFF4, L.FFFF9 ],
couplings = {(1,6):C.GC_43,(0,7):C.GC_4109,(1,0):C.GC_3575,(3,0):C.GC_3585,(0,5):C.GC_3570,(2,5):C.GC_3580,(1,4):C.GC_429,(3,4):C.GC_430,(1,2):C.GC_4056,(3,2):C.GC_4061,(1,3):C.GC_841,(3,3):C.GC_844,(1,8):C.GC_3575,(3,8):C.GC_3585,(0,1):C.GC_3570,(2,1):C.GC_3580})
V_1838 = Vertex(name = 'V_1838',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_4078,(0,3):C.GC_3516,(1,1):C.GC_3667,(2,1):C.GC_3675,(1,0):C.GC_3627,(2,0):C.GC_3630})
V_1839 = Vertex(name = 'V_1839',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3919,(0,1):C.GC_3920})
V_1840 = Vertex(name = 'V_1840',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3507})
V_1841 = Vertex(name = 'V_1841',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3525})
V_1842 = Vertex(name = 'V_1842',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3908})
V_1843 = Vertex(name = 'V_1843',
particles = [ P.t__tilde__, P.b, P.b__tilde__, P.t ],
color = [ 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3 ],
couplings = {(0,0):C.GC_3929})
V_1844 = Vertex(name = 'V_1844',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_1023,(0,9):C.GC_1022,(0,10):C.GC_1022,(0,6):C.GC_1018,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_1023,(0,4):C.GC_1022,(0,5):C.GC_1022,(0,0):C.GC_1018})
V_1845 = Vertex(name = 'V_1845',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_4028,(0,9):C.GC_4027,(0,10):C.GC_4027,(0,6):C.GC_4024,(0,1):C.GC_3706,(0,2):C.GC_1066,(0,3):C.GC_1065,(0,7):C.GC_4028,(0,4):C.GC_4027,(0,5):C.GC_4027,(0,0):C.GC_4024})
V_1846 = Vertex(name = 'V_1846',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3694})
V_1847 = Vertex(name = 'V_1847',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3700})
V_1848 = Vertex(name = 'V_1848',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_2399,(0,9):C.GC_2642,(0,7):C.GC_2641,(0,8):C.GC_2641,(0,4):C.GC_2640,(0,1):C.GC_2408,(0,5):C.GC_2531,(0,2):C.GC_2530,(0,3):C.GC_2530,(0,0):C.GC_2527})
V_1849 = Vertex(name = 'V_1849',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2403})
V_1850 = Vertex(name = 'V_1850',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_3691,(0,9):C.GC_4026,(0,7):C.GC_4025,(0,8):C.GC_4025,(0,4):C.GC_4023,(0,1):C.GC_3703,(0,5):C.GC_3885,(0,2):C.GC_3884,(0,3):C.GC_3884,(0,0):C.GC_3881})
V_1851 = Vertex(name = 'V_1851',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3696})
V_1852 = Vertex(name = 'V_1852',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_1621,(0,9):C.GC_1676,(0,7):C.GC_1675,(0,8):C.GC_1675,(0,4):C.GC_1674,(0,1):C.GC_1627,(0,5):C.GC_1776,(0,2):C.GC_1775,(0,3):C.GC_1775,(0,0):C.GC_1773})
V_1853 = Vertex(name = 'V_1853',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1624})
V_1854 = Vertex(name = 'V_1854',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_590,(0,9):C.GC_589,(0,10):C.GC_589,(0,6):C.GC_585,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_590,(0,4):C.GC_589,(0,5):C.GC_589,(0,0):C.GC_585})
V_1855 = Vertex(name = 'V_1855',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_2533,(0,9):C.GC_2532,(0,10):C.GC_2532,(0,6):C.GC_2528,(0,1):C.GC_2409,(0,2):C.GC_481,(0,3):C.GC_480,(0,7):C.GC_2533,(0,4):C.GC_2532,(0,5):C.GC_2532,(0,0):C.GC_2528})
V_1856 = Vertex(name = 'V_1856',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2400})
V_1857 = Vertex(name = 'V_1857',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2405})
V_1858 = Vertex(name = 'V_1858',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_3692,(0,9):C.GC_3793,(0,7):C.GC_3792,(0,8):C.GC_3792,(0,4):C.GC_3791,(0,1):C.GC_3704,(0,5):C.GC_3887,(0,2):C.GC_3886,(0,3):C.GC_3886,(0,0):C.GC_3882})
V_1859 = Vertex(name = 'V_1859',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3698})
V_1860 = Vertex(name = 'V_1860',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_1622,(0,9):C.GC_1709,(0,7):C.GC_1708,(0,8):C.GC_1708,(0,4):C.GC_1707,(0,1):C.GC_1628,(0,5):C.GC_1778,(0,2):C.GC_1777,(0,3):C.GC_1777,(0,0):C.GC_1774})
V_1861 = Vertex(name = 'V_1861',
particles = [ P.e__plus__, P.e__minus__, P.u__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1626})
V_1862 = Vertex(name = 'V_1862',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_2401,(0,9):C.GC_2581,(0,7):C.GC_2580,(0,8):C.GC_2580,(0,4):C.GC_2579,(0,1):C.GC_2410,(0,5):C.GC_2535,(0,2):C.GC_2534,(0,3):C.GC_2534,(0,0):C.GC_2529})
V_1863 = Vertex(name = 'V_1863',
particles = [ P.e__plus__, P.e__minus__, P.c__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2407})
V_1864 = Vertex(name = 'V_1864',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_801,(0,9):C.GC_800,(0,10):C.GC_800,(0,6):C.GC_796,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_801,(0,4):C.GC_800,(0,5):C.GC_800,(0,0):C.GC_796})
V_1865 = Vertex(name = 'V_1865',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_3889,(0,9):C.GC_3888,(0,10):C.GC_3888,(0,6):C.GC_3883,(0,1):C.GC_3705,(0,2):C.GC_830,(0,3):C.GC_829,(0,7):C.GC_3889,(0,4):C.GC_3888,(0,5):C.GC_3888,(0,0):C.GC_3883})
V_1866 = Vertex(name = 'V_1866',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3693})
V_1867 = Vertex(name = 'V_1867',
particles = [ P.e__plus__, P.e__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3701})
V_1868 = Vertex(name = 'V_1868',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_1035,(0,9):C.GC_1034,(0,10):C.GC_1034,(0,6):C.GC_1030,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_1035,(0,4):C.GC_1034,(0,5):C.GC_1034,(0,0):C.GC_1030})
V_1869 = Vertex(name = 'V_1869',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_4034,(0,9):C.GC_4033,(0,10):C.GC_4033,(0,6):C.GC_4030,(0,1):C.GC_3706,(0,2):C.GC_1066,(0,3):C.GC_1065,(0,7):C.GC_4034,(0,4):C.GC_4033,(0,5):C.GC_4033,(0,0):C.GC_4030})
V_1870 = Vertex(name = 'V_1870',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3694})
V_1871 = Vertex(name = 'V_1871',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3700})
V_1872 = Vertex(name = 'V_1872',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_2399,(0,9):C.GC_2645,(0,7):C.GC_2644,(0,8):C.GC_2644,(0,4):C.GC_2643,(0,1):C.GC_2408,(0,5):C.GC_2544,(0,2):C.GC_2543,(0,3):C.GC_2543,(0,0):C.GC_2540})
V_1873 = Vertex(name = 'V_1873',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2403})
V_1874 = Vertex(name = 'V_1874',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_3691,(0,9):C.GC_4032,(0,7):C.GC_4031,(0,8):C.GC_4031,(0,4):C.GC_4029,(0,1):C.GC_3703,(0,5):C.GC_3894,(0,2):C.GC_3893,(0,3):C.GC_3893,(0,0):C.GC_3890})
V_1875 = Vertex(name = 'V_1875',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3696})
V_1876 = Vertex(name = 'V_1876',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_1621,(0,9):C.GC_1679,(0,7):C.GC_1678,(0,8):C.GC_1678,(0,4):C.GC_1677,(0,1):C.GC_1627,(0,5):C.GC_1782,(0,2):C.GC_1781,(0,3):C.GC_1781,(0,0):C.GC_1779})
V_1877 = Vertex(name = 'V_1877',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1624})
V_1878 = Vertex(name = 'V_1878',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_647,(0,9):C.GC_646,(0,10):C.GC_646,(0,6):C.GC_642,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_647,(0,4):C.GC_646,(0,5):C.GC_646,(0,0):C.GC_642})
V_1879 = Vertex(name = 'V_1879',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_2546,(0,9):C.GC_2545,(0,10):C.GC_2545,(0,6):C.GC_2541,(0,1):C.GC_2409,(0,2):C.GC_481,(0,3):C.GC_480,(0,7):C.GC_2546,(0,4):C.GC_2545,(0,5):C.GC_2545,(0,0):C.GC_2541})
V_1880 = Vertex(name = 'V_1880',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2400})
V_1881 = Vertex(name = 'V_1881',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2405})
V_1882 = Vertex(name = 'V_1882',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_3692,(0,9):C.GC_3796,(0,7):C.GC_3795,(0,8):C.GC_3795,(0,4):C.GC_3794,(0,1):C.GC_3704,(0,5):C.GC_3896,(0,2):C.GC_3895,(0,3):C.GC_3895,(0,0):C.GC_3891})
V_1883 = Vertex(name = 'V_1883',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3698})
V_1884 = Vertex(name = 'V_1884',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_1622,(0,9):C.GC_1712,(0,7):C.GC_1711,(0,8):C.GC_1711,(0,4):C.GC_1710,(0,1):C.GC_1628,(0,5):C.GC_1784,(0,2):C.GC_1783,(0,3):C.GC_1783,(0,0):C.GC_1780})
V_1885 = Vertex(name = 'V_1885',
particles = [ P.mu__plus__, P.mu__minus__, P.u__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1626})
V_1886 = Vertex(name = 'V_1886',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_2401,(0,9):C.GC_2584,(0,7):C.GC_2583,(0,8):C.GC_2583,(0,4):C.GC_2582,(0,1):C.GC_2410,(0,5):C.GC_2548,(0,2):C.GC_2547,(0,3):C.GC_2547,(0,0):C.GC_2542})
V_1887 = Vertex(name = 'V_1887',
particles = [ P.mu__plus__, P.mu__minus__, P.c__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2407})
V_1888 = Vertex(name = 'V_1888',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_813,(0,9):C.GC_812,(0,10):C.GC_812,(0,6):C.GC_808,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_813,(0,4):C.GC_812,(0,5):C.GC_812,(0,0):C.GC_808})
V_1889 = Vertex(name = 'V_1889',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_3898,(0,9):C.GC_3897,(0,10):C.GC_3897,(0,6):C.GC_3892,(0,1):C.GC_3705,(0,2):C.GC_830,(0,3):C.GC_829,(0,7):C.GC_3898,(0,4):C.GC_3897,(0,5):C.GC_3897,(0,0):C.GC_3892})
V_1890 = Vertex(name = 'V_1890',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3693})
V_1891 = Vertex(name = 'V_1891',
particles = [ P.mu__plus__, P.mu__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3701})
V_1892 = Vertex(name = 'V_1892',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_1056,(0,9):C.GC_1055,(0,10):C.GC_1055,(0,6):C.GC_1051,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_1056,(0,4):C.GC_1055,(0,5):C.GC_1055,(0,0):C.GC_1051})
V_1893 = Vertex(name = 'V_1893',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_4044,(0,9):C.GC_4043,(0,10):C.GC_4043,(0,6):C.GC_4040,(0,1):C.GC_3706,(0,2):C.GC_1066,(0,3):C.GC_1065,(0,7):C.GC_4044,(0,4):C.GC_4043,(0,5):C.GC_4043,(0,0):C.GC_4040})
V_1894 = Vertex(name = 'V_1894',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3694})
V_1895 = Vertex(name = 'V_1895',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3700})
V_1896 = Vertex(name = 'V_1896',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_2399,(0,9):C.GC_2648,(0,7):C.GC_2647,(0,8):C.GC_2647,(0,4):C.GC_2646,(0,1):C.GC_2408,(0,5):C.GC_2605,(0,2):C.GC_2604,(0,3):C.GC_2604,(0,0):C.GC_2601})
V_1897 = Vertex(name = 'V_1897',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2403})
V_1898 = Vertex(name = 'V_1898',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_3691,(0,9):C.GC_4042,(0,7):C.GC_4041,(0,8):C.GC_4041,(0,4):C.GC_4039,(0,1):C.GC_3703,(0,5):C.GC_3948,(0,2):C.GC_3947,(0,3):C.GC_3947,(0,0):C.GC_3944})
V_1899 = Vertex(name = 'V_1899',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.u ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3696})
V_1900 = Vertex(name = 'V_1900',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_1621,(0,9):C.GC_1715,(0,7):C.GC_1714,(0,8):C.GC_1714,(0,4):C.GC_1713,(0,1):C.GC_1627,(0,5):C.GC_1788,(0,2):C.GC_1787,(0,3):C.GC_1787,(0,0):C.GC_1785})
V_1901 = Vertex(name = 'V_1901',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1624})
V_1902 = Vertex(name = 'V_1902',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_918,(0,9):C.GC_917,(0,10):C.GC_917,(0,6):C.GC_913,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_918,(0,4):C.GC_917,(0,5):C.GC_917,(0,0):C.GC_913})
V_1903 = Vertex(name = 'V_1903',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_2607,(0,9):C.GC_2606,(0,10):C.GC_2606,(0,6):C.GC_2602,(0,1):C.GC_2409,(0,2):C.GC_481,(0,3):C.GC_480,(0,7):C.GC_2607,(0,4):C.GC_2606,(0,5):C.GC_2606,(0,0):C.GC_2602})
V_1904 = Vertex(name = 'V_1904',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2400})
V_1905 = Vertex(name = 'V_1905',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2405})
V_1906 = Vertex(name = 'V_1906',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_3692,(0,9):C.GC_3943,(0,7):C.GC_3942,(0,8):C.GC_3942,(0,4):C.GC_3941,(0,1):C.GC_3704,(0,5):C.GC_3950,(0,2):C.GC_3949,(0,3):C.GC_3949,(0,0):C.GC_3945})
V_1907 = Vertex(name = 'V_1907',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.c ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3698})
V_1908 = Vertex(name = 'V_1908',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_1622,(0,9):C.GC_1718,(0,7):C.GC_1717,(0,8):C.GC_1717,(0,4):C.GC_1716,(0,1):C.GC_1628,(0,5):C.GC_1790,(0,2):C.GC_1789,(0,3):C.GC_1789,(0,0):C.GC_1786})
V_1909 = Vertex(name = 'V_1909',
particles = [ P.ta__plus__, P.ta__minus__, P.u__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1626})
V_1910 = Vertex(name = 'V_1910',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,6):C.GC_2401,(0,9):C.GC_2612,(0,7):C.GC_2611,(0,8):C.GC_2611,(0,4):C.GC_2610,(0,1):C.GC_2410,(0,5):C.GC_2609,(0,2):C.GC_2608,(0,3):C.GC_2608,(0,0):C.GC_2603})
V_1911 = Vertex(name = 'V_1911',
particles = [ P.ta__plus__, P.ta__minus__, P.c__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2407})
V_1912 = Vertex(name = 'V_1912',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_25,(0,11):C.GC_938,(0,9):C.GC_937,(0,10):C.GC_937,(0,6):C.GC_933,(0,1):C.GC_40,(0,2):C.GC_37,(0,3):C.GC_14,(0,7):C.GC_938,(0,4):C.GC_937,(0,5):C.GC_937,(0,0):C.GC_933})
V_1913 = Vertex(name = 'V_1913',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF11, L.FFFF12, L.FFFF16, L.FFFF17, L.FFFF18, L.FFFF19, L.FFFF2, L.FFFF20, L.FFFF4, L.FFFF5, L.FFFF6, L.FFFF7 ],
couplings = {(0,8):C.GC_26,(0,11):C.GC_3952,(0,9):C.GC_3951,(0,10):C.GC_3951,(0,6):C.GC_3946,(0,1):C.GC_3705,(0,2):C.GC_830,(0,3):C.GC_829,(0,7):C.GC_3952,(0,4):C.GC_3951,(0,5):C.GC_3951,(0,0):C.GC_3946})
V_1914 = Vertex(name = 'V_1914',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3693})
V_1915 = Vertex(name = 'V_1915',
particles = [ P.ta__plus__, P.ta__minus__, P.t__tilde__, P.t ],
color = [ 'Identity(3,4)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3701})
V_1916 = Vertex(name = 'V_1916',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_41,(3,3):C.GC_48,(1,1):C.GC_47,(3,1):C.GC_48,(1,2):C.GC_53,(0,4):C.GC_47,(2,4):C.GC_48,(0,5):C.GC_53,(0,0):C.GC_47,(2,0):C.GC_48,(1,3):C.GC_47})
V_1917 = Vertex(name = 'V_1917',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_42,(0,7):C.GC_42,(3,3):C.GC_3690,(1,1):C.GC_3685,(3,1):C.GC_3690,(1,2):C.GC_54,(0,4):C.GC_3685,(2,4):C.GC_3690,(0,5):C.GC_54,(0,0):C.GC_3685,(2,0):C.GC_3690,(1,3):C.GC_3685})
V_1918 = Vertex(name = 'V_1918',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_44,(0,7):C.GC_44,(3,3):C.GC_1064,(1,1):C.GC_1063,(3,1):C.GC_1064,(1,2):C.GC_1067,(0,4):C.GC_1063,(2,4):C.GC_1064,(0,5):C.GC_1067,(0,0):C.GC_1063,(2,0):C.GC_1064,(1,3):C.GC_1063})
V_1919 = Vertex(name = 'V_1919',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF14, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_45,(0,3):C.GC_45,(1,0):C.GC_1068,(0,1):C.GC_1068})
V_1920 = Vertex(name = 'V_1920',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3716,(0,1):C.GC_3716})
V_1921 = Vertex(name = 'V_1921',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3723,(0,1):C.GC_3723})
V_1922 = Vertex(name = 'V_1922',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3732,(0,1):C.GC_3732})
V_1923 = Vertex(name = 'V_1923',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3741,(0,1):C.GC_3741})
V_1924 = Vertex(name = 'V_1924',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2411,(0,3):C.GC_2411,(1,0):C.GC_2391,(3,0):C.GC_2395,(0,1):C.GC_2391,(2,1):C.GC_2395})
V_1925 = Vertex(name = 'V_1925',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2415,(0,1):C.GC_2415})
V_1926 = Vertex(name = 'V_1926',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2420,(0,1):C.GC_2420})
V_1927 = Vertex(name = 'V_1927',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2427,(0,1):C.GC_2427})
V_1928 = Vertex(name = 'V_1928',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3707,(0,3):C.GC_3707,(1,0):C.GC_3681,(3,0):C.GC_3686,(0,1):C.GC_3681,(2,1):C.GC_3686})
V_1929 = Vertex(name = 'V_1929',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3717,(0,1):C.GC_3717})
V_1930 = Vertex(name = 'V_1930',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3724,(0,1):C.GC_3724})
V_1931 = Vertex(name = 'V_1931',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.u ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3733,(0,1):C.GC_3733})
V_1932 = Vertex(name = 'V_1932',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_1629,(0,3):C.GC_1629,(0,0):C.GC_1617,(2,0):C.GC_1619,(1,1):C.GC_1617,(3,1):C.GC_1619})
V_1933 = Vertex(name = 'V_1933',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1631,(0,1):C.GC_1631})
V_1934 = Vertex(name = 'V_1934',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1633,(0,1):C.GC_1633})
V_1935 = Vertex(name = 'V_1935',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1637,(0,1):C.GC_1637})
V_1936 = Vertex(name = 'V_1936',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_42,(0,0):C.GC_1009,(2,0):C.GC_1010,(1,3):C.GC_47,(3,3):C.GC_48,(1,1):C.GC_47,(3,1):C.GC_48,(1,2):C.GC_53,(0,4):C.GC_1009,(2,4):C.GC_1010,(0,5):C.GC_54})
V_1937 = Vertex(name = 'V_1937',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_44,(0,5):C.GC_45,(1,2):C.GC_2360,(2,2):C.GC_2361,(1,0):C.GC_1601,(2,0):C.GC_1603,(1,1):C.GC_1072,(0,3):C.GC_1074})
V_1938 = Vertex(name = 'V_1938',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3712,(0,1):C.GC_3721})
V_1939 = Vertex(name = 'V_1939',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3730,(0,1):C.GC_3737})
V_1940 = Vertex(name = 'V_1940',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3708,(0,2):C.GC_3718,(1,0):C.GC_3682,(2,0):C.GC_3687})
V_1941 = Vertex(name = 'V_1941',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3726,(0,1):C.GC_3734})
V_1942 = Vertex(name = 'V_1942',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_1630,(0,3):C.GC_1630,(0,0):C.GC_1618,(2,0):C.GC_1620,(1,1):C.GC_1618,(3,1):C.GC_1620})
V_1943 = Vertex(name = 'V_1943',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1632,(0,1):C.GC_1632})
V_1944 = Vertex(name = 'V_1944',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1635,(0,1):C.GC_1635})
V_1945 = Vertex(name = 'V_1945',
particles = [ P.u__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1638,(0,1):C.GC_1638})
V_1946 = Vertex(name = 'V_1946',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2414,(0,2):C.GC_2419,(1,0):C.GC_2394,(2,0):C.GC_2398})
V_1947 = Vertex(name = 'V_1947',
particles = [ P.c__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2425,(0,1):C.GC_2431})
V_1948 = Vertex(name = 'V_1948',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_42,(0,0):C.GC_1049,(2,0):C.GC_1050,(1,3):C.GC_47,(3,3):C.GC_48,(1,1):C.GC_47,(3,1):C.GC_48,(1,2):C.GC_53,(0,4):C.GC_1049,(2,4):C.GC_1050,(0,5):C.GC_54})
V_1949 = Vertex(name = 'V_1949',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_44,(0,5):C.GC_45,(1,2):C.GC_3653,(2,2):C.GC_3654,(1,0):C.GC_1602,(2,0):C.GC_1604,(1,1):C.GC_1073,(0,3):C.GC_1075})
V_1950 = Vertex(name = 'V_1950',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2412,(0,1):C.GC_2417})
V_1951 = Vertex(name = 'V_1951',
particles = [ P.t__tilde__, P.u, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2423,(0,1):C.GC_2429})
V_1952 = Vertex(name = 'V_1952',
particles = [ P.c__tilde__, P.u, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2411,(0,3):C.GC_2411,(1,0):C.GC_2391,(3,0):C.GC_2395,(0,1):C.GC_2391,(2,1):C.GC_2395})
V_1953 = Vertex(name = 'V_1953',
particles = [ P.c__tilde__, P.u, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2415,(0,1):C.GC_2415})
V_1954 = Vertex(name = 'V_1954',
particles = [ P.c__tilde__, P.u, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2420,(0,1):C.GC_2420})
V_1955 = Vertex(name = 'V_1955',
particles = [ P.c__tilde__, P.u, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2427,(0,1):C.GC_2427})
V_1956 = Vertex(name = 'V_1956',
particles = [ P.t__tilde__, P.u, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)' ],
lorentz = [ L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_3717,(0,2):C.GC_3707,(0,0):C.GC_3681,(2,0):C.GC_3686})
V_1957 = Vertex(name = 'V_1957',
particles = [ P.t__tilde__, P.u, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3733,(0,1):C.GC_3724})
V_1958 = Vertex(name = 'V_1958',
particles = [ P.t__tilde__, P.u, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_2411,(0,2):C.GC_2415,(1,0):C.GC_2391,(2,0):C.GC_2395})
V_1959 = Vertex(name = 'V_1959',
particles = [ P.t__tilde__, P.u, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2420,(0,1):C.GC_2427})
V_1960 = Vertex(name = 'V_1960',
particles = [ P.t__tilde__, P.u, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3707,(0,3):C.GC_3707,(1,0):C.GC_3681,(3,0):C.GC_3686,(0,1):C.GC_3681,(2,1):C.GC_3686})
V_1961 = Vertex(name = 'V_1961',
particles = [ P.t__tilde__, P.u, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3717,(0,1):C.GC_3717})
V_1962 = Vertex(name = 'V_1962',
particles = [ P.t__tilde__, P.u, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3724,(0,1):C.GC_3724})
V_1963 = Vertex(name = 'V_1963',
particles = [ P.t__tilde__, P.u, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3733,(0,1):C.GC_3733})
V_1964 = Vertex(name = 'V_1964',
particles = [ P.c__tilde__, P.c, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_1629,(0,3):C.GC_1629,(0,0):C.GC_1617,(2,0):C.GC_1619,(1,1):C.GC_1617,(3,1):C.GC_1619})
V_1965 = Vertex(name = 'V_1965',
particles = [ P.c__tilde__, P.c, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1631,(0,1):C.GC_1631})
V_1966 = Vertex(name = 'V_1966',
particles = [ P.c__tilde__, P.c, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1633,(0,1):C.GC_1633})
V_1967 = Vertex(name = 'V_1967',
particles = [ P.c__tilde__, P.c, P.u__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1637,(0,1):C.GC_1637})
V_1968 = Vertex(name = 'V_1968',
particles = [ P.c__tilde__, P.c, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)' ],
lorentz = [ L.FFFF12, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1632,(0,2):C.GC_1630,(0,0):C.GC_1618,(2,0):C.GC_1620})
V_1969 = Vertex(name = 'V_1969',
particles = [ P.c__tilde__, P.c, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1638,(0,1):C.GC_1635})
V_1970 = Vertex(name = 'V_1970',
particles = [ P.t__tilde__, P.c, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,1):C.GC_1629,(0,2):C.GC_1631,(1,0):C.GC_1617,(2,0):C.GC_1619})
V_1971 = Vertex(name = 'V_1971',
particles = [ P.t__tilde__, P.c, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1633,(0,1):C.GC_1637})
V_1972 = Vertex(name = 'V_1972',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_41,(0,0):C.GC_47,(2,0):C.GC_48,(1,3):C.GC_47,(3,3):C.GC_48,(1,1):C.GC_47,(3,1):C.GC_48,(1,2):C.GC_53,(0,4):C.GC_47,(2,4):C.GC_48,(0,5):C.GC_53})
V_1973 = Vertex(name = 'V_1973',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_42,(0,7):C.GC_42,(0,0):C.GC_478,(2,0):C.GC_479,(1,3):C.GC_478,(3,3):C.GC_479,(1,1):C.GC_478,(3,1):C.GC_479,(1,2):C.GC_54,(0,4):C.GC_478,(2,4):C.GC_479,(0,5):C.GC_54})
V_1974 = Vertex(name = 'V_1974',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_44,(0,7):C.GC_44,(0,0):C.GC_2392,(2,0):C.GC_2396,(1,3):C.GC_2392,(3,3):C.GC_2396,(1,1):C.GC_2392,(3,1):C.GC_2396,(1,2):C.GC_482,(0,4):C.GC_2392,(2,4):C.GC_2396,(0,5):C.GC_482})
V_1975 = Vertex(name = 'V_1975',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF14, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_45,(0,3):C.GC_45,(1,0):C.GC_483,(0,1):C.GC_483})
V_1976 = Vertex(name = 'V_1976',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2413,(0,1):C.GC_2413})
V_1977 = Vertex(name = 'V_1977',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2418,(0,1):C.GC_2418})
V_1978 = Vertex(name = 'V_1978',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2424,(0,1):C.GC_2424})
V_1979 = Vertex(name = 'V_1979',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2430,(0,1):C.GC_2430})
V_1980 = Vertex(name = 'V_1980',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF15, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3708,(0,3):C.GC_3708,(1,0):C.GC_3682,(3,0):C.GC_3687,(0,1):C.GC_3682,(2,1):C.GC_3687})
V_1981 = Vertex(name = 'V_1981',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3718,(0,1):C.GC_3718})
V_1982 = Vertex(name = 'V_1982',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3726,(0,1):C.GC_3726})
V_1983 = Vertex(name = 'V_1983',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.c ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3734,(0,1):C.GC_3734})
V_1984 = Vertex(name = 'V_1984',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF15, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2414,(0,3):C.GC_2414,(0,0):C.GC_2394,(2,0):C.GC_2398,(1,1):C.GC_2394,(3,1):C.GC_2398})
V_1985 = Vertex(name = 'V_1985',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2419,(0,1):C.GC_2419})
V_1986 = Vertex(name = 'V_1986',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2425,(0,1):C.GC_2425})
V_1987 = Vertex(name = 'V_1987',
particles = [ P.c__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2431,(0,1):C.GC_2431})
V_1988 = Vertex(name = 'V_1988',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_42,(0,0):C.GC_787,(2,0):C.GC_788,(1,3):C.GC_47,(3,3):C.GC_48,(1,1):C.GC_47,(3,1):C.GC_48,(1,2):C.GC_53,(0,4):C.GC_787,(2,4):C.GC_788,(0,5):C.GC_54})
V_1989 = Vertex(name = 'V_1989',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,4):C.GC_44,(0,5):C.GC_45,(1,2):C.GC_3683,(2,2):C.GC_3688,(1,0):C.GC_2393,(2,0):C.GC_2397,(1,1):C.GC_836,(0,3):C.GC_837})
V_1990 = Vertex(name = 'V_1990',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3709,(0,1):C.GC_3720})
V_1991 = Vertex(name = 'V_1991',
particles = [ P.t__tilde__, P.c, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3729,(0,1):C.GC_3736})
V_1992 = Vertex(name = 'V_1992',
particles = [ P.t__tilde__, P.c, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF13, L.FFFF16, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_3708,(0,3):C.GC_3708,(1,0):C.GC_3682,(3,0):C.GC_3687,(0,1):C.GC_3682,(2,1):C.GC_3687})
V_1993 = Vertex(name = 'V_1993',
particles = [ P.t__tilde__, P.c, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3718,(0,1):C.GC_3718})
V_1994 = Vertex(name = 'V_1994',
particles = [ P.t__tilde__, P.c, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3726,(0,1):C.GC_3726})
V_1995 = Vertex(name = 'V_1995',
particles = [ P.t__tilde__, P.c, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3734,(0,1):C.GC_3734})
V_1996 = Vertex(name = 'V_1996',
particles = [ P.t__tilde__, P.t, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_1630,(0,3):C.GC_1630,(0,0):C.GC_1618,(2,0):C.GC_1620,(1,1):C.GC_1618,(3,1):C.GC_1620})
V_1997 = Vertex(name = 'V_1997',
particles = [ P.t__tilde__, P.t, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1632,(0,1):C.GC_1632})
V_1998 = Vertex(name = 'V_1998',
particles = [ P.t__tilde__, P.t, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1635,(0,1):C.GC_1635})
V_1999 = Vertex(name = 'V_1999',
particles = [ P.t__tilde__, P.t, P.u__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_1638,(0,1):C.GC_1638})
V_2000 = Vertex(name = 'V_2000',
particles = [ P.t__tilde__, P.t, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_2414,(0,3):C.GC_2414,(0,0):C.GC_2394,(2,0):C.GC_2398,(1,1):C.GC_2394,(3,1):C.GC_2398})
V_2001 = Vertex(name = 'V_2001',
particles = [ P.t__tilde__, P.t, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2419,(0,1):C.GC_2419})
V_2002 = Vertex(name = 'V_2002',
particles = [ P.t__tilde__, P.t, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2425,(0,1):C.GC_2425})
V_2003 = Vertex(name = 'V_2003',
particles = [ P.t__tilde__, P.t, P.c__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_2431,(0,1):C.GC_2431})
V_2004 = Vertex(name = 'V_2004',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_41,(0,7):C.GC_41,(0,0):C.GC_47,(2,0):C.GC_48,(1,3):C.GC_47,(3,3):C.GC_48,(1,1):C.GC_47,(3,1):C.GC_48,(1,2):C.GC_53,(0,4):C.GC_47,(2,4):C.GC_48,(0,5):C.GC_53})
V_2005 = Vertex(name = 'V_2005',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_42,(0,7):C.GC_42,(0,0):C.GC_3684,(2,0):C.GC_3689,(1,3):C.GC_3684,(3,3):C.GC_3689,(1,1):C.GC_3684,(3,1):C.GC_3689,(1,2):C.GC_54,(0,4):C.GC_3684,(2,4):C.GC_3689,(0,5):C.GC_54})
V_2006 = Vertex(name = 'V_2006',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF14, L.FFFF15, L.FFFF16, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,6):C.GC_44,(0,7):C.GC_44,(0,0):C.GC_827,(2,0):C.GC_828,(1,3):C.GC_827,(3,3):C.GC_828,(1,1):C.GC_827,(3,1):C.GC_828,(1,2):C.GC_831,(0,4):C.GC_827,(2,4):C.GC_828,(0,5):C.GC_831})
V_2007 = Vertex(name = 'V_2007',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF14, L.FFFF17, L.FFFF3, L.FFFF4 ],
couplings = {(1,2):C.GC_45,(0,3):C.GC_45,(1,0):C.GC_832,(0,1):C.GC_832})
V_2008 = Vertex(name = 'V_2008',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3715,(0,1):C.GC_3715})
V_2009 = Vertex(name = 'V_2009',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3722,(0,1):C.GC_3722})
V_2010 = Vertex(name = 'V_2010',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3731,(0,1):C.GC_3731})
V_2011 = Vertex(name = 'V_2011',
particles = [ P.t__tilde__, P.t, P.t__tilde__, P.t ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(1,0):C.GC_3738,(0,1):C.GC_3738})
V_2012 = Vertex(name = 'V_2012',
particles = [ P.e__plus__, P.e__minus__, P.ve__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2013 = Vertex(name = 'V_2013',
particles = [ P.e__plus__, P.e__minus__, P.ve__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2014 = Vertex(name = 'V_2014',
particles = [ P.e__plus__, P.e__minus__, P.vm__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2015 = Vertex(name = 'V_2015',
particles = [ P.e__plus__, P.e__minus__, P.vt__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2016 = Vertex(name = 'V_2016',
particles = [ P.mu__plus__, P.e__minus__, P.ve__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2017 = Vertex(name = 'V_2017',
particles = [ P.ta__plus__, P.e__minus__, P.ve__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2018 = Vertex(name = 'V_2018',
particles = [ P.e__plus__, P.mu__minus__, P.vm__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2019 = Vertex(name = 'V_2019',
particles = [ P.mu__plus__, P.mu__minus__, P.ve__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2020 = Vertex(name = 'V_2020',
particles = [ P.mu__plus__, P.mu__minus__, P.vm__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2021 = Vertex(name = 'V_2021',
particles = [ P.mu__plus__, P.mu__minus__, P.vm__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2022 = Vertex(name = 'V_2022',
particles = [ P.mu__plus__, P.mu__minus__, P.vt__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2023 = Vertex(name = 'V_2023',
particles = [ P.ta__plus__, P.mu__minus__, P.vm__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2024 = Vertex(name = 'V_2024',
particles = [ P.e__plus__, P.ta__minus__, P.vt__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2025 = Vertex(name = 'V_2025',
particles = [ P.mu__plus__, P.ta__minus__, P.vt__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2026 = Vertex(name = 'V_2026',
particles = [ P.ta__plus__, P.ta__minus__, P.ve__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2027 = Vertex(name = 'V_2027',
particles = [ P.ta__plus__, P.ta__minus__, P.vm__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2028 = Vertex(name = 'V_2028',
particles = [ P.ta__plus__, P.ta__minus__, P.vt__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_23,(0,0):C.GC_22})
V_2029 = Vertex(name = 'V_2029',
particles = [ P.ta__plus__, P.ta__minus__, P.vt__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_24})
V_2030 = Vertex(name = 'V_2030',
particles = [ P.ve__tilde__, P.ve, P.ve__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_23,(0,1):C.GC_23})
V_2031 = Vertex(name = 'V_2031',
particles = [ P.ve__tilde__, P.ve, P.ve__tilde__, P.ve ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_24,(0,1):C.GC_24})
V_2032 = Vertex(name = 'V_2032',
particles = [ P.vm__tilde__, P.ve, P.ve__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_23,(0,1):C.GC_24})
V_2033 = Vertex(name = 'V_2033',
particles = [ P.vt__tilde__, P.ve, P.ve__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_23,(0,1):C.GC_24})
V_2034 = Vertex(name = 'V_2034',
particles = [ P.vm__tilde__, P.vm, P.vm__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_23,(0,1):C.GC_23})
V_2035 = Vertex(name = 'V_2035',
particles = [ P.vm__tilde__, P.vm, P.vm__tilde__, P.vm ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_24,(0,1):C.GC_24})
V_2036 = Vertex(name = 'V_2036',
particles = [ P.vt__tilde__, P.vm, P.vm__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_23,(0,1):C.GC_24})
V_2037 = Vertex(name = 'V_2037',
particles = [ P.vt__tilde__, P.vt, P.vt__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_23,(0,1):C.GC_23})
V_2038 = Vertex(name = 'V_2038',
particles = [ P.vt__tilde__, P.vt, P.vt__tilde__, P.vt ],
color = [ '1' ],
lorentz = [ L.FFFF3, L.FFFF4 ],
couplings = {(0,0):C.GC_24,(0,1):C.GC_24})
V_2039 = Vertex(name = 'V_2039',
particles = [ P.u__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2040 = Vertex(name = 'V_2040',
particles = [ P.u__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_1066})
V_2041 = Vertex(name = 'V_2041',
particles = [ P.u__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3694})
V_2042 = Vertex(name = 'V_2042',
particles = [ P.u__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3702})
V_2043 = Vertex(name = 'V_2043',
particles = [ P.u__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2044 = Vertex(name = 'V_2044',
particles = [ P.u__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_1066})
V_2045 = Vertex(name = 'V_2045',
particles = [ P.u__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3694})
V_2046 = Vertex(name = 'V_2046',
particles = [ P.u__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3702})
V_2047 = Vertex(name = 'V_2047',
particles = [ P.u__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2048 = Vertex(name = 'V_2048',
particles = [ P.u__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_1066})
V_2049 = Vertex(name = 'V_2049',
particles = [ P.u__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3694})
V_2050 = Vertex(name = 'V_2050',
particles = [ P.u__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3702})
V_2051 = Vertex(name = 'V_2051',
particles = [ P.c__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2399})
V_2052 = Vertex(name = 'V_2052',
particles = [ P.c__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2402})
V_2053 = Vertex(name = 'V_2053',
particles = [ P.c__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2399})
V_2054 = Vertex(name = 'V_2054',
particles = [ P.c__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2402})
V_2055 = Vertex(name = 'V_2055',
particles = [ P.c__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2399})
V_2056 = Vertex(name = 'V_2056',
particles = [ P.c__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2402})
V_2057 = Vertex(name = 'V_2057',
particles = [ P.t__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3691})
V_2058 = Vertex(name = 'V_2058',
particles = [ P.t__tilde__, P.u, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3695})
V_2059 = Vertex(name = 'V_2059',
particles = [ P.t__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3691})
V_2060 = Vertex(name = 'V_2060',
particles = [ P.t__tilde__, P.u, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3695})
V_2061 = Vertex(name = 'V_2061',
particles = [ P.t__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3691})
V_2062 = Vertex(name = 'V_2062',
particles = [ P.t__tilde__, P.u, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3695})
V_2063 = Vertex(name = 'V_2063',
particles = [ P.u__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1621})
V_2064 = Vertex(name = 'V_2064',
particles = [ P.u__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1623})
V_2065 = Vertex(name = 'V_2065',
particles = [ P.u__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1621})
V_2066 = Vertex(name = 'V_2066',
particles = [ P.u__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1623})
V_2067 = Vertex(name = 'V_2067',
particles = [ P.u__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1621})
V_2068 = Vertex(name = 'V_2068',
particles = [ P.u__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1623})
V_2069 = Vertex(name = 'V_2069',
particles = [ P.c__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2070 = Vertex(name = 'V_2070',
particles = [ P.c__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_481})
V_2071 = Vertex(name = 'V_2071',
particles = [ P.c__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2400})
V_2072 = Vertex(name = 'V_2072',
particles = [ P.c__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2404})
V_2073 = Vertex(name = 'V_2073',
particles = [ P.c__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2074 = Vertex(name = 'V_2074',
particles = [ P.c__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_481})
V_2075 = Vertex(name = 'V_2075',
particles = [ P.c__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2400})
V_2076 = Vertex(name = 'V_2076',
particles = [ P.c__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2404})
V_2077 = Vertex(name = 'V_2077',
particles = [ P.c__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2078 = Vertex(name = 'V_2078',
particles = [ P.c__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_481})
V_2079 = Vertex(name = 'V_2079',
particles = [ P.c__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2400})
V_2080 = Vertex(name = 'V_2080',
particles = [ P.c__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2404})
V_2081 = Vertex(name = 'V_2081',
particles = [ P.t__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3692})
V_2082 = Vertex(name = 'V_2082',
particles = [ P.t__tilde__, P.c, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3697})
V_2083 = Vertex(name = 'V_2083',
particles = [ P.t__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3692})
V_2084 = Vertex(name = 'V_2084',
particles = [ P.t__tilde__, P.c, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3697})
V_2085 = Vertex(name = 'V_2085',
particles = [ P.t__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3692})
V_2086 = Vertex(name = 'V_2086',
particles = [ P.t__tilde__, P.c, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3697})
V_2087 = Vertex(name = 'V_2087',
particles = [ P.u__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1622})
V_2088 = Vertex(name = 'V_2088',
particles = [ P.u__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1625})
V_2089 = Vertex(name = 'V_2089',
particles = [ P.u__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1622})
V_2090 = Vertex(name = 'V_2090',
particles = [ P.u__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1625})
V_2091 = Vertex(name = 'V_2091',
particles = [ P.u__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1622})
V_2092 = Vertex(name = 'V_2092',
particles = [ P.u__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_1625})
V_2093 = Vertex(name = 'V_2093',
particles = [ P.c__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2401})
V_2094 = Vertex(name = 'V_2094',
particles = [ P.c__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2406})
V_2095 = Vertex(name = 'V_2095',
particles = [ P.c__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2401})
V_2096 = Vertex(name = 'V_2096',
particles = [ P.c__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2406})
V_2097 = Vertex(name = 'V_2097',
particles = [ P.c__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2401})
V_2098 = Vertex(name = 'V_2098',
particles = [ P.c__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2406})
V_2099 = Vertex(name = 'V_2099',
particles = [ P.t__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2100 = Vertex(name = 'V_2100',
particles = [ P.t__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_830})
V_2101 = Vertex(name = 'V_2101',
particles = [ P.t__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3693})
V_2102 = Vertex(name = 'V_2102',
particles = [ P.t__tilde__, P.t, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3699})
V_2103 = Vertex(name = 'V_2103',
particles = [ P.t__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2104 = Vertex(name = 'V_2104',
particles = [ P.t__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_830})
V_2105 = Vertex(name = 'V_2105',
particles = [ P.t__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3693})
V_2106 = Vertex(name = 'V_2106',
particles = [ P.t__tilde__, P.t, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3699})
V_2107 = Vertex(name = 'V_2107',
particles = [ P.t__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_37})
V_2108 = Vertex(name = 'V_2108',
particles = [ P.t__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_27,(0,0):C.GC_830})
V_2109 = Vertex(name = 'V_2109',
particles = [ P.t__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3693})
V_2110 = Vertex(name = 'V_2110',
particles = [ P.t__tilde__, P.t, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3699})
V_2111 = Vertex(name = 'V_2111',
particles = [ P.u__tilde__, P.d, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1143,(0,4):C.GC_1234,(0,2):C.GC_1233,(0,3):C.GC_1233,(0,0):C.GC_1196,(0,1):C.GC_1232})
V_2112 = Vertex(name = 'V_2112',
particles = [ P.u__tilde__, P.d, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1261,(0,4):C.GC_1285,(0,2):C.GC_1284,(0,3):C.GC_1284,(0,0):C.GC_1280,(0,1):C.GC_1283})
V_2113 = Vertex(name = 'V_2113',
particles = [ P.c__tilde__, P.d, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1834,(0,4):C.GC_1902,(0,2):C.GC_1901,(0,3):C.GC_1901,(0,0):C.GC_1903,(0,1):C.GC_1900})
V_2114 = Vertex(name = 'V_2114',
particles = [ P.c__tilde__, P.d, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1971,(0,4):C.GC_1983,(0,2):C.GC_1982,(0,3):C.GC_1982,(0,0):C.GC_1984,(0,1):C.GC_1981})
V_2115 = Vertex(name = 'V_2115',
particles = [ P.t__tilde__, P.d, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2720,(0,4):C.GC_2817,(0,2):C.GC_2816,(0,3):C.GC_2816,(0,0):C.GC_2776,(0,1):C.GC_2815})
V_2116 = Vertex(name = 'V_2116',
particles = [ P.t__tilde__, P.d, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2867,(0,4):C.GC_2896,(0,2):C.GC_2895,(0,3):C.GC_2895,(0,0):C.GC_2892,(0,1):C.GC_2894})
V_2117 = Vertex(name = 'V_2117',
particles = [ P.u__tilde__, P.d, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1143,(0,4):C.GC_1237,(0,2):C.GC_1236,(0,3):C.GC_1236,(0,0):C.GC_1197,(0,1):C.GC_1235})
V_2118 = Vertex(name = 'V_2118',
particles = [ P.u__tilde__, P.d, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1261,(0,4):C.GC_1288,(0,2):C.GC_1287,(0,3):C.GC_1287,(0,0):C.GC_1281,(0,1):C.GC_1286})
V_2119 = Vertex(name = 'V_2119',
particles = [ P.c__tilde__, P.d, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1834,(0,4):C.GC_1906,(0,2):C.GC_1905,(0,3):C.GC_1905,(0,0):C.GC_1907,(0,1):C.GC_1904})
V_2120 = Vertex(name = 'V_2120',
particles = [ P.c__tilde__, P.d, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1971,(0,4):C.GC_1987,(0,2):C.GC_1986,(0,3):C.GC_1986,(0,0):C.GC_1988,(0,1):C.GC_1985})
V_2121 = Vertex(name = 'V_2121',
particles = [ P.t__tilde__, P.d, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2720,(0,4):C.GC_2820,(0,2):C.GC_2819,(0,3):C.GC_2819,(0,0):C.GC_2777,(0,1):C.GC_2818})
V_2122 = Vertex(name = 'V_2122',
particles = [ P.t__tilde__, P.d, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2867,(0,4):C.GC_2899,(0,2):C.GC_2898,(0,3):C.GC_2898,(0,0):C.GC_2893,(0,1):C.GC_2897})
V_2123 = Vertex(name = 'V_2123',
particles = [ P.u__tilde__, P.d, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1143,(0,4):C.GC_1254,(0,2):C.GC_1253,(0,3):C.GC_1253,(0,0):C.GC_1212,(0,1):C.GC_1252})
V_2124 = Vertex(name = 'V_2124',
particles = [ P.u__tilde__, P.d, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1261,(0,4):C.GC_1291,(0,2):C.GC_1290,(0,3):C.GC_1290,(0,0):C.GC_1282,(0,1):C.GC_1289})
V_2125 = Vertex(name = 'V_2125',
particles = [ P.c__tilde__, P.d, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1834,(0,4):C.GC_1955,(0,2):C.GC_1954,(0,3):C.GC_1954,(0,0):C.GC_1956,(0,1):C.GC_1953})
V_2126 = Vertex(name = 'V_2126',
particles = [ P.c__tilde__, P.d, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1971,(0,4):C.GC_2000,(0,2):C.GC_1999,(0,3):C.GC_1999,(0,0):C.GC_2001,(0,1):C.GC_1998})
V_2127 = Vertex(name = 'V_2127',
particles = [ P.t__tilde__, P.d, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2720,(0,4):C.GC_2842,(0,2):C.GC_2841,(0,3):C.GC_2841,(0,0):C.GC_2839,(0,1):C.GC_2840})
V_2128 = Vertex(name = 'V_2128',
particles = [ P.t__tilde__, P.d, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2867,(0,4):C.GC_2903,(0,2):C.GC_2902,(0,3):C.GC_2902,(0,0):C.GC_2900,(0,1):C.GC_2901})
V_2129 = Vertex(name = 'V_2129',
particles = [ P.u__tilde__, P.s, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1292,(0,4):C.GC_1396,(0,2):C.GC_1395,(0,3):C.GC_1395,(0,0):C.GC_1357,(0,1):C.GC_1394})
V_2130 = Vertex(name = 'V_2130',
particles = [ P.u__tilde__, P.s, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1428,(0,4):C.GC_1458,(0,2):C.GC_1457,(0,3):C.GC_1457,(0,0):C.GC_1453,(0,1):C.GC_1456})
V_2131 = Vertex(name = 'V_2131',
particles = [ P.c__tilde__, P.s, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2008,(0,4):C.GC_2075,(0,2):C.GC_2074,(0,3):C.GC_2074,(0,0):C.GC_2114,(0,1):C.GC_2073})
V_2132 = Vertex(name = 'V_2132',
particles = [ P.c__tilde__, P.s, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2162,(0,4):C.GC_2177,(0,2):C.GC_2176,(0,3):C.GC_2176,(0,0):C.GC_2181,(0,1):C.GC_2175})
V_2133 = Vertex(name = 'V_2133',
particles = [ P.t__tilde__, P.s, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3068,(0,4):C.GC_3168,(0,2):C.GC_3167,(0,3):C.GC_3167,(0,0):C.GC_3134,(0,1):C.GC_3166})
V_2134 = Vertex(name = 'V_2134',
particles = [ P.t__tilde__, P.s, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3231,(0,4):C.GC_3263,(0,2):C.GC_3262,(0,3):C.GC_3262,(0,0):C.GC_3259,(0,1):C.GC_3261})
V_2135 = Vertex(name = 'V_2135',
particles = [ P.u__tilde__, P.s, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1292,(0,4):C.GC_1399,(0,2):C.GC_1398,(0,3):C.GC_1398,(0,0):C.GC_1358,(0,1):C.GC_1397})
V_2136 = Vertex(name = 'V_2136',
particles = [ P.u__tilde__, P.s, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1428,(0,4):C.GC_1461,(0,2):C.GC_1460,(0,3):C.GC_1460,(0,0):C.GC_1454,(0,1):C.GC_1459})
V_2137 = Vertex(name = 'V_2137',
particles = [ P.c__tilde__, P.s, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2008,(0,4):C.GC_2078,(0,2):C.GC_2077,(0,3):C.GC_2077,(0,0):C.GC_2115,(0,1):C.GC_2076})
V_2138 = Vertex(name = 'V_2138',
particles = [ P.c__tilde__, P.s, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2162,(0,4):C.GC_2180,(0,2):C.GC_2179,(0,3):C.GC_2179,(0,0):C.GC_2182,(0,1):C.GC_2178})
V_2139 = Vertex(name = 'V_2139',
particles = [ P.t__tilde__, P.s, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3068,(0,4):C.GC_3171,(0,2):C.GC_3170,(0,3):C.GC_3170,(0,0):C.GC_3135,(0,1):C.GC_3169})
V_2140 = Vertex(name = 'V_2140',
particles = [ P.t__tilde__, P.s, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3231,(0,4):C.GC_3266,(0,2):C.GC_3265,(0,3):C.GC_3265,(0,0):C.GC_3260,(0,1):C.GC_3264})
V_2141 = Vertex(name = 'V_2141',
particles = [ P.u__tilde__, P.s, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1292,(0,4):C.GC_1421,(0,2):C.GC_1420,(0,3):C.GC_1420,(0,0):C.GC_1371,(0,1):C.GC_1419})
V_2142 = Vertex(name = 'V_2142',
particles = [ P.u__tilde__, P.s, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1428,(0,4):C.GC_1464,(0,2):C.GC_1463,(0,3):C.GC_1463,(0,0):C.GC_1455,(0,1):C.GC_1462})
V_2143 = Vertex(name = 'V_2143',
particles = [ P.c__tilde__, P.s, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2008,(0,4):C.GC_2142,(0,2):C.GC_2141,(0,3):C.GC_2141,(0,0):C.GC_2143,(0,1):C.GC_2140})
V_2144 = Vertex(name = 'V_2144',
particles = [ P.c__tilde__, P.s, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2162,(0,4):C.GC_2194,(0,2):C.GC_2193,(0,3):C.GC_2193,(0,0):C.GC_2195,(0,1):C.GC_2192})
V_2145 = Vertex(name = 'V_2145',
particles = [ P.t__tilde__, P.s, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3068,(0,4):C.GC_3204,(0,2):C.GC_3203,(0,3):C.GC_3203,(0,0):C.GC_3201,(0,1):C.GC_3202})
V_2146 = Vertex(name = 'V_2146',
particles = [ P.t__tilde__, P.s, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3231,(0,4):C.GC_3270,(0,2):C.GC_3269,(0,3):C.GC_3269,(0,0):C.GC_3267,(0,1):C.GC_3268})
V_2147 = Vertex(name = 'V_2147',
particles = [ P.u__tilde__, P.b, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1465,(0,4):C.GC_1574,(0,2):C.GC_1573,(0,3):C.GC_1573,(0,0):C.GC_1524,(0,1):C.GC_1572})
V_2148 = Vertex(name = 'V_2148',
particles = [ P.u__tilde__, P.b, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1791,(0,4):C.GC_1827,(0,2):C.GC_1826,(0,3):C.GC_1826,(0,0):C.GC_1822,(0,1):C.GC_1825})
V_2149 = Vertex(name = 'V_2149',
particles = [ P.c__tilde__, P.b, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2205,(0,4):C.GC_2285,(0,2):C.GC_2284,(0,3):C.GC_2284,(0,0):C.GC_2282,(0,1):C.GC_2283})
V_2150 = Vertex(name = 'V_2150',
particles = [ P.c__tilde__, P.b, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2366,(0,4):C.GC_2526,(0,2):C.GC_2525,(0,3):C.GC_2525,(0,0):C.GC_2523,(0,1):C.GC_2524})
V_2151 = Vertex(name = 'V_2151',
particles = [ P.t__tilde__, P.b, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3498,(0,4):C.GC_3609,(0,2):C.GC_3608,(0,3):C.GC_3608,(0,0):C.GC_3558,(0,1):C.GC_3607})
V_2152 = Vertex(name = 'V_2152',
particles = [ P.t__tilde__, P.b, P.e__plus__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3901,(0,4):C.GC_3937,(0,2):C.GC_3936,(0,3):C.GC_3936,(0,0):C.GC_3933,(0,1):C.GC_3935})
V_2153 = Vertex(name = 'V_2153',
particles = [ P.u__tilde__, P.b, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1465,(0,4):C.GC_1577,(0,2):C.GC_1576,(0,3):C.GC_1576,(0,0):C.GC_1525,(0,1):C.GC_1575})
V_2154 = Vertex(name = 'V_2154',
particles = [ P.u__tilde__, P.b, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1791,(0,4):C.GC_1830,(0,2):C.GC_1829,(0,3):C.GC_1829,(0,0):C.GC_1823,(0,1):C.GC_1828})
V_2155 = Vertex(name = 'V_2155',
particles = [ P.c__tilde__, P.b, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2205,(0,4):C.GC_2289,(0,2):C.GC_2288,(0,3):C.GC_2288,(0,0):C.GC_2286,(0,1):C.GC_2287})
V_2156 = Vertex(name = 'V_2156',
particles = [ P.c__tilde__, P.b, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2366,(0,4):C.GC_2539,(0,2):C.GC_2538,(0,3):C.GC_2538,(0,0):C.GC_2536,(0,1):C.GC_2537})
V_2157 = Vertex(name = 'V_2157',
particles = [ P.t__tilde__, P.b, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3498,(0,4):C.GC_3612,(0,2):C.GC_3611,(0,3):C.GC_3611,(0,0):C.GC_3559,(0,1):C.GC_3610})
V_2158 = Vertex(name = 'V_2158',
particles = [ P.t__tilde__, P.b, P.mu__plus__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3901,(0,4):C.GC_3940,(0,2):C.GC_3939,(0,3):C.GC_3939,(0,0):C.GC_3934,(0,1):C.GC_3938})
V_2159 = Vertex(name = 'V_2159',
particles = [ P.u__tilde__, P.b, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1465,(0,4):C.GC_1594,(0,2):C.GC_1593,(0,3):C.GC_1593,(0,0):C.GC_1544,(0,1):C.GC_1592})
V_2160 = Vertex(name = 'V_2160',
particles = [ P.u__tilde__, P.b, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_1791,(0,4):C.GC_1833,(0,2):C.GC_1832,(0,3):C.GC_1832,(0,0):C.GC_1824,(0,1):C.GC_1831})
V_2161 = Vertex(name = 'V_2161',
particles = [ P.c__tilde__, P.b, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2205,(0,4):C.GC_2341,(0,2):C.GC_2340,(0,3):C.GC_2340,(0,0):C.GC_2338,(0,1):C.GC_2339})
V_2162 = Vertex(name = 'V_2162',
particles = [ P.c__tilde__, P.b, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_2366,(0,4):C.GC_2600,(0,2):C.GC_2599,(0,3):C.GC_2599,(0,0):C.GC_2597,(0,1):C.GC_2598})
V_2163 = Vertex(name = 'V_2163',
particles = [ P.t__tilde__, P.b, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3498,(0,4):C.GC_3634,(0,2):C.GC_3633,(0,3):C.GC_3633,(0,0):C.GC_3631,(0,1):C.GC_3632})
V_2164 = Vertex(name = 'V_2164',
particles = [ P.t__tilde__, P.b, P.ta__plus__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF10, L.FFFF11, L.FFFF18, L.FFFF19, L.FFFF20, L.FFFF4 ],
couplings = {(0,5):C.GC_3901,(0,4):C.GC_3956,(0,2):C.GC_3955,(0,3):C.GC_3955,(0,0):C.GC_3953,(0,1):C.GC_3954})
V_2165 = Vertex(name = 'V_2165',
particles = [ P.d__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2166 = Vertex(name = 'V_2166',
particles = [ P.d__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_540})
V_2167 = Vertex(name = 'V_2167',
particles = [ P.d__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2657})
V_2168 = Vertex(name = 'V_2168',
particles = [ P.d__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2658})
V_2169 = Vertex(name = 'V_2169',
particles = [ P.d__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2170 = Vertex(name = 'V_2170',
particles = [ P.d__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_540})
V_2171 = Vertex(name = 'V_2171',
particles = [ P.d__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2657})
V_2172 = Vertex(name = 'V_2172',
particles = [ P.d__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2658})
V_2173 = Vertex(name = 'V_2173',
particles = [ P.d__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2174 = Vertex(name = 'V_2174',
particles = [ P.d__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_540})
V_2175 = Vertex(name = 'V_2175',
particles = [ P.d__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2657})
V_2176 = Vertex(name = 'V_2176',
particles = [ P.d__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2658})
V_2177 = Vertex(name = 'V_2177',
particles = [ P.s__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2928})
V_2178 = Vertex(name = 'V_2178',
particles = [ P.s__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2931})
V_2179 = Vertex(name = 'V_2179',
particles = [ P.s__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2928})
V_2180 = Vertex(name = 'V_2180',
particles = [ P.s__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2931})
V_2181 = Vertex(name = 'V_2181',
particles = [ P.s__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2928})
V_2182 = Vertex(name = 'V_2182',
particles = [ P.s__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2931})
V_2183 = Vertex(name = 'V_2183',
particles = [ P.b__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2929})
V_2184 = Vertex(name = 'V_2184',
particles = [ P.b__tilde__, P.d, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2933})
V_2185 = Vertex(name = 'V_2185',
particles = [ P.b__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2929})
V_2186 = Vertex(name = 'V_2186',
particles = [ P.b__tilde__, P.d, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2933})
V_2187 = Vertex(name = 'V_2187',
particles = [ P.b__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2929})
V_2188 = Vertex(name = 'V_2188',
particles = [ P.b__tilde__, P.d, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_2933})
V_2189 = Vertex(name = 'V_2189',
particles = [ P.d__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3305})
V_2190 = Vertex(name = 'V_2190',
particles = [ P.d__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3309})
V_2191 = Vertex(name = 'V_2191',
particles = [ P.d__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3305})
V_2192 = Vertex(name = 'V_2192',
particles = [ P.d__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3309})
V_2193 = Vertex(name = 'V_2193',
particles = [ P.d__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3305})
V_2194 = Vertex(name = 'V_2194',
particles = [ P.d__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3309})
V_2195 = Vertex(name = 'V_2195',
particles = [ P.s__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2196 = Vertex(name = 'V_2196',
particles = [ P.s__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_723})
V_2197 = Vertex(name = 'V_2197',
particles = [ P.s__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3306})
V_2198 = Vertex(name = 'V_2198',
particles = [ P.s__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3311})
V_2199 = Vertex(name = 'V_2199',
particles = [ P.s__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2200 = Vertex(name = 'V_2200',
particles = [ P.s__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_723})
V_2201 = Vertex(name = 'V_2201',
particles = [ P.s__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3306})
V_2202 = Vertex(name = 'V_2202',
particles = [ P.s__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3311})
V_2203 = Vertex(name = 'V_2203',
particles = [ P.s__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2204 = Vertex(name = 'V_2204',
particles = [ P.s__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_723})
V_2205 = Vertex(name = 'V_2205',
particles = [ P.s__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3306})
V_2206 = Vertex(name = 'V_2206',
particles = [ P.s__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3311})
V_2207 = Vertex(name = 'V_2207',
particles = [ P.b__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3307})
V_2208 = Vertex(name = 'V_2208',
particles = [ P.b__tilde__, P.s, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3313})
V_2209 = Vertex(name = 'V_2209',
particles = [ P.b__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3307})
V_2210 = Vertex(name = 'V_2210',
particles = [ P.b__tilde__, P.s, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3313})
V_2211 = Vertex(name = 'V_2211',
particles = [ P.b__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3307})
V_2212 = Vertex(name = 'V_2212',
particles = [ P.b__tilde__, P.s, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_3313})
V_2213 = Vertex(name = 'V_2213',
particles = [ P.d__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4063})
V_2214 = Vertex(name = 'V_2214',
particles = [ P.d__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4067})
V_2215 = Vertex(name = 'V_2215',
particles = [ P.d__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4063})
V_2216 = Vertex(name = 'V_2216',
particles = [ P.d__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4067})
V_2217 = Vertex(name = 'V_2217',
particles = [ P.d__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4063})
V_2218 = Vertex(name = 'V_2218',
particles = [ P.d__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4067})
V_2219 = Vertex(name = 'V_2219',
particles = [ P.s__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4064})
V_2220 = Vertex(name = 'V_2220',
particles = [ P.s__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4069})
V_2221 = Vertex(name = 'V_2221',
particles = [ P.s__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4064})
V_2222 = Vertex(name = 'V_2222',
particles = [ P.s__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4069})
V_2223 = Vertex(name = 'V_2223',
particles = [ P.s__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4064})
V_2224 = Vertex(name = 'V_2224',
particles = [ P.s__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4069})
V_2225 = Vertex(name = 'V_2225',
particles = [ P.b__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2226 = Vertex(name = 'V_2226',
particles = [ P.b__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_434})
V_2227 = Vertex(name = 'V_2227',
particles = [ P.b__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4065})
V_2228 = Vertex(name = 'V_2228',
particles = [ P.b__tilde__, P.b, P.ve__tilde__, P.ve ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4071})
V_2229 = Vertex(name = 'V_2229',
particles = [ P.b__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2230 = Vertex(name = 'V_2230',
particles = [ P.b__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_434})
V_2231 = Vertex(name = 'V_2231',
particles = [ P.b__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4065})
V_2232 = Vertex(name = 'V_2232',
particles = [ P.b__tilde__, P.b, P.vm__tilde__, P.vm ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4071})
V_2233 = Vertex(name = 'V_2233',
particles = [ P.b__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_25,(0,0):C.GC_21})
V_2234 = Vertex(name = 'V_2234',
particles = [ P.b__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF12, L.FFFF4 ],
couplings = {(0,1):C.GC_26,(0,0):C.GC_434})
V_2235 = Vertex(name = 'V_2235',
particles = [ P.b__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4065})
V_2236 = Vertex(name = 'V_2236',
particles = [ P.b__tilde__, P.b, P.vt__tilde__, P.vt ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.FFFF4 ],
couplings = {(0,0):C.GC_4071})
V_2237 = Vertex(name = 'V_2237',
particles = [ P.s__tilde__, P.d, P.s__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_1928,(2,0):C.GC_1931,(1,2):C.GC_1928,(3,2):C.GC_1931,(1,1):C.GC_1928,(3,1):C.GC_1931,(0,3):C.GC_1928,(2,3):C.GC_1931})
V_2238 = Vertex(name = 'V_2238',
particles = [ P.b__tilde__, P.d, P.s__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_2763,(2,0):C.GC_2766,(1,2):C.GC_1929,(3,2):C.GC_1932,(1,1):C.GC_2763,(3,1):C.GC_2766,(0,3):C.GC_1929,(2,3):C.GC_1932})
V_2239 = Vertex(name = 'V_2239',
particles = [ P.s__tilde__, P.b, P.s__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_1909,(2,0):C.GC_1912,(1,2):C.GC_1909,(3,2):C.GC_1912,(1,1):C.GC_2309,(3,1):C.GC_2312,(0,3):C.GC_2309,(2,3):C.GC_2312})
V_2240 = Vertex(name = 'V_2240',
particles = [ P.b__tilde__, P.d, P.b__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_2764,(2,0):C.GC_2767,(1,2):C.GC_2764,(3,2):C.GC_2767,(1,1):C.GC_2764,(3,1):C.GC_2767,(0,3):C.GC_2764,(2,3):C.GC_2767})
V_2241 = Vertex(name = 'V_2241',
particles = [ P.b__tilde__, P.s, P.b__tilde__, P.d ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_2780,(2,0):C.GC_2783,(1,2):C.GC_2780,(3,2):C.GC_2783,(1,1):C.GC_3112,(3,1):C.GC_3115,(0,3):C.GC_3112,(2,3):C.GC_3115})
V_2242 = Vertex(name = 'V_2242',
particles = [ P.d__tilde__, P.s, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_1351,(2,0):C.GC_1354,(1,2):C.GC_1351,(3,2):C.GC_1354,(1,1):C.GC_1351,(3,1):C.GC_1354,(0,3):C.GC_1351,(2,3):C.GC_1354})
V_2243 = Vertex(name = 'V_2243',
particles = [ P.b__tilde__, P.s, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_3116,(2,0):C.GC_3119,(1,2):C.GC_1353,(3,2):C.GC_1356,(1,1):C.GC_3116,(3,1):C.GC_3119,(0,3):C.GC_1353,(2,3):C.GC_1356})
V_2244 = Vertex(name = 'V_2244',
particles = [ P.d__tilde__, P.b, P.d__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_1327,(2,0):C.GC_1330,(1,2):C.GC_1327,(3,2):C.GC_1330,(1,1):C.GC_1526,(3,1):C.GC_1529,(0,3):C.GC_1526,(2,3):C.GC_1529})
V_2245 = Vertex(name = 'V_2245',
particles = [ P.b__tilde__, P.s, P.b__tilde__, P.s ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_3118,(2,0):C.GC_3121,(1,2):C.GC_3118,(3,2):C.GC_3121,(1,1):C.GC_3118,(3,1):C.GC_3121,(0,3):C.GC_3118,(2,3):C.GC_3121})
V_2246 = Vertex(name = 'V_2246',
particles = [ P.d__tilde__, P.b, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_1512,(2,0):C.GC_1515,(1,2):C.GC_1512,(3,2):C.GC_1515,(1,1):C.GC_1512,(3,1):C.GC_1515,(0,3):C.GC_1512,(2,3):C.GC_1515})
V_2247 = Vertex(name = 'V_2247',
particles = [ P.s__tilde__, P.b, P.d__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_2290,(2,0):C.GC_2293,(1,2):C.GC_1513,(3,2):C.GC_1516,(1,1):C.GC_2290,(3,1):C.GC_2293,(0,3):C.GC_1513,(2,3):C.GC_1516})
V_2248 = Vertex(name = 'V_2248',
particles = [ P.s__tilde__, P.b, P.s__tilde__, P.b ],
color = [ 'Identity(1,2)*Identity(3,4)', 'Identity(1,4)*Identity(2,3)', 'T(-1,2,1)*T(-1,4,3)', 'T(-1,2,3)*T(-1,4,1)' ],
lorentz = [ L.FFFF12, L.FFFF13, L.FFFF15, L.FFFF16 ],
couplings = {(0,0):C.GC_2291,(2,0):C.GC_2294,(1,2):C.GC_2291,(3,2):C.GC_2294,(1,1):C.GC_2291,(3,1):C.GC_2294,(0,3):C.GC_2291,(2,3):C.GC_2294})
V_2249 = Vertex(name = 'V_2249',
particles = [ P.a, P.a, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_207})
V_2250 = Vertex(name = 'V_2250',
particles = [ P.g, P.g, P.H1 ],
color = [ 'Identity(1,2)' ],
lorentz = [ L.VVS2, L.VVS3, L.VVS4, L.VVS5 ],
couplings = {(0,0):C.GC_208,(0,2):C.GC_221,(0,1):C.GC_217,(0,3):C.GC_212})
V_2251 = Vertex(name = 'V_2251',
particles = [ P.a, P.Z, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_211})
V_2252 = Vertex(name = 'V_2252',
particles = [ P.a, P.Z1, P.H ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_211})
V_2253 = Vertex(name = 'V_2253',
particles = [ P.a, P.Z1, P.H1 ],
color = [ '1' ],
lorentz = [ L.VVS2 ],
couplings = {(0,0):C.GC_225})
V_2254 = Vertex(name = 'V_2254',
particles = [ P.g, P.g, P.g, P.H1 ],
color = [ 'f(1,2,3)' ],
lorentz = [ L.VVVS3, L.VVVS4, L.VVVS5, L.VVVS6, L.VVVS7 ],
couplings = {(0,2):C.GC_213,(0,4):C.GC_222,(0,3):C.GC_218,(0,1):C.GC_215,(0,0):C.GC_209})
V_2255 = Vertex(name = 'V_2255',
particles = [ P.g, P.g, P.g, P.g, P.H1 ],
color = [ 'f(-1,1,2)*f(-1,3,4)', 'f(-1,1,3)*f(-1,2,4)', 'f(-1,1,4)*f(-1,2,3)' ],
lorentz = [ L.VVVVS1, L.VVVVS10, L.VVVVS11, L.VVVVS12, L.VVVVS13, L.VVVVS14, L.VVVVS15, L.VVVVS16, L.VVVVS18, L.VVVVS2, L.VVVVS20, L.VVVVS3, L.VVVVS5, L.VVVVS7, L.VVVVS8 ],
couplings = {(2,5):C.GC_214,(2,8):C.GC_223,(1,4):C.GC_214,(1,10):C.GC_223,(2,6):C.GC_220,(0,11):C.GC_216,(0,12):C.GC_224,(1,7):C.GC_220,(0,3):C.GC_219,(1,2):C.GC_216,(2,1):C.GC_216,(0,9):C.GC_214,(1,13):C.GC_210,(0,0):C.GC_210,(2,14):C.GC_210})
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| 646,263
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d1a2733dc3ad45b92fb286ce40d129a0d2db645f
| 155
|
py
|
Python
|
rcds/__init__.py
|
jordanbertasso/rcds
|
d3d655a59a350042d65476793db84e761de04829
|
[
"BSD-3-Clause"
] | 5
|
2020-04-08T06:26:13.000Z
|
2020-06-23T04:33:40.000Z
|
rcds/__init__.py
|
jordanbertasso/rcds
|
d3d655a59a350042d65476793db84e761de04829
|
[
"BSD-3-Clause"
] | 144
|
2020-07-06T11:26:49.000Z
|
2022-02-01T14:33:28.000Z
|
rcds/__init__.py
|
jordanbertasso/rcds
|
d3d655a59a350042d65476793db84e761de04829
|
[
"BSD-3-Clause"
] | 7
|
2020-07-22T12:38:32.000Z
|
2021-12-21T14:27:54.000Z
|
from rcds.challenge import Challenge # noqa: F401
from rcds.challenge import ChallengeLoader # noqa: F401
from rcds.project import Project # noqa: F401
| 38.75
| 56
| 0.787097
| 21
| 155
| 5.809524
| 0.380952
| 0.196721
| 0.278689
| 0.377049
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068702
| 0.154839
| 155
| 3
| 57
| 51.666667
| 0.862595
| 0.206452
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ae689b00bbe46999be89f16ee4bcddd411b3e80e
| 2,093
|
py
|
Python
|
pyproct/data/handler/featurearray/test/data/__init__.py
|
victor-gil-sepulveda/pyProCT
|
2777c73efb48d5ca6543c69a31719421c4d54694
|
[
"MIT"
] | 10
|
2015-03-07T09:00:06.000Z
|
2019-01-25T15:00:59.000Z
|
pyproct/data/handler/featurearray/test/data/__init__.py
|
victor-gil-sepulveda/pyProCT
|
2777c73efb48d5ca6543c69a31719421c4d54694
|
[
"MIT"
] | 6
|
2015-01-08T11:17:14.000Z
|
2018-10-08T15:17:32.000Z
|
pyproct/data/handler/featurearray/test/data/__init__.py
|
victor-gil-sepulveda/pyProCT
|
2777c73efb48d5ca6543c69a31719421c4d54694
|
[
"MIT"
] | 11
|
2015-03-02T11:13:24.000Z
|
2022-02-22T16:21:56.000Z
|
expected_1 = ({
'__feature_4': [ 42., 67., 6., 92., 80., 10., 90., 5., 100.,
40., 23., 44., 81., 53., 37., 7., 79., 45., 87.],
'__feature_2': [ 91., 15., 36., 51., 32., 11., 38., 56., 21., 34., 75.,
77., 98., 71., 95., 4., 83., 70., 33.],
'__feature_3': [ 17., 82., 26., 99., 72., 35., 54., 22., 20., 25., 29.,
94., 66., 84., 55., 12., 43., 1., 16.],
'__feature_0': [ 63., 89., 49., 24., 41., 48., 58., 47., 61., 14., 59.,
96., 88., 65., 19., 74., 97., 50., 57.],
'__feature_1': [ 27., 52., 18., 76., 60., 62., 30., 8., 86., 78., 31.,
39., 93., 2., 28., 46., 85., 3., 73.]}, 19)
expected_2 = ({
'cuatro': [ 17., 82., 26., 99., 72., 35., 54., 22., 20., 25., 29.,
94., 66., 84., 55., 12., 43., 1., 16.],
'dos': [ 27., 52., 18., 76., 60., 62., 30., 8., 86., 78., 31.,
39., 93., 2., 28., 46., 85., 3., 73.],
'tres': [ 91., 15., 36., 51., 32., 11., 38., 56., 21., 34., 75.,
77., 98., 71., 95., 4., 83., 70., 33.],
'cinco': [ 42., 67., 6., 92., 80., 10., 90., 5., 100.,
40., 23., 44., 81., 53., 37., 7., 79., 45., 87.],
'uno': [ 63., 89., 49., 24., 41., 48., 58., 47., 61., 14., 59.,
96., 88., 65., 19., 74., 97., 50., 57.]}, 19)
expected_3 = ({
'cuatro': [ 17., 82., 26., 99., 72., 35., 54., 22., 20., 25., 29.,
94., 66., 84., 55., 12., 43., 1., 16.],
'tres': [ 91., 15., 36., 51., 32., 11., 38., 56., 21., 34., 75.,
77., 98., 71., 95., 4., 83., 70., 33.]}, 19)
expected_single_row = {
'single': [ 91., 15., 36., 51., 32., 11., 38., 56., 21., 34., 75.,
77., 98., 71., 95., 4., 83., 70., 33.]}
| 58.138889
| 96
| 0.298137
| 277
| 2,093
| 2.180505
| 0.379061
| 0.02649
| 0.039735
| 0.05298
| 0.822848
| 0.822848
| 0.822848
| 0.822848
| 0.822848
| 0.822848
| 0
| 0.409167
| 0.42666
| 2,093
| 36
| 97
| 58.138889
| 0.094167
| 0
| 0
| 0.366667
| 0
| 0
| 0.043977
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ae84df8f048a8e2c453aaf07113dafe336bd52a2
| 2,911
|
py
|
Python
|
Trikfb/trik_dec.py
|
shyamjangid07/Reverse-Engineering
|
469efabcd6057f7895d8d891f1fabdf2ffe730b0
|
[
"Apache-2.0"
] | 337
|
2020-08-15T12:22:14.000Z
|
2022-03-29T06:05:15.000Z
|
Trikfb/trik_dec.py
|
Wh014M/Reverse-Engineering
|
f7aae2c43f7ea4a6730964d085c07814b6660a53
|
[
"Apache-2.0"
] | 3
|
2020-11-12T14:30:48.000Z
|
2021-05-18T16:56:22.000Z
|
Trikfb/trik_dec.py
|
Wh014M/Reverse-Engineering
|
f7aae2c43f7ea4a6730964d085c07814b6660a53
|
[
"Apache-2.0"
] | 83
|
2020-08-15T00:22:58.000Z
|
2022-03-31T08:40:23.000Z
|
# Decompiled by HTR-TECH | TAHMID RAYAT
# Github : https://github.com/htr-tech
#---------------------------------------
# Auto Dis Parser 2.2.0
# Source File : trik_1.pyc
# Bytecode Version : 2.7
# Time : Sun Aug 9 12:07:31 2020
#---------------------------------------
import os
import sys
import time
os.system('clear')
logo = '\n[RG4] Black_Coder Team<<<\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x97\nRemaja berkarya \xe2\x95\x91\nBukan Bergaya Facebook \xe2\x95\x91\nAuthor : Tegar ID \xe2\x95\x91\nKontak : 08212506xxxx \xe2\x95\x91\nGithub : Https://github.com/Tegar-ID \xe2\x95\x91\n\xe2\x95\x91-\xe2\x96\xba {01}./P3R50N G4N5 \xe2\x95\x94\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x9d\n\xe2\x95\x91-\xe2\x96\xba {02}Mr XHamster \xe2\x95\x94\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x97\n\xe2\x95\x91-\xe2\x96\xba {03}MeYouSue \xe2\x95\x91 Tegar \xe2\x95\x91]\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x97\n\xe2\x95\x91-\xe2\x96\xba {04}Lucky Know \xe2\x95\x9a\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x9d \xe2\x95\x91\n\xe2\x95\x91-\xe2\x96\xba {05}XGanz \xe2\x95\x91\n\xe2\x95\x91-\xe2\x96\xba {06}Mr.Cekliz \xe2\x95\x91\n\xe2\x95\x91-\xe2\x96\xba {07}Bidadari Viee \xe2\x95\x91\n\xe2\x95\x91-\xe2\x96\xba {08}Rice \xe2\x95\x91\n\xe2\x95\x91-\xe2\x96\xba {09}./Nahrun Ganz \xe2\x95\x91\n\xe2\x95\x9a\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90[Trik Facebook]\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x9d\n '
CorrectUsername = 'tegarsalsa'
CorrectPassword = 'trik'
loop = 'true'
while loop == 'true':
print logo
username = raw_input(' TOOL USERNAME: ')
if username == CorrectUsername:
password = raw_input(' TOOL PASSWORD: ')
if password == CorrectPassword:
print ' Logged in successfully as ' + username
time.sleep(1)
loop = 'false'
else:
print ' Wrong Password !'
os.system('xdg-open http://wa.me/+6282125068665')
os.system('clear')
print ' Wrong Username !'
os.system('xdg-open http://wa.me/+6282125068665')
os.system('clear')
os.system('bash kshehd')
| 80.861111
| 1,919
| 0.607008
| 496
| 2,911
| 3.554435
| 0.227823
| 0.333522
| 0.336926
| 0.442428
| 0.590471
| 0.577425
| 0.571753
| 0.561543
| 0.561543
| 0.561543
| 0
| 0.249894
| 0.191687
| 2,911
| 35
| 1,920
| 83.171429
| 0.499363
| 0.087255
| 0
| 0.2
| 0
| 0.04
| 0.803929
| 0.495655
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.16
| 0.12
| null | null | 0.16
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 10
|
8875e02d181881fbe3ec9aa48011c47accf207b3
| 360
|
py
|
Python
|
tests/helpers/examples/cart/views.py
|
nicoddemus/dependencies
|
74180e2c6098d8ad03bc53c5703bdf8dc61c3ed9
|
[
"BSD-2-Clause"
] | null | null | null |
tests/helpers/examples/cart/views.py
|
nicoddemus/dependencies
|
74180e2c6098d8ad03bc53c5703bdf8dc61c3ed9
|
[
"BSD-2-Clause"
] | null | null | null |
tests/helpers/examples/cart/views.py
|
nicoddemus/dependencies
|
74180e2c6098d8ad03bc53c5703bdf8dc61c3ed9
|
[
"BSD-2-Clause"
] | null | null | null |
from dependencies import Injector
from dependencies.contrib.django import view
from dependencies.contrib.rest_framework import api_view
from dependencies.contrib.rest_framework import model_view_set
@view
class ShowCartWithDiscount(Injector):
pass
@api_view
class CartAPIView(Injector):
pass
@model_view_set
class UserViewSet(Injector):
pass
| 18
| 62
| 0.822222
| 45
| 360
| 6.4
| 0.377778
| 0.222222
| 0.239583
| 0.1875
| 0.319444
| 0.319444
| 0.319444
| 0
| 0
| 0
| 0
| 0
| 0.127778
| 360
| 19
| 63
| 18.947368
| 0.917197
| 0
| 0
| 0.230769
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.230769
| 0.307692
| 0
| 0.538462
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
88a2f27758a0c8c2007fc7387c1b30bbbf3a7140
| 1,910
|
py
|
Python
|
tests_without_pytest/lesson2.1_step7.py
|
adilgereev/selenium_course
|
7b7ca68bb7d915c3e973292ec18d8dbaf4dc363e
|
[
"Apache-2.0"
] | null | null | null |
tests_without_pytest/lesson2.1_step7.py
|
adilgereev/selenium_course
|
7b7ca68bb7d915c3e973292ec18d8dbaf4dc363e
|
[
"Apache-2.0"
] | null | null | null |
tests_without_pytest/lesson2.1_step7.py
|
adilgereev/selenium_course
|
7b7ca68bb7d915c3e973292ec18d8dbaf4dc363e
|
[
"Apache-2.0"
] | null | null | null |
from selenium import webdriver
import time
import math
<<<<<<< HEAD
link = "http://suninjuly.github.io/get_attribute.html"
browser = webdriver.Chrome()
browser.get(link)
# Вычисление требуемое на странице link
def calc(x):
return str(math.log(abs(12 * math.sin(int(x)))))
x_element = browser.find_element_by_css_selector("#treasure")
x = x_element.get_attribute("valuex")
y = calc(x)
# Ввод вычесленного значения в input
input = browser.find_element_by_css_selector("#answer")
input.send_keys(y)
checkbox = browser.find_element_by_css_selector("#robotCheckbox")
checkbox.click()
radiobutton = browser.find_element_by_css_selector("#robotsRule")
radiobutton.click()
button = browser.find_element_by_css_selector("body > div > form > div > div > button")
button.click()
# ожидание чтобы визуально оценить результаты прохождения скрипта
time.sleep(10)
# закрываем браузер после всех манипуляций
browser.quit()
=======
try:
link = "http://suninjuly.github.io/get_attribute.html"
browser = webdriver.Chrome()
browser.get(link)
# Вычисление требуемое на странице link
def calc(x):
return str(math.log(abs(12 * math.sin(int(x)))))
x_element = browser.find_elements_by_tag_name("img")
value = browser.get_attribute("valuex")
y = calc(x)
# Ввод вычисленного значения в input
input = browser.find_element_by_css_selector("#answer")
input.send_keys(y)
checkbox = browser.find_element_by_css_selector("#robotCheckbox")
checkbox.click()
radiobutton = browser.find_element_by_css_selector("#robotsRule")
radiobutton.click()
button = browser.find_element_by_css_selector("body > div > form > button")
button.click()
finally:
# ожидание чтобы визуально оценить результаты прохождения скрипта
time.sleep(10)
# закрываем браузер после всех манипуляций
browser.quit()
>>>>>>> 0dfa7eac25517f0e7e9b3fe73f953af2c2537a41
| 27.681159
| 87
| 0.737696
| 247
| 1,910
| 5.506073
| 0.323887
| 0.080882
| 0.119118
| 0.132353
| 0.842647
| 0.842647
| 0.827941
| 0.786765
| 0.786765
| 0.786765
| 0
| 0.01896
| 0.143979
| 1,910
| 68
| 88
| 28.088235
| 0.812844
| 0.185864
| 0
| 0.681818
| 0
| 0
| 0.156533
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.068182
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ee7593e6060042e4275196983da2f2370e2691d3
| 17,180
|
py
|
Python
|
old/control/ValueIteration.py
|
ali493/pyro
|
1245340077a733e2ab35765eae783b358d2f3af9
|
[
"MIT"
] | null | null | null |
old/control/ValueIteration.py
|
ali493/pyro
|
1245340077a733e2ab35765eae783b358d2f3af9
|
[
"MIT"
] | null | null | null |
old/control/ValueIteration.py
|
ali493/pyro
|
1245340077a733e2ab35765eae783b358d2f3af9
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 12 12:09:37 2017
@author: alxgr
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import RectBivariateSpline as interpol2D
from scipy.interpolate import griddata
from scipy.interpolate import LinearNDInterpolator
'''
################################################################################
'''
class ValueIteration_2D:
""" Dynamic programming for 2D continous dynamic system, one continuous input u """
############################
def __init__(self, dDS , cost_function ):
# Dynamic system
self.dDS = dDS # Discretized Dynamic system class
self.DS = dDS.DS # Base Dynamic system class
# Cost function
self.CF = cost_function
# Options
self.uselookuptable = True
##############################
def initialize(self):
""" initialize cost-to-go and policy """
self.J = np.zeros( self.dDS.xgriddim , dtype = float )
self.action_policy = np.zeros( self.dDS.xgriddim , dtype = int )
self.Jnew = self.J.copy()
self.Jplot = self.J.copy()
# Initial evaluation
# For all state nodes
for node in range( self.dDS.nodes_n ):
x = self.dDS.nodes_state[ node , : ]
i = self.dDS.nodes_index[ node , 0 ]
j = self.dDS.nodes_index[ node , 1 ]
# Final Cost
self.J[i,j] = self.CF.h( x )
###############################
def compute_step(self):
""" One step of value iteration """
# Get interpolation of current cost space
J_interpol = interpol2D( self.dDS.xd[0] , self.dDS.xd[1] , self.J , bbox=[None, None, None, None], kx=1, ky=1,)
# For all state nodes
for node in range( self.dDS.nodes_n ):
x = self.dDS.nodes_state[ node , : ]
i = self.dDS.nodes_index[ node , 0 ]
j = self.dDS.nodes_index[ node , 1 ]
# One steps costs - Q values
Q = np.zeros( self.dDS.actions_n )
# For all control actions
for action in range( self.dDS.actions_n ):
u = self.dDS.actions_input[ action , : ]
# Compute next state and validity of the action
if self.uselookuptable:
x_next = self.dDS.x_next[node,action,:]
action_isok = self.dDS.action_isok[node,action]
else:
x_next = self.DS.fc( x , u ) * self.dt + x
x_ok = self.DS.isavalidstate(x_next)
u_ok = self.DS.isavalidinput(x,u)
action_isok = ( u_ok & x_ok )
# If the current option is allowable
if action_isok:
J_next = J_interpol( x_next[0] , x_next[1] )
# Cost-to-go of a given action
Q[action] = self.CF.g( x , u ) + J_next[0,0]
else:
# Not allowable states or inputs/states combinations
Q[action] = self.CF.INF
self.Jnew[i,j] = Q.min()
self.action_policy[i,j] = Q.argmin()
# Impossible situation ( unaceptable situation for any control actions )
if self.Jnew[i,j] > (self.CF.INF-1) :
self.action_policy[i,j] = -1
# Convergence check
delta = self.J - self.Jnew
j_max = self.Jnew.max()
delta_max = delta.max()
delta_min = delta.min()
print('Max:',j_max,'Delta max:',delta_max, 'Delta min:',delta_min)
self.J = self.Jnew.copy()
################################
def compute_steps(self, l = 50, plot = False):
""" compute number of step """
for i in range(l):
print('Step:',i)
self.compute_step()
################################
def plot_J(self):
""" print graphic """
xname = self.DS.state_label[0] + ' ' + self.DS.state_units[0]
yname = self.DS.state_label[1] + ' ' + self.DS.state_units[1]
self.Jplot = self.J.copy()
###################
fs = 10
self.fig1 = plt.figure(figsize=(4, 4),dpi=300, frameon=True)
self.fig1.canvas.set_window_title('Cost-to-go')
self.ax1 = self.fig1.add_subplot(1,1,1)
plt.ylabel(yname, fontsize = fs)
plt.xlabel(xname, fontsize = fs)
self.im1 = plt.pcolormesh( self.dDS.xd[0] , self.dDS.xd[1] , self.Jplot.T )
plt.axis([self.DS.x_lb[0] , self.DS.x_ub[0], self.DS.x_lb[1] , self.DS.x_ub[1]])
plt.colorbar()
plt.grid(True)
plt.tight_layout()
################################
def plot_policy(self, i = 0 ):
""" print graphic """
xname = self.DS.state_label[0] + ' ' + self.DS.state_units[0]
yname = self.DS.state_label[1] + ' ' + self.DS.state_units[1]
policy_plot = self.u_policy_grid[i].copy()
###################
fs = 10
self.fig1 = plt.figure(figsize=(4, 4),dpi=300, frameon=True)
self.fig1.canvas.set_window_title('Policy for u[%i]'%i)
self.ax1 = self.fig1.add_subplot(1,1,1)
plt.ylabel(yname, fontsize = fs)
plt.xlabel(xname, fontsize = fs)
self.im1 = plt.pcolormesh( self.dDS.xd[0] , self.dDS.xd[1] , policy_plot.T )
plt.axis([self.DS.x_lb[0] , self.DS.x_ub[0], self.DS.x_lb[1] , self.DS.x_ub[1]])
plt.colorbar()
plt.grid(True)
plt.tight_layout()
################################
def assign_interpol_controller(self):
""" controller from optimal actions """
# Compute grid of u
self.u_policy_grid = [ None ]
self.u_policy_grid[0] = np.zeros( self.dDS.xgriddim , dtype = float )
# For all state nodes
for node in range( self.dDS.nodes_n ):
i = self.dDS.nodes_index[ node , 0 ]
j = self.dDS.nodes_index[ node , 1 ]
if ( self.action_policy[i,j] == -1 ):
self.u_policy_grid[0][i,j] = 0
else:
self.u_policy_grid[0][i,j] = self.dDS.actions_input[ self.action_policy[i,j] , 0 ]
# Compute Interpol function
self.x2u0 = interpol2D( self.dDS.xd[0] , self.dDS.xd[1] , self.u_policy_grid[0] , bbox=[None, None, None, None], kx=1, ky=1,)
# Asign Controller
self.DS.ctl = self.ctl_interpol
################################
def ctl_interpol(self, x , t = 0 ):
""" controller from optimal actions """
u = np.zeros( self.DS.m )
u[0] = self.x2u0( x[0] , x[1] )
return u
################################
def load_data(self, name = 'DP_data'):
""" Save optimal controller policy and cost to go """
try:
self.J = np.load( name + '_J' + '.npy' )
self.action_policy = np.load( name + '_a' + '.npy' ).astype(int)
except:
print('Failed to load DP data ' )
################################
def save_data(self, name = 'DP_data'):
""" Save optimal controller policy and cost to go """
np.save( name + '_J' , self.J )
np.save( name + '_a' , self.action_policy.astype(int))
'''
################################################################################
'''
class ValueIteration_3D:
""" Dynamic programming for 3D continous dynamic system, 2 continuous input u """
############################
def __init__(self, dDS , cost_function ):
# Dynamic system
self.dDS = dDS # Discretized Dynamic system class
self.DS = dDS.DS # Base Dynamic system class
# Cost function
self.CF = cost_function
# Options
self.uselookuptable = False
##############################
def initialize(self):
""" initialize cost-to-go and policy """
self.J = np.zeros( self.dDS.xgriddim , dtype = float )
self.J_1D = np.zeros( self.dDS.nodes_n , dtype = float )
self.action_policy = np.zeros( self.dDS.xgriddim , dtype = int )
self.Jnew = self.J.copy()
self.J_1D_new = self.J_1D.copy()
self.Jplot = self.J.copy()
# Initial evaluation
# For all state nodes
for node in range( self.dDS.nodes_n ):
x = self.dDS.nodes_state[ node , : ]
i = self.dDS.nodes_index[ node , 0 ]
j = self.dDS.nodes_index[ node , 1 ]
k = self.dDS.nodes_index[ node , 2 ]
# Final Cost
j = self.CF.h( x )
self.J[i,j,k] = j
self.J_1D[node] = j
###############################
def compute_step(self):
""" One step of value iteration """
# Get interpolation of current cost space
#J_interpol = interpol2D( self.dDS.xd[0] , self.dDS.xd[1] , self.J , bbox=[None, None, None, None], kx=1, ky=1,)
cartcoord = self.dDS.nodes_state
values = self.J_1D
J_interpol = LinearNDInterpolator(cartcoord, values, fill_value=0)
# For all state nodes
for node in range( self.dDS.nodes_n ):
x = self.dDS.nodes_state[ node , : ]
i = self.dDS.nodes_index[ node , 0 ]
j = self.dDS.nodes_index[ node , 1 ]
k = self.dDS.nodes_index[ node , 3 ]
# One steps costs - Q values
Q = np.zeros( self.dDS.actions_n )
# For all control actions
for action in range( self.dDS.actions_n ):
u = self.dDS.actions_input[ action , : ]
# Compute next state and validity of the action
x_next = self.DS.fc( x , u ) * self.dt + x
x_ok = self.DS.isavalidstate(x_next)
u_ok = self.DS.isavalidinput(x,u)
action_isok = ( u_ok & x_ok )
# If the current option is allowable
if action_isok:
J_next = J_interpol( x_next )
# Cost-to-go of a given action
Q[action] = self.CF.g( x , u ) + J_next[0,0]
else:
# Not allowable states or inputs/states combinations
Q[action] = self.CF.INF
self.Jnew[i,j,k] = Q.min()
self.J_1D_new[node] = self.Jnew[i,j,k]
self.action_policy[i,j,k] = Q.argmin()
# Impossible situation ( unaceptable situation for any control actions )
if self.Jnew[i,j,k] > (self.CF.INF-1) :
self.action_policy[i,j,k] = -1
# Convergence check
delta = self.J - self.Jnew
j_max = self.Jnew.max()
delta_max = delta.max()
delta_min = delta.min()
print('Max:',j_max,'Delta max:',delta_max, 'Delta min:',delta_min)
self.J = self.Jnew.copy()
self.J_1D = self.J_1D_new.copy()
################################
def compute_steps(self, l = 50, plot = False):
""" compute number of step """
for i in range(l):
print('Step:',i)
self.compute_step()
################################
def plot_J_ij(self, k ):
""" print graphic """
xname = self.DS.state_label[0] + ' ' + self.DS.state_units[0]
yname = self.DS.state_label[1] + ' ' + self.DS.state_units[1]
self.Jplot = self.J[:,:,i].copy()
###################
fs = 10
self.fig1 = plt.figure(figsize=(4, 4),dpi=300, frameon=True)
self.fig1.canvas.set_window_title('Cost-to-go')
self.ax1 = self.fig1.add_subplot(1,1,1)
plt.ylabel(yname, fontsize = fs)
plt.xlabel(xname, fontsize = fs)
self.im1 = plt.pcolormesh( self.dDS.xd[0] , self.dDS.xd[1] , self.Jplot.T )
plt.axis([self.DS.x_lb[0] , self.DS.x_ub[0], self.DS.x_lb[1] , self.DS.x_ub[1]])
plt.colorbar()
plt.grid(True)
plt.tight_layout()
################################
def plot_policy_ij(self, k , i = 0 ):
""" print graphic """
xname = self.DS.state_label[0] + ' ' + self.DS.state_units[0]
yname = self.DS.state_label[1] + ' ' + self.DS.state_units[1]
policy_plot = self.u_policy_grid[i][:,:,k].copy()
###################
fs = 10
self.fig1 = plt.figure(figsize=(4, 4),dpi=300, frameon=True)
self.fig1.canvas.set_window_title('Policy for u[%i]'%i)
self.ax1 = self.fig1.add_subplot(1,1,1)
plt.ylabel(yname, fontsize = fs)
plt.xlabel(xname, fontsize = fs)
self.im1 = plt.pcolormesh( self.dDS.xd[0] , self.dDS.xd[1] , policy_plot.T )
plt.axis([self.DS.x_lb[0] , self.DS.x_ub[0], self.DS.x_lb[1] , self.DS.x_ub[1]])
plt.colorbar()
plt.grid(True)
plt.tight_layout()
################################
def assign_interpol_controller(self):
""" controller from optimal actions """
# Compute grid of u
self.u_policy_grid = [ None ]
self.u_policy_grid[0] = np.zeros( self.dDS.xgriddim , dtype = float )
# For all state nodes
for node in range( self.dDS.nodes_n ):
i = self.dDS.nodes_index[ node , 0 ]
j = self.dDS.nodes_index[ node , 1 ]
if ( self.action_policy[i,j] == -1 ):
self.u_policy_grid[0][i,j] = 0
else:
self.u_policy_grid[0][i,j] = self.dDS.actions_input[ self.action_policy[i,j] , 0 ]
# Compute Interpol function
self.x2u0 = interpol2D( self.dDS.xd[0] , self.dDS.xd[1] , self.u_policy_grid[0] , bbox=[None, None, None, None], kx=1, ky=1,)
# Asign Controller
self.DS.ctl = self.ctl_interpol
################################
def ctl_interpol(self, x , t = 0 ):
""" controller from optimal actions """
u = np.zeros( self.DS.m )
u[0] = self.x2u0( x[0] , x[1] )
return u
################################
def load_data(self, name = 'DP_data'):
""" Save optimal controller policy and cost to go """
try:
self.J = np.load( name + '_J' + '.npy' )
self.action_policy = np.load( name + '_a' + '.npy' ).astype(int)
except:
print('Failed to load DP data ' )
################################
def save_data(self, name = 'DP_data'):
""" Save optimal controller policy and cost to go """
np.save( name + '_J' , self.J )
np.save( name + '_a' , self.action_policy.astype(int))
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0
| 7
|
c9e75603734a66e685158266276c1afc496e013a
| 22,308
|
py
|
Python
|
test/test_function_backward.py
|
basicv8vc/miniautodiff
|
1ce038276df45760ab3cb7875b35b46d5ee0b27f
|
[
"MIT"
] | 1
|
2021-09-01T09:05:14.000Z
|
2021-09-01T09:05:14.000Z
|
test/test_function_backward.py
|
shaoyf9/miniautodiff
|
1ce038276df45760ab3cb7875b35b46d5ee0b27f
|
[
"MIT"
] | null | null | null |
test/test_function_backward.py
|
shaoyf9/miniautodiff
|
1ce038276df45760ab3cb7875b35b46d5ee0b27f
|
[
"MIT"
] | 1
|
2021-08-10T09:29:35.000Z
|
2021-08-10T09:29:35.000Z
|
# encoding:utf-8
import unittest
import copy
import random
import numpy as np
import miniad
import miniad.functional as F
random.seed(123)
np.random.seed(123)
EPS = 1e-5
class TestAddBackward(unittest.TestCase):
def test_tensor_add_scalar(self):
'''tensor + scalar, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = 2
output = F.add(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.add(left_p, right)
appro_m = F.add(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_add_tensor_left(self):
'''tensor + tensor, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
output = F.add(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.add(left_p, right)
appro_m = F.add(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_add_tensor_right(self):
'''tensor + tensor, test right operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
right = miniad.Tensor(input_data)
output = F.add(left, right)
output.backward()
grad = output.children[1].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
right_p = copy.deepcopy(input_data)
right_p[i] += EPS
right_m = copy.deepcopy(input_data)
right_m[i] -= EPS
right_p = miniad.Tensor(right_p)
right_m = miniad.Tensor(right_m)
appro_p = F.add(left, right_p)
appro_m = F.add(left, right_m)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestMinusBackward(unittest.TestCase):
def test_tensor_minus_scalar(self):
'''tensor - scalar, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = 2
output = F.minus(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.minus(left_p, right)
appro_m = F.minus(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_minus_tensor_left(self):
'''tensor - tensor, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
output = F.minus(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.minus(left_p, right)
appro_m = F.minus(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_minus_tensor_right(self):
'''tensor - tensor, test right operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
right = miniad.Tensor(input_data)
output = F.minus(left, right)
output.backward()
grad = output.children[1].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
right_p = copy.deepcopy(input_data)
right_p[i] += EPS
right_m = copy.deepcopy(input_data)
right_m[i] -= EPS
right_p = miniad.Tensor(right_p)
right_m = miniad.Tensor(right_m)
appro_p = F.minus(left, right_p)
appro_m = F.minus(left, right_m)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestMultiplyBackward(unittest.TestCase):
def test_tensor_multiply_scalar(self):
'''tensor * scalar, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = 2.
output = F.multiply(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.multiply(left_p, right)
appro_m = F.multiply(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_multiply_tensor_left(self):
'''tensor * tensor, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
output = F.multiply(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.multiply(left_p, right)
appro_m = F.multiply(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_multiply_tensor_right(self):
'''tensor * tensor, test right operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
right = miniad.Tensor(input_data)
output = F.multiply(left, right)
output.backward()
grad = output.children[1].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
right_p = copy.deepcopy(input_data)
right_p[i] += EPS
right_m = copy.deepcopy(input_data)
right_m[i] -= EPS
right_p = miniad.Tensor(right_p)
right_m = miniad.Tensor(right_m)
appro_p = F.multiply(left, right_p)
appro_m = F.multiply(left, right_m)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestDivisionBackward(unittest.TestCase):
def test_tensor_division_scalar(self):
'''tensor / scalar '''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = 2.
output = F.division(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.division(left_p, right)
appro_m = F.division(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_division_tensor(self):
'''tensor / tensor, test left operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(input_data)
right = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
output = F.division(left, right)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
left_p = copy.deepcopy(input_data)
left_p[i] += EPS
left_m = copy.deepcopy(input_data)
left_m[i] -= EPS
left_p = miniad.Tensor(left_p)
left_m = miniad.Tensor(left_m)
appro_p = F.division(left_p, right)
appro_m = F.division(left_m, right)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_tensor_division_tensor_right(self):
'''tensor / tensor, test right operand'''
input_data = np.array([1., 2., 3.])
left = miniad.Tensor(np.array([-1., 100.9, 1e-5]))
right = miniad.Tensor(input_data)
output = F.division(left, right)
output.backward()
grad = output.children[1].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
right_p = copy.deepcopy(input_data)
right_p[i] += EPS
right_m = copy.deepcopy(input_data)
right_m[i] -= EPS
right_p = miniad.Tensor(right_p)
right_m = miniad.Tensor(right_m)
appro_p = F.division(left, right_p)
appro_m = F.division(left, right_m)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestPowerBackward(unittest.TestCase):
def test_power_backward_function(self):
input_data = np.array([1., 2., 3.])
base = miniad.Tensor(input_data)
exponent = 2.5
output = F.power(base, exponent)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(len(input_data)):
base_p = copy.deepcopy(input_data)
base_p[i] += EPS
base_m = copy.deepcopy(input_data)
base_m[i] -= EPS
base_p = miniad.Tensor(base_p)
base_m = miniad.Tensor(base_m)
appro_p = F.power(base_p, exponent)
appro_m = F.power(base_m, exponent)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestLinearBackward(unittest.TestCase):
def test_linear_backward_function_x(self):
'''x @ weight + bias, test x operand
Note:
x: (B, M)
weight: (M, N)
bias: (N)
'''
B = 32
M = 16
N = 64
input_data = np.random.randn(B, M)
x = miniad.Tensor(input_data)
weight = miniad.Tensor(np.random.randn(M, N))
bias = miniad.Tensor(np.random.randn(N))
output = F.linear(x, weight, bias)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(B):
for j in range(M):
x_p = copy.deepcopy(input_data)
x_p[i][j] += EPS
x_m = copy.deepcopy(input_data)
x_m[i][j] -= EPS
x_p = miniad.Tensor(x_p)
x_m = miniad.Tensor(x_m)
appro_p = F.linear(x_p, weight, bias)
appro_m = F.linear(x_m, weight, bias)
appro_grad[i][j] = np.sum(
(appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_linear_backward_function_weight(self):
'''x @ weight + bias, test weight operand
Note:
x: (B, M)
weight: (M, N)
bias: (N)
'''
B = 32
M = 16
N = 64
input_data = np.random.randn(M, N)
x = miniad.Tensor(np.random.randn(B, M))
weight = miniad.Tensor(input_data)
bias = miniad.Tensor(np.random.randn(N))
output = F.linear(x, weight, bias)
output.backward()
grad = output.children[1].grad
appro_grad = np.zeros_like(grad)
for i in range(M):
for j in range(N):
weight_p = copy.deepcopy(input_data)
weight_p[i][j] += EPS
weight_m = copy.deepcopy(input_data)
weight_m[i][j] -= EPS
weight_p = miniad.Tensor(weight_p)
weight_m = miniad.Tensor(weight_m)
appro_p = F.linear(x, weight_p, bias)
appro_m = F.linear(x, weight_m, bias)
appro_grad[i][j] = np.sum(
(appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_linear_backward_function_bias(self):
'''x @ weight + bias, test bias operand
Note:
x: (B, M)
weight: (M, N)
bias: (N)
'''
B = 32
M = 16
N = 64
input_data = np.random.randn(N)
x = miniad.Tensor(np.random.randn(B, M))
weight = miniad.Tensor(np.random.randn(M, N))
bias = miniad.Tensor(input_data)
output = F.linear(x, weight, bias)
output.backward()
grad = output.children[2].grad
appro_grad = np.zeros_like(grad)
for i in range(N):
bias_p = copy.deepcopy(input_data)
bias_p[i] += EPS
bias_m = copy.deepcopy(input_data)
bias_m[i] -= EPS
bias_p = miniad.Tensor(bias_p)
bias_m = miniad.Tensor(bias_m)
appro_p = F.linear(x, weight, bias_p)
appro_m = F.linear(x, weight, bias_m)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestReLUBackward(unittest.TestCase):
def test_relu_backward_function(self):
'''relu(x)'''
input_data = np.random.randn(16, 8)
x = miniad.Tensor(input_data)
output = F.relu(x)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(16):
for j in range(8):
x_p = copy.deepcopy(input_data)
x_p[i][j] += EPS
x_m = copy.deepcopy(input_data)
x_m[i][j] -= EPS
x_p = miniad.Tensor(x_p)
x_m = miniad.Tensor(x_m)
appro_p = F.relu(x_p)
appro_m = F.relu(x_m)
appro_grad[i][j] = np.sum(
(appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestSigmoidBackward(unittest.TestCase):
def test_sigmoid_backward_function(self):
'''sigmoid(x)'''
input_data = np.random.randn(16, 8)
x = miniad.Tensor(input_data)
output = F.sigmoid(x)
output.backward()
grad = output.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(16):
for j in range(8):
x_p = copy.deepcopy(input_data)
x_p[i][j] += EPS
x_m = copy.deepcopy(input_data)
x_m[i][j] -= EPS
x_p = miniad.Tensor(x_p)
x_m = miniad.Tensor(x_m)
appro_p = F.sigmoid(x_p)
appro_m = F.sigmoid(x_m)
appro_grad[i][j] = np.sum(
(appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestSqueezeBackward(unittest.TestCase):
def test_squeeze_all(self):
'''squeeze(x, dim=None)'''
x = miniad.Tensor(
np.random.randn(16, 8).reshape((1, 16, 1, 1, 8, 1, 1)))
output = F.squeeze(x)
output.backward()
x_grad = output.children[0].grad
output_grad = output.grad.reshape(x_grad.shape)
difference = np.linalg.norm(x_grad - output_grad) / (
np.linalg.norm(x_grad) + np.linalg.norm(output_grad))
self.assertAlmostEqual(difference, 0, places=7)
def test_squeeze_some_dim(self):
'''squeeze(x, dim=3)'''
x = miniad.Tensor(
np.random.randn(16, 8).reshape((1, 16, 1, 1, 8, 1, 1)))
output = F.squeeze(x, dim=3)
output.backward()
x_grad = output.children[0].grad
output_grad = output.grad.reshape(x_grad.shape)
difference = np.linalg.norm(x_grad - output_grad) / (
np.linalg.norm(x_grad) + np.linalg.norm(output_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestBinaryCrossEntropyLossBackward(unittest.TestCase):
def test_bce_backward_function(self):
'''binary_cross_entropy(y_hat, y)'''
input_data = np.random.uniform(size=(10, ))
y_hat = miniad.Tensor(input_data)
y = np.random.randint(2, size=(10, ))
loss = F.binary_cross_entropy_loss(y_hat, y)
loss.backward()
grad = loss.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(10):
x_p = copy.deepcopy(input_data)
x_p[i] += EPS
x_m = copy.deepcopy(input_data)
x_m[i] -= EPS
x_p = miniad.Tensor(x_p)
x_m = miniad.Tensor(x_m)
appro_p = F.binary_cross_entropy_loss(x_p, y)
appro_m = F.binary_cross_entropy_loss(x_m, y)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
class TestMSELossBackward(unittest.TestCase):
def test_mse_backward_function(self):
'''mse(y_hat, y)'''
input_data = np.random.randn(10)
y_hat = miniad.Tensor(input_data)
y = np.random.randn(10)
loss = F.mse_loss(y_hat, y)
loss.backward()
grad = loss.children[0].grad
appro_grad = np.zeros_like(grad)
for i in range(10):
x_p = copy.deepcopy(input_data)
x_p[i] += EPS
x_m = copy.deepcopy(input_data)
x_m[i] -= EPS
x_p = miniad.Tensor(x_p)
x_m = miniad.Tensor(x_m)
appro_p = F.mse_loss(x_p, y)
appro_m = F.mse_loss(x_m, y)
appro_grad[i] = np.sum((appro_p.data - appro_m.data) / (2 * EPS))
difference = np.linalg.norm(grad - appro_grad) / (
np.linalg.norm(grad) + np.linalg.norm(appro_grad))
self.assertAlmostEqual(difference, 0, places=7)
if __name__ == '__main__':
unittest.main()
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0
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|
4e4a834f87b0d3d3310ace428d10600e32204b8f
| 87
|
py
|
Python
|
test/fixtures/python/corpus/import-from-statement.A.py
|
matsubara0507/semantic
|
67899f701abc0f1f0cb4374d8d3c249afc33a272
|
[
"MIT"
] | 8,844
|
2019-05-31T15:47:12.000Z
|
2022-03-31T18:33:51.000Z
|
test/fixtures/python/corpus/import-from-statement.A.py
|
matsubara0507/semantic
|
67899f701abc0f1f0cb4374d8d3c249afc33a272
|
[
"MIT"
] | 401
|
2019-05-31T18:30:26.000Z
|
2022-03-31T16:32:29.000Z
|
test/fixtures/python/corpus/import-from-statement.A.py
|
matsubara0507/semantic
|
67899f701abc0f1f0cb4374d8d3c249afc33a272
|
[
"MIT"
] | 504
|
2019-05-31T17:55:03.000Z
|
2022-03-30T04:15:04.000Z
|
from a import b
from a import (b, c)
from a import *
from a.b import c
from . import b
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4e742208d9668fcc54ea01e52790366c38ef2cbf
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py
|
Python
|
buildings/tests/gui/test_processes_edit_attribute_production.py
|
strk/nz-buildings
|
8dc8ee19d322837380bb4f016b01eccee2c1bd0a
|
[
"PostgreSQL",
"CC-BY-4.0"
] | 2
|
2020-02-21T00:46:31.000Z
|
2020-08-17T14:22:19.000Z
|
buildings/tests/gui/test_processes_edit_attribute_production.py
|
strk/nz-buildings
|
8dc8ee19d322837380bb4f016b01eccee2c1bd0a
|
[
"PostgreSQL",
"CC-BY-4.0"
] | 243
|
2018-12-16T22:01:54.000Z
|
2022-01-10T20:09:24.000Z
|
buildings/tests/gui/test_processes_edit_attribute_production.py
|
strk/nz-buildings
|
8dc8ee19d322837380bb4f016b01eccee2c1bd0a
|
[
"PostgreSQL",
"CC-BY-4.0"
] | 1
|
2020-03-24T10:35:43.000Z
|
2020-03-24T10:35:43.000Z
|
# -*- coding: utf-8 -*-
"""
################################################################################
#
# Copyright 2018 Crown copyright (c)
# Land Information New Zealand and the New Zealand Government.
# All rights reserved
#
# This program is released under the terms of the 3 clause BSD license. See the
# LICENSE file for more information.
#
################################################################################
Tests: Edit Production Outlines Processes
***************************************************************************/
"""
import unittest
from qgis.PyQt.QtCore import Qt, QTimer
from qgis.PyQt.QtWidgets import QMessageBox
from qgis.PyQt.QtTest import QTest
from qgis.core import QgsCoordinateReferenceSystem, QgsPointXY, QgsRectangle
from qgis.gui import QgsMapTool
from qgis.utils import plugins, iface
from buildings.utilities import database as db
class ProcessProductionEditOutlinesTest(unittest.TestCase):
"""Test Edit Production Outline Processes"""
@classmethod
def setUpClass(cls):
"""Runs at TestCase init."""
db.connect()
@classmethod
def tearDownClass(cls):
"""Runs at TestCase teardown."""
db.close_connection()
def setUp(self):
"""Runs before each test."""
self.building_plugin = plugins.get("buildings")
self.building_plugin.main_toolbar.actions()[0].trigger()
self.dockwidget = self.building_plugin.dockwidget
sub_menu = self.dockwidget.lst_sub_menu
sub_menu.setCurrentItem(sub_menu.findItems("Edit Outlines", Qt.MatchExactly)[0])
self.production_frame = self.dockwidget.current_frame
self.edit_dialog = self.production_frame.edit_dialog
for action in iface.building_toolbar.actions():
if action.text() == "Edit Attributes":
action.trigger()
def tearDown(self):
"""Runs after each test."""
self.production_frame.btn_exit.click()
def test_ui_on_geom_selected(self):
"""UI and Canvas behave correctly when geometry is selected"""
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878035.0, 5555256.0)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878132.1, 5555323.9)),
delay=30,
)
QTest.qWait(10)
self.assertTrue(self.edit_dialog.btn_edit_save.isEnabled())
self.assertTrue(self.edit_dialog.btn_edit_reset.isEnabled())
self.assertTrue(self.edit_dialog.cmb_capture_method.isEnabled())
self.assertTrue(self.edit_dialog.cmb_capture_source.isEnabled())
self.assertTrue(self.edit_dialog.cmb_lifecycle_stage.isEnabled())
self.assertTrue(self.edit_dialog.cmb_ta.isEnabled())
self.assertTrue(self.edit_dialog.cmb_town.isEnabled())
self.assertTrue(self.edit_dialog.cmb_suburb.isEnabled())
self.assertEqual(self.edit_dialog.cmb_lifecycle_stage.currentText(), "Current")
self.assertEqual(
self.edit_dialog.cmb_capture_method.currentText(), "Feature Extraction"
)
self.assertEqual(
self.edit_dialog.cmb_capture_source.currentText(),
u"1- Imagery One- NZ Aerial Imagery",
)
self.assertEqual(self.edit_dialog.cmb_ta.currentText(), "Wellington")
self.assertEqual(self.edit_dialog.cmb_town.currentText(), "Wellington")
self.assertEqual(self.edit_dialog.cmb_suburb.currentText(), "Aro Valley")
def test_reset_clicked(self):
"""Check comboboxes reset correctly when 'reset' called"""
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878132.1, 5555323.9)),
delay=30,
)
QTest.qWait(10)
self.edit_dialog.btn_edit_reset.click()
self.assertFalse(self.edit_dialog.btn_edit_save.isEnabled())
self.assertFalse(self.edit_dialog.btn_edit_reset.isEnabled())
self.assertFalse(self.edit_dialog.cmb_capture_method.isEnabled())
self.assertEqual(self.edit_dialog.cmb_capture_method.currentText(), "")
self.assertFalse(self.edit_dialog.cmb_capture_source.isEnabled())
self.assertEqual(self.edit_dialog.cmb_capture_source.currentText(), "")
self.assertFalse(self.edit_dialog.cmb_lifecycle_stage.isEnabled())
self.assertEqual(self.edit_dialog.cmb_lifecycle_stage.currentText(), "")
self.assertFalse(self.edit_dialog.cmb_ta.isEnabled())
self.assertEqual(self.edit_dialog.cmb_ta.currentText(), "")
self.assertFalse(self.edit_dialog.cmb_town.isEnabled())
self.assertEqual(self.edit_dialog.cmb_town.currentText(), "")
self.assertFalse(self.edit_dialog.cmb_suburb.isEnabled())
self.assertEqual(self.edit_dialog.cmb_suburb.currentText(), "")
def test_save_clicked(self):
"""Check attributes are updated when save clicked"""
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878137.41, 5555313.84)),
delay=30,
)
QTest.qWait(10)
self.edit_dialog.cmb_lifecycle_stage.setCurrentIndex(
self.edit_dialog.cmb_lifecycle_stage.findText("Replaced")
)
self.edit_dialog.cmb_capture_method.setCurrentIndex(
self.edit_dialog.cmb_capture_method.findText("Unknown")
)
self.edit_dialog.cmb_ta.setCurrentIndex(
self.edit_dialog.cmb_ta.findText("Manawatu-Whanganui")
)
self.edit_dialog.cmb_town.setCurrentIndex(
self.edit_dialog.cmb_town.findText("Palmerston North")
)
self.edit_dialog.cmb_suburb.setCurrentIndex(
self.edit_dialog.cmb_suburb.findText("Hokowhitu")
)
self.edit_dialog.change_instance.edit_save_clicked(False)
sql = "SELECT lifecycle_stage_id, capture_method_id, suburb_locality_id, town_city_id, territorial_authority_id FROM buildings.building_outlines WHERE building_outline_id = %s"
result = db._execute(sql, (self.edit_dialog.building_outline_id,))
result = result.fetchall()[0]
# lifecycle_stage
sql = (
"SELECT value FROM buildings.lifecycle_stage WHERE lifecycle_stage_id = %s;"
)
lifecycle_stage = db._execute(sql, (result[0],))
lifecycle_stage = lifecycle_stage.fetchall()[0][0]
self.assertEqual("Replaced", lifecycle_stage)
# capture method
sql = "SELECT value FROM buildings_common.capture_method WHERE capture_method_id = %s;"
capture_method = db._execute(sql, (result[1],))
capture_method = capture_method.fetchall()[0][0]
self.assertEqual("Unknown", capture_method)
# suburb
sql = "SELECT suburb_4th FROM buildings_reference.suburb_locality WHERE suburb_locality_id = %s;"
suburb = db._execute(sql, (result[2],))
suburb = suburb.fetchall()[0][0]
self.assertEqual("Hokowhitu", suburb)
# town
sql = "SELECT name FROM buildings_reference.town_city WHERE town_city_id = %s;"
town_city = db._execute(sql, (result[3],))
town_city = town_city.fetchall()[0][0]
self.assertEqual("Palmerston North", town_city)
# territorial Authority
sql = "SELECT name FROM buildings_reference.territorial_authority WHERE territorial_authority_id = %s;"
territorial_authority = db._execute(sql, (result[4],))
territorial_authority = territorial_authority.fetchall()[0][0]
self.assertEqual("Manawatu-Whanganui", territorial_authority)
self.assertEqual(self.edit_dialog.cmb_lifecycle_stage.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_capture_method.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_suburb.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_town.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_ta.currentText(), "")
self.edit_dialog.ids = []
self.edit_dialog.building_outline_id = None
self.edit_dialog.db.rollback_open_cursor()
def test_edit_mutiple_attributes(self):
"""Checks Multiple outlines with the same attributes can be edited together"""
iface.actionSelectPolygon().trigger()
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878042, 5555668, 1878327, 5555358)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878149, 5555640)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878203, 5555640)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878203, 5555384)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878149, 5555384)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878203, 5555384)),
delay=50,
)
QTest.qWait(100)
self.edit_dialog.cmb_lifecycle_stage.setCurrentIndex(
self.edit_dialog.cmb_lifecycle_stage.findText("Replaced")
)
self.edit_dialog.cmb_capture_method.setCurrentIndex(
self.edit_dialog.cmb_capture_method.findText("Unknown")
)
self.edit_dialog.cmb_ta.setCurrentIndex(
self.edit_dialog.cmb_ta.findText("Manawatu-Whanganui")
)
self.edit_dialog.cmb_town.setCurrentIndex(
self.edit_dialog.cmb_town.findText("Palmerston North")
)
self.edit_dialog.cmb_suburb.setCurrentIndex(
self.edit_dialog.cmb_suburb.findText("Hokowhitu")
)
self.edit_dialog.change_instance.edit_save_clicked(False)
for i in self.edit_dialog.ids:
sql = "SELECT lifecycle_stage_id, capture_method_id, suburb_locality_id, town_city_id, territorial_authority_id FROM buildings.building_outlines WHERE building_outline_id = %s;"
result = db._execute(sql, (i,))
result = result.fetchall()[0]
# lifecycle_stage
sql = "SELECT value FROM buildings.lifecycle_stage WHERE lifecycle_stage_id = %s;"
lifecycle_stage = db._execute(sql, (result[0],))
lifecycle_stage = lifecycle_stage.fetchall()[0][0]
self.assertEqual("Replaced", lifecycle_stage)
# capture method
sql = "SELECT value FROM buildings_common.capture_method WHERE capture_method_id = %s;"
capture_method = db._execute(sql, (result[1],))
capture_method = capture_method.fetchall()[0][0]
self.assertEqual("Unknown", capture_method)
# suburb
sql = "SELECT suburb_4th FROM buildings_reference.suburb_locality WHERE suburb_locality_id = %s;"
suburb = db._execute(sql, (result[2],))
suburb = suburb.fetchall()[0][0]
self.assertEqual("Hokowhitu", suburb)
# town
sql = "SELECT name FROM buildings_reference.town_city WHERE town_city_id = %s;"
town_city = db._execute(sql, (result[3],))
town_city = town_city.fetchall()[0][0]
self.assertEqual("Palmerston North", town_city)
# territorial Authority
sql = "SELECT name FROM buildings_reference.territorial_authority WHERE territorial_authority_id = %s;"
territorial_authority = db._execute(sql, (result[4],))
territorial_authority = territorial_authority.fetchall()[0][0]
self.assertEqual("Manawatu-Whanganui", territorial_authority)
self.assertEqual(len(self.edit_dialog.ids), 4)
self.assertEqual(self.edit_dialog.cmb_lifecycle_stage.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_capture_method.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_suburb.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_town.currentText(), "")
self.assertEqual(self.edit_dialog.cmb_ta.currentText(), "")
self.edit_dialog.ids = []
self.edit_dialog.building_outline_id = None
self.edit_dialog.db.rollback_open_cursor()
def test_selection_change(self):
"""Check change only occurs on currently selected outlines.
This test protects against a regression of #55."""
iface.actionSelectPolygon().trigger()
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878042, 5555668, 1878327, 5555358)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878149, 5555640)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878203, 5555640)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878203, 5555384)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878149, 5555384)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878203, 5555384)),
delay=50,
)
QTest.qWait(100)
iface.actionSelect().trigger()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878202.1, 5555618.9)),
delay=50,
)
self.edit_dialog.cmb_capture_method.setCurrentIndex(
self.edit_dialog.cmb_capture_method.findText("Unknown")
)
self.edit_dialog.change_instance.edit_save_clicked(False)
sql = "SELECT capture_method_id FROM buildings.building_outlines WHERE building_outline_id = 1031;"
result = db._execute(sql)
self.assertEqual(result.fetchall()[0][0], 1)
sql = "SELECT capture_method_id FROM buildings.building_outlines WHERE building_outline_id = 1030;"
result = db._execute(sql)
self.assertNotEqual(result.fetchall()[0][0], 1)
self.edit_dialog.ids = []
self.edit_dialog.building_outline_id = None
self.edit_dialog.db.rollback_open_cursor()
def test_select_geom_before_edit(self):
"""UI and Canvas behave correctly when one geometry is selected before edits button clicked"""
self.production_frame.edit_dialog.close()
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878035.0, 5555256.0)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878132.1, 5555323.9)),
delay=30,
)
QTest.qWait(10)
for action in iface.building_toolbar.actions():
if action.text() == "Edit Attributes":
action.trigger()
self.assertTrue(self.edit_dialog.btn_edit_save.isEnabled())
self.assertTrue(self.edit_dialog.btn_edit_reset.isEnabled())
self.assertTrue(self.edit_dialog.cmb_capture_method.isEnabled())
self.assertTrue(self.edit_dialog.cmb_capture_source.isEnabled())
self.assertTrue(self.edit_dialog.cmb_lifecycle_stage.isEnabled())
self.assertTrue(self.edit_dialog.cmb_ta.isEnabled())
self.assertTrue(self.edit_dialog.cmb_town.isEnabled())
self.assertTrue(self.edit_dialog.cmb_suburb.isEnabled())
self.assertEqual(self.edit_dialog.cmb_lifecycle_stage.currentText(), "Current")
self.assertEqual(
self.edit_dialog.cmb_capture_method.currentText(), "Feature Extraction"
)
self.assertEqual(
self.edit_dialog.cmb_capture_source.currentText(),
u"1- Imagery One- NZ Aerial Imagery",
)
self.assertEqual(self.edit_dialog.cmb_ta.currentText(), "Wellington")
self.assertEqual(self.edit_dialog.cmb_town.currentText(), "Wellington")
self.assertEqual(self.edit_dialog.cmb_suburb.currentText(), "Aro Valley")
def test_select_multiple_geom_before_edit(self):
"""UI and Canvas behave correctly when multiple geometries are selected before edits button clicked"""
self.production_frame.edit_dialog.close()
iface.actionSelectPolygon().trigger()
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878053.0, 5555587.0, 1878315.0, 5555655.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878053, 5555631)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878053, 5555612)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878315, 5555612)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878315, 5555631)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878315, 5555631)),
delay=50,
)
QTest.qWait(100)
for action in iface.building_toolbar.actions():
if action.text() == "Edit Attributes":
action.trigger()
self.assertTrue(self.edit_dialog.btn_edit_save.isEnabled())
self.assertTrue(self.edit_dialog.btn_edit_reset.isEnabled())
self.assertTrue(self.edit_dialog.cmb_capture_method.isEnabled())
self.assertTrue(self.edit_dialog.cmb_capture_source.isEnabled())
self.assertTrue(self.edit_dialog.cmb_lifecycle_stage.isEnabled())
self.assertTrue(self.edit_dialog.cmb_ta.isEnabled())
self.assertTrue(self.edit_dialog.cmb_town.isEnabled())
self.assertTrue(self.edit_dialog.cmb_suburb.isEnabled())
self.assertEqual(self.edit_dialog.cmb_lifecycle_stage.currentText(), "Current")
self.assertEqual(
self.edit_dialog.cmb_capture_method.currentText(), "Feature Extraction"
)
self.assertEqual(
self.edit_dialog.cmb_capture_source.currentText(),
u"1- Imagery One- NZ Aerial Imagery",
)
self.assertEqual(self.edit_dialog.cmb_ta.currentText(), "Wellington")
self.assertEqual(self.edit_dialog.cmb_town.currentText(), "Wellington")
self.assertEqual(self.edit_dialog.cmb_suburb.currentText(), "Kelburn")
def test_cannot_select_nonidentical_multiple_geoms_before_edit(self):
"""UI and Canvas behave correctly when multiple geometries are selected before edits button clicked"""
self.production_frame.edit_dialog.close()
iface.actionSelectPolygon().trigger()
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878155.0, 5555119.0, 1878219.0, 5555190.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878155, 5555190)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878155, 5555119)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878219, 5555612)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878219, 5555190)),
delay=50,
)
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878219, 5555190)),
delay=50,
)
QTest.qWait(100)
for action in iface.building_toolbar.actions():
if action.text() == "Edit Attributes":
action.trigger()
self.edit_dialog.change_instance.error_dialog.close()
self.assertFalse(self.edit_dialog.btn_edit_save.isEnabled())
self.assertFalse(self.edit_dialog.btn_edit_reset.isEnabled())
self.assertFalse(self.edit_dialog.cmb_capture_method.isEnabled())
self.assertFalse(self.edit_dialog.cmb_capture_source.isEnabled())
self.assertFalse(self.edit_dialog.cmb_lifecycle_stage.isEnabled())
self.assertFalse(self.edit_dialog.cmb_ta.isEnabled())
self.assertFalse(self.edit_dialog.cmb_town.isEnabled())
self.assertFalse(self.edit_dialog.cmb_suburb.isEnabled())
def test_end_lifespan_of_building_pass(self):
"""test that ending lifespan of removed building works"""
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878035.0, 5555256.0)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878038.1, 5555312.6)),
delay=30,
)
btn_yes = self.edit_dialog.change_instance.msgbox_remove.button(QMessageBox.Yes)
QTimer.singleShot(500, btn_yes.click)
self.edit_dialog.change_instance.end_lifespan(False)
sql = "SELECT end_lifespan FROM buildings.building_outlines WHERE building_outline_id = 1006;"
result = db._execute(sql)
self.assertNotEqual(result.fetchone()[0], None)
sql = "SELECT end_lifespan FROM buildings.buildings WHERE building_id = 10006;"
result = db._execute(sql)
self.assertNotEqual(result.fetchone()[0], None)
sql = "SELECT count(*) FROM buildings_bulk_load.existing_subset_extracts WHERE building_outline_id = 1006;"
result = db._execute(sql)
self.assertEquals(result.fetchone()[0], 0)
sql = "SELECT count(*) FROM buildings_bulk_load.removed WHERE building_outline_id = 1006;"
result = db._execute(sql)
self.assertEquals(result.fetchone()[0], 0)
self.edit_dialog.db.rollback_open_cursor()
self.edit_dialog.ids = []
self.edit_dialog.building_outline_id = None
self.edit_dialog.editing_layer.removeSelection()
def test_end_lifespan_of_building_fails(self):
"""test that ending lifespan of related building fails"""
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1878035.0, 5555256.0)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878420.4, 5555426.8)),
delay=30,
)
btn_yes = self.edit_dialog.change_instance.msgbox_remove.button(QMessageBox.Yes)
QTimer.singleShot(500, btn_yes.click)
self.edit_dialog.change_instance.end_lifespan(False)
self.edit_dialog.change_instance.error_dialog.close()
sql = "SELECT end_lifespan FROM buildings.building_outlines WHERE building_outline_id = 1033;"
result = db._execute(sql)
self.assertEquals(result.fetchone()[0], None)
sql = "SELECT end_lifespan FROM buildings.buildings WHERE building_id = 10033;"
result = db._execute(sql)
self.assertEquals(result.fetchone()[0], None)
sql = "SELECT count(*) FROM buildings_bulk_load.existing_subset_extracts WHERE building_outline_id = 1033;"
result = db._execute(sql)
self.assertEquals(result.fetchone()[0], 1)
sql = "SELECT count(*) FROM buildings_bulk_load.related WHERE building_outline_id = 1033;"
result = db._execute(sql)
self.assertEquals(result.fetchone()[0], 2)
self.edit_dialog.db.rollback_open_cursor()
self.edit_dialog.ids = []
self.edit_dialog.building_outline_id = None
self.edit_dialog.editing_layer.removeSelection()
def test_modified_date_on_save(self):
"""Check modified date is updated when save clicked"""
widget = iface.mapCanvas().viewport()
canvas_point = QgsMapTool(iface.mapCanvas()).toCanvasCoordinates
QTest.mouseClick(
widget,
Qt.RightButton,
pos=canvas_point(QgsPointXY(1747651, 5428152)),
delay=50,
)
canvas = iface.mapCanvas()
selectedcrs = "EPSG:2193"
target_crs = QgsCoordinateReferenceSystem()
target_crs.createFromUserInput(selectedcrs)
canvas.setDestinationCrs(target_crs)
zoom_rectangle = QgsRectangle(1878035.0, 5555256.0, 1878345.0, 5555374.0)
canvas.setExtent(zoom_rectangle)
canvas.refresh()
QTest.mouseClick(
widget,
Qt.LeftButton,
pos=canvas_point(QgsPointXY(1878137.41, 5555313.84)),
delay=30,
)
QTest.qWait(10)
self.edit_dialog.cmb_lifecycle_stage.setCurrentIndex(
self.edit_dialog.cmb_lifecycle_stage.findText("Replaced")
)
self.edit_dialog.cmb_capture_method.setCurrentIndex(
self.edit_dialog.cmb_capture_method.findText("Unknown")
)
self.edit_dialog.cmb_ta.setCurrentIndex(
self.edit_dialog.cmb_ta.findText("Manawatu-Whanganui")
)
self.edit_dialog.cmb_town.setCurrentIndex(
self.edit_dialog.cmb_town.findText("Palmerston North")
)
self.edit_dialog.cmb_suburb.setCurrentIndex(
self.edit_dialog.cmb_suburb.findText("Hokowhitu")
)
self.edit_dialog.change_instance.edit_save_clicked(False)
sql = "SELECT now()::timestamp;"
result = db._execute(sql)
time = result.fetchall()[0]
# building_outline modified date
sql = "SELECT last_modified FROM buildings.building_outlines WHERE building_outline_id = %s;"
result = db._execute(sql, (self.edit_dialog.building_outline_id,))
bo_modified_date = result.fetchall()[0]
self.assertEqual(bo_modified_date, time)
self.edit_dialog.ids = []
self.edit_dialog.building_outline_id = None
self.edit_dialog.db.rollback_open_cursor()
| 42.5
| 189
| 0.642886
| 3,349
| 31,875
| 5.906241
| 0.096148
| 0.073812
| 0.100506
| 0.082508
| 0.898787
| 0.894793
| 0.888878
| 0.880789
| 0.872447
| 0.851264
| 0
| 0.049221
| 0.249161
| 31,875
| 749
| 190
| 42.556742
| 0.777253
| 0.050855
| 0
| 0.792793
| 0
| 0
| 0.094835
| 0.024845
| 0
| 0
| 0
| 0
| 0.144144
| 1
| 0.022523
| false
| 0.001502
| 0.012012
| 0
| 0.036036
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
4ea1527a18a627da4e61dae90634d58a2081c89a
| 24,529
|
py
|
Python
|
lib/fama/output/pdf_report.py
|
aekazakov/FamaProfiling
|
d9db15ea217e3be2aab65c356564a6d345b4f410
|
[
"MIT"
] | null | null | null |
lib/fama/output/pdf_report.py
|
aekazakov/FamaProfiling
|
d9db15ea217e3be2aab65c356564a6d345b4f410
|
[
"MIT"
] | null | null | null |
lib/fama/output/pdf_report.py
|
aekazakov/FamaProfiling
|
d9db15ea217e3be2aab65c356564a6d345b4f410
|
[
"MIT"
] | null | null | null |
"""Functions for PDF report generations"""
import os
from collections import defaultdict, Counter, OrderedDict
from fpdf import FPDF, HTMLMixin
from fama.diamond_parser.hit_utils import get_paired_end
from fama.utils.utils import cleanup_protein_id
from fama.output.report import get_scores_per_tax_rank
class MyFPDF(FPDF, HTMLMixin):
"""MyFPDF class has two parents, FPDF and HTMLMixin, for easy use of HTML
formatting for generation of PDF document"""
pass
def generate_pdf_report(parser):
"""Generates PDF report for processing of one FASTQ file
Args:
parser (:obj:DiamondParser): parser object with annotated reads
"""
pdf = MyFPDF('P', 'mm', 'Letter')
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Run info')
pdf.ln(h='')
pdf.set_font('Arial', 'B', 12)
pdf.cell(60, 10, 'Sequence data')
pdf.ln(h='')
pdf.set_font('Arial', '', 12)
pdf.cell(75, 10, 'Sample ID: ' + parser.options.get_sample_name(parser.sample.sample_id))
pdf.ln(h=5)
pdf.cell(90, 10, 'Paired end: ' + parser.end)
pdf.ln(h=5)
pdf.cell(105, 10, 'FASTQ file: '
+ parser.options.get_fastq_path(parser.sample.sample_id, parser.end))
pdf.ln(h=5)
if parser.options.get_fastq_path(parser.sample.sample_id, get_paired_end(parser.end)):
pdf.cell(120, 10, 'Paired-end FASTQ file: '
+ parser.options.get_fastq_path(parser.sample.sample_id,
get_paired_end(parser.end)))
pdf.ln(h=5)
pdf.cell(135, 10, 'Total number of reads: '
+ str(parser.options.get_fastq1_readcount(parser.sample.sample_id)))
pdf.ln(h=20)
pdf.set_font('Arial', 'B', 12)
pdf.cell(160, 10, 'Reference data ')
pdf.ln(h='')
pdf.set_font('Arial', '', 12)
pdf.cell(175, 10, 'Reference collection: ' + parser.collection)
pdf.ln(h=5)
pdf.cell(190, 10, 'Number of functions in reference collection: '
+ str(len(parser.ref_data.functions_dict)))
pdf.ln(h=5)
pdf.cell(205, 10, 'Number of proteins in reference collection: '
+ str(len(parser.ref_data.proteins_dict)))
pdf.ln(h=5)
pdf.cell(220, 10, 'Reference DB size (aa): '
+ str(parser.config.get_reference_db_size(parser.collection)))
pdf.ln(h=5)
pdf.cell(235, 10, 'Background DB size (aa): '
+ str(parser.config.get_background_db_size(parser.collection)))
pdf.ln(h=20)
pdf.set_font('Arial', 'B', 12)
pdf.cell(260, 10, 'Parameters ')
pdf.ln(h='')
pdf.set_font('Arial', '', 12)
pdf.cell(275, 10, 'Protein identity threshold (%): '
+ str(parser.config.get_identity_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(290, 10, 'Alignment length threshold (aa): '
+ str(parser.config.get_length_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(305, 10, 'Hits overlap threshold (aa): '
+ str(parser.config.get_overlap_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(320, 10, 'Bitscore range threshold (%): '
+ str(100 * parser.config.get_biscore_range_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(335, 10, 'e-value threshold for reference DB search: '
+ '{:.2e}'.format(parser.config.get_evalue_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(335, 10, 'e-value threshold for background DB search: ' + '{:.2e}'.format(
parser.config.get_background_db_size(
parser.options.get_collection(parser.sample.sample_id)
) * parser.config.get_evalue_cutoff(
parser.options.get_collection(parser.sample.sample_id)
) / parser.config.get_reference_db_size(
parser.options.get_collection(parser.sample.sample_id)
)
))
pdf.ln(h=20)
# Write search stats
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Search stats')
pdf.ln(h='')
pdf.set_font('Arial', 'B', 12)
pdf.cell(65, 10, 'Number of reads found in reference DB search: ' + str(len(parser.reads)))
pdf.ln(h=10)
read_stats = Counter()
for read in sorted(parser.reads.keys()):
read_stats[parser.reads[read].status] += 1
pdf.cell(
65, 10, 'Number of reads found in background DB search: '
+ str(len(parser.reads) - read_stats['unaccounted'])
)
pdf.ln(h=5)
pdf.set_font('Arial', '', 12)
table_rows = ['<table border="0" align="center" width="100%">',
'<thead><tr><th width="90%">Status</th><th width="10%">Read count</th>'
+ '</tr></thead>',
'<tbody>']
for status in OrderedDict(read_stats.most_common()):
if status == 'unaccounted':
pass
elif status == 'nofunction':
table_rows.append('<tr><td>Reads not mapped to any function</td><td>'
+ str(read_stats[status]) + '</td></tr>')
elif status == 'function':
table_rows.append('<tr><td>Reads mapped to a function of interest</td><td>'
+ str(read_stats[status]) + '</td></tr>')
else:
table_rows.append('<tr><td>' + status + '</td><td>'
+ str(read_stats[status]) + '</td></tr>')
table_rows.append('</tbody>\n</table>')
pdf.write_html('\n'.join(table_rows))
# Write group scores
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Function statistics by category')
pdf.ln(h=5)
func_stats = defaultdict(float)
func_counts = Counter()
func_identity = defaultdict(float)
func_hit_counts = Counter()
for read in parser.reads.keys():
if parser.reads[read].status == 'function':
functions = parser.reads[read].functions
for function in functions:
func_stats[parser.ref_data.lookup_function_group(function)] += functions[function]
func_counts[parser.ref_data.lookup_function_group(function)] += 1/len(functions)
for hit in parser.reads[read].hit_list.hits:
for function in hit.functions:
func_identity[parser.ref_data.lookup_function_group(function)] += hit.identity
func_hit_counts[parser.ref_data.lookup_function_group(function)] += 1
for function in func_identity:
func_identity[function] = func_identity[function]/func_hit_counts[function]
table_rows = ['<table border="0" align="center" width="100%">',
'<thead><tr><<th width="55%">Definition</th><th width="15%">RPKM Score</th>' +
'<th width="15%">Read count</th><th width="15%">Avg. identity</th></tr></thead>',
'<tbody>']
for function in sorted(func_stats.keys()):
table_rows.append('<tr><td>' + function
+ '</td><td>' + '{0:.2f}'.format(func_stats[function])
+ '</td><td>' + '{0:g}'.format(func_counts[function])
+ '</td><td>' + '{0:.1f}'.format(func_identity[function])
+ '</td></tr>')
table_rows.append('</tbody></table>')
pdf.write_html('\n'.join(table_rows))
# Write function scores
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Function statistics')
pdf.ln(h=5)
func_stats = defaultdict(float)
func_counts = Counter()
func_identity = defaultdict(float)
func_hit_counts = Counter()
for read in parser.reads.keys():
if parser.reads[read].status == 'function':
functions = parser.reads[read].functions
for function in functions:
func_stats[function] += functions[function]
func_counts[function] += 1/len(functions)
for hit in parser.reads[read].hit_list.hits:
for function in hit.functions:
func_identity[function] += hit.identity
func_hit_counts[function] += 1
for function in func_identity:
func_identity[function] = func_identity[function]/func_hit_counts[function]
table_rows = ['<font size="8"><table border="1" align="center" width="100%">',
'<thead><tr><th width="12%">ID</th><th width="61%">Definition</th>' +
'<th width="9%">RPKM Score</th><th width="9%">Read count</th>' +
'<th width="9%">Avg. identity</th></tr></thead>',
'<tbody>']
for function in sorted(func_stats.keys()):
function_definition = parser.ref_data.lookup_function_name(function)
if len(function_definition) > 80:
function_definition = function_definition[:81] + '...'
table_rows.append('<tr><td>' + function
+ '</td><td>' + function_definition
+ '</td><td>' + '{0:.2f}'.format(func_stats[function])
+ '</td><td>' + '{0:g}'.format(func_counts[function])
+ '</td><td>' + '{0:.1f}'.format(func_identity[function])
+ '</td></tr>')
table_rows.append('</tbody></table></font>')
pdf.write_html('\n'.join(table_rows))
# Write taxonomy stats
tax_stats = Counter()
identity_stats = Counter()
rpkm_stats = defaultdict(float)
for read in parser.reads.keys():
if parser.reads[read].status == 'function':
hits = parser.reads[read].hit_list.hits
for hit in hits:
protein_taxid = parser.ref_data.lookup_protein_tax(
cleanup_protein_id(hit.subject_id)
)
tax_stats[protein_taxid] += 1
identity_stats[protein_taxid] += hit.identity
if len(hits) == 1:
read_functions = parser.reads[read].functions
for function in read_functions:
rpkm_stats[parser.ref_data.lookup_protein_tax(
cleanup_protein_id(hits[0].subject_id)
)] += read_functions[function]
else:
read_functions = parser.reads[read].functions
protein_taxids = {}
for hit in hits:
hit_taxid = parser.ref_data.lookup_protein_tax(
cleanup_protein_id(hit.subject_id)
)
for hit_function in hit.functions:
protein_taxids[hit_taxid] = hit_function
for taxid in protein_taxids:
if protein_taxids[taxid] in read_functions:
rpkm_stats[taxid] += read_functions[protein_taxids[taxid]]
tax_data = parser.taxonomy_data
counts_per_rank, identity_per_rank, rpkm_per_rank = get_scores_per_tax_rank(
counts=tax_stats, identity=identity_stats, scores=rpkm_stats, taxonomy_data=tax_data
)
ranks = ['superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']
for rank in ranks:
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Taxonomy statistics for best hits (rank: ' + rank + ')')
pdf.ln(h=6)
pdf.set_font('Arial', '', 10)
pdf.cell(50, 10, '*top 100 entries are shown')
table_rows = ['<table border="0" align="center" width="100%">',
'<thead><tr><<th width="60%">Taxon</th><th width="15%">RPKM score</th>'
+ '<th width="15%">Read count</th><th width="15%">Avg. identity</th>'
+ '</tr></thead>',
'<tbody>']
for tax in OrderedDict(Counter(rpkm_per_rank[rank]).most_common(100)):
table_rows.append('<tr><td>' + tax
+ '</td><td>' + '{0:.2f}'.format(rpkm_per_rank[rank][tax])
+ '</td><td>' + '{0:g}'.format(counts_per_rank[rank][tax])
+ '</td><td>' + '{0:.1f}'.format(identity_per_rank[rank][tax])
+ '</td></tr>')
table_rows.append('</tbody>\n</table>')
pdf.write_html('\n'.join(table_rows))
outfile = os.path.join(
parser.options.get_project_dir(parser.sample.sample_id),
parser.options.get_output_subdir(parser.sample.sample_id),
parser.sample.sample_id + '_' + parser.end + '_' + parser.options.report_name+'.pdf'
)
print('Writing PDF output to ', outfile)
pdf.output(outfile, 'F')
def generate_protein_pdf_report(parser):
"""Generates PDF report for processing of one protein FASTA file
Args:
parser (:obj:DiamondParser): parser object with annotated proteins
"""
pdf = MyFPDF('P', 'mm', 'Letter')
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Run info')
pdf.ln(h='')
pdf.set_font('Arial', 'B', 12)
pdf.cell(60, 10, 'Sequence data')
pdf.ln(h='')
pdf.set_font('Arial', '', 12)
pdf.cell(75, 10, 'Sample ID: ' + parser.options.get_sample_id(parser.sample.sample_id))
pdf.ln(h=5)
pdf.cell(105, 10, 'FASTA file: '
+ parser.options.get_fastq_path(parser.sample.sample_id, parser.end))
pdf.ln(h=5)
pdf.cell(135, 10, 'Number of reads: '
+ str(parser.options.get_fastq1_readcount(parser.sample.sample_id)))
pdf.ln(h=20)
pdf.set_font('Arial', 'B', 12)
pdf.cell(160, 10, 'Reference data ')
pdf.ln(h='')
pdf.set_font('Arial', '', 12)
pdf.cell(175, 10, 'Reference collection: ' + parser.collection)
pdf.ln(h=5)
pdf.cell(190, 10, 'Number of functions in reference collection: '
+ str(len(parser.ref_data.functions_dict)))
pdf.ln(h=5)
pdf.cell(205, 10, 'Number of proteins in reference collection: '
+ str(len(parser.ref_data.proteins_dict)))
pdf.ln(h=5)
pdf.cell(220, 10, 'Reference DB size (aa): '
+ str(parser.config.get_reference_db_size(parser.collection)))
pdf.ln(h=5)
pdf.cell(235, 10, 'Background DB size (aa): '
+ str(parser.config.get_background_db_size(parser.collection)))
pdf.ln(h=20)
pdf.set_font('Arial', 'B', 12)
pdf.cell(260, 10, 'Parameters ')
pdf.ln(h='')
pdf.set_font('Arial', '', 12)
pdf.cell(275, 10, 'Protein identity threshold (%): '
+ str(parser.config.get_identity_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(290, 10, 'Alignment length threshold (aa): '
+ str(parser.config.get_length_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(305, 10, 'Hits overlap threshold (aa): '
+ str(parser.config.get_overlap_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(320, 10, 'Bitscore range threshold (%): '
+ str(100 * parser.config.get_biscore_range_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(335, 10, 'e-value threshold for reference DB search: '
+ '{:.2e}'.format(parser.config.get_evalue_cutoff(parser.collection)))
pdf.ln(h=5)
pdf.cell(335, 10, 'e-value threshold for background DB search: ' + '{:.2e}'.format(
parser.config.get_background_db_size(
parser.options.get_collection(parser.sample.sample_id)
) * parser.config.get_evalue_cutoff(
parser.options.get_collection(parser.sample.sample_id)
) / parser.config.get_reference_db_size(
parser.options.get_collection(parser.sample.sample_id)
)
))
pdf.ln(h=20)
# Write search stats
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Search stats')
pdf.ln(h='')
pdf.set_font('Arial', 'B', 12)
pdf.cell(65, 10, 'Number of proteins found in reference DB search: '
+ str(len(parser.reads)))
pdf.ln(h=10)
read_stats = Counter()
for read in sorted(parser.reads.keys()):
read_stats[parser.reads[read].get_status()] += 1
pdf.cell(65, 10, 'Number of proteins found in background DB search: '
+ str(len(parser.reads) - read_stats['unaccounted']))
pdf.ln(h=5)
pdf.set_font('Arial', '', 12)
table_rows = ['<table border="0" align="center" width="100%">',
'<thead><tr><th width="90%">Status</th><th width="10%">Read count</th>'
+ '</tr></thead>',
'<tbody>']
for status in OrderedDict(read_stats.most_common()):
if status == 'unaccounted':
pass
elif status == 'nofunction':
table_rows.append('<tr><td>Proteins not mapped to any function</td><td>'
+ str(read_stats[status]) + '</td></tr>')
elif status == 'function':
table_rows.append('<tr><td>Proteins mapped to a function of interest</td><td>'
+ str(read_stats[status]) + '</td></tr>')
elif status == 'function,besthit':
table_rows.append('<tr><td>Proteins mapped to a function of interest AND '
+ 'functional protein</td><td>'
+ str(read_stats[status]) + '</td></tr>')
else:
table_rows.append('<tr><td>' + status + '</td><td>'
+ str(read_stats[status]) + '</td></tr>')
table_rows.append('</tbody>\n</table>')
pdf.write_html('\n'.join(table_rows))
# Write group scores
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Function statistics by category')
pdf.ln(h=5)
func_stats = defaultdict(float)
func_counts = Counter()
func_identity = defaultdict(float)
func_hit_counts = Counter()
for read in parser.reads.keys():
if parser.reads[read].get_status() == 'function':
functions = parser.reads[read].functions
for function in functions:
func_stats[parser.ref_data.lookup_function_group(function)] += functions[function]
func_counts[parser.ref_data.lookup_function_group(function)] += 1/len(functions)
for hit in parser.reads[read].get_hit_list().get_hits():
for function in hit.get_functions():
func_identity[parser.ref_data.lookup_function_group(function)] += hit.identity
func_hit_counts[parser.ref_data.lookup_function_group(function)] += 1
for function in func_identity:
func_identity[function] = func_identity[function]/func_hit_counts[function]
table_rows = ['<table border="0" align="center" width="100%">',
'<thead><tr><<th width="55%">Definition</th><th width="15%">Norm. abundance</th>'
+ '<th width="15%">Prot. count</th><th width="15%">Avg. identity</th>'
+ '</tr></thead>',
'<tbody>']
for function in sorted(func_stats.keys()):
table_rows.append('<tr><td>' + function
+ '</td><td>' + '{0:.2f}'.format(func_stats[function])
+ '</td><td>' + '{0:g}'.format(func_counts[function])
+ '</td><td>' + '{0:.1f}'.format(func_identity[function])
+ '</td></tr>')
table_rows.append('</tbody></table>')
pdf.write_html('\n'.join(table_rows))
# Write function scores
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Function statistics')
pdf.ln(h=5)
func_stats = defaultdict(float)
func_counts = Counter()
func_identity = defaultdict(float)
func_hit_counts = Counter()
for read in parser.reads.keys():
if parser.reads[read].get_status() == 'function':
functions = parser.reads[read].get_functions()
for function in functions:
func_stats[function] += functions[function]
func_counts[function] += 1/len(functions)
for hit in parser.reads[read].get_hit_list().get_hits():
for function in hit.get_functions():
func_identity[function] += hit.get_identity()
func_hit_counts[function] += 1
for function in func_identity:
func_identity[function] = func_identity[function]/func_hit_counts[function]
table_rows = ['<font size="8"><table border="1" align="center" width="100%">',
'<thead><tr><th width="12%">ID</th><th width="61%">Definition</th><th width="9%">'
+ 'Norm. abundance</th><th width="9%">Prot. count</th><th width="9%">'
+ 'Avg. identity</th></tr></thead>',
'<tbody>']
for function in sorted(func_stats.keys()):
function_definition = parser.ref_data.lookup_function_name(function)
if len(function_definition) > 80:
function_definition = function_definition[:81] + '...'
table_rows.append('<tr><td>' + function
+ '</td><td>' + function_definition
+ '</td><td>' + '{0:.2f}'.format(func_stats[function])
+ '</td><td>' + '{0:g}'.format(func_counts[function])
+ '</td><td>' + '{0:.1f}'.format(func_identity[function])
+ '</td></tr>')
table_rows.append('</tbody></table></font>')
pdf.write_html('\n'.join(table_rows))
# Write taxonomy stats
tax_stats = Counter()
identity_stats = Counter()
rpkm_stats = defaultdict(float)
for read in parser.reads.keys():
if parser.reads[read].status == 'function':
hits = parser.reads[read].hit_list.hits
for hit in hits:
protein_taxid = parser.ref_data.lookup_protein_tax(
cleanup_protein_id(hit.get_subject_id())
)
tax_stats[protein_taxid] += 1
identity_stats[protein_taxid] += hit.get_identity()
if len(hits) == 1:
read_functions = parser.reads[read].get_functions()
for function in read_functions:
rpkm_stats[parser.ref_data.lookup_protein_tax(
cleanup_protein_id(hits[0].get_subject_id())
)] += read_functions[function]
else:
read_functions = parser.reads[read].get_functions()
protein_taxids = {}
for hit in hits:
hit_taxid = parser.ref_data.lookup_protein_tax(
cleanup_protein_id(hit.get_subject_id())
)
hit_functions = hit.get_functions()
for hit_function in hit_functions:
protein_taxids[hit_taxid] = hit_function
for taxid in protein_taxids:
if protein_taxids[taxid] in read_functions:
rpkm_stats[taxid] += read_functions[protein_taxids[taxid]]
tax_data = parser.taxonomy_data
counts_per_rank, identity_per_rank, rpkm_per_rank = tax_data.get_taxonomy_profile(
counts=tax_stats, identity=identity_stats, scores=rpkm_stats
)
ranks = ['superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']
for rank in ranks:
pdf.add_page()
pdf.set_font('Arial', 'B', 16)
pdf.cell(40, 10, 'Taxonomy statistics for best hits (rank: ' + rank + ')')
pdf.ln(h=6)
pdf.set_font('Arial', '', 10)
pdf.cell(50, 10, '*top 100 entries are shown')
table_rows = ['<table border="0" align="center" width="100%">',
'<thead><tr><<th width="60%">Taxon</th><th width="15%">Norm. abundance</th>'
+ '<th width="15%">Prot. count</th><th width="15%">Avg. identity</th>'
+ '</tr></thead>',
'<tbody>']
for tax in OrderedDict(Counter(rpkm_per_rank[rank]).most_common(100)):
table_rows.append('<tr><td>' + tax
+ '</td><td>' + '{0:.2f}'.format(rpkm_per_rank[rank][tax])
+ '</td><td>' + '{0:g}'.format(counts_per_rank[rank][tax])
+ '</td><td>' + '{0:.1f}'.format(identity_per_rank[rank][tax])
+ '</td></tr>')
table_rows.append('</tbody>\n</table>')
pdf.write_html('\n'.join(table_rows))
outfile = os.path.join(
parser.options.get_project_dir(parser.sample.sample_id),
parser.options.get_output_subdir(parser.sample.sample_id),
parser.sample.sample_id + '_' + parser.end + '_' + parser.options.get_report_name()+'.pdf'
)
print('Writing PDF output to ', outfile)
pdf.output(outfile, 'F')
| 45.848598
| 100
| 0.572873
| 3,074
| 24,529
| 4.40566
| 0.075472
| 0.026877
| 0.022152
| 0.015506
| 0.958724
| 0.95134
| 0.949051
| 0.94883
| 0.947058
| 0.926383
| 0
| 0.028544
| 0.273024
| 24,529
| 534
| 101
| 45.934457
| 0.730933
| 0.023605
| 0
| 0.841004
| 0
| 0.029289
| 0.186339
| 0.016198
| 0
| 0
| 0
| 0
| 0
| 1
| 0.004184
| false
| 0.006276
| 0.012552
| 0
| 0.018828
| 0.004184
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
14f8741847baa6cb25f02f552277793a6a73a289
| 290
|
py
|
Python
|
tests/test_simple.py
|
godotgildor/urban-octo-palm-tree
|
2c48dd07c2f92e94da61c46644dcf30a30462434
|
[
"MIT"
] | null | null | null |
tests/test_simple.py
|
godotgildor/urban-octo-palm-tree
|
2c48dd07c2f92e94da61c46644dcf30a30462434
|
[
"MIT"
] | null | null | null |
tests/test_simple.py
|
godotgildor/urban-octo-palm-tree
|
2c48dd07c2f92e94da61c46644dcf30a30462434
|
[
"MIT"
] | null | null | null |
import pytest
@pytest.mark.parametrize("input_value", [1, True, False, "hello"])
def test_success(input_value):
assert input_value == input_value
@pytest.mark.parametrize("input_value", [1, True, False, "hello"])
def test_failure(input_value):
assert input_value != input_value
| 24.166667
| 66
| 0.737931
| 40
| 290
| 5.1
| 0.375
| 0.392157
| 0.205882
| 0.254902
| 0.872549
| 0.872549
| 0.872549
| 0.519608
| 0.519608
| 0.519608
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| 0.007874
| 0.124138
| 290
| 11
| 67
| 26.363636
| 0.795276
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| 0.110345
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| 0.285714
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| null | 1
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0
| 7
|
116dd6c5e1017c31f8ff7189eeadd8b19333c6d3
| 15,983
|
py
|
Python
|
tests/test_todo_api.py
|
shuhaowu/projecto
|
4dec9dbde43874f35d2ea38d3c3496db883c0c42
|
[
"Apache-2.0"
] | 29
|
2015-02-07T01:28:30.000Z
|
2022-01-18T17:04:36.000Z
|
tests/test_todo_api.py
|
shuhaowu/projecto
|
4dec9dbde43874f35d2ea38d3c3496db883c0c42
|
[
"Apache-2.0"
] | null | null | null |
tests/test_todo_api.py
|
shuhaowu/projecto
|
4dec9dbde43874f35d2ea38d3c3496db883c0c42
|
[
"Apache-2.0"
] | 6
|
2015-02-07T12:02:50.000Z
|
2020-03-05T10:09:19.000Z
|
from __future__ import absolute_import
from datetime import datetime, timedelta
from kvkit import NotFoundError
from projecto.models import Comment
from projecto.apiv1.todos.models import Todo, ArchivedTodo
import unittest
from .utils import ProjectTestCase, new_todo, new_comment
class TestTodoAPI(ProjectTestCase):
def base_url(self, postfix):
return "/api/v1/projects/{}/todos{}".format(self.project.key, postfix)
# We need to test for security problems like XSS here.
def test_new_todo(self):
self.login()
response, data = self.postJSON(self.base_url("/"), data={"title": "A title"})
self.assertStatus(200, response)
self.assertTrue("key" in data)
self.assertTrue("title" in data)
self.assertEquals("A title", data["title"])
self.assertTrue("author" in data)
self.assertEquals(self.user.key, data["author"]["key"])
response, data = self.postJSON(self.base_url("/"), data={"title": "A title", "content": "some content", "tags": ["a", "b", "c"]})
self.assertStatus(200, response)
self.assertTrue("key" in data)
self.assertTrue("title" in data)
self.assertEquals("A title", data["title"])
self.assertTrue("author" in data)
self.assertEquals(self.user.key, data["author"]["key"])
self.assertTrue("content" in data)
self.assertTrue("markdown" in data["content"])
self.assertEquals("some content", data["content"]["markdown"])
self.assertTrue("html" in data["content"])
self.assertTrue("<p>some content</p>" in data["content"]["html"])
self.assertTrue("tags" in data)
self.assertEquals(["a", "b", "c"], data["tags"])
def test_new_todo_reject_badrequest(self):
self.login()
response, data = self.postJSON(self.base_url("/"), data={"invalid": "invald"})
self.assertStatus(400, response)
self.postJSON(self.base_url("/"), data={"title": "title", "content": "content", "author": "invalid"})
self.assertStatus(400, response)
def test_new_todo_reject_permission(self):
response, data = self.postJSON(self.base_url("/"), data={"title": "todo"})
self.assertStatus(403, response)
user2 = self.create_user("test2@test.com")
self.login(user2)
response, data = self.postJSON(self.base_url("/"), data={"title": "todo"})
self.assertStatus(403, response)
# TODO: this method
# def test_new_todo_filter_xss(self):
def test_update_todo(self):
todo = new_todo(self.user, self.project, save=True)
self.login()
response, data = self.putJSON(self.base_url("/" + todo.key), data={"title": "todo2"})
self.assertStatus(200, response)
self.assertTrue("key" in data)
self.assertEquals(todo.key, data["key"])
self.assertEquals("todo2", data["title"])
response, data = self.putJSON(self.base_url("/" + todo.key), data={"content": {"markdown": "aaaa"}})
self.assertStatus(200, response)
self.assertEquals("todo2", data["title"])
self.assertTrue("content" in data)
self.assertTrue("markdown" in data["content"])
self.assertEquals("aaaa", data["content"]["markdown"])
self.assertTrue("<p>aaaa</p>" in data["content"]["html"])
def test_update_todo_reject_badrequest(self):
todo = new_todo(self.user, self.project, save=True)
self.login()
response, data = self.putJSON(self.base_url("/" + todo.key), data={"author": "someauthor"})
self.assertStatus(400, response)
response, data = self.putJSON(self.base_url("/" + todo.key), data={"title": "title", "adfaf": "adfa"})
self.assertStatus(400, response)
def test_update_todo_reject_permission(self):
todo = new_todo(self.user, self.project, save=True)
response, data = self.putJSON(self.base_url("/" + todo.key), data={"title": "todo2"})
self.assertStatus(403, response)
user2 = self.create_user("test2@test.com")
self.login(user2)
response, data = self.putJSON(self.base_url("/" + todo.key), data={"title": "todo2"})
self.assertStatus(403, response)
def test_get_todo(self):
todo = new_todo(self.user, self.project, title="todo", save=True)
self.login()
response, data = self.getJSON(self.base_url("/" + todo.key))
self.assertStatus(200, response)
self.assertEqual(todo.key, data["key"])
self.assertEqual("todo", data["title"])
self.assertEqual(self.user.key, data["author"]["key"])
self.assertEqual(self.user.name, data["author"]["name"])
def test_get_todo_reject_permission(self):
todo = new_todo(self.user, self.project, save=True)
response, data = self.getJSON(self.base_url("/" + todo.key))
self.assertStatus(403, response)
user2 = self.create_user("test2@test.com")
self.login(user2)
response, data = self.getJSON(self.base_url("/" + todo.key))
self.assertStatus(403, response)
def test_delete_todo(self):
todo = new_todo(self.user, self.project, save=True)
self.login()
response, data = self.deleteJSON(self.base_url("/" + todo.key))
self.assertStatus(200, response)
with self.assertRaises(NotFoundError):
Todo.get(todo.key)
response, data = self.deleteJSON(self.base_url("/" + todo.key))
self.assertStatus(404, response)
def test_delete_todo_reject_permission(self):
todo = new_todo(self.user, self.project, save=True)
response, data = self.deleteJSON(self.base_url("/" + todo.key))
self.assertStatus(403, response)
def test_markdone_todo(self):
todo = new_todo(self.user, self.project, save=True)
self.login()
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": True})
self.assertStatus(200, response)
todo = Todo.get(todo.key)
self.assertTrue(todo.done)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": False})
self.assertStatus(200, response)
todo = Todo.get(todo.key)
self.assertFalse(todo.done)
def test_markdone_todo_reject_badrequest(self):
todo = new_todo(self.user, self.project, save=True)
self.login()
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"notdone": False})
self.assertStatus(400, response)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": False, "invalid": "invalid"})
self.assertStatus(400, response)
def test_markdone_todo_reject_permission(self):
todo = new_todo(self.user, self.project, save=True)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": True})
self.assertStatus(403, response)
user2 = self.create_user("test2@test.com")
self.login(user2)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": True})
self.assertStatus(403, response)
def test_index_todos(self):
self.login()
keys = set()
for i in xrange(50):
todo = new_todo(self.user, self.project, date=datetime.now() + timedelta(seconds=i*10), title=str(i), save=True)
keys.add(todo.key)
response, data = self.getJSON(self.base_url("/"))
self.assertStatus(200, response)
self.assertEquals(4, len(data))
self.assertTrue("todos" in data)
self.assertEquals(1, data["currentPage"])
self.assertEquals(50, data["totalTodos"])
self.assertEquals(20, data["todosPerPage"])
self.assertEquals(20, len(data["todos"]))
k = {t["key"] for t in data["todos"]}
self.assertEquals(20, len(k))
response, data = self.getJSON(self.base_url("/?page=2"))
self.assertEquals(20, len(data["todos"]))
self.assertEquals(2, data["currentPage"])
self.assertEquals(50, data["totalTodos"])
self.assertEquals(20, data["todosPerPage"])
k.update({t["key"] for t in data["todos"]})
self.assertEquals(40, len(k))
response, data = self.getJSON(self.base_url("/?page=3"))
self.assertEquals(10, len(data["todos"]))
self.assertEquals(3, data["currentPage"])
self.assertEquals(50, data["totalTodos"])
self.assertEquals(20, data["todosPerPage"])
k.update({t["key"] for t in data["todos"]})
self.assertEquals(50, len(k))
self.assertEquals(keys, k)
def test_index_todos_reject_permission(self):
response, data = self.getJSON(self.base_url("/"))
self.assertStatus(403, response)
user2 = self.create_user("test2@test.com")
self.login(user2)
response, data = self.getJSON(self.base_url("/"))
self.assertStatus(403, response)
def test_filter_todos(self):
self.login()
keys = []
for i in xrange(30):
todo = new_todo(self.user, self.project, date=datetime.now() + timedelta(seconds=i*10), title=str(i), tags=["tag1"], save=True)
keys.append(todo.key)
for i in xrange(30, 50):
todo = new_todo(self.user, self.project, date=datetime.now() + timedelta(seconds=i*10), title=str(i), tags=["tag2"], save=True)
keys.append(todo.key)
for i in xrange(50, 57):
new_todo(self.user, self.project, date=datetime.now() + timedelta(seconds=i*10), title=str(i), tags=["tag3"], save=True)
response, data = self.getJSON(self.base_url("/filter?tags=tag1&tags=tag2&page=1"))
self.assertStatus(200, response)
self.assertEquals(4, len(data))
self.assertTrue("todos" in data)
self.assertEquals(1, data["currentPage"])
self.assertEquals(50, data["totalTodos"])
self.assertEquals(20, data["todosPerPage"])
self.assertEquals(20, len(data["todos"]))
k = [t["key"] for t in data["todos"]]
response, data = self.getJSON(self.base_url("/filter?tags=tag1&tags=tag2&page=2"))
self.assertEquals(2, data["currentPage"])
self.assertEquals(50, data["totalTodos"])
self.assertEquals(20, data["todosPerPage"])
self.assertEquals(20, len(data["todos"]))
k.extend([t["key"] for t in data["todos"]])
response, data = self.getJSON(self.base_url("/filter?tags=tag1&tags=tag2&page=3"))
self.assertEquals(3, data["currentPage"])
self.assertEquals(50, data["totalTodos"])
self.assertEquals(20, data["todosPerPage"])
self.assertEquals(10, len(data["todos"]))
k.extend([t["key"] for t in data["todos"]])
self.assertEquals(set(keys), set(k))
def test_filter_todos_reject_permission(self):
response, data = self.getJSON(self.base_url("/filter"))
self.assertStatus(403, response)
user2 = self.create_user("test2@test.com")
self.login(user2)
response, data = self.getJSON(self.base_url("/filter"))
self.assertStatus(403, response)
def test_markdone(self):
todo = new_todo(self.user, self.project, save=True)
self.login()
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": True})
self.assertStatus(200, response)
self.assertTrue(Todo.get(todo.key).done)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": False})
self.assertStatus(200, response)
self.assertFalse(Todo.get(todo.key).done)
def test_markdone_reject_badrequest(self):
todo = new_todo(self.user, self.project, save=True)
self.login() # TODO: we really gotta refactor these
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"title": True})
self.assertStatus(400, response)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"title": True, "done": True})
self.assertStatus(400, response)
def test_markdone_reject_permission(self):
todo = new_todo(self.user, self.project, save=True)
response, data = self.postJSON(self.base_url("/" + todo.key + "/markdone"), data={"done": False})
self.assertStatus(403, response)
def test_list_tags(self):
new_todo(self.user, self.project, tags=["tag1", "tag2", "another tag"], save=True)
new_todo(self.user, self.project, tags=["tag1", "mrrow", "wut"], save=True)
self.login()
response, data = self.getJSON(self.base_url("/tags/"))
self.assertStatus(200, response)
self.assertEquals(1, len(data))
self.assertTrue("tags" in data)
data["tags"].sort()
self.assertTrue(sorted(["tag1", "tag2", "mrrow", "wut", "another tag"]), data["tags"])
def test_list_tags_reject_permission(self):
new_todo(self.user, self.project, tags=["tag1", "tag2", "another tag"], save=True)
response, data = self.getJSON(self.base_url("/tags/"))
self.assertStatus(403, response)
def test_archived_index(self):
todo1 = new_todo(self.user, self.project, save=True)
todo2 = new_todo(self.user, self.project, done=True, save=True)
todo1.archive()
todo2.archive()
self.login()
response, data = self.getJSON(self.base_url("/"), query_string={"archived": "1"})
self.assertStatus(200, response)
self.assertTrue("todos" in data)
self.assertEquals(2, len(data["todos"]))
k = [t["key"] for t in data["todos"]]
self.assertTrue(todo1.key in k)
self.assertTrue(todo2.key in k)
response, data = self.getJSON(self.base_url("/"))
self.assertStatus(200, response)
self.assertEquals(0, len(data["todos"]))
def test_archived_delete(self):
todo1 = new_todo(self.user, self.project, save=True)
self.login()
response = self.delete(self.base_url("/" + todo1.key))
self.assertStatus(200, response)
with self.assertRaises(NotFoundError):
Todo.get(todo1.key)
todo1_again = ArchivedTodo.get(todo1.key)
self.assertEquals(todo1.key, todo1_again.key)
def test_really_delete(self):
todo1 = new_todo(self.user, self.project, save=True)
self.login()
response = self.delete(self.base_url("/" + todo1.key), query_string={"really": "1"})
self.assertStatus(200, response)
with self.assertRaises(NotFoundError):
ArchivedTodo.get(todo1.key)
with self.assertRaises(NotFoundError):
Todo.get(todo1.key)
def test_delete_todo_with_comments(self):
todo1 = new_todo(self.user, self.project, save=True)
comment = new_comment(self.user, todo1.key, save=True)
self.login()
response = self.delete(self.base_url("/" + todo1.key), query_string={"really": "1"})
self.assertStatus(200, response)
with self.assertRaises(NotFoundError):
Todo.get(todo1.key)
with self.assertRaises(NotFoundError):
ArchivedTodo.get(todo1.key)
with self.assertRaises(NotFoundError):
Comment.get(comment.key)
def test_delete_archived(self):
todo1 = new_todo(self.user, self.project, save=True)
todo2 = new_todo(self.user, self.project, save=True)
todo1 = todo1.archive()
todo2 = todo2.archive()
self.login()
response = self.delete(self.base_url("/" + todo1.key), query_string={"really": "1", "archived": "1"})
self.assertStatus(200, response)
with self.assertRaises(NotFoundError):
todo1.reload()
# Should not have an effect.
response = self.delete(self.base_url("/" + todo2.key), query_string={"archived": "1"})
self.assertStatus(304, response)
todo2.reload()
def test_get_archived(self):
todo1 = new_todo(self.user, self.project, save=True)
todo1 = todo1.archive()
self.login()
response, data = self.getJSON(self.base_url("/" + todo1.key))
self.assertStatus(404, response)
response, data = self.getJSON(self.base_url("/" + todo1.key), query_string={"archived": "1"})
self.assertStatus(200, response)
self.assertEquals(todo1.key, data["key"])
def test_markdone_archived(self):
todo1 = new_todo(self.user, self.project, save=True)
todo1 = todo1.archive()
self.login()
response, data = self.postJSON(self.base_url("/" + todo1.key + "/markdone"), data={"done": True})
self.assertStatus(404, response)
response, data = self.postJSON(self.base_url("/" + todo1.key + "/markdone"), query_string={"archived": "1"}, data={"done": True})
self.assertStatus(200, response)
response, data = self.getJSON(self.base_url("/" + todo1.key), query_string={"archived": "1"})
self.assertStatus(200, response)
self.assertEquals(True, data["done"])
if __name__ == "__main__":
unittest.main()
| 36.490868
| 133
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0
| 7
|
11d57121431fb051b5dbdd0dc8d42d08c9b14729
| 8,978
|
py
|
Python
|
python/lib/lib_care/measure/compute_relative_velocities.py
|
timtyree/bgmc
|
891e003a9594be9e40c53822879421c2b8c44eed
|
[
"MIT"
] | null | null | null |
python/lib/lib_care/measure/compute_relative_velocities.py
|
timtyree/bgmc
|
891e003a9594be9e40c53822879421c2b8c44eed
|
[
"MIT"
] | null | null | null |
python/lib/lib_care/measure/compute_relative_velocities.py
|
timtyree/bgmc
|
891e003a9594be9e40c53822879421c2b8c44eed
|
[
"MIT"
] | null | null | null |
import pandas as pd, numpy as np
from ..utils.projection_func import get_subtract_pbc
def compute_DT(df,round_t_to_n_digits=3):
'''DT is the time between two observations'''
DT=np.around(df[(df.frame==1)].t.values[0]-df[(df.frame==0)].t.values[0],round_t_to_n_digits)
return DT
# def compute_angle_between_initial_velocities(d1,d2):
# '''computes angle between initial velocities near birth for one tip pair.
# Updates d1,d2 with fields.
# Example Usage:
# tbirth_values,angle_between_values=compute_angle_between_initial_velocities(d1,d2)
# '''
# d1[['dx','dy','dt']]=d1[['x','y','t']].diff().shift(-1).iloc[1:-1]
# d1['displacement']=np.sqrt(d1['dx']**2+d1['dy']**2)
# d1['dx_hat']=d1['dx']/d1['displacement']
# d1['dy_hat']=d1['dy']/d1['displacement']
#
# d2[['dx','dy','dt']]=d2[['x','y','t']].diff().shift(-1).iloc[1:-1]
# d2['displacement']=np.sqrt(d2['dx']**2+d2['dy']**2)
# d2['dx_hat']=d2['dx']/d2['displacement']
# d2['dy_hat']=d2['dy']/d2['displacement']
#
# cosine_series=d1['dx_hat']*d2['dx_hat']+d1['dy_hat']*d2['dy_hat']
# d1['angle_between']=np.arccos(cosine_series) #radians
# angle_between_values=d1['angle_between'].values
# tbirth_values=d1['t'].values-d1['t'].values[0] #ms
# return tbirth_values,angle_between_values
def get_compute_angle_between_final_velocities(width,height):
# compute_displacements_between=get_compute_displacements_between(width,height)
subtract_pbc=get_subtract_pbc(width=width,height=height)
def compute_angle_between_final_velocities(d1,d2):
'''computes angle between final velocities near birth for one tip pair.
Updates d1,d2 with fields. aligns locations by index
Example Usage:
compute_angle_between_final_velocities=get_compute_angle_between_final_velocities(width,height)
tdeath_values,angle_between_values=compute_angle_between_final_velocities(d1,d2)
'''
#compute displacement of d1 with pbc
xy_values=np.array(list(zip(d1['x'],d1['y'])))
dshifted=d1.shift(1).copy()
# dshifted=d1.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy1_values=np.zeros_like(xy_values)+np.nan
# compute displacement unit vector from tip 1 to tip 2
xy_values=np.array(list(zip(d1['x'],d1['y'])))
dshifted=d1.shift(1).copy()
# dshifted=d1.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy1_values=np.zeros_like(xy_values)+np.nan
#compute displacements between
for j in range(dxy1_values.shape[0]):
dxy1_values[j]=subtract_pbc(xy_next_values[j],xy_values[j])
d1['dx']=dxy1_values[:,0]
d1['dy']=dxy1_values[:,1]
d1['dt']=d1['t'].diff().shift(-1).iloc[1:-1]
# d1[['dx','dy','dt']]=d1[['x','y','t']].diff().shift(-1).iloc[1:-1]
d1['displacement']=np.sqrt(d1['dx']**2+d1['dy']**2)
d1['dx_hat']=d1['dx']/d1['displacement']
d1['dy_hat']=d1['dy']/d1['displacement']
#compute displacement of d2 with pbc
xy_values=np.array(list(zip(d2['x'],d2['y'])))
dshifted=d2.shift(1).copy()
# dshifted=d2.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy2_values=np.zeros_like(xy_values)+np.nan
# compute displacement unit vector from tip 1 to tip 2
xy_values=np.array(list(zip(d2['x'],d2['y'])))
dshifted=d2.shift(1).copy()
# dshifted=d1.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy2_values=np.zeros_like(xy_values)+np.nan
#compute displacements between
for j in range(dxy2_values.shape[0]):
dxy2_values[j]=subtract_pbc(xy_next_values[j],xy_values[j])
d2['dx']=dxy2_values[:,0]
d2['dy']=dxy2_values[:,1]
d2['dt']=d2['t'].diff().shift(-1).iloc[1:-1]
d2[['dx','dy','dt']]=d2[['x','y','t']].diff().shift(-1).iloc[1:-1]
d2['displacement']=np.sqrt(d2['dx']**2+d2['dy']**2)
d2['dx_hat']=d2['dx']/d2['displacement']
d2['dy_hat']=d2['dy']/d2['displacement']
# compute dot product between tip 1 and tip 2
cosine_series=d1['dx_hat']*d2['dx_hat']+d1['dy_hat']*d2['dy_hat']
d1['angle_between']=np.arccos(cosine_series) #radians
angle_between_values=d1['angle_between'].values
tdeath_values=d1['t'].values[-1]-d1['t'].values #ms
# # limit the values of tdeath to d1 or d2 depending on who is shorter
# tdeath2_values=d2['t'].values[-1]-d2['t'].values
# t1_min=np.min(tdeath_values)
# t2_min=np.min(tdeath_values)
# t_min=np.min((t1_min,t2_min))
# boo=tdeath_values>=t_min
# tdeath_values=tdeath_values[boo]
# angle_between_values=angle_between_values[boo]
d1.dropna(inplace=True)
return tdeath_values,angle_between_values
return compute_angle_between_final_velocities
# compute the ray beginning at the center of mass of the two spiral tips and extending towards the given spiral tip
#TODO: compute (un)signed angle
#DONT: sort by right/left handed spiral tips?
def get_compute_angle_between_initial_velocities(width,height):
# compute_displacements_between=get_compute_displacements_between(width,height)
subtract_pbc=get_subtract_pbc(width=width,height=height)
def compute_angle_between_initial_velocities(d1,d2):
'''computes angle between final velocities near birth for one tip pair.
Updates d1,d2 with fields. aligns locations by index
Example Usage:
compute_angle_between_initial_velocities=get_compute_angle_between_initial_velocities(width,height)
tbirth_values,angle_between_values=compute_angle_between_initial_velocities(d1,d2)
'''
#compute displacement of d1 with pbc
xy_values=np.array(list(zip(d1['x'],d1['y'])))
dshifted=d1.shift(1).copy()
# dshifted=d1.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy1_values=np.zeros_like(xy_values)+np.nan
# compute displacement unit vector from tip 1 to tip 2
xy_values=np.array(list(zip(d1['x'],d1['y'])))
dshifted=d1.shift(1).copy()
# dshifted=d1.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy1_values=np.zeros_like(xy_values)+np.nan
#compute displacements between
for j in range(dxy1_values.shape[0]):
dxy1_values[j]=subtract_pbc(xy_next_values[j],xy_values[j])
d1['dx']=dxy1_values[:,0]
d1['dy']=dxy1_values[:,1]
d1['dt']=d1['t'].diff().shift(-1).iloc[1:-1]
# d1[['dx','dy','dt']]=d1[['x','y','t']].diff().shift(-1).iloc[1:-1]
d1['displacement']=np.sqrt(d1['dx']**2+d1['dy']**2)
d1['dx_hat']=d1['dx']/d1['displacement']
d1['dy_hat']=d1['dy']/d1['displacement']
#compute displacement of d2 with pbc
xy_values=np.array(list(zip(d2['x'],d2['y'])))
dshifted=d2.shift(1).copy()
# dshifted=d2.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy2_values=np.zeros_like(xy_values)+np.nan
# compute displacement unit vector from tip 1 to tip 2
xy_values=np.array(list(zip(d2['x'],d2['y'])))
dshifted=d2.shift(1).copy()
# dshifted=d1.shift(-1).copy()
xy_next_values=np.array(list(zip(dshifted['x'],dshifted['y'])))
dxy2_values=np.zeros_like(xy_values)+np.nan
#compute displacements between
for j in range(dxy2_values.shape[0]):
dxy2_values[j]=subtract_pbc(xy_next_values[j],xy_values[j])
d2['dx']=dxy2_values[:,0]
d2['dy']=dxy2_values[:,1]
d2['dt']=d2['t'].diff().shift(-1).iloc[1:-1]
d2[['dx','dy','dt']]=d2[['x','y','t']].diff().shift(-1).iloc[1:-1]
d2['displacement']=np.sqrt(d2['dx']**2+d2['dy']**2)
d2['dx_hat']=d2['dx']/d2['displacement']
d2['dy_hat']=d2['dy']/d2['displacement']
# compute dot product between tip 1 and tip 2
cosine_series=d1['dx_hat']*d2['dx_hat']+d1['dy_hat']*d2['dy_hat']
d1['angle_between']=np.arccos(cosine_series) #radians
angle_between_values=d1['angle_between'].values
tbirth_values=d1['t'].values-d1['t'].values[0] #ms
# # limit the values of tdeath to d1 or d2 depending on who is shorter
# tdeath2_values=d2['t'].values[-1]-d2['t'].values
# t1_min=np.min(tdeath_values)
# t2_min=np.min(tdeath_values)
# t_min=np.min((t1_min,t2_min))
# boo=tdeath_values>=t_min
# tdeath_values=tdeath_values[boo]
# angle_between_values=angle_between_values[boo]
d1.dropna(inplace=True)
return tbirth_values,angle_between_values
return compute_angle_between_initial_velocities
| 49.60221
| 115
| 0.635554
| 1,369
| 8,978
| 3.975895
| 0.096421
| 0.079368
| 0.052912
| 0.049972
| 0.931655
| 0.911078
| 0.903362
| 0.893625
| 0.847327
| 0.847327
| 0
| 0.040371
| 0.183337
| 8,978
| 180
| 116
| 49.877778
| 0.701991
| 0.39775
| 0
| 0.835165
| 0
| 0
| 0.07754
| 0
| 0
| 0
| 0
| 0.005556
| 0
| 1
| 0.054945
| false
| 0
| 0.021978
| 0
| 0.131868
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
|
0
| 7
|
eea955f8e83ed37872d0e491911e3dbbd46c7a10
| 127,503
|
py
|
Python
|
script/gmos-spike.py
|
thallislp/GMOS-SPiKE
|
b43aea468cb5b09663d239052fa474c4a6313941
|
[
"MIT"
] | null | null | null |
script/gmos-spike.py
|
thallislp/GMOS-SPiKE
|
b43aea468cb5b09663d239052fa474c4a6313941
|
[
"MIT"
] | null | null | null |
script/gmos-spike.py
|
thallislp/GMOS-SPiKE
|
b43aea468cb5b09663d239052fa474c4a6313941
|
[
"MIT"
] | null | null | null |
# Import Python Packages.
import numpy as np
from pyraf import iraf
from pyraf.iraf import gemini, gemtools, gmos, onedspec
import os
from astropy.io import fits
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator)
# Directory path for the uncalibrated files.
raw_path = '/raw/'
# Print the central wavelength and class for all files in the directory.
# The Sciece Object file must match its wavelength coverage with BIAS,
# FLAT and ARC calibration files.
print('')
print("The available values for central wavelength in this directory are: ")
print('')
print('ObsClass \ CentWave')
print('')
iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path), #IRAF task 'hselect'.
'obsclass && GrWlen', 'ObsType="OBJECT"')
print('')
# Empty lists for saving the names of the selected files.
obj_std_name=[]
obj_sci_name=[]
arc_std_name=[]
arc_sci_name=[]
bias_std_list=[]
flat_std_list=[]
bias_obj_list=[]
flat_obj_list=[]
def selec_std():
""" Select a Standard Star and its calibration files matching their
central wavelength and CCD binning. """
while True:
try:
# Ask for a central wavelength value.
wavelength = int(input("Type the central wavelength for the "
"STANDARD STAR: "))
# Parameters for the IRAF task 'hselect'.
select_std = ('ObsType="OBJECT" && obsclass?= "partnerCal" && '
'CentWave = {}'.format(wavelength))
print('')
# Print the selected parameters.
print("Selecting " + select_std)
# Confirm the existence of files with the selected parameters.
selected_files=[]
selected_files.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_std, Stdout=1))
if selected_files == [[]]:
print('')
print("There is no file matching the specified value.")
else:
print('')
# Print the number of STANDARD STAR files matching the specified
# values.
print("It has been found {} STANDARD STAR file(s) matching the "
"specified values:".format(len(selected_files[0])))
print('')
print(" Name \ CCDsum ")
print('')
std_title=[]
std_name=[]
std_ccdsum=[]
std_name.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_std, Stdout=1)) # File name.
std_ccdsum.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$Ccdsum', select_std, Stdout=1)) # File CCD Binning.
std_title.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$title', select_std, Stdout=1)) # File title
for j in range(len(std_name[0])):
# Print STANDARD STAR names, CCD binning and title.
# Remove '[2,inherit=yes]' and 'raw_path' from their names.
print(std_name[0][j].replace('[2,inherit=yes]','').replace(raw_path,''),
std_ccdsum[0][j], std_title[0][j])
break
except (NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct central wavelength. ")
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option for choosing another file.
while True:
try:
first_std_name=std_name[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected STANDARD STAR file: {}".format(first_std_name))
print('')
answer = raw_input("Continue? [y/n] ")
ccdsum_file = iraf.hselect("{}{}[2,inherit=yes]".format(raw_path,first_std_name),
'Ccdsum', select_std, Stdout=1)[0]
if answer == 'y':
filename = first_std_name
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
ccdsum_file = iraf.hselect("{}{}[2,inherit=yes]".format(raw_path,filename),
'Ccdsum', select_std, Stdout=1)[0]
break
else:
print('')
print('Type y or n')
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Append the selected file to the empty list.
obj_std_name.append(filename)
# Select the calibration files based on the previous selected parameters.
print('')
print("Selecting ARC, FLAT and BIAS...")
# ARC
arc_file=[]
# Parameters for the IRAF task 'hselect'.
select_arc_std = ("ObsType='ARC' && Ccdsum = {} "
"&& CentWave = {} ".format(ccdsum_file, wavelength))
arc_file.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_arc_std, Stdout=1))
if arc_file == [[]]:
print('')
print("There is no ARC file matching the specified values")
print('')
else:
print('')
# Print the number of ARC files matching the specified values.
print("It has been found {} ARC file(s) matching"
"the specified values:".format(len(arc_file[0])))
print('')
arc_name=[]
arc_name.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_arc_std, Stdout=1)) # File name.
for j in range(len(arc_name[0])):
# Print ARC names, CCD binning and title.
# Remove '[2,inherit=yes]' and the 'raw_path' from their names.
print(arc_name[0][j].replace('[2,inherit=yes]','').replace(raw_path,''))
print('')
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option to choose another file.
while True:
try:
first_arc_name=arc_name[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected ARC file: {}".format(first_arc_name))
print('')
answer = raw_input("Continue? [y/n] ")
if answer == 'y':
filename = first_arc_name
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
break
else:
print('')
print('Type y or n')
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Append the selected file to the empty list.
arc_std_name.append(filename)
# FLAT
flat_file=[]
# Parameters for the IRAF task 'hselect'.
select_flat_std = ("ObsType='FLAT' && Ccdsum = {} && "
"CentWave = {}".format(ccdsum_file, wavelength))
flat_file.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_flat_std, Stdout=1))
if flat_file == [[]]:
print('')
print("There is no FLAT file matching the specified values")
print('')
else:
print('')
# Print the number of FLAT files matching the specified values.
print("It has been found {} FLAT file(s) matching "
"the specified values:".format(len(flat_file[0])))
print('')
flat_name=[]
flat_name.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_flat_std, Stdout=1)) # File name.
for j in range(len(flat_name[0])):
# Print FLAT names, CCD binning and title.
# Remove '[2,inherit=yes]' and the 'raw_path' from their names.
print(flat_name[0][j].replace('[2,inherit=yes]','').replace(raw_path,''))
print('')
# Remove pre-existing FLAT list.
if os.path.exists("flat_std.txt"):
os.remove("flat_std.txt")
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option to choose another file.
while True:
try:
first_flat_name=flat_name[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected FLAT file: {}".format(first_flat_name))
print('')
answer = raw_input("Continue? [y/n] ")
if answer == 'y':
flat_std_list.append(first_flat_name)
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
flat_std_list.append(filename)
break
else:
print('')
print('Type y or n')
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Give the user option to add more FLAT files to the list.
while True:
try:
print('')
answer = raw_input('Do you wish to add more files? [y/n] ')
if answer == "y":
print('')
filename = raw_input("Type your selected file name: ")
flat_std_list.append(filename)
if answer == "n":
break
else:
print('')
print("Type y or n")
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Create a '.txt' FLAT list.
for j in range(len(flat_std_list)):
flat_std_txt = open("flat_std.txt","a")
flat_std_txt.write(flat_std_list[j])
flat_std_txt.write("\n")
flat_std_txt.close()
# BIAS
bias_file=[]
# Parameters for the IRAF task 'hselect'.
select_bias_std = 'ObsType="BIAS" && Ccdsum = {}'.format(ccdsum_file)
bias_file.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_bias_std, Stdout=1))
if bias_file == [[]]:
print('')
print("There is no BIAS file matching the specified values")
print('')
else:
print('')
# Print the number of BIAS files matching the specified values.
print("It has been found {} BIAS file(s) matching the"
"specified value:".format(len(bias_file[0])))
print('')
bias_name=[]
bias_name.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_bias_std, Stdout=1))# File name.
for j in range(len(bias_name[0])):
# Print BIAS names, CCD binning and title.
# Remove '[2,inherit=yes]' and the 'raw_path' from their names.
print(bias_name[0][j].replace('[2,inherit=yes]','').replace(raw_path,''))
print('')
# Remove pre-existing BIAS list.
if os.path.exists("bias_std.txt"):
os.remove("bias_std.txt")
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option to choose another file.
while True:
try:
first_bias_name=bias_name[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected BIAS file: {}".format(first_bias_name))
print('')
answer = raw_input("Continue? [y/n] ")
if answer == 'y':
bias_std_list.append(first_bias_name)
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
bias_std_list.append(filename)
break
else:
print('')
print('Type y or n')
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Give the user option to add more BIAS files to the list.
while True:
try:
print('')
answer = raw_input('Do you wish to add more files? [y/n] ')
if answer == "y":
print('')
filename = raw_input("Type your selected file name: ")
bias_std_list.append(filename)
if answer == "n":
break
else:
print('')
print("Type y or n")
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Create a '.txt' FLAT list.
for j in range(len(bias_std_list)):
bias_std_txt = open("bias_std.txt","a")
bias_std_txt.write(bias_std_list[j])
bias_std_txt.write("\n")
bias_std_txt.close()
# Ask the user to continue with the selected calibration files.
while True:
print('')
answer = raw_input("Do you want to continue the data reduction"
"with the selected files [y/n]? ")
if answer == "y":
break
if answer == "n":
exit(0)
else:
print("Type y or n")
def selec_obj():
""" Select a Science Object and its calibration files matching their
central wavelength and CCD binning. """
while True:
try:
print('')
# Ask for a central wavelength value.
wavelength = int(input("Type the central wavelength for the SCIENCE OBJECT: "))
# Parameters for the IRAF task 'hselect'.
select_obj = ("ObsType='OBJECT' && obsclass?='science' && CentWave = {}".format(wavelength))
print('')
# Print the selected parameters.
print("Selecting " + select_obj)
# Confirm the existence of files with the selected parameters.
selected_files=[]
selected_files.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_obj, Stdout=1))
if selected_files == [[]]:
print('')
print("There is no file matching the specified value.")
else:
print('')
# Print the number of SCIENCE OBJECT files matching the specified
# values.
print("It has been found {} SCIENCE OBJECT file(s)"
"matching the specified values:".format(len(selected_files[0])))
print('')
print(" Name \ CCDsum ")
print('')
obj_name=[]
obj_ccdsum=[]
obj_name.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_obj, Stdout=1)) # File name.
obj_ccdsum.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$Ccdsum', select_obj, Stdout=1)) # File CCD Binning.
for j in range(len(obj_name[0])):
# Print SCIENCE OBJECT names, CCD binning and title.
# Remove '[2,inherit=yes]' and 'raw_path' from their names.
print(obj_name[0][j].replace('[2,inherit=yes]','').replace(raw_path,''),
obj_ccdsum[0][j])
break
except (NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct central wavelength. ")
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option for choosing another file.
while True:
try:
first_obj_name=obj_name[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected SCIENCE OBJECT file: {}".format(first_obj_name))
print('')
answer = raw_input("Continue? [y/n] ")
ccdsum_file = iraf.hselect("{}{}[2,inherit=yes]".format(raw_path,first_obj_name),
'Ccdsum', select_obj, Stdout=1)[0]
if answer == 'y':
filename = first_obj_name
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
ccdsum_file = iraf.hselect("{}{}[2,inherit=yes]".format(raw_path,filename),
'Ccdsum', select_obj, Stdout=1)[0]
break
else:
print('')
print('Type y or n')
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Append the selected file to the empty list.
obj_sci_name.append(filename)
# Select the calibration files based on the previous selected parameters.
print('')
print("Selecting ARC, FLAT and BIAS...")
# ARC
arc_file_obj=[]
# Parameters for the IRAF task 'hselect'.
select_arc_obj = ("ObsType='ARC' && Ccdsum = {} &&"
"CentWave = {}".format(ccdsum_file, wavelength))
arc_file_obj.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_arc_obj, Stdout=1))
if arc_file_obj == [[]]:
print('')
print("There is no ARC file matching the specified values")
print('')
else:
print('')
# Print the number of ARC files matching the specified values.
print("It has been found {} ARC file(s) matching the"
"specified values:".format(len(arc_file_obj[0])))
print('')
arc_name_obj=[]
arc_name_obj.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_arc_obj, Stdout=1)) # File name.
for j in range(len(arc_name_obj[0])):
# Print ARC names, CCD binning and title.
# Remove '[2,inherit=yes]' and the 'raw_path' from their names.
print(arc_name_obj[0][j].replace('[2,inherit=yes]','').replace(raw_path,''))
print('')
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option to choose another file.
while True:
try:
first_arc_name=arc_name_obj[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected ARC file: {}".format(first_arc_name))
print('')
answer = raw_input("Continue? [y/n] ")
if answer == 'y':
filename = first_arc_name
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
break
else:
print('')
print("Type y or n")
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Append the selected file to the empty list.
arc_sci_name.append(filename)
# FLAT
flat_file_obj=[]
# Parameters for the IRAF task 'hselect'.
select_flat_obj = ("ObsType='FLAT' && Ccdsum = {}"
"&& CentWave = {}".format(ccdsum_file, wavelength))
flat_file_obj.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_flat_obj, Stdout=1))
if flat_file_obj == [[]]:
print('')
print("There is no FLAT file matching the specified values")
print('')
else:
print('')
# Print the number of FLAT files matching the specified values.
print("It has been found {} FLAT file(s) matching the specified"
"values:".format(len(flat_file_obj[0])))
print('')
flat_name_obj=[]
flat_name_obj.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_flat_obj, Stdout=1)) # File name.
for j in range(len(flat_name_obj[0])):
# Print FLAT names, CCD binning and title.
# Remove '[2,inherit=yes]' and the 'raw_path' from their names.
print(flat_name_obj[0][j].replace('[2,inherit=yes]','').replace(raw_path,''))
print('')
# Remove pre-existing FLAT list.
if os.path.exists("flat_obj.txt"):
os.remove("flat_obj.txt")
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option to choose another file.
while True:
try:
first_flat_name=flat_name_obj[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected FLAT file: {}".format(first_flat_name))
print('')
answer = raw_input("Continue? [y/n] ")
if answer == 'y':
flat_obj_list.append(first_flat_name)
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
flat_obj_list.append(filename)
break
else:
print('')
print("Type y or n")
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Give the user option to add more FLAT files to the list.
while True:
try:
print('')
answer = raw_input('Do you wish to add more files? [y/n] ')
if answer == "y":
print('')
filename = raw_input("Type your selected file name: ")
flat_obj_list.append(filename)
if answer == "n":
break
else:
print('')
print("Type y or n")
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Create a '.txt' FLAT list.
for j in range(len(flat_obj_list)):
flat_obj_txt = open("flat_obj.txt","a")
flat_obj_txt.write(flat_obj_list[j])
flat_obj_txt.write("\n")
flat_obj_txt.close()
# BIAS
bias_file_obj=[]
# Parameters for the IRAF task 'hselect'.
select_bias_obj = 'ObsType="BIAS" && Ccdsum = {}'.format(ccdsum_file)
bias_file_obj.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_bias_obj, Stdout=1))
if bias_file_obj == [[]]:
print('')
print("There is no BIAS file matching the specified values")
print('')
else:
print('')
# Print the number of BIAS files matching the specified values.
print("It has been found {} BIAS file(s) matching the specified"
"values:".format(len(bias_file_obj[0])))
print('')
bias_name_obj=[]
bias_name_obj.append(iraf.hselect("{}*.fits[2,inherit=yes]".format(raw_path),
'$I', select_bias_obj, Stdout=1))
for j in range(len(bias_name_obj[0])):
# Print BIAS names, CCD binning and title.
# Remove '[2,inherit=yes]' and the 'raw_path' from their names.
print(bias_name_obj[0][j].replace('[2,inherit=yes]','').replace(raw_path,''))
print('')
# Remove pre-existing BIAS list.
if os.path.exists("bias_obj.txt"):
os.remove("bias_obj.txt")
# Select a file matching the values for central wavelength and CCD binning,
# and ask the user to continue. Give the user option to choose another file.
while True:
try:
first_bias_name=bias_name_obj[0][0].replace(raw_path,'').replace('[2,inherit=yes]','')
print('')
print("Selected BIAS file: {}".format(first_bias_name))
print('')
answer = raw_input("Continue? [y/n] ")
if answer == 'y':
bias_obj_list.append(first_bias_name)
break
if answer == 'n':
print('')
filename = raw_input("Type your selected file name: ")
bias_obj_list.append(filename)
break
else:
print('')
print("Type y or n")
print('')
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Give the user option to add more BIAS files to the list.
while True:
try:
print('')
answer = raw_input('Do you wish to add more files? [y/n] ')
if answer == "y":
print('')
filename = raw_input("Type your selected file name: ")
bias_std_list.append(filename)
if answer == "n":
break
else:
print('')
print "Type y or n"
except (IndexError):
print('')
print("Check if you typed the correct name. ")
# Create a '.txt' FLAT list.
for j in range(len(bias_obj_list)):
bias_obj_txt = open("bias_obj.txt","a")
bias_obj_txt.write(bias_obj_list[j])
bias_obj_txt.write("\n")
bias_obj_txt.close()
# Ask the user to continue with the selected calibration files.
while True:
print('')
answer = raw_input("Do you want to continue the data reduction "
"with the selected files [y/n]? ")
if answer == "y":
break
if answer == "n":
exit(0)
else:
print("Type y or n")
# Reduction and calibration of STANDARD STAR files.
print "------------------------------"
print "# REDUCTION OF STANDARD STAR #"
print "------------------------------"
def std_gbias():
""" Apply overscan correction and trim individual bias frames.
Create Master Bias. Plot Master Bias and pixel counting. """
print('# CREATING MASTER BIAS #')
# Remove pre-existing Master Bias.
if os.path.exists('Bias_std.fits'):
os.remove('Bias_std.fits')
gmos.gbias.unlearn() # Debug gbias.
# Set the task parameters.
biasFlags = {'rawpath':'raw', 'fl_over':'yes',
'fl_trim':'yes', 'fl_vardq':'yes'}
# Create Master Bias.
gmos.gbias('@bias_std.txt', 'Bias_std.fits', **biasFlags) # IRAF task gbias.
# Load Master Bias.
obj=fits.open('Bias_std.fits')
obj_data = obj[2].data
obj_shape = obj_data.shape
# Print Master Bias.
fig = plt.figure(figsize=(14.0,4.0))
for i in range(len(np.arange(0,12,1))):
ax = plt.subplot(1, 12,i+1)
plt.imshow(obj[i+1].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[i+1].data,5),
vmax=np.percentile(obj[i+1].data,90), aspect='auto')
if i == 0:
ax.yaxis.set_visible(True)
ax.set_ylabel('Position Along Slit', fontsize=14)
else:
ax.yaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Master BIAS', y=1.0, fontsize=14)
plt.savefig('master-bias-std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
# Print the pixel counting through a line cut.
plt.figure(figsize=(16.0,8.0))
for j in range(len(np.arange(0,12,1))):
ax1 = plt.subplot(3, 12,j+1)
ax1.set_ylim(min(obj[1].data[int(0.3*obj_shape[0]),1:obj_shape[1]]),
max(obj[1].data[int(0.3*obj_shape[0]),1:obj_shape[1]]))
ax1.plot(np.arange(1,obj_shape[1],1),
obj[j+1].data[int(0.3*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax2 = plt.subplot(3, 12,12+j+1)
ax2.set_ylim(min(obj[1].data[int(0.5*obj_shape[0]),1:obj_shape[1]]),
max(obj[1].data[int(0.5*obj_shape[0]),1:obj_shape[1]]))
ax2.plot(np.arange(1,obj_shape[1],1),
obj[j+1].data[int(0.5*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax3 = plt.subplot(3, 12, 24+j+1)
ax3.set_ylim(min(obj[1].data[int(0.7*obj_shape[0]),1:obj_shape[1]]),
max(obj[1].data[int(0.7*obj_shape[0]),1:obj_shape[1]]))
ax3.plot(np.arange(1,obj_shape[1],1),
obj[j+1].data[int(0.7*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
if j == 0:
ax1.yaxis.set_visible(True)
ax1.set_ylabel('Counts [row={}]'.format(int(0.3*obj_shape[0])), fontsize=14)
ax2.yaxis.set_visible(True)
ax2.set_ylabel('Counts [row={}]'.format(int(0.5*obj_shape[0])), fontsize=14)
ax3.yaxis.set_visible(True)
ax3.set_ylabel('Counts [row={}]'.format(int(0.7*obj_shape[0])), fontsize=14)
else:
ax1.yaxis.set_visible(False)
ax2.yaxis.set_visible(False)
ax3.yaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pixel Counting',y=1.0, fontsize=14)
plt.savefig('pixel-counting-bias-std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.close()
def std_reduc_arc():
""" Apply overscan correction and mosaic arc frames. """
print('# REDUCING ARC LAMP #')
# Remove pre-existing calibrated files.
if os.path.exists('gs{}'.format(arc_std_name[0])):
os.remove('gs{}'.format(arc_std_name[0]))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Set the task parameters.
gsreduceFlags1={'rawpath':'raw', 'fl_bias':'yes',
'fl_flat':'no', 'fl_over':'yes',
'bias':'Bias_std', 'fl_gmos':'yes'}
# Reduce arc frames.
gmos.gsreduce(str(arc_std_name[0]), **gsreduceFlags1) # IRAF task gsreduce.
def std_wavelength_arc():
""" Create a wavelength solution based on arc frames.
Print the difference of the calculated and correct wavelength values
for the estimated lines."""
print('# CALIBRATING WAVELENGTH #')
# Remove pre-existing wavelength solution files on database directory.
if os.path.exists('database/idgs{}_001'.format(arc_std_name[0].replace('.fits',''))):
os.remove('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')))
# Load reduced arc frame.
obj=fits.open('gs{}'.format(arc_std_name[0]))
obj_data = obj[2].data
arc_shape = obj_data.shape
gmos.gswavelength.unlearn() # Debug gswavelength.
# Set the task parameters.
gswavelengthFlags={'nsum':str(arc_shape[0]/3), 'step':str(arc_shape[0]/3),
'fwidth':'7', 'gsigma':'1.5','cradius':'12',
'minsep':'7', 'order':'6','fl_inter':'no'}
# Create wavelength solution.
gmos.gswavelength('gs{}'.format(arc_std_name[0]), **gswavelengthFlags) # IRAF task gswavelength.
# Read wavelength solution file on database directory.
# Autoidentify.
# Number of calculated features.
number_of_features_auto = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=6, usecols=1, max_rows=1, invalid_raise=False)
# Number of calculated coefficients.
number_of_coefficients_auto = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+8, usecols=1,
max_rows=1, invalid_raise=False)
# gswavelength flags to discard outlier values.
wave_flag_auto = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')), skip_header=7,
usecols=5, max_rows=int(number_of_features_auto), invalid_raise=False)
# Row number.
wave_auto_col_0 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')), skip_header=7,
usecols=0, max_rows=int(number_of_features_auto), invalid_raise=False)
# Corrected line wavelength.
wave_auto_col_1 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')), skip_header=7,
usecols=1, max_rows=int(number_of_features_auto), invalid_raise=False)
# Calculated line wavelength.
wave_auto_col_2 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')), skip_header=7,
usecols=2, max_rows=int(number_of_features_auto), invalid_raise=False)
# Reidentify.
# Number of calculated features.
number_of_features_re1 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+7,
usecols=1, max_rows=1, invalid_raise=False)
number_of_coefficients_re1 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8+int(number_of_features_re1)+8,
usecols=1, max_rows=1, invalid_raise=False)
# gswavelength flags to discard outlier values.
wave_flag_re1 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8,
usecols=5, max_rows=int(number_of_features_re1), invalid_raise=False)
# Row number.
wave_re1_col_0 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8, usecols=0,
max_rows=int(number_of_features_re1), invalid_raise=False)
# Corrected line wavelength.
wave_re1_col_1 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8, usecols=1,
max_rows=int(number_of_features_re1), invalid_raise=False)
# Calculated line wavelength.
wave_re1_col_2 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8, usecols=2,
max_rows=int(number_of_features_re1), invalid_raise=False)
# Reidentify.
# Number of calculated features.
number_of_features_re2 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+7),
usecols=1, max_rows=1, invalid_raise=False)
# gswavelength flags to discard outlier values.
wave_flag_re2 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=5, max_rows=int(number_of_features_re2), invalid_raise=False)
# Row number.
wave_re2_col_0 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=0, max_rows=int(number_of_features_re2), invalid_raise=False)
# Corrected line wavelength.
wave_re2_col_1 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=1, max_rows=int(number_of_features_re2), invalid_raise=False)
# Calculated line wavelength.
wave_re2_col_2 = np.genfromtxt('database/idgs{}_001'.format(arc_std_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=2, max_rows=int(number_of_features_re2), invalid_raise=False)
# gswavelength flags
flag_auto = np.where(wave_flag_auto[0:,] == 1) # Autoidentify.
flag_re1 = np.where(wave_flag_re1[0:,] == 1) # Reidentify.
flag_re2 = np.where(wave_flag_re2[0:,] == 1) # Reidentify.
# RMS estimate.
# Autoidentify.
rows_auto = wave_auto_col_0[0:,][flag_auto]
delta_lambda_auto = wave_auto_col_2[0:,][flag_auto] - wave_auto_col_1[0:,][flag_auto]
rms_auto = np.sqrt(np.sum(delta_lambda_auto**2) / len(rows_auto))
print('[AUTOIDENTIFY] RMS = ', rms_auto)
hig_than_rms_auto=[] # Values higher than RMS.
for l in range(len(delta_lambda_auto)):
if np.abs(delta_lambda_auto[l]) > rms_auto:
hig_than_rms_auto.append( wave_auto_col_2[0:,][flag_auto][l])
# Print number of lines with values diverging from the correct value
# whith a difference higher thant the RMS.
print("[AUTOIDENTIFY] There are {} identified lines diverging from the"
" observed value with differences higher than the RMS :".format(len(hig_than_rms_auto)), hig_than_rms_auto)
# Reidentify.
rows_re1 = wave_re1_col_0[0:,][flag_re1]
delta_lambda_re1 = wave_re1_col_2[0:,][flag_re1] - wave_re1_col_1[0:,][flag_re1]
rms_re1 = np.sqrt(np.sum(delta_lambda_re1**2) / len(rows_re1))
print('[REIDENTIFY] RMS = ', rms_re1)
hig_than_rms_re1=[] # Values higher than RMS.
for l in range(len(delta_lambda_re1)):
if np.abs(delta_lambda_re1[l]) > rms_re1:
hig_than_rms_re1.append(wave_re1_col_2[0:,][flag_re1][l])
# Print number of lines with values diverging from the correct value
# whith a difference higher thant the RMS.
print("[REIDENTIFY] There are {} identified lines diverging from the"
" observed value with differences higher than the RMS :".format(len(hig_than_rms_re1)), hig_than_rms_re1)
# Reidentify.
rows_re2 = wave_re2_col_0[0:,][flag_re2]
delta_lambda_re2 = wave_re2_col_2[0:,][flag_re2] - wave_re2_col_1[0:,][flag_re2]
rms_re2 = np.sqrt(np.sum(delta_lambda_re2**2) / len(rows_re2))
print('[REIDENTIFY] RMS = ', rms_re2)
hig_than_rms_re2=[] # Values higher than RMS.
for l in range(len(delta_lambda_re2)):
if np.abs(delta_lambda_re2[l]) > rms_re2:
hig_than_rms_re2.append(wave_re2_col_2[0:,][flag_re2][l])
# Print number of lines with values diverging from the correct value
# whith a difference higher thant the RMS.
print("[REIDENTIFY] There are {} identified lines diverging from the"
" observed value with differences higher than the RMS :".format(len(hig_than_rms_re2)), hig_than_rms_re2)
# Print the difference of the calculated and correct wavelength values
# for the estimated lines.
# Autoidentify.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()
ax4 = ax1.twiny()
ax1.scatter(rows_auto,np.abs(delta_lambda_auto), color='white' )
ax2.scatter(wave_auto_col_2[0:,][flag_auto], np.abs(delta_lambda_auto), color='coral')
ax2.invert_xaxis()
plt.title('Autoidentify', y=1.10, fontsize=14)
ax2.set_xlabel(r'$\lambda_{identified} \ [\AA]$', fontsize=14)
ax1.set_xlabel('Pixel position', fontsize=14)
ax1.set_ylabel(r'|$\lambda_{identified} - \lambda_{fitted}| \ [\AA]$', fontsize=14)
ax3.axhline(y=0.2, linestyle='--')
ax3.xaxis.set_visible(False)
ax4.axhline(y=rms_auto, linestyle=':', label='RMS = {}'.format(rms_auto))
ax4.xaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
ax4.legend()
plt.savefig('autoidentify_std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.close()
# Reidentify.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()
ax4 = ax1.twiny()
ax1.scatter(rows_re1,np.abs(delta_lambda_re1), color='white' )
ax2.scatter(wave_re1_col_2[0:,][flag_re1] ,np.abs(delta_lambda_re1), color='coral')
ax2.invert_xaxis()
plt.title('Reidentify', y=1.10, fontsize=14)
ax2.set_xlabel(r'$\lambda_{identified} \ [\AA]$', fontsize=14)
ax1.set_xlabel('Pixel position', fontsize=14)
ax1.set_ylabel(r'|$\lambda_{identified} - \lambda_{fitted}| \ [\AA]$', fontsize=14)
ax3.axhline(y=0.2, linestyle='--')
ax3.xaxis.set_visible(False)
ax4.axhline(y=rms_re1, linestyle=':', label='RMS = {}'.format(rms_re1))
ax4.xaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
ax4.legend()
plt.show()
plt.close()
# Reidentify.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()
ax4 = ax1.twiny()
ax1.scatter(rows_re2,np.abs(delta_lambda_re2), color='white' )
ax2.scatter(wave_re2_col_2[0:,][flag_re2] ,np.abs(delta_lambda_re2), color='coral')
ax2.invert_xaxis()
plt.title('Reidentify', y=1.10, fontsize=14)
ax2.set_xlabel(r'$\lambda_{identified} \ [\AA]$', fontsize=14)
ax1.set_xlabel('Pixel position', fontsize=14)
ax1.set_ylabel(r'|$\lambda_{identified} - \lambda_{fitted}| \ [\AA]$', fontsize=14)
ax3.axhline(y=0.2, linestyle='--')
ax3.xaxis.set_visible(False)
ax4.axhline(y=rms_re2, linestyle=':', label='RMS = {}'.format(rms_re2))
ax4.xaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
ax4.legend()
plt.show()
plt.close()
def std_transf_arc():
""" Transform arc files. """
print('# TRANSFORMING ARC FILES #')
# Remove pre-existing transformed files.
if os.path.exists('tgs{}'.format(arc_std_name[0])):
os.remove('tgs{}'.format(arc_std_name[0]))
gmos.gstransform.unlearn() # Debug gstransform.
# Set the task parameters.
gstransformFlags={'wavtranam':'gs{}'.format(arc_std_name[0])}
# Transform arc files.
gmos.gstransform('gs{}'.format(arc_std_name[0]), **gstransformFlags) # IRAF task gstransform.
def std_reduc_flat():
""" Subtract bias from individual flat frames. """
print('# REDUCING RAW FLATS #')
# Open individual flat frames.
f = open('flat_std.txt', 'r')
for flatfile in f:
# Remove pre-existing processed files.
if os.path.exists('gs{}'.format(flatfile.replace('\n',''))):
os.remove('gs{}'.format(flatfile.replace('\n','')))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Set the task parameters.
gsreduceFlags={'rawpath':'raw', 'fl_bias':'yes',
'fl_flat':'no', 'fl_fixpix':'no',
'bias':'Bias_std.fits','fl_gmos':'no',
'fl_gsappwave':'no', 'fl_cut':'no'}
# Reduce flat files.
gmos.gsreduce(str(flatfile.replace('\n','')), **gsreduceFlags) # IRAF task gsreduce.
f.close()
def std_qecorr_flat():
""" Apply quantum efficiency correction to individual
flat frames. """
print('# QUANTUM-CORRECTING FLAT#')
# Open individual flat frames.
f = open('flat_std.txt', 'r')
for flatfile in f:
# Remove pre-existing processed files.
if os.path.exists('qgs{}'.format(flatfile.replace('\n',''))):
os.remove('qgs{}'.format(flatfile.replace('\n','')))
gmos.gqecorr.unlearn() # Debug gqecorr.
# Set the task parameters.
qecorrFlags= {'refimage':'gs{}'.format(arc_std_name[0]),
'fl_keep':'yes'}
# Apply quantum efficiency correction.
gmos.gqecorr('gs{}'.format(flatfile.replace('\n','')), **qecorrFlags) # IRAF task gqecorr.
f.close()
def std_gmosaic_flat():
""" Mosaic individual flat frames. """
print('# MOSAIC FLAT#')
# Remove pre-existing list.
if os.path.exists("mqgsflat_std.txt"):
os.remove("mqgsflat_std.txt")
# Open individual flat frames.
f = open('flat_std.txt', 'r')
for flatfile in f:
# Remove pre-existing processed files.
if os.path.exists('mqgs{}'.format(flatfile.replace('\n',''))):
os.remove('mqgs{}'.format(flatfile.replace('\n','')))
gmos.gmosaic.unlearn() # Debug gmosaic.
# Set the task parameters.
gmosaicFlags= {'fl_fixpix':'yes'}
# Mosaic flat frames.
gmos.gmosaic('qgs{}'.format(flatfile.replace('\n','')), **gmosaicFlags) # IRAF task gmosaic.
# Write name of processed files on list.
mqgsflat = open("mqgsflat_std.txt","a")
mqgsflat.write('mqgs{}'.format(flatfile.replace('\n','')))
mqgsflat.write("\n")
mqgsflat.close()
f.close()
def std_masterflat():
""" Trim individual flat frames and create Master Flat.
Print Master Flat and pixel counting. """
print('# CREATING MASTER FLAT #')
# Remove pre-existing Master Flat.
if os.path.exists('qFlat_std.fits'):
os.remove('qFlat_std.fits')
gmos.gsflat.unlearn() # Debug gsflat.
# Set the task parameters.
flatFlags = {'fl_bias':'no', 'order':'29',
'fl_over':'no', 'fl_trim':'no',
'fl_usegrad':'yes'}
# Create Master Flat.
gmos.gsflat('@mqgsflat_std.txt', 'qFlat_std.fits', **flatFlags) # IRAF task gsflat.
# Load Master Flat.
obj=fits.open('qFlat_std.fits')
obj_data = obj[2].data
obj_shape = obj_data.shape
# Plot Master Flat.
plt.figure(figsize=(12.0,4.0))
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
plt.ylabel('Position Along Slit', fontsize=14)
plt.xlabel('Dispersion Axis', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Master FLAT', y=1.0, fontsize=14)
plt.savefig('master-flat-std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
# Print the pixel counting through a line cut.
plt.figure(figsize=(16.0,8.0))
ax1 = plt.subplot(3, 1, 1)
ax1.set_ylim(min(obj[2].data[int(0.3*obj_shape[0]),1:obj_shape[1]]),
max(obj[2].data[int(0.3*obj_shape[0]),1:obj_shape[1]]))
ax1.set_ylabel('Counts [row={}]'.format(int(0.3*obj_shape[0])), fontsize=14)
ax1.plot(np.arange(1,obj_shape[1],1),
obj[2].data[int(0.3*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax2 = plt.subplot(3, 1, 2)
ax2.set_ylim(min(obj[2].data[int(0.5*obj_shape[0]),1:obj_shape[1]]),
max(obj[2].data[int(0.5*obj_shape[0]),1:obj_shape[1]]))
ax2.set_ylabel('Counts [row={}]'.format(int(0.5*obj_shape[0])), fontsize=14)
ax2.plot(np.arange(1,obj_shape[1],1),
obj[2].data[int(0.5*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax3 = plt.subplot(3, 1, 3)
ax3.set_ylim(min(obj[2].data[int(0.7*obj_shape[0]),1:obj_shape[1]]),
max(obj[2].data[int(0.7*obj_shape[0]),1:obj_shape[1]]))
ax3.set_ylabel('Counts [row={}]'.format(int(0.7*obj_shape[0])), fontsize=14)
ax3.plot(np.arange(1,obj_shape[1],1),
obj[2].data[int(0.7*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax3.set_xlabel('Dispersion Axis', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pixel Counting',y=1.0, fontsize=14)
plt.savefig('pixel-counting-flat-std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.close()
def std_reduc1_std():
""" Subtract bias, apply overscan and cosmic ray correction
to standard star frames. """
print('# 1st REDUCTION #')
# Remove pre-existing processed files.
if os.path.exists('gs{}'.format(obj_std_name[0])):
os.remove('gs{}'.format(obj_std_name[0]))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Task parameters.
gsreduceFlags={'rawpath':'raw','fl_gmos':'no', 'fl_fixpix':'no',
'fl_flat':'no', 'fl_vardq':'yes',
'fl_fulldq':'yes', 'fl_bias':'yes',
'bias':'Bias_std', 'fl_inter':'no',
'fl_cut':'no', 'fl_gsappwave':'no',
'fl_over':'yes', 'fl_crspec':'yes'}
# Reduce standard star files.
gmos.gsreduce(str(obj_std_name[0]), **gsreduceFlags) # IRAF task gsreduce.
def std_gemfix_std():
""" Improve cosmic ray and bad pixel correction.
Print raw and corrected files for comparison. """
print('# GEMFIX #')
# Remove pre-existing processed files.
if os.path.exists('gemgs{}'.format(obj_std_name[0])):
os.remove('gemgs{}'.format(obj_std_name[0]))
if os.path.exists('copy_gemgs{}'.format(obj_std_name[0])):
os.remove('copy_gemgs{}'.format(obj_std_name[0]))
gemini.gemfix.unlearn() # Debug gemfix.
# Task parameters.
gemfixFlags={'outimages':'gemgs{}'.format(obj_std_name[0]),
'method':'fixpix', 'bitmask':'8'}
# Fix bad pixels.
gemini.gemfix('gs{}'.format(obj_std_name[0]), **gemfixFlags) # IRAF task gemfix.
# Load raw files.
obj=fits.open('raw/{}'.format(obj_std_name[0]))
obj_data = obj[2].data
obj_shape = obj_data.shape
# Load processed files.
gemobj=fits.open('gemgs{}'.format(obj_std_name[0]))
gemobj_data = gemobj[2].data
gemobj_shape = gemobj_data.shape
# Create copy of processed file.
copy_gemobj = fits.HDUList([gemobj[0]])
for n in np.arange(2,37,3):
copy_gemobj.append(gemobj[n])
copy_gemobj.writeto('copy_gemgs{}'.format(obj_std_name[0]))
new_gemobj = fits.open('copy_gemgs{}'.format(obj_std_name[0]))
gemobj_data = gemobj[2].data
gemobj_shape = gemobj_data.shape
# Print raw and corrected files.
plt.figure(figsize=(16.0,8.0))
for i in range(len(np.arange(0,12,1))):
ax1 = plt.subplot(2,12,i+1)
ax1.imshow(obj[i+1].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[i+1].data,5),
vmax=np.percentile(obj[i+1].data,90), aspect='auto')
ax2 = plt.subplot(2,12,12+i+1)
ax2.imshow(new_gemobj[i+1].data, origin='lower',cmap='afmhot',
vmin=np.percentile(new_gemobj[i+1].data,5),
vmax=np.percentile(new_gemobj[i+1].data,90), aspect='auto')
if i == 0:
ax1.yaxis.set_visible(True)
ax1.set_ylabel('Position Along Slit', fontsize=14)
ax2.yaxis.set_visible(True)
ax2.set_ylabel('Position Along Slit', fontsize=14)
else:
ax1.yaxis.set_visible(False)
ax2.yaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pre- and Post- Cosmic Ray Rejection', y=1.0, fontsize=14)
plt.savefig('gemfix_std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
def std_qecorr_std():
""" Apply quantum efficiency correction to standard
star frames. """
print('# QUANTUM-CORRECTING #')
# Remove pre-existing processed files.
if os.path.exists('qgemgs{}'.format(obj_std_name[0])):
os.remove('qgemgs{}'.format(obj_std_name[0]))
# Open list of individual flat frames.
flatfile_list=[]
f = open('flat_std.txt', 'r')
for flatfile in f:
flatfile_list.append(flatfile)
f.close()
gmos.gqecorr.unlearn() # Debug gqecorr.
# Task parameters
qecorrFlags= {'refimage':'gs{}'.format(arc_std_name[0]),
'corrima':'qgs{}'.format(flatfile_list[0].replace('\n',''))}
# Apply quantum efficiency correction.
gmos.gqecorr('gemgs{}'.format(obj_std_name[0]), **qecorrFlags) # IRAF task gqecorr.
def std_reduc2_std():
""" Apply flat field correction to standard star frames. """
print('# 2nd REDUCTION #')
# Remove pre-existing processed files.
if os.path.exists('gsqgemgs{}'.format(obj_std_name[0])):
os.remove('gsqgemgs{}'.format(obj_std_name[0]))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Task parameters.
gsreduceFlags={'fl_bias':'no', 'fl_flat':'yes',
'fl_over':'no', 'flat':'qFlat_std',
'fl_trim':'no'}
# Apply flat correction.
gmos.gsreduce('qgemgs{}'.format(obj_std_name[0]), **gsreduceFlags) # IRAF task gsreduce.
def std_badcolumn_std():
""" Print standard star frame for bad column checking.
Interpolate bad columns. """
print('# BAD COLUMN CHECKING #')
# Remove pre-existing processed files.
if os.path.exists('bcgsqgemgs{}'.format(obj_std_name[0])):
os.remove('bcgsqgemgs{}'.format(obj_std_name[0]))
# Create copy of science object file to apply bad column correction.
iraf.copy('gsqgemgs{}'.format(obj_std_name[0]), 'bcgsqgemgs{}'.format(obj_std_name[0]))
# Load copy.
obj=fits.open('bcgsqgemgs{}'.format(obj_std_name[0]))
# Check if a bad column mask is already available in the directory.
if os.path.exists("maskbadcol.txt"):
while True:
try:
print('')
answer = raw_input("The file 'maskbadcol.txt' is available in this directory. "
" Do you wish to apply this mask to your spectrum? (y/n) ")
if answer=='y':
# Remove pre-existing mask.
if os.path.exists("maskbadcol.pl"):
os.remove("maskbadcol.pl")
# Create mask based on coordinates text file.
iraf.text2mask('maskbadcol.txt', 'maskbadcol.pl',
obj[2].shape[1], obj[2].shape[0]) # IRAF task 'text2mask'.
# Interpolate bad columns using mask.
iraf.fixpix('bcgsqgemgs{}[2]'.format(obj_std_name[0]),
'maskbadcol.pl', linterp='1,2,3,4') # IRAF task 'fixpix'.
break
if answer=='n':
break
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
# Select a line in the science object frame and print the pixel counting
# through the line for bad column checking.
while True:
try:
print('')
# Selected line.
line = int(input('Choose a line for bad column checking: ' ))
print('')
xaxis=np.arange(1, obj[2].data.shape[1],1)
yaxis=obj[2].data[line,1:obj[2].data.shape[1]]
# Print science object frame and pixel counting
# through the selected line.
plt.figure(figsize=(16.0,8.0))
plt.figure(figsize=(16.0,8.0))
ax1=plt.subplot(211)
ax1.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,1),
vmax=np.percentile(obj[2].data,99), aspect='auto')
ax1.axhline(y=line, linestyle='--', color='red',linewidth=2., alpha=0.7)
ax1.set_ylabel('Position Along Slit', fontsize=14)
ax2=plt.subplot(212)
ax2.plot(xaxis, yaxis, color='red' )
ax2.set_xlim(left=np.min(xaxis), right=np.max(xaxis))
ax2.set_ylabel('Counts', fontsize=14)
ax2.set_xlabel('Dispersion Axis', fontsize=14)
plt.show()
answer = raw_input('Do you wish to select another line? (y/n) ')
if answer=='n':
break
if answer=='y':
print('')
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
# Select the inital and final position along the x-axis of
# the columns to interpolate.
# Create a mask and interpolate bad column.
while True:
try:
print('')
answer2 = raw_input('Do you wish to interpolate a bad column? (y/n) ')
print('')
if answer2=='n':
break
if answer2=='y':
while True:
try:
print('')
# Inital x position of the bad column.
x1 = int(input('Select a column to interpolate (x min): ' ))
print('')
# Final x position of the bad column.
x2 = int(input('Select a column to interpolate (x max): ' ))
# Create text file with the column coordinates.
bias_obj_txt = open("maskbadcol.txt","a")
bias_obj_txt.write('{} {} 1 {}'.format(x1, x2, obj[2].shape[0]))
bias_obj_txt.write("\n")
bias_obj_txt.close()
# Remove pre-existing mask.
if os.path.exists("maskbadcol.pl"):
os.remove("maskbadcol.pl")
# Create mask based on coordinates text file.
iraf.text2mask('maskbadcol.txt', 'maskbadcol.pl',
obj[2].shape[1], obj[2].shape[0]) # IRAF task 'text2mask'.
# Interpolate bad columns using mask.
iraf.fixpix('bcgsqgemgs{}[2]'.format(obj_std_name[0]),
'maskbadcol.pl', linterp='1,2,3,4') # IRAF task 'fixpix'.
# Print corrected science object frame and pixel counting
# through the selected line.
obj2=fits.open('bcgsqgemgs{}'.format(obj_std_name[0]))
xaxis=np.arange(1, obj[2].data.shape[1],1)
yaxis=obj[2].data[line,1:obj[2].data.shape[1]]
plt.figure(figsize=(16.0,8.0))
ax1=plt.subplot(211)
ax1.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,1),
vmax=np.percentile(obj[2].data,99), aspect='auto')
ax1.axhline(y=line, linestyle='--', color='red',linewidth=2., alpha=0.7)
ax1.set_ylabel('Position Along Slit', fontsize=14)
ax2=plt.subplot(212)
ax2.plot(xaxis, yaxis, color='red' )
ax2.set_xlim(left=np.min(xaxis), right=np.max(xaxis))
ax2.set_ylabel('Counts', fontsize=14)
ax2.set_xlabel('Dispersion Axis', fontsize=14)
plt.show()
print('')
answer3 = raw_input('Do you wish to interpolate another bad column? (y/n) ' )
if answer3=='n':
break
if answer3=='y':
print('')
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
break
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
def std_transf_std():
""" Transform standard star frame. """
print('# TRANSFORMING STANDARD STAR #')
# Remove pre-existing processed files.
if os.path.exists('tbcgsqgemgs{}'.format(obj_std_name[0])):
os.remove('tbcgsqgemgs{}'.format(obj_std_name[0]))
gmos.gstransform.unlearn() # Debug gstransform.
# Task parameters.
gstransformFlags={'wavtranam':'gs{}'.format(arc_std_name[0]),
'fl_vardq':'yes'}
# Transform spectrum.
gmos.gstransform('bcgsqgemgs{}'.format(obj_std_name[0]), **gstransformFlags) # IRAF task gstransform.
def std_sky_sub_std():
""" Subtract sky background from standard star frame.
Print raw and sky subtracted frames for comparison. """
print('# SKY-SUBTRACTING #')
# Remove pre-existing processed files.
if os.path.exists('stbcgsqgemgs{}'.format(obj_std_name[0])):
os.remove('stbcgsqgemgs{}'.format(obj_std_name[0]))
gmos.gsskysub.unlearn() # Debug gsskysub.
# Task parameters.
gsskysubFlags={'fl_int':'no'}
# Subtract sky background.
gmos.gsskysub('tbcgsqgemgs{}'.format(obj_std_name[0]), **gsskysubFlags) # IRAF task gsskysub.
# Print frames with and without sky correction.
obj=fits.open('tbcgsqgemgs{}'.format(obj_std_name[0]))
subobj=fits.open('stbcgsqgemgs{}'.format(obj_std_name[0]))
obj_data = obj[2].data
subobj_data = subobj[2].data
obj_shape = obj_data.shape
subobj_shape = subobj_data.shape
plt.figure(figsize=(16.0,8.0))
plt.subplot(2, 1, 1)
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
plt.ylabel('Position Along Slit', fontsize=14)
plt.subplot(2, 1, 2)
plt.imshow(subobj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(subobj[2].data,5),
vmax=np.percentile(subobj[2].data,90), aspect='auto')
plt.xlabel('Dispersion Axis', fontsize=14)
plt.ylabel('Position Along Slit', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pre- and Post- Sky Subtraction', y=1.0)
plt.savefig('skysub-std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
def std_extract_std():
""" Extract standard star spectrum.
Print position of extracted spectrum for checking. """
print('# EXTRACTING SPECTRUM #')
# Remove pre-existing extraction info file in the database directory.
if os.path.exists('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_std_name[0].replace('.fits',''))):
os.remove('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_std_name[0].replace('.fits','')))
# Remove pre-existing extracted spectrum.
if os.path.exists('estbcgsqgemgs{}'.format(obj_std_name[0])):
os.remove('estbcgsqgemgs{}'.format(obj_std_name[0]))
gmos.gsextract.unlearn() # Debug gsextract.
# Task parameters.
gsextractFlags={'fl_inter':'no', 'apwidth':'4', 'torder':'20'}
# Extract spectrum.
gmos.gsextract('stbcgsqgemgs{}'.format(obj_std_name[0]), **gsextractFlags) # IRAF task gsextract.
# Row position of extracted spectrum in the standard star frame.
center = np.genfromtxt('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_std_name[0].replace('.fits','')), skip_header=5,
max_rows=1, usecols=2, invalid_raise=False)
# Load standard star frame.
obj=fits.open('stbcgsqgemgs{}'.format(obj_std_name[0]))
obj_data = obj[2].data
obj_shape = obj_data.shape
# Print frame with row of extracted spectrum.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
ax1.axhline(y=center, linestyle='--', color='red',linewidth=2., alpha=0.7)
plt.tight_layout(w_pad=-0.9)
plt.ylabel('Position Along Slit', fontsize=14)
plt.xlabel('Dispersion Axis', fontsize=14)
plt.suptitle('Extracted Spectrum Position', y=1.0, fontsize=14)
plt.savefig('extract_std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
# Check if the spectrum position is correct.
# Select another row to extract the spectrum.
while True:
print('')
answer1 = raw_input('Are you satisfied with the extracted spectrum position? [y/n]')
if answer1 == "y":
break
if answer1 == "n":
print('')
answer2 = raw_input('Type the new row position for extracting the spectrum: ')
# Remove extraction info (last) file in the database directory.
os.remove('database/aplast')
# Remove extracted spectrum.
os.remove('estbcgsqgemgs{}'.format(obj_std_name[0]))
# Open extraction info file in the database directory.
lines = open('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_std_name[0].replace('.fits',''))).read().splitlines()
lines[5] = ' center 1566. {}'.format(answer2)
# Write new selected row to extract spectrum.
open('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_std_name[0].replace('.fits','')),'w').write('\n'.join(lines))
gmos.gsextract.unlearn() # Debug gsextract.
# Task parameter.
gsextractFlags={'fl_inter':'no', 'apwidth':'4', 'torder':'20'}
# Extract spectrum.
gmos.gsextract('stbcgsqgemgs{}'.format(obj_std_name[0]), **gsextractFlags) # IRAF task gsextract.
# Row position of extracted spectrum in the standard star frame.
center = np.genfromtxt('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_std_name[0].replace('.fits','')), skip_header=5,
max_rows=1, usecols=2, invalid_raise=False)
# Load standard star frame.
obj=fits.open('stbcgsqgemgs{}'.format(obj_std_name[0]))
obj_data = obj[2].data
obj_shape = obj_data.shape
# Print frame with row of extracted spectrum.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
ax1.axhline(y=center, linestyle='--', color='red',linewidth=2., alpha=0.7)
plt.tight_layout(w_pad=-0.9)
plt.ylabel('Position Along Slit', fontsize=14)
plt.xlabel('Dispersion Axis', fontsize=14)
plt.suptitle('Extracted Spectrum Position', y=1.0, fontsize=14)
plt.savefig('extract_std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
else:
print "Type y or n"
# Load extracted standard star spectrum.
obj=fits.open('estbcgsqgemgs{}'.format(obj_std_name[0]))
obj_header = obj[2].header
obj_data = obj[2].data
obj_shape = obj_data.shape
crval = obj_header['CRVAL1']
cd1_1 = obj_header['CD1_1']
# Print extracted standard star spectrum
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(crval + cd1_1*np.arange(1,len(obj[2].data)+1,1), obj[2].data, linewidth=0.55)
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.set_ylim(np.percentile(obj[2].data,5), np.percentile(obj[2].data,95))
plt.ylabel('Counts', fontsize=14)
plt.xlabel(r'Wavelength $\ [\AA]$', fontsize=14)
plt.title('Extracted Spectrum', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.savefig('extracted_spec_std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
def std_calib_std():
""" Create sensitivity function for the standard star.
Calibrate standard star spectrum.
Plot sensitivity function and the calibrated spectrum
of the standard star. """
print('# CREATING CALIBRATION FILES #')
# Remove pre-existing sensitivity function.
if os.path.exists('sens'):
os.remove('sens')
# Remove pre-existing std file.
if os.path.exists('std'):
os.remove('std')
# Remove pre-existing log file.
if os.path.exists('logstandard'):
os.remove('logstandard')
# Load standard star file.
obj = fits.open('estbcgsqgemgs{}'.format(obj_std_name[0]))
obj_header = obj[0].header
# Name of standard star.
obj_name = obj_header['OBJECT']
# Convert name to IRAF format.
std_iraf_name={'L745-46A': 'l745', 'EG21': 'eg21', 'HD217086': 'hd217086',
'EG81': 'eg81','Feige34': 'feige34', 'LTT1788': 'l1788', 'LTT3864': 'l3864',
'G191-B2B': 'g191b2b', 'HZ43': 'hz43', 'Feige56': 'f56',
'HD192281': 'hd192281', 'HZ44': 'hz44', 'HZ21': 'hz21',
'LTT3218': 'l3218', 'eg131': 'eg131', 'LTT4816': 'l4816', 'LTT377': 'l377',
'Feige110': 'feige110', 'LTT9491': 'l9491',
'Hiltner600': 'hilt600', 'LTT1020': 'l1020',
'LTT4364': 'l4364', 'CD-329927': 'cd32',
'BD+75325': 'bd75325', 'LTT7987': 'l7987',
'BD+284211': 'bd284211', 'LTT6248': 'l6248',
'LTT9239': 'l9239', 'LTT2415': 'l2415',
'Feige67': 'feige67', 'Feige66': 'feige66',
'EG274': 'eg274', 'LTT7379': 'l7379'}
std_name = std_iraf_name[str(obj_name)]
# Dictionary of GEMINI/IRAF standard stars and their directories.
dic_std = {'bd284211':'onedstds$spec50cal/','bd75325':'onedstds$oke90/',
'cd32':'onedstds$ctionewcal/','eg21':'onedstds$ctionewcal/',
'eg81':'onedstds$spec50cal/','eg131':'gmos$calib/',
'eg274':'onedstds$ctionewcal/','feige34':'onedstds$spec50cal/',
'f56':'onedstds$ctionewcal/', 'feige66':'onedstds$spec50cal/',
'feige67':'onedstds$spec50cal/','feige110':'onedstds$spec50cal/',
'g191b2b':'onedstds$spec50cal/', 'hilt600':'onedstds$spec50cal/',
'hd192281':'onedstds$spec50cal/','hd217086':'onedstds$spec50cal/',
'hz21':'onedstds$oke90/', 'hz43':'onedstds$iidscal/',
'hz44':'onedstds$spec50cal/','l745':'onedstds$ctionewcal/',
'l377':'onedstds$ctionewcal/','l1020':'onedstds$ctionewcal/',
'l1788':'onedstds$ctionewcal/','l2415':'onedstds$ctionewcal/',
'l3218':'onedstds$ctionewcal/','l3864':'onedstds$ctionewcal/',
'l4364':'onedstds$ctionewcal/','l4816':'onedstds$ctionewcal/',
'l6248':'onedstds$ctionewcal/','l7379':'onedstds$ctionewcal/',
'l7987':'onedstds$ctionewcal/','l9239':'onedstds$ctionewcal/',
'l9491':'onedstds$ctionewcal/',}
func='spline3' # Function used to interpolate sensitivity function.
ord='25' # Order of interpolating fuction.
gmos.gsstandard.unlearn() # Debug gsstandard.
# Task parameters.
gsstandardFlags={'sfile':'std','sfunction':'sens',
'starname':str(std_name),'logfile':'logstandard',
'caldir':str(dic_std[str(std_name)]),
'fl_inter':'no', 'function':func, 'order':ord}
# Create sensitivity function.
gmos.gsstandard('estbcgsqgemgs{}'.format(obj_std_name[0]), **gsstandardFlags) # IRAF task gsstandard.
# Load sensitivity function.
sens = fits.open('sens.fits')
sens_data = sens[0].data
sens_header = sens[0].header
crval1 = sens[0].header['CRVAL1']
cd1_1 = sens[0].header['CD1_1']
wavelength_sens = crval1 + cd1_1*(np.arange(1,len(sens_data)+1,1))
# Read 'logstandard' file.
# Number of lines for the needed information.
number_of_lines = np.genfromtxt('logstandard', skip_header=42, usecols=2,
max_rows=1, invalid_raise=False)
# RMS of function interpolation.
RMS_logstandard = np.genfromtxt('logstandard', skip_header=42, usecols=4,
max_rows = 1, invalid_raise=False)
# Wavelength for each point.
wavelength_logstandard = np.genfromtxt('logstandard', skip_header=45, usecols=0,
max_rows = number_of_lines, invalid_raise=False)
# Fitted points.
fit_logstandard = np.genfromtxt('logstandard', skip_header=45, usecols=1,
max_rows = number_of_lines, invalid_raise=False)
# Residual points.
resid_logstandard = np.genfromtxt('logstandard', skip_header=45, usecols=3,
max_rows = number_of_lines, invalid_raise=False)
# Plot sensitivity function, its order, RMS and residual.
plt.figure(figsize=(14.0,6.0))
ax1 = plt.subplot(2,1,1)
ax1.plot(wavelength_sens, sens_data)
ax1.scatter(wavelength_logstandard, fit_logstandard, color='red', s=25, marker='x')
ax1.set_ylabel('Sensitivity', fontsize=12)
ax1.set_xlabel('Wavelength', fontsize=12)
ax2 = plt.subplot(2,1,2)
ax2.scatter(wavelength_logstandard, resid_logstandard, s=25, color='red', marker='x')
ax2.axhline(y=0, linestyle='--')
ax2.set_xlabel('Wavelength', fontsize=12)
ax2.set_ylabel('Residual', fontsize=12)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Function = {} Order = {} RMS = {}'.format(func, ord, RMS_logstandard), fontsize=12,y=1.0)
plt.savefig('plot-gsstandard-{}.png'.format(obj_std_name[0]))
plt.show()
print('# CALIBRATING... # ')
# Remove pre-existing calibrated spectrum.
if os.path.exists('cestbcgsqgemgs{}'.format(obj_std_name[0])):
os.remove('cestbcgsqgemgs{}'.format(obj_std_name[0]))
gmos.gscalibrate.unlearn() # Debug gscalibrate.
# Calibrate standard star spectrum.
gmos.gscalibrate('estbcgsqgemgs{}'.format(obj_std_name[0])) # IRAF task gscalibrate.
# Load calibrated spectrum.
obj=fits.open('cestbcgsqgemgs{}'.format(obj_std_name[0]))
obj_header = obj[2].header
obj_data = obj[2].data
obj_shape = obj_data.shape
crval = obj_header['CRVAL1']
cd1_1 = obj_header['CD1_1']
# Plot calibrated spectrum of standard star.
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(crval + cd1_1*np.arange(1,obj_shape[0]+1,1), obj[2].data,linewidth=0.55)
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.set_ylim(np.percentile(obj[2].data,5), np.percentile(obj[2].data,95))
plt.ylabel('Flux [ergs cm$^{-2}$ s$^{-1}$ $\ \AA$$^{-1}$] ', fontsize=14)
plt.xlabel(r'Wavelength $\ [\AA]$', fontsize=14)
plt.title('Calibrated Spectrum', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.savefig('calib-spec-std-{}.png'.format(obj_std_name[0]))
plt.show()
plt.clf()
plt.close()
# Reduction and calibration of SCIENCE OBJECT files.
print "------------------------------"
print "# REDUCTION OF SCIENCE OBJECT #"
print "------------------------------"
def obj_gbias():
""" Apply overscan correction and trim individual bias frames.
Create Master Bias. Plot Master Bias and pixel counting. """
print('# CREATING MASTER BIAS #')
# Remove pre-existing Master Bias.
if os.path.exists('Bias.fits'):
os.remove('Bias.fits')
gmos.gbias.unlearn() # Debug gbias.
# Set the task parameters.
biasFlags = {'rawpath':'raw', 'fl_over':'yes',
'fl_trim':'yes', 'fl_vardq':'yes'}
# Create Master Bias.
gmos.gbias('@bias_obj.txt', 'Bias.fits', **biasFlags) # IRAF task gbias.
# Load Master Bias.
obj=fits.open('Bias.fits')
obj_data = obj[2].data
obj_shape = obj_data.shape
# Print Master Bias.
plt.figure(figsize=(14.0,4.0))
for i in range(len(np.arange(0,12,1))):
ax = plt.subplot(1, 12,i+1)
plt.imshow(obj[i+1].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[i+1].data,5),
vmax=np.percentile(obj[i+1].data,90), aspect='auto')
if i == 0:
ax.yaxis.set_visible(True)
ax.set_ylabel('Position Along Slit', fontsize=14)
else:
ax.yaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Master BIAS', y=1.0, fontsize=14)
plt.savefig('master-bias-obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
# Print the pixel counting through a line cut.
plt.figure(figsize=(16.0,8.0))
for j in range(len(np.arange(0,12,1))):
ax1 = plt.subplot(3, 12,j+1)
ax1.set_ylim(min(obj[1].data[int(0.3*obj_shape[0]),1:obj_shape[1]]),
max(obj[1].data[int(0.3*obj_shape[0]),1:obj_shape[1]]))
ax1.plot(np.arange(1,obj_shape[1],1),
obj[j+1].data[int(0.3*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax2 = plt.subplot(3, 12,12+j+1)
ax2.set_ylim(min(obj[1].data[int(0.5*obj_shape[0]),1:obj_shape[1]]),
max(obj[1].data[int(0.5*obj_shape[0]),1:obj_shape[1]]))
ax2.plot(np.arange(1,obj_shape[1],1),
obj[j+1].data[int(0.5*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax3 = plt.subplot(3, 12, 24+j+1)
ax3.set_ylim(min(obj[1].data[int(0.7*obj_shape[0]),1:obj_shape[1]]),
max(obj[1].data[int(0.7*obj_shape[0]),1:obj_shape[1]]))
ax3.plot(np.arange(1,obj_shape[1],1),
obj[j+1].data[int(0.7*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
if j == 0:
ax1.yaxis.set_visible(True)
ax1.set_ylabel('Counts [row={}]'.format(int(0.3*obj_shape[0])), fontsize=14)
ax2.yaxis.set_visible(True)
ax2.set_ylabel('Counts [row={}]'.format(int(0.5*obj_shape[0])), fontsize=14)
ax3.yaxis.set_visible(True)
ax3.set_ylabel('Counts [row={}]'.format(int(0.7*obj_shape[0])), fontsize=14)
else:
ax1.yaxis.set_visible(False)
ax2.yaxis.set_visible(False)
ax3.yaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pixel Counting',y=1.0, fontsize=14)
plt.savefig('pixel-counting-bias-obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.close()
def obj_reduc_arc():
""" Apply overscan correction and mosaic arc frames. """
print('# REDUCING ARC LAMP #')
# Remove pre-existing calibrated files.
if os.path.exists('gs{}'.format(arc_sci_name[0])):
os.remove('gs{}'.format(arc_sci_name[0]))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Set the task parameters.
gsreduceFlags1={'rawpath':'raw', 'fl_bias':'yes',
'fl_flat':'no', 'fl_over':'yes',
'bias':'Bias', 'fl_gmos':'yes'}
# Reduce arc frames.
gmos.gsreduce(str(arc_sci_name[0]), **gsreduceFlags1) # IRAF task gsreduce.
def obj_wavelength_arc():
""" Create a wavelength solution based on arc frames.
Print the difference of the calculated and correct wavelength values
for the estimated lines."""
print('# CALIBRATING WAVELENGTH #')
# Remove pre-existing wavelength solution files on database directory.
if os.path.exists('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits',''))):
os.remove('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')))
# Load reduced arc frame.
obj=fits.open('gs{}'.format(arc_sci_name[0]))
obj_data = obj[2].data
arc_shape = obj_data.shape
gmos.gswavelength.unlearn() # Debug gswavelength.
# Set the task parameters.
gswavelengthFlags={'nsum':str(arc_shape[0]/3), 'step':str(arc_shape[0]/3),
'fwidth':'7', 'gsigma':'1.5','cradius':'12',
'minsep':'7', 'order':'6', 'fl_inter':'no'}
gmos.gswavelength('gs{}'.format(arc_sci_name[0]), **gswavelengthFlags) # IRAF task gswavelength.
# Read wavelength solution file on database directory.
# Autoidentify.
# Number of calculated features.
number_of_features_auto = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=6, usecols=1, max_rows=1, invalid_raise=False)
# Number of calculated coefficients.
number_of_coefficients_auto = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+8, usecols=1,
max_rows=1, invalid_raise=False)
# gswavelength flags to discard outlier values.
wave_flag_auto = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')), skip_header=7,
usecols=5, max_rows=int(number_of_features_auto), invalid_raise=False)
# Row number.
wave_auto_col_0 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')), skip_header=7,
usecols=0, max_rows=int(number_of_features_auto), invalid_raise=False)
# Corrected line wavelength.
wave_auto_col_1 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')), skip_header=7,
usecols=1, max_rows=int(number_of_features_auto), invalid_raise=False)
# Calculated line wavelength.
wave_auto_col_2 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')), skip_header=7,
usecols=2, max_rows=int(number_of_features_auto), invalid_raise=False)
# Reidentify.
# Number of calculated features.
number_of_features_re1 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+7,
usecols=1, max_rows=1, invalid_raise=False)
number_of_coefficients_re1 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8+int(number_of_features_re1)+8,
usecols=1, max_rows=1, invalid_raise=False)
# gswavelength flags to discard outlier values.
wave_flag_re1 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8,
usecols=5, max_rows=int(number_of_features_re1), invalid_raise=False)
# Row number.
wave_re1_col_0 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8, usecols=0,
max_rows=int(number_of_features_re1), invalid_raise=False)
# Corrected line wavelength.
wave_re1_col_1 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8, usecols=1,
max_rows=int(number_of_features_re1), invalid_raise=False)
# Calculated line wavelength.
wave_re1_col_2 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)+8, usecols=2,
max_rows=int(number_of_features_re1), invalid_raise=False)
# Reidentify.
# Number of calculated features.
number_of_features_re2 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+7),
usecols=1, max_rows=1, invalid_raise=False)
# gswavelength flags to discard outlier values.
wave_flag_re2 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=5, max_rows=int(number_of_features_re2), invalid_raise=False)
# Row number.
wave_re2_col_0 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=0, max_rows=int(number_of_features_re2), invalid_raise=False)
# Corrected line wavelength.
wave_re2_col_1 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=1, max_rows=int(number_of_features_re2), invalid_raise=False)
# Calculated line wavelength.
wave_re2_col_2 = np.genfromtxt('database/idgs{}_001'.format(arc_sci_name[0].replace('.fits','')),
skip_header=(7+int(number_of_features_auto)+9+int(number_of_coefficients_auto)
+8+int(number_of_features_re1)+9+int(number_of_coefficients_re1)+8),
usecols=2, max_rows=int(number_of_features_re2), invalid_raise=False)
# gswavelength flags
flag_auto = np.where(wave_flag_auto[0:,] == 1) # Autoidentify.
flag_re1 = np.where(wave_flag_re1[0:,] == 1) # Reidentify.
flag_re2 = np.where(wave_flag_re2[0:,] == 1) # Reidentify.
# RMS estimate.
# Autoidentify.
rows_auto = wave_auto_col_0[0:,][flag_auto]
delta_lambda_auto = wave_auto_col_2[0:,][flag_auto] - wave_auto_col_1[0:,][flag_auto]
rms_auto = np.sqrt(np.sum(delta_lambda_auto**2) / len(rows_auto))
print("[AUTOIDENTIFY] RMS = ", rms_auto)
hig_than_rms_auto=[] # Values higher than RMS.
for l in range(len(delta_lambda_auto)):
if np.abs(delta_lambda_auto[l]) > rms_auto:
hig_than_rms_auto.append( wave_auto_col_2[0:,][flag_auto][l])
# Print number of lines with values diverging from the correct value
# whith a difference higher thant the RMS.
print("[AUTOIDENTIFY] There are {} identified lines diverging from the"
" observed value with differences higher than the RMS :".format(len(hig_than_rms_auto)), hig_than_rms_auto)
# Reidentify.
rows_re1 = wave_re1_col_0[0:,][flag_re1]
delta_lambda_re1 = wave_re1_col_2[0:,][flag_re1] - wave_re1_col_1[0:,][flag_re1]
rms_re1 = np.sqrt(np.sum(delta_lambda_re1**2) / len(rows_re1))
print('[REIDENTIFY] RMS = ', rms_re1)
hig_than_rms_re1=[] # Values higher than RMS.
for l in range(len(delta_lambda_re1)):
if np.abs(delta_lambda_re1[l]) > rms_re1:
hig_than_rms_re1.append(wave_re1_col_2[0:,][flag_re1][l])
# Print number of lines with values diverging from the correct value
# whith a difference higher thant the RMS.
print("[REIDENTIFY] There are {} identified lines diverging from the"
" observed value with differences higher than the RMS :".format(len(hig_than_rms_re1)), hig_than_rms_re1)
# Reidentify.
rows_re2 = wave_re2_col_0[0:,][flag_re2]
delta_lambda_re2 = wave_re2_col_2[0:,][flag_re2] - wave_re2_col_1[0:,][flag_re2]
rms_re2 = np.sqrt(np.sum(delta_lambda_re2**2) / len(rows_re2))
print('[REIDENTIFY] RMS = ', rms_re2)
hig_than_rms_re2=[] # Values higher than RMS.
for l in range(len(delta_lambda_re2)):
if np.abs(delta_lambda_re2[l]) > rms_re2:
hig_than_rms_re2.append(wave_re2_col_2[0:,][flag_re2][l])
# Print number of lines with values diverging from the correct value
# whith a difference higher thant the RMS.
print("[REIDENTIFY] There are {} identified lines diverging from the"
" observed value with differences higher than the RMS :".format(len(hig_than_rms_re2)), hig_than_rms_re2)
# Print the difference of the calculated and correct wavelength values
# for the estimated lines.
# Autoidentify.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()
ax4 = ax1.twiny()
ax1.scatter(rows_auto,np.abs(delta_lambda_auto), color='white' )
ax2.scatter(wave_auto_col_2[0:,][flag_auto], np.abs(delta_lambda_auto), color='coral')
ax2.invert_xaxis()
plt.title('Autoidentify', y=1.10, fontsize=14)
ax2.set_xlabel(r'$\lambda_{identified} \ [\AA]$', fontsize=14)
ax1.set_xlabel('Pixel position', fontsize=14)
ax1.set_ylabel(r'|$\lambda_{identified} - \lambda_{fitted}| \ [\AA]$', fontsize=14)
ax3.axhline(y=0.2, linestyle='--')
ax3.xaxis.set_visible(False)
ax4.axhline(y=rms_auto, linestyle=':', label='RMS = {}'.format(rms_auto))
ax4.xaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
ax4.legend()
plt.savefig('autoidentify_obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.close()
# Reidentify.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()
ax4 = ax1.twiny()
ax1.scatter(rows_re1,np.abs(delta_lambda_re1), color='white' )
ax2.scatter(wave_re1_col_2[0:,][flag_re1] ,np.abs(delta_lambda_re1), color='coral')
ax2.invert_xaxis()
plt.title('Reidentify', y=1.10, fontsize=14)
ax2.set_xlabel(r'$\lambda_{identified} \ [\AA]$', fontsize=14)
ax1.set_xlabel('Pixel position', fontsize=14)
ax1.set_ylabel(r'|$\lambda_{identified} - \lambda_{fitted}| \ [\AA]$', fontsize=14)
ax3.axhline(y=0.2, linestyle='--')
ax3.xaxis.set_visible(False)
ax4.axhline(y=rms_re1, linestyle=':', label='RMS = {}'.format(rms_re1))
ax4.xaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
ax4.legend()
plt.show()
plt.close()
# Reidentify.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()
ax4 = ax1.twiny()
ax1.scatter(rows_re2,np.abs(delta_lambda_re2), color='white' )
ax2.scatter(wave_re2_col_2[0:,][flag_re2] ,np.abs(delta_lambda_re2), color='coral')
ax2.invert_xaxis()
plt.title('Reidentify', y=1.10, fontsize=14)
ax2.set_xlabel(r'$\lambda_{identified} \ [\AA]$', fontsize=14)
ax1.set_xlabel('Pixel position', fontsize=14)
ax1.set_ylabel(r'|$\lambda_{identified} - \lambda_{fitted}| \ [\AA]$', fontsize=14)
ax3.axhline(y=0.2, linestyle='--')
ax3.xaxis.set_visible(False)
ax4.axhline(y=rms_re2, linestyle=':', label='RMS = {}'.format(rms_re2))
ax4.xaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
ax4.legend()
plt.show()
plt.close()
def obj_transf_arc():
""" Transform arc files. """
print('# TRANSFORMING ARC #')
# Remove pre-existing transformed files.
if os.path.exists('tgs{}'.format(arc_sci_name[0])):
os.remove('tgs{}'.format(arc_sci_name[0]))
gmos.gstransform.unlearn() # Debug gstransform.
# Set the task parameters.
gstransformFlags={'wavtranam':'gs{}'.format(arc_sci_name[0])}
# Transform arc files.
gmos.gstransform('gs{}'.format(arc_sci_name[0]), **gstransformFlags) # IRAF task gstransform.
def obj_reduc_flat():
""" Subtract bias from individual flat frames. """
print('# REDUCING RAW FLATS #')
# Open individual flat frames.
f = open('flat_obj.txt', 'r')
for flatfile in f:
# Remove pre-existing processed files.
if os.path.exists('gs{}'.format(flatfile.replace('\n',''))):
os.remove('gs{}'.format(flatfile.replace('\n','')))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Set the task parameters.
gsreduceFlags={'rawpath':'raw', 'fl_bias':'yes',
'fl_flat':'no', 'fl_fixpix':'no',
'bias':'Bias.fits','fl_gmos':'no',
'fl_gsappwave':'no', 'fl_cut':'no'}
# Reduce flat files.
gmos.gsreduce(str(flatfile.replace('\n','')), **gsreduceFlags) # IRAF task gsreduce.
f.close()
def obj_qecorr_flat():
""" Apply quantum efficiency correction to individual
flat frames. """
print('# QUANTUM-CORRECTING FLAT #')
# Open individual flat frames.
f = open('flat_obj.txt', 'r')
for flatfile in f:
# Remove pre-existing processed files.
if os.path.exists('qgs{}'.format(flatfile.replace('\n',''))):
os.remove('qgs{}'.format(flatfile.replace('\n','')))
gmos.gqecorr.unlearn() # Debug gqecorr.
# Set the task parameters.
qecorrFlags= {'refimage':'gs{}'.format(arc_sci_name[0]),
'fl_keep':'yes'}
# Apply quantum efficiency correction.
gmos.gqecorr('gs{}'.format(flatfile.replace('\n','')), **qecorrFlags) # IRAF task gqecorr.
f.close()
def obj_gmosaic_flat():
""" Mosaic individual flat frames. """
print('# MOSAIC FLAT#')
# Remove pre-existing list.
if os.path.exists("mqgsflat_obj.txt"):
os.remove("mqgsflat_obj.txt")
# Open individual flat frames.
f = open('flat_obj.txt', 'r')
for flatfile in f:
# Remove pre-existing processed files.
if os.path.exists('mqgs{}'.format(flatfile.replace('\n',''))):
os.remove('mqgs{}'.format(flatfile.replace('\n','')))
gmos.gmosaic.unlearn() # Debug gmosaic.
# Set the task parameters.
gmosaicFlags= {'fl_fixpix':'yes'}
# Mosaic flat frames.
gmos.gmosaic('qgs{}'.format(flatfile.replace('\n','')), **gmosaicFlags) # IRAF task gmosaic.
# Write name of processed files on list.
mqgsflat = open("mqgsflat_obj.txt","a")
mqgsflat.write('mqgs{}'.format(flatfile.replace('\n','')))
mqgsflat.write("\n")
mqgsflat.close()
f.close()
def obj_masterflat():
""" Trim individual flat frames and create Master Flat.
Print Master Flat and pixel counting. """
print('# CREATING MASTER FLAT #')
# Remove pre-existing Master Flat.
if os.path.exists('qFlat.fits'):
os.remove('qFlat.fits')
gmos.gsflat.unlearn() # Debug gsflat.
# Set the task parameters.
flatFlags = {'fl_bias':'no', 'order':'29',
'fl_over':'no', 'fl_trim':'no',
'fl_usegrad':'yes'}
# Create Master Flat.
gmos.gsflat('@mqgsflat_obj.txt', 'qFlat.fits', **flatFlags) # IRAF task gsflat.
# Load Master Flat.
obj=fits.open('qFlat.fits')
obj_data = obj[2].data
obj_shape = obj_data.shape
# Plot Master Flat.
plt.figure(figsize=(12.0,4.0))
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
plt.ylabel('Position Along Slit', fontsize=14)
plt.xlabel('Dispersion Axis', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Master FLAT', y=1.0, fontsize=14)
plt.savefig('master-flat-obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
# Print the pixel counting through a line cut.
plt.figure(figsize=(16.0,8.0))
ax1 = plt.subplot(3, 1, 1)
ax1.set_ylim(min(obj[2].data[int(0.3*obj_shape[0]),1:obj_shape[1]]),
max(obj[2].data[int(0.3*obj_shape[0]),1:obj_shape[1]]))
ax1.set_ylabel('Counts [row={}]'.format(int(0.3*obj_shape[0])), fontsize=14)
ax1.plot(np.arange(1,obj_shape[1],1),
obj[2].data[int(0.3*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax2 = plt.subplot(3, 1, 2)
ax2.set_ylim(min(obj[2].data[int(0.5*obj_shape[0]),1:obj_shape[1]]),
max(obj[2].data[int(0.5*obj_shape[0]),1:obj_shape[1]]))
ax2.set_ylabel('Counts [row={}]'.format(int(0.5*obj_shape[0])), fontsize=14)
ax2.plot(np.arange(1,obj_shape[1],1),
obj[2].data[int(0.5*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax3 = plt.subplot(3, 1, 3)
ax3.set_ylim(min(obj[2].data[int(0.7*obj_shape[0]),1:obj_shape[1]]),
max(obj[2].data[int(0.7*obj_shape[0]),1:obj_shape[1]]))
ax3.set_ylabel('Counts [row={}]'.format(int(0.7*obj_shape[0])), fontsize=14)
ax3.plot(np.arange(1,obj_shape[1],1),
obj[2].data[int(0.7*obj_shape[0]),1:obj_shape[1]],linewidth=0.5)
ax3.set_xlabel('Dispersion Axis', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pixel Counting',y=1.0, fontsize=14)
plt.savefig('pixel-counting-flat-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.close()
def obj_reduc1_obj():
""" Subtract bias, apply overscan and cosmic ray correction
to science object frames. """
print('# 1st REDUCTION #')
# Remove pre-existing processed files.
if os.path.exists('gs{}'.format(obj_sci_name[0])):
os.remove('gs{}'.format(obj_sci_name[0]))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Task parameters.
gsreduceFlags={'rawpath':'raw','fl_gmos':'no', 'fl_fixpix':'no',
'fl_flat':'no', 'fl_vardq':'yes',
'fl_fulldq':'yes', 'fl_bias':'yes',
'bias':'Bias', 'fl_inter':'no',
'fl_cut':'no', 'fl_gsappwave':'no',
'fl_over':'yes', 'fl_crspec':'yes'}
# Reduce science object files.
gmos.gsreduce(str(obj_sci_name[0]), **gsreduceFlags) # IRAF task gsreduce.
def obj_gemfix_obj():
""" Improve cosmic ray and bad pixel correction.
Print raw and corrected files for comparison. """
print('# GEMFIX #')
# Remove pre-existing processed files.
if os.path.exists('gemgs{}'.format(obj_sci_name[0])):
os.remove('gemgs{}'.format(obj_sci_name[0]))
if os.path.exists('copy_gemgs{}'.format(obj_sci_name[0])):
os.remove('copy_gemgs{}'.format(obj_sci_name[0]))
gemini.gemfix.unlearn() # Debug gemfix.
# Task parameters.
gemfixFlags={'outimages':'gemgs{}'.format(obj_sci_name[0]),
'method':'fixpix', 'bitmask':'8'}
# Fix bad pixels.
gemini.gemfix('gs{}'.format(obj_sci_name[0]), **gemfixFlags) # IRAF task gemfix.
# Load raw files.
obj=fits.open('raw/{}'.format(obj_sci_name[0]))
obj_data = obj[2].data
obj_shape = obj_data.shape
# Load processed files.
gemobj=fits.open('gemgs{}'.format(obj_sci_name[0]))
gemobj_data = gemobj[len(gemobj)-1].data
gemobj_shape = gemobj_data.shape
# Create copy of processed file.
copy_gemobj = fits.HDUList([gemobj[0]])
for n in np.arange(2,37,3):
copy_gemobj.append(gemobj[n])
copy_gemobj.writeto('copy_gemgs{}'.format(obj_sci_name[0]))
new_gemobj = fits.open('copy_gemgs{}'.format(obj_sci_name[0]))
gemobj_data = gemobj[len(gemobj)-1].data
gemobj_shape = gemobj_data.shape
# Print raw and corrected files.
plt.figure(figsize=(16.0,8.0))
for i in range(len(np.arange(0,12,1))):
ax1 = plt.subplot(2,12,i+1)
ax1.imshow(obj[i+1].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[i+1].data,5),
vmax=np.percentile(obj[i+1].data,90), aspect='auto')
ax2 = plt.subplot(2,12,12+i+1)
ax2.imshow(new_gemobj[i+1].data, origin='lower',cmap='afmhot',
vmin=np.percentile(new_gemobj[i+1].data,5),
vmax=np.percentile(new_gemobj[i+1].data,90), aspect='auto')
if i == 0:
ax1.yaxis.set_visible(True)
ax1.set_ylabel('Position Along Slit', fontsize=14)
ax2.yaxis.set_visible(True)
ax2.set_ylabel('Position Along Slit', fontsize=14)
else:
ax1.yaxis.set_visible(False)
ax2.yaxis.set_visible(False)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pre- and Post- Cosmic Ray Rejection', y=1.0, fontsize=14)
plt.savefig('gemfix_obj_{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
def obj_qecorr_obj():
""" Apply quantum efficiency correction to science object frames. """
print('# QUANTUM-CORRECTING #')
# Remove pre-existing processed files.
if os.path.exists('qgemgs{}'.format(obj_sci_name[0])):
os.remove('qgemgs{}'.format(obj_sci_name[0]))
# Open list of individual flat frames.
flatfile_list=[]
f = open('flat_obj.txt', 'r')
for flatfile in f:
flatfile_list.append(flatfile)
f.close()
gmos.gqecorr.unlearn() # Debug gqecorr.
# Task parameters
qecorrFlags= {'refimage':'gs{}'.format(arc_sci_name[0]),
'corrima':'qgs{}'.format(flatfile_list[0].replace('\n',''))}
# Apply quantum efficiency correction.
gmos.gqecorr('gemgs{}'.format(obj_sci_name[0]), **qecorrFlags) # IRAF task gqecorr.
def obj_reduc2_obj():
""" Apply flat field correction to science object frames. """
print '# 2nd REDUCTION #'
# Remove pre-existing processed files.
if os.path.exists('gsqgemgs{}'.format(obj_sci_name[0])):
os.remove('gsqgemgs{}'.format(obj_sci_name[0]))
gmos.gsreduce.unlearn() # Debug gsreduce.
# Task parameters.
gsreduceFlags={'fl_bias':'no', 'fl_flat':'yes',
'fl_over':'no', 'flat':'qFlat',
'fl_trim':'no'}
# Apply flat correction.
gmos.gsreduce('qgemgs{}'.format(obj_sci_name[0]), **gsreduceFlags) # IRAF task gsreduce.
def obj_badcolumn_obj():
""" Print science object frame for bad column checking.
Interpolate bad columns. """
print('# BAD COLUMN CHECKING #')
# Remove pre-existing processed files.
if os.path.exists('bcgsqgemgs{}'.format(obj_sci_name[0])):
os.remove('bcgsqgemgs{}'.format(obj_sci_name[0]))
# Create copy of science object file to apply bad column correction.
iraf.copy('gsqgemgs{}'.format(obj_sci_name[0]), 'bcgsqgemgs{}'.format(obj_sci_name[0]))
# Load copy.
obj=fits.open('bcgsqgemgs{}'.format(obj_sci_name[0]))
# Check if a bad column mask is already available in the directory.
if os.path.exists("maskbadcol.txt"):
while True:
try:
print('')
answer = raw_input("The file 'maskbadcol.txt' is available in this directory. "
" Do you wish to apply this mask to your spectrum? (y/n) ")
if answer=='y':
# Remove pre-existing mask.
if os.path.exists("maskbadcol.pl"):
os.remove("maskbadcol.pl")
# Create mask based on coordinates text file.
iraf.text2mask('maskbadcol.txt', 'maskbadcol.pl',
obj[2].shape[1], obj[2].shape[0]) # IRAF task 'text2mask'.
# Interpolate bad columns using mask.
iraf.fixpix('bcgsqgemgs{}[2]'.format(obj_sci_name[0]),
'maskbadcol.pl', linterp='1,2,3,4') # IRAF task 'fixpix'.
break
if answer=='n':
break
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
# Select a line in the science object frame and print the pixel counting
# through the line for bad column checking.
while True:
try:
print('')
# Selected line.
line = int(input('Choose a line for bad column checking: ' ))
print('')
xaxis=np.arange(1, obj[2].data.shape[1],1)
yaxis=obj[2].data[line,1:obj[2].data.shape[1]]
# Print science object frame and pixel counting
# through the selected line.
plt.figure(figsize=(16.0,8.0))
plt.figure(figsize=(16.0,8.0))
ax1=plt.subplot(211)
ax1.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,1),
vmax=np.percentile(obj[2].data,99), aspect='auto')
ax1.axhline(y=line, linestyle='--', color='red',linewidth=2., alpha=0.7)
ax1.set_ylabel('Position Along Slit', fontsize=14)
ax2=plt.subplot(212)
ax2.plot(xaxis, yaxis, color='red' )
ax2.set_xlim(left=np.min(xaxis), right=np.max(xaxis))
ax2.set_ylabel('Counts', fontsize=14)
ax2.set_xlabel('Dispersion Axis', fontsize=14)
plt.show()
answer = raw_input('Do you wish to select another line? (y/n) ')
if answer=='n':
break
if answer=='y':
print('')
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
# Select the inital and final position along the x-axis of
# the columns to interpolate.
# Create a mask and interpolate bad column.
while True:
try:
print('')
answer2 = raw_input('Do you wish to interpolate a bad column? (y/n) ')
print('')
if answer2=='n':
break
if answer2=='y':
while True:
try:
print('')
# Inital x position of the bad column.
x1 = int(input('Select a column to interpolate (x min): ' ))
print('')
# Final x position of the bad column.
x2 = int(input('Select a column to interpolate (x max): ' ))
# Create text file with the column coordinates.
bias_obj_txt = open("maskbadcol.txt","a")
bias_obj_txt.write('{} {} 1 {}'.format(x1, x2, obj[2].shape[0]))
bias_obj_txt.write("\n")
bias_obj_txt.close()
# Remove pre-existing mask.
if os.path.exists("maskbadcol.pl"):
os.remove("maskbadcol.pl")
# Create mask based on coordinates text file.
iraf.text2mask('maskbadcol.txt', 'maskbadcol.pl',
obj[2].shape[1], obj[2].shape[0]) # IRAF task 'text2mask'.
# Interpolate bad columns using mask.
iraf.fixpix('bcgsqgemgs{}[2]'.format(obj_sci_name[0]),
'maskbadcol.pl', linterp='1,2,3,4') # IRAF task 'fixpix'.
# Print corrected science object frame and pixel counting
# through the selected line.
obj2=fits.open('bcgsqgemgs{}'.format(obj_sci_name[0]))
xaxis=np.arange(1, obj[2].data.shape[1],1)
yaxis=obj[2].data[line,1:obj[2].data.shape[1]]
plt.figure(figsize=(16.0,8.0))
ax1=plt.subplot(211)
ax1.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,1),
vmax=np.percentile(obj[2].data,99), aspect='auto')
ax1.axhline(y=line, linestyle='--', color='red',linewidth=2., alpha=0.7)
ax1.set_ylabel('Position Along Slit', fontsize=14)
ax2=plt.subplot(212)
ax2.plot(xaxis, yaxis, color='red' )
ax2.set_xlim(left=np.min(xaxis), right=np.max(xaxis))
ax2.set_ylabel('Counts', fontsize=14)
ax2.set_xlabel('Dispersion Axis', fontsize=14)
plt.show()
print('')
answer3 = raw_input('Do you wish to interpolate another bad column? (y/n) ' )
if answer3=='n':
break
if answer3=='y':
print('')
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
break
else:
print('Please, type y or n ')
except(NameError, SyntaxError,IndexError):
print('')
print("Check if you typed a correct line number. ")
def obj_transf_obj():
""" Transform science object frame. """
print('# TRANSFORMING SCIENCE SPECTRUM #')
# Remove pre-existing processed files.
if os.path.exists('tbcgsqgemgs{}'.format(obj_sci_name[0])):
os.remove('tbcgsqgemgs{}'.format(obj_sci_name[0]))
gmos.gstransform.unlearn() # Debug gstransform.
# Task parameters.
gstransformFlags={'wavtranam':'gs{}'.format(arc_sci_name[0]),
'fl_vardq':'yes'}
# Transform spectrum.
gmos.gstransform('bcgsqgemgs{}'.format(obj_sci_name[0]), **gstransformFlags) # IRAF task gstransform.
def obj_sky_sub_obj():
""" Subtract sky background from science object frame.
Print raw and sky subtracted frames for comparison. """
print('# SKY-SUBTRACTING #')
# Remove pre-existing processed files.
if os.path.exists('stbcgsqgemgs{}'.format(obj_sci_name[0])):
os.remove('stbcgsqgemgs{}'.format(obj_sci_name[0]))
gmos.gsskysub.unlearn() # Debug gsskysub.
# Task parameters.
gsskysubFlags={'fl_int':'no'}
# Subtract sky background.
gmos.gsskysub('tbcgsqgemgs{}'.format(obj_sci_name[0]), **gsskysubFlags) # IRAF task gsskysub.
# Print frames with and without sky correction.
obj=fits.open('tbcgsqgemgs{}'.format(obj_sci_name[0]))
subobj=fits.open('stbcgsqgemgs{}'.format(obj_sci_name[0]))
obj_data = obj[2].data
subobj_data = subobj[2].data
obj_shape = obj_data.shape
subobj_shape = subobj_data.shape
plt.figure(figsize=(16.0,8.0))
plt.subplot(2, 1, 1)
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
plt.ylabel('Position Along Slit', fontsize=14)
plt.subplot(2, 1, 2)
plt.imshow(subobj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(subobj[2].data,5),
vmax=np.percentile(subobj[2].data,90), aspect='auto')
plt.xlabel('Dispersion Axis', fontsize=14)
plt.ylabel('Position Along Slit', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.suptitle('Pre- and Post- Sky Subtraction', y=1.0)
plt.savefig('skysub-obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
def obj_extract_obj():
""" Extract science object spectrum.
Print position of extracted spectrum for checking. """
print('# EXTRACTING SCIENCE SPECTRUM #')
# Remove pre-existing extraction info file in the database directory.
if os.path.exists('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_sci_name[0].replace('.fits',''))):
os.remove('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_sci_name[0].replace('.fits','')))
# Remove pre-existing extracted spectrum.
if os.path.exists('estbcgsqgemgs{}'.format(obj_sci_name[0])):
os.remove('estbcgsqgemgs{}'.format(obj_sci_name[0]))
gmos.gsextract.unlearn() # Debug gsextract.
# Task parameters.
gsextractFlags={'fl_inter':'no', 'apwidth':'4', 'torder':'20'}
# Extract spectrum.
gmos.gsextract('stbcgsqgemgs{}'.format(obj_sci_name[0]), **gsextractFlags) # IRAF task gsextract.
# Row position of extracted spectrum in the science object frame.
center = np.genfromtxt('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_sci_name[0].replace('.fits','')), skip_header=5,
max_rows=1, usecols=2, invalid_raise=False)
# Load science object frame.
obj=fits.open('stbcgsqgemgs{}'.format(obj_sci_name[0]))
obj_data = obj[2].data
obj_shape = obj_data.shape
# Print frame with row of extracted spectrum.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
ax1.axhline(y=center, linestyle='--', color='red',linewidth=2., alpha=0.7)
plt.tight_layout(w_pad=-0.9)
plt.ylabel('Position Along Slit', fontsize=14)
plt.xlabel('Dispersion Axis', fontsize=14)
plt.suptitle('Extracted Spectrum Position', y=1.0, fontsize=14)
plt.savefig('extract_obj_{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
# Check if the spectrum position is correct.
# Select another row to extract the spectrum.
while True:
print('')
answer1 = raw_input('Are you satisfied with the extracted spectrum position? [y/n]')
if answer1 == "y":
break
if answer1 == "n":
print('')
answer2 = raw_input('Type the new row position for extracting the spectrum: ')
# Remove extraction info (last) file in the database directory.
os.remove('database/aplast')
# Remove extracted spectrum.
os.remove('estbcgsqgemgs{}'.format(obj_sci_name[0]))
# Open extraction info file in the database directory.
lines = open('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_sci_name[0].replace('.fits',''))).read().splitlines()
lines[5] = ' center 1566. {}'.format(answer2)
# Write new selected row to extract spectrum.
open('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_sci_name[0].replace('.fits','')),'w').write('\n'.join(lines))
gmos.gsextract.unlearn() # Debug gsextract.
# Task parameter.
gsextractFlags={'fl_inter':'no', 'apwidth':'4', 'torder':'20'}
# Extract spectrum.
gmos.gsextract('stbcgsqgemgs{}'.format(obj_sci_name[0]), **gsextractFlags) # IRAF task gsextract.
# Row position of extracted spectrum in the science object frame.
center = np.genfromtxt('database/apstbcgsqgemgs{}_SCI_1_'.format(obj_sci_name[0].replace('.fits','')), skip_header=5,
max_rows=1, usecols=2, invalid_raise=False)
# Load standard star frame.
obj=fits.open('stbcgsqgemgs{}'.format(obj_sci_name[0]))
obj_data = obj[2].data
obj_shape = obj_data.shape
# Print frame with row of extracted spectrum.
fig = plt.figure(figsize=(14.0,6.0))
ax1 = fig.add_subplot(111)
plt.imshow(obj[2].data, origin='lower',cmap='afmhot',
vmin=np.percentile(obj[2].data,5),
vmax=np.percentile(obj[2].data,90), aspect='auto')
ax1.axhline(y=center, linestyle='--', color='red',linewidth=2., alpha=0.7)
plt.tight_layout(w_pad=-0.9)
plt.ylabel('Position Along Slit', fontsize=14)
plt.xlabel('Dispersion Axis', fontsize=14)
plt.suptitle('Extracted Spectrum Position', y=1.0, fontsize=14)
plt.savefig('extract_obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
else:
print("Type y or n")
# Load extracted science object spectrum.
obj=fits.open('estbcgsqgemgs{}'.format(obj_sci_name[0]))
obj_header = obj[2].header
obj_data = obj[2].data
obj_shape = obj_data.shape
crval = obj_header['CRVAL1']
cd1_1 = obj_header['CD1_1']
# Print extracted standard star spectrum
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(crval + cd1_1*np.arange(1,len(obj[2].data)+1,1), obj[2].data, linewidth=0.55)
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.set_ylim(np.percentile(obj[2].data,5), np.percentile(obj[2].data,95))
plt.ylabel('Counts', fontsize=14)
plt.xlabel(r'Wavelength $\ [\AA]$', fontsize=14)
plt.title('Extracted Spectrum', fontsize=14)
plt.tight_layout(w_pad=-0.9)
plt.savefig('extracted_spec_obj{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
def obj_calib_obj():
""" Calibrate science object spectrum.
Plot calibrated spectrum of the science object. """
print('# CALIBRATING SCIENCE SPECTRUM # ')
# Remove pre-existing calibrated spectrum.
if os.path.exists('cestbcgsqgemgs{}'.format(obj_sci_name[0])):
os.remove('cestbcgsqgemgs{}'.format(obj_sci_name[0]))
gmos.gscalibrate.unlearn() # Debug gscalibrate.
# Calibrate standard star spectrum.
calibrateFlags={'observatory':'gemini-south'}
gmos.gscalibrate('estbcgsqgemgs{}'.format(obj_sci_name[0]), **calibrateFlags ) # IRAF task gscalibrate.
# Load calibrated spectrum.
obj=fits.open('cestbcgsqgemgs{}'.format(obj_sci_name[0]))
obj_header = obj[2].header
obj_data = obj[2].data
obj_shape = obj_data.shape
crval = obj_header['CRVAL1']
cd1_1 = obj_header['CD1_1']
# Plot calibrated spectrum of the science object.
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(crval + cd1_1*np.arange(1,len(obj[2].data)+1,1), obj[2].data, linewidth=0.55)
plt.grid(alpha=0.8, ls='--')
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.set_ylim(np.percentile(obj[2].data,5), np.percentile(obj[2].data,95))
plt.ylabel('Flux [ergs cm$^{-2}$ s$^{-1}$ $\ \AA$$^{-1}$] ', fontsize=14)
plt.xlabel(r'Wavelength $\ [\AA]$', fontsize=14)
plt.title('Calibrated Spectrum', fontsize=14)
plt.xticks(fontsize = 11)
plt.yticks(fontsize = 11)
plt.tight_layout(w_pad=-0.9)
plt.savefig('calib-spec-obj-{}.png'.format(obj_sci_name[0]))
plt.show()
plt.clf()
plt.close()
def obj_despike_obj():
""" Use the modified z-score detection of outlying points
to exclude bad pixels and spikes from the spectrum. """
while True:
print('')
answer = raw_input('Do you wish to remove spikes in your spectrum? ')
if answer == 'n':
break
if answer == 'y':
# Load calibrated spectrum.
obj=fits.open('cestbcgsqgemgs{}'.format(obj_sci_name[0]))
obj_header = obj[2].header
obj_data = obj[2].data
obj_shape = obj_data.shape
crval_obj = obj_header['CRVAL1']
cd1_1_obj = obj_header['CD1_1']
intensity = obj[2].data
wavelength = crval_obj + cd1_1_obj*np.arange(1,len(intensity)+1,1)
## Whitaker and Hayes' modified Z-score based approach for spike detection
## in spectra.
def modified_z_score(intensity):
median_int = np.median(intensity)
mad_int = np.median([np.abs(intensity - median_int)])
modified_z_scores = 0.6745 * (intensity - median_int) / mad_int
return modified_z_scores
delta_int = np.diff(intensity)
threshold = 7 #z-score threshold.
intensity_modified_z_score=np.array(np.abs(modified_z_score(delta_int)))
# Print the modified z-score of Delta X (i) for the points along the spectrum.
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(wavelength[1:], intensity_modified_z_score)
plt.plot(wavelength[1:], threshold*np.ones(len(wavelength[1:])), label = 'threshold = {}'.format(threshold))
plt.title('Modified z-Score of Delta x (i) [Whitaker and Hayes Approach ]', fontsize = 15)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel('Wavelength ', fontsize = 15)
plt.ylabel('|z-scores|', fontsize = 15)
plt.legend()
# plt.savefig('z_scores.png')
plt.show()
# 1 is assigned to spikes, 0 to non-spikes:
spikes = abs(np.array(modified_z_score(intensity))) > threshold
# Print the detected spikes along the spectrum.
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(wavelength, spikes, color = 'red')
plt.title('Spikes: ' + str(np.sum(spikes)), fontsize = 15)
plt.grid()
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel( 'Wavelength' ,fontsize = 15)
# plt.savefig('z_score_spikes.png')
plt.show()
# Delete the spikes and fix the spectrum.
def fixer(y,m):
threshold = 7
spikes = abs(np.array(modified_z_score(np.diff(y)))) > threshold
y_out = y.copy()
for i in np.arange(len(spikes)):
if spikes[i] != 0:
w = np.arange(i - m, i + 1 + m)
w2 = w[spikes[w] == 0]
y_out[i] = np.mean(y[w2])
return y_out
# Print a comparison between the original and fixed spectrum.
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(wavelength, intensity, 'darkgrey',linewidth=0.55, label = 'Original Spectrum')
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(50))
plt.plot(wavelength, fixer(intensity,m=3), alpha=0.85, linewidth=0.55, label = 'Fixed Spectrum')
ax.set_ylim(np.percentile(intensity,5), np.percentile(intensity,95))
plt.grid(alpha=0.8, ls='--')
plt.ylabel('Flux [ergs cm$^{-2}$ s$^{-1}$ $\ \AA$$^{-1}$] ', fontsize=14)
plt.xlabel(r'Wavelength $\ [\AA]$', fontsize=14)
plt.title('Calibrated Spectrum', fontsize=14)
plt.xticks(fontsize = 11)
plt.yticks(fontsize = 11)
plt.tight_layout(w_pad=-0.9)
plt.legend()
plt.show()
# Print the fixed spectrum.
fig, ax = plt.subplots(figsize=(14.0,6.0))
plt.plot(wavelength, fixer(intensity,m=3),alpha=0.85, linewidth=0.55)
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.set_ylim(np.percentile(intensity,5), np.percentile(intensity,95))
plt.grid(alpha=0.8, ls='--')
plt.ylabel('Flux [ergs cm$^{-2}$ s$^{-1}$ $\ \AA$$^{-1}$] ', fontsize=14)
plt.xlabel(r'Wavelength $\ [\AA]$', fontsize=14)
plt.title('Calibrated Spectrum', fontsize=14)
plt.xticks(fontsize = 11)
plt.yticks(fontsize = 11)
plt.tight_layout(w_pad=-0.9)
plt.legend()
plt.show()
break
else:
print('')
print "Type y or n"
print('')
# Call the reduction functions.
while True:
# Check if the calibration sensitivity functions is already available in
# the directory.
if os.path.exists('sens.fits'):
print('The file SENS.FITS is already available in the current directory. ')
print('')
# Ask if the user wants to remake the reduction of the standard star file.
answer = raw_input('Do you wish to REMAKE the reduction of the STANDARD STAR file? [y/n] ')
if answer == "n":
break
if answer == "y":
# Remove pre-existing sensitivity function files.
if os.path.exists('std'):
os.remove('std')
if os.path.exists('sens.fits'):
os.remove('sens.fits')
selec_std()
print "------------------------------"
print "# REDUCTION OF STANDARD STAR #"
print "------------------------------"
# Call standard star reduction functions.
std_gbias()
std_reduc_arc()
std_wavelength_arc()
std_transf_arc()
std_reduc_flat()
std_qecorr_flat()
std_gmosaic_flat()
std_masterflat()
std_reduc1_std()
std_gemfix_std()
std_qecorr_std()
std_reduc2_std()
std_badcolumn_std()
std_transf_std()
std_sky_sub_std()
std_extract_std()
std_calib_std()
break
else:
print('')
print("Type y or n")
print('')
else:
# Call standard star reduction functions.
selec_std()
std_gbias()
std_reduc_arc()
std_wavelength_arc()
std_transf_arc()
std_reduc_flat()
std_qecorr_flat()
std_gmosaic_flat()
std_masterflat()
std_reduc1_std()
std_gemfix_std()
std_qecorr_std()
std_reduc2_std()
std_badcolumn_std()
std_transf_std()
std_sky_sub_std()
std_extract_std()
std_calib_std()
print "--------------------------------------"
print "# END OF THE STANDARD STAR REDUCTION #"
print "--------------------------------------"
break
# Call science object reduction functions.
selec_obj()
print "-------------------------------"
print "# REDUCTION OF SCIENCE OBJECT #"
print "-------------------------------"
obj_gbias()
obj_reduc_arc()
obj_wavelength_arc()
obj_transf_arc()
obj_reduc_flat()
obj_qecorr_flat()
obj_gmosaic_flat()
obj_masterflat()
obj_reduc1_obj()
obj_gemfix_obj()
obj_qecorr_obj()
obj_reduc2_obj()
obj_badcolumn_obj()
obj_transf_obj()
obj_sky_sub_obj()
obj_extract_obj()
obj_calib_obj()
obj_despike_obj()
print '#------------------#'
print '# END OF REDUCTION #'
print '#------------------#'
| 41.599674
| 129
| 0.582112
| 16,449
| 127,503
| 4.356557
| 0.038787
| 0.014583
| 0.011275
| 0.010285
| 0.902848
| 0.893638
| 0.883577
| 0.864585
| 0.850156
| 0.840583
| 0
| 0.033633
| 0.270347
| 127,503
| 3,064
| 130
| 41.613251
| 0.736642
| 0.133299
| 0
| 0.74858
| 0
| 0
| 0.168529
| 0.016712
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.003788
| null | null | 0.14536
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
eeda49f9070fae4f93cbe6a147581acb429f2412
| 92
|
py
|
Python
|
vvsa/economic_indicators/inc/series.py
|
goncalovf/security-analysis
|
72b80ea7c0c5c93b6fd80a4e347ecdb401b7667e
|
[
"MIT"
] | 1
|
2021-09-16T13:36:13.000Z
|
2021-09-16T13:36:13.000Z
|
vvsa/economic_indicators/inc/series.py
|
goncalovf/security-analysis
|
72b80ea7c0c5c93b6fd80a4e347ecdb401b7667e
|
[
"MIT"
] | null | null | null |
vvsa/economic_indicators/inc/series.py
|
goncalovf/security-analysis
|
72b80ea7c0c5c93b6fd80a4e347ecdb401b7667e
|
[
"MIT"
] | null | null | null |
from vvsa.abstracts.time_series import Time_Series
class EI_Series(Time_Series):
pass
| 15.333333
| 50
| 0.804348
| 14
| 92
| 5
| 0.642857
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.141304
| 92
| 5
| 51
| 18.4
| 0.886076
| 0
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| 0
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| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
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| 1
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| null | 0
| 0
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| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
eefb7b17f2ecd0e3bd1c35338e8fc627de34a3ec
| 65,523
|
py
|
Python
|
files/plusobf.py
|
ruby-rust-perl/N-WEB
|
423c8b646a91bea547e3951c6599525856661e9b
|
[
"MIT"
] | 1
|
2021-10-01T18:32:45.000Z
|
2021-10-01T18:32:45.000Z
|
files/plusobf.py
|
ruby-rust-perl/N-WEB
|
423c8b646a91bea547e3951c6599525856661e9b
|
[
"MIT"
] | null | null | null |
files/plusobf.py
|
ruby-rust-perl/N-WEB
|
423c8b646a91bea547e3951c6599525856661e9b
|
[
"MIT"
] | null | null | null |
# coding=utf-8
# obfuscated with plusobf: https://github.com/loolzec/plusobf
d=['+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++', '++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++', '++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++', '++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++', '++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++', '+++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++', '++++++++++', '+++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++', '+++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++', '+++++++++', '+++++++++', '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', '++++++++++++++++++++++++++++++++++++++++++++++', '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', 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py
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Python
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test/functions/decl7.py
|
kylebarron/MagicPython
|
da6fa0793e2c85d3bf7709ff1d4f65ccf468db11
|
[
"MIT"
] | 1,482
|
2015-10-16T21:59:32.000Z
|
2022-03-30T11:44:40.000Z
|
test/functions/decl7.py
|
kylebarron/MagicPython
|
da6fa0793e2c85d3bf7709ff1d4f65ccf468db11
|
[
"MIT"
] | 226
|
2015-10-15T15:53:44.000Z
|
2022-03-25T03:08:27.000Z
|
test/functions/decl7.py
|
kylebarron/MagicPython
|
da6fa0793e2c85d3bf7709ff1d4f65ccf468db11
|
[
"MIT"
] | 129
|
2015-10-20T02:41:49.000Z
|
2022-03-22T01:44:36.000Z
|
def foo(*, a): pass
def foo(*a): pass
def foo(**a): pass
def : meta.function.python, source.python, storage.type.function.python
: meta.function.python, source.python
foo : entity.name.function.python, meta.function.python, source.python
( : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.begin.python, source.python
* : keyword.operator.unpacking.parameter.python, meta.function.parameters.python, meta.function.python, source.python
, : meta.function.parameters.python, meta.function.python, source.python
a : meta.function.parameters.python, meta.function.python, source.python, variable.parameter.function.language.python
) : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.end.python, source.python
: : meta.function.python, punctuation.section.function.begin.python, source.python
: source.python
pass : keyword.control.flow.python, source.python
def : meta.function.python, source.python, storage.type.function.python
: meta.function.python, source.python
foo : entity.name.function.python, meta.function.python, source.python
( : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.begin.python, source.python
* : keyword.operator.unpacking.parameter.python, meta.function.parameters.python, meta.function.python, source.python
a : meta.function.parameters.python, meta.function.python, source.python, variable.parameter.function.language.python
) : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.end.python, source.python
: : meta.function.python, punctuation.section.function.begin.python, source.python
: source.python
pass : keyword.control.flow.python, source.python
def : meta.function.python, source.python, storage.type.function.python
: meta.function.python, source.python
foo : entity.name.function.python, meta.function.python, source.python
( : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.begin.python, source.python
** : keyword.operator.unpacking.parameter.python, meta.function.parameters.python, meta.function.python, source.python
a : meta.function.parameters.python, meta.function.python, source.python, variable.parameter.function.language.python
) : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.end.python, source.python
: : meta.function.python, punctuation.section.function.begin.python, source.python
: source.python
pass : keyword.control.flow.python, source.python
| 76.684211
| 132
| 0.707618
| 324
| 2,914
| 6.364198
| 0.080247
| 0.221145
| 0.27934
| 0.256062
| 1
| 1
| 1
| 1
| 1
| 0.983996
| 0
| 0
| 0.179822
| 2,914
| 37
| 133
| 78.756757
| 0.862762
| 0
| 0
| 0.852941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.176471
| 0
| null | null | 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 12
|
014811aaf66ea156d4bb7da6dc5d8ae4f0310ca6
| 129
|
py
|
Python
|
prometheus/utils/datetime.py
|
face-digital/prometheus_cms
|
95f8a8f90165cbcac53976d9792989f1d30b0ab9
|
[
"MIT"
] | null | null | null |
prometheus/utils/datetime.py
|
face-digital/prometheus_cms
|
95f8a8f90165cbcac53976d9792989f1d30b0ab9
|
[
"MIT"
] | null | null | null |
prometheus/utils/datetime.py
|
face-digital/prometheus_cms
|
95f8a8f90165cbcac53976d9792989f1d30b0ab9
|
[
"MIT"
] | null | null | null |
from django.utils.timezone import utc
import datetime
def utcnow():
return datetime.datetime.utcnow().replace(tzinfo=utc)
| 16.125
| 57
| 0.767442
| 17
| 129
| 5.823529
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131783
| 129
| 7
| 58
| 18.428571
| 0.883929
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.5
| 0.25
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
016603713f8d0b0da78ead42f03eaf252e4ee616
| 87
|
py
|
Python
|
plugins/Bots/PyrogramBot/__init__.py
|
pr0stre1/tbot
|
90aacc1e9b8ae2cc323974b0872fa8b496a2ecb3
|
[
"MIT"
] | null | null | null |
plugins/Bots/PyrogramBot/__init__.py
|
pr0stre1/tbot
|
90aacc1e9b8ae2cc323974b0872fa8b496a2ecb3
|
[
"MIT"
] | 1
|
2022-03-30T18:56:14.000Z
|
2022-03-30T18:56:14.000Z
|
plugins/Bots/PyrogramBot/__init__.py
|
pr0stre1/tbot
|
90aacc1e9b8ae2cc323974b0872fa8b496a2ecb3
|
[
"MIT"
] | null | null | null |
from plugins.Bots.PyrogramBot import bot
from plugins.Bots.PyrogramBot import handlers
| 29
| 45
| 0.862069
| 12
| 87
| 6.25
| 0.583333
| 0.293333
| 0.4
| 0.693333
| 0.853333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091954
| 87
| 2
| 46
| 43.5
| 0.949367
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 9
|
6d9f2fa86fdd755841bcae04e35483d85850bc0e
| 166
|
py
|
Python
|
python/dagster-fusion/dagster_fusion/io/__init__.py
|
roeap/flight-fusion
|
14f73c99c5214277d0abcced633d83b37f1d5292
|
[
"Apache-2.0",
"MIT"
] | 5
|
2021-12-24T06:21:40.000Z
|
2022-01-16T12:21:06.000Z
|
python/dagster-fusion/dagster_fusion/io/__init__.py
|
roeap/flight-fusion
|
14f73c99c5214277d0abcced633d83b37f1d5292
|
[
"Apache-2.0",
"MIT"
] | 66
|
2021-12-15T17:08:21.000Z
|
2022-03-29T10:36:18.000Z
|
python/dagster-fusion/dagster_fusion/io/__init__.py
|
roeap/flight-fusion
|
14f73c99c5214277d0abcced633d83b37f1d5292
|
[
"Apache-2.0",
"MIT"
] | 1
|
2022-02-08T21:07:08.000Z
|
2022-02-08T21:07:08.000Z
|
from .io_manager import flight_fusion_io_manager
from .root_input_manager import flight_fusion_loader
__all__ = ("flight_fusion_io_manager", "flight_fusion_loader")
| 33.2
| 62
| 0.861446
| 24
| 166
| 5.25
| 0.416667
| 0.380952
| 0.301587
| 0.396825
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078313
| 166
| 4
| 63
| 41.5
| 0.823529
| 0
| 0
| 0
| 0
| 0
| 0.26506
| 0.144578
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
6da89608445e841a068d59d45767a1502ce7ef67
| 31,001
|
py
|
Python
|
bash/listener.py
|
HewlettPackard/Aruba-FlaskwithNetworking
|
d19e4a542f72806b1d22ed7519b0a59a697d275a
|
[
"MIT"
] | 10
|
2018-08-10T12:01:20.000Z
|
2022-01-21T20:44:11.000Z
|
bash/listener.py
|
HewlettPackard/Aruba-FlaskwithNetworking
|
d19e4a542f72806b1d22ed7519b0a59a697d275a
|
[
"MIT"
] | null | null | null |
bash/listener.py
|
HewlettPackard/Aruba-FlaskwithNetworking
|
d19e4a542f72806b1d22ed7519b0a59a697d275a
|
[
"MIT"
] | 4
|
2019-03-24T16:24:05.000Z
|
2022-02-18T05:49:51.000Z
|
import pyshark
from datetime import datetime, time, timedelta
import time
import json
import os
import sys
import pymysql.cursors
pathname = os.path.dirname(sys.argv[0])
appPath = os.path.abspath(pathname) + "/globals.json"
with open(appPath, 'r') as myfile:
data=myfile.read()
globalconf=json.loads(data)
listenerlog = open('/var/www/html/log/listener.log', 'a')
activeInterface=str(sys.argv[1])
syslogFacilities=("Kernel messages","User-level","Mail system","System daemons","Security authorization","Messages generated internally by syslogd",\
"Line printer subsystem","Network news subsystem","UUCP subsystem","Clock daemon","Security authorization","FTP daemon","NTP subsystem",\
"Log audit","Log alert","Clock daemon","Local use 0 (local0)","Local use 1 (local1)","Local use 2 (local2)","Local use 3 (local3)","Local use 4 (local4)",\
"Local use 5 (local5)","Local use 6 (local6)","Local use 7 (local7))")
syslogSeverity=("Emergency","Alert","Critical","Error","Warning","Notice","Informational","Debug")
dbconnection=pymysql.connect(host='localhost',user='aruba',password='ArubaRocks',db='aruba', autocommit=True)
cursor=dbconnection.cursor(pymysql.cursors.DictCursor)
def capture_live_packets(network_interface,listenerlog,cursor,syslogFacilities,syslogSeverity):
# Need to capture DHCP, SNMP and Syslog packets
capture = pyshark.LiveCapture(interface=network_interface, bpf_filter='udp port 67 or udp port 68 or udp port 161 or udp port 514')
for raw_packet in capture.sniff_continuously():
filter_udp_traffic_file(raw_packet,listenerlog,cursor,syslogFacilities,syslogSeverity)
def analyzeDHCP(packet,listenerlog,cursor):
if "BOOTP" in str(packet.layers):
bord="bootp"
else:
bord="dhcp"
if bord=="bootp":
fieldnames=list(packet.bootp.field_names)
else:
fieldnames=list(packet.dhcp.field_names)
if bord=="dhcp":
# Match DHCP Discover
if packet.dhcp.option_value == "01":
try:
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if not macaddress.startswith("204c03"):
information="Host " + macaddress + " asked for an IP address"
options=""
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP Discover",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP Discover','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP Discover packet\n".format(datetime.now()))
# Match DHCP offer
elif packet.dhcp.option_value == "02":
try:
if "ip_server" in fieldnames:
dhcpserver=packet.dhcp.ip_server
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
offeredip=packet.dhcp.ip_your
else:
offeredip="Unknown"
if "option_subnet_mask" in fieldnames:
subnetmask=packet.dhcp.option_subnet_mask
else:
subnetmask="Unknown"
if "option_ip_address_lease_time" in fieldnames:
leasetime=packet.dhcp.option_ip_address_lease_time
else:
leasetime="Unknown"
if "option_router" in fieldnames:
router=packet.dhcp.option_router
else:
router="Unknown"
if "option_domain_name_server" in fieldnames:
nameserver=packet.dhcp.option_domain_name_server
else:
nameserver="Unknown"
if "option_domain_name" in fieldnames:
domainname=packet.dhcp.option_domain_name
else:
domainname="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if domainname!="Unknown" and nameserver!="Unknown" and router!="Unknown" and leasetime!="Unknown" and offeredip!="Unknown" and dhcpserver!="Unknown":
if not macaddress.startswith("204c03"):
information="DHCP Server " + dhcpserver + " offered " + offeredip
options="Subnet mask: " + subnetmask + ", Lease time: " + leasetime + ", Router: " + router + ", Name Server: " + nameserver + ", domain: " + domainname
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP Offer",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP Offer','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP Offer packet\n".format(datetime.now()))
# Match DHCP request
elif packet.dhcp.option_value == "03":
try:
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if "option_requested_ip_address" in fieldnames:
requestedip=packet.dhcp.option_requested_ip_address
else:
requestedip="Unknown"
if not macaddress.startswith("204c03"):
information="Host " + macaddress + " requested " + requestedip
options=""
if macaddress!="000000000000" and requestedip!="Unknown":
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP Request",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP Request','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP Request packet\n".format(datetime.now()))
# Match DHCP ack
elif packet.dhcp.option_value == "05":
try:
if "option_subnet_mask" in fieldnames:
netmask=packet.dhcp.option_subnet_mask
else:
netmask="Unknown"
if "option_ip_address_lease_time" in fieldnames:
leasetime=packet.dhcp.option_ip_address_lease_time
else:
leasetime="Unknown"
if "option_router" in fieldnames:
router=packet.dhcp.option_router
else:
router="Unknown"
if "option_domain_name_server" in fieldnames:
dnsserver=packet.dhcp.option_domain_name_server
else:
dnsserver="Unknown"
if "option_dhcp_server_id" in fieldnames:
dhcpserver=packet.dhcp.option_dhcp_server_id
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
clientip=packet.dhcp.ip_your
else:
clientip="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
# The listener class is also used for ZTP. Goal is to have the switch keep it's DHCP IP address.
try:
if macaddress!="000000000000" and clientip!="Unknown" and netmask!="Unknown" and router!="Unknown":
ztpnetmask=sum(bin(int(x)).count('1') for x in netmask.split('.'))
queryStr="select * from ztpdevices where macaddress='{}'".format(macaddress)
cursor.execute(queryStr)
result=cursor.fetchall()
if result:
# We found a ZTP device entry and need to update the IP address information, but only if DHCP ZTP is enabled
if result[0]['ztpdhcp']==1:
queryStr="update ztpdevices set ipaddress='{}', netmask='{}', gateway='{}' where id='{}'".format(clientip,ztpnetmask,router,result[0]['id'])
cursor.execute(queryStr)
logEntry="{}: Listener {} Updated IP address of ZTP Device with MAC Address {} to {}\n".format(datetime.now(),bord,macaddress, clientip)
ztplog = open('/var/www/html/log/ztp.log', 'a')
ztplog.write(logEntry)
ztplog.close()
except:
listenerlog.write("{}: Could not analyze ZTP packet in listener\n".format(datetime.now()))
if not macaddress.startswith("204c03"):
information="DHCP Server " + dhcpserver + " acknowledged " + clientip
options="Subnet_mask: " + netmask + ", Lease time: " + leasetime + ", Router: " + router + ", Name Server: " + dnsserver
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP Ack",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP Ack','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP Ack packet\n".format(datetime.now()))
# Match DHCP NAK
elif packet.dhcp.option_value == "06":
try:
if "option_dhcp_server_id" in fieldnames:
dhcpserver=packet.dhcp.option_dhcp_server_id
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
ip_your=packet.dhcp.ip_your
else:
ip_your="Unknown"
if "ip_client" in fieldnames:
clientip=packet.dhcp.ip_client
else:
clientip="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if "option_message" in fieldnames:
optionmessage=packet.dhcp.option_message
else:
optionmessage="Unknown"
if not macaddress.startswith("204c03"):
information="DHCP NAK: " + optionmessage + ". " + ip_your + " not available on " + dhcpserver
options="IP client: " + clientip + ", MAC address: " + macaddress
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP NAK",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP NAK','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP NAK packet\n".format(datetime.now()))
# Match DHCP release
elif packet.dhcp.option_value == "07":
try:
if "option_dhcp_server_id" in fieldnames:
dhcpserver=packet.dhcp.option_dhcp_server_id
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
ip_your=packet.dhcp.ip_your
else:
ip_your="Unknown"
if "ip_client" in fieldnames:
clientip=packet.dhcp.ip_client
else:
clientip="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if "option_hostname" in fieldnames:
hostname=packet.dhcp.option_hostname
else:
hostname="Unknown"
if not macaddress.startswith("204c03"):
information="DHCP Server " + dhcpserver + " released " + ip_your
options="IP client: " + clientip + ", MAC address: " + macaddress + ", Hostname: " + hostname
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP Release",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP Release','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP Release packet\n".format(datetime.now()))
# Match DHCP inform
elif packet.dhcp.option_value == "08":
try:
information="DHCP Inform from " + packet.ip.src + " (" + packet.eth.src + ") Hostname: " + packet.dhcp.option_hostname + ", Vendor Class ID: " + packet.dhcp.option_vendor_class_id
options=""
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"DHCP Inform",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','DHCP Inform','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze DHCP Inform packet\n".format(datetime.now()))
else:
# Match bootp discover
if packet.bootp.option_value == "01":
try:
if "hw_mac_addr" in fieldnames:
macaddress=packet.bootp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if not macaddress.startswith("204c03"):
information="Host " + macaddress + " asked for an IP address"
options=""
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"Bootp Discover",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp Discover','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp Discover packet\n".format(datetime.now()))
# Match bootp offer
elif packet.bootp.option_value == "02":
try:
if "ip_server" in fieldnames:
dhcpserver=packet.bootp.ip_server
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
offeredip=packet.bootp.ip_your
else:
offeredip="Unknown"
if "option_subnet_mask" in fieldnames:
subnetmask=packet.bootp.option_subnet_mask
else:
subnetmask="Unknown"
if "option_ip_address_lease_time" in fieldnames:
leasetime=packet.bootp.option_ip_address_lease_time
else:
leasetime="Unknown"
if "option_router" in fieldnames:
router=packet.bootp.option_router
else:
router="Unknown"
if "option_domain_name_server" in fieldnames:
nameserver=packet.bootp.option_domain_name_server
else:
nameserver="Unknown"
if "option_domain_name" in fieldnames:
domainname=packet.bootp.option_domain_name
else:
domainname="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.bootp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if domainname!="Unknown" and nameserver!="Unknown" and router!="Unknown" and leasetime!="Unknown" and offeredip!="Unknown" and dhcpserver!="Unknown":
if not macaddress.startswith("204c03"):
information="DHCP Server " + dhcpserver + " offered " + offeredip
options="Subnet mask: " + subnetmask + ", Lease time: " + leasetime + ", Router: " + router + ", Name Server: " + nameserver + ", domain: " + domainname
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"Bootp Offer",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp Offer','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp Offer packet\n".format(datetime.now()))
# Match bootp request
elif packet.bootp.option_value == "03":
try:
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if "option_requested_ip_address" in fieldnames:
requestedip=packet.bootp.option_requested_ip_address
else:
requestedip="Unknown"
if not macaddress.startswith("204c03"):
information="Host " + macaddress + " requested " + requestedip
options=""
if macaddress!="000000000000" and requestedip!="Unknown":
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"Bootp Request",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp Request','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp Request packet\n".format(datetime.now()))
# Match bootp ack
elif packet.bootp.option_value == "05":
try:
if "option_subnet_mask" in fieldnames:
netmask=packet.bootp.option_subnet_mask
else:
netmask="Unknown"
if "option_ip_address_lease_time" in fieldnames:
leasetime=packet.bootp.option_ip_address_lease_time
else:
leasetime="Unknown"
if "option_router" in fieldnames:
router=packet.bootp.option_router
else:
router="Unknown"
if "option_domain_name_server" in fieldnames:
dnsserver=packet.bootp.option_domain_name_server
else:
dnsserver="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.bootp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
# The listener class is also used for ZTP. Goal is to have the switch keep it's DHCP IP address.
try:
if "hw_mac_addr" in fieldnames and "ip_your" in fieldnames and netmask!="Unknown" and router!="Unknown":
ztpnetmask=sum(bin(int(x)).count('1') for x in netmask.split('.'))
queryStr="select * from ztpdevices where macaddress='{}'".format(ztpmacaddress)
cursor.execute(queryStr)
result=cursor.fetchall()
if result:
# We found a ZTP device entry and need to update the IP address information, but only if DHCP ZTP is enabled
if result[0]['ztpdhcp']==1:
queryStr="update ztpdevices set ipaddress='{}', netmask='{}', gateway='{}' where id='{}'".format(packet.bootp.ip_your,ztpnetmask,router,result[0]['id'])
cursor.execute(queryStr)
logEntry="{}: Listener {} Updated IP address of ZTP Device with MAC Address {} to {}\n".format(datetime.now(),bord,macaddress, clientip)
ztplog = open('/var/www/html/log/ztp.log', 'a')
ztplog.write(logEntry)
ztplog.close()
except:
listenerlog.write("{}: Could not analyze ZTP packet in listener\n".format(datetime.now()))
if not macaddress.startswith("204c03"):
information="DHCP Server " + packet.bootp.option_dhcp_server_id + " acknowledged " + packet.bootp.ip_your
options="Subnet_mask: " + netmask + ", Lease time: " + leasetime + ", Router: " + router + ", Name Server: " + dnsserver
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"Bootp Ack",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp Ack','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp Ack packet\n".format(datetime.now()))
# Match bootp NAK
elif packet.bootp.option_value == "06":
try:
if "option_dhcp_server_id" in fieldnames:
dhcpserver=packet.bootp.option_dhcp_server_id
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
ip_your=packet.bootp.ip_your
else:
ip_your="Unknown"
if "ip_client" in fieldnames:
clientip=packet.bootp.ip_client
else:
clientip="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.bootp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if "option_message" in fieldnames:
optionmessage=packet.bootp.option_message
else:
optionmessage="Unknown"
if not macaddress.startswith("204c03"):
information="DHCP NAK: " + optionmessage + ". " + ip_your + " not available on " + dhcpserver
options="IP client: " + clientip + ", MAC address: " + macaddress
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"BootpNAK",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp NAK','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp NAK packet\n".format(datetime.now()))
# Match bootp release
elif packet.bootp.option_value == "07":
try:
if "option_dhcp_server_id" in fieldnames:
dhcpserver=packet.bootp.option_dhcp_server_id
else:
dhcpserver="Unknown"
if "ip_your" in fieldnames:
ip_your=packet.bootp.ip_your
else:
ip_your="Unknown"
if "ip_client" in fieldnames:
clientip=packet.bootp.ip_client
else:
clientip="Unknown"
if "hw_mac_addr" in fieldnames:
macaddress=packet.dhcp.hw_mac_addr
macaddress=macaddress.replace(":","")
else:
macaddress="000000000000"
if "option_hostname" in fieldnames:
hostname=packet.bootp.option_hostname
else:
hostname="Unknown"
if not macaddress.startswith("204c03"):
information="DHCP Server " + dhcpserver + " released " + ip_your
options="IP client: " + clientip + ", MAC address: " + macaddress + ", Hostname: " + hostname
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"Bootp Release",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp Release','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp Release packet\n".format(datetime.now()))
# Match DHCP inform
elif packet.bootp.option_value == "08":
try:
information="DHCP Inform from " + packet.ip.src + " (" + packet.eth.src + ") Hostname: " + packet.bootp.option_hostname + ", Vendor Class ID: " + packet.bootp.option_vendor_class_id
options=""
timestamp=packet.sniff_timestamp.split(".")
if checkDuplicate(timestamp[0],"Bootp Inform",information,cursor,"dhcptracker")==False:
queryStr="insert into dhcptracker (utctime,dhcptype,information,options) values ('{}','Bootp Inform','{}','{}')".format(timestamp[0],information,options)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Bootp Inform packet\n".format(datetime.now()))
def analyzeSNMP(packet,listenerlog,cursor):
fieldnames=list(packet.snmp.field_names)
try:
generictraps=['Cold start','Warm start','Link down','Link up','Authentication failure','EGP Neighbor loss']
snmpversions=['V1', 'V2c']
if "generic_trap" in fieldnames:
if packet.snmp.generic_trap=="6":
# This is an enterprise trap. Need to analyze a bit further
trapMessage="Enterprise trap {}".format(packet.snmp.generic_trap)
else:
trapMessage=generictraps[int(packet.snmp.generic_trap)]
trapMessage=trapMessage.replace("'","\\'")
if "version" in fieldnames:
snmpversion=snmpversions[int(packet.snmp.version)]
else:
snmpversion="Unknown"
if trapMessage!="":
timestamp=packet.sniff_timestamp.split(".")
queryStr="insert into snmptracker (utctime,source,version,community,information) values ('{}','{}','{}','{}','{}')".format(timestamp[0],packet.ip.src_host,snmpversion,packet.snmp.community,trapMessage)
cursor.execute(queryStr)
else:
trapMessage=""
if "version" in fieldnames:
snmpversion=snmpversions[int(packet.snmp.version)]
else:
snmpversion="Unknown"
if trapMessage!="":
timestamp=packet.sniff_timestamp.split(".")
queryStr="insert into snmptracker (utctime,source,version,community,information) values ('{}','{}','{}','{}','{}')".format(timestamp[0],packet.ip.src_host,snmpversion,packet.snmp.community,trapMessage)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze SNMP packet\n".format(datetime.now()))
listenerlog.write(packet)
def analyzeSyslog(packet,listenerlog,cursor,syslogFacilities, syslogSeverity):
try:
message=str(packet.syslog.msg)
message=message.replace("'","\\'")
timestamp=packet.sniff_timestamp.split(".")
queryStr="insert into syslog (utctime,source,facility,severity,information) values ('{}','{}','{}','{}','{}')".format(timestamp[0],packet.ip.src_host,syslogFacilities[int(packet.syslog.facility)],syslogSeverity[int(packet.syslog.level)],message)
cursor.execute(queryStr)
except:
listenerlog.write("{}: Could not analyze Syslog packet\n".format(datetime.now()))
listenerlog.write(packet)
def filter_udp_traffic_file(packet,listenerlog,cursor,syslogFacilities,syslogSeverity):
if hasattr(packet, 'udp'):
if packet.udp.dstport=="67" or packet.udp.dstport=="68":
analyzeDHCP(packet,listenerlog,cursor)
elif packet.udp.dstport=="161" or packet.udp.dstport=="162":
analyzeSNMP(packet,listenerlog,cursor)
elif packet.udp.dstport=="514":
analyzeSyslog(packet,listenerlog,cursor,syslogFacilities, syslogSeverity)
def checkDuplicate(utctime,dhcptype,information,cursor,dbtable):
queryStr="select * from {} where utctime='{}' AND dhcptype='{}' AND information='{}'".format(dbtable,utctime,dhcptype,information)
cursor.execute(queryStr)
result=cursor.fetchall()
if result:
return True
else:
return False
capture_live_packets(activeInterface,listenerlog,cursor,syslogFacilities,syslogSeverity)
| 56.778388
| 253
| 0.554111
| 2,890
| 31,001
| 5.840484
| 0.10519
| 0.041946
| 0.01333
| 0.021328
| 0.845962
| 0.829788
| 0.807038
| 0.776705
| 0.766159
| 0.751289
| 0
| 0.017035
| 0.335344
| 31,001
| 545
| 254
| 56.882569
| 0.802135
| 0.024515
| 0
| 0.708415
| 0
| 0
| 0.213005
| 0.039676
| 0
| 0
| 0
| 0
| 0
| 1
| 0.011742
| false
| 0.001957
| 0.013699
| 0
| 0.029354
| 0.001957
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6de9c4b19dc22deb5c0b78ad6d8ca54515d97323
| 563
|
py
|
Python
|
EX109/moeda.py
|
LucasLCarreira/Python
|
03bd64837d74315687e567261a149f0176496348
|
[
"MIT"
] | 1
|
2020-04-21T19:14:50.000Z
|
2020-04-21T19:14:50.000Z
|
EX109/moeda.py
|
LucasLCarreira/Python
|
03bd64837d74315687e567261a149f0176496348
|
[
"MIT"
] | null | null | null |
EX109/moeda.py
|
LucasLCarreira/Python
|
03bd64837d74315687e567261a149f0176496348
|
[
"MIT"
] | null | null | null |
def metade(preco=0, formato=False):
res = preco / 2
return res if formato is False else moeda(res)
def dobro(preco=0, formato=False):
res = preco * 2
return res if formato is False else moeda(res)
def aumento(preco=0, taxa=0, formato=False):
res = preco * (1 + taxa/100)
return res if formato is False else moeda(res)
def reducao(preco=0, taxa=0, formato=False):
res = preco * (1 - taxa/100)
return res if formato is False else moeda(res)
def moeda(preco=0, moeda='R$'):
return f' {moeda} {preco:7.2f}'.replace('.',',')
| 25.590909
| 52
| 0.646536
| 93
| 563
| 3.913978
| 0.258065
| 0.082418
| 0.142857
| 0.175824
| 0.802198
| 0.802198
| 0.802198
| 0.802198
| 0.802198
| 0.802198
| 0
| 0.042889
| 0.213144
| 563
| 22
| 52
| 25.590909
| 0.778781
| 0
| 0
| 0.285714
| 0
| 0
| 0.044326
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.357143
| false
| 0
| 0
| 0.071429
| 0.714286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 9
|
6dfa1d7d8d88b53420c25b493c7bb643b100724a
| 12,744
|
py
|
Python
|
pypeln/task/queue_task_test.py
|
quarckster/pypeln
|
f4160d0f4d4718b67f79a0707d7261d249459a4b
|
[
"MIT"
] | 1,281
|
2018-09-20T05:35:27.000Z
|
2022-03-30T01:29:48.000Z
|
pypeln/task/queue_task_test.py
|
webclinic017/pypeln
|
5231806f2cac9d2019dacbbcf913484fd268b8c1
|
[
"MIT"
] | 78
|
2018-09-18T20:38:12.000Z
|
2022-03-30T20:16:02.000Z
|
pypeln/task/queue_task_test.py
|
webclinic017/pypeln
|
5231806f2cac9d2019dacbbcf913484fd268b8c1
|
[
"MIT"
] | 88
|
2018-09-24T10:46:14.000Z
|
2022-03-28T09:34:50.000Z
|
import asyncio
from dataclasses import dataclass
import sys
import sys
import time
import typing as tp
from unittest import TestCase
import unittest
from unittest import mock
import cytoolz as cz
import hypothesis as hp
from hypothesis import strategies as st
import pytest
from pypeln import utils as pypeln_utils
import pypeln as pl
from pypeln.task.utils import run_test_async
MAX_EXAMPLES = 10
T = tp.TypeVar("T")
# ----------------------------------------------------------------
# queue
# ----------------------------------------------------------------
class MyException(Exception):
pass
class TestQueue(TestCase):
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_done(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.worker_done()
processes = pl.task.start_workers(worker)
nums_pl = list(queue)
assert len(processes) == 1
assert nums_pl == nums
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_done_async(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.worker_done()
processes = pl.task.start_workers(worker)
nums_pl = [x async for x in queue]
assert len(processes) == 1
assert nums_pl == nums
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_get(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.worker_done()
processes = pl.task.start_workers(worker)
if len(nums) > 0:
x = await queue.get()
assert x == nums[0]
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_get_2(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.worker_done()
processes = pl.task.start_workers(worker)
await asyncio.sleep(0.01)
if len(nums) > 0:
x = queue._get_nowait()
assert x == nums[0]
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_done_nowait(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
queue.worker_done_nowait()
processes = pl.task.start_workers(worker)
nums_pl = list(queue)
assert len(processes) == 1
assert nums_pl == nums
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_done_many(self, nums):
n_workers = 3
queue = pl.task.IterableQueue(total_sources=n_workers)
async def worker():
for i in nums:
await queue.put(i)
await queue.worker_done()
processes = pl.task.start_workers(worker, n_workers=n_workers)
nums_pl = list(queue)
assert len(processes) == n_workers
assert len(nums_pl) == (len(nums) * 3)
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_done_many_async(self, nums):
n_workers = 3
queue = pl.task.IterableQueue(total_sources=n_workers)
async def worker():
for i in nums:
await queue.put(i)
await queue.worker_done()
processes = pl.task.start_workers(worker, n_workers=n_workers)
nums_pl = [x async for x in queue]
assert len(processes) == n_workers
assert len(nums_pl) == (len(nums) * 3)
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_stop(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.stop()
processes = pl.task.start_workers(worker)
nums_pl = list(queue)
assert len(processes) == 1
assert nums_pl == nums
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_stop_async(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.stop()
processes = pl.task.start_workers(worker)
nums_pl = [x async for x in queue]
assert len(processes) == 1
assert nums_pl == nums
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_stop_nowait(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
queue.stop_nowait()
processes = pl.task.start_workers(worker)
nums_pl = list(queue)
assert len(processes) == 1
assert nums_pl == nums
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_kill(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.kill()
processes = pl.task.start_workers(worker)
nums_pl = list(queue)
assert len(processes) == 1
assert len(nums_pl) <= len(nums)
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_kill_async(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
await queue.kill()
processes = pl.task.start_workers(worker)
nums_pl = [x async for x in queue]
assert len(processes) == 1
assert len(nums_pl) <= len(nums)
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_kill_nowait(self, nums):
queue = pl.task.IterableQueue()
async def worker():
for i in nums:
await queue.put(i)
queue.kill_nowait()
processes = pl.task.start_workers(worker)
nums_pl = list(queue)
assert len(processes) == 1
assert len(nums_pl) <= len(nums)
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_raise(self, nums):
queue = pl.task.IterableQueue()
async def worker():
try:
raise MyException()
except BaseException as e:
await queue.raise_exception(e)
processes = pl.task.start_workers(worker)
with pytest.raises(MyException):
nums_pl = list(queue)
assert len(processes) == 1
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
@run_test_async
async def test_raise_async(self, nums):
queue = pl.task.IterableQueue()
async def worker():
try:
raise MyException()
except BaseException as e:
await queue.raise_exception(e)
processes = pl.task.start_workers(worker)
with pytest.raises(MyException):
nums_pl = [x async for x in queue]
assert len(processes) == 1
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_raise_nowait(self, nums):
queue = pl.task.IterableQueue()
async def worker():
try:
raise MyException()
except BaseException as e:
queue.raise_exception_nowait(e)
processes = pl.task.start_workers(worker)
with pytest.raises(MyException):
nums_pl = list(queue)
assert len(processes) == 1
class TestOutputQueues(TestCase):
def test_basic_nowait(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue: pl.task.IterableQueue[int] = pl.task.IterableQueue()
queues.append(queue)
queues.put_nowait(3)
x = queue._get_nowait()
assert isinstance(queues, list)
assert x == 3
@run_test_async
async def test_basic(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue: pl.task.IterableQueue[int] = pl.task.IterableQueue()
queues.append(queue)
await queues.put(3)
x = await queue.get()
assert isinstance(queues, list)
assert x == 3
@run_test_async
async def test_done(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue = pl.task.IterableQueue()
queues.append(queue)
await queues.worker_done()
x = await queue.get()
assert isinstance(x, pl.utils.Done)
def test_done_nowait(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue = pl.task.IterableQueue()
queues.append(queue)
queues.worker_done_nowait()
with pytest.raises(asyncio.QueueEmpty):
x = queue._get_nowait()
@run_test_async
async def test_stop(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue = pl.task.IterableQueue()
queues.append(queue)
assert queue.namespace.remaining == 1
await queues.stop()
assert queue.namespace.remaining == 0
def test_stop_nowait(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue = pl.task.IterableQueue()
queues.append(queue)
assert queue.namespace.remaining == 1
queues.stop_nowait()
assert queue.namespace.remaining == 0
@run_test_async
async def test_kill(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue = pl.task.IterableQueue()
queues.append(queue)
assert queue.namespace.force_stop == False
await queues.kill()
assert queue.namespace.remaining == True
def test_kill_nowait(self):
queues: pl.task.OutputQueues[int] = pl.task.OutputQueues()
queue = pl.task.IterableQueue()
queues.append(queue)
assert queue.namespace.force_stop == False
queues.kill_nowait()
assert queue.namespace.remaining == True
class TestTaskPool(unittest.TestCase):
@run_test_async
async def test_basic(self):
namespace = pl.task.Namespace(x=0)
async def task():
await asyncio.sleep(0.1)
namespace.x = 1
tasks = pl.task.TaskPool.create(workers=0)
await tasks.put(task)
assert namespace.x == 0
await tasks.join()
assert namespace.x == 1
@run_test_async
async def test_context(self):
namespace = pl.task.Namespace(x=0)
async def task():
await asyncio.sleep(0.1)
namespace.x = 1
async with pl.task.TaskPool.create(workers=0) as tasks:
await tasks.put(task)
assert namespace.x == 1
@run_test_async
async def test_put_wait(self):
timeout = 0.1
namespace = pl.task.Namespace(x=0)
async def task():
await asyncio.sleep(timeout)
namespace.x = 1
async def no_task():
pass
async with pl.task.TaskPool.create(workers=1) as tasks:
await tasks.put(task)
assert len(tasks.tasks) == 1
t0 = time.time()
await tasks.put(no_task)
assert time.time() - t0 > timeout
assert namespace.x == 1
@run_test_async
async def test_put_no_wait(self):
timeout = 0.1
namespace = pl.task.Namespace(x=0)
async def task():
await asyncio.sleep(timeout)
namespace.x = 1
async def no_task():
pass
async with pl.task.TaskPool.create(workers=2) as tasks:
await tasks.put(task)
assert len(tasks.tasks) == 1
t0 = time.time()
await tasks.put(no_task)
assert len(tasks.tasks) == 2
assert time.time() - t0 < timeout
assert namespace.x == 1
| 24
| 70
| 0.586315
| 1,583
| 12,744
| 4.595073
| 0.06633
| 0.05444
| 0.067913
| 0.079186
| 0.89868
| 0.864586
| 0.85455
| 0.830217
| 0.822519
| 0.814682
| 0
| 0.007398
| 0.299984
| 12,744
| 530
| 71
| 24.045283
| 0.807981
| 0.010593
| 0
| 0.795918
| 0
| 0
| 0.000079
| 0
| 0
| 0
| 0
| 0
| 0.145773
| 1
| 0.037901
| false
| 0.008746
| 0.046647
| 0
| 0.09621
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a3062b381d13aa4b4f37260c5d0049ac41f895bc
| 49
|
py
|
Python
|
obstacle-avoidance/lbi/envs/__init__.py
|
irom-lab/performance-limits
|
7ef091d962946a6c35415039e9e29d9c1591ed52
|
[
"MIT"
] | 3
|
2022-02-02T15:10:37.000Z
|
2022-02-16T18:09:19.000Z
|
obstacle-avoidance/lbi/envs/__init__.py
|
irom-lab/performance-limits
|
7ef091d962946a6c35415039e9e29d9c1591ed52
|
[
"MIT"
] | null | null | null |
obstacle-avoidance/lbi/envs/__init__.py
|
irom-lab/performance-limits
|
7ef091d962946a6c35415039e9e29d9c1591ed52
|
[
"MIT"
] | null | null | null |
from .envs import generate_random_env, plot_env
| 24.5
| 48
| 0.836735
| 8
| 49
| 4.75
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122449
| 49
| 1
| 49
| 49
| 0.883721
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
0977d7b30548cbbb80b688241a2f4246c91ffe30
| 2,290
|
py
|
Python
|
pytorch/softsort.py
|
sprillo/softsort
|
8dcb552804ccb3638ade1a53ef12c0e84f3831d8
|
[
"MIT"
] | 27
|
2020-07-01T12:22:28.000Z
|
2022-02-17T16:49:00.000Z
|
pytorch/softsort.py
|
lbruand/softsort
|
8dcb552804ccb3638ade1a53ef12c0e84f3831d8
|
[
"MIT"
] | 11
|
2020-09-25T22:41:58.000Z
|
2022-02-10T03:32:24.000Z
|
pytorch/softsort.py
|
lbruand/softsort
|
8dcb552804ccb3638ade1a53ef12c0e84f3831d8
|
[
"MIT"
] | 4
|
2020-06-30T17:30:13.000Z
|
2021-12-29T15:15:11.000Z
|
import torch
from torch import Tensor
class SoftSort(torch.nn.Module):
def __init__(self, tau=1.0, hard=False, pow=1.0):
super(SoftSort, self).__init__()
self.hard = hard
self.tau = tau
self.pow = pow
def forward(self, scores: Tensor):
"""
scores: elements to be sorted. Typical shape: batch_size x n
"""
scores = scores.unsqueeze(-1)
sorted = scores.sort(descending=True, dim=1)[0]
pairwise_diff = (scores.transpose(1, 2) - sorted).abs().pow(self.pow).neg() / self.tau
P_hat = pairwise_diff.softmax(-1)
if self.hard:
P = torch.zeros_like(P_hat, device=P_hat.device)
P.scatter_(-1, P_hat.topk(1, -1)[1], value=1)
P_hat = (P - P_hat).detach() + P_hat
return P_hat
class SoftSort_p1(torch.nn.Module):
def __init__(self, tau=1.0, hard=False):
super(SoftSort_p1, self).__init__()
self.hard = hard
self.tau = tau
def forward(self, scores: Tensor):
"""
scores: elements to be sorted. Typical shape: batch_size x n
"""
scores = scores.unsqueeze(-1)
sorted = scores.sort(descending=True, dim=1)[0]
pairwise_diff = (scores.transpose(1, 2) - sorted).abs().neg() / self.tau
P_hat = pairwise_diff.softmax(-1)
if self.hard:
P = torch.zeros_like(P_hat, device=P_hat.device)
P.scatter_(-1, P_hat.topk(1, -1)[1], value=1)
P_hat = (P - P_hat).detach() + P_hat
return P_hat
class SoftSort_p2(torch.nn.Module):
def __init__(self, tau=1.0, hard=False):
super(SoftSort_p2, self).__init__()
self.hard = hard
self.tau = tau
def forward(self, scores: Tensor):
"""
scores: elements to be sorted. Typical shape: batch_size x n
"""
scores = scores.unsqueeze(-1)
sorted = scores.sort(descending=True, dim=1)[0]
pairwise_diff = ((scores.transpose(1, 2) - sorted) ** 2).neg() / self.tau
P_hat = pairwise_diff.softmax(-1)
if self.hard:
P = torch.zeros_like(P_hat, device=P_hat.device)
P.scatter_(-1, P_hat.topk(1, -1)[1], value=1)
P_hat = (P - P_hat).detach() + P_hat
return P_hat
| 32.714286
| 94
| 0.575546
| 328
| 2,290
| 3.814024
| 0.167683
| 0.076739
| 0.047962
| 0.052758
| 0.926459
| 0.926459
| 0.926459
| 0.926459
| 0.902478
| 0.902478
| 0
| 0.0281
| 0.285153
| 2,290
| 69
| 95
| 33.188406
| 0.736103
| 0.079476
| 0
| 0.729167
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| false
| 0
| 0.041667
| 0
| 0.291667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
098eb34100ccf888fa1b89ac14d5506eb18d5441
| 45,212
|
py
|
Python
|
sdk/python/pulumi_databricks/mws_log_delivery.py
|
pulumi/pulumi-databricks
|
43580d4adbd04b72558f368ff0eef3d03432ebc1
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_databricks/mws_log_delivery.py
|
pulumi/pulumi-databricks
|
43580d4adbd04b72558f368ff0eef3d03432ebc1
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_databricks/mws_log_delivery.py
|
pulumi/pulumi-databricks
|
43580d4adbd04b72558f368ff0eef3d03432ebc1
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['MwsLogDeliveryArgs', 'MwsLogDelivery']
@pulumi.input_type
class MwsLogDeliveryArgs:
def __init__(__self__, *,
account_id: pulumi.Input[str],
credentials_id: pulumi.Input[str],
log_type: pulumi.Input[str],
output_format: pulumi.Input[str],
storage_configuration_id: pulumi.Input[str],
config_id: Optional[pulumi.Input[str]] = None,
config_name: Optional[pulumi.Input[str]] = None,
delivery_path_prefix: Optional[pulumi.Input[str]] = None,
delivery_start_time: Optional[pulumi.Input[str]] = None,
status: Optional[pulumi.Input[str]] = None,
workspace_ids_filters: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None):
"""
The set of arguments for constructing a MwsLogDelivery resource.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
:param pulumi.Input[str] credentials_id: The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
:param pulumi.Input[str] log_type: The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
:param pulumi.Input[str] output_format: The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
:param pulumi.Input[str] storage_configuration_id: The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
:param pulumi.Input[str] config_id: Databricks log delivery configuration ID.
:param pulumi.Input[str] config_name: The optional human-readable name of the log delivery configuration. Defaults to empty.
:param pulumi.Input[str] delivery_path_prefix: Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
:param pulumi.Input[str] delivery_start_time: The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
:param pulumi.Input[Sequence[pulumi.Input[int]]] workspace_ids_filters: By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
pulumi.set(__self__, "account_id", account_id)
pulumi.set(__self__, "credentials_id", credentials_id)
pulumi.set(__self__, "log_type", log_type)
pulumi.set(__self__, "output_format", output_format)
pulumi.set(__self__, "storage_configuration_id", storage_configuration_id)
if config_id is not None:
pulumi.set(__self__, "config_id", config_id)
if config_name is not None:
pulumi.set(__self__, "config_name", config_name)
if delivery_path_prefix is not None:
pulumi.set(__self__, "delivery_path_prefix", delivery_path_prefix)
if delivery_start_time is not None:
pulumi.set(__self__, "delivery_start_time", delivery_start_time)
if status is not None:
pulumi.set(__self__, "status", status)
if workspace_ids_filters is not None:
pulumi.set(__self__, "workspace_ids_filters", workspace_ids_filters)
@property
@pulumi.getter(name="accountId")
def account_id(self) -> pulumi.Input[str]:
"""
Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
"""
return pulumi.get(self, "account_id")
@account_id.setter
def account_id(self, value: pulumi.Input[str]):
pulumi.set(self, "account_id", value)
@property
@pulumi.getter(name="credentialsId")
def credentials_id(self) -> pulumi.Input[str]:
"""
The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
"""
return pulumi.get(self, "credentials_id")
@credentials_id.setter
def credentials_id(self, value: pulumi.Input[str]):
pulumi.set(self, "credentials_id", value)
@property
@pulumi.getter(name="logType")
def log_type(self) -> pulumi.Input[str]:
"""
The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
"""
return pulumi.get(self, "log_type")
@log_type.setter
def log_type(self, value: pulumi.Input[str]):
pulumi.set(self, "log_type", value)
@property
@pulumi.getter(name="outputFormat")
def output_format(self) -> pulumi.Input[str]:
"""
The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
"""
return pulumi.get(self, "output_format")
@output_format.setter
def output_format(self, value: pulumi.Input[str]):
pulumi.set(self, "output_format", value)
@property
@pulumi.getter(name="storageConfigurationId")
def storage_configuration_id(self) -> pulumi.Input[str]:
"""
The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
"""
return pulumi.get(self, "storage_configuration_id")
@storage_configuration_id.setter
def storage_configuration_id(self, value: pulumi.Input[str]):
pulumi.set(self, "storage_configuration_id", value)
@property
@pulumi.getter(name="configId")
def config_id(self) -> Optional[pulumi.Input[str]]:
"""
Databricks log delivery configuration ID.
"""
return pulumi.get(self, "config_id")
@config_id.setter
def config_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config_id", value)
@property
@pulumi.getter(name="configName")
def config_name(self) -> Optional[pulumi.Input[str]]:
"""
The optional human-readable name of the log delivery configuration. Defaults to empty.
"""
return pulumi.get(self, "config_name")
@config_name.setter
def config_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config_name", value)
@property
@pulumi.getter(name="deliveryPathPrefix")
def delivery_path_prefix(self) -> Optional[pulumi.Input[str]]:
"""
Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
"""
return pulumi.get(self, "delivery_path_prefix")
@delivery_path_prefix.setter
def delivery_path_prefix(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "delivery_path_prefix", value)
@property
@pulumi.getter(name="deliveryStartTime")
def delivery_start_time(self) -> Optional[pulumi.Input[str]]:
"""
The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
"""
return pulumi.get(self, "delivery_start_time")
@delivery_start_time.setter
def delivery_start_time(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "delivery_start_time", value)
@property
@pulumi.getter
def status(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "status")
@status.setter
def status(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "status", value)
@property
@pulumi.getter(name="workspaceIdsFilters")
def workspace_ids_filters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]:
"""
By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
return pulumi.get(self, "workspace_ids_filters")
@workspace_ids_filters.setter
def workspace_ids_filters(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]):
pulumi.set(self, "workspace_ids_filters", value)
@pulumi.input_type
class _MwsLogDeliveryState:
def __init__(__self__, *,
account_id: Optional[pulumi.Input[str]] = None,
config_id: Optional[pulumi.Input[str]] = None,
config_name: Optional[pulumi.Input[str]] = None,
credentials_id: Optional[pulumi.Input[str]] = None,
delivery_path_prefix: Optional[pulumi.Input[str]] = None,
delivery_start_time: Optional[pulumi.Input[str]] = None,
log_type: Optional[pulumi.Input[str]] = None,
output_format: Optional[pulumi.Input[str]] = None,
status: Optional[pulumi.Input[str]] = None,
storage_configuration_id: Optional[pulumi.Input[str]] = None,
workspace_ids_filters: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None):
"""
Input properties used for looking up and filtering MwsLogDelivery resources.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
:param pulumi.Input[str] config_id: Databricks log delivery configuration ID.
:param pulumi.Input[str] config_name: The optional human-readable name of the log delivery configuration. Defaults to empty.
:param pulumi.Input[str] credentials_id: The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
:param pulumi.Input[str] delivery_path_prefix: Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
:param pulumi.Input[str] delivery_start_time: The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
:param pulumi.Input[str] log_type: The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
:param pulumi.Input[str] output_format: The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
:param pulumi.Input[str] storage_configuration_id: The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
:param pulumi.Input[Sequence[pulumi.Input[int]]] workspace_ids_filters: By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
if account_id is not None:
pulumi.set(__self__, "account_id", account_id)
if config_id is not None:
pulumi.set(__self__, "config_id", config_id)
if config_name is not None:
pulumi.set(__self__, "config_name", config_name)
if credentials_id is not None:
pulumi.set(__self__, "credentials_id", credentials_id)
if delivery_path_prefix is not None:
pulumi.set(__self__, "delivery_path_prefix", delivery_path_prefix)
if delivery_start_time is not None:
pulumi.set(__self__, "delivery_start_time", delivery_start_time)
if log_type is not None:
pulumi.set(__self__, "log_type", log_type)
if output_format is not None:
pulumi.set(__self__, "output_format", output_format)
if status is not None:
pulumi.set(__self__, "status", status)
if storage_configuration_id is not None:
pulumi.set(__self__, "storage_configuration_id", storage_configuration_id)
if workspace_ids_filters is not None:
pulumi.set(__self__, "workspace_ids_filters", workspace_ids_filters)
@property
@pulumi.getter(name="accountId")
def account_id(self) -> Optional[pulumi.Input[str]]:
"""
Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
"""
return pulumi.get(self, "account_id")
@account_id.setter
def account_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "account_id", value)
@property
@pulumi.getter(name="configId")
def config_id(self) -> Optional[pulumi.Input[str]]:
"""
Databricks log delivery configuration ID.
"""
return pulumi.get(self, "config_id")
@config_id.setter
def config_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config_id", value)
@property
@pulumi.getter(name="configName")
def config_name(self) -> Optional[pulumi.Input[str]]:
"""
The optional human-readable name of the log delivery configuration. Defaults to empty.
"""
return pulumi.get(self, "config_name")
@config_name.setter
def config_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config_name", value)
@property
@pulumi.getter(name="credentialsId")
def credentials_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
"""
return pulumi.get(self, "credentials_id")
@credentials_id.setter
def credentials_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "credentials_id", value)
@property
@pulumi.getter(name="deliveryPathPrefix")
def delivery_path_prefix(self) -> Optional[pulumi.Input[str]]:
"""
Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
"""
return pulumi.get(self, "delivery_path_prefix")
@delivery_path_prefix.setter
def delivery_path_prefix(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "delivery_path_prefix", value)
@property
@pulumi.getter(name="deliveryStartTime")
def delivery_start_time(self) -> Optional[pulumi.Input[str]]:
"""
The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
"""
return pulumi.get(self, "delivery_start_time")
@delivery_start_time.setter
def delivery_start_time(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "delivery_start_time", value)
@property
@pulumi.getter(name="logType")
def log_type(self) -> Optional[pulumi.Input[str]]:
"""
The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
"""
return pulumi.get(self, "log_type")
@log_type.setter
def log_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "log_type", value)
@property
@pulumi.getter(name="outputFormat")
def output_format(self) -> Optional[pulumi.Input[str]]:
"""
The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
"""
return pulumi.get(self, "output_format")
@output_format.setter
def output_format(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "output_format", value)
@property
@pulumi.getter
def status(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "status")
@status.setter
def status(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "status", value)
@property
@pulumi.getter(name="storageConfigurationId")
def storage_configuration_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
"""
return pulumi.get(self, "storage_configuration_id")
@storage_configuration_id.setter
def storage_configuration_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "storage_configuration_id", value)
@property
@pulumi.getter(name="workspaceIdsFilters")
def workspace_ids_filters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]:
"""
By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
return pulumi.get(self, "workspace_ids_filters")
@workspace_ids_filters.setter
def workspace_ids_filters(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]):
pulumi.set(self, "workspace_ids_filters", value)
class MwsLogDelivery(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
account_id: Optional[pulumi.Input[str]] = None,
config_id: Optional[pulumi.Input[str]] = None,
config_name: Optional[pulumi.Input[str]] = None,
credentials_id: Optional[pulumi.Input[str]] = None,
delivery_path_prefix: Optional[pulumi.Input[str]] = None,
delivery_start_time: Optional[pulumi.Input[str]] = None,
log_type: Optional[pulumi.Input[str]] = None,
output_format: Optional[pulumi.Input[str]] = None,
status: Optional[pulumi.Input[str]] = None,
storage_configuration_id: Optional[pulumi.Input[str]] = None,
workspace_ids_filters: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None,
__props__=None):
"""
> **Note** This resource has an evolving API, which will change in the upcoming versions of the provider in order to simplify user experience.
Make sure you have authenticated with username and password for Accounts Console. This resource configures the delivery of the two supported log types from Databricks workspaces: [billable usage logs](https://docs.databricks.com/administration-guide/account-settings/billable-usage-delivery.html) and [audit logs](https://docs.databricks.com/administration-guide/account-settings/audit-logs.html). You cannot delete a log delivery configuration, but you can disable it when you no longer need it. This fact is important because there is a limit to the number of enabled log delivery configurations that you can create for an account. You can create a maximum of two enabled using the account level *(without workspace filter)* and two that use the workspace filter. There is an additional uniqueness constraint that two enabled configurations cannot share all their fields (not including the `config_name`). Re-enabling may fail when there's a violation of limit or uniqueness constraints.
## Billable Usage
CSV files are delivered to `<delivery_path_prefix>/billable-usage/csv/` and are named `workspaceId=<workspace-id>-usageMonth=<month>.csv`, which are delivered daily by overwriting the month's CSV file for each workspace. Format of CSV file, as well as some usage examples, can be found [here](https://docs.databricks.com/administration-guide/account-settings/usage.html#download-usage-as-a-csv-file).
Common processing scenario is to apply [cost allocation tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html), that could be enforced by setting custom_tags on a cluster or through cluster policy. Report contains `clusterId` field, that could be joined with data from AWS [cost and usage reports](https://docs.aws.amazon.com/cur/latest/userguide/cur-create.html), that can be joined with `user:ClusterId` tag from AWS usage report.
```python
import pulumi
import pulumi_databricks as databricks
usage_logs = databricks.MwsLogDelivery("usageLogs",
account_id=var["databricks_account_id"],
credentials_id=databricks_mws_credentials["log_writer"]["credentials_id"],
storage_configuration_id=databricks_mws_storage_configurations["log_bucket"]["storage_configuration_id"],
delivery_path_prefix="billable-usage",
config_name="Usage Logs",
log_type="BILLABLE_USAGE",
output_format="CSV")
```
## Audit Logs
JSON files with [static schema](https://docs.databricks.com/administration-guide/account-settings/audit-logs.html#audit-log-schema) are delivered to `<delivery_path_prefix>/workspaceId=<workspaceId>/date=<yyyy-mm-dd>/auditlogs_<internal-id>.json`. Logs are available within 15 minutes of activation for audit logs. New JSON files are delivered every few minutes, potentially overwriting existing files for each workspace. Sometimes data may arrive later than 15 minutes. Databricks can overwrite the delivered log files in your bucket at any time. If a file is overwritten, the existing content remains, but there may be additional lines for more auditable events. Overwriting ensures exactly-once semantics without requiring read or delete access to your account.
```python
import pulumi
import pulumi_databricks as databricks
audit_logs = databricks.MwsLogDelivery("auditLogs",
account_id=var["databricks_account_id"],
credentials_id=databricks_mws_credentials["log_writer"]["credentials_id"],
storage_configuration_id=databricks_mws_storage_configurations["log_bucket"]["storage_configuration_id"],
delivery_path_prefix="audit-logs",
config_name="Audit Logs",
log_type="AUDIT_LOGS",
output_format="JSON")
```
## Related Resources
The following resources are used in the same context:
* Provisioning Databricks on AWS guide.
* MwsCredentials to configure the cross-account role for creation of new workspaces within AWS.
* MwsCustomerManagedKeys to configure KMS keys for new workspaces within AWS.
* MwsNetworks to [configure VPC](https://docs.databricks.com/administration-guide/cloud-configurations/aws/customer-managed-vpc.html) & subnets for new workspaces within AWS.
* MwsStorageConfigurations to configure root bucket new workspaces within AWS.
* MwsWorkspaces to set up [workspaces in E2 architecture on AWS](https://docs.databricks.com/getting-started/overview.html#e2-architecture-1).
## Import
-> **Note** Importing this resource is not currently supported.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
:param pulumi.Input[str] config_id: Databricks log delivery configuration ID.
:param pulumi.Input[str] config_name: The optional human-readable name of the log delivery configuration. Defaults to empty.
:param pulumi.Input[str] credentials_id: The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
:param pulumi.Input[str] delivery_path_prefix: Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
:param pulumi.Input[str] delivery_start_time: The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
:param pulumi.Input[str] log_type: The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
:param pulumi.Input[str] output_format: The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
:param pulumi.Input[str] storage_configuration_id: The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
:param pulumi.Input[Sequence[pulumi.Input[int]]] workspace_ids_filters: By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: MwsLogDeliveryArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
> **Note** This resource has an evolving API, which will change in the upcoming versions of the provider in order to simplify user experience.
Make sure you have authenticated with username and password for Accounts Console. This resource configures the delivery of the two supported log types from Databricks workspaces: [billable usage logs](https://docs.databricks.com/administration-guide/account-settings/billable-usage-delivery.html) and [audit logs](https://docs.databricks.com/administration-guide/account-settings/audit-logs.html). You cannot delete a log delivery configuration, but you can disable it when you no longer need it. This fact is important because there is a limit to the number of enabled log delivery configurations that you can create for an account. You can create a maximum of two enabled using the account level *(without workspace filter)* and two that use the workspace filter. There is an additional uniqueness constraint that two enabled configurations cannot share all their fields (not including the `config_name`). Re-enabling may fail when there's a violation of limit or uniqueness constraints.
## Billable Usage
CSV files are delivered to `<delivery_path_prefix>/billable-usage/csv/` and are named `workspaceId=<workspace-id>-usageMonth=<month>.csv`, which are delivered daily by overwriting the month's CSV file for each workspace. Format of CSV file, as well as some usage examples, can be found [here](https://docs.databricks.com/administration-guide/account-settings/usage.html#download-usage-as-a-csv-file).
Common processing scenario is to apply [cost allocation tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html), that could be enforced by setting custom_tags on a cluster or through cluster policy. Report contains `clusterId` field, that could be joined with data from AWS [cost and usage reports](https://docs.aws.amazon.com/cur/latest/userguide/cur-create.html), that can be joined with `user:ClusterId` tag from AWS usage report.
```python
import pulumi
import pulumi_databricks as databricks
usage_logs = databricks.MwsLogDelivery("usageLogs",
account_id=var["databricks_account_id"],
credentials_id=databricks_mws_credentials["log_writer"]["credentials_id"],
storage_configuration_id=databricks_mws_storage_configurations["log_bucket"]["storage_configuration_id"],
delivery_path_prefix="billable-usage",
config_name="Usage Logs",
log_type="BILLABLE_USAGE",
output_format="CSV")
```
## Audit Logs
JSON files with [static schema](https://docs.databricks.com/administration-guide/account-settings/audit-logs.html#audit-log-schema) are delivered to `<delivery_path_prefix>/workspaceId=<workspaceId>/date=<yyyy-mm-dd>/auditlogs_<internal-id>.json`. Logs are available within 15 minutes of activation for audit logs. New JSON files are delivered every few minutes, potentially overwriting existing files for each workspace. Sometimes data may arrive later than 15 minutes. Databricks can overwrite the delivered log files in your bucket at any time. If a file is overwritten, the existing content remains, but there may be additional lines for more auditable events. Overwriting ensures exactly-once semantics without requiring read or delete access to your account.
```python
import pulumi
import pulumi_databricks as databricks
audit_logs = databricks.MwsLogDelivery("auditLogs",
account_id=var["databricks_account_id"],
credentials_id=databricks_mws_credentials["log_writer"]["credentials_id"],
storage_configuration_id=databricks_mws_storage_configurations["log_bucket"]["storage_configuration_id"],
delivery_path_prefix="audit-logs",
config_name="Audit Logs",
log_type="AUDIT_LOGS",
output_format="JSON")
```
## Related Resources
The following resources are used in the same context:
* Provisioning Databricks on AWS guide.
* MwsCredentials to configure the cross-account role for creation of new workspaces within AWS.
* MwsCustomerManagedKeys to configure KMS keys for new workspaces within AWS.
* MwsNetworks to [configure VPC](https://docs.databricks.com/administration-guide/cloud-configurations/aws/customer-managed-vpc.html) & subnets for new workspaces within AWS.
* MwsStorageConfigurations to configure root bucket new workspaces within AWS.
* MwsWorkspaces to set up [workspaces in E2 architecture on AWS](https://docs.databricks.com/getting-started/overview.html#e2-architecture-1).
## Import
-> **Note** Importing this resource is not currently supported.
:param str resource_name: The name of the resource.
:param MwsLogDeliveryArgs 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(MwsLogDeliveryArgs, 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,
account_id: Optional[pulumi.Input[str]] = None,
config_id: Optional[pulumi.Input[str]] = None,
config_name: Optional[pulumi.Input[str]] = None,
credentials_id: Optional[pulumi.Input[str]] = None,
delivery_path_prefix: Optional[pulumi.Input[str]] = None,
delivery_start_time: Optional[pulumi.Input[str]] = None,
log_type: Optional[pulumi.Input[str]] = None,
output_format: Optional[pulumi.Input[str]] = None,
status: Optional[pulumi.Input[str]] = None,
storage_configuration_id: Optional[pulumi.Input[str]] = None,
workspace_ids_filters: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = 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__ = MwsLogDeliveryArgs.__new__(MwsLogDeliveryArgs)
if account_id is None and not opts.urn:
raise TypeError("Missing required property 'account_id'")
__props__.__dict__["account_id"] = account_id
__props__.__dict__["config_id"] = config_id
__props__.__dict__["config_name"] = config_name
if credentials_id is None and not opts.urn:
raise TypeError("Missing required property 'credentials_id'")
__props__.__dict__["credentials_id"] = credentials_id
__props__.__dict__["delivery_path_prefix"] = delivery_path_prefix
__props__.__dict__["delivery_start_time"] = delivery_start_time
if log_type is None and not opts.urn:
raise TypeError("Missing required property 'log_type'")
__props__.__dict__["log_type"] = log_type
if output_format is None and not opts.urn:
raise TypeError("Missing required property 'output_format'")
__props__.__dict__["output_format"] = output_format
__props__.__dict__["status"] = status
if storage_configuration_id is None and not opts.urn:
raise TypeError("Missing required property 'storage_configuration_id'")
__props__.__dict__["storage_configuration_id"] = storage_configuration_id
__props__.__dict__["workspace_ids_filters"] = workspace_ids_filters
super(MwsLogDelivery, __self__).__init__(
'databricks:index/mwsLogDelivery:MwsLogDelivery',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
account_id: Optional[pulumi.Input[str]] = None,
config_id: Optional[pulumi.Input[str]] = None,
config_name: Optional[pulumi.Input[str]] = None,
credentials_id: Optional[pulumi.Input[str]] = None,
delivery_path_prefix: Optional[pulumi.Input[str]] = None,
delivery_start_time: Optional[pulumi.Input[str]] = None,
log_type: Optional[pulumi.Input[str]] = None,
output_format: Optional[pulumi.Input[str]] = None,
status: Optional[pulumi.Input[str]] = None,
storage_configuration_id: Optional[pulumi.Input[str]] = None,
workspace_ids_filters: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None) -> 'MwsLogDelivery':
"""
Get an existing MwsLogDelivery resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
:param pulumi.Input[str] config_id: Databricks log delivery configuration ID.
:param pulumi.Input[str] config_name: The optional human-readable name of the log delivery configuration. Defaults to empty.
:param pulumi.Input[str] credentials_id: The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
:param pulumi.Input[str] delivery_path_prefix: Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
:param pulumi.Input[str] delivery_start_time: The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
:param pulumi.Input[str] log_type: The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
:param pulumi.Input[str] output_format: The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
:param pulumi.Input[str] storage_configuration_id: The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
:param pulumi.Input[Sequence[pulumi.Input[int]]] workspace_ids_filters: By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _MwsLogDeliveryState.__new__(_MwsLogDeliveryState)
__props__.__dict__["account_id"] = account_id
__props__.__dict__["config_id"] = config_id
__props__.__dict__["config_name"] = config_name
__props__.__dict__["credentials_id"] = credentials_id
__props__.__dict__["delivery_path_prefix"] = delivery_path_prefix
__props__.__dict__["delivery_start_time"] = delivery_start_time
__props__.__dict__["log_type"] = log_type
__props__.__dict__["output_format"] = output_format
__props__.__dict__["status"] = status
__props__.__dict__["storage_configuration_id"] = storage_configuration_id
__props__.__dict__["workspace_ids_filters"] = workspace_ids_filters
return MwsLogDelivery(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="accountId")
def account_id(self) -> pulumi.Output[str]:
"""
Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/).
"""
return pulumi.get(self, "account_id")
@property
@pulumi.getter(name="configId")
def config_id(self) -> pulumi.Output[str]:
"""
Databricks log delivery configuration ID.
"""
return pulumi.get(self, "config_id")
@property
@pulumi.getter(name="configName")
def config_name(self) -> pulumi.Output[Optional[str]]:
"""
The optional human-readable name of the log delivery configuration. Defaults to empty.
"""
return pulumi.get(self, "config_name")
@property
@pulumi.getter(name="credentialsId")
def credentials_id(self) -> pulumi.Output[str]:
"""
The ID for a Databricks credential configuration that represents the AWS IAM role with policy and trust relationship as described in the main billable usage documentation page.
"""
return pulumi.get(self, "credentials_id")
@property
@pulumi.getter(name="deliveryPathPrefix")
def delivery_path_prefix(self) -> pulumi.Output[Optional[str]]:
"""
Defaults to empty, which means that logs are delivered to the root of the bucket. The value must be a valid S3 object key. It must not start or end with a slash character.
"""
return pulumi.get(self, "delivery_path_prefix")
@property
@pulumi.getter(name="deliveryStartTime")
def delivery_start_time(self) -> pulumi.Output[str]:
"""
The optional start month and year for delivery, specified in YYYY-MM format. Defaults to current year and month. Usage is not available before 2019-03.
"""
return pulumi.get(self, "delivery_start_time")
@property
@pulumi.getter(name="logType")
def log_type(self) -> pulumi.Output[str]:
"""
The type of log delivery. `BILLABLE_USAGE` and `AUDIT_LOGS` are supported.
"""
return pulumi.get(self, "log_type")
@property
@pulumi.getter(name="outputFormat")
def output_format(self) -> pulumi.Output[str]:
"""
The file type of log delivery. Currently `CSV` (for `BILLABLE_USAGE`) and `JSON` (for `AUDIT_LOGS`) are supported.
"""
return pulumi.get(self, "output_format")
@property
@pulumi.getter
def status(self) -> pulumi.Output[str]:
return pulumi.get(self, "status")
@property
@pulumi.getter(name="storageConfigurationId")
def storage_configuration_id(self) -> pulumi.Output[str]:
"""
The ID for a Databricks storage configuration that represents the S3 bucket with bucket policy as described in the main billable usage documentation page.
"""
return pulumi.get(self, "storage_configuration_id")
@property
@pulumi.getter(name="workspaceIdsFilters")
def workspace_ids_filters(self) -> pulumi.Output[Optional[Sequence[int]]]:
"""
By default, this log configuration applies to all workspaces associated with your account ID. If your account is on the E2 version of the platform or on a select custom plan that allows multiple workspaces per account, you may have multiple workspaces associated with your account ID. You can optionally set the field as mentioned earlier to an array of workspace IDs. If you plan to use different log delivery configurations for several workspaces, set this explicitly rather than leaving it blank. If you leave this blank and your account ID gets additional workspaces in the future, this configuration will also apply to the new workspaces.
"""
return pulumi.get(self, "workspace_ids_filters")
| 62.794444
| 997
| 0.706007
| 5,898
| 45,212
| 5.244659
| 0.06765
| 0.055475
| 0.057932
| 0.053341
| 0.944978
| 0.933275
| 0.927876
| 0.914881
| 0.910452
| 0.906766
| 0
| 0.002239
| 0.209635
| 45,212
| 719
| 998
| 62.88178
| 0.863407
| 0.530611
| 0
| 0.761155
| 1
| 0
| 0.116506
| 0.028017
| 0
| 0
| 0
| 0
| 0
| 1
| 0.16273
| false
| 0.002625
| 0.013123
| 0.007874
| 0.272966
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
61deaf37a4cc4c5832e2c213d466e1ac71bab0e9
| 150
|
py
|
Python
|
loldib/getratings/models/NA/na_rammus/__init__.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_rammus/__init__.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_rammus/__init__.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from .na_rammus_top import *
from .na_rammus_jng import *
from .na_rammus_mid import *
from .na_rammus_bot import *
from .na_rammus_sup import *
| 25
| 29
| 0.766667
| 25
| 150
| 4.2
| 0.36
| 0.285714
| 0.571429
| 0.685714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 150
| 5
| 30
| 30
| 0.84
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
61e10752124341e4a29bc703a29494d6d3bc3f02
| 20,020
|
py
|
Python
|
config.py
|
thuiar/cmcnn
|
a18f09fa63baf74bb083779fa0a8881d55226e1a
|
[
"MIT"
] | null | null | null |
config.py
|
thuiar/cmcnn
|
a18f09fa63baf74bb083779fa0a8881d55226e1a
|
[
"MIT"
] | null | null | null |
config.py
|
thuiar/cmcnn
|
a18f09fa63baf74bb083779fa0a8881d55226e1a
|
[
"MIT"
] | null | null | null |
import os
import random
import argparse
__all__ = ['Config', 'ConfigDebug']
class Storage(dict):
"""
A Storage object is like a dictionary except `obj.foo` can be used inadition to `obj['foo']`
ref: https://blog.csdn.net/a200822146085/article/details/88430450
"""
def __getattr__(self, key):
try:
return self[key]
except KeyError as k:
raise AttributeError(k)
def __setattr__(self, key, value):
self[key] = value
def __delattr__(self, key):
try:
del self[key]
except KeyError as k:
raise AttributeError(k)
def __str__(self):
return "<" + self.__class__.__name__ + dict.__repr__(self) + ">"
class Config():
def __init__(self, input_args):
# global parameters for running
try:
self.global_running = vars(input_args)
except TypeError:
self.global_running = input_args
# hyper parameters for models
self.HYPER_MODEL_MAP = {
'FER_DCNN': self.__FER_DCNN,
'LM_DCNN': self.__LM_DCNN,
'CMCNN': self.__CMCNN,
}
# hyper parameters for datasets
self.HYPER_DATASET_MAP = self.__datasetCommonParams()
def __datasetCommonParams(self):
data_root_dir = '/home/sharing/disk3/dataset/facial-expression-recognition'
tmp = {
'RAF':{
'data_dir': os.path.join(data_root_dir, 'RAF-Basic/Processed/Faces'),
'label_dir': os.path.join(data_root_dir, 'RAF-Basic/Processed/Label'),
'fer_num_classes': 7,
# 'fer_num_classes': 6,
'num_landmarks': 68,
'num_tasks': 2,
},
'SFEW2':{
'data_dir': os.path.join(data_root_dir, 'SFEW2/Processed/AlignedFaces'),
'label_dir': os.path.join(data_root_dir, 'SFEW2/Processed/AlignedLabels'),
'fer_num_classes': 7,
'num_landmarks': 68,
'num_tasks': 2,
},
'CK+':{
'data_dir': os.path.join(data_root_dir, 'CK+/Processed/AlignedFaces'),
'label_dir': os.path.join(data_root_dir, 'CK+/Processed/AlignedLabels'),
'fer_num_classes': 7,
# 'fer_num_classes': 6,
'num_landmarks': 68,
'num_tasks': 2,
},
'MMI':{
'data_dir': os.path.join(data_root_dir, 'MMI/Processed/AlignedFaces'),
'label_dir': os.path.join(data_root_dir, 'MMI/Processed/AlignedLabels'),
'fer_num_classes': 6,
'num_landmarks': 68,
'num_tasks': 2,
},
'OULU_CASIA':{
'data_dir': os.path.join(data_root_dir, 'Oulu_CASIA/Processed/AlignedFaces'),
'label_dir': os.path.join(data_root_dir, 'Oulu_CASIA/Processed/AlignedLabels'),
'fer_num_classes': 6,
'num_landmarks': 68,
'num_tasks': 2,
},
}
return tmp
def __FER_DCNN(self):
tmp = {
'commonParas':{
# Tuning
'metricsName': 'FER',
'epochs': 50,
'patience': 15, # when to decay learning rate
# Logistics
'keyEval': 'Average_Acc',
},
# dataset
'datasetParas':{
'RAF':{
'batch_size': 32,
'embedding_size': 128,
'weight_decay': 0.005,
'lr': 0.01,
},
'SFEW2':{
'batch_size': 128,
'embedding_size': 128,
'weight_decay': 0.0001,
'lr': 0.01,
},
'CK+':{
'batch_size': 32,
'embedding_size': 128,
'weight_decay': 0.005,
'lr': 0.01,
},
'MMI':{
'batch_size': 16,
'embedding_size': 256,
'weight_decay': 0.005,
'lr': 0.01,
},
'OULU_CASIA':{
'batch_size': 16,
'embedding_size': 256,
'weight_decay': 0.005,
'lr': 0.01,
},
},
}
return tmp
def __LM_DCNN(self):
tmp = {
'commonParas':{
# Tuning
'metricsName': 'LM',
'epochs': 50,
'patience': 15, # when to decay learning rate
# Logistics
'keyEval': 'NME',
},
# dataset
'datasetParas':{
'RAF':{
'batch_size': 32,
'embedding_size': 2048,
'weight_decay': 5e-3,
'learning_rate': 0.01,
},
'SFEW2':{
'batch_size': 32,
'embedding_size': 1024,
'weight_decay': 0.05,
'learning_rate': 0.1,
},
'CK+':{
'batch_size': 8,
'embedding_size': 1024,
'weight_decay': 0.01,
'learning_rate': 0.001,
},
'MMI':{
'batch_size': 8,
'embedding_size': 1024,
'weight_decay': 0.05,
'learning_rate': 0.001,
},
'OULU_CASIA':{
'batch_size': 8,
'embedding_size': 512,
'weight_decay': 0.05,
'learning_rate': 0.001,
},
},
}
return tmp
def __CMCNN(self):
tmp = {
'commonParas':{
# Tuning
'metricsName': 'FER_LM',
'epochs': 50,
'patience': 15, # when to decay learning rate
# Logistics
'keyEval': 'Average_Acc',
},
# dataset
'datasetParas':{
'RAF':{
'batch_size': 32,
'e_ratio': 0.2,
'fer_embedding': 512,
'lm_embedding': 512,
'alphaBetas': [[0.5, 0.5]],
'lm_threshold': 0.5,
'lambda_e2l': 0.2,
'lambda_l2e': 1.0,
# 'alphaBetas': [[0.0, 1.0]],
'weight_decay': 0.005,
'loss_fer': 1.0,
'loss_lm': 0.5,
'loss_mtl': 1.0,
'lr': 0.01,
},
'SFEW2':{
'batch_size': 32,
'e_ratio': 0.2,
'fer_embedding': 128,
'lm_embedding': 2048,
'alphaBetas': [[0.5, 0.5]],
'lm_threshold': 0.5,
'lambda_e2l': 0.2,
'lambda_l2e': 0.2,
# 'alphaBetas': [[0.0, 1.0]],
'weight_decay': 0.005,
'loss_fer': 1.0,
'loss_lm': 0.5,
'loss_mtl': 1.0,
'lr': 0.01,
},
'CK+':{
'batch_size': 16,
'e_ratio': 0.5,
'fer_embedding': 128,
'lm_embedding': 2048,
'alphaBetas': [[0.5, 0.5]],
'lm_threshold': 0.5,
'lambda_e2l': 0.2,
'lambda_l2e': 0.2,
# 'alphaBetas': [[0.0, 1.0]],
'weight_decay': 0.005,
'loss_fer': 1.0,
'loss_lm': 0.5,
'loss_mtl': 1.0,
'lr': 0.01,
},
'MMI':{
'batch_size': 32,
'e_ratio': 0.5,
'fer_embedding': 128,
'lm_embedding': 2048,
'alphaBetas': [[0.5, 0.5]],
'lm_threshold': 0.5,
'lambda_e2l': 0.2,
'lambda_l2e': 0.2,
# 'alphaBetas': [[0.0, 1.0]],
'weight_decay': 0.0001,
'loss_fer': 1.0,
'loss_lm': 0.5,
'loss_mtl': 1.0,
'lr': 0.01,
},
'OULU_CASIA':{
'batch_size': 32,
'e_ratio': 0.5,
'fer_embedding': 128,
'lm_embedding': 2048,
'alphaBetas': [[0.5, 0.5]],
'lm_threshold': 0.5,
'lambda_e2l': 0.2,
'lambda_l2e': 0.2,
# 'alphaBetas': [[0.0, 1.0]],
'weight_decay': 0.0001,
'loss_fer': 1.0,
'loss_lm': 0.5,
'loss_mtl': 1.0,
'lr': 0.01,
},
},
}
return tmp
def get_config(self):
# normalize
model_name = self.global_running['modelName'].upper()
dataset_name = self.global_running['datasetName'].upper()
# integrate all parameters
res = Storage(dict(self.global_running,
**self.HYPER_MODEL_MAP[model_name]()['datasetParas'][dataset_name],
**self.HYPER_MODEL_MAP[model_name]()['commonParas'],
**self.HYPER_DATASET_MAP[dataset_name]))
return res
class ConfigDebug():
def __init__(self, input_args):
# global parameters for running
try:
self.global_running = vars(input_args)
except TypeError:
self.global_running = input_args
# hyper parameters for models
self.HYPER_MODEL_MAP = {
'FER_DCNN': self.__FER_DCNN,
'LM_DCNN': self.__LM_DCNN,
'CMCNN': self.__CMCNN
}
# hyper parameters for datasets
self.HYPER_DATASET_MAP = self.__datasetCommonParams()
def __datasetCommonParams(self):
data_root_dir = '/home/sharing/disk3/dataset/facial-expression-recognition'
tmp = {
'RAF':{
'data_dir': os.path.join(data_root_dir, 'RAF-Basic/Processed/Faces'),
'label_dir': os.path.join(data_root_dir, 'RAF-Basic/Processed/Label'),
'fer_num_classes': 7,
'num_landmarks': 68,
},
'SFEW':{
'data_dir': os.path.join(data_root_dir, 'SFEW2/Processed/AlignedFaces'),
'label_dir': os.path.join(data_root_dir, 'SFEW2/Processed/Label'),
'fer_num_classes': 7,
'num_landmarks': 68,
},
'CK+':{
'data_dir': os.path.join(data_root_dir, 'CK+/Processed/AlignedFacesEqual'),
'label_dir': os.path.join(data_root_dir, 'CK+/Processed/Label'),
'fer_num_classes': 7,
'num_landmarks': 68,
},
'MMI':{
'data_dir': os.path.join(data_root_dir, 'MMI/Processed/AlignedFacesEqual'),
'label_dir': os.path.join(data_root_dir, 'MMI/Processed/Label'),
'fer_num_classes': 6,
'num_landmarks': 68,
},
'OULU_CASIA':{
'data_dir': os.path.join(data_root_dir, 'Oulu_CASIA/Processed/AlignedFacesEqual'),
'label_dir': os.path.join(data_root_dir, 'Oulu_CASIA/Processed/Label'),
'fer_num_classes': 6,
'num_landmarks': 68,
},
}
return tmp
def __FER_DCNN(self):
tmp = {
'commonParas':{
# Tuning
'metricsName': 'FER',
'epochs': 50,
'patience': 15, # when to decay learning rate
# Logistics
'keyEval': 'Average_Acc',
},
# dataset
'datasetParas':{
'RAF':{
# ref Original Paper
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'lambda_islandLoss', 'weight_islandLoss', 'lr'],
'batch_size': random.choice([128,64]),
'embedding_size': random.choice([128,256]),
'weight_decay': random.choice([0.1,0.005,0.0]),
'lambda_islandLoss': random.choice([1,10,20]),
'weight_islandLoss': random.choice([0.1,0.01]),
'lr': random.choice([0.01]),
'loss_lr': random.choice([0.01]),
},
'CK+':{
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'lambda_islandLoss', 'weight_islandLoss', 'lr'],
'batch_size': random.choice([64,32,16]),
'embedding_size': random.choice([128]),
'weight_decay': random.choice([0.005]),
'lambda_islandLoss': random.choice([20]),
'weight_islandLoss': random.choice([0.1]),
'lr': random.choice([0.01]),
'loss_lr': random.choice([0.01]),
},
'OULU_CASIA':{
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'lambda_islandLoss', 'weight_islandLoss', 'lr'],
'batch_size': random.choice([8,16]),
'embedding_size': random.choice([128,256]),
'weight_decay': random.choice([0.1,0.01,0.005,0.0]),
'lambda_islandLoss': random.choice([1,10,20]),
'weight_islandLoss': random.choice([0.1,0.01]),
'lr': random.choice([0.01]),
'loss_lr': random.choice([0.01]),
},
},
}
return tmp
def __LM_DCNN(self):
tmp = {
'commonParas':{
# Tuning
'metricsName': 'LM',
'epochs': 100,
'patience': 20, # when to decay learning rate
# Logistics
'keyEval': 'NME',
},
# dataset
'datasetParas':{
'RAF':{
# ref Original Paper
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'learning_rate'],
'batch_size': random.choice([128,64]),
'embedding_size': random.choice([512, 1024, 2048]),
'weight_decay': random.choice([0.02,0.1,0.05,0.01,0.005]),
'learning_rate': random.choice([0.1, 0.01, 0.001, 0.0001]),
},
'SFEW':{
# ref Original Paper
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'learning_rate'],
'batch_size': random.choice([64,32,16,8]),
'embedding_size': random.choice([512, 1024, 2048]),
'weight_decay': random.choice([0.02,0.1,0.05,0.01,0.005]),
'learning_rate': random.choice([0.1, 0.01, 0.001, 0.0001]),
},
'CK+':{
# ref Original Paper
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'learning_rate'],
'batch_size': random.choice([64,32,16,8]),
'embedding_size': random.choice([512, 1024, 2048]),
'weight_decay': random.choice([0.02,0.1,0.05,0.01,0.005]),
'learning_rate': random.choice([0.1, 0.01, 0.001, 0.0001]),
},
'OULU_CASIA':{
# ref Original Paper
'd_paras': ['batch_size', 'embedding_size', 'weight_decay', 'learning_rate'],
'batch_size': random.choice([64,32,16,8]),
'embedding_size': random.choice([512, 1024, 2048]),
'weight_decay': random.choice([0.02,0.1,0.05,0.01,0.005]),
'learning_rate': random.choice([0.1, 0.01, 0.001, 0.0001]),
},
},
}
return tmp
def __CMCNN(self):
tmp = {
'commonParas':{
# Tuning
'metricsName': 'FER_LM',
'epochs': 22,
'patience': 10, # when to decay learning rate
# Logistics
'keyEval': 'Average_Acc',
},
# dataset
'datasetParas':{
'RAF':{
'batch_size': 32,
'e_ratio': 0.5,
'lr': 0.01,
'fer_embedding': 128,
'lm_embedding': 512,
'alphaBetas': [[0.5, 0.5]],
'weight_decay': 0.005,
'lm_threshold': 0.5,
'd_paras': ['lambda_e2l', 'lambda_l2e', 'loss_fer', \
'loss_lm', 'loss_mtl'],
'lambda_e2l': random.choice([0.1, 1.0]),
'lambda_l2e': random.choice([0.1, 1.0]),
'loss_fer': random.choice([1.0, 0.5]),
'loss_lm': random.choice([1.0, 0.5]),
'loss_mtl': random.choice([1.0, 0.1, 0.01]),
},
'CK+':{
'd_paras': ['batch_size', 'e_ratio', 'fer_embedding', 'lm_embedding', \
'alphaBetas', 'weight_decay', 'weight_lm', 'weight_lp', 'lr'],
'batch_size': random.choice([16,32]),
'e_ratio': random.choice([0.2, 0.5]),
'fer_embedding': random.choice([128]),
'lm_embedding': random.choice([2048]),
'alphaBetas': random.choice([[[0.5, 0.5]], [[1.0, 0.0]], [[0.0, 1.0]], [[0.2, 0.8]], [[0.8, 0.2]]]),
'weight_decay': random.choice([0.005]),
'weight_lm': random.choice([0.5]),
'weight_lp': random.choice([0.01, 0.1, 1]),
'lr': random.choice([0.01]),
},
'OULU_CASIA':{
'd_paras': ['batch_size', 'e_ratio', 'fer_embedding', 'lm_embedding', \
'alphaBetas', 'weight_decay', 'weight_lm', 'weight_lp', 'lr'],
'batch_size': random.choice([16,32]),
'e_ratio': random.choice([0.2, 0.5]),
'fer_embedding': random.choice([128]),
'lm_embedding': random.choice([1024]),
'alphaBetas': random.choice([[[0.5, 0.5]], [[1.0, 0.0]], [[0.0, 1.0]], [[0.2, 0.8]], [[0.8, 0.2]]]),
'weight_decay': random.choice([0.005]),
'weight_lm': random.choice([0.1, 0.5]),
'weight_lp': random.choice([0.01, 0.1, 1]),
'lr': random.choice([0.01]),
}
},
}
return tmp
def get_config(self):
# normalize
model_name = self.global_running['modelName'].upper()
dataset_name = self.global_running['datasetName'].upper()
# integrate all parameters
res = Storage(dict(self.global_running,
**self.HYPER_MODEL_MAP[model_name]()['datasetParas'][dataset_name],
**self.HYPER_MODEL_MAP[model_name]()['commonParas'],
**self.HYPER_DATASET_MAP[dataset_name]))
return res
| 39.643564
| 128
| 0.422927
| 1,918
| 20,020
| 4.172576
| 0.090198
| 0.089966
| 0.055229
| 0.032488
| 0.923529
| 0.916781
| 0.890041
| 0.873298
| 0.860927
| 0.832563
| 0
| 0.074949
| 0.437512
| 20,020
| 505
| 129
| 39.643564
| 0.635734
| 0.05005
| 0
| 0.730769
| 0
| 0
| 0.228184
| 0.032658
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036199
| false
| 0
| 0.006787
| 0.002262
| 0.076923
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
112648289ff90dd78c8313d5fc6c711a7ee8a951
| 2,530
|
py
|
Python
|
tests/test_tutorials.py
|
gitter-badger/tudatpy
|
f5faef4ecfe8667cb9c989dd839185eeed5f9662
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_tutorials.py
|
gitter-badger/tudatpy
|
f5faef4ecfe8667cb9c989dd839185eeed5f9662
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_tutorials.py
|
gitter-badger/tudatpy
|
f5faef4ecfe8667cb9c989dd839185eeed5f9662
|
[
"BSD-3-Clause"
] | null | null | null |
import unittest
import numpy as np
from tudatpy.apps import satellite_propagator
from tudatpy.kernel import constants
class TestTutorials(unittest.TestCase):
def test_tutorial_1(self):
test_output = satellite_propagator.single(
start_epoch=0.0,
fixed_step_size=10.0,
end_epoch=constants.JULIAN_DAY,
parent_body="Earth",
frame_orientation="ECLIPJ2000",
frame_origin="SSB",
satellite_name="Delfi-C3",
sat_sma=7500.0E3,
sat_ecc=0.1,
sat_inc=np.deg2rad(85.3),
sat_raan=np.deg2rad(23.4),
sat_argp=np.deg2rad(235.7),
sat_nu=np.deg2rad(139.87),
return_output=True,
print_output=True,
output_type='array'
)
np.testing.assert_almost_equal(
actual=test_output,
desired=np.array(
[[+7.037484001334376e+03, -4.560454309804188e+03],
[+3.238059017921996e+03, -1.438318384929149e+03],
[+2.150724187502574e+03, +5.973990914838605e+03],
[-1.465657627242447e+00, -4.550213315831223e+00],
[-4.095839489293598e-02, -2.412544138773537e+00],
[+6.622797609422496e+00, -4.950630568956334e+00]]
).T
)
def test_tutorial_2(self):
test_output = satellite_propagator.single(
start_epoch=0.0,
fixed_step_size=10.0,
end_epoch=constants.JULIAN_DAY,
parent_body="Earth",
frame_orientation="ECLIPJ2000",
frame_origin="SSB",
satellite_name="Delfi-C3",
sat_sma=7500.0E3,
sat_ecc=0.1,
sat_inc=np.deg2rad(85.3),
sat_raan=np.deg2rad(23.4),
sat_argp=np.deg2rad(235.7),
sat_nu=np.deg2rad(139.87),
return_output=True,
print_output=True,
output_type='array'
)
np.testing.assert_almost_equal(
actual=test_output,
desired=np.array(
[[+7.037484001334376e+03, -4.560454309804188e+03],
[+3.238059017921996e+03, -1.438318384929149e+03],
[+2.150724187502574e+03, +5.973990914838605e+03],
[-1.465657627242447e+00, -4.550213315831223e+00],
[-4.095839489293598e-02, -2.412544138773537e+00],
[+6.622797609422496e+00, -4.950630568956334e+00]]
).T
)
| 35.138889
| 66
| 0.557312
| 273
| 2,530
| 4.978022
| 0.333333
| 0.05298
| 0.022075
| 0.033848
| 0.877116
| 0.877116
| 0.877116
| 0.877116
| 0.877116
| 0.877116
| 0
| 0.298824
| 0.328063
| 2,530
| 71
| 67
| 35.633803
| 0.500588
| 0
| 0
| 0.830769
| 0
| 0
| 0.024506
| 0
| 0
| 0
| 0
| 0
| 0.030769
| 1
| 0.030769
| false
| 0
| 0.061538
| 0
| 0.107692
| 0.030769
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
3a2ddf0963feff86e69d10186cbb94e97250251f
| 7,396
|
py
|
Python
|
tests/test_client.py
|
AppVelox/alicemsg
|
2cf3abe3eb2596e306734c3cec74205f1586869b
|
[
"MIT"
] | null | null | null |
tests/test_client.py
|
AppVelox/alicemsg
|
2cf3abe3eb2596e306734c3cec74205f1586869b
|
[
"MIT"
] | null | null | null |
tests/test_client.py
|
AppVelox/alicemsg
|
2cf3abe3eb2596e306734c3cec74205f1586869b
|
[
"MIT"
] | null | null | null |
import pytest
from alicemsg import AliceClient
from alicemsg.models import messages
class TestMessengerClient:
def test_init(self):
client = AliceClient()
assert client.text_message_processor is None
assert client.callback_processor is None
msg_json = {
"meta": {
"client_id": "ru.yandex.searchplugin/7.16 (none none; android 4.4.2)",
"interfaces": {
"screen": {}
},
"locale": "ru-RU",
"timezone": "UTC"
},
"request": {
"command": "",
"nlu": {
"entities": [],
"tokens": []
},
"original_utterance": "",
"type": "SimpleUtterance"
},
"session": {
"message_id": 0,
"new": True,
"session_id": "4d561804-a51d901c-fbb986a4-1699b7",
"skill_id": "934d5268-3d49-4b5a-bb53-97200edd0a0b",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
with pytest.raises(AttributeError):
client.process_json(msg_json)
msg_json = {
"meta": {
"client_id": "ru.yandex.searchplugin/7.16 (none none; android 4.4.2)",
"interfaces": {
"screen": {}
},
"locale": "ru-RU",
"timezone": "UTC"
},
"request": {
"nlu": {
"entities": [],
"tokens": []
},
"payload": {
"aaa": "bbb"
},
"type": "ButtonPressed"
},
"session": {
"message_id": 3,
"new": False,
"session_id": "1cd02173-43c63383-3f975d2e-54738",
"skill_id": "934d5268-3d49-4b5a-bb53-97200edd0a0b",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
with pytest.raises(AttributeError):
client.process_json(msg_json)
def test_text_message_processor(self):
client = AliceClient()
@client.register_text_message_processor()
def f(request):
return messages.Message('text')
assert client.text_message_processor == f
msg_json = {
"meta": {
"client_id": "ru.yandex.searchplugin/7.16 (none none; android 4.4.2)",
"interfaces": {
"screen": {}
},
"locale": "ru-RU",
"timezone": "UTC"
},
"request": {
"command": "",
"nlu": {
"entities": [],
"tokens": []
},
"original_utterance": "",
"type": "SimpleUtterance"
},
"session": {
"message_id": 0,
"new": True,
"session_id": "4d561804-a51d901c-fbb986a4-1699b7",
"skill_id": "934d5268-3d49-4b5a-bb53-97200edd0a0b",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
response = client.process_json(msg_json)
assert response.to_dict() == {
'response': {
'text': 'text',
'end_session': False,
},
"session": {
"message_id": 0,
"session_id": "4d561804-a51d901c-fbb986a4-1699b7",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
@client.register_text_message_processor()
def f(request):
return 1
with pytest.raises(TypeError):
client.process_json(msg_json)
def test_callback_processor(self):
client = AliceClient()
@client.register_callback_processor()
def f(request):
return messages.Message('text')
assert client.callback_processor == f
msg_json = {
"meta": {
"client_id": "ru.yandex.searchplugin/7.16 (none none; android 4.4.2)",
"interfaces": {
"screen": {}
},
"locale": "ru-RU",
"timezone": "UTC"
},
"request": {
"nlu": {
"entities": [],
"tokens": []
},
"payload": {
"aaa": "bbb"
},
"type": "ButtonPressed"
},
"session": {
"message_id": 3,
"new": False,
"session_id": "1cd02173-43c63383-3f975d2e-54738",
"skill_id": "934d5268-3d49-4b5a-bb53-97200edd0a0b",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
response = client.process_json(msg_json)
assert response.to_dict() == {
'response': {
'text': 'text',
'end_session': False,
},
"session": {
"message_id": 3,
"session_id": "1cd02173-43c63383-3f975d2e-54738",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
@client.register_callback_processor()
def f(request):
return 1
with pytest.raises(TypeError):
client.process_json(msg_json)
def test_incorrect_request(self):
client = AliceClient()
msg_json = {
"meta": {
"client_id": "ru.yandex.searchplugin/7.16 (none none; android 4.4.2)",
"interfaces": {
"screen": {}
},
"locale": "ru-RU",
"timezone": "UTC"
},
"request": {
"nlu": {
"entities": [],
"tokens": []
},
"payload": {
"aaa": "bbb"
},
"type": "FAIL"
},
"session": {
"message_id": 3,
"new": False,
"session_id": "1cd02173-43c63383-3f975d2e-54738",
"skill_id": "934d5268-3d49-4b5a-bb53-97200edd0a0b",
"user_id": "1AF98159AEAC9F1ABA46DC479ECD790CB97E75F3D0DA5E84331279FF57296926"
},
"version": "1.0"
}
with pytest.raises(ValueError):
client.process_json(msg_json)
with pytest.raises(TypeError):
client.process_json(1)
with pytest.raises(KeyError):
client.process_json({})
with pytest.raises(KeyError):
client.process_json({'request': {}})
with pytest.raises(TypeError):
client.process_json({'request': {'type': 1}})
| 32.017316
| 93
| 0.443213
| 534
| 7,396
| 5.981273
| 0.170412
| 0.026299
| 0.058547
| 0.168754
| 0.915467
| 0.89449
| 0.851597
| 0.797433
| 0.734189
| 0.718535
| 0
| 0.140067
| 0.432396
| 7,396
| 231
| 94
| 32.017316
| 0.620772
| 0
| 0
| 0.728571
| 0
| 0
| 0.285521
| 0.133838
| 0
| 0
| 0
| 0
| 0.028571
| 1
| 0.038095
| false
| 0
| 0.014286
| 0.019048
| 0.07619
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
3a5af104bcdce18fdc75c10f2f00a69ff3ee722d
| 161
|
py
|
Python
|
desktop/core/ext-py/nose-1.3.7/functional_tests/support/ctx/mod_import_skip.py
|
kokosing/hue
|
2307f5379a35aae9be871e836432e6f45138b3d9
|
[
"Apache-2.0"
] | 5,079
|
2015-01-01T03:39:46.000Z
|
2022-03-31T07:38:22.000Z
|
desktop/core/ext-py/nose-1.3.7/functional_tests/support/ctx/mod_import_skip.py
|
zks888/hue
|
93a8c370713e70b216c428caa2f75185ef809deb
|
[
"Apache-2.0"
] | 1,623
|
2015-01-01T08:06:24.000Z
|
2022-03-30T19:48:52.000Z
|
desktop/core/ext-py/nose-1.3.7/functional_tests/support/ctx/mod_import_skip.py
|
zks888/hue
|
93a8c370713e70b216c428caa2f75185ef809deb
|
[
"Apache-2.0"
] | 2,033
|
2015-01-04T07:18:02.000Z
|
2022-03-28T19:55:47.000Z
|
from nose import SkipTest
raise SkipTest("Don't run me")
def test():
assert False, "Should not be run"
def test2():
assert False, "Should not be run"
| 16.1
| 37
| 0.677019
| 26
| 161
| 4.192308
| 0.653846
| 0.201835
| 0.311927
| 0.366972
| 0.458716
| 0.458716
| 0
| 0
| 0
| 0
| 0
| 0.007937
| 0.217391
| 161
| 9
| 38
| 17.888889
| 0.857143
| 0
| 0
| 0.333333
| 0
| 0
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.166667
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
28ce672bd9d65a14d76339419473baaa1c8d4a36
| 128
|
py
|
Python
|
python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_0/_pkg1_0_0_0/_pkg1_0_0_0_1/_mod1_0_0_0_1_1.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_0/_pkg1_0_0_0/_pkg1_0_0_0_1/_mod1_0_0_0_1_1.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_0/_pkg1_0_0_0/_pkg1_0_0_0_1/_mod1_0_0_0_1_1.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
name1_0_0_0_1_1_0 = None
name1_0_0_0_1_1_1 = None
name1_0_0_0_1_1_2 = None
name1_0_0_0_1_1_3 = None
name1_0_0_0_1_1_4 = None
| 14.222222
| 24
| 0.820313
| 40
| 128
| 1.875
| 0.175
| 0.266667
| 0.466667
| 0.533333
| 0.88
| 0.88
| 0.746667
| 0
| 0
| 0
| 0
| 0.318182
| 0.140625
| 128
| 9
| 25
| 14.222222
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
e91506705dbb145d0523cd604c4ddec00eb32803
| 161
|
py
|
Python
|
ramda/zip_test.py
|
jakobkolb/ramda.py
|
982b2172f4bb95b9a5b09eff8077362d6f2f0920
|
[
"MIT"
] | 56
|
2018-08-06T08:44:58.000Z
|
2022-03-17T09:49:03.000Z
|
ramda/zip_test.py
|
jakobkolb/ramda.py
|
982b2172f4bb95b9a5b09eff8077362d6f2f0920
|
[
"MIT"
] | 28
|
2019-06-17T11:09:52.000Z
|
2022-02-18T16:59:21.000Z
|
ramda/zip_test.py
|
jakobkolb/ramda.py
|
982b2172f4bb95b9a5b09eff8077362d6f2f0920
|
[
"MIT"
] | 5
|
2019-09-18T09:24:38.000Z
|
2021-07-21T08:40:23.000Z
|
from ramda import *
from ramda.private.asserts import *
def zip_test():
assert_equal(zip([1, 2, 3], ["a", "b", "c", "e"]), [[1, "a"], [2, "b"], [3, "c"]])
| 23
| 86
| 0.515528
| 27
| 161
| 3
| 0.62963
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.045455
| 0.180124
| 161
| 6
| 87
| 26.833333
| 0.568182
| 0
| 0
| 0
| 0
| 0
| 0.043478
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.25
| true
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
3a771f10943a2b41dea4053499724ddf9ac3a8ca
| 329
|
py
|
Python
|
ex108/teste.py
|
eng-lenin/Python
|
6038f052b8343878760ed133dd43869927e39b3d
|
[
"MIT"
] | 1
|
2021-01-30T14:50:55.000Z
|
2021-01-30T14:50:55.000Z
|
ex108/teste.py
|
eng-lenin/Python
|
6038f052b8343878760ed133dd43869927e39b3d
|
[
"MIT"
] | null | null | null |
ex108/teste.py
|
eng-lenin/Python
|
6038f052b8343878760ed133dd43869927e39b3d
|
[
"MIT"
] | null | null | null |
import moeda
preço = float(input('Digite o preço: R$ '))
print(f'A metade do preço {moeda.moeda(preço)} é {moeda.moeda(moeda.metade(preço))}')
print(f'O dobro do preço {moeda.moeda(preço)} é {moeda.moeda(moeda.dobro(preço))}')
print(f'Aumentndo o preço {moeda.moeda(preço)} em 10% temos {moeda.moeda(moeda.aumentar(preço, 10))}')
| 65.8
| 102
| 0.717325
| 56
| 329
| 4.214286
| 0.357143
| 0.381356
| 0.190678
| 0.254237
| 0.322034
| 0.322034
| 0.322034
| 0.322034
| 0.322034
| 0
| 0
| 0.013423
| 0.094225
| 329
| 5
| 102
| 65.8
| 0.778523
| 0
| 0
| 0
| 0
| 0.6
| 0.784848
| 0.306061
| 0
| 0
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| 0.6
| 0
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| null | 1
| 1
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|
0
| 7
|
3a791a78b5d314313444bc1fbc8ea53122548b2e
| 7,403
|
py
|
Python
|
tests/html/test_button.py
|
elliterate/xpath.py
|
6a244d8067cebf6f9f3cc9d9f05bfb5c1ca9ed40
|
[
"MIT",
"Unlicense"
] | 18
|
2016-07-28T22:05:50.000Z
|
2021-11-30T13:29:40.000Z
|
tests/html/test_button.py
|
elliterate/xpath.py
|
6a244d8067cebf6f9f3cc9d9f05bfb5c1ca9ed40
|
[
"MIT",
"Unlicense"
] | 6
|
2016-08-02T22:02:36.000Z
|
2020-04-15T09:41:38.000Z
|
tests/html/test_button.py
|
elliterate/xpath.py
|
6a244d8067cebf6f9f3cc9d9f05bfb5c1ca9ed40
|
[
"MIT",
"Unlicense"
] | 7
|
2017-01-15T16:55:24.000Z
|
2020-09-15T05:45:52.000Z
|
from tests.case import HTMLTestCase
class ButtonTestCase(HTMLTestCase):
__fixture__ = "form.html"
__matcher__ = "button"
class TestButtonTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_id(self):
self.assertEqual(self.get("button-with-id"), "id-button")
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("button-with-value"), "value-button")
def test_finds_buttons_by_approximate_value(self):
self.assertEqual(self.get("ton-with-val"), "value-button")
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My button title"), "title-button")
def test_finds_buttons_by_approximate_title(self):
self.assertEqual(self.get("button title"), "title-button")
class TestExactButtonTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("button-with-value", exact=True), "value-button")
def test_does_not_find_buttons_by_approximate_value(self):
self.assertIsNone(self.get("ton-with-val", exact=True))
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My button title", exact=True), "title-button")
def test_does_not_find_buttons_by_title(self):
self.assertIsNone(self.get("button title", exact=True))
class TestImageTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_id(self):
self.assertEqual(self.get("imgbut-with-id"), "id-imgbut")
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("imgbut-with-value"), "value-imgbut")
def test_finds_buttons_by_approximate_value(self):
self.assertEqual(self.get("gbut-with-val"), "value-imgbut")
def test_finds_buttons_by_alt_attribute(self):
self.assertEqual(self.get("imgbut-with-alt"), "alt-imgbut")
def test_finds_buttons_by_approximate_alt_attribute(self):
self.assertEqual(self.get("mgbut-with-al"), "alt-imgbut")
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My imgbut title"), "title-imgbut")
def test_finds_buttons_by_approximate_title(self):
self.assertEqual(self.get("imgbut title"), "title-imgbut")
class TestExactImageTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("imgbut-with-value", exact=True), "value-imgbut")
def test_does_not_find_buttons_by_approximate_value(self):
self.assertIsNone(self.get("gbut-with-val", exact=True))
def test_finds_buttons_by_alt_attribute(self):
self.assertEqual(self.get("imgbut-with-alt", exact=True), "alt-imgbut")
def test_does_not_find_buttons_by_approximate_alt_attribute(self):
self.assertIsNone(self.get("mgbut-with-al", exact=True))
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My imgbut title", exact=True), "title-imgbut")
def test_does_not_find_buttons_by_title(self):
self.assertIsNone(self.get("imgbut title", exact=True))
class TestResetTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_id(self):
self.assertEqual(self.get("reset-with-id"), "id-reset")
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("reset-with-value"), "value-reset")
def test_finds_buttons_by_approximate_value(self):
self.assertEqual(self.get("set-with-val"), "value-reset")
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My reset title"), "title-reset")
def test_finds_buttons_by_approximate_title(self):
self.assertEqual(self.get("reset title"), "title-reset")
class TestExactResetTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("reset-with-value", exact=True), "value-reset")
def test_does_not_find_buttons_by_approximate_value(self):
self.assertIsNone(self.get("set-with-val", exact=True))
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My reset title", exact=True), "title-reset")
def test_does_not_find_buttons_by_title(self):
self.assertIsNone(self.get("reset title", exact=True))
class TestSubmitTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_id(self):
self.assertEqual(self.get("submit-with-id"), "id-submit")
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("submit-with-value"), "value-submit")
def test_finds_buttons_by_approximate_value(self):
self.assertEqual(self.get("mit-with-val"), "value-submit")
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My submit title"), "title-submit")
def test_finds_buttons_by_approximate_title(self):
self.assertEqual(self.get("submit title"), "title-submit")
class TestExactSubmitTypeInputTag(ButtonTestCase):
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("submit-with-value", exact=True), "value-submit")
def test_does_not_find_buttons_by_approximate_value(self):
self.assertIsNone(self.get("mit-with-val", exact=True))
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My submit title", exact=True), "title-submit")
def test_does_not_find_buttons_by_approximate_title(self):
self.assertIsNone(self.get("submit title", exact=True))
class TestButtonTag(ButtonTestCase):
def test_finds_buttons_by_id(self):
self.assertEqual(self.get("btag-with-id"), "id-btag")
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("btag-with-value"), "value-btag")
def test_finds_buttons_by_approximate_value(self):
self.assertEqual(self.get("tag-with-val"), "value-btag")
def test_finds_buttons_by_text(self):
self.assertEqual(self.get("btag-with-text"), "text-btag")
def test_finds_buttons_by_text_ignoring_whitespace(self):
self.assertEqual(self.get("My whitespaced button"), "btag-with-whitespace")
def test_finds_buttons_by_approximate_text(self):
self.assertEqual(self.get("tag-with-tex"), "text-btag")
def test_finds_buttons_with_child_tags_by_text(self):
self.assertEqual(self.get("An emphatic button"), "btag-with-children")
def test_finds_buttons_by_text_of_their_children(self):
self.assertEqual(self.get("emphatic"), "btag-with-children")
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My btag title"), "title-btag")
def test_finds_buttons_by_approximate_title(self):
self.assertEqual(self.get("btag title"), "title-btag")
class TestExactButtonTag(ButtonTestCase):
def test_finds_buttons_by_value(self):
self.assertEqual(self.get("btag-with-value", exact=True), "value-btag")
def test_does_not_find_buttons_by_approximate_value(self):
self.assertIsNone(self.get("tag-with-val", exact=True))
def test_finds_buttons_by_text(self):
self.assertEqual(self.get("btag-with-text", exact=True), "text-btag")
def test_does_not_find_buttons_by_approximate_text(self):
self.assertIsNone(self.get("tag-with-tex", exact=True))
def test_finds_buttons_by_title(self):
self.assertEqual(self.get("My btag title", exact=True), "title-btag")
def test_does_not_find_buttons_by_approximate_title(self):
self.assertIsNone(self.get("btag title", exact=True))
| 37.964103
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| 0.847086
| 0.807978
| 0.777278
| 0.694368
| 0.67853
| 0.650372
| 0
| 0
| 0.141834
| 7,403
| 194
| 84
| 38.159794
| 0.804974
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| 0.388889
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| 0.172633
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| 0.444444
| 1
| 0.444444
| false
| 0
| 0.007937
| 0
| 0.555556
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| 0
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| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
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| null | 0
| 0
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| 1
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| 0
| 0
| 0
| 1
| 0
|
0
| 8
|
3a9ff2073657f1328d725c73baa45d742a12bfe4
| 13,625
|
py
|
Python
|
demo2.py
|
lucas7788/ontology-python-vm
|
22988c6826d6c8546965016cd6f8cdbc5ce3fa13
|
[
"MIT"
] | null | null | null |
demo2.py
|
lucas7788/ontology-python-vm
|
22988c6826d6c8546965016cd6f8cdbc5ce3fa13
|
[
"MIT"
] | null | null | null |
demo2.py
|
lucas7788/ontology-python-vm
|
22988c6826d6c8546965016cd6f8cdbc5ce3fa13
|
[
"MIT"
] | null | null | null |
from ontology.account.account import Account
from ontology.common.address import Address
from ontology.ont_sdk import OntologySdk
from ontology.smart_contract.neo_contract.abi.abi_info import AbiInfo
from ontology.smart_contract.neo_contract.abi.build_params import BuildParams
from src.types.bool_item import BoolItem
from src.types.bytearray_item import ByteArrayItem
from src.types.integer_item import IntegerItem
from src.utils.config import Config
from src.utils.script_op import ScriptOp
from src.utils.service_map import ServiceMap
from src.vm.execution_context import ExecutionContext
from src.vm.execution_engine import ExecutionEngine
privatekey1 = "1094e90dd7c4fdfd849c14798d725ac351ae0d924b29a279a9ffa77d5737bd96"
privatekey2 = "bc254cf8d3910bc615ba6bf09d4553846533ce4403bc24f58660ae150a6d64cf"
# nep5abi = {"hash":"0x5bb169f915c916a5e30a3c13a5e0cd228ea26826","entrypoint":"Main","functions":
# [{"name":"Name","parameters":[],"returntype":"String"},{"name":"Symbol","parameters":[],"returntype":"String"},
# {"name":"Decimals","parameters":[],"returntype":"Integer"},{"name":"Main","parameters":
# [{"name":"operation","type":"String"},{"name":"args","type":"Array"}],"returntype":"Any"},
# {"name":"Init","parameters":[],"returntype":"Boolean"},{"name":"TotalSupply","parameters":[],"returntype":"Integer"},
# {"name":"Transfer","parameters":[{"name":"from","type":"ByteArray"},{"name":"to","type":"ByteArray"},
# {"name":"value","type":"Integer"}],"returntype":"Boolean"},
# {"name":"BalanceOf","parameters":[{"name":"address","type":"ByteArray"}],"returntype":"Integer"}],
# "events":[
# {"name":"transfer","parameters":[{"name":"arg1","type":"ByteArray"},{"name":"arg2","type":"ByteArray"},
# {"name":"arg3","type":"Integer"}],"returntype":"Void"}]}
nep5abi = {"hash":"0xb80b05b998e017b0b170bc51c52b0f148cb990d3","entrypoint":"Main","functions":[
{"name":"name","parameters":[],"returntype":"String"},
{"name":"symbol","parameters":[],"returntype":"String"},
{"name":"decimals","parameters":[],"returntype":"Integer"},
{"name":"Main","parameters":[
{"name":"operation","type":"String"},
{"name":"args","type":"Array"}],"returntype":"ByteArray"},
{"name":"deploy","parameters":[],"returntype":"Boolean"},{"name":"totalSupply","parameters":[],"returntype":"Integer"},{"name":"transfer","parameters":[{"name":"from","type":"ByteArray"},{"name":"to","type":"ByteArray"},{"name":"value","type":"Integer"}],"returntype":"Boolean"},{"name":"balanceOf","parameters":[{"name":"address","type":"ByteArray"}],"returntype":"Integer"},{"name":"inflation","parameters":[{"name":"count","type":"Integer"}],"returntype":"Boolean"},{"name":"recycle","parameters":[{"name":"count","type":"Integer"}],"returntype":"Boolean"},{"name":"transferMulti","parameters":[{"name":"args","type":"Array"}],"returntype":"Boolean"}],"events":[]}
# codeStr = "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";
codeStr = "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"
sdk = OntologySdk()
acct1 = Account(privatekey1)
acct2 = Account(privatekey2)
def execute_test():
code_bytes = bytearray.fromhex(codeStr)
config = Config('b80b05b998e017b0b170bc51c52b0f148cb990d3')
params = build_params()
print("params: ", params.hex())
config.tx = build_tx()
# print(config.tx.serialize(True))
engine = ExecutionEngine()
engine.push_context(ExecutionContext(engine, params))
num = 0
while(True):
if len(engine.contexts) == 0:
break
engine.execute_code()
if engine.op_code == ScriptOp.OP_RET:
break
if engine.op_code is None:
pass
elif ScriptOp.OP_PUSHBYTES1.value <= engine.op_code.value <= ScriptOp.OP_PUSHBYTES75.value:
pass
elif not engine.validate_op():
break
else:
if engine.op_code is not None:
print(num, "> " + str(engine.evaluation_stack.count()) + " " + hex(engine.op_code.value) + " " + engine.op_exec.name + " " + engine.evaluation_stack.info2())
num += 1
if ScriptOp.OP_APPCALL == engine.op_code:
print("####APPCALL####")
num = 0
engine2 = ExecutionEngine()
engine2.push_context(ExecutionContext(engine2, code_bytes))
engine.evaluation_stack.copy_to(engine2.evaluation_stack)
engine = engine2
elif ScriptOp.OP_SYSCALL == engine.op_code:
bys = engine.context.op_reader.read_var_bytes()
print("####SYSCALL#### ", bys.decode())
service = ServiceMap.get_service(bys.decode())
if service is None:
print(bys.decode())
return
service.exec(config, engine)
else:
engine.step_info()
print("##########end############")
print("Stack Count:", engine.evaluation_stack.count())
items = engine.evaluation_stack.peek(0)
if type(items) is ByteArrayItem:
print("Result ByteArrayItem:", engine.evaluation_stack.peek(0).get_bytearray().hex() + engine.evaluation_stack.peek(0).get_bytearray().decode())
elif type(items) is IntegerItem:
print("Result GetBigInteger:", engine.evaluation_stack.peek(0).get_biginteger())
elif type(items) is BoolItem:
print("Result BoolItem:", engine.evaluation_stack.peek(0).get_bool())
return
def build_params():
abi = AbiInfo(nep5abi['hash'], nep5abi['entrypoint'], nep5abi["functions"])
# func = abi.get_function("Transfer")
# func = abi.get_function("BalanceOf")
func = abi.get_function("balanceOf")
# func = abi.get_function("deploy")
# func.set_params_value((acct1.get_address().to_array(), acct2.get_address().to_array(), 19 * 10000000))
# add = acct1.get_address().to_reverse_hex_str()
func.set_params_value((Address.b58decode('AHX1wzvdw9Yipk7E9MuLY4GGX4Ym9tHeDe').to_array(),))
params = BuildParams.serialize_abi_function(func)
params += bytearray([0x67])
return params
def build_tx():
contract_address = Address.address_from_vm_code(codeStr)
params = build_params()
tx = sdk.neo_vm().make_invoke_transaction(contract_address.to_array(), params, None, 0, 0)
# tx_hash = tx.hash256_bytes()
# sig_data = acct1.generate_signature(tx_hash, acct1.get_signature_scheme())
# sig = Sig([acct1.serialize_public_key()], 1, [sig_data])
# tx.sigs = list()
# tx.sigs.append(sig)
return tx
if __name__ == "__main__":
execute_test()
| 105.620155
| 6,168
| 0.832734
| 694
| 13,625
| 16.195965
| 0.23487
| 0.017794
| 0.016815
| 0.011121
| 0.118772
| 0.11121
| 0.10605
| 0.092349
| 0.083274
| 0.076868
| 0
| 0.465914
| 0.077358
| 13,625
| 128
| 6,169
| 106.445313
| 0.428208
| 0.183266
| 0
| 0.139785
| 0
| 0
| 0.650126
| 0.57862
| 0
| 1
| 0.004143
| 0
| 0
| 1
| 0.032258
| false
| 0.021505
| 0.139785
| 0
| 0.215054
| 0.107527
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
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| null | 1
| 0
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|
0
| 8
|
c9054536fc1255b14e6796a95621cd8b3e6fe673
| 9,730
|
py
|
Python
|
haychecker/_test/dhc/mutual_info_test.py
|
fruttasecca/hay_checker
|
2bbf4e8e90e0abc590dd74080fb6e4f445056354
|
[
"MIT"
] | 2
|
2019-05-22T08:24:38.000Z
|
2020-12-04T13:36:30.000Z
|
haychecker/_test/dhc/mutual_info_test.py
|
fruttasecca/hay_checker
|
2bbf4e8e90e0abc590dd74080fb6e4f445056354
|
[
"MIT"
] | null | null | null |
haychecker/_test/dhc/mutual_info_test.py
|
fruttasecca/hay_checker
|
2bbf4e8e90e0abc590dd74080fb6e4f445056354
|
[
"MIT"
] | 3
|
2018-09-15T13:40:40.000Z
|
2021-06-29T23:31:18.000Z
|
import random
import unittest
import numpy as np
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType, StructField, StructType, IntegerType, FloatType
from sklearn.metrics import mutual_info_score
from haychecker.dhc.metrics import mutual_info
replace_empty_with_null = udf(lambda x: None if x == "" else x, StringType())
replace_0_with_null = udf(lambda x: None if x == 0 else x, IntegerType())
replace_0dot_with_null = udf(lambda x: None if x == 0. else x, FloatType())
replace_every_string_with_null = udf(lambda x: None, StringType())
replace_every_int_with_null = udf(lambda x: None, IntegerType())
replace_every_float_with_null = udf(lambda x: None, FloatType())
class TestMutualInfo(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestMutualInfo, self).__init__(*args, **kwargs)
self.spark = SparkSession.builder.master("local[2]").appName("mutual_info_test").getOrCreate()
self.spark.sparkContext.setLogLevel("ERROR")
self.spark.conf.set("spark.sql.crossJoin.enabled", "true")
def mi(self, df, x, y):
index = (df[x].isna()) | (df[y].isna())
index = ~index
if sum(index) > 0:
return mutual_info_score(df[x][index], df[y][index])
else:
return 0
def test_empty(self):
data = pd.DataFrame()
data["c1"] = []
data["c2"] = []
schema = [StructField("c1", IntegerType(), True), StructField("c2", StringType(), True)]
df = self.spark.createDataFrame(data, StructType(schema))
r1 = mutual_info(0, 1, df)[0]
self.assertEqual(r1, 0.)
def test_allnull(self):
data = pd.DataFrame()
data["c1"] = [" " for i in range(100)]
data["c2"] = [1 for i in range(100)]
df = self.spark.createDataFrame(data)
df = df.withColumn("c1", replace_every_string_with_null(df["c1"]))
df = df.withColumn("c2", replace_every_int_with_null(df["c2"]))
data["c1"] = [None for i in range(100)]
data["c2"] = [np.NaN for i in range(100)]
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertEqual(r, pmi)
r = mutual_info(1, 0, df)[0]
self.assertEqual(r, pmi)
def test_allequal(self):
data = pd.DataFrame()
data["c1"] = [chr(0) for _ in range(100)]
data["c2"] = [1 for _ in range(100)]
df = self.spark.createDataFrame(data)
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertEqual(r, pmi)
r = mutual_info(1, 0, df)[0]
self.assertEqual(r, pmi)
def test_halfnull_halfequal(self):
data = pd.DataFrame()
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c1.extend(["" for _ in range(50)])
c2.extend([0 for _ in range(50)])
data["c1"] = c1
data["c2"] = c2
df = self.spark.createDataFrame(data)
df = df.withColumn("c1", replace_empty_with_null(df["c1"]))
df = df.withColumn("c2", replace_0_with_null(df["c2"]))
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c1.extend([None for _ in range(50)])
c2.extend([np.NaN for _ in range(50)])
data["c1"] = c1
data["c2"] = c2
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
def test_halfhalf(self):
data = pd.DataFrame()
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c3 = [0.7 for _ in range(50)]
c1.extend(["zz" for _ in range(50)])
c2.extend([100 for _ in range(50)])
c3.extend([32. for _ in range(50)])
data["c1"] = c1
data["c2"] = c2
data["c3"] = c3
df = self.spark.createDataFrame(data)
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c1", "c3")
r = mutual_info(0, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c2", "c3")
r = mutual_info(1, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
def test_halfhalf_shuffled(self):
for _ in range(2):
data = pd.DataFrame()
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c3 = [0.7 for _ in range(50)]
c1.extend(["zz" for _ in range(50)])
c2.extend([100 for _ in range(50)])
c3.extend([32. for _ in range(50)])
random.shuffle(c1)
random.shuffle(c2)
random.shuffle(c3)
data["c1"] = c1
data["c2"] = c2
data["c3"] = c3
df = self.spark.createDataFrame(data)
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c1", "c3")
r = mutual_info(0, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c2", "c3")
r = mutual_info(1, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
def test_halfhalf_shuffled_withnull(self):
for _ in range(2):
data = pd.DataFrame()
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c3 = [0.7 for _ in range(50)]
c1.extend(["" for _ in range(50)])
c2.extend([0 for _ in range(50)])
c3.extend([0. for _ in range(50)])
random.shuffle(c1)
random.shuffle(c2)
random.shuffle(c3)
data["c1"] = c1
data["c2"] = c2
data["c3"] = c3
df = self.spark.createDataFrame(data)
df = df.withColumn("c1", replace_empty_with_null(df["c1"]))
df = df.withColumn("c2", replace_0_with_null(df["c2"]))
df = df.withColumn("c3", replace_0dot_with_null(df["c3"]))
data = pd.DataFrame()
c1 = [(el if el != "" else None) for el in c1]
c2 = [(el if el != 0 else None) for el in c2]
c3 = [(el if el != 0. else None) for el in c3]
data["c1"] = c1
data["c2"] = c2
data["c3"] = c3
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c1", "c3")
r = mutual_info(0, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c2", "c3")
r = mutual_info(1, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
def test_mixed_shuffled_with_null(self):
for _ in range(2):
data = pd.DataFrame()
c1 = [chr(i) for i in range(50)]
c2 = [i for i in range(1, 51)]
c3 = [i / 0.7 for i in range(1, 51)]
c1.extend(["" for _ in range(50)])
c2.extend([0 for _ in range(50)])
c3.extend([0. for _ in range(50)])
random.shuffle(c1)
random.shuffle(c2)
random.shuffle(c3)
data["c1"] = c1
data["c2"] = c2
data["c3"] = c3
df = self.spark.createDataFrame(data)
df = df.withColumn("c1", replace_empty_with_null(df["c1"]))
df = df.withColumn("c2", replace_0_with_null(df["c2"]))
df = df.withColumn("c3", replace_0dot_with_null(df["c3"]))
data = pd.DataFrame()
c1 = [(el if el != "" else None) for el in c1]
c2 = [(el if el != 0 else np.NaN) for el in c2]
c3 = [(el if el != 0. else np.NaN) for el in c3]
data["c1"] = c1
data["c2"] = c2
data["c3"] = c3
pmi = self.mi(data, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c1", "c3")
r = mutual_info(0, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(data, "c2", "c3")
r = mutual_info(1, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
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0
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|
c90dc4782ce74a1107e8aabf68563c1847c29131
| 46
|
py
|
Python
|
whoisit/__main__.py
|
karakays/who-is-it
|
1acc317b9f11d40110cf7a30b910dba53a525deb
|
[
"MIT"
] | null | null | null |
whoisit/__main__.py
|
karakays/who-is-it
|
1acc317b9f11d40110cf7a30b910dba53a525deb
|
[
"MIT"
] | null | null | null |
whoisit/__main__.py
|
karakays/who-is-it
|
1acc317b9f11d40110cf7a30b910dba53a525deb
|
[
"MIT"
] | null | null | null |
from . import app
def main():
app.run()
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0
| 7
|
c9626850de096356edd211d9314f58870bdf1eb3
| 20,646
|
py
|
Python
|
policykit/constitution/migrations/0001_initial.py
|
mashton/policyk
|
623523d76d63c06b6d559ad7b477d80512fbd2e7
|
[
"MIT"
] | 78
|
2020-05-08T17:25:38.000Z
|
2022-01-13T05:44:50.000Z
|
policykit/constitution/migrations/0001_initial.py
|
mashton/policyk
|
623523d76d63c06b6d559ad7b477d80512fbd2e7
|
[
"MIT"
] | 302
|
2020-02-20T07:04:30.000Z
|
2022-02-25T17:44:23.000Z
|
policykit/constitution/migrations/0001_initial.py
|
mashton/policyk
|
623523d76d63c06b6d559ad7b477d80512fbd2e7
|
[
"MIT"
] | 13
|
2020-04-17T19:44:26.000Z
|
2022-02-25T17:18:04.000Z
|
# Generated by Django 3.2.2 on 2021-09-02 15:10
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('auth', '0012_alter_user_first_name_max_length'),
('policyengine', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='ConstitutionCommunity',
fields=[
('communityplatform_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.communityplatform')),
],
options={
'abstract': False,
'base_manager_name': 'objects',
},
bases=('policyengine.communityplatform',),
),
migrations.CreateModel(
name='PolicykitAddCommunityDoc',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.TextField()),
('text', models.TextField()),
],
options={
'permissions': (('can_execute_policykitaddcommunitydoc', 'Can execute policykit add community doc'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRemoveUserRole',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('role', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='policyengine.communityrole')),
('users', models.ManyToManyField(to='policyengine.CommunityUser')),
],
options={
'permissions': (('can_execute_policykitremoveuserrole', 'Can execute policykit remove user role'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRemoveTriggerPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitremovetriggerpolicy', 'Can execute policykit remove trigger policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRemovePlatformPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitremoveplatformpolicy', 'Can execute policykit remove platform policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRemoveConstitutionPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitremoveconstitutionpolicy', 'Can execute policykit remove constitution policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRecoverTriggerPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitrecovertriggerpolicy', 'Can execute policykit recover trigger policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRecoverPlatformPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitrecoverplatformpolicy', 'Can execute policykit recover platform policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRecoverConstitutionPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitrecoverconstitutionpolicy', 'Can execute policykit recover constitution policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitRecoverCommunityDoc',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('doc', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.communitydoc')),
],
options={
'permissions': (('can_execute_policykitrecovercommunitydoc', 'Can execute policykit recover community doc'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitEditRole',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=300, verbose_name='name')),
('description', models.TextField(blank=True, default='', null=True)),
('permissions', models.ManyToManyField(to='auth.Permission')),
('role', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.communityrole')),
],
options={
'permissions': (('can_execute_policykiteditrole', 'Can execute policykit edit role'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitDeleteRole',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('role', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.communityrole')),
],
options={
'permissions': (('can_execute_policykitdeleterole', 'Can execute policykit delete role'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitDeleteCommunityDoc',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('doc', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.communitydoc')),
],
options={
'permissions': (('can_execute_policykitdeletecommunitydoc', 'Can execute policykit delete community doc'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitChangeTriggerPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('filter', models.TextField(blank=True, default='return True\n\n', verbose_name='Filter')),
('initialize', models.TextField(blank=True, default='pass\n\n', verbose_name='Initialize')),
('check', models.TextField(blank=True, default='return PASSED\n\n', verbose_name='Check')),
('notify', models.TextField(blank=True, default='pass\n\n', verbose_name='Notify')),
('success', models.TextField(blank=True, default='action.execute()\n\n', verbose_name='Pass')),
('fail', models.TextField(blank=True, default='pass\n\n', verbose_name='Fail')),
('action_types', models.ManyToManyField(to='policyengine.ActionType')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitchangetriggerpolicy', 'Can execute policykit change trigger policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitChangePlatformPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('filter', models.TextField(blank=True, default='return True\n\n', verbose_name='Filter')),
('initialize', models.TextField(blank=True, default='pass\n\n', verbose_name='Initialize')),
('check', models.TextField(blank=True, default='return PASSED\n\n', verbose_name='Check')),
('notify', models.TextField(blank=True, default='pass\n\n', verbose_name='Notify')),
('success', models.TextField(blank=True, default='action.execute()\n\n', verbose_name='Pass')),
('fail', models.TextField(blank=True, default='pass\n\n', verbose_name='Fail')),
('action_types', models.ManyToManyField(to='policyengine.ActionType')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitchangeplatformpolicy', 'Can execute policykit change platform policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitChangeConstitutionPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('filter', models.TextField(blank=True, default='return True\n\n', verbose_name='Filter')),
('initialize', models.TextField(blank=True, default='pass\n\n', verbose_name='Initialize')),
('check', models.TextField(blank=True, default='return PASSED\n\n', verbose_name='Check')),
('notify', models.TextField(blank=True, default='pass\n\n', verbose_name='Notify')),
('success', models.TextField(blank=True, default='action.execute()\n\n', verbose_name='Pass')),
('fail', models.TextField(blank=True, default='pass\n\n', verbose_name='Fail')),
('action_types', models.ManyToManyField(to='policyengine.ActionType')),
('policy', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.policy')),
],
options={
'permissions': (('can_execute_policykitchangeconstitutionpolicy', 'Can execute policykit change constitution policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitChangeCommunityDoc',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.TextField()),
('text', models.TextField()),
('doc', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='policyengine.communitydoc')),
],
options={
'permissions': (('can_execute_policykitchangecommunitydoc', 'Can execute policykit change community doc'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitAddUserRole',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('role', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='policyengine.communityrole')),
('users', models.ManyToManyField(to='policyengine.CommunityUser')),
],
options={
'permissions': (('can_execute_policykitadduserrole', 'Can execute policykit add user role'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitAddTriggerPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('filter', models.TextField(blank=True, default='return True\n\n', verbose_name='Filter')),
('initialize', models.TextField(blank=True, default='pass\n\n', verbose_name='Initialize')),
('check', models.TextField(blank=True, default='return PASSED\n\n', verbose_name='Check')),
('notify', models.TextField(blank=True, default='pass\n\n', verbose_name='Notify')),
('success', models.TextField(blank=True, default='action.execute()\n\n', verbose_name='Pass')),
('fail', models.TextField(blank=True, default='pass\n\n', verbose_name='Fail')),
('action_types', models.ManyToManyField(to='policyengine.ActionType')),
],
options={
'permissions': (('can_execute_policykitaddtriggerpolicy', 'Can execute policykit add trigger policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitAddRole',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=300, verbose_name='name')),
('description', models.TextField(blank=True, default='', null=True)),
('permissions', models.ManyToManyField(to='auth.Permission')),
],
options={
'permissions': (('can_execute_policykitaddrole', 'Can execute policykit add role'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitAddPlatformPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('filter', models.TextField(blank=True, default='return True\n\n', verbose_name='Filter')),
('initialize', models.TextField(blank=True, default='pass\n\n', verbose_name='Initialize')),
('check', models.TextField(blank=True, default='return PASSED\n\n', verbose_name='Check')),
('notify', models.TextField(blank=True, default='pass\n\n', verbose_name='Notify')),
('success', models.TextField(blank=True, default='action.execute()\n\n', verbose_name='Pass')),
('fail', models.TextField(blank=True, default='pass\n\n', verbose_name='Fail')),
('action_types', models.ManyToManyField(to='policyengine.ActionType')),
],
options={
'permissions': (('can_execute_addpolicykitplatformpolicy', 'Can execute policykit add platform policy'),),
},
bases=('policyengine.governableaction',),
),
migrations.CreateModel(
name='PolicykitAddConstitutionPolicy',
fields=[
('governableaction_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='policyengine.governableaction')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('filter', models.TextField(blank=True, default='return True\n\n', verbose_name='Filter')),
('initialize', models.TextField(blank=True, default='pass\n\n', verbose_name='Initialize')),
('check', models.TextField(blank=True, default='return PASSED\n\n', verbose_name='Check')),
('notify', models.TextField(blank=True, default='pass\n\n', verbose_name='Notify')),
('success', models.TextField(blank=True, default='action.execute()\n\n', verbose_name='Pass')),
('fail', models.TextField(blank=True, default='pass\n\n', verbose_name='Fail')),
('action_types', models.ManyToManyField(to='policyengine.ActionType')),
],
options={
'permissions': (('can_execute_policykitaddconstitutionpolicy', 'Can execute policykit add constitution policy'),),
},
bases=('policyengine.governableaction',),
),
]
| 64.317757
| 218
| 0.625932
| 1,893
| 20,646
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| 0.083281
| 0.835804
| 0.831782
| 0.831782
| 0.817114
| 0.72153
| 0.72153
| 0
| 0.002972
| 0.233992
| 20,646
| 320
| 219
| 64.51875
| 0.798799
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| 0.295548
| 0.146415
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| 0.095847
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|
0
| 9
|
a392be2b03c7996f15c54dc71e3762ee9e7cacdc
| 6,420
|
py
|
Python
|
convlstm/test/tests.py
|
AlbertoCenzato/pytorch_model_zoo
|
ffd9a70b95141c75dea4d71546ca509f88b1b63a
|
[
"MIT"
] | 11
|
2019-08-13T14:21:19.000Z
|
2022-02-17T12:22:01.000Z
|
convlstm/test/tests.py
|
AlbertoCenzato/pytorch_model_zoo
|
ffd9a70b95141c75dea4d71546ca509f88b1b63a
|
[
"MIT"
] | null | null | null |
convlstm/test/tests.py
|
AlbertoCenzato/pytorch_model_zoo
|
ffd9a70b95141c75dea4d71546ca509f88b1b63a
|
[
"MIT"
] | 3
|
2019-10-20T17:46:05.000Z
|
2021-12-10T22:29:50.000Z
|
import unittest
import random
import time
import torch
from ..package import convlstm, convlstm_cpp, convlstm_cuda
class TestConvLSTMCPP(unittest.TestCase):
def setUp(self):
input_size = (30,30)
input_dim = 1
hidden_dim = 1
kernel_size = (5,5)
bias = True
self.convlstm_cell = convlstm.ConvLSTMCell (input_size, input_dim, hidden_dim, kernel_size, bias)
self.convlstmcpp_cell = convlstm_cpp.ConvLSTMCPPCell(input_size, input_dim, hidden_dim, kernel_size, bias)
# The two models must have the same initial conditions
self.convlstmcpp_cell.weights.data = self.convlstm_cell.conv.weight.data
self.convlstmcpp_cell.bias.data = self.convlstm_cell.conv.bias.data
batch_size = 16
self.input = torch.rand((batch_size, input_dim) + input_size)
self.state = (torch.rand((batch_size, hidden_dim) + input_size), torch.rand((batch_size, hidden_dim) + input_size))
def tearDown(self):
self.convlstm_cell = None
self.convlstmcpp_cell = None
self.input = None
self.state = None
def test_forward(self):
output = self.convlstm_cell(self.input, self.state)
output_cpp = self.convlstmcpp_cell(self.input, self.state)
self.assertTrue(torch.equal(output[0], output_cpp[0]), 'The two output tensors are not equal')
def test_backpropagation(self):
output = self.convlstm_cell(self.input, self.state)
output_cpp = self.convlstmcpp_cell(self.input, self.state)
ground_truth = torch.full_like(output[0], 20)
criterion = torch.nn.MSELoss()
loss = criterion(output[0], ground_truth)
loss_cpp = criterion(output_cpp[0], ground_truth)
loss.backward()
loss_cpp.backward()
weight_grad = self.convlstm_cell.conv.weight._grad
weight_grad_cpp = self.convlstmcpp_cell.weights._grad
self.assertTrue(torch.allclose(weight_grad, weight_grad_cpp), 'The two weights gradients are not equal')
def test_bptt(self):
time_steps = 100 #random.randint(1,20)
criterion = torch.nn.MSELoss()
# python model
start = time.perf_counter()
output = self.convlstm_cell(self.input, self.state)
for _ in range(time_steps):
output = self.convlstm_cell(output[0], self.state)
ground_truth = torch.full_like(output[0], 20)
loss = criterion(output[0], ground_truth)
loss.backward()
grad = self.convlstm_cell.conv.weight._grad
print('Python time: {}'.format(time.perf_counter() - start))
# c++ model
start = time.perf_counter()
output_cpp = self.convlstmcpp_cell(self.input, self.state)
for _ in range(time_steps):
output_cpp = self.convlstmcpp_cell(output_cpp[0], self.state)
loss_cpp = criterion(output_cpp[0], ground_truth)
loss_cpp.backward()
grad_cpp = self.convlstmcpp_cell.weights._grad
print('C++ time: {}'.format(time.perf_counter() - start))
self.assertTrue(torch.allclose(grad, grad_cpp), 'The two gradients are not equal')
class TestConvLSTMCuda(unittest.TestCase):
def setUp(self):
input_size = (30,30)
input_dim = 1
hidden_dim = 1
kernel_size = (5,5)
bias = True
self.convlstm_cell = convlstm.ConvLSTMCell (input_size, input_dim, hidden_dim, kernel_size, bias)
self.convlstmcuda_cell = convlstm_cuda.ConvLSTMCudaCell(input_size, input_dim, hidden_dim, kernel_size, bias)
# The two models must have the same initial conditions
self.convlstmcuda_cell.weights.data = self.convlstm_cell.conv.weight.data
self.convlstmcuda_cell.bias.data = self.convlstm_cell.conv.bias.data
batch_size = 16
self.input = torch.rand((batch_size, input_dim) + input_size)
self.state = (torch.rand((batch_size, hidden_dim) + input_size), torch.rand((batch_size, hidden_dim) + input_size))
def tearDown(self):
self.convlstm_cell = None
self.convlstmcuda_cell = None
self.input = None
self.state = None
def test_forward(self):
output = self.convlstm_cell(self.input, self.state)
output_cpp = self.convlstmcuda_cell(self.input, self.state)
self.assertTrue(torch.equal(output[0], output_cpp[0]), 'The two output tensors are not equal')
def test_backpropagation(self):
output = self.convlstm_cell(self.input, self.state)
output_cpp = self.convlstmcuda_cell(self.input, self.state)
ground_truth = torch.full_like(output[0], 20)
criterion = torch.nn.MSELoss()
loss = criterion(output[0], ground_truth)
loss_cpp = criterion(output_cpp[0], ground_truth)
loss.backward()
loss_cpp.backward()
weight_grad = self.convlstm_cell.conv.weight._grad
weight_grad_cpp = self.convlstmcuda_cell.weights._grad
self.assertTrue(torch.allclose(weight_grad, weight_grad_cpp), 'The two weights gradients are not equal')
def test_bptt(self):
time_steps = 100 #random.randint(1,20)
criterion = torch.nn.MSELoss()
# python model
start = time.perf_counter()
output = self.convlstm_cell(self.input, self.state)
for _ in range(time_steps):
output = self.convlstm_cell(output[0], self.state)
ground_truth = torch.full_like(output[0], 20)
loss = criterion(output[0], ground_truth)
loss.backward()
grad = self.convlstm_cell.conv.weight._grad
print('Python time: {}'.format(time.perf_counter() - start))
# c++ model
start = time.perf_counter()
output_cpp = self.convlstmcuda_cell(self.input, self.state)
for _ in range(time_steps):
output_cpp = self.convlstmcuda_cell(output_cpp[0], self.state)
loss_cpp = criterion(output_cpp[0], ground_truth)
loss_cpp.backward()
grad_cpp = self.convlstmcuda_cell.weights._grad
print('C++ time: {}'.format(time.perf_counter() - start))
self.assertTrue(torch.allclose(grad, grad_cpp), 'The two gradients are not equal')
if __name__ == '__main__':
unittest.main()
| 36.896552
| 123
| 0.645794
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| 0.921252
| 0.915447
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| 0.898031
| 0
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| 6,420
| 174
| 124
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0
| 7
|
6e8a010f2834256ef316ba8ac7246302d665caa8
| 124,986
|
py
|
Python
|
wrfSection.py
|
fossabot/Ekman
|
2b688ac156159b8499736f4663716252ae90bec9
|
[
"MIT"
] | null | null | null |
wrfSection.py
|
fossabot/Ekman
|
2b688ac156159b8499736f4663716252ae90bec9
|
[
"MIT"
] | null | null | null |
wrfSection.py
|
fossabot/Ekman
|
2b688ac156159b8499736f4663716252ae90bec9
|
[
"MIT"
] | null | null | null |
"""
Author: Ueslei Adriano Sutil
Created: 08 Apr 2019
Last modified: 05 Jan 2021
Version: 2.12
This file generates a new WRF output file from scratch.
It is netCDF4 CF-compliant.
WARNING: Do not change anything in this file.
"""
from netCDF4 import Dataset
from setOptions import *
from numpy import dtype
from matplotlib import path
from wrf import getvar
from progress.bar import IncrementalBar
import numpy as np
import time
if wrfTemp or wrfPotTemp or wrfRh or wrfTd or wrfTwb or wrfTv or wrfSST or wrfPressure or wrfUnstaggeredU or wrfUnstaggeredV or wrfUnstaggeredW or wrfUvmet or wrfUvmet10m or wrfLatent or wrfSensible or wrfSlp or wrfAvo or wrfDbz or wrfGeopt or wrfOmega or wrfPvo or wrfTerrain or wrfRh2 or wrfTd2 or wrfLandmask== True:
wrfMassPoints = True
else:
wrfMassPoints = False
if wrfU or wrfV == True:
wrfUVPoints = True
else:
wrfUVPoints = False
wrfFillVal = 1.e+37
def bbox2ij(lon,lat,wrfBox=[-160., -155., 18., 23.]):
"""Return indices for i,j that will completely cover the specified bounding box.
i0,i1,j0,j1 = bbox2ij(lon,lat,wrfBox)
lon,lat = 2D arrays that are the target of the subset
wrfBox = list containing the bounding box: [lon_min, lon_max, lat_min, lat_max]
Example
-------
>>> i0,i1,j0,j1 = bbox2ij(lon_rho,[-71, -63., 39., 46])
>>> h_subset = nc.variables['h'][j0:j1,i0:i1]
"""
wrfBox = np.array(wrfBox)
mypath = np.array([wrfBox[[0,1,1,0]],wrfBox[[2,2,3,3]]]).T
p = path.Path(mypath)
points = np.vstack((lon.flatten(),lat.flatten())).T
n,m = np.shape(lon)
inside = p.contains_points(points).reshape((n,m))
ii,jj = np.meshgrid(range(m),range(n))
return min(ii[inside]),max(ii[inside]),min(jj[inside]),max(jj[inside])
def wrfVars(wrfOriDir,wrfNewDir):
"""
Generates a new WRF output file from scratch.
"""
wrfRawFile = Dataset(wrfOriDir, mode='r')
wrfNewFile = Dataset(wrfNewDir, 'w', format='NETCDF4')
wrfNewFile.title = "WRF output file made by "+projectAuthor
wrfNewFile.description = "Created with Ekman Toolbox in " + time.ctime(time.time())
wrfNewFile.link = "https://github.com/uesleisutil/Ekman"
# If a variable on mass point has been chosen.
if wrfMassPoints == True:
# Define Lat and Lon variables.
if selectWrfBox == True:
lon_wrf = wrfRawFile.variables['XLONG'][0,:,:]
lat_wrf = wrfRawFile.variables['XLAT'][0,:,:]
i0,i1,j0,j1 = bbox2ij(lon_wrf[:,:],lat_wrf[:,:],wrfBox)
lon_wrf = wrfRawFile.variables['XLONG'][0,j0:j1, i0:i1]
lat_wrf = wrfRawFile.variables['XLAT'][0,j0:j1, i0:i1]
wrfNewFile.createDimension('south_north', len(lat_wrf[:,0]))
wrfNewFile.createDimension('west_east', len(lon_wrf[0,:]))
print("Bounding box selected. New domain limits are: Longitude "+str(wrfBox[0])+"/"+str(wrfBox[1])+" and Latitude "+str(wrfBox[2])+"/"+str(wrfBox[3])+".")
else:
print("No bounding box selected: Using XLAT and XLONG variables from input file.")
lon_wrf = wrfRawFile.variables['XLONG'][0,:,:]
lat_wrf = wrfRawFile.variables['XLAT'][0,:,:]
wrfNewFile.createDimension('south_north', len(lat_wrf[:,0]))
wrfNewFile.createDimension('west_east', len(lon_wrf[0,:]))
wrfNewLon = wrfNewFile.createVariable('XLONG', 'd', ('south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewLon.long_name = 'Longitude on RHO-points'
wrfNewLon.units = 'degree_east'
wrfNewLon.standard_name = 'longitude'
wrfNewLon[:,:] = lon_wrf
wrfNewLat = wrfNewFile.createVariable('XLAT', 'd', ('south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewLat.units = 'degree_north'
wrfNewLat.standard_name = 'latitude'
wrfNewLat[:, :] = lat_wrf
# Define vertical levels and time-steps.
if selectWrfLevel == True and len(wrfLevel) == 1:
print("One vertical level selected: Working on vertical level "+str(wrfLevel)+".")
wrfNewFile.createDimension('bottom_top', len(wrfLevel))
if selectWrfLevel == True and len(wrfLevel) > 1:
print("Multiple vertical levels selected: Working from level "+str(wrfLevel[0])+" to "+str(wrfLevel[-1])+".")
wrfNewFile.createDimension('bottom_top', len(wrfLevel))
if selectWrfLevel == False:
print("No selected vertical levels specified: Using entire vertical level from input file.")
levels = getvar(wrfRawFile,'z',meta=False)[:,0,0]
wrfNewFile.createDimension('bottom_top', len(levels))
if selectWrfTimeStep == True:
ntimes = wrfTimeStep
print("Time-step selected: Working from time-step "+str(ntimes[0])+" to "+str(ntimes[-1])+".")
wrfNewFile.createDimension('Time', len(ntimes))
else:
print("No time-step selected. Working with entire time-step.")
ntimes = getvar(wrfRawFile,'LH',meta=False, timeidx=None)[:,0,0]
ntimes = np.arange(np.argmin(ntimes), len(ntimes))
wrfNewFile.createDimension('Time', len(ntimes))
# If WRF Temperature has been chosen.
if wrfTemp == True:
print('Working on WRF Temperature.')
bar = IncrementalBar('WRF Temperature:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('temp', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('temp', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('temp', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('temp', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('temp', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'temp',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = wrfRawFile.variables['temp'][i,wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('temp', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = wrfRawFile.variables['temp'][i,wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Potential Temperature has been chosen.
if wrfPotTemp == True:
print('Working on WRF Potential Temperature.')
bar = IncrementalBar('WRF Potential Temperature:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('th', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('th', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('th', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('th', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('th', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'th',units="degC",meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = wrfRawFile.variables['th'][i,wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('th', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = wrfRawFile.variables['th'][i,wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Relative Humidity has been chosen.
if wrfRh == True:
print('Working on WRF Relative Humidity.')
bar = IncrementalBar('WRF Relative Humidity:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = wrfRawFile.variables['rh'][i,wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = wrfRawFile.variables['rh'][i,wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Dew Point Temperature has been chosen.
if wrfTd == True:
print('Working on WRF Dew Point Temperature.')
bar = IncrementalBar('WRF Dew Point Temperature:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td',units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Wet Bulb Temperature has been chosen.
if wrfTwb == True:
print('Working on WRF Wet Bulb Temperature.')
bar = IncrementalBar('WRF Wet Bulb Temperature:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('twb', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Wet Bulb Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('twb', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Wet Bulb Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('twb', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Wet Bulb Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('twb', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Wet Bulb Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('twb', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Wet Bulb Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'twb', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('twb', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Wet Bulb Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'twb',units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Virtual Temperature has been chosen.
if wrfTv == True:
print('Working on WRF Virtual Temperature.')
bar = IncrementalBar('WRF Virtual Temperature:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('tv', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Virtual Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('tv', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Virtual Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('tv', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Virtual Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('tv', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Virtual Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('tv', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Virtual Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'tv', units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('tv', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Virtual Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tv',units="degC", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Pressure has been chosen.
if wrfPressure == True:
print('Working on WRF Full Model Pressure.')
bar = IncrementalBar('WRF Full Model Pressure:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('p', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Full Model Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('p', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Full Model Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('p', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Full Model Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('p', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Full Model Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('p', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Full Model Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'p', units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('p', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Full Model Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'p',units="hPa", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Absolute Vorticity has been chosen.
if wrfAvo == True:
print('Working on WRF Absolute Vorticity.')
bar = IncrementalBar('WRF Absolute Vorticity:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('avo', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Absolute Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('avo', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Absolute Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'avo',meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('avo', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Absolute Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('avo', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Absolute Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('avo', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Absolute Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('avo', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Absolute Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'avo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Potential Vorticity has been chosen.
if wrfPvo == True:
print('Working on WRF Potential Vorticity.')
bar = IncrementalBar('WRF Potential Vorticity:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('pvo', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('pvo', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'pvo',meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('pvo', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('pvo', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('pvo', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('pvo', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Potential Vorticity'
wrfNewVar.units = '10-5 s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'pvo', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Reflectivity has been chosen.
if wrfDbz == True:
print('Working on WRF Reflectivity.')
bar = IncrementalBar('WRF Reflectivity:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('dbz', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Reflectivity'
wrfNewVar.units = 'dBZ'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('dbz', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Reflectivity'
wrfNewVar.units = 'dBZ'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'dbz',meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('dbz', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Reflectivity'
wrfNewVar.units = 'dBZ'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('dbz', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Reflectivity'
wrfNewVar.units = 'dBZ'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('dbz', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Reflectivity'
wrfNewVar.units = 'dBZ'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('dbz', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Reflectivity'
wrfNewVar.units = 'dBZ'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'dbz', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Geopotential Height for the Mass Grid has been chosen.
if wrfGeopt == True:
print('Working on WRF Geopotential Height for the Mass Grid.')
bar = IncrementalBar('WRF Geopotential Height for the Mass Grid:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('geopt', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Geopotential Height for the Mass Grid'
wrfNewVar.units = 'm2 s-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('geopt', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Geopotential Height for the Mass Grid'
wrfNewVar.units = 'm2 s-2'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'geopt',meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('geopt', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Geopotential Height for the Mass Grid'
wrfNewVar.units = 'm2 s-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('geopt', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Geopotential Height for the Mass Grid'
wrfNewVar.units = 'm2 s-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('geopt', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Geopotential Height for the Mass Grid'
wrfNewVar.units = 'm2 s-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('geopt', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Geopotential Height for the Mass Grid'
wrfNewVar.units = 'm2 s-2'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'geopt', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Omega has been chosen.
if wrfOmega == True:
print('Working on WRF Omega.')
bar = IncrementalBar('WRF Omega:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('omega', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Omega'
wrfNewVar.units = 'Pa s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('omega', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Omega'
wrfNewVar.units = 'Pa s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'omega',meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('omega', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Omega'
wrfNewVar.units = 'Pa s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('omega', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Omega'
wrfNewVar.units = 'Pa s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('omega', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Omega'
wrfNewVar.units = 'Pa s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('omega', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Omega'
wrfNewVar.units = 'Pa s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'omega', meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF U-component of Wind on Mass Points has been chosen.
if wrfUnstaggeredU == True:
print('Working on WRF U-component of Wind on Mass Points.')
bar = IncrementalBar('WRF U-component of Wind on Mass Points:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ua', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ua', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ua', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ua', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile, 'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ua', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ua', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ua', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ua',units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF V-component of Wind on Mass Points has been chosen.
if wrfUnstaggeredV == True:
print('Working on WRF V-component of Wind on Mass Points.')
bar = IncrementalBar('WRF V-component of Wind on Mass Points:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('va', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'V-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('va', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'V-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('va', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'V-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('va', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'V-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile, 'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('va', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'V-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'va', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('va', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'V-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'va',units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF W-component of Wind on Mass Points has been chosen.
if wrfUnstaggeredW == True:
print('Working on WRF W-component of Wind on Mass Points.')
bar = IncrementalBar('WRF W-component of Wind on Mass Points:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('wa', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'W-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('wa', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'W-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('wa', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'W-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('wa', 'f', ('Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'W-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile, 'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('wa', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'W-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfLevel,:,:]
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'wa', units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('wa', 'f', ('Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'W-component of Wind on Mass Points'
wrfNewVar.units = 'm s-1'
wrfNewVar[i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'wa',units="m s-1", meta=False, timeidx=ntimes[0]+i)[wrfStart:wrfStop,:, :]
wrfNewVar[i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF U and V Components of Wind Rotated to Earth Coordinates has been chosen.
if wrfUvmet == True:
print('Working on WRF U and V Components of Wind Rotated to Earth Coordinates.')
bar = IncrementalBar('WRF U and V Components of Wind Rotated to Earth Coordinates:', max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,wrfLevel,j0:j1, i0:i1]
wrfNewVar = np.zeros([2,len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet', 'f', ('UV','Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U and V Components of Wind Rotated to Earth Coordinates'
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,wrfLevel,j0:j1, i0:i1]
wrfNewVar[:,i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar = np.zeros([2,len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet', 'f', ('UV','Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U and V Components of Wind Rotated to Earth Coordinates'
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, wrfStart:wrfStop,j0:j1, i0:i1]
wrfNewVar[:,i,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, :,:,:]
wrfNewVar = np.zeros([2, len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet', 'f', ('UV','Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U and V Components of Wind Rotated to Earth Coordinates'
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, :,:,:]
wrfNewVar[:,i,:,:,:] = wrfRawVar
elif selectWrfBox == True and selectWrfLevel == False:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, :,j0:j1, i0:i1]
wrfNewVar = np.zeros([2,len(ntimes),len(levels),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet', 'f', ('UV','Time', 'bottom_top', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U and V Components of Wind Rotated to Earth Coordinates'
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, :,j0:j1, i0:i1]
wrfNewVar[i,:,:,:] = wrfRawVar
elif selectWrfBox == False and selectWrfLevel == True:
if len(wrfLevel) == 1:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile, 'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, wrfLevel,:,:]
wrfNewVar = np.zeros([2,len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet', 'f', ('UV','Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U and V Components of Wind Rotated to Earth Coordinates'
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, wrfLevel,:,:]
wrfNewVar[:,i,:,:] = wrfRawVar
else:
wrfStart = slice(min(wrfLevel),max(wrfLevel)+1).start
wrfStop = slice(min(wrfLevel),max(wrfLevel)+1).stop
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, wrfStart:wrfStop,:, :]
wrfNewVar = np.zeros([2,len(ntimes),len(wrfLevel),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet', 'f', ('UV','Time','bottom_top','south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'U and V Components of Wind Rotated to Earth Coordinates'
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet',units="m s-1", meta=False, timeidx=ntimes[0]+i)[:, wrfStart:wrfStop,:, :]
wrfNewVar[:,i,:,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF 10 m U and V Components of Wind Rotated to Earth Coordinates been chosen.
if wrfUvmet10m == True:
print('Working on 10 m U and V Components of Wind Rotated to Earth Coordinates .')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet10', units="m s-1",meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar = np.zeros([2,len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet10', 'f', ('UV','Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = '10 m U and V Components of Wind Rotated to Earth Coordinates '
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet10', units="m s-1",meta=False, timeidx=ntimes[0]+i)[:,j0:j1, i0:i1]
wrfNewVar[:,i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'uvmet10', units="m s-1",meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar = np.zeros([2, len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewFile.createDimension('UV', 2)
wrfNewVar = wrfNewFile.createVariable('uvmet10', 'f', ('UV', 'Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = '10 m U and V Components of Wind Rotated to Earth Coordinates '
wrfNewVar.units = 'm s-1'
wrfNewVar[:,i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'uvmet10', units="m s-1", meta=False, timeidx=ntimes[0]+i)[:,:,:]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Latent Heat Flux has been chosen.
if wrfLatent == True:
print('Working on WRF Latent Heat Flux.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'LH', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('LH', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Latent Heat Flux'
wrfNewVar.units = 'W m-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'LH', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'LH', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('LH', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Latent Heat Flux'
wrfNewVar.units = 'W m-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'LH', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Sensible Heat Flux has been chosen.
if wrfSensible == True:
print('Working on WRF Sensible Heat Flux.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'HFX', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('HFX', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Sensible Heat Flux'
wrfNewVar.units = 'W m-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'HFX', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'HFX', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('HFX', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Sensible Heat Flux'
wrfNewVar.units = 'W m-2'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'HFX', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Sea Level Pressure has been chosen.
if wrfSlp == True:
print('Working on WRF Sea Level Pressure.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'slp', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('slp', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Sea Level Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'slp', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'slp', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('slp', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Sea Level Pressure'
wrfNewVar.units = 'hPa'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'slp', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF 2m Relative Humidity has been chosen.
if wrfRh2 == True:
print('Working on WRF 2m Relative Humidity.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh2', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh2', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = '2m Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh2', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'rh2', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('rh2', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = '2m Relative Humidity'
wrfNewVar.units = '%'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'rh2', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Model Terrain Height has been chosen.
if wrfTerrain == True:
print('Working on WRF Model Terrain Height.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ter', units='m', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ter', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Model Terrain Height'
wrfNewVar.units = 'm'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ter', units='m', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'ter', units='m', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('ter', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Model Terrain Height'
wrfNewVar.units = 'm'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'ter', units='m',meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF 2m Dew Point Temperature has been chosen.
if wrfTd2 == True:
print('Working on WRF 2m Dew Point Temperature.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td2', units='degC', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td2', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = '2m Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td2', units='degC', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'td2', units='degC', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('td2', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = '2m Dew Point Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'td2', units='degC', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
# If WRF Landmask has been chosen.
if wrfLandmask == True:
print('Working on WRF Landmask.')
if selectWrfBox == True:
wrfRawVar = getvar(wrfRawFile,'LANDMASK', meta=False)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('LANDMASK', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Land mask'
wrfNewVar.units = '1=land, 0=water'
wrfNewVar[:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'tLANDMASKd2', meta=False)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('LANDMASK', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Land mask'
wrfNewVar.units = '1=land, 0=water'
wrfNewVar[:,:] = wrfRawVar
# If WRF Sea Surface Temperature has been chosen.
if wrfSST == True:
print('Working on WRF Sea Surface Temperature.')
bar = IncrementalBar(max=len(ntimes))
for i in range(np.argmin(ntimes),len(ntimes),1):
if selectWrfBox == True:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'SST', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('SST', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Sea Surface Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'SST', meta=False, timeidx=ntimes[0]+i)[j0:j1, i0:i1]
wrfNewVar[i,:,:] = wrfRawVar
else:
if i == np.argmin(ntimes):
wrfRawVar = getvar(wrfRawFile,'SST', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar = np.zeros([len(ntimes),len(lat_wrf[:,0]), len(lon_wrf[0,:])])
wrfNewVar = wrfNewFile.createVariable('SST', 'f', ('Time', 'south_north', 'west_east'), fill_value=wrfFillVal)
wrfNewVar.long_name = 'Sea Surface Temperature'
wrfNewVar.units = 'Degree Celsius'
wrfNewVar[i,:,:] = wrfRawVar
else:
wrfRawVar = getvar(wrfRawFile,'SST', meta=False, timeidx=ntimes[0]+i)[:, :]
wrfNewVar[i,:,:] = wrfRawVar
bar.next()
bar.finish()
| 75.156945
| 319
| 0.467636
| 11,882
| 124,986
| 4.852803
| 0.026847
| 0.016094
| 0.073811
| 0.083176
| 0.940289
| 0.926831
| 0.919079
| 0.91613
| 0.909402
| 0.906956
| 0
| 0.017767
| 0.398805
| 124,986
| 1,663
| 320
| 75.156945
| 0.749604
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| 0.919211
| 1
| 0
| 0.088363
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| 0
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| 1
| 0.001272
| false
| 0
| 0.005089
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| 0.006997
| 0.020356
| 0
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| null | 0
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| 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
6e9e820508a9ccdc3f51b73e7eebe8b5da7af0de
| 792
|
py
|
Python
|
ex016.py
|
CarlosHenriqueEvangelista/PythonExercies
|
f5082d425d57a386f6d0a9655bc6b54e2304e405
|
[
"MIT"
] | null | null | null |
ex016.py
|
CarlosHenriqueEvangelista/PythonExercies
|
f5082d425d57a386f6d0a9655bc6b54e2304e405
|
[
"MIT"
] | null | null | null |
ex016.py
|
CarlosHenriqueEvangelista/PythonExercies
|
f5082d425d57a386f6d0a9655bc6b54e2304e405
|
[
"MIT"
] | null | null | null |
import math
print("{:=^50}".format("> Elimating Floating Numbers <"))
NumberWithFloatingNumber = float(input("Enter a floating number to convert it to an integer: "))
print("This number with only it's integer part is: {}".format(math.trunc(NumberWithFloatingNumber)))
print("{:=^50}".format("> Elimating Floating Numbers <"))
NumberWithFloatingNumber = float(input("Enter a floating number to convert it to an integer: "))
print("This number with only it's integer part is: {}".format(math.floor(NumberWithFloatingNumber)))
print("{:=^50}".format("> Elimating Floating Numbers <"))
NumberWithFloatingNumber = float(input("Enter a floating number to convert it to an integer: "))
print("This number with only it's integer part is: {}".format((int(NumberWithFloatingNumber))))
| 28.285714
| 100
| 0.728535
| 100
| 792
| 5.77
| 0.28
| 0.036395
| 0.067591
| 0.114385
| 0.918544
| 0.918544
| 0.918544
| 0.918544
| 0.918544
| 0.918544
| 0
| 0.008671
| 0.126263
| 792
| 27
| 101
| 29.333333
| 0.825145
| 0
| 0
| 0.6
| 0
| 0
| 0.516456
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.1
| 0
| 0.1
| 0.6
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
28709750043722126a0dc1790a5800281eb4515d
| 145
|
py
|
Python
|
tests/parser/aggregates.grounding.2.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
tests/parser/aggregates.grounding.2.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
tests/parser/aggregates.grounding.2.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
input = """
a(1) | a(2).
b(1).
:- b(N), not #count{ V : a(V) } = N.
"""
output = """
a(1) | a(2).
b(1).
:- b(N), not #count{ V : a(V) } = N.
"""
| 13.181818
| 36
| 0.351724
| 30
| 145
| 1.7
| 0.333333
| 0.078431
| 0.117647
| 0.156863
| 0.784314
| 0.784314
| 0.784314
| 0.784314
| 0.784314
| 0.784314
| 0
| 0.055046
| 0.248276
| 145
| 10
| 37
| 14.5
| 0.412844
| 0
| 0
| 0.8
| 0
| 0
| 0.786207
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
250b0a9dc27636b0d1518930fec96350efb41616
| 1,296
|
py
|
Python
|
tests/test_verbose.py
|
pengyan510/torchtest
|
f84e4a7f1c3e0cda2430ba09880af4a964b1d3ba
|
[
"MIT"
] | 24
|
2021-06-09T16:12:45.000Z
|
2022-03-08T17:50:47.000Z
|
tests/test_verbose.py
|
pengyan510/torchtest
|
f84e4a7f1c3e0cda2430ba09880af4a964b1d3ba
|
[
"MIT"
] | 1
|
2021-11-19T09:17:30.000Z
|
2021-11-19T09:17:30.000Z
|
tests/test_verbose.py
|
pengyan510/torchtest
|
f84e4a7f1c3e0cda2430ba09880af4a964b1d3ba
|
[
"MIT"
] | 1
|
2021-06-11T05:23:33.000Z
|
2021-06-11T05:23:33.000Z
|
import pytest
import torcheck
def test_verbose_on(
unchanging_model_optimizer, unchanging_model, dataloader, run_training
):
torcheck.verbose_on()
torcheck.register(unchanging_model_optimizer)
torcheck.add_module_changing_check(unchanging_model, module_name="NeuralNet")
with pytest.raises(
RuntimeError,
match=(
r"Module NeuralNet's fc1\.weight should change\.\n"
r"The tensor is:(.|\n)*"
r"fc1\.bias should change\.\n"
r"The tensor is:(.|\n)*"
),
):
run_training(unchanging_model, dataloader, unchanging_model_optimizer)
def test_verbose_off(
unchanging_model_optimizer, unchanging_model, dataloader, run_training
):
torcheck.register(unchanging_model_optimizer)
torcheck.add_module_changing_check(unchanging_model, module_name="NeuralNet")
torcheck.verbose_on()
with pytest.raises(RuntimeError):
run_training(unchanging_model, dataloader, unchanging_model_optimizer)
torcheck.verbose_off()
with pytest.raises(
RuntimeError,
match=(
r"Module NeuralNet's fc1\.weight should change\.\n"
r".*fc1.bias should change"
),
):
run_training(unchanging_model, dataloader, unchanging_model_optimizer)
| 32.4
| 81
| 0.693673
| 144
| 1,296
| 5.958333
| 0.25
| 0.244755
| 0.195804
| 0.111888
| 0.847319
| 0.847319
| 0.815851
| 0.815851
| 0.561772
| 0.412587
| 0
| 0.003922
| 0.212963
| 1,296
| 39
| 82
| 33.230769
| 0.837255
| 0
| 0
| 0.771429
| 0
| 0
| 0.159722
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057143
| false
| 0
| 0.057143
| 0
| 0.114286
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
250fbe77306254e873c313b9671ef699ebce58c7
| 341
|
py
|
Python
|
src/final_exam/q_employee/hourly_employee.py
|
acc-cosc-1336/cosc-1336-spring-2018-BarndonSelesino
|
c2ee6a9914383716347765f148d2d15b9e3211b7
|
[
"MIT"
] | null | null | null |
src/final_exam/q_employee/hourly_employee.py
|
acc-cosc-1336/cosc-1336-spring-2018-BarndonSelesino
|
c2ee6a9914383716347765f148d2d15b9e3211b7
|
[
"MIT"
] | 1
|
2018-02-03T03:41:28.000Z
|
2018-02-03T03:41:28.000Z
|
src/final_exam/q_employee/hourly_employee.py
|
acc-cosc-1336/cosc-1336-spring-2018-BarndonSelesino
|
c2ee6a9914383716347765f148d2d15b9e3211b7
|
[
"MIT"
] | 1
|
2018-04-13T01:17:50.000Z
|
2018-04-13T01:17:50.000Z
|
from employee import Employee
class HourlyEmployee():
def __init__(self, hourly_rate, worked_hours):
self.hourly_rate = hourly_rate
self.worked_hours = worked_hours
Employee.__init__(self,employee_id,name,hourly_rate,worked_hours)
def calculate(self):
return self.hourly_rate * worked_hours
| 34.1
| 74
| 0.715543
| 42
| 341
| 5.357143
| 0.380952
| 0.222222
| 0.186667
| 0.28
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.214076
| 341
| 9
| 75
| 37.888889
| 0.839552
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.125
| 0.125
| 0.625
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
c269d683858debf91f5de1a1d4890a78737f11f3
| 149
|
py
|
Python
|
bluesteel/settings/__init__.py
|
imvu/bluesteel
|
ab52133249a693b3cd2d8593c5d47408a3b0fce6
|
[
"MIT"
] | 10
|
2017-01-13T06:28:04.000Z
|
2020-11-18T13:00:26.000Z
|
bluesteel/settings/__init__.py
|
imvu/bluesteel
|
ab52133249a693b3cd2d8593c5d47408a3b0fce6
|
[
"MIT"
] | null | null | null |
bluesteel/settings/__init__.py
|
imvu/bluesteel
|
ab52133249a693b3cd2d8593c5d47408a3b0fce6
|
[
"MIT"
] | 2
|
2018-03-29T14:10:53.000Z
|
2019-11-20T08:21:57.000Z
|
""" Settings module """
from bluesteel.settings import production
from bluesteel.settings import development
from bluesteel.settings import testing
| 24.833333
| 42
| 0.825503
| 17
| 149
| 7.235294
| 0.470588
| 0.317073
| 0.512195
| 0.658537
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114094
| 149
| 5
| 43
| 29.8
| 0.931818
| 0.100671
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
6c1517529b5ac7652955d04bf04ecc150a288881
| 134,090
|
py
|
Python
|
gaugette/fonts/arial_32.py
|
wsiffer/Google-Bartender
|
37018d3efe33a84074a6dccbce9e82f20ef3c923
|
[
"MIT"
] | 6
|
2020-07-30T00:21:29.000Z
|
2022-03-16T23:31:09.000Z
|
gaugette/fonts/arial_32.py
|
antndeb/Google-Bartender
|
37018d3efe33a84074a6dccbce9e82f20ef3c923
|
[
"MIT"
] | null | null | null |
gaugette/fonts/arial_32.py
|
antndeb/Google-Bartender
|
37018d3efe33a84074a6dccbce9e82f20ef3c923
|
[
"MIT"
] | 1
|
2022-03-16T23:39:29.000Z
|
2022-03-16T23:39:29.000Z
|
# coding=utf-8
# Module arial_32
# generated from Arial 24.75pt
name = "Arial 32"
start_char = '!'
end_char = chr(127)
char_height = 32
space_width = 16
gap_width = 4
bitmaps = (
# @0 '!' (3 pixels wide)
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x40, # O
0x40, # O
0x40, # O
0x40, # O
0x40, # O
0x40, # O
0x40, # O
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @32 '"' (9 pixels wide)
0x00, 0x00, #
0xE3, 0x80, # OOO OOO
0xE3, 0x80, # OOO OOO
0xE3, 0x80, # OOO OOO
0xE3, 0x80, # OOO OOO
0xE3, 0x80, # OOO OOO
0xE3, 0x80, # OOO OOO
0xE3, 0x80, # OOO OOO
0x41, 0x00, # O O
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @96 '#' (17 pixels wide)
0x00, 0x00, 0x00, #
0x03, 0x87, 0x00, # OOO OOO
0x03, 0x87, 0x00, # OOO OOO
0x07, 0x0E, 0x00, # OOO OOO
0x07, 0x0E, 0x00, # OOO OOO
0x07, 0x0E, 0x00, # OOO OOO
0x07, 0x0E, 0x00, # OOO OOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0x0E, 0x1C, 0x00, # OOO OOO
0x0E, 0x1C, 0x00, # OOO OOO
0x0E, 0x1C, 0x00, # OOO OOO
0x1C, 0x38, 0x00, # OOO OOO
0x1C, 0x38, 0x00, # OOO OOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0x38, 0x70, 0x00, # OOO OOO
0x38, 0x70, 0x00, # OOO OOO
0x38, 0x70, 0x00, # OOO OOO
0x38, 0x70, 0x00, # OOO OOO
0x38, 0x70, 0x00, # OOO OOO
0x70, 0xE0, 0x00, # OOO OOO
0x70, 0xE0, 0x00, # OOO OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @192 '$' (16 pixels wide)
0x01, 0x80, # OO
0x07, 0xE0, # OOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x39, 0x9C, # OOO OO OOO
0x71, 0x8E, # OOO OO OOO
0x71, 0x8E, # OOO OO OOO
0x71, 0x80, # OOO OO
0x71, 0x80, # OOO OO
0x79, 0x80, # OOOO OO
0x3F, 0x80, # OOOOOOO
0x1F, 0xE0, # OOOOOOOO
0x07, 0xF8, # OOOOOOOO
0x01, 0xFC, # OOOOOOO
0x01, 0xBE, # OO OOOOO
0x01, 0x8F, # OO OOOO
0x01, 0x87, # OO OOO
0x01, 0x87, # OO OOO
0xE1, 0x87, # OOO OO OOO
0xE1, 0x87, # OOO OO OOO
0x71, 0x8F, # OOO OO OOOO
0x79, 0x9E, # OOOO OO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x07, 0xF0, # OOOOOOO
0x01, 0x80, # OO
0x01, 0x80, # OO
0x01, 0x80, # OO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @256 '%' (25 pixels wide)
0x00, 0x00, 0x00, 0x00, #
0x1F, 0x00, 0x70, 0x00, # OOOOO OOO
0x3F, 0x80, 0xE0, 0x00, # OOOOOOO OOO
0x71, 0xC0, 0xE0, 0x00, # OOO OOO OOO
0xE0, 0xE1, 0xC0, 0x00, # OOO OOO OOO
0xE0, 0xE1, 0xC0, 0x00, # OOO OOO OOO
0xE0, 0xE1, 0xC0, 0x00, # OOO OOO OOO
0xE0, 0xE3, 0x80, 0x00, # OOO OOO OOO
0xE0, 0xE3, 0x80, 0x00, # OOO OOO OOO
0xE0, 0xE7, 0x00, 0x00, # OOO OOO OOO
0xE0, 0xE7, 0x00, 0x00, # OOO OOO OOO
0x71, 0xCE, 0x00, 0x00, # OOO OOO OOO
0x3F, 0x8E, 0x7C, 0x00, # OOOOOOO OOO OOOOO
0x1F, 0x1C, 0xFE, 0x00, # OOOOO OOO OOOOOOO
0x00, 0x1D, 0xC7, 0x00, # OOO OOO OOO
0x00, 0x3B, 0x83, 0x80, # OOO OOO OOO
0x00, 0x3B, 0x83, 0x80, # OOO OOO OOO
0x00, 0x73, 0x83, 0x80, # OOO OOO OOO
0x00, 0x73, 0x83, 0x80, # OOO OOO OOO
0x00, 0xE3, 0x83, 0x80, # OOO OOO OOO
0x00, 0xE3, 0x83, 0x80, # OOO OOO OOO
0x01, 0xC3, 0x83, 0x80, # OOO OOO OOO
0x01, 0xC1, 0xC7, 0x00, # OOO OOO OOO
0x03, 0x80, 0xFE, 0x00, # OOO OOOOOOO
0x03, 0x80, 0x7C, 0x00, # OOO OOOOO
0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, #
# @384 '&' (19 pixels wide)
0x00, 0x00, 0x00, #
0x03, 0xF0, 0x00, # OOOOOO
0x07, 0xF8, 0x00, # OOOOOOOO
0x0F, 0xFC, 0x00, # OOOOOOOOOO
0x1E, 0x1E, 0x00, # OOOO OOOO
0x1C, 0x0E, 0x00, # OOO OOO
0x1C, 0x0E, 0x00, # OOO OOO
0x1C, 0x0E, 0x00, # OOO OOO
0x0E, 0x1C, 0x00, # OOO OOO
0x0F, 0x3C, 0x00, # OOOO OOOO
0x07, 0xF8, 0x00, # OOOOOOOO
0x03, 0xE0, 0x00, # OOOOO
0x0F, 0xC0, 0x00, # OOOOOO
0x1E, 0xE0, 0x00, # OOOO OOO
0x78, 0x70, 0x80, # OOOO OOO O
0x70, 0x71, 0xE0, # OOO OOO OOOO
0xE0, 0x39, 0xC0, # OOO OOO OOO
0xE0, 0x1F, 0xC0, # OOO OOOOOOO
0xE0, 0x0F, 0x80, # OOO OOOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xF0, 0x0F, 0x80, # OOOO OOOOO
0x78, 0x3F, 0xC0, # OOOO OOOOOOOO
0x3F, 0xFD, 0xE0, # OOOOOOOOOOOO OOOO
0x1F, 0xF8, 0xE0, # OOOOOOOOOO OOO
0x07, 0xE0, 0x40, # OOOOOO O
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @480 ''' (3 pixels wide)
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x40, # O
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @512 '(' (8 pixels wide)
0x00, #
0x03, # OO
0x06, # OO
0x0E, # OOO
0x0C, # OO
0x18, # OO
0x38, # OOO
0x38, # OOO
0x30, # OO
0x70, # OOO
0x70, # OOO
0x60, # OO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x70, # OOO
0x70, # OOO
0x70, # OOO
0x30, # OO
0x38, # OOO
0x38, # OOO
0x1C, # OOO
0x0C, # OO
0x0E, # OOO
0x06, # OO
0x03, # OO
# @544 ')' (8 pixels wide)
0x00, #
0xC0, # OO
0x60, # OO
0x70, # OOO
0x30, # OO
0x18, # OO
0x1C, # OOO
0x1C, # OOO
0x0C, # OO
0x0E, # OOO
0x0E, # OOO
0x06, # OO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x0E, # OOO
0x0E, # OOO
0x0E, # OOO
0x0C, # OO
0x1C, # OOO
0x1C, # OOO
0x38, # OOO
0x30, # OO
0x70, # OOO
0x60, # OO
0xC0, # OO
# @576 '*' (10 pixels wide)
0x00, 0x00, #
0x0C, 0x00, # OO
0x0C, 0x00, # OO
0x0C, 0x00, # OO
0xED, 0xC0, # OOO OO OOO
0xFF, 0xC0, # OOOOOOOOOO
0x3F, 0x00, # OOOOOO
0x1E, 0x00, # OOOO
0x3F, 0x00, # OOOOOO
0x73, 0x80, # OOO OOO
0x21, 0x00, # O O
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @640 '+' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @704 ',' (3 pixels wide)
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x60, # OO
0x60, # OO
0x60, # OO
0x40, # O
0xC0, # OO
0x00, #
0x00, #
# @736 '-' (9 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xFF, 0x80, # OOOOOOOOO
0xFF, 0x80, # OOOOOOOOO
0xFF, 0x80, # OOOOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @800 '.' (3 pixels wide)
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @832 '/' (9 pixels wide)
0x00, 0x00, #
0x01, 0x80, # OO
0x01, 0x80, # OO
0x03, 0x00, # OO
0x03, 0x00, # OO
0x03, 0x00, # OO
0x06, 0x00, # OO
0x06, 0x00, # OO
0x06, 0x00, # OO
0x06, 0x00, # OO
0x0C, 0x00, # OO
0x0C, 0x00, # OO
0x0C, 0x00, # OO
0x18, 0x00, # OO
0x18, 0x00, # OO
0x18, 0x00, # OO
0x38, 0x00, # OOO
0x30, 0x00, # OO
0x30, 0x00, # OO
0x30, 0x00, # OO
0x60, 0x00, # OO
0x60, 0x00, # OO
0x60, 0x00, # OO
0xE0, 0x00, # OOO
0xC0, 0x00, # OO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @896 '0' (16 pixels wide)
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x3C, 0x3C, # OOOO OOOO
0x70, 0x1E, # OOO OOOO
0x70, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x70, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0x3C, 0x3C, # OOOO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @960 '1' (8 pixels wide)
0x00, #
0x03, # OO
0x07, # OOO
0x0F, # OOOO
0x1F, # OOOOO
0x3F, # OOOOOO
0x7F, # OOOOOOO
0xF7, # OOOO OOO
0xC7, # OO OOO
0x87, # O OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x07, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @992 '2' (16 pixels wide)
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x78, 0x1E, # OOOO OOOO
0x70, 0x0F, # OOO OOOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x1C, # OOO
0x00, 0x38, # OOO
0x00, 0x70, # OOO
0x00, 0xE0, # OOO
0x01, 0xC0, # OOO
0x03, 0x80, # OOO
0x0F, 0x00, # OOOO
0x1E, 0x00, # OOOO
0x38, 0x00, # OOO
0x70, 0x00, # OOO
0x7F, 0xFF, # OOOOOOOOOOOOOOO
0xFF, 0xFF, # OOOOOOOOOOOOOOOO
0xFF, 0xFF, # OOOOOOOOOOOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1056 '3' (16 pixels wide)
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x78, 0x3C, # OOOO OOOO
0xF0, 0x0E, # OOOO OOO
0xE0, 0x0E, # OOO OOO
0x00, 0x0E, # OOO
0x00, 0x1E, # OOOO
0x00, 0x3C, # OOOO
0x03, 0xF8, # OOOOOOO
0x03, 0xF8, # OOOOOOO
0x03, 0xFC, # OOOOOOOO
0x00, 0x1E, # OOOO
0x00, 0x0F, # OOOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x70, 0x0E, # OOO OOO
0x78, 0x1E, # OOOO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1120 '4' (17 pixels wide)
0x00, 0x00, 0x00, #
0x00, 0x0C, 0x00, # OO
0x00, 0x1C, 0x00, # OOO
0x00, 0x3C, 0x00, # OOOO
0x00, 0x7C, 0x00, # OOOOO
0x00, 0xFC, 0x00, # OOOOOO
0x00, 0xFC, 0x00, # OOOOOO
0x01, 0xDC, 0x00, # OOO OOO
0x03, 0x9C, 0x00, # OOO OOO
0x07, 0x1C, 0x00, # OOO OOO
0x0E, 0x1C, 0x00, # OOO OOO
0x0E, 0x1C, 0x00, # OOO OOO
0x1C, 0x1C, 0x00, # OOO OOO
0x38, 0x1C, 0x00, # OOO OOO
0x70, 0x1C, 0x00, # OOO OOO
0xE0, 0x1C, 0x00, # OOO OOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @1216 '5' (16 pixels wide)
0x00, 0x00, #
0x3F, 0xFE, # OOOOOOOOOOOOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x70, 0x00, # OOO
0x70, 0x00, # OOO
0x70, 0x00, # OOO
0x70, 0x00, # OOO
0x70, 0x00, # OOO
0x77, 0xE0, # OOO OOOOOO
0xEF, 0xF8, # OOO OOOOOOOOO
0xFF, 0xFC, # OOOOOOOOOOOOOO
0xF0, 0x1E, # OOOO OOOO
0x00, 0x0E, # OOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x70, 0x0E, # OOO OOO
0x78, 0x1E, # OOOO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1280 '6' (16 pixels wide)
0x00, 0x00, #
0x03, 0xF0, # OOOOOO
0x0F, 0xFC, # OOOOOOOOOO
0x1F, 0xFE, # OOOOOOOOOOOO
0x3C, 0x1E, # OOOO OOOO
0x78, 0x0F, # OOOO OOOO
0x70, 0x07, # OOO OOO
0x70, 0x00, # OOO
0xE0, 0x00, # OOO
0xE3, 0xF0, # OOO OOOOOO
0xEF, 0xF8, # OOO OOOOOOOOO
0xFF, 0xFC, # OOOOOOOOOOOOOO
0xFC, 0x1E, # OOOOOO OOOO
0xF0, 0x0E, # OOOO OOO
0xF0, 0x07, # OOOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x60, 0x07, # OO OOO
0x70, 0x07, # OOO OOO
0x70, 0x0E, # OOO OOO
0x3C, 0x1E, # OOOO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x0F, 0xF8, # OOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1344 '7' (16 pixels wide)
0x00, 0x00, #
0xFF, 0xFF, # OOOOOOOOOOOOOOOO
0xFF, 0xFF, # OOOOOOOOOOOOOOOO
0xFF, 0xFF, # OOOOOOOOOOOOOOOO
0x00, 0x06, # OO
0x00, 0x0C, # OO
0x00, 0x1C, # OOO
0x00, 0x38, # OOO
0x00, 0x30, # OO
0x00, 0x70, # OOO
0x00, 0xE0, # OOO
0x00, 0xE0, # OOO
0x01, 0xC0, # OOO
0x01, 0xC0, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x07, 0x00, # OOO
0x07, 0x00, # OOO
0x07, 0x00, # OOO
0x07, 0x00, # OOO
0x0E, 0x00, # OOO
0x0E, 0x00, # OOO
0x0E, 0x00, # OOO
0x0E, 0x00, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1408 '8' (16 pixels wide)
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x78, 0x1C, # OOOO OOO
0x70, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0x38, 0x1C, # OOO OOO
0x1F, 0xF8, # OOOOOOOOOO
0x0F, 0xF0, # OOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x78, 0x1E, # OOOO OOOO
0x70, 0x0E, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x70, 0x0E, # OOO OOO
0x78, 0x1E, # OOOO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1472 '9' (16 pixels wide)
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF0, # OOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x7C, 0x1C, # OOOOO OOO
0x70, 0x0E, # OOO OOO
0xF0, 0x06, # OOOO OO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0xE0, 0x0F, # OOO OOOO
0x70, 0x0F, # OOO OOOO
0x78, 0x3F, # OOOO OOOOOO
0x3F, 0xFF, # OOOOOOOOOOOOOO
0x1F, 0xF7, # OOOOOOOOO OOO
0x07, 0xC7, # OOOOO OOO
0x00, 0x07, # OOO
0x00, 0x0E, # OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x1E, # OOOO OOOO
0x78, 0x3C, # OOOO OOOO
0x7F, 0xF8, # OOOOOOOOOOOO
0x3F, 0xF0, # OOOOOOOOOO
0x0F, 0xC0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1536 ':' (3 pixels wide)
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @1568 ';' (3 pixels wide)
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x60, # OO
0x60, # OO
0x60, # OO
0x40, # O
0xC0, # OO
0x00, #
0x00, #
# @1600 '<' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x02, # O
0x00, 0x1E, # OOOO
0x00, 0x7E, # OOOOOO
0x03, 0xFC, # OOOOOOOO
0x0F, 0xE0, # OOOOOOO
0x7F, 0x00, # OOOOOOO
0xFC, 0x00, # OOOOOO
0xE0, 0x00, # OOO
0xFC, 0x00, # OOOOOO
0x7F, 0x00, # OOOOOOO
0x0F, 0xE0, # OOOOOOO
0x03, 0xFC, # OOOOOOOO
0x00, 0x7E, # OOOOOO
0x00, 0x1E, # OOOO
0x00, 0x02, # O
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1664 '=' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1728 '>' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x80, 0x00, # O
0xF0, 0x00, # OOOO
0xFC, 0x00, # OOOOOO
0x7F, 0x80, # OOOOOOOO
0x0F, 0xE0, # OOOOOOO
0x01, 0xFC, # OOOOOOO
0x00, 0x7E, # OOOOOO
0x00, 0x0E, # OOO
0x00, 0x7E, # OOOOOO
0x01, 0xFC, # OOOOOOO
0x0F, 0xE0, # OOOOOOO
0x7F, 0x80, # OOOOOOOO
0xFC, 0x00, # OOOOOO
0xF0, 0x00, # OOOO
0x80, 0x00, # O
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1792 '?' (16 pixels wide)
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x7C, 0x1E, # OOOOO OOOO
0x70, 0x0F, # OOO OOOO
0xE0, 0x07, # OOO OOO
0xE0, 0x07, # OOO OOO
0x00, 0x07, # OOO
0x00, 0x07, # OOO
0x00, 0x0E, # OOO
0x00, 0x1E, # OOOO
0x00, 0x3C, # OOOO
0x00, 0x78, # OOOO
0x00, 0xF0, # OOOO
0x00, 0xE0, # OOO
0x01, 0xC0, # OOO
0x01, 0xC0, # OOO
0x01, 0xC0, # OOO
0x01, 0xC0, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x01, 0xC0, # OOO
0x01, 0xC0, # OOO
0x01, 0xC0, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @1856 '@' (30 pixels wide)
0x00, 0x00, 0x00, 0x00, #
0x00, 0x1F, 0xF0, 0x00, # OOOOOOOOO
0x00, 0xFF, 0xFC, 0x00, # OOOOOOOOOOOOOO
0x01, 0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOOO
0x07, 0xF0, 0x1F, 0x80, # OOOOOOO OOOOOO
0x0F, 0x80, 0x07, 0xC0, # OOOOO OOOOO
0x0F, 0x00, 0x01, 0xE0, # OOOO OOOO
0x1E, 0x1E, 0x1C, 0xE0, # OOOO OOOO OOO OOO
0x3C, 0x7F, 0x9C, 0x70, # OOOO OOOOOOOO OOO OOO
0x38, 0xFF, 0xF8, 0x70, # OOO OOOOOOOOOOOOO OOO
0x79, 0xF1, 0xF8, 0x78, # OOOO OOOOO OOOOOO OOOO
0x71, 0xC0, 0xF8, 0x38, # OOO OOO OOOOO OOO
0x73, 0xC0, 0x78, 0x38, # OOO OOOO OOOO OOO
0xE3, 0x80, 0x78, 0x38, # OOO OOO OOOO OOO
0xE7, 0x80, 0x70, 0x38, # OOO OOOO OOO OOO
0xE7, 0x00, 0x70, 0x38, # OOO OOO OOO OOO
0xE7, 0x00, 0x70, 0x38, # OOO OOO OOO OOO
0xE7, 0x00, 0xF0, 0x70, # OOO OOO OOOO OOO
0xE7, 0x00, 0xE0, 0x70, # OOO OOO OOO OOO
0xE7, 0x01, 0xE0, 0xE0, # OOO OOO OOOO OOO
0xE7, 0x81, 0xE1, 0xE0, # OOO OOOO OOOO OOOO
0xF3, 0xC7, 0xE3, 0xC0, # OOOO OOOO OOOOOO OOOO
0x73, 0xFF, 0xFF, 0x80, # OOO OOOOOOOOOOOOOOOOOOO
0x79, 0xFE, 0xFF, 0x00, # OOOO OOOOOOOO OOOOOOOO
0x38, 0x78, 0x7C, 0x00, # OOO OOOO OOOOO
0x3C, 0x00, 0x00, 0x1C, # OOOO OOO
0x1E, 0x00, 0x00, 0x78, # OOOO OOOO
0x0F, 0x80, 0x00, 0xF0, # OOOOO OOOO
0x07, 0xF0, 0x07, 0xE0, # OOOOOOO OOOOOO
0x03, 0xFF, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOOOOO
0x00, 0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0x00, 0x1F, 0xF8, 0x00, # OOOOOOOOOO
# @1984 'A' (21 pixels wide)
0x00, 0x00, 0x00, #
0x00, 0xF8, 0x00, # OOOOO
0x00, 0xF8, 0x00, # OOOOO
0x00, 0xF8, 0x00, # OOOOO
0x01, 0xDC, 0x00, # OOO OOO
0x01, 0xDC, 0x00, # OOO OOO
0x01, 0xDC, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x07, 0x07, 0x00, # OOO OOO
0x07, 0x07, 0x00, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x1F, 0xFF, 0xC0, # OOOOOOOOOOOOOOO
0x1F, 0xFF, 0xC0, # OOOOOOOOOOOOOOO
0x1F, 0xFF, 0xC0, # OOOOOOOOOOOOOOO
0x38, 0x00, 0xE0, # OOO OOO
0x38, 0x00, 0xE0, # OOO OOO
0x38, 0x00, 0xE0, # OOO OOO
0x70, 0x00, 0x70, # OOO OOO
0x70, 0x00, 0x70, # OOO OOO
0x70, 0x00, 0x70, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2080 'B' (17 pixels wide)
0x00, 0x00, 0x00, #
0xFF, 0xF0, 0x00, # OOOOOOOOOOOO
0xFF, 0xFC, 0x00, # OOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xE0, 0x1E, 0x00, # OOO OOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xE0, 0x07, 0x00, # OOO OOO
0xE0, 0x07, 0x00, # OOO OOO
0xE0, 0x07, 0x00, # OOO OOO
0xE0, 0x0E, 0x00, # OOO OOO
0xE0, 0x1E, 0x00, # OOO OOOO
0xFF, 0xFC, 0x00, # OOOOOOOOOOOOOO
0xFF, 0xFC, 0x00, # OOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xF8, 0x00, # OOOOOOOOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2176 'C' (20 pixels wide)
0x00, 0x00, 0x00, #
0x01, 0xFC, 0x00, # OOOOOOO
0x07, 0xFE, 0x00, # OOOOOOOOOO
0x0F, 0xFF, 0x80, # OOOOOOOOOOOOO
0x1E, 0x07, 0x80, # OOOO OOOO
0x3C, 0x03, 0xC0, # OOOO OOOO
0x78, 0x01, 0xE0, # OOOO OOOO
0x70, 0x00, 0xE0, # OOO OOO
0x70, 0x00, 0x80, # OOO O
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x40, # OOO O
0x70, 0x00, 0x70, # OOO OOO
0x70, 0x00, 0xF0, # OOO OOOO
0x78, 0x00, 0xE0, # OOOO OOO
0x3C, 0x01, 0xE0, # OOOO OOOO
0x1F, 0x07, 0xC0, # OOOOO OOOOO
0x1F, 0xFF, 0x80, # OOOOOOOOOOOOOO
0x07, 0xFF, 0x00, # OOOOOOOOOOO
0x01, 0xFC, 0x00, # OOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2272 'D' (19 pixels wide)
0x00, 0x00, 0x00, #
0xFF, 0xF8, 0x00, # OOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xE0, 0x0F, 0x80, # OOO OOOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xE0, # OOO OOOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x00, 0xE0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x0F, 0x80, # OOO OOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xF0, 0x00, # OOOOOOOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2368 'E' (17 pixels wide)
0x00, 0x00, 0x00, #
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2464 'F' (15 pixels wide)
0x00, 0x00, #
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @2528 'G' (22 pixels wide)
0x00, 0x00, 0x00, #
0x00, 0xFF, 0x00, # OOOOOOOO
0x07, 0xFF, 0xC0, # OOOOOOOOOOOOO
0x0F, 0xFF, 0xE0, # OOOOOOOOOOOOOOO
0x1F, 0x01, 0xF0, # OOOOO OOOOO
0x3C, 0x00, 0x78, # OOOO OOOO
0x38, 0x00, 0x38, # OOO OOO
0x70, 0x00, 0x38, # OOO OOO
0x70, 0x00, 0x18, # OOO OO
0xF0, 0x00, 0x00, # OOOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x0F, 0xFC, # OOO OOOOOOOOOO
0xE0, 0x0F, 0xFC, # OOO OOOOOOOOOO
0xE0, 0x0F, 0xFC, # OOO OOOOOOOOOO
0xE0, 0x00, 0x1C, # OOO OOO
0x70, 0x00, 0x1C, # OOO OOO
0x70, 0x00, 0x1C, # OOO OOO
0x38, 0x00, 0x1C, # OOO OOO
0x3C, 0x00, 0x3C, # OOOO OOOO
0x1F, 0x81, 0xFC, # OOOOOO OOOOOOO
0x0F, 0xFF, 0xF0, # OOOOOOOOOOOOOOOO
0x03, 0xFF, 0xE0, # OOOOOOOOOOOOO
0x00, 0xFF, 0x00, # OOOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2624 'H' (18 pixels wide)
0x00, 0x00, 0x00, #
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xFF, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2720 'I' (3 pixels wide)
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @2752 'J' (13 pixels wide)
0x00, 0x00, #
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0x00, 0x38, # OOO
0xE0, 0x38, # OOO OOO
0xE0, 0x38, # OOO OOO
0xF0, 0x78, # OOOO OOOO
0xF0, 0xF0, # OOOO OOOO
0x7F, 0xF0, # OOOOOOOOOOO
0x3F, 0xE0, # OOOOOOOOO
0x1F, 0x80, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @2816 'K' (20 pixels wide)
0x00, 0x00, 0x00, #
0xE0, 0x01, 0xE0, # OOO OOOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xE0, 0x1E, 0x00, # OOO OOOO
0xE0, 0x3C, 0x00, # OOO OOOO
0xE0, 0x78, 0x00, # OOO OOOO
0xE0, 0xF0, 0x00, # OOO OOOO
0xE1, 0xE0, 0x00, # OOO OOOO
0xE3, 0xC0, 0x00, # OOO OOOO
0xE7, 0x80, 0x00, # OOO OOOO
0xEF, 0xC0, 0x00, # OOO OOOOOO
0xFF, 0xE0, 0x00, # OOOOOOOOOOO
0xFC, 0xE0, 0x00, # OOOOOO OOO
0xF8, 0xF0, 0x00, # OOOOO OOOO
0xF0, 0x78, 0x00, # OOOO OOOO
0xE0, 0x3C, 0x00, # OOO OOOO
0xE0, 0x1E, 0x00, # OOO OOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xE0, 0x07, 0x00, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x01, 0xE0, # OOO OOOO
0xE0, 0x00, 0xF0, # OOO OOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @2912 'L' (14 pixels wide)
0x00, 0x00, #
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xFF, 0xFC, # OOOOOOOOOOOOOO
0xFF, 0xFC, # OOOOOOOOOOOOOO
0xFF, 0xFC, # OOOOOOOOOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @2976 'M' (23 pixels wide)
0x00, 0x00, 0x00, #
0xF8, 0x00, 0x3E, # OOOOO OOOOO
0xFC, 0x00, 0x3E, # OOOOOO OOOOO
0xFC, 0x00, 0x7E, # OOOOOO OOOOOO
0xFC, 0x00, 0x7E, # OOOOOO OOOOOO
0xFE, 0x00, 0x7E, # OOOOOOO OOOOOO
0xEE, 0x00, 0xEE, # OOO OOO OOO OOO
0xEE, 0x00, 0xEE, # OOO OOO OOO OOO
0xEE, 0x00, 0xEE, # OOO OOO OOO OOO
0xE7, 0x01, 0xCE, # OOO OOO OOO OOO
0xE7, 0x01, 0xCE, # OOO OOO OOO OOO
0xE7, 0x01, 0xCE, # OOO OOO OOO OOO
0xE3, 0x83, 0x8E, # OOO OOO OOO OOO
0xE3, 0x83, 0x8E, # OOO OOO OOO OOO
0xE3, 0x83, 0x8E, # OOO OOO OOO OOO
0xE1, 0xC7, 0x0E, # OOO OOO OOO OOO
0xE1, 0xC7, 0x0E, # OOO OOO OOO OOO
0xE1, 0xC7, 0x0E, # OOO OOO OOO OOO
0xE0, 0xEE, 0x0E, # OOO OOO OOO OOO
0xE0, 0xEE, 0x0E, # OOO OOO OOO OOO
0xE0, 0xEE, 0x0E, # OOO OOO OOO OOO
0xE0, 0x7C, 0x0E, # OOO OOOOO OOO
0xE0, 0x7C, 0x0E, # OOO OOOOO OOO
0xE0, 0x7C, 0x0E, # OOO OOOOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3072 'N' (18 pixels wide)
0x00, 0x00, 0x00, #
0xF0, 0x01, 0xC0, # OOOO OOO
0xF0, 0x01, 0xC0, # OOOO OOO
0xF8, 0x01, 0xC0, # OOOOO OOO
0xF8, 0x01, 0xC0, # OOOOO OOO
0xFC, 0x01, 0xC0, # OOOOOO OOO
0xEE, 0x01, 0xC0, # OOO OOO OOO
0xEE, 0x01, 0xC0, # OOO OOO OOO
0xE7, 0x01, 0xC0, # OOO OOO OOO
0xE7, 0x01, 0xC0, # OOO OOO OOO
0xE3, 0x81, 0xC0, # OOO OOO OOO
0xE1, 0xC1, 0xC0, # OOO OOO OOO
0xE1, 0xC1, 0xC0, # OOO OOO OOO
0xE0, 0xE1, 0xC0, # OOO OOO OOO
0xE0, 0xE1, 0xC0, # OOO OOO OOO
0xE0, 0x71, 0xC0, # OOO OOO OOO
0xE0, 0x39, 0xC0, # OOO OOO OOO
0xE0, 0x39, 0xC0, # OOO OOO OOO
0xE0, 0x1D, 0xC0, # OOO OOO OOO
0xE0, 0x1D, 0xC0, # OOO OOO OOO
0xE0, 0x0F, 0xC0, # OOO OOOOOO
0xE0, 0x07, 0xC0, # OOO OOOOO
0xE0, 0x07, 0xC0, # OOO OOOOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x03, 0xC0, # OOO OOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3168 'O' (22 pixels wide)
0x00, 0x00, 0x00, #
0x01, 0xFC, 0x00, # OOOOOOO
0x07, 0xFF, 0x80, # OOOOOOOOOOOO
0x0F, 0xFF, 0xC0, # OOOOOOOOOOOOOO
0x1F, 0x03, 0xE0, # OOOOO OOOOO
0x3C, 0x00, 0xF0, # OOOO OOOO
0x78, 0x00, 0x70, # OOOO OOO
0x70, 0x00, 0x38, # OOO OOO
0x70, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0xE0, 0x00, 0x1C, # OOO OOO
0x70, 0x00, 0x38, # OOO OOO
0x70, 0x00, 0x38, # OOO OOO
0x38, 0x00, 0x78, # OOO OOOO
0x3C, 0x00, 0xF0, # OOOO OOOO
0x1F, 0x03, 0xE0, # OOOOO OOOOO
0x0F, 0xFF, 0xC0, # OOOOOOOOOOOOOO
0x07, 0xFF, 0x80, # OOOOOOOOOOOO
0x00, 0xFC, 0x00, # OOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3264 'P' (17 pixels wide)
0x00, 0x00, 0x00, #
0xFF, 0xF8, 0x00, # OOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFE, 0x00, # OOOOOOOOOOOOOOO
0xFF, 0xF8, 0x00, # OOOOOOOOOOOOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0xE0, 0x00, 0x00, # OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3360 'Q' (22 pixels wide)
0x00, 0x00, 0x00, #
0x01, 0xFC, 0x00, # OOOOOOO
0x07, 0xFF, 0x00, # OOOOOOOOOOO
0x0F, 0xFF, 0x80, # OOOOOOOOOOOOO
0x1F, 0x07, 0xC0, # OOOOO OOOOO
0x3C, 0x01, 0xE0, # OOOO OOOO
0x78, 0x00, 0xF0, # OOOO OOOO
0x70, 0x00, 0x70, # OOO OOO
0x70, 0x00, 0x70, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0xE0, 0x00, 0x38, # OOO OOO
0x70, 0x00, 0x70, # OOO OOO
0x70, 0x18, 0x70, # OOO OO OOO
0x78, 0x1E, 0xE0, # OOOO OOOO OOO
0x3C, 0x0F, 0xE0, # OOOO OOOOOOO
0x1F, 0x07, 0xC0, # OOOOO OOOOO
0x0F, 0xFF, 0xE0, # OOOOOOOOOOOOOOO
0x07, 0xFF, 0xF0, # OOOOOOOOOOOOOOO
0x01, 0xFC, 0x7C, # OOOOOOO OOOOO
0x00, 0x00, 0x38, # OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3456 'R' (19 pixels wide)
0x00, 0x00, 0x00, #
0xFF, 0xFC, 0x00, # OOOOOOOOOOOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xFF, 0xFF, 0x80, # OOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0x00, # OOOOOOOOOOOOOOOO
0xFF, 0xFC, 0x00, # OOOOOOOOOOOOOO
0xE0, 0x70, 0x00, # OOO OOO
0xE0, 0x38, 0x00, # OOO OOO
0xE0, 0x3C, 0x00, # OOO OOOO
0xE0, 0x1E, 0x00, # OOO OOOO
0xE0, 0x0E, 0x00, # OOO OOO
0xE0, 0x0F, 0x00, # OOO OOOO
0xE0, 0x07, 0x80, # OOO OOOO
0xE0, 0x03, 0x80, # OOO OOO
0xE0, 0x03, 0xC0, # OOO OOOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xE0, # OOO OOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3552 'S' (18 pixels wide)
0x00, 0x00, 0x00, #
0x07, 0xF0, 0x00, # OOOOOOO
0x1F, 0xFC, 0x00, # OOOOOOOOOOO
0x3F, 0xFE, 0x00, # OOOOOOOOOOOOO
0x3C, 0x0F, 0x00, # OOOO OOOO
0x78, 0x07, 0x80, # OOOO OOOO
0x70, 0x03, 0x80, # OOO OOO
0x70, 0x03, 0x80, # OOO OOO
0x70, 0x00, 0x00, # OOO
0x78, 0x00, 0x00, # OOOO
0x3E, 0x00, 0x00, # OOOOO
0x1F, 0xE0, 0x00, # OOOOOOOO
0x0F, 0xFC, 0x00, # OOOOOOOOOO
0x01, 0xFF, 0x00, # OOOOOOOOO
0x00, 0x1F, 0x80, # OOOOOO
0x00, 0x07, 0x80, # OOOO
0x00, 0x03, 0xC0, # OOOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xF0, 0x01, 0xC0, # OOOO OOO
0x78, 0x03, 0xC0, # OOOO OOOO
0x7C, 0x07, 0x80, # OOOOO OOOO
0x3F, 0xFF, 0x00, # OOOOOOOOOOOOOO
0x1F, 0xFE, 0x00, # OOOOOOOOOOOO
0x03, 0xF8, 0x00, # OOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3648 'T' (19 pixels wide)
0x00, 0x00, 0x00, #
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0xE0, 0x00, # OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3744 'U' (18 pixels wide)
0x00, 0x00, 0x00, #
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0xE0, 0x01, 0xC0, # OOO OOO
0x70, 0x03, 0x80, # OOO OOO
0x78, 0x03, 0x80, # OOOO OOO
0x7C, 0x0F, 0x80, # OOOOO OOOOO
0x3F, 0xFF, 0x00, # OOOOOOOOOOOOOO
0x1F, 0xFE, 0x00, # OOOOOOOOOOOO
0x07, 0xF8, 0x00, # OOOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3840 'V' (21 pixels wide)
0x00, 0x00, 0x00, #
0xE0, 0x00, 0x38, # OOO OOO
0xF0, 0x00, 0x78, # OOOO OOOO
0x70, 0x00, 0x70, # OOO OOO
0x70, 0x00, 0x70, # OOO OOO
0x78, 0x00, 0xF0, # OOOO OOOO
0x38, 0x00, 0xE0, # OOO OOO
0x38, 0x00, 0xE0, # OOO OOO
0x1C, 0x01, 0xC0, # OOO OOO
0x1C, 0x01, 0xC0, # OOO OOO
0x1C, 0x01, 0xC0, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x07, 0x07, 0x00, # OOO OOO
0x07, 0x07, 0x00, # OOO OOO
0x07, 0x8F, 0x00, # OOOO OOOO
0x03, 0x8E, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x03, 0xDE, 0x00, # OOOO OOOO
0x01, 0xDC, 0x00, # OOO OOO
0x01, 0xDC, 0x00, # OOO OOO
0x01, 0xFC, 0x00, # OOOOOOO
0x00, 0xF8, 0x00, # OOOOO
0x00, 0xF8, 0x00, # OOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @3936 'W' (33 pixels wide)
0x00, 0x00, 0x00, 0x00, 0x00, #
0xE0, 0x03, 0xE0, 0x03, 0x80, # OOO OOOOO OOO
0xE0, 0x03, 0xE0, 0x03, 0x80, # OOO OOOOO OOO
0x70, 0x03, 0xE0, 0x07, 0x00, # OOO OOOOO OOO
0x70, 0x07, 0x70, 0x07, 0x00, # OOO OOO OOO OOO
0x70, 0x07, 0x70, 0x07, 0x00, # OOO OOO OOO OOO
0x70, 0x07, 0x70, 0x07, 0x00, # OOO OOO OOO OOO
0x38, 0x0E, 0x30, 0x0E, 0x00, # OOO OOO OO OOO
0x38, 0x0E, 0x38, 0x0E, 0x00, # OOO OOO OOO OOO
0x38, 0x0E, 0x38, 0x0E, 0x00, # OOO OOO OOO OOO
0x1C, 0x0E, 0x38, 0x1C, 0x00, # OOO OOO OOO OOO
0x1C, 0x1C, 0x1C, 0x1C, 0x00, # OOO OOO OOO OOO
0x1C, 0x1C, 0x1C, 0x1C, 0x00, # OOO OOO OOO OOO
0x1C, 0x1C, 0x1C, 0x1C, 0x00, # OOO OOO OOO OOO
0x0E, 0x1C, 0x0C, 0x38, 0x00, # OOO OOO OO OOO
0x0E, 0x38, 0x0E, 0x38, 0x00, # OOO OOO OOO OOO
0x0E, 0x38, 0x0E, 0x38, 0x00, # OOO OOO OOO OOO
0x0E, 0x38, 0x0E, 0x38, 0x00, # OOO OOO OOO OOO
0x07, 0x70, 0x07, 0x70, 0x00, # OOO OOO OOO OOO
0x07, 0x70, 0x07, 0x70, 0x00, # OOO OOO OOO OOO
0x07, 0x70, 0x07, 0x70, 0x00, # OOO OOO OOO OOO
0x07, 0x70, 0x07, 0x70, 0x00, # OOO OOO OOO OOO
0x03, 0xE0, 0x03, 0xE0, 0x00, # OOOOO OOOOO
0x03, 0xE0, 0x03, 0xE0, 0x00, # OOOOO OOOOO
0x03, 0xE0, 0x03, 0xE0, 0x00, # OOOOO OOOOO
0x00, 0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, 0x00, 0x00, #
# @4096 'X' (21 pixels wide)
0x00, 0x00, 0x00, #
0x78, 0x00, 0xF0, # OOOO OOOO
0x3C, 0x01, 0xE0, # OOOO OOOO
0x1C, 0x01, 0xC0, # OOO OOO
0x1E, 0x03, 0xC0, # OOOO OOOO
0x0F, 0x07, 0x80, # OOOO OOOO
0x07, 0x07, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x03, 0xCE, 0x00, # OOOO OOO
0x01, 0xDC, 0x00, # OOO OOO
0x00, 0xF8, 0x00, # OOOOO
0x00, 0xF8, 0x00, # OOOOO
0x00, 0x70, 0x00, # OOO
0x00, 0xF8, 0x00, # OOOOO
0x01, 0xDC, 0x00, # OOO OOO
0x01, 0xDC, 0x00, # OOO OOO
0x03, 0xCE, 0x00, # OOOO OOO
0x07, 0x8F, 0x00, # OOOO OOOO
0x07, 0x07, 0x00, # OOO OOO
0x0E, 0x03, 0x80, # OOO OOO
0x1E, 0x03, 0xC0, # OOOO OOOO
0x3C, 0x01, 0xE0, # OOOO OOOO
0x38, 0x00, 0xE0, # OOO OOO
0x78, 0x00, 0xF0, # OOOO OOOO
0xF0, 0x00, 0x78, # OOOO OOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @4192 'Y' (21 pixels wide)
0x00, 0x00, 0x00, #
0xE0, 0x00, 0x78, # OOO OOOO
0x70, 0x00, 0xF0, # OOO OOOO
0x38, 0x00, 0xE0, # OOO OOO
0x3C, 0x01, 0xE0, # OOOO OOOO
0x1C, 0x03, 0xC0, # OOO OOOO
0x0E, 0x03, 0x80, # OOO OOO
0x0F, 0x07, 0x80, # OOOO OOOO
0x07, 0x07, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x03, 0x8E, 0x00, # OOO OOO
0x01, 0xDC, 0x00, # OOO OOO
0x01, 0xFC, 0x00, # OOOOOOO
0x00, 0xF8, 0x00, # OOOOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x70, 0x00, # OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @4288 'Z' (19 pixels wide)
0x00, 0x00, 0x00, #
0x7F, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOO
0x7F, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOO
0x7F, 0xFF, 0xC0, # OOOOOOOOOOOOOOOOO
0x00, 0x03, 0xC0, # OOOO
0x00, 0x07, 0x80, # OOOO
0x00, 0x0F, 0x00, # OOOO
0x00, 0x0E, 0x00, # OOO
0x00, 0x1C, 0x00, # OOO
0x00, 0x3C, 0x00, # OOOO
0x00, 0x78, 0x00, # OOOO
0x00, 0xF0, 0x00, # OOOO
0x00, 0xE0, 0x00, # OOO
0x01, 0xC0, 0x00, # OOO
0x03, 0xC0, 0x00, # OOOO
0x07, 0x80, 0x00, # OOOO
0x0F, 0x00, 0x00, # OOOO
0x0E, 0x00, 0x00, # OOO
0x1C, 0x00, 0x00, # OOO
0x3C, 0x00, 0x00, # OOOO
0x78, 0x00, 0x00, # OOOO
0xF0, 0x00, 0x00, # OOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @4384 '[' (6 pixels wide)
0x00, #
0xFC, # OOOOOO
0xFC, # OOOOOO
0xFC, # OOOOOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xFC, # OOOOOO
0xFC, # OOOOOO
0xFC, # OOOOOO
# @4416 '\' (9 pixels wide)
0x00, 0x00, #
0xC0, 0x00, # OO
0xC0, 0x00, # OO
0x60, 0x00, # OO
0x60, 0x00, # OO
0x60, 0x00, # OO
0x30, 0x00, # OO
0x30, 0x00, # OO
0x30, 0x00, # OO
0x30, 0x00, # OO
0x18, 0x00, # OO
0x18, 0x00, # OO
0x18, 0x00, # OO
0x0C, 0x00, # OO
0x0C, 0x00, # OO
0x0C, 0x00, # OO
0x0E, 0x00, # OOO
0x06, 0x00, # OO
0x06, 0x00, # OO
0x06, 0x00, # OO
0x03, 0x00, # OO
0x03, 0x00, # OO
0x03, 0x00, # OO
0x03, 0x80, # OOO
0x01, 0x80, # OO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @4480 ']' (6 pixels wide)
0x00, #
0xFC, # OOOOOO
0xFC, # OOOOOO
0xFC, # OOOOOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0xFC, # OOOOOO
0xFC, # OOOOOO
0xFC, # OOOOOO
# @4512 '^' (12 pixels wide)
0x00, 0x00, #
0x06, 0x00, # OO
0x0F, 0x00, # OOOO
0x0F, 0x00, # OOOO
0x0F, 0x00, # OOOO
0x1F, 0x80, # OOOOOO
0x19, 0x80, # OO OO
0x39, 0xC0, # OOO OOO
0x39, 0xC0, # OOO OOO
0x30, 0xC0, # OO OO
0x70, 0xE0, # OOO OOO
0x70, 0xE0, # OOO OOO
0x60, 0x60, # OO OO
0xE0, 0x70, # OOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @4576 '_' (19 pixels wide)
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
0xFF, 0xFF, 0xE0, # OOOOOOOOOOOOOOOOOOO
# @4672 '`' (6 pixels wide)
0x00, #
0xF0, # OOOO
0x78, # OOOO
0x38, # OOO
0x18, # OO
0x0C, # OO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @4704 'a' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x0F, 0xE0, # OOOOOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x7F, 0xF8, # OOOOOOOOOOOO
0xF0, 0x3C, # OOOO OOOO
0xE0, 0x1C, # OOO OOO
0x00, 0x1C, # OOO
0x00, 0x7C, # OOOOO
0x0F, 0xFC, # OOOOOOOOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x7F, 0xDC, # OOOOOOOOO OOO
0xF0, 0x1C, # OOOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x3C, # OOO OOOO
0xF0, 0xFC, # OOOO OOOOOO
0x7F, 0xFC, # OOOOOOOOOOOOO
0x7F, 0xDC, # OOOOOOOOO OOO
0x1F, 0x8E, # OOOOOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @4768 'b' (15 pixels wide)
0x00, 0x00, #
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE3, 0xC0, # OOO OOOO
0xEF, 0xF0, # OOO OOOOOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xFC, 0x3C, # OOOOOO OOOO
0xF0, 0x1C, # OOOO OOO
0xF0, 0x1E, # OOOO OOOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x1E, # OOOO OOOO
0xF0, 0x1C, # OOOO OOO
0xF8, 0x7C, # OOOOO OOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xEF, 0xF0, # OOO OOOOOOOO
0xE3, 0xC0, # OOO OOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @4832 'c' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x07, 0xE0, # OOOOOO
0x1F, 0xF0, # OOOOOOOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x7C, 0x3C, # OOOOO OOOO
0x70, 0x1C, # OOO OOO
0xF0, 0x0E, # OOOO OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x0E, # OOOO OOO
0x70, 0x1C, # OOO OOO
0x7C, 0x3C, # OOOOO OOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x1F, 0xF0, # OOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @4896 'd' (15 pixels wide)
0x00, 0x00, #
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x07, 0x8E, # OOOO OOO
0x1F, 0xEE, # OOOOOOOO OOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x78, 0x7E, # OOOO OOOOOO
0x70, 0x1E, # OOO OOOO
0xF0, 0x1E, # OOOO OOOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x1E, # OOOO OOOO
0x70, 0x1E, # OOO OOOO
0x7C, 0x3E, # OOOOO OOOOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x1F, 0xEE, # OOOOOOOO OOO
0x07, 0x8E, # OOOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @4960 'e' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x07, 0xC0, # OOOOO
0x1F, 0xF0, # OOOOOOOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x78, 0x3C, # OOOO OOOO
0x70, 0x1C, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xF0, 0x00, # OOOO
0x70, 0x0E, # OOO OOO
0x7C, 0x3C, # OOOOO OOOO
0x3F, 0xFC, # OOOOOOOOOOOO
0x1F, 0xF8, # OOOOOOOOOO
0x07, 0xE0, # OOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5024 'f' (10 pixels wide)
0x00, 0x00, #
0x0F, 0xC0, # OOOOOO
0x1F, 0x80, # OOOOOO
0x3F, 0x80, # OOOOOOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0xFF, 0x00, # OOOOOOOO
0xFF, 0x00, # OOOOOOOO
0xFF, 0x00, # OOOOOOOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x38, 0x00, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5088 'g' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x07, 0xCE, # OOOOO OOO
0x1F, 0xEE, # OOOOOOOO OOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x78, 0x7E, # OOOO OOOOOO
0x70, 0x1E, # OOO OOOO
0xF0, 0x1E, # OOOO OOOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x1E, # OOOO OOOO
0x70, 0x1E, # OOO OOOO
0x78, 0x3E, # OOOO OOOOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x1F, 0xEE, # OOOOOOOO OOO
0x07, 0xCE, # OOOOO OOO
0x00, 0x0E, # OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xF8, 0x3C, # OOOOO OOOO
0x7F, 0xF8, # OOOOOOOOOOOO
0x3F, 0xF0, # OOOOOOOOOO
0x0F, 0xC0, # OOOOOO
# @5152 'h' (14 pixels wide)
0x00, 0x00, #
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE3, 0xE0, # OOO OOOOO
0xEF, 0xF0, # OOO OOOOOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xF8, 0x7C, # OOOOO OOOOO
0xF0, 0x3C, # OOOO OOOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5216 'i' (3 pixels wide)
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @5248 'j' (6 pixels wide)
0x00, #
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x00, #
0x00, #
0x00, #
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0x1C, # OOO
0xFC, # OOOOOO
0xF8, # OOOOO
0xF0, # OOOO
# @5280 'k' (14 pixels wide)
0x00, 0x00, #
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x3C, # OOO OOOO
0xE0, 0x78, # OOO OOOO
0xE0, 0xF0, # OOO OOOO
0xE1, 0xE0, # OOO OOOO
0xE3, 0xC0, # OOO OOOO
0xE7, 0x80, # OOO OOOO
0xEF, 0x00, # OOO OOOO
0xFF, 0x00, # OOOOOOOO
0xFF, 0x80, # OOOOOOOOO
0xF7, 0x80, # OOOO OOOO
0xE3, 0xC0, # OOO OOOO
0xE1, 0xC0, # OOO OOO
0xE1, 0xE0, # OOO OOOO
0xE0, 0xE0, # OOO OOO
0xE0, 0x70, # OOO OOO
0xE0, 0x78, # OOO OOOO
0xE0, 0x38, # OOO OOO
0xE0, 0x3C, # OOO OOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5344 'l' (3 pixels wide)
0x00, #
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0xE0, # OOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @5376 'm' (23 pixels wide)
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0xE3, 0xC1, 0xF0, # OOO OOOO OOOOO
0xEF, 0xF3, 0xFC, # OOO OOOOOOOO OOOOOOOO
0xFF, 0xF7, 0xFC, # OOOOOOOOOOOO OOOOOOOOO
0xF8, 0x7E, 0x1E, # OOOOO OOOOOO OOOO
0xF0, 0x3C, 0x0E, # OOOO OOOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @5472 'n' (14 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE7, 0xE0, # OOO OOOOOO
0xEF, 0xF0, # OOO OOOOOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xF8, 0x7C, # OOOOO OOOOO
0xF0, 0x3C, # OOOO OOOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5536 'o' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x07, 0xC0, # OOOOO
0x1F, 0xF0, # OOOOOOOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x78, 0x3C, # OOOO OOOO
0x70, 0x1C, # OOO OOO
0xF0, 0x1E, # OOOO OOOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x1E, # OOOO OOOO
0x70, 0x1C, # OOO OOO
0x78, 0x3C, # OOOO OOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x1F, 0xF0, # OOOOOOOOO
0x07, 0xC0, # OOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5600 'p' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE3, 0xE0, # OOO OOOOO
0xEF, 0xF0, # OOO OOOOOOOO
0xEF, 0xF8, # OOO OOOOOOOOO
0xFC, 0x3C, # OOOOOO OOOO
0xF0, 0x1C, # OOOO OOO
0xF0, 0x1E, # OOOO OOOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x1C, # OOOO OOO
0xF0, 0x1C, # OOOO OOO
0xF8, 0x7C, # OOOOO OOOOO
0xFF, 0xF8, # OOOOOOOOOOOOO
0xEF, 0xF0, # OOO OOOOOOOO
0xE7, 0xC0, # OOO OOOOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
# @5664 'q' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x07, 0x8E, # OOOO OOO
0x1F, 0xEE, # OOOOOOOO OOO
0x3F, 0xEE, # OOOOOOOOO OOO
0x78, 0x7E, # OOOO OOOOOO
0x70, 0x1E, # OOO OOOO
0xF0, 0x1E, # OOOO OOOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0xE0, 0x0E, # OOO OOO
0x70, 0x1E, # OOO OOOO
0x70, 0x1E, # OOO OOOO
0x7C, 0x3E, # OOOOO OOOOO
0x3F, 0xFE, # OOOOOOOOOOOOO
0x1F, 0xEE, # OOOOOOOO OOO
0x07, 0xCE, # OOOOO OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
0x00, 0x0E, # OOO
# @5728 'r' (9 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE7, 0x80, # OOO OOOO
0xEF, 0x80, # OOO OOOOO
0xFF, 0x00, # OOOOOOOO
0xF8, 0x00, # OOOOO
0xF0, 0x00, # OOOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0xE0, 0x00, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5792 's' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x0F, 0xC0, # OOOOOO
0x3F, 0xF0, # OOOOOOOOOO
0x7F, 0xF8, # OOOOOOOOOOOO
0xF0, 0x3C, # OOOO OOOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x00, # OOO
0xF8, 0x00, # OOOOO
0x7F, 0xC0, # OOOOOOOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x0F, 0xFC, # OOOOOOOOOO
0x01, 0xFE, # OOOOOOOO
0x00, 0x1E, # OOOO
0xE0, 0x0E, # OOO OOO
0xF0, 0x0E, # OOOO OOO
0x78, 0x1E, # OOOO OOOO
0x7F, 0xFC, # OOOOOOOOOOOOO
0x3F, 0xF8, # OOOOOOOOOOO
0x0F, 0xE0, # OOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5856 't' (8 pixels wide)
0x00, #
0x08, # O
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0xFF, # OOOOOOOO
0xFF, # OOOOOOOO
0xFF, # OOOOOOOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x38, # OOO
0x3F, # OOOOOO
0x1F, # OOOOO
0x0F, # OOOO
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
0x00, #
# @5888 'u' (14 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xE0, 0x1C, # OOO OOO
0xF0, 0x3C, # OOOO OOOO
0xF8, 0x7C, # OOOOO OOOOO
0x7F, 0xDC, # OOOOOOOOO OOO
0x3F, 0xDC, # OOOOOOOO OOO
0x1F, 0x1C, # OOOOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @5952 'v' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE0, 0x0E, # OOO OOO
0x70, 0x0C, # OOO OO
0x70, 0x1C, # OOO OOO
0x70, 0x1C, # OOO OOO
0x30, 0x18, # OO OO
0x38, 0x38, # OOO OOO
0x38, 0x38, # OOO OOO
0x18, 0x30, # OO OO
0x1C, 0x70, # OOO OOO
0x1C, 0x70, # OOO OOO
0x0C, 0x60, # OO OO
0x0E, 0xE0, # OOO OOO
0x0E, 0xE0, # OOO OOO
0x06, 0xC0, # OO OO
0x07, 0xC0, # OOOOO
0x07, 0xC0, # OOOOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @6016 'w' (23 pixels wide)
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x38, 0x0E, # OOO OOO OOO
0xE0, 0x7C, 0x0E, # OOO OOOOO OOO
0x70, 0x7C, 0x1C, # OOO OOOOO OOO
0x70, 0x6C, 0x1C, # OOO OO OO OOO
0x70, 0x6C, 0x1C, # OOO OO OO OOO
0x30, 0xEE, 0x18, # OO OOO OOO OO
0x38, 0xC6, 0x38, # OOO OO OO OOO
0x38, 0xC6, 0x38, # OOO OO OO OOO
0x38, 0xC6, 0x30, # OOO OO OO OO
0x1D, 0xC7, 0x70, # OOO OOO OOO OOO
0x1D, 0x83, 0x70, # OOO OO OO OOO
0x1D, 0x83, 0x70, # OOO OO OO OOO
0x0D, 0x83, 0x60, # OO OO OO OO
0x0F, 0x83, 0xE0, # OOOOO OOOOO
0x0F, 0x83, 0xE0, # OOOOO OOOOO
0x07, 0x01, 0xC0, # OOO OOO
0x07, 0x01, 0xC0, # OOO OOO
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
0x00, 0x00, 0x00, #
# @6112 'x' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE0, 0x0E, # OOO OOO
0x70, 0x1C, # OOO OOO
0x78, 0x3C, # OOOO OOOO
0x38, 0x38, # OOO OOO
0x1C, 0x70, # OOO OOO
0x1E, 0xF0, # OOOO OOOO
0x0E, 0xE0, # OOO OOO
0x0F, 0xE0, # OOOOOOO
0x07, 0xC0, # OOOOO
0x07, 0xC0, # OOOOO
0x0F, 0xE0, # OOOOOOO
0x0E, 0xE0, # OOO OOO
0x1E, 0xF0, # OOOO OOOO
0x1C, 0x70, # OOO OOO
0x38, 0x38, # OOO OOO
0x78, 0x3C, # OOOO OOOO
0x70, 0x1C, # OOO OOO
0xE0, 0x0E, # OOO OOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @6176 'y' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0xE0, 0x0E, # OOO OOO
0x70, 0x0E, # OOO OOO
0x70, 0x1C, # OOO OOO
0x70, 0x1C, # OOO OOO
0x38, 0x1C, # OOO OOO
0x38, 0x38, # OOO OOO
0x38, 0x38, # OOO OOO
0x1C, 0x38, # OOO OOO
0x1C, 0x70, # OOO OOO
0x1C, 0x70, # OOO OOO
0x0E, 0x70, # OOO OOO
0x0E, 0xE0, # OOO OOO
0x0E, 0xE0, # OOO OOO
0x07, 0xE0, # OOOOOO
0x07, 0xC0, # OOOOO
0x07, 0xC0, # OOOOO
0x03, 0xC0, # OOOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x03, 0x80, # OOO
0x07, 0x00, # OOO
0x07, 0x00, # OOO
0x3E, 0x00, # OOOOO
0x3E, 0x00, # OOOOO
0x3C, 0x00, # OOOO
# @6240 'z' (15 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x7F, 0xFE, # OOOOOOOOOOOOOO
0x7F, 0xFE, # OOOOOOOOOOOOOO
0x7F, 0xFE, # OOOOOOOOOOOOOO
0x00, 0x1E, # OOOO
0x00, 0x3C, # OOOO
0x00, 0x78, # OOOO
0x00, 0xF0, # OOOO
0x01, 0xE0, # OOOO
0x03, 0xC0, # OOOO
0x07, 0x80, # OOOO
0x0F, 0x00, # OOOO
0x1E, 0x00, # OOOO
0x3C, 0x00, # OOOO
0x78, 0x00, # OOOO
0xF0, 0x00, # OOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0xFF, 0xFE, # OOOOOOOOOOOOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @6304 '{' (9 pixels wide)
0x00, 0x00, #
0x07, 0x80, # OOOO
0x0F, 0x80, # OOOOO
0x1F, 0x80, # OOOOOO
0x1E, 0x00, # OOOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x3C, 0x00, # OOOO
0x38, 0x00, # OOO
0xF0, 0x00, # OOOO
0xC0, 0x00, # OO
0xF0, 0x00, # OOOO
0x38, 0x00, # OOO
0x3C, 0x00, # OOOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1E, 0x00, # OOOO
0x1F, 0x80, # OOOOOO
0x0F, 0x80, # OOOOO
0x07, 0x80, # OOOO
# @6368 '|' (2 pixels wide)
0x00, #
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
0xC0, # OO
# @6400 '}' (9 pixels wide)
0x00, 0x00, #
0xF0, 0x00, # OOOO
0xF8, 0x00, # OOOOO
0xFC, 0x00, # OOOOOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1E, 0x00, # OOOO
0x0E, 0x00, # OOO
0x07, 0x80, # OOOO
0x01, 0x80, # OO
0x07, 0x80, # OOOO
0x0E, 0x00, # OOO
0x1E, 0x00, # OOOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x1C, 0x00, # OOO
0x3C, 0x00, # OOOO
0xFC, 0x00, # OOOOOO
0xF8, 0x00, # OOOOO
0xF0, 0x00, # OOOO
# @6464 '~' (16 pixels wide)
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x3E, 0x00, # OOOOO
0x7F, 0x81, # OOOOOOOO O
0xFF, 0xC3, # OOOOOOOOOO OO
0xC3, 0xFF, # OO OOOOOOOOOO
0x80, 0xFE, # O OOOOOOO
0x00, 0x7C, # OOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
# @6528 '°' (9 pixels wide)
0x00, 0x00, #
0x3E, 0x00, # OOOOO
0x7F, 0x00, # OOOOOOO
0xE3, 0x80, # OOO OOO
0xC1, 0x80, # OO OO
0xC1, 0x80, # OO OO
0xC1, 0x80, # OO OO
0xE3, 0x80, # OOO OOO
0x7F, 0x00, # OOOOOOO
0x3E, 0x00, # OOOOO
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
0x00, 0x00, #
)
descriptors = (
(3,0),# !
(9,32),# "
(17,96),# #
(16,192),# $
(25,256),# %
(19,384),# &
(3,480),# '
(8,512),# (
(8,544),# )
(10,576),# *
(15,640),# +
(3,704),# ,
(9,736),# -
(3,800),# .
(9,832),# /
(16,896),# 0
(8,960),# 1
(16,992),# 2
(16,1056),# 3
(17,1120),# 4
(16,1216),# 5
(16,1280),# 6
(16,1344),# 7
(16,1408),# 8
(16,1472),# 9
(3,1536),# :
(3,1568),# ;
(15,1600),# <
(15,1664),# =
(15,1728),# >
(16,1792),# ?
(30,1856),# @
(21,1984),# A
(17,2080),# B
(20,2176),# C
(19,2272),# D
(17,2368),# E
(15,2464),# F
(22,2528),# G
(18,2624),# H
(3,2720),# I
(13,2752),# J
(20,2816),# K
(14,2912),# L
(23,2976),# M
(18,3072),# N
(22,3168),# O
(17,3264),# P
(22,3360),# Q
(19,3456),# R
(18,3552),# S
(19,3648),# T
(18,3744),# U
(21,3840),# V
(33,3936),# W
(21,4096),# X
(21,4192),# Y
(19,4288),# Z
(6,4384),# [
(9,4416),# \
(6,4480),# ]
(12,4512),# ^
(19,4576),# _
(6,4672),# `
(15,4704),# a
(15,4768),# b
(15,4832),# c
(15,4896),# d
(15,4960),# e
(10,5024),# f
(15,5088),# g
(14,5152),# h
(3,5216),# i
(6,5248),# j
(14,5280),# k
(3,5344),# l
(23,5376),# m
(14,5472),# n
(15,5536),# o
(15,5600),# p
(15,5664),# q
(9,5728),# r
(15,5792),# s
(8,5856),# t
(14,5888),# u
(15,5952),# v
(23,6016),# w
(15,6112),# x
(15,6176),# y
(15,6240),# z
(9,6304),# {
(2,6368),# |
(9,6400),# }
(16,6464),# ~
(9,6528),# °
)
kerning = (
(3,3,3,2,3,2,3,3,3,3,3,3,2,3,3,3,3,3,3,2,3,3,3,2,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,2,0,3,3,3,3,3,3,3,3,3,3,0,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,2,3,),
(9,9,9,8,9,6,9,7,9,9,3,6,0,6,4,8,9,9,9,2,8,8,9,8,9,9,9,0,8,9,9,6,3,9,8,9,9,9,8,9,9,0,9,9,9,9,8,9,8,9,8,9,9,9,9,8,9,8,9,9,9,7,0,9,6,9,5,5,5,9,5,9,9,6,9,9,9,9,5,9,5,9,6,9,9,9,9,9,9,8,6,9,9,0,9,),
(17,17,17,16,17,17,17,17,16,16,14,14,17,14,15,17,17,17,15,17,16,17,16,17,17,17,17,16,17,17,17,17,14,17,17,17,17,17,17,17,17,12,17,17,17,17,17,17,17,17,17,16,17,16,16,15,16,15,17,16,16,15,0,16,17,17,17,17,17,17,17,17,17,14,17,17,17,17,17,17,17,17,16,17,17,17,17,17,17,16,17,17,16,17,17,),
(15,15,16,16,15,16,15,16,13,15,15,14,16,14,15,16,14,15,16,16,16,16,14,16,16,14,14,15,16,16,15,16,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,14,16,14,14,13,12,15,16,14,14,14,0,12,16,16,16,16,16,14,16,16,16,13,16,16,16,16,16,16,16,16,16,14,16,13,14,13,13,15,16,16,14,16,15,),
(24,20,25,25,20,25,22,25,21,18,24,24,25,24,24,25,20,24,25,25,25,25,21,25,25,24,24,24,25,25,18,25,24,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,20,25,20,21,23,20,25,25,21,22,23,6,20,25,25,25,25,25,23,25,25,25,22,25,25,25,25,25,25,25,25,25,23,25,22,23,23,22,25,25,25,22,25,19,),
(19,15,19,17,15,18,16,19,15,15,17,19,19,19,19,19,15,19,17,19,17,19,15,19,18,19,19,18,19,17,15,19,18,19,19,19,19,19,19,19,19,18,19,19,19,19,19,19,19,19,18,14,19,14,15,18,13,19,19,15,16,13,0,13,18,19,19,19,19,17,19,19,19,16,19,19,19,19,19,19,19,19,18,17,19,16,17,18,16,19,19,19,16,19,15,),
(3,3,3,2,3,0,3,1,3,3,0,0,0,0,0,2,3,3,3,0,2,2,3,2,3,3,3,0,2,3,3,0,0,3,2,3,3,3,2,3,3,0,3,3,3,3,2,3,2,3,2,3,3,3,3,2,3,2,3,3,3,1,0,3,0,3,0,0,0,3,0,3,3,0,3,3,3,3,0,3,0,3,0,3,3,3,3,3,3,2,0,3,3,0,3,),
(8,8,5,5,6,3,8,3,8,6,3,7,3,5,4,5,5,5,5,3,6,4,8,5,5,5,7,3,4,5,5,4,4,8,4,8,8,8,4,8,8,4,8,8,8,8,4,8,4,8,5,8,8,8,8,7,8,7,8,8,8,3,8,8,4,8,3,3,3,5,6,8,8,8,8,8,5,5,3,8,3,5,4,5,5,5,5,5,6,4,4,8,8,3,7,),
(8,6,8,8,7,8,6,8,3,6,8,7,8,7,7,8,7,7,8,8,8,8,4,8,8,7,7,8,8,8,6,8,6,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,4,5,6,2,7,8,5,5,8,4,2,8,8,8,8,8,7,8,8,8,5,8,8,8,8,8,8,8,8,8,7,8,6,7,6,6,7,8,8,5,8,6,),
(10,10,9,9,10,7,10,8,8,10,3,7,1,7,4,9,9,9,10,4,9,9,6,9,9,9,9,4,9,8,9,7,3,10,9,10,10,10,9,10,10,0,10,10,10,10,9,10,9,10,9,6,10,9,9,7,8,5,10,9,7,7,0,7,8,10,7,7,7,9,7,10,9,6,10,10,9,9,7,9,7,9,8,9,9,8,9,8,8,8,7,10,7,0,10,),
(15,9,12,10,13,14,12,15,10,8,15,12,9,12,12,15,10,9,9,14,15,15,9,14,14,12,12,15,9,9,9,15,11,15,15,15,15,15,15,15,15,9,15,15,15,15,15,15,15,15,11,7,15,11,12,8,8,9,15,12,12,15,0,9,11,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,14,12,13,8,13,15,12,15,9,),
(3,0,2,1,0,1,0,2,1,0,0,3,0,3,3,1,0,3,1,0,1,1,0,1,2,3,3,0,0,0,0,2,3,3,0,3,3,3,0,3,3,2,3,3,3,3,0,3,0,3,1,0,1,0,0,3,0,3,3,0,2,0,2,0,2,3,1,1,1,1,3,3,3,2,3,3,3,3,1,3,1,3,2,1,2,0,0,3,0,3,0,3,2,0,0,),
(8,0,9,2,0,9,6,9,4,0,3,6,9,6,7,9,4,3,0,9,0,9,4,9,6,6,6,8,9,8,2,9,6,9,9,9,9,9,9,9,9,0,9,9,9,9,9,9,9,9,9,1,9,4,5,4,0,5,9,5,6,0,0,3,9,9,9,9,9,7,9,9,9,6,9,9,9,9,9,9,9,9,7,7,9,6,7,5,6,5,9,9,4,9,0,),
(3,0,2,1,0,1,0,2,0,0,0,3,0,3,3,1,0,3,1,0,1,1,0,1,2,3,3,0,0,0,0,2,3,3,0,3,3,3,0,3,3,2,3,3,3,3,0,3,0,3,1,0,1,0,0,3,0,3,3,0,0,0,0,0,2,3,1,1,1,1,1,3,3,0,3,3,3,3,1,3,1,3,2,1,2,0,0,3,0,3,0,3,0,0,0,),
(9,9,7,7,8,5,9,6,9,8,6,6,5,6,3,7,7,7,8,5,7,7,9,7,7,7,7,6,7,7,7,5,2,9,7,9,9,9,7,9,9,4,9,9,9,9,7,9,7,9,7,9,9,9,9,8,9,8,9,9,9,5,0,9,6,9,6,6,6,7,6,9,9,6,9,9,7,7,6,7,6,7,6,7,7,7,7,7,7,6,5,9,9,5,8,),
(16,15,16,15,16,16,15,16,13,15,16,14,16,14,14,16,16,15,15,16,16,16,14,16,16,16,16,16,16,16,15,16,13,16,16,16,16,16,16,16,16,15,16,16,16,16,16,16,16,16,16,14,16,14,14,12,12,14,16,14,14,16,0,12,16,16,16,16,16,16,16,16,16,13,16,16,16,16,16,16,16,16,16,16,16,15,16,15,15,15,16,16,14,16,15,),
(8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,0,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,),
(16,16,16,15,16,14,16,15,13,16,14,16,11,16,16,16,16,16,16,11,15,16,14,15,16,16,16,14,16,16,16,15,16,16,16,16,16,16,16,16,16,15,16,16,16,16,16,16,16,16,15,14,16,15,15,16,13,16,16,15,14,14,0,12,15,16,14,14,14,16,14,16,16,13,16,16,16,16,14,16,14,16,15,16,16,16,16,16,16,16,13,16,14,13,16,),
(15,15,16,16,15,16,15,16,13,15,16,14,16,14,14,16,15,15,16,16,16,16,14,16,16,15,15,16,16,16,15,16,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,14,16,14,14,13,12,15,16,14,14,15,0,12,16,16,16,16,16,15,16,16,16,13,16,16,16,16,16,16,16,16,16,15,16,15,15,15,15,15,16,16,14,16,15,),
(16,14,17,17,14,17,14,17,14,14,14,14,17,14,15,17,14,14,17,17,17,17,14,17,14,14,14,14,17,17,14,17,15,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,14,17,14,14,14,14,14,17,14,14,14,0,14,17,17,17,17,17,15,17,17,17,14,17,17,17,17,17,17,17,17,14,15,17,14,15,14,14,14,17,17,14,14,14,),
(15,15,16,16,14,16,15,16,15,11,16,14,16,14,14,16,11,14,16,16,16,16,15,16,16,14,14,16,16,16,13,16,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,15,16,15,15,14,15,15,16,15,15,15,0,15,16,16,16,16,16,14,16,16,16,13,16,16,16,16,16,16,16,16,16,14,16,13,14,13,13,15,16,16,15,16,15,),
(16,16,16,16,16,16,16,16,14,16,16,14,16,14,14,16,15,16,16,16,16,16,15,16,16,14,14,16,16,16,16,16,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,15,16,15,15,13,13,15,16,15,15,15,0,13,16,16,16,16,16,14,16,16,16,13,16,16,16,16,16,16,16,16,16,14,16,13,14,13,13,15,16,16,15,16,16,),
(16,16,13,14,15,12,16,12,16,15,10,13,9,13,10,14,13,14,14,9,14,13,16,14,14,13,13,10,12,14,14,10,8,16,13,16,16,16,12,16,16,8,16,16,16,16,13,16,13,16,14,16,16,16,16,15,16,15,16,16,16,12,0,16,11,16,10,10,10,14,10,16,16,13,16,16,13,13,10,13,10,13,11,14,13,13,13,13,13,12,13,16,16,10,16,),
(15,15,16,16,15,16,15,16,13,15,15,14,16,14,14,16,15,15,16,16,16,16,14,16,16,15,15,15,16,16,15,16,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,14,16,14,14,13,12,15,16,14,14,15,0,12,16,16,16,16,16,15,16,16,16,13,16,16,16,16,16,16,16,16,16,15,16,15,15,15,15,15,16,16,14,16,15,),
(16,16,16,15,16,16,16,16,13,15,16,13,16,13,14,16,16,16,15,16,16,16,14,16,16,16,16,16,16,16,16,16,13,16,16,16,16,16,16,16,16,15,16,16,16,16,16,16,16,16,16,14,16,14,14,12,12,14,16,14,14,16,0,12,16,16,16,16,16,16,16,16,16,13,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,15,16,16,14,16,16,),
(3,3,3,2,3,1,3,2,0,2,0,3,0,3,3,3,3,3,1,0,2,3,0,2,3,3,3,0,3,3,3,2,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,2,0,3,1,1,3,0,3,3,1,0,1,0,0,2,3,1,1,1,3,1,3,3,0,3,3,3,3,1,3,1,3,2,3,3,3,3,3,3,3,0,3,0,0,3,),
(3,3,3,2,3,1,3,2,1,2,0,3,0,3,3,3,3,3,1,0,2,3,0,2,3,3,3,0,3,3,3,2,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,2,0,3,1,1,3,0,3,3,1,2,1,2,0,2,3,1,1,1,3,3,3,3,2,3,3,3,3,1,3,1,3,2,3,3,3,3,3,3,3,0,3,2,0,3,),
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(15,11,15,15,14,15,12,15,10,12,15,13,15,13,13,15,13,13,15,15,15,15,10,15,15,13,13,15,15,15,10,15,13,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,7,15,11,12,12,8,14,15,12,12,15,0,9,15,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,14,12,13,14,15,15,12,15,11,),
(15,11,13,15,14,15,12,15,10,12,15,13,6,13,13,15,13,13,15,15,15,15,10,15,15,13,13,15,14,15,11,15,13,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,7,15,11,12,12,8,14,15,12,12,14,0,9,15,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,14,12,13,14,13,15,12,14,11,),
(15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,0,15,15,15,15,15,15,15,15,15,15,12,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,),
(15,11,15,14,14,15,12,15,10,12,15,14,15,14,14,15,13,13,14,15,15,15,10,15,15,14,14,15,15,15,10,15,13,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,14,7,15,11,12,12,8,14,15,12,12,15,0,9,15,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,14,13,13,14,15,15,12,15,11,),
(10,10,8,7,8,5,10,7,10,7,5,7,5,7,5,8,8,8,7,5,8,8,10,7,8,8,8,5,8,8,8,6,5,10,8,10,10,10,8,10,10,5,10,10,10,10,8,10,8,10,7,10,10,10,10,9,10,9,10,10,10,6,0,10,7,10,6,6,6,8,6,10,10,7,10,10,8,8,6,8,6,8,7,8,8,8,8,8,8,7,6,10,10,5,9,),
(15,15,15,15,15,15,15,15,12,14,15,15,15,15,15,15,15,15,15,15,15,15,11,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,7,15,13,13,15,11,15,15,13,13,15,13,9,15,15,15,15,15,15,15,15,15,13,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,13,15,15,),
(14,11,14,14,14,14,11,14,10,12,14,14,14,14,14,14,13,14,14,14,14,14,10,14,14,14,14,14,14,14,11,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,6,14,11,11,14,8,14,14,11,11,14,0,8,14,14,14,14,14,13,14,14,14,11,14,14,14,14,14,14,14,14,14,13,14,13,13,14,13,14,14,14,11,14,11,),
(3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,3,3,3,3,3,3,3,3,3,3,0,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,),
(6,6,6,6,6,6,6,6,6,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,),
(14,14,14,13,14,11,14,12,11,11,9,14,10,14,14,13,14,14,11,10,13,13,10,13,14,14,14,9,13,14,14,12,14,14,13,14,14,14,13,14,14,12,14,14,14,14,13,14,13,14,13,6,14,12,12,14,10,14,14,12,11,12,0,8,12,14,11,11,11,14,11,14,14,11,14,14,14,14,11,14,11,14,12,14,14,14,14,14,14,14,11,14,11,9,14,),
(3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,3,3,3,3,3,3,3,3,3,3,0,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,),
(23,21,23,23,23,23,21,23,19,21,23,23,23,23,23,23,22,23,23,23,23,23,19,23,23,23,23,23,23,23,20,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,15,23,20,20,23,17,23,23,20,20,23,4,17,23,23,23,23,23,22,23,23,23,20,23,23,23,23,23,23,23,23,23,22,23,22,22,23,22,23,23,23,20,23,21,),
(14,11,14,14,14,14,11,14,10,12,14,14,14,14,14,14,13,14,14,14,14,14,10,14,14,14,14,14,14,14,11,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,6,14,11,11,14,8,14,14,11,11,14,0,8,14,14,14,14,14,13,14,14,14,11,14,14,14,14,14,14,14,14,14,13,14,13,13,14,13,14,14,14,11,14,11,),
(15,11,15,15,14,15,12,15,10,12,15,13,15,13,13,15,13,13,15,15,15,15,10,15,15,13,13,15,15,15,10,15,13,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,7,15,11,12,12,8,14,15,12,12,15,0,9,15,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,14,12,13,14,15,15,12,15,11,),
(15,11,15,15,14,15,12,15,10,12,15,13,15,13,13,15,13,13,15,15,15,15,10,15,14,13,13,15,15,15,11,15,13,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,7,15,11,12,12,8,14,15,12,12,15,3,9,15,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,14,12,13,14,15,15,12,15,11,),
(15,15,15,15,15,15,15,15,15,14,15,15,15,15,15,15,15,15,15,15,15,15,11,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,7,15,13,13,15,11,15,15,13,15,15,15,9,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,),
(9,9,9,8,9,6,9,7,6,7,3,6,3,6,4,9,9,9,3,3,8,9,0,8,9,9,9,3,9,9,9,7,3,9,8,9,9,9,8,9,9,3,9,9,9,9,8,9,8,9,8,1,9,7,7,3,5,3,9,7,6,7,0,3,7,9,6,6,6,9,6,9,9,6,9,9,9,9,6,9,6,9,7,9,9,9,9,9,9,8,6,9,6,3,9,),
(14,11,15,15,14,15,12,15,11,12,10,14,15,14,14,15,13,14,15,15,15,15,11,15,15,14,14,13,15,15,10,15,14,15,14,15,15,15,14,15,15,15,15,15,15,15,14,15,14,15,15,7,15,11,11,13,8,15,15,11,12,13,0,9,15,15,15,15,15,13,15,15,15,12,15,15,15,15,15,15,15,15,15,13,15,13,13,13,13,15,15,15,12,13,11,),
(8,8,8,7,8,6,8,7,5,7,5,8,5,8,8,8,8,8,6,5,7,8,5,7,8,8,8,5,8,8,8,7,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,8,8,8,7,5,8,6,6,8,5,8,8,6,5,6,0,5,7,8,6,6,6,8,6,8,8,5,8,8,8,8,6,8,6,8,7,8,8,8,8,8,8,8,5,8,5,5,8,),
(14,14,14,14,14,14,14,14,11,13,14,14,14,14,14,14,14,14,14,14,14,14,10,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,6,14,12,12,14,10,14,14,12,11,14,0,8,14,14,14,14,14,14,14,14,14,11,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,11,14,14,),
(15,15,15,14,15,12,15,13,12,13,13,12,12,12,10,14,15,15,11,12,14,14,6,14,15,15,15,13,14,15,15,13,9,15,14,15,15,15,14,15,15,11,15,15,15,15,14,15,14,15,14,7,15,13,13,9,11,10,15,13,12,13,0,9,14,15,13,13,13,15,13,15,15,12,15,15,15,15,13,15,13,15,14,15,15,15,15,15,15,14,12,15,12,13,15,),
(23,23,23,22,23,20,23,22,20,22,22,20,21,20,18,23,23,23,20,21,22,23,15,22,23,23,23,22,23,23,23,21,18,23,23,23,23,23,23,23,23,20,23,23,23,23,23,23,23,23,22,15,23,21,21,18,19,19,23,21,20,21,4,17,22,23,22,22,22,23,22,23,23,20,23,23,23,23,22,23,22,23,22,23,23,23,23,23,23,22,21,23,20,21,23,),
(15,15,15,14,15,12,15,13,12,13,12,15,11,15,15,14,15,15,12,11,14,14,11,14,15,15,15,12,14,15,15,13,15,15,14,15,15,15,14,15,15,13,15,15,15,15,14,15,14,15,14,7,15,13,13,15,11,15,15,13,12,13,0,9,13,15,12,12,12,15,12,15,15,12,15,15,15,15,12,15,12,15,13,15,15,15,15,15,15,15,12,15,12,11,15,),
(15,15,15,14,15,12,15,13,12,13,13,12,12,12,10,15,15,15,11,12,14,15,7,14,15,15,15,13,15,15,15,13,9,15,14,15,15,15,14,15,15,11,15,15,15,15,14,15,14,15,14,7,15,13,13,9,11,10,15,13,12,13,7,9,14,15,13,13,13,15,13,15,15,12,15,15,15,15,13,15,13,15,14,15,15,15,15,15,15,14,12,15,12,13,15,),
(15,15,15,14,15,13,15,14,12,14,13,15,10,15,15,15,15,15,13,11,15,15,11,14,15,15,15,13,15,15,15,14,15,15,15,15,15,15,15,15,15,14,15,15,15,15,15,15,15,15,14,7,15,13,13,15,11,15,15,13,12,14,0,9,15,15,14,14,14,15,14,15,15,12,15,15,15,15,14,15,14,15,15,15,15,15,15,15,15,15,12,15,12,12,15,),
(9,9,6,7,8,6,9,6,9,7,6,9,4,6,6,7,6,7,7,5,7,6,9,7,7,6,9,6,6,6,7,6,6,9,6,9,9,9,6,9,9,6,9,9,9,9,6,9,6,9,7,9,9,9,9,8,9,8,9,9,9,6,9,9,6,9,6,6,6,7,8,9,9,9,9,9,6,6,6,9,6,6,6,7,6,6,6,6,7,6,6,9,9,6,9,),
(2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,),
(8,6,9,7,6,9,6,9,5,6,7,6,9,6,7,9,6,6,7,9,7,9,6,9,7,6,6,8,9,8,6,9,6,9,9,9,9,9,9,9,9,7,9,9,9,9,9,9,9,9,9,6,9,5,5,6,4,6,9,5,6,7,6,4,9,9,9,9,9,7,9,9,9,6,9,9,9,9,9,9,9,9,7,7,9,6,7,6,6,6,9,9,6,9,6,),
(16,7,15,11,14,15,13,16,11,6,16,13,15,13,13,16,11,7,10,15,16,16,10,15,15,13,13,16,15,10,8,16,12,16,16,16,16,16,16,16,16,6,16,16,16,16,16,16,16,16,12,8,16,12,13,9,9,10,16,13,13,16,0,10,13,16,16,16,16,14,16,16,16,13,16,16,16,16,16,16,16,16,16,14,16,14,15,13,14,9,15,16,13,16,7,),
(9,9,9,8,9,6,9,7,8,9,3,6,0,6,4,8,9,9,9,2,8,8,9,8,9,9,9,1,8,9,9,6,3,9,8,9,9,9,8,9,9,0,9,9,9,9,8,9,8,9,8,9,9,8,8,6,7,8,9,8,9,7,0,7,6,9,5,5,5,9,5,9,9,6,9,9,9,9,5,9,5,9,6,9,9,9,9,9,9,8,6,9,9,0,9,),
)
# End of font
| 38.957002
| 292
| 0.419472
| 19,377
| 134,090
| 2.902462
| 0.013831
| 0.289184
| 0.387049
| 0.475667
| 0.818246
| 0.762771
| 0.713003
| 0.674952
| 0.633079
| 0.60207
| 0
| 0.43484
| 0.430778
| 134,090
| 3,441
| 293
| 38.968323
| 0.301977
| 0.365769
| 0
| 0.867983
| 1
| 0
| 0.00011
| 0
| 0
| 0
| 0.322324
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
6c24ce4c7bea1ecb61064d6a72153eae3a8f10c7
| 2,206
|
py
|
Python
|
src/pdc2/scripts/transrate_wrapper.py
|
jlanga/smsk_selection
|
08070c6d4a6fbd9320265e1e698c95ba80f81123
|
[
"MIT"
] | 4
|
2021-07-18T05:20:20.000Z
|
2022-01-03T10:22:33.000Z
|
src/pdc2/scripts/transrate_wrapper.py
|
jlanga/smsk_selection
|
08070c6d4a6fbd9320265e1e698c95ba80f81123
|
[
"MIT"
] | 1
|
2017-08-21T07:26:13.000Z
|
2018-11-08T13:59:48.000Z
|
src/pdc2/scripts/transrate_wrapper.py
|
jlanga/smsk_orthofinder
|
08070c6d4a6fbd9320265e1e698c95ba80f81123
|
[
"MIT"
] | 2
|
2021-07-18T05:20:26.000Z
|
2022-03-31T18:23:31.000Z
|
"Wrapper for transrate"
import sys,os
def transrate_ref(assembly,pe_fq1,pe_fq2,num_cores,DIR,reference):
if DIR == ".": DIR = os.getcwd()
if DIR != os.getcwd() : os.chdir(DIR)
if DIR[-1] != "/": DIR += "/"
if DIR[0] != "/": DIR = "/" + str(DIR)
path_assembly, file_assembly = (os.path.split(assembly)) #splits the path from the file name
assembly_base_name = (os.path.splitext(file_assembly)[0])
results_name = assembly_base_name+"_transrate_results"
if os.path.exists(DIR+results_name):
print "Found transrate results folder"
else:
cmd = ["transrate","--assembly",assembly,
"--left",pe_fq1, \
"--right",pe_fq2, \
"--threads",str(num_cores), \
"--reference",reference,\
"--output",results_name]
print " ".join(cmd)
os.system(" ".join(cmd))
assert os.path.exists(DIR+results_name), "Transrate not completed"
def transrate_no_ref(assembly,pe_fq1,pe_fq2,num_cores,DIR):
if DIR == ".": DIR = os.getcwd()
if DIR != os.getcwd() : os.chdir(DIR)
if DIR[-1] != "/": DIR += "/"
if DIR[0] != "/": DIR = "/" + str(DIR)
path_assembly, file_assembly = (os.path.split(assembly)) #splits the path from the file name
assembly_base_name = (os.path.splitext(file_assembly)[0])
results_name = assembly_base_name+"_transrate_results"
if os.path.exists(DIR+results_name):
print "Found transrate results folder"
else:
cmd = ["transrate","--assembly",assembly,
"--left",pe_fq1, \
"--right",pe_fq2, \
"--threads",str(num_cores),\
"--output",results_name]
print " ".join(cmd)
os.system(" ".join(cmd))
print (DIR+results_name)
assert os.path.exists(DIR+results_name), "Transrate not completed"
if __name__ == "__main__":
if len(sys.argv) == 6:
transrate_no_ref(assembly=sys.argv[1],pe_fq1=sys.argv[2],pe_fq2=sys.argv[3],num_cores=int(sys.argv[4]),DIR=sys.argv[5])
elif len(sys.argv) == 7:
transrate_ref(assembly=sys.argv[1],pe_fq1=sys.argv[2],pe_fq2=sys.argv[3],num_cores=int(sys.argv[4]),DIR=sys.argv[5],reference=sys.argv[6])
else:
print "Usage:"
print "python transrate_wrapper.py assembly_fasta fastq_filtered_pe_reads1 fastq_filtered_pe_reads2 num_cores output_dir reference(optional) "
sys.exit(0)
| 27.234568
| 144
| 0.676337
| 333
| 2,206
| 4.273273
| 0.207207
| 0.063949
| 0.02811
| 0.056219
| 0.777231
| 0.777231
| 0.777231
| 0.777231
| 0.777231
| 0.732256
| 0
| 0.017989
| 0.143246
| 2,206
| 80
| 145
| 27.575
| 0.734921
| 0.030825
| 0
| 0.72549
| 0
| 0
| 0.203469
| 0.022504
| 0
| 0
| 0
| 0
| 0.039216
| 0
| null | null | 0
| 0.019608
| null | null | 0.137255
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6c3bb55cf14477add4354b17f71ae37445f64de3
| 6,890
|
py
|
Python
|
tests/grammpy_test/oldapi_tests/rules_tests/grammarManipulation_tests/ValidAddTest.py
|
PatrikValkovic/grammpy
|
8308a1fd349bf9ea0d267360cc9a4ab20d1629e8
|
[
"MIT"
] | 1
|
2021-02-04T12:41:08.000Z
|
2021-02-04T12:41:08.000Z
|
tests/grammpy_test/oldapi_tests/rules_tests/grammarManipulation_tests/ValidAddTest.py
|
PatrikValkovic/grammpy
|
8308a1fd349bf9ea0d267360cc9a4ab20d1629e8
|
[
"MIT"
] | 3
|
2017-07-08T16:28:52.000Z
|
2020-04-23T18:06:24.000Z
|
tests/grammpy_test/oldapi_tests/rules_tests/grammarManipulation_tests/ValidAddTest.py
|
PatrikValkovic/grammpy
|
8308a1fd349bf9ea0d267360cc9a4ab20d1629e8
|
[
"MIT"
] | 1
|
2021-02-04T12:41:10.000Z
|
2021-02-04T12:41:10.000Z
|
#!/usr/bin/env python
"""
:Author Patrik Valkovic
:Created 03.08.2017 12:28
:Licence MIT
Part of grammpy
"""
from unittest import main, TestCase
from grammpy.old_api import Rule as _R, Grammar
from ..grammar import *
class ValidAddTest(TestCase):
def __init__(self, *args):
super().__init__(*args)
self.g = Grammar()
def setUp(self):
g = Grammar()
g.add_term([0, 1, 2, 'a', 'b', 'c'])
g.add_nonterm([NFirst, NSecond, NThird, NFourth])
self.g = g
def test_addOne(self):
class Tmp(_R):
rule = ([NFirst], ['a', 0])
self.assertEqual(self.g.rules_count(), 0)
self.assertFalse(self.g.have_rule(Tmp))
self.assertIsNone(self.g.get_rule(Tmp))
self.assertIsNone(self.g.rule(Tmp))
self.assertEqual(self.g.add_rule(Tmp)[0].rule, Tmp.rule)
self.assertEqual(self.g.rules_count(), 1)
self.assertTrue(self.g.have_rule(Tmp))
self.assertEqual(self.g.get_rule(Tmp), Tmp)
self.assertEqual(self.g.rule(Tmp), Tmp)
def test_addOneInArray(self):
class Tmp(_R):
rule = ([NFirst], ['a', 0])
self.assertEqual(self.g.rules_count(), 0)
self.assertFalse(self.g.have_rule(Tmp))
self.assertIsNone(self.g.get_rule(Tmp))
self.assertIsNone(self.g.rule(Tmp))
self.assertEqual(self.g.add_rule([Tmp])[0].rule, ([NFirst], ['a', 0]))
self.assertEqual(self.g.rules_count(), 1)
self.assertTrue(self.g.have_rule(Tmp))
self.assertEqual(self.g.get_rule(Tmp), Tmp)
self.assertEqual(self.g.rule(Tmp), Tmp)
def test_addOneInTuple(self):
class Tmp(_R):
rule = ([NFirst], ['a', 0])
self.assertEqual(self.g.rules_count(), 0)
self.assertFalse(self.g.have_rule(Tmp))
self.assertIsNone(self.g.get_rule(Tmp))
self.assertIsNone(self.g.rule(Tmp))
self.assertEqual(self.g.add_rule((Tmp,))[0].rule, Tmp.rule)
self.assertEqual(self.g.rules_count(), 1)
self.assertTrue(self.g.have_rule(Tmp))
self.assertEqual(self.g.get_rule(Tmp), Tmp)
self.assertEqual(self.g.rule(Tmp), Tmp)
def test_addThree(self):
class Tmp1(_R):
rule = ([NFirst], ['a', 0])
class Tmp2(_R):
rule = ([NSecond], ['a', 0, NFourth])
class Tmp3(_R):
rule = ([NThird], [0])
self.assertEqual(self.g.rules_count(), 0)
self.assertFalse(self.g.have_rule(Tmp1))
self.assertIsNone(self.g.get_rule(Tmp1))
self.assertIsNone(self.g.rule(Tmp1))
self.assertFalse(self.g.have_rule(Tmp2))
self.assertIsNone(self.g.get_rule(Tmp2))
self.assertIsNone(self.g.rule(Tmp2))
self.assertFalse(self.g.have_rule(Tmp3))
self.assertIsNone(self.g.get_rule(Tmp3))
self.assertIsNone(self.g.rule(Tmp3))
self.assertEqual(self.g.add_rule(Tmp1)[0].rule, Tmp1.rule)
self.assertEqual(self.g.rules_count(), 1)
self.assertTrue(self.g.have_rule(Tmp1))
self.assertEqual(self.g.get_rule(Tmp1), Tmp1)
self.assertEqual(self.g.rule(Tmp1), Tmp1)
self.assertFalse(self.g.have_rule(Tmp2))
self.assertIsNone(self.g.get_rule(Tmp2))
self.assertIsNone(self.g.rule(Tmp2))
self.assertFalse(self.g.have_rule(Tmp3))
self.assertIsNone(self.g.get_rule(Tmp3))
self.assertIsNone(self.g.rule(Tmp3))
self.assertEqual(self.g.add_rule(Tmp2)[0].rule, Tmp2.rule)
self.assertEqual(self.g.rules_count(), 2)
self.assertTrue(self.g.have_rule(Tmp1))
self.assertEqual(self.g.get_rule(Tmp1), Tmp1)
self.assertEqual(self.g.rule(Tmp1), Tmp1)
self.assertTrue(self.g.have_rule(Tmp2))
self.assertEqual(self.g.get_rule(Tmp2), Tmp2)
self.assertEqual(self.g.rule(Tmp2), Tmp2)
self.assertFalse(self.g.have_rule(Tmp3))
self.assertIsNone(self.g.get_rule(Tmp3))
self.assertIsNone(self.g.rule(Tmp3))
self.assertEqual(self.g.add_rule(Tmp3)[0].rule, Tmp3.rule)
self.assertEqual(self.g.rules_count(), 3)
self.assertTrue(self.g.have_rule(Tmp1))
self.assertEqual(self.g.get_rule(Tmp1), Tmp1)
self.assertEqual(self.g.rule(Tmp1), Tmp1)
self.assertTrue(self.g.have_rule(Tmp2))
self.assertEqual(self.g.get_rule(Tmp2), Tmp2)
self.assertEqual(self.g.rule(Tmp2), Tmp2)
self.assertTrue(self.g.have_rule(Tmp3))
self.assertEqual(self.g.get_rule(Tmp3), Tmp3)
self.assertEqual(self.g.rule(Tmp3), Tmp3)
def test_addThreeInArray(self):
class Tmp1(_R):
rule = ([NFirst], ['a', 0])
class Tmp2(_R):
rule = ([NSecond], ['a', 0, NFourth])
class Tmp3(_R):
rule = ([NThird], [0])
self.assertEqual(self.g.rules_count(), 0)
add = self.g.add_rule([Tmp1, Tmp2, Tmp3])
self.assertEqual(add[0].rule, Tmp1.rule)
self.assertEqual(add[1].rule, Tmp2.rule)
self.assertEqual(add[2].rule, Tmp3.rule)
self.assertEqual(self.g.rules_count(), 3)
self.assertTrue(self.g.have_rule(Tmp1))
self.assertEqual(self.g.get_rule(Tmp1), Tmp1)
self.assertEqual(self.g.rule(Tmp1), Tmp1)
self.assertTrue(self.g.have_rule(Tmp2))
self.assertEqual(self.g.get_rule(Tmp2), Tmp2)
self.assertEqual(self.g.rule(Tmp2), Tmp2)
self.assertTrue(self.g.have_rule(Tmp3))
self.assertEqual(self.g.get_rule(Tmp3), Tmp3)
self.assertEqual(self.g.rule(Tmp3), Tmp3)
def test_addThreeInTuple(self):
class Tmp1(_R):
rule = ([NFirst], ['a', 0])
class Tmp2(_R):
rule = ([NSecond], ['a', 0, NFourth])
class Tmp3(_R):
rule = ([NThird], [0])
self.assertEqual(self.g.rules_count(), 0)
add = self.g.add_rule((Tmp1, Tmp2, Tmp3))
self.assertEqual(add[0].rule, Tmp1.rule)
self.assertEqual(add[1].rule, Tmp2.rule)
self.assertEqual(add[2].rule, Tmp3.rule)
self.assertEqual(self.g.rules_count(), 3)
self.assertTrue(self.g.have_rule(Tmp1))
self.assertEqual(self.g.get_rule(Tmp1), Tmp1)
self.assertEqual(self.g.rule(Tmp1), Tmp1)
self.assertTrue(self.g.have_rule(Tmp2))
self.assertEqual(self.g.get_rule(Tmp2), Tmp2)
self.assertEqual(self.g.rule(Tmp2), Tmp2)
self.assertTrue(self.g.have_rule(Tmp3))
self.assertEqual(self.g.get_rule(Tmp3), Tmp3)
self.assertEqual(self.g.rule(Tmp3), Tmp3)
def test_shouldAddIntoGrammar(self):
class Tmp1(_R):
rule = ([NFirst], ['a', 0])
g = Grammar(terminals=['a', 0],
nonterminals=[NFirst],
rules=[Tmp1])
g.add_rule(Tmp1)
self.assertEqual(g.get_rule(Tmp1), Tmp1)
if __name__ == '__main__':
main()
| 36.648936
| 78
| 0.610305
| 948
| 6,890
| 4.317511
| 0.080169
| 0.118495
| 0.232104
| 0.24432
| 0.878329
| 0.870511
| 0.854386
| 0.846079
| 0.839726
| 0.839726
| 0
| 0.031279
| 0.229753
| 6,890
| 187
| 79
| 36.84492
| 0.739966
| 0.014369
| 0
| 0.75
| 0
| 0
| 0.003391
| 0
| 0
| 0
| 0
| 0
| 0.651316
| 1
| 0.059211
| false
| 0
| 0.019737
| 0
| 0.171053
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
6c84ce571418c28bbb52bcbba7fc089810db3277
| 1,109
|
py
|
Python
|
Trabajo_final.py
|
carlos99c/Programming-2020B
|
53b08cb1d8b84558112f68f44aa3256f26fc93ca
|
[
"Apache-2.0"
] | null | null | null |
Trabajo_final.py
|
carlos99c/Programming-2020B
|
53b08cb1d8b84558112f68f44aa3256f26fc93ca
|
[
"Apache-2.0"
] | null | null | null |
Trabajo_final.py
|
carlos99c/Programming-2020B
|
53b08cb1d8b84558112f68f44aa3256f26fc93ca
|
[
"Apache-2.0"
] | null | null | null |
list = ["2,4,6,8,10","1,3,7,9,10"]
listA = list[0].split(sep=',')
listB = list[1].split(sep=',')
print("\n::: INTERSECCIÓN ::: \n")
i=0
counter=0
while i < len(listA) :
j=0
while j < len(listB) :
if listA[i] == listB[j] :
print ("lista A",listA[i])
counter+=1
j+=1
i+=1
if counter == 0 :
print("no hay numeros repetidos en la lista")
list = ["1,2,3,4,5,6","1,2,3,4,5,6"]
listA = list[0].split(sep=',')
listB = list[1].split(sep=',')
print(" ::: INTERSECCIÓN ::: ")
i=0
counter=0
while i < len(listA) :
j=0
while j < len(listB) :
if listA[i] == listB[j] :
print ("lista B",listA[i])
counter+=1
j+=1
i+=1
if counter == 0 :
print("no hay numeros repetidos en la lista")
list = ["5,10,15,20,25,30","1,14,21,28,35"]
listA = list[0].split(sep=',')
listB = list[1].split(sep=',')
print("\n::: INTERSECCIÓN ::: \n")
i=0
counter=0
while i < len(listA) :
j=0
while j < len(listB) :
if listA[i] == listB[j] :
print ("lista c",listA[i])
counter+=1
j+=1
i+=1
if counter == 0 :
print("no hay numeros repetidos en la lista")
| 17.328125
| 47
| 0.543733
| 199
| 1,109
| 3.030151
| 0.211055
| 0.079602
| 0.049751
| 0.074627
| 0.915423
| 0.915423
| 0.895522
| 0.895522
| 0.895522
| 0.895522
| 0
| 0.082367
| 0.222723
| 1,109
| 64
| 48
| 17.328125
| 0.617169
| 0
| 0
| 0.854167
| 0
| 0
| 0.25045
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.1875
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
66974cd714d24e51490ff83bcb3bb659675baa82
| 19,587
|
py
|
Python
|
tests/frameworks/test_aiohttp_server.py
|
tirkarthi/python-sensor
|
9872d146ac00baff2673fde5ba97fdbe596869a4
|
[
"MIT"
] | 61
|
2017-09-27T02:50:17.000Z
|
2022-03-22T12:13:37.000Z
|
tests/frameworks/test_aiohttp_server.py
|
tirkarthi/python-sensor
|
9872d146ac00baff2673fde5ba97fdbe596869a4
|
[
"MIT"
] | 82
|
2017-07-11T13:47:33.000Z
|
2022-03-22T10:10:38.000Z
|
tests/frameworks/test_aiohttp_server.py
|
takeaway/python-sensor
|
52d6eaa2d6a8e625201bad36ac2448201c4bd63d
|
[
"MIT"
] | 27
|
2017-09-11T16:22:32.000Z
|
2022-03-11T17:21:49.000Z
|
# (c) Copyright IBM Corp. 2021
# (c) Copyright Instana Inc. 2020
from __future__ import absolute_import
import aiohttp
import asyncio
import unittest
import tests.apps.aiohttp_app
from ..helpers import testenv
from instana.singletons import async_tracer, agent
class TestAiohttpServer(unittest.TestCase):
async def fetch(self, session, url, headers=None):
try:
async with session.get(url, headers=headers) as response:
return response
except aiohttp.web_exceptions.HTTPException:
pass
def setUp(self):
""" Clear all spans before a test run """
self.recorder = async_tracer.recorder
self.recorder.clear_spans()
# New event loop for every test
self.loop = asyncio.new_event_loop()
asyncio.set_event_loop(None)
def tearDown(self):
pass
def test_server_get(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/")
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
traceId = test_span.t
self.assertEqual(traceId, aioclient_span.t)
self.assertEqual(traceId, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Synthetic
self.assertIsNone(test_span.sy)
self.assertIsNone(aioclient_span.sy)
self.assertIsNone(aioserver_span.sy)
# Error logging
self.assertIsNone(test_span.ec)
self.assertIsNone(aioclient_span.ec)
self.assertIsNone(aioserver_span.ec)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(200, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(200, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(type(aioclient_span.stack) is list)
self.assertTrue(len(aioclient_span.stack) > 1)
assert "X-INSTANA-T" in response.headers
self.assertEqual(response.headers["X-INSTANA-T"], traceId)
assert "X-INSTANA-S" in response.headers
self.assertEqual(response.headers["X-INSTANA-S"], aioserver_span.s)
assert "X-INSTANA-L" in response.headers
self.assertEqual(response.headers["X-INSTANA-L"], '1')
assert "Server-Timing" in response.headers
self.assertEqual(
response.headers["Server-Timing"], "intid;desc=%s" % traceId)
def test_server_get_204(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/204")
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
trace_id = test_span.t
self.assertEqual(trace_id, aioclient_span.t)
self.assertEqual(trace_id, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Synthetic
self.assertIsNone(test_span.sy)
self.assertIsNone(aioclient_span.sy)
self.assertIsNone(aioserver_span.sy)
# Error logging
self.assertIsNone(test_span.ec)
self.assertIsNone(aioclient_span.ec)
self.assertIsNone(aioserver_span.ec)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(204, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/204", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(204, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/204", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(isinstance(aioclient_span.stack, list))
self.assertTrue(len(aioclient_span.stack) > 1)
assert "X-INSTANA-T" in response.headers
self.assertEqual(response.headers["X-INSTANA-T"], trace_id)
assert "X-INSTANA-S" in response.headers
self.assertEqual(response.headers["X-INSTANA-S"], aioserver_span.s)
assert "X-INSTANA-L" in response.headers
self.assertEqual(response.headers["X-INSTANA-L"], '1')
assert "Server-Timing" in response.headers
self.assertEqual(
response.headers["Server-Timing"], "intid;desc=%s" % trace_id)
def test_server_synthetic_request(self):
async def test():
headers = {
'X-INSTANA-SYNTHETIC': '1'
}
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/", headers=headers)
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertTrue(aioserver_span.sy)
self.assertIsNone(aioclient_span.sy)
self.assertIsNone(test_span.sy)
def test_server_get_with_params_to_scrub(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/?secret=iloveyou")
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
traceId = test_span.t
self.assertEqual(traceId, aioclient_span.t)
self.assertEqual(traceId, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Error logging
self.assertIsNone(test_span.ec)
self.assertIsNone(aioclient_span.ec)
self.assertIsNone(aioserver_span.ec)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(200, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertEqual("secret=<redacted>",
aioserver_span.data["http"]["params"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(200, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertEqual("secret=<redacted>",
aioclient_span.data["http"]["params"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(type(aioclient_span.stack) is list)
self.assertTrue(len(aioclient_span.stack) > 1)
assert "X-INSTANA-T" in response.headers
self.assertEqual(response.headers["X-INSTANA-T"], traceId)
assert "X-INSTANA-S" in response.headers
self.assertEqual(response.headers["X-INSTANA-S"], aioserver_span.s)
assert "X-INSTANA-L" in response.headers
self.assertEqual(response.headers["X-INSTANA-L"], '1')
assert "Server-Timing" in response.headers
self.assertEqual(
response.headers["Server-Timing"], "intid;desc=%s" % traceId)
def test_server_custom_header_capture(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
# Hack together a manual custom headers list
agent.options.extra_http_headers = [
u'X-Capture-This', u'X-Capture-That']
headers = dict()
headers['X-Capture-This'] = 'this'
headers['X-Capture-That'] = 'that'
return await self.fetch(session, testenv["aiohttp_server"] + "/?secret=iloveyou", headers=headers)
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
traceId = test_span.t
self.assertEqual(traceId, aioclient_span.t)
self.assertEqual(traceId, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Error logging
self.assertIsNone(test_span.ec)
self.assertIsNone(aioclient_span.ec)
self.assertIsNone(aioserver_span.ec)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(200, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertEqual("secret=<redacted>",
aioserver_span.data["http"]["params"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(200, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertEqual("secret=<redacted>",
aioclient_span.data["http"]["params"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(type(aioclient_span.stack) is list)
self.assertTrue(len(aioclient_span.stack) > 1)
assert "X-INSTANA-T" in response.headers
self.assertEqual(response.headers["X-INSTANA-T"], traceId)
assert "X-INSTANA-S" in response.headers
self.assertEqual(response.headers["X-INSTANA-S"], aioserver_span.s)
assert "X-INSTANA-L" in response.headers
self.assertEqual(response.headers["X-INSTANA-L"], '1')
assert "Server-Timing" in response.headers
self.assertEqual(
response.headers["Server-Timing"], "intid;desc=%s" % traceId)
assert "X-Capture-This" in aioserver_span.data["http"]["header"]
self.assertEqual("this", aioserver_span.data["http"]["header"]["X-Capture-This"])
assert "X-Capture-That" in aioserver_span.data["http"]["header"]
self.assertEqual("that", aioserver_span.data["http"]["header"]["X-Capture-That"])
def test_server_get_401(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/401")
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
traceId = test_span.t
self.assertEqual(traceId, aioclient_span.t)
self.assertEqual(traceId, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Error logging
self.assertIsNone(test_span.ec)
self.assertIsNone(aioclient_span.ec)
self.assertIsNone(aioserver_span.ec)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(401, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/401", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(401, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/401", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(type(aioclient_span.stack) is list)
self.assertTrue(len(aioclient_span.stack) > 1)
assert "X-INSTANA-T" in response.headers
self.assertEqual(response.headers["X-INSTANA-T"], traceId)
assert "X-INSTANA-S" in response.headers
self.assertEqual(response.headers["X-INSTANA-S"], aioserver_span.s)
assert "X-INSTANA-L" in response.headers
self.assertEqual(response.headers["X-INSTANA-L"], '1')
assert "Server-Timing" in response.headers
self.assertEqual(
response.headers["Server-Timing"], "intid;desc=%s" % traceId)
def test_server_get_500(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/500")
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
traceId = test_span.t
self.assertEqual(traceId, aioclient_span.t)
self.assertEqual(traceId, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Error logging
self.assertIsNone(test_span.ec)
self.assertEqual(aioclient_span.ec, 1)
self.assertEqual(aioserver_span.ec, 1)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(500, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/500", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(500, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/500", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertEqual('I must simulate errors.',
aioclient_span.data["http"]["error"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(type(aioclient_span.stack) is list)
self.assertTrue(len(aioclient_span.stack) > 1)
assert "X-INSTANA-T" in response.headers
self.assertEqual(response.headers["X-INSTANA-T"], traceId)
assert "X-INSTANA-S" in response.headers
self.assertEqual(response.headers["X-INSTANA-S"], aioserver_span.s)
assert "X-INSTANA-L" in response.headers
self.assertEqual(response.headers["X-INSTANA-L"], '1')
assert "Server-Timing" in response.headers
self.assertEqual(
response.headers["Server-Timing"], "intid;desc=%s" % traceId)
def test_server_get_exception(self):
async def test():
with async_tracer.start_active_span('test'):
async with aiohttp.ClientSession() as session:
return await self.fetch(session, testenv["aiohttp_server"] + "/exception")
response = self.loop.run_until_complete(test())
spans = self.recorder.queued_spans()
self.assertEqual(3, len(spans))
aioserver_span = spans[0]
aioclient_span = spans[1]
test_span = spans[2]
self.assertIsNone(async_tracer.active_span)
# Same traceId
traceId = test_span.t
self.assertEqual(traceId, aioclient_span.t)
self.assertEqual(traceId, aioserver_span.t)
# Parent relationships
self.assertEqual(aioclient_span.p, test_span.s)
self.assertEqual(aioserver_span.p, aioclient_span.s)
# Error logging
self.assertIsNone(test_span.ec)
self.assertEqual(aioclient_span.ec, 1)
self.assertEqual(aioserver_span.ec, 1)
self.assertEqual("aiohttp-server", aioserver_span.n)
self.assertEqual(500, aioserver_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/exception", aioserver_span.data["http"]["url"])
self.assertEqual("GET", aioserver_span.data["http"]["method"])
self.assertIsNone(aioserver_span.stack)
self.assertEqual("aiohttp-client", aioclient_span.n)
self.assertEqual(500, aioclient_span.data["http"]["status"])
self.assertEqual(testenv["aiohttp_server"] +
"/exception", aioclient_span.data["http"]["url"])
self.assertEqual("GET", aioclient_span.data["http"]["method"])
self.assertEqual('Internal Server Error',
aioclient_span.data["http"]["error"])
self.assertIsNotNone(aioclient_span.stack)
self.assertTrue(type(aioclient_span.stack) is list)
self.assertTrue(len(aioclient_span.stack) > 1)
| 40.552795
| 118
| 0.63634
| 2,221
| 19,587
| 5.461054
| 0.063935
| 0.158298
| 0.051447
| 0.046747
| 0.911864
| 0.908978
| 0.904526
| 0.898755
| 0.8915
| 0.88713
| 0
| 0.009123
| 0.238934
| 19,587
| 482
| 119
| 40.636929
| 0.804521
| 0.026803
| 0
| 0.809524
| 0
| 0
| 0.108151
| 0
| 0
| 0
| 0
| 0
| 0.602241
| 1
| 0.028011
| false
| 0.005602
| 0.019608
| 0
| 0.07563
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
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| 0
|
0
| 8
|
66b82c09498b6ec90c69d90f29418a3cfa3f42e2
| 27,499
|
py
|
Python
|
tests/unit/classes_test_io/classes_1_functions_io.py
|
MickyHCorbett/MorfLess
|
9761197d7767c250cc27262e1ab41adf21c59333
|
[
"MIT"
] | null | null | null |
tests/unit/classes_test_io/classes_1_functions_io.py
|
MickyHCorbett/MorfLess
|
9761197d7767c250cc27262e1ab41adf21c59333
|
[
"MIT"
] | null | null | null |
tests/unit/classes_test_io/classes_1_functions_io.py
|
MickyHCorbett/MorfLess
|
9761197d7767c250cc27262e1ab41adf21c59333
|
[
"MIT"
] | null | null | null |
#
import libraries.constants as ct
import libraries.globals as gb
import libraries.schematics as sch
from unit.second_processes_test_io.second_processes_supplemental import LIST_META_1
from unit.second_processes_test_io.second_processes_supplemental import LIST_META_BLANK_1
from unit.second_processes_test_io.second_processes_supplemental import LIST_META_BLANK_2
from unit.second_processes_test_io.second_processes_supplemental import POSTLIST_2
from unit.second_processes_test_io.second_processes_supplemental import POSTLIST_SINGLE
from unit.second_processes_test_io.second_processes_supplemental import POSTLIST_BLANK
from unit.second_processes_test_io.second_processes_supplemental import ARCHIVE_1
from unit.lists_test_io.lists_supplemental import DEPENDENCIES_1
from collections import OrderedDict
#
test_values_1 = [\
{ 'remark': 'Test Case 1:HtmlOut:init - empty init parameters',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': {},
'filename': '',
'dependencies': [],
'postlist': {},
'assertEqual': {\
'postlist_constant_name': 'postlist--.js',
'pagination_name': 'pagination--.js',
'is_template': False,
'is_root': False,
'is_search': False,
'fileroot': ''
}
},
{ 'remark': 'Test Case 2:HtmlOut:init - empty init parameters except filename',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': {},
'filename': 'this-post.page',
'dependencies': [],
'postlist': {},
'assertEqual': {\
'postlist_constant_name': 'postlist--this-post.js',
'pagination_name': 'pagination--this-post.js',
'is_template': False,
'is_root': False,
'is_search': False,
'fileroot': 'this-post'
}
},
{ 'remark': 'Test Case 3:HtmlOut:init - empty init parameters except filename = search.page',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': {},
'filename': 'search.page',
'dependencies': [],
'postlist': {},
'assertEqual': {\
'postlist_constant_name': 'postlist--search.js',
'pagination_name': 'pagination--search.js',
'is_template': True,
'is_root': False,
'is_search': True,
'fileroot': 'search'
}
},
{ 'remark': 'Test Case 4:HtmlOut:init - empty init parameters except filename = index.page',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': {},
'filename': 'index.page',
'dependencies': [],
'postlist': {},
'assertEqual': {\
'postlist_constant_name': 'postlist--index.js',
'pagination_name': 'pagination--index.js',
'is_template': False,
'is_root': True,
'is_search': False,
'fileroot': 'index'
}
},
{ 'remark': 'Test Case 5:HtmlOut:init - empty init parameters except filename = categories.page',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': {},
'filename': 'categories.page',
'dependencies': [],
'postlist': {},
'assertEqual': {\
'postlist_constant_name': 'postlist--categories.js',
'pagination_name': 'pagination--categories.js',
'is_template': True,
'is_root': False,
'is_search': False,
'fileroot': 'categories'
}
},
]
test_values_2 = [\
{ 'remark': 'Test Case 1:HtmlOut:process_pagination - filename test2.post - empty html array',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test2.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': False
},
'html_array': [''],
'assertEqual': {\
}
},
{ 'remark': 'Test Case 2:HtmlOut:process_pagination - filename test2.post - pagination in html array',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test2.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': False
},
'html_array': ['Something', 'PAGINATION=[HEADER:my-class:]:', 'This thing'],
'assertEqual': {\
'fileroot': 'test2',
'index': 1,
'next_ref': '',
'postname': 'test2.post',
'prev_ref': '',
'type': True
}
},
{ 'remark': 'Test Case 3:HtmlOut:process_pagination - filename test2.post - pagination in html array - unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test2.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': True
},
'html_array': ['Something', 'PAGINATION=[HEADER:my-class:]:', 'This thing'],
'assertEqual': {\
}
},
{ 'remark': 'Test Case 4:HtmlOut:process_pagination - filename index.page - pagination in html array',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'index.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': False
},
'html_array': ['Something', 'PAGINATION=[HEADER:my-class:]:', 'This thing'],
'assertEqual': {\
}
},
{ 'remark': 'Test Case 5:HtmlOut:process_pagination - filename authors.page - pagination in html array',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'authors.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': False
},
'html_array': ['Something', 'PAGINATION=[HEADER:my-class:]:', 'This thing'],
'assertEqual': {\
}
},
]
test_values_3 = [\
{ 'remark': 'Test Case 1:HtmlOut:insert_additions_into_html - header additions',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test2.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': False
},
'placement': ct.PCOM_HEADER_PLACEMENT,
'add_default_header_additions': True,
'add_default_footer_additions': False,
'default_header_additions': 'Something in the header',
'default_footer_additions': 'Something in the footer',
'assertEqual': {\
'header_additions': ['Something in the header'],
'footer_additions': [],
}
},
{ 'remark': 'Test Case 1:HtmlOut:insert_additions_into_html - footer additions',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test2.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'unlisted': False
},
'placement': ct.PCOM_FOOTER_PLACEMENT,
'add_default_header_additions': False,
'add_default_footer_additions': True,
'default_header_additions': 'Something in the header',
'default_footer_additions': 'Something in the footer',
'assertEqual': {\
'header_additions': [],
'footer_additions': ['Something in the footer'],
}
},
]
test_values_4 = [\
{ 'remark': 'Test Case 1:HtmlOut:get_raw_content - not search or unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test2.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'html_content': """
<head>
Content head_start_end
</head>
<section id="main">
More here
</section>
<style>
<!-- things here to add {{}} -->
</style>
<script src="this script" />
<footer>
<div class="footer content">
<div class="inner1"><h1>A title</h1></div>
<div class="inner2"><p>Lots of data to talk about</p></div>
<ul>
<li>one</li>
<li>Another here what!</li>
</ul>
</div>
</footer>
""",
'meta': {\
'unlisted': False,
'page_title': 'A name',
'page_description': 'A description',
'page_extract': 'An extract'
},
'assertEqual': """
More hereA title
Lots of data to talk aboutone
Another here what!
A name
A description
An extract
"""
},
{ 'remark': 'Test Case 2:HtmlOut:get_raw_content - search or unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'search.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'html_content': """
<head>
Content head_start_end
</head>
<section id="main">
More here
</section>
<style>
<!-- things here to add {{}} -->
</style>
<script src="this script" />
<footer>
<div class="footer content">
<div class="inner1"><h1>A title</h1></div>
<div class="inner2"><p>Lots of data to talk about</p></div>
<ul>
<li>one</li>
<li>Another here what!</li>
</ul>
</div>
</footer>
""",
'meta': {\
'unlisted': False,
'page_title': 'A name',
'page_description': 'A description',
'page_extract': 'An extract'
},
'assertEqual': """
"""
},
{ 'remark': 'Test Case 3:HtmlOut:get_raw_content - not search but unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'search-this.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'html_content': """
<head>
Content head_start_end
</head>
<section id="main">
More here
</section>
<style>
<!-- things here to add {{}} -->
</style>
<script src="this script" />
<footer>
<div class="footer content">
<div class="inner1"><h1>A title</h1></div>
<div class="inner2"><p>Lots of data to talk about</p></div>
<ul>
<li>one</li>
<li>Another here what!</li>
</ul>
</div>
</footer>
""",
'meta': {\
'unlisted': True,
'page_title': 'A name',
'page_description': 'A description',
'page_extract': 'An extract'
},
'assertEqual': """
"""
},
{ 'remark': 'Test Case 4:HtmlOut:get_raw_content - meta is NONE',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'search-this.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'html_content': """
<head>
Content head_start_end
</head>
<section id="main">
More here
</section>
<style>
<!-- things here to add {{}} -->
</style>
<script src="this script" />
<footer>
<div class="footer content">
<div class="inner1"><h1>A title</h1></div>
<div class="inner2"><p>Lots of data to talk about</p></div>
<ul>
<li>one</li>
<li>Another here what!</li>
</ul>
</div>
</footer>
""",
'meta': ct.PCOM_NO_ENTRY,
'assertEqual': """
"""
},
]
test_values_5 = [\
{ 'remark': 'Test Case 1:HtmlOut:update_postlist - new post',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test-update1.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_2,
'meta': {\
'page_title': 'Something',
'page_description': 'A short description',
'authors': 'Charles',
'categories': 'stuff',
'thumb_link': 'default/image/link.png',
'page_extract': 'An extract',
'url': 'this/link',
'meta_valid': ct.PCOM_META_VALID,
'sticky': '',
'unlisted': False
},
'assertEqual': 'test-update1.post'
},
{ 'remark': 'Test Case 2:HtmlOut:update_postlist - new post meta = no entry',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test-update1.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_BLANK,
'meta': ct.PCOM_NO_ENTRY,
'assertEqual': ct.PCOM_NO_ENTRY
},
{ 'remark': 'Test Case 3:HtmlOut:update_postlist - unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test-update1.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_BLANK,
'meta': {\
'page_title': 'Something',
'page_description': 'A short description',
'authors': 'Charles',
'categories': 'stuff',
'thumb_link': 'default/image/link.png',
'page_extract': 'An extract',
'url': 'this/link',
'meta_valid': ct.PCOM_META_VALID,
'sticky': '',
'unlisted': True
},
'assertEqual': ct.PCOM_NO_ENTRY
},
{ 'remark': 'Test Case 4:HtmlOut:update_postlist - template = authors.page',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'authors.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_BLANK,
'meta': {\
'page_title': 'Something',
'page_description': 'A short description',
'authors': 'Charles',
'categories': 'stuff',
'thumb_link': 'default/image/link.png',
'page_extract': 'An extract',
'url': 'this/link',
'meta_valid': ct.PCOM_META_VALID,
'sticky': '',
'unlisted': False
},
'assertEqual': ct.PCOM_NO_ENTRY
},
{ 'remark': 'Test Case 5:HtmlOut:update_postlist - root = 404.page',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': '404.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_BLANK,
'meta': {\
'page_title': 'Something',
'page_description': 'A short description',
'authors': 'Charles',
'categories': 'stuff',
'thumb_link': 'default/image/link.png',
'page_extract': 'An extract',
'url': 'this/link',
'meta_valid': ct.PCOM_META_VALID,
'sticky': '',
'unlisted': False
},
'assertEqual': ct.PCOM_NO_ENTRY
},
{ 'remark': 'Test Case 6:HtmlOut:update_postlist - meta is none',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_1,
'filename': 'test-update1.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_BLANK,
'meta': ct.PCOM_NO_ENTRY,
'assertEqual': ct.PCOM_NO_ENTRY
},
]
test_values_6 = [\
{ 'remark': 'Test Case 1:HtmlOut:update_categories - update category',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_1,
'filename': 'contact.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': False
},
'assertEqual': {\
"no_of_category_pages": 1,
"categories": [
OrderedDict([('name', 'stuff'),
('thumbnail', '/images/Polimorf-shapes-background.jpg'),
('description', '')])
]
},
},
{ 'remark': 'Test Case 2:HtmlOut:update_categories - unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'contact.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': True
},
'assertEqual': {\
"no_of_category_pages": 1,
"categories": []
},
},
{ 'remark': 'Test Case 3:HtmlOut:update_categories - template',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'categories.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': False
},
'assertEqual': {\
"no_of_category_pages": 1,
"categories": []
},
},
{ 'remark': 'Test Case 4:HtmlOut:update_categories - root = index ',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'index.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': False
},
'assertEqual': {\
"no_of_category_pages": 1,
"categories": []
},
},
{ 'remark': 'Test Case 5:HtmlOut:update_categories - meta is none',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'contact.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': ct.PCOM_NO_ENTRY,
'assertEqual': {\
"no_of_category_pages": 1,
"categories": []
},
},
]
test_values_7 = [\
{ 'remark': 'Test Case 1:HtmlOut:update_authors - update author',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_1,
'filename': 'contact.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': False
},
'assertEqual': {\
"no_of_author_pages": 1,
"authors": [
OrderedDict([('name', 'Clive'), ('shortname', 'Clive'),
('thumbnail', '/images/Polimorf-shapes-background.jpg'),
('description', '')])
]
},
},
{ 'remark': 'Test Case 2:HtmlOut:update_authors - unlisted',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'contact.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': True
},
'assertEqual': {\
"no_of_author_pages": 1,
"authors": []
},
},
{ 'remark': 'Test Case 3:HtmlOut:update_authors - meta is none',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'contact.post',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': ct.PCOM_NO_ENTRY,
'assertEqual': {\
"no_of_author_pages": 1,
"authors": []
},
},
{ 'remark': 'Test Case 4:HtmlOut:update_authors - template',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'categories.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': False
},
'assertEqual': {\
"no_of_author_pages": 1,
"authors": []
},
},
{ 'remark': 'Test Case 5:HtmlOut:update_authors - root = index ',
'content': '',
'log': {\
'inserts_processed': [],
'search_content': [],
'files_processed': 'N',
'default_header_additions': [],
'default_footer_additions': [],
'file_header_additions': [],
'file_footer_additions': []
},
'site_settings': gb.DEFAULT_SETTINGS,
'list_meta': LIST_META_BLANK_2,
'filename': 'index.page',
'dependencies': DEPENDENCIES_1 ,
'postlist': POSTLIST_SINGLE,
'meta': {\
'unlisted': False
},
'assertEqual': {\
"no_of_author_pages": 1,
"authors": []
},
},
]
| 28.525934
| 115
| 0.568166
| 2,632
| 27,499
| 5.620821
| 0.06193
| 0.071989
| 0.053535
| 0.056239
| 0.920238
| 0.899283
| 0.868933
| 0.850548
| 0.829593
| 0.818778
| 0
| 0.007886
| 0.262228
| 27,499
| 963
| 116
| 28.555556
| 0.721313
| 0
| 0
| 0.764387
| 0
| 0
| 0.505037
| 0.16009
| 0
| 0
| 0
| 0
| 0.034745
| 1
| 0
| false
| 0
| 0.013029
| 0
| 0.013029
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
66b8d250510c4f902683e0891b91ead4000ac2dc
| 646
|
py
|
Python
|
tests/regex_demo.py
|
arisp99/sphinx-toolbox
|
2987080e2d65c0dd2d392dcf7f1f5a904a9231f5
|
[
"MIT"
] | 30
|
2021-03-01T00:15:55.000Z
|
2022-03-01T13:23:59.000Z
|
tests/regex_demo.py
|
arisp99/sphinx-toolbox
|
2987080e2d65c0dd2d392dcf7f1f5a904a9231f5
|
[
"MIT"
] | 56
|
2020-12-17T12:39:04.000Z
|
2022-03-21T19:00:55.000Z
|
tests/regex_demo.py
|
domdfcoding/sphinx-toolbox
|
fe5a35d6b4fce617514c4c243ad94fb8bd86b0bf
|
[
"MIT"
] | 4
|
2021-07-04T16:57:52.000Z
|
2022-03-21T19:35:31.000Z
|
# stdlib
import re
no_flags = re.compile(r"Hello\s+[Ww]orld[.,](Lovely|Horrible) weather, isn't it (.*)\?")
one_flag = re.compile(r"Hello\s+[Ww]orld[.,](Lovely|Horrible) weather, isn't it (.*)\?", flags=re.IGNORECASE)
two_flags = re.compile(
r"Hello \s+ [Ww]orld [.,] (Lovely|Horrible)\ weather,\ isn't\ it (.*) \?",
flags=re.ASCII | re.VERBOSE,
)
backticks = re.compile(":py:class:`([A-Za-z_][A-Za-z0-9._]+)`")
leading_whitespace = re.compile(" :py:class:`([A-Za-z_][A-Za-z0-9._]+)`")
trailing_whitespace = re.compile(":py:class:`([A-Za-z_][A-Za-z0-9._]+)` ")
single_whitespace = re.compile(" :py:class:`([A-Za-z_][A-Za-z0-9._]+)` ")
| 46.142857
| 109
| 0.617647
| 107
| 646
| 3.598131
| 0.327103
| 0.062338
| 0.114286
| 0.166234
| 0.792208
| 0.792208
| 0.792208
| 0.792208
| 0.792208
| 0.792208
| 0
| 0.013722
| 0.097523
| 646
| 13
| 110
| 49.692308
| 0.646655
| 0.009288
| 0
| 0
| 0
| 0.454545
| 0.548589
| 0.347962
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.090909
| 0
| 0.090909
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
66e73ddb617fbb45166fd9c065b0ded1f73633ee
| 919
|
py
|
Python
|
tests/test_cimmodel.py
|
nalu-svk/cimsparql
|
e69b0799a2bbd70027e2c8bb9970574991597ca5
|
[
"MIT"
] | 9
|
2021-04-30T11:37:44.000Z
|
2022-03-24T12:20:17.000Z
|
tests/test_cimmodel.py
|
nalu-svk/cimsparql
|
e69b0799a2bbd70027e2c8bb9970574991597ca5
|
[
"MIT"
] | 15
|
2021-05-01T21:21:00.000Z
|
2022-03-16T07:09:03.000Z
|
tests/test_cimmodel.py
|
nalu-svk/cimsparql
|
e69b0799a2bbd70027e2c8bb9970574991597ca5
|
[
"MIT"
] | 3
|
2021-09-14T09:50:25.000Z
|
2022-02-21T15:44:57.000Z
|
from mock import MagicMock
from cimsparql.model import CimModel
def test_map_data_types(monkeypatch):
def cim_init(self, *args):
self._mapper = MagicMock(have_cim_version=MagicMock(return_value=True))
self._prefixes = {"cim": None}
monkeypatch.setattr(CimModel, "__init__", cim_init)
cim_model = CimModel()
assert cim_model.map_data_types
def test_not_map_data_types(monkeypatch):
def cim_init(self, *args):
self._mapper = MagicMock(have_cim_version=MagicMock(return_value=False))
self._prefixes = {"cim": None}
monkeypatch.setattr(CimModel, "__init__", cim_init)
cim_model = CimModel()
assert not cim_model.map_data_types
def test_not_map_data_types_on_exception(monkeypatch):
def cim_init(self, *args):
pass
monkeypatch.setattr(CimModel, "__init__", cim_init)
cim_model = CimModel()
assert not cim_model.map_data_types
| 27.848485
| 80
| 0.727965
| 122
| 919
| 5.04918
| 0.262295
| 0.068182
| 0.116883
| 0.102273
| 0.86039
| 0.86039
| 0.813312
| 0.813312
| 0.813312
| 0.813312
| 0
| 0
| 0.176279
| 919
| 32
| 81
| 28.71875
| 0.813738
| 0
| 0
| 0.590909
| 0
| 0
| 0.032644
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 1
| 0.272727
| false
| 0.045455
| 0.090909
| 0
| 0.363636
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
dd050c6ccda50e5f2e47b0df9c972f445bc9238c
| 18,737
|
py
|
Python
|
dual_encoder/metrics_test.py
|
garyxcheng/federated
|
ba7133ead6127af71ea9356e26bfd05c02f8324a
|
[
"Apache-2.0"
] | 330
|
2020-09-14T23:10:16.000Z
|
2022-03-30T19:49:19.000Z
|
dual_encoder/metrics_test.py
|
garyxcheng/federated
|
ba7133ead6127af71ea9356e26bfd05c02f8324a
|
[
"Apache-2.0"
] | 52
|
2020-09-30T06:10:51.000Z
|
2022-03-31T19:25:16.000Z
|
dual_encoder/metrics_test.py
|
garyxcheng/federated
|
ba7133ead6127af71ea9356e26bfd05c02f8324a
|
[
"Apache-2.0"
] | 119
|
2020-09-24T04:54:46.000Z
|
2022-03-31T21:46:57.000Z
|
# Copyright 2021, Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from absl.testing import absltest
import tensorflow as tf
from dual_encoder import metrics
from dual_encoder import model_utils as utils
class MetricsTest(absltest.TestCase):
def test_batch_recall(self):
metric = metrics.BatchRecall(recall_k=2)
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.8
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_recall_similarities(self):
metric = metrics.BatchRecall(recall_k=2, expect_embeddings=False)
y_pred = tf.constant(
[[0.9999999, 0.97463185, 0.92582005, 0.92582005, -0.9999999],
[0.97463185, 1.0000001, 0.98692757, 0.98692757, -0.97463185],
[0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005],
[0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005],
[0.98198044, 0.9770084, 0.942809, 0.942809, -0.98198044]])
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.8
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_recall_y_true_2d(self):
metric = metrics.BatchRecall(recall_k=2)
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([[1.0], [1.0], [1.0], [1.0], [1.0]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.8
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_recall_dot_product(self):
metric = metrics.BatchRecall(recall_k=2, normalization_fn=None)
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.4
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_recall_get_config(self):
metric = metrics.BatchRecall()
config = metric.get_config()
expected_config = {
'normalization_fn': utils.l2_normalize_fn,
'expect_embeddings': True,
'recall_k': 10,
'name': 'batch_recall',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_recall_get_config_keyword_args(self):
metric = metrics.BatchRecall(recall_k=5,
normalization_fn=None,
expect_embeddings=False)
config = metric.get_config()
expected_config = {
'normalization_fn': None,
'expect_embeddings': False,
'recall_k': 5,
'name': 'batch_recall',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_recall_with_global_similarity(self):
metric = metrics.BatchRecallWithGlobalSimilarity(recall_k=1)
y_pred = tf.constant(
[[1, 0],
[1.0, 1.0],
[1, 1],
[0., 1.0],
[0, 1],
[1, 0]]
)
y_true = tf.constant([[0], [2]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_compare_batch_recall_and_batch_recall_with_global_similarity(self):
metric_1 = metrics.BatchRecallWithGlobalSimilarity(recall_k=1)
metric_2 = metrics.BatchRecall(recall_k=1)
y_pred_1 = tf.constant(
[[1, 0],
[1.0, 1.0],
[1, 1],
[0., 1.0],
[0, 1],
[1, 0]]
)
y_pred_2 = tf.constant(
[[1, 0],
[1.0, 1.0],
[1, 1],
[0, 1]]
)
y_true = tf.constant([[0], [2]])
metric_value_1 = metric_1(y_true, y_pred_1)
metric_value_2 = metric_2(y_true, y_pred_2)
tf.debugging.assert_near(metric_value_1, metric_value_2)
def test_batch_recall_with_global_similarity_similarities(self):
metric = metrics.BatchRecallWithGlobalSimilarity(
recall_k=1, expect_embeddings=False)
y_pred = tf.constant(
[[0.7071067, 0.0, 0.0, 1.0],
[1.0, 0.7071067, 0.7071067, 0.7071067]])
y_true = tf.constant([[0], [2]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_compare_batch_recall_and_batch_recall_with_global_similarity_similarities(self): # pylint: disable=line-too-long
metric_1 = metrics.BatchRecallWithGlobalSimilarity(
recall_k=1, expect_embeddings=False)
metric_2 = metrics.BatchRecall(
recall_k=1, expect_embeddings=False)
y_pred_1 = tf.constant(
[[0.7071067, 0.0, 0.0, 1.0],
[1.0, 0.7071067, 0.7071067, 0.7071067]])
y_pred_2 = tf.constant(
[[0.7071067, 0.0],
[1.0, 0.7071067]])
y_true = tf.constant([[0], [2]])
metric_value_1 = metric_1(y_true, y_pred_1)
metric_value_2 = metric_2(y_true, y_pred_2)
tf.debugging.assert_near(metric_value_1, metric_value_2)
def test_batch_recall_with_global_similarity_dot_product(self):
metric = metrics.BatchRecallWithGlobalSimilarity(
recall_k=1, normalization_fn=None)
y_pred = tf.constant(
[[1.0, 0.0],
[0.7071067, 0.7071067],
[0.7071067, 0.7071067],
[0.0, 1.0],
[0, 1],
[1.0, 0.0]]
)
y_true = tf.constant([[0], [2]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_compare_batch_recall_and_batch_recall_with_global_similarity_dot_product(self): # pylint: disable=line-too-long
metric_1 = metrics.BatchRecallWithGlobalSimilarity(
recall_k=1, normalization_fn=None)
metric_2 = metrics.BatchRecall(
recall_k=1, normalization_fn=None)
y_pred_1 = tf.constant(
[[1.0, 0.0],
[0.7071067, 0.7071067],
[0.7071067, 0.7071067],
[0.0, 1.0],
[0, 1],
[1.0, 0.0]]
)
y_pred_2 = tf.constant(
[[1.0, 0.0],
[0.7071067, 0.7071067],
[0.7071067, 0.7071067],
[0, 1]]
)
y_true = tf.constant([[0], [2]])
metric_value_1 = metric_1(y_true, y_pred_1)
metric_value_2 = metric_2(y_true, y_pred_2)
tf.debugging.assert_near(metric_value_1, metric_value_2)
def test_batch_recall_with_global_similarity_get_config(self):
metric = metrics.BatchRecallWithGlobalSimilarity()
config = metric.get_config()
expected_config = {
'normalization_fn': utils.l2_normalize_fn,
'expect_embeddings': True,
'recall_k': 10,
'name': 'batch_recall_with_global_similarity',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_recall_with_global_similarity_get_config_keyword_args(self):
metric = metrics.BatchRecallWithGlobalSimilarity(
recall_k=5, normalization_fn=None, expect_embeddings=False)
config = metric.get_config()
expected_config = {
'normalization_fn': None,
'expect_embeddings': False,
'recall_k': 5,
'name': 'batch_recall_with_global_similarity',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_global_recall(self):
metric = metrics.GlobalRecall(recall_k=2)
y_pred = tf.constant(
[[1, 0],
[0.0, 1.0],
[1, 1],
[0, 1],
[1, 0],
[1, 0]]
)
y_true = tf.constant([[2], [3]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_global_recall_similarities(self):
metric = metrics.GlobalRecall(recall_k=2, expect_embeddings=False)
y_pred = tf.constant(
[[0.7071067, 0.0, 1.0, 1.0],
[0.7071067, 1.0, 0.0, 0.0]])
y_true = tf.constant([[2], [3]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_global_recall_dot_product(self):
metric = metrics.GlobalRecall(recall_k=2, normalization_fn=None)
y_pred = tf.constant(
[[1.0, 0.0],
[0, 1],
[0.7071067, 0.7071067],
[0.0, 1.0],
[1, 0],
[1.0, 0.0]]
)
y_true = tf.constant([[2], [3]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 0.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_global_recall_get_config(self):
metric = metrics.GlobalRecall()
config = metric.get_config()
expected_config = {
'normalization_fn': utils.l2_normalize_fn,
'expect_embeddings': True,
'recall_k': 10,
'name': 'global_recall',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_global_recall_get_config_keyword_args(self):
metric = metrics.GlobalRecall(recall_k=5,
normalization_fn=None,
expect_embeddings=False)
config = metric.get_config()
expected_config = {
'normalization_fn': None,
'expect_embeddings': False,
'recall_k': 5,
'name': 'global_recall',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_mean_rank(self):
metric = metrics.BatchMeanRank()
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 1.0
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_mean_rank_similarities(self):
metric = metrics.BatchMeanRank(expect_embeddings=False)
y_pred = tf.constant(
[[0.9999999, 0.97463185, 0.92582005, 0.92582005, -0.9999999],
[0.97463185, 1.0000001, 0.98692757, 0.98692757, -0.97463185],
[0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005],
[0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005],
[0.98198044, 0.9770084, 0.942809, 0.942809, -0.98198044]])
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 1.0
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_mean_rank_y_true_2d(self):
metric = metrics.BatchMeanRank()
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([[1.0], [1.0], [1.0], [1.0], [1.0]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 1.0
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_mean_rank_dot_product(self):
metric = metrics.BatchMeanRank(normalization_fn=None)
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 2.0
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_mean_rank_get_config(self):
metric = metrics.BatchMeanRank()
config = metric.get_config()
expected_config = {
'normalization_fn': utils.l2_normalize_fn,
'expect_embeddings': True,
'name': 'batch_mean_rank',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_mean_rank_get_config_keyword_args(self):
metric = metrics.BatchMeanRank(normalization_fn=None,
expect_embeddings=False)
config = metric.get_config()
expected_config = {
'normalization_fn': None,
'expect_embeddings': False,
'name': 'batch_mean_rank',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_global_mean_rank(self):
metric = metrics.GlobalMeanRank()
y_pred = tf.constant(
[[1, 0],
[0.0, 1.0],
[1, 1],
[0, 1],
[1, 0],
[1, 0]]
)
y_true = tf.constant([[2], [3]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 1.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_global_mean_rank_similarities(self):
metric = metrics.GlobalMeanRank(expect_embeddings=False)
y_pred = tf.constant(
[[0.7071067, 0.0, 1.0, 1.0],
[0.7071067, 1.0, 0.0, 0.0]])
y_true = tf.constant([[2], [3]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 1.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_global_mean_rank_dot_product(self):
metric = metrics.GlobalMeanRank(normalization_fn=None)
y_pred = tf.constant(
[[1.0, 0.0],
[0, 1],
[0.7071067, 0.7071067],
[0.0, 1.0],
[1, 0],
[1.0, 0.0]]
)
y_true = tf.constant([[2], [3]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 1.5
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_global_mean_rank_get_config(self):
metric = metrics.GlobalMeanRank()
config = metric.get_config()
expected_config = {
'normalization_fn': utils.l2_normalize_fn,
'expect_embeddings': True,
'name': 'global_mean_rank',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_global_mean_rank_get_config_keyword_args(self):
metric = metrics.GlobalMeanRank(normalization_fn=None,
expect_embeddings=False)
config = metric.get_config()
expected_config = {
'normalization_fn': None,
'expect_embeddings': False,
'name': 'global_mean_rank',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_similarities_norm(self):
metric = metrics.BatchSimilaritiesNorm()
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 4.311066
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_similarities_norm_similarities(self):
metric = metrics.BatchSimilaritiesNorm(expect_embeddings=False)
y_pred = tf.constant(
[[0.9999999, 0.97463185, 0.92582005, 0.92582005, -0.9999999],
[0.97463185, 1.0000001, 0.98692757, 0.98692757, -0.97463185],
[0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005],
[0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005],
[0.98198044, 0.9770084, 0.942809, 0.942809, -0.98198044]])
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 4.311066
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_similarities_norm_y_true_2d(self):
metric = metrics.BatchSimilaritiesNorm()
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([[1.0], [1.0], [1.0], [1.0], [1.0]])
metric_value = metric(y_true, y_pred)
expected_metric_value = 4.311066
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_similarities_norm_dot_product(self):
metric = metrics.BatchSimilaritiesNorm(normalization_fn=None)
y_pred = tf.constant(
[[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[1, 1, 2],
[1, 2, 3],
[4.0, 5.0, 6.0],
[1, 1, 1],
[1, 1, 1],
[-1, -2, -3]]
)
y_true = tf.constant([1.0, 1.0, 1.0, 1.0, 1.0])
metric_value = metric(y_true, y_pred)
expected_metric_value = 70.398865
tf.debugging.assert_near(expected_metric_value, metric_value)
def test_batch_similarities_norm_get_config(self):
metric = metrics.BatchSimilaritiesNorm()
config = metric.get_config()
expected_config = {
'normalization_fn': utils.l2_normalize_fn,
'expect_embeddings': True,
'name': 'batch_similarities_norm',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
def test_batch_similarities_norm_get_config_keyword_args(self):
metric = metrics.BatchSimilaritiesNorm(normalization_fn=None,
expect_embeddings=False)
config = metric.get_config()
expected_config = {
'normalization_fn': None,
'expect_embeddings': False,
'name': 'batch_similarities_norm',
'dtype': 'float32'
}
self.assertEqual(config, expected_config)
if __name__ == '__main__':
absltest.main()
| 28.826154
| 124
| 0.599829
| 2,603
| 18,737
| 4.066462
| 0.058778
| 0.024185
| 0.028059
| 0.030609
| 0.921776
| 0.913179
| 0.89334
| 0.829192
| 0.797449
| 0.788758
| 0
| 0.122963
| 0.259967
| 18,737
| 649
| 125
| 28.87057
| 0.640415
| 0.032556
| 0
| 0.800391
| 0
| 0
| 0.048148
| 0.006405
| 0
| 0
| 0
| 0
| 0.07045
| 1
| 0.07045
| false
| 0
| 0.007828
| 0
| 0.080235
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
dd857f9d4037abdc91f7ab75e796ce78d3f4378f
| 6,521
|
py
|
Python
|
tests/test_torque_driven_ocp.py
|
mickaelbegon/BiorbdOptim
|
77e058e96ab0393953a83ffef08693b3049477be
|
[
"Apache-2.0"
] | null | null | null |
tests/test_torque_driven_ocp.py
|
mickaelbegon/BiorbdOptim
|
77e058e96ab0393953a83ffef08693b3049477be
|
[
"Apache-2.0"
] | null | null | null |
tests/test_torque_driven_ocp.py
|
mickaelbegon/BiorbdOptim
|
77e058e96ab0393953a83ffef08693b3049477be
|
[
"Apache-2.0"
] | null | null | null |
"""
Test for file IO
"""
import importlib.util
from pathlib import Path
import pytest
import numpy as np
from biorbd_optim import Data, OdeSolver
from .utils import TestUtils
# Load align_markers
PROJECT_FOLDER = Path(__file__).parent / ".."
spec = importlib.util.spec_from_file_location(
"align_markers", str(PROJECT_FOLDER) + "/examples/torque_driven_ocp/align_markers.py"
)
align_markers = importlib.util.module_from_spec(spec)
spec.loader.exec_module(align_markers)
@pytest.mark.parametrize("ode_solver", [OdeSolver.RK, OdeSolver.COLLOCATION])
def test_align_markers(ode_solver):
ocp = align_markers.prepare_ocp(
biorbd_model_path=str(PROJECT_FOLDER) + "/examples/torque_driven_ocp/cube.bioMod",
number_shooting_points=30,
final_time=2,
ode_solver=ode_solver,
)
sol = ocp.solve()
# Check objective function value
f = np.array(sol["f"])
np.testing.assert_equal(f.shape, (1, 1))
np.testing.assert_almost_equal(f[0, 0], 1317.835541713015)
# Check constraints
g = np.array(sol["g"])
np.testing.assert_equal(g.shape, (186, 1))
np.testing.assert_almost_equal(g, np.zeros((186, 1)))
# Check some of the results
states, controls = Data.get_data(ocp, sol["x"])
q, qdot, tau = states["q"], states["q_dot"], controls["tau"]
# initial and final position
np.testing.assert_almost_equal(q[:, 0], np.array((1, 0, 0)))
np.testing.assert_almost_equal(q[:, -1], np.array((2, 0, 1.57)))
# initial and final velocities
np.testing.assert_almost_equal(qdot[:, 0], np.array((0, 0, 0)))
np.testing.assert_almost_equal(qdot[:, -1], np.array((0, 0, 0)))
# initial and final controls
np.testing.assert_almost_equal(tau[:, 0], np.array((1.4516128810214546, 9.81, 2.2790322540381487)))
np.testing.assert_almost_equal(tau[:, -1], np.array((-1.4516128810214546, 9.81, -2.2790322540381487)))
# save and load
TestUtils.save_and_load(sol, ocp, False)
# Load multiphase_align_markers
PROJECT_FOLDER = Path(__file__).parent / ".."
spec = importlib.util.spec_from_file_location(
"multiphase_align_markers", str(PROJECT_FOLDER) + "/examples/torque_driven_ocp/multiphase_align_markers.py"
)
multiphase_align_markers = importlib.util.module_from_spec(spec)
spec.loader.exec_module(multiphase_align_markers)
@pytest.mark.parametrize("ode_solver", [OdeSolver.RK, OdeSolver.COLLOCATION])
def test_multiphase_align_markers(ode_solver):
ocp = multiphase_align_markers.prepare_ocp(
biorbd_model_path=str(PROJECT_FOLDER) + "/examples/torque_driven_ocp/cube.bioMod", ode_solver=ode_solver
)
sol = ocp.solve()
# Check objective function value
f = np.array(sol["f"])
np.testing.assert_equal(f.shape, (1, 1))
np.testing.assert_almost_equal(f[0, 0], 17672.950313589874)
# Check constraints
g = np.array(sol["g"])
np.testing.assert_equal(g.shape, (444, 1))
np.testing.assert_almost_equal(g, np.zeros((444, 1)))
# Check some of the results
states, controls = Data.get_data(ocp, sol["x"], concatenate=False)
q, qdot, tau = states["q"], states["q_dot"], controls["tau"]
# initial and final position
np.testing.assert_almost_equal(q[0][:, 0], np.array((1, 0, 0)))
np.testing.assert_almost_equal(q[0][:, -1], np.array((2, 0, 0)))
np.testing.assert_almost_equal(q[1][:, 0], np.array((2, 0, 0)))
np.testing.assert_almost_equal(q[1][:, -1], np.array((1, 0, 0)))
np.testing.assert_almost_equal(q[2][:, 0], np.array((1, 0, 0)))
np.testing.assert_almost_equal(q[2][:, -1], np.array((2, 0, 1.57)))
# initial and final velocities
np.testing.assert_almost_equal(qdot[0][:, 0], np.array((0, 0, 0)))
np.testing.assert_almost_equal(qdot[0][:, -1], np.array((0, 0, 0)))
np.testing.assert_almost_equal(qdot[1][:, 0], np.array((0, 0, 0)))
np.testing.assert_almost_equal(qdot[1][:, -1], np.array((0, 0, 0)))
np.testing.assert_almost_equal(qdot[2][:, 0], np.array((0, 0, 0)))
np.testing.assert_almost_equal(qdot[2][:, -1], np.array((0, 0, 0)))
# initial and final controls
np.testing.assert_almost_equal(tau[0][:, 0], np.array((1.42857142, 9.81, 0)))
np.testing.assert_almost_equal(tau[0][:, -1], np.array((-1.42857144, 9.81, 0)))
np.testing.assert_almost_equal(tau[1][:, 0], np.array((-0.2322581, 9.81, 0.0)))
np.testing.assert_almost_equal(tau[1][:, -1], np.array((0.2322581, 9.81, -0.0)))
np.testing.assert_almost_equal(tau[2][:, 0], np.array((0.35714285, 9.81, 0.56071428)))
np.testing.assert_almost_equal(tau[2][:, -1], np.array((-0.35714285, 9.81, -0.56071428)))
# save and load
TestUtils.save_and_load(sol, ocp, False)
# Load external_forces
PROJECT_FOLDER = Path(__file__).parent / ".."
spec = importlib.util.spec_from_file_location(
"external_forces", str(PROJECT_FOLDER) + "/examples/torque_driven_ocp/external_forces.py"
)
external_forces = importlib.util.module_from_spec(spec)
spec.loader.exec_module(external_forces)
@pytest.mark.parametrize("ode_solver", [OdeSolver.RK])
def test_external_forces(ode_solver):
ocp = external_forces.prepare_ocp(
biorbd_model_path=str(PROJECT_FOLDER) + "/examples/torque_driven_ocp/cube_with_forces.bioMod",
ode_solver=ode_solver,
)
sol = ocp.solve()
# Check objective function value
f = np.array(sol["f"])
np.testing.assert_equal(f.shape, (1, 1))
np.testing.assert_almost_equal(f[0, 0], 658.3925125)
# Check constraints
g = np.array(sol["g"])
np.testing.assert_equal(g.shape, (246, 1))
np.testing.assert_almost_equal(g, np.zeros((246, 1)))
# Check some of the results
states, controls = Data.get_data(ocp, sol["x"])
q, qdot, tau = states["q"], states["q_dot"], controls["tau"]
# initial and final position
np.testing.assert_almost_equal(q[:, 0], np.array((0, 0, 0, 0)))
np.testing.assert_almost_equal(q[:, -1], np.array((0, 2, 0, 0)))
# initial and final velocities
np.testing.assert_almost_equal(qdot[:, 0], np.array((0, 0, 0, 0)))
np.testing.assert_almost_equal(qdot[:, -1], np.array((0, 0, 0, 0)))
# initial and final controls
np.testing.assert_almost_equal(tau[:, 0], np.array((0, 9.71322593, 0, 0)))
np.testing.assert_almost_equal(tau[:, 10], np.array((0, 7.71100122, 0, 0)))
np.testing.assert_almost_equal(tau[:, 20], np.array((0, 5.70877651, 0, 0)))
np.testing.assert_almost_equal(tau[:, -1], np.array((0, 3.90677425, 0, 0)))
# save and load
TestUtils.save_and_load(sol, ocp, True)
| 39.521212
| 112
| 0.681797
| 1,012
| 6,521
| 4.192688
| 0.123518
| 0.09333
| 0.15555
| 0.188074
| 0.865425
| 0.850342
| 0.850342
| 0.823474
| 0.777987
| 0.686778
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| 113
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| 0.05062
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| 0.419048
| 1
| 0.028571
| false
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| 0.114286
| 0
| 0.142857
| 0
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| 0
| null | 0
| 0
| 1
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0
| 8
|
06fd23ba166ed184598f05dfb526fff84d88d979
| 870,660
|
py
|
Python
|
rubikscubennnsolver/swaps.py
|
zimyang/Rubiks-Cube-Server
|
48649174851043664983f68908876fa9fb2c84e8
|
[
"MIT"
] | null | null | null |
rubikscubennnsolver/swaps.py
|
zimyang/Rubiks-Cube-Server
|
48649174851043664983f68908876fa9fb2c84e8
|
[
"MIT"
] | null | null | null |
rubikscubennnsolver/swaps.py
|
zimyang/Rubiks-Cube-Server
|
48649174851043664983f68908876fa9fb2c84e8
|
[
"MIT"
] | null | null | null |
swaps_222 = {
"B": (0, 14, 16, 3, 4, 2, 6, 1, 8, 9, 10, 11, 12, 13, 24, 15, 23, 19, 17, 20, 18, 21, 22, 5, 7),
"B'": (0, 7, 5, 3, 4, 23, 6, 24, 8, 9, 10, 11, 12, 13, 1, 15, 2, 18, 20, 17, 19, 21, 22, 16, 14),
"B2": (0, 24, 23, 3, 4, 16, 6, 14, 8, 9, 10, 11, 12, 13, 7, 15, 5, 20, 19, 18, 17, 21, 22, 2, 1),
"D": (0, 1, 2, 3, 4, 5, 6, 19, 20, 9, 10, 7, 8, 13, 14, 11, 12, 17, 18, 15, 16, 23, 21, 24, 22),
"D'": (0, 1, 2, 3, 4, 5, 6, 11, 12, 9, 10, 15, 16, 13, 14, 19, 20, 17, 18, 7, 8, 22, 24, 21, 23),
"D2": (0, 1, 2, 3, 4, 5, 6, 15, 16, 9, 10, 19, 20, 13, 14, 7, 8, 17, 18, 11, 12, 24, 23, 22, 21),
"F": (0, 1, 2, 8, 6, 5, 21, 7, 22, 11, 9, 12, 10, 3, 14, 4, 16, 17, 18, 19, 20, 15, 13, 23, 24),
"F'": (0, 1, 2, 13, 15, 5, 4, 7, 3, 10, 12, 9, 11, 22, 14, 21, 16, 17, 18, 19, 20, 6, 8, 23, 24),
"F2": (0, 1, 2, 22, 21, 5, 15, 7, 13, 12, 11, 10, 9, 8, 14, 6, 16, 17, 18, 19, 20, 4, 3, 23, 24),
"L": (0, 20, 2, 18, 4, 7, 5, 8, 6, 1, 10, 3, 12, 13, 14, 15, 16, 17, 23, 19, 21, 9, 22, 11, 24),
"L'": (0, 9, 2, 11, 4, 6, 8, 5, 7, 21, 10, 23, 12, 13, 14, 15, 16, 17, 3, 19, 1, 20, 22, 18, 24),
"L2": (0, 21, 2, 23, 4, 8, 7, 6, 5, 20, 10, 18, 12, 13, 14, 15, 16, 17, 11, 19, 9, 1, 22, 3, 24),
"R": (0, 1, 10, 3, 12, 5, 6, 7, 8, 9, 22, 11, 24, 15, 13, 16, 14, 4, 18, 2, 20, 21, 19, 23, 17),
"R'": (0, 1, 19, 3, 17, 5, 6, 7, 8, 9, 2, 11, 4, 14, 16, 13, 15, 24, 18, 22, 20, 21, 10, 23, 12),
"R2": (0, 1, 22, 3, 24, 5, 6, 7, 8, 9, 19, 11, 17, 16, 15, 14, 13, 12, 18, 10, 20, 21, 2, 23, 4),
"U": (0, 3, 1, 4, 2, 9, 10, 7, 8, 13, 14, 11, 12, 17, 18, 15, 16, 5, 6, 19, 20, 21, 22, 23, 24),
"U'": (0, 2, 4, 1, 3, 17, 18, 7, 8, 5, 6, 11, 12, 9, 10, 15, 16, 13, 14, 19, 20, 21, 22, 23, 24),
"U2": (0, 4, 3, 2, 1, 13, 14, 7, 8, 17, 18, 11, 12, 5, 6, 15, 16, 9, 10, 19, 20, 21, 22, 23, 24),
"x": (0, 9, 10, 11, 12, 6, 8, 5, 7, 21, 22, 23, 24, 15, 13, 16, 14, 4, 3, 2, 1, 20, 19, 18, 17),
"x'": (0, 20, 19, 18, 17, 7, 5, 8, 6, 1, 2, 3, 4, 14, 16, 13, 15, 24, 23, 22, 21, 9, 10, 11, 12),
"x2": (0, 21, 22, 23, 24, 8, 7, 6, 5, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 1, 2, 3, 4),
"y": (0, 3, 1, 4, 2, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 5, 6, 7, 8, 22, 24, 21, 23),
"y'": (0, 2, 4, 1, 3, 17, 18, 19, 20, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 23, 21, 24, 22),
"y2": (0, 4, 3, 2, 1, 13, 14, 15, 16, 17, 18, 19, 20, 5, 6, 7, 8, 9, 10, 11, 12, 24, 23, 22, 21),
"z": (0, 7, 5, 8, 6, 23, 21, 24, 22, 11, 9, 12, 10, 3, 1, 4, 2, 18, 20, 17, 19, 15, 13, 16, 14),
"z'": (0, 14, 16, 13, 15, 2, 4, 1, 3, 10, 12, 9, 11, 22, 24, 21, 23, 19, 17, 20, 18, 6, 8, 5, 7),
"z2": (0, 24, 23, 22, 21, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 20, 19, 18, 17, 4, 3, 2, 1),
}
swaps_333 = {
"B": (
0,
30,
33,
36,
4,
5,
6,
7,
8,
9,
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11,
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38,
45,
42,
39,
46,
47,
48,
49,
50,
51,
10,
13,
16,
),
"B'": (
0,
16,
13,
10,
4,
5,
6,
7,
8,
9,
52,
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12,
53,
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1,
31,
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2,
34,
35,
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39,
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38,
41,
44,
37,
40,
43,
46,
47,
48,
49,
50,
51,
36,
33,
30,
),
"B2": (
0,
54,
53,
52,
4,
5,
6,
7,
8,
9,
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11,
12,
33,
14,
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38,
37,
46,
47,
48,
49,
50,
51,
3,
2,
1,
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"D": (
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49,
46,
53,
50,
47,
54,
51,
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),
"D'": (
0,
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41,
42,
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17,
18,
48,
51,
54,
47,
50,
53,
46,
49,
52,
),
"D2": (
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
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35,
36,
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21,
22,
23,
24,
43,
44,
45,
28,
29,
30,
31,
32,
33,
16,
17,
18,
37,
38,
39,
40,
41,
42,
25,
26,
27,
54,
53,
52,
51,
50,
49,
48,
47,
46,
),
"F": (
0,
1,
2,
3,
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5,
6,
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15,
12,
10,
11,
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13,
14,
47,
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"F'": (
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"L": (
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53,
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"L'": (
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1,
45,
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48,
42,
50,
51,
39,
53,
54,
),
"L2": (
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"R": (
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"R'": (
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"R2": (
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"U": (
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31,
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43,
44,
45,
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47,
48,
49,
50,
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53,
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"U'": (
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"U2": (
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"x": (
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35,
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6606c957b52d448501c54fbefd26e89aa81abc7a
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py
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Python
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codeiq/data_deuce.py
|
takahasi/utility
|
60ea9d2f97aa1917ebdac97121cd21e483e714f2
|
[
"MIT"
] | null | null | null |
codeiq/data_deuce.py
|
takahasi/utility
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60ea9d2f97aa1917ebdac97121cd21e483e714f2
|
[
"MIT"
] | null | null | null |
codeiq/data_deuce.py
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takahasi/utility
|
60ea9d2f97aa1917ebdac97121cd21e483e714f2
|
[
"MIT"
] | null | null | null |
3 4 2
10 9 8
15 0 14
15 17 20
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0
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66083dc83c6df1c0ac08805c4fb7632bba0dfcc4
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py
|
Python
|
dist/Basilisk/simulation/ephemeris_converter/__init__.py
|
ian-cooke/basilisk_mag
|
a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14
|
[
"0BSD"
] | null | null | null |
dist/Basilisk/simulation/ephemeris_converter/__init__.py
|
ian-cooke/basilisk_mag
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a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14
|
[
"0BSD"
] | 1
|
2019-03-13T20:52:22.000Z
|
2019-03-13T20:52:22.000Z
|
dist/Basilisk/simulation/ephemeris_converter/__init__.py
|
ian-cooke/basilisk_mag
|
a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14
|
[
"0BSD"
] | null | null | null |
# This __init__.py file for the ephemeris_converter package is automatically generated by the build system
from ephemeris_converter import *
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9fcfd86090683374c4df4e45f63c8d8b9d9e8496
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py
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Python
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GoogleScraper/user_agents.py
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keepit1/GoogleScraper
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595dc9504f06a10e8dc999f3f03c690bcd247274
|
[
"Apache-2.0"
] | 2,294
|
2015-01-01T02:49:13.000Z
|
2022-03-29T11:13:22.000Z
|
GoogleScraper/user_agents.py
|
keepit1/GoogleScraper
|
595dc9504f06a10e8dc999f3f03c690bcd247274
|
[
"Apache-2.0"
] | 179
|
2015-01-04T02:27:41.000Z
|
2021-11-04T07:03:46.000Z
|
GoogleScraper/user_agents.py
|
keepit1/GoogleScraper
|
595dc9504f06a10e8dc999f3f03c690bcd247274
|
[
"Apache-2.0"
] | 773
|
2015-01-03T02:41:49.000Z
|
2022-03-31T22:53:20.000Z
|
# -*- coding: utf-8 -*-
import random
# Several different User-Agents to diversify the requests.
# Keep the User-Agents updated. Last update: 13th November 2014
# Get them here: http://techblog.willshouse.com/2012/01/03/most-common-user-agents/
user_agents = ['Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_2) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.2 Safari/605.1.15',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36 Edge/17.17134',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (X11; Linux x86_64; rv:60.0) Gecko/20100101 Firefox/60.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko',
'Mozilla/5.0 (X11; Linux x86_64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.2 Safari/605.1.15',
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 6.3; Win64; x64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:65.0) Gecko/20100101 Firefox/65.0',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:60.0) Gecko/20100101 Firefox/60.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0 Safari/605.1.15',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 OPR/57.0.3098.116',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 YaBrowser/18.11.1.805 Yowser/2.5 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36 Edge/18.17763',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36 Edge/16.16299',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36',
'Mozilla/5.0 (iPad; CPU OS 12_1_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0 Mobile/15E148 Safari/604.1',
'Mozilla/5.0 (Windows NT 6.1; rv:60.0) Gecko/20100101 Firefox/60.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_1) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.1 Safari/605.1.15',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.81 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 YaBrowser/18.11.1.805 Yowser/2.5 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.3 Safari/605.1.15',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/11.1.2 Safari/605.1.15',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/71.0.3578.98 Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.3 Safari/605.1.15',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.2 Safari/605.1.15',
'Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; Touch; rv:11.0) like Gecko',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 OPR/57.0.3098.106',
'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 OPR/57.0.3098.116',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:65.0) Gecko/20100101 Firefox/65.0',
'Mozilla/5.0 (Windows NT 6.1; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:60.0) Gecko/20100101 Firefox/60.0',
'Mozilla/5.0 (X11; Fedora; Linux x86_64; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.106 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/11.1.2 Safari/605.1.15',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.80 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.80 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:63.0) Gecko/20100101 Firefox/63.0',
'Mozilla/5.0 (X11; CrOS x86_64 11151.59.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.94 Safari/537.36',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.12; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:64.0) Gecko/20100101 Firefox/64.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
'Mozilla/5.0 (Windows NT 6.1; Trident/7.0; rv:11.0) like Gecko',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:65.0) Gecko/20100101 Firefox/65.0',
'Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7.0; rv:11.0) like Gecko']
desktop_user_agents = [
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/601.2.7 (KHTML, like Gecko) Version/9.0.1 Safari/601.2.7',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.80 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.80 Safari/537.36',
]
def random_user_agent(only_desktop=False):
if only_desktop:
return random.choice(desktop_user_agents)
return random.choice(user_agents)
| 90.164948
| 147
| 0.714498
| 1,735
| 8,746
| 3.566571
| 0.081268
| 0.063025
| 0.111991
| 0.103426
| 0.905301
| 0.902715
| 0.902715
| 0.899321
| 0.888332
| 0.883807
| 0
| 0.247932
| 0.115481
| 8,746
| 96
| 148
| 91.104167
| 0.551965
| 0.025383
| 0
| 0.02381
| 0
| 0.916667
| 0.927104
| 0.007395
| 0
| 0
| 0
| 0
| 0
| 1
| 0.011905
| false
| 0
| 0.011905
| 0
| 0.047619
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
b0285cfd610a5b36f90bb3833c8426f45f079102
| 278
|
py
|
Python
|
component/widget/__init__.py
|
i-m-amit/sdg_indicators_module
|
7446d44aa52c55520a5030d64388bdcccd50d0df
|
[
"MIT"
] | null | null | null |
component/widget/__init__.py
|
i-m-amit/sdg_indicators_module
|
7446d44aa52c55520a5030d64388bdcccd50d0df
|
[
"MIT"
] | null | null | null |
component/widget/__init__.py
|
i-m-amit/sdg_indicators_module
|
7446d44aa52c55520a5030d64388bdcccd50d0df
|
[
"MIT"
] | null | null | null |
from .climate_regime import *
from .picker_line import *
from .picker_line_productivity import *
from .picker_line_soc import *
from .picker_line_landcover import *
from .sensor_select import *
from .transition_matrix import *
from .result_map import *
from .select_lc import *
| 27.8
| 39
| 0.805755
| 39
| 278
| 5.435897
| 0.410256
| 0.377358
| 0.301887
| 0.377358
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129496
| 278
| 9
| 40
| 30.888889
| 0.876033
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
b0548e8a613b20039f0e8dd150ccfe056cb23e15
| 103
|
py
|
Python
|
tests/test_ansi_escapes.py
|
shawwn/ansi-escapes-python
|
f37b0c3d815a7a126e97811c0f402c9cac59d466
|
[
"MIT"
] | 2
|
2021-10-17T19:16:17.000Z
|
2021-11-13T05:11:20.000Z
|
tests/test_ansi_escapes.py
|
shawwn/ansi-escapes-python
|
f37b0c3d815a7a126e97811c0f402c9cac59d466
|
[
"MIT"
] | null | null | null |
tests/test_ansi_escapes.py
|
shawwn/ansi-escapes-python
|
f37b0c3d815a7a126e97811c0f402c9cac59d466
|
[
"MIT"
] | null | null | null |
from ansi_escapes import ansiEscapes
def test_ansi_escapes():
assert ansiEscapes.BEL == '\u0007'
| 17.166667
| 38
| 0.757282
| 13
| 103
| 5.769231
| 0.769231
| 0.293333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.045977
| 0.15534
| 103
| 5
| 39
| 20.6
| 0.816092
| 0
| 0
| 0
| 0
| 0
| 0.058252
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c6aaf0c2e53b2ff71d804cf6a0a51d51239d49e3
| 3,017
|
py
|
Python
|
examples/plot_simulation_example.py
|
richford/skmediate
|
b1c2767f942ba1ceec9f4c615c85a3e84a68d973
|
[
"MIT"
] | 1
|
2021-07-02T16:20:41.000Z
|
2021-07-02T16:20:41.000Z
|
examples/plot_simulation_example.py
|
richford/skmediate
|
b1c2767f942ba1ceec9f4c615c85a3e84a68d973
|
[
"MIT"
] | 9
|
2021-01-07T22:33:21.000Z
|
2021-10-04T22:47:42.000Z
|
examples/plot_simulation_example.py
|
richford/skmediate
|
b1c2767f942ba1ceec9f4c615c85a3e84a68d973
|
[
"MIT"
] | 4
|
2020-12-23T19:38:16.000Z
|
2021-07-27T14:45:52.000Z
|
"""
Simulation Example.
===============================
Shows the different combinations of parameters used to
simulate null and true data.
"""
import numpy as np
from skmediate.datasets import make_null_mediation, make_mediation
outcomes, exposures, mediators, true_alpha, true_beta, true_gam = make_null_mediation(
n_mediators=10, dag_type="null-dag1"
)
print("Parameter values when simulating null confounding motif:")
print("Alpha: {}".format(true_alpha))
print("Beta: {}".format(np.round(np.transpose(true_beta), 1)))
print("Gamma: {}".format(np.round(np.transpose(true_gam), 1)))
total_ie = np.multiply(np.transpose(true_beta), true_gam)
fraction_ie = np.round(np.divide(total_ie, total_ie + true_alpha), 2)
print("Indirect-to-Total Signal Strength: {}".format(fraction_ie))
outcomes, exposures, mediators, true_alpha, true_beta, true_gam = make_null_mediation(
n_mediators=10, dag_type="null-dag2"
)
print("Parameter values when simulating null independent variables motif:")
print("Alpha: {}".format(true_alpha))
print("Beta: {}".format(np.round(np.transpose(true_beta), 1)))
print("Gamma: {}".format(np.round(np.transpose(true_gam), 1)))
total_ie = np.multiply(np.transpose(true_beta), true_gam)
fraction_ie = np.round(np.divide(total_ie, total_ie + true_alpha), 2)
print("Indirect-to-Total Signal Strength: {}".format(fraction_ie))
outcomes, exposures, mediators, true_alpha, true_beta, true_gam = make_null_mediation(
n_mediators=10, dag_type="null-dag3"
)
print("Parameter values when simulating null noise motif:")
print("Alpha: {}".format(true_alpha))
print("Beta: {}".format(np.round(np.transpose(true_beta), 1)))
print("Gamma: {}".format(np.round(np.transpose(true_gam), 1)))
total_ie = np.multiply(np.transpose(true_beta), true_gam)
fraction_ie = np.round(np.divide(total_ie, total_ie + true_alpha), 2)
print("Indirect-to-Total Signal Strength: {}".format(fraction_ie))
outcomes, exposures, mediators, true_alpha, true_beta, true_gam = make_mediation(
n_mediators=10
)
print("Parameter values when simulating true mediation motif, no sparsity:")
print("Alpha: {}".format(true_alpha))
print("Beta: {}".format(np.round(np.transpose(true_beta), 1)))
print("Gamma: {}".format(np.round(np.transpose(true_gam), 1)))
total_ie = np.multiply(np.transpose(true_beta), true_gam)
fraction_ie = np.round(np.divide(total_ie, total_ie + true_alpha), 2)
print("Indirect-to-Total Signal Strength: {}".format(fraction_ie))
outcomes, exposures, mediators, true_alpha, true_beta, true_gam = make_mediation(
n_mediators=10, n_informative_mo=5
)
print("Parameter values when simulating true but partial mediation motif, 60% sparse :")
print("Alpha: {}".format(true_alpha))
print("Beta: {}".format(np.round(np.transpose(true_beta), 1)))
print("Gamma: {}".format(np.round(np.transpose(true_gam), 1)))
total_ie = np.multiply(np.transpose(true_beta), true_gam)
fraction_ie = np.round(np.divide(total_ie, total_ie + true_alpha), 2)
print("Indirect-to-Total Signal Strength: {}".format(fraction_ie))
| 45.029851
| 88
| 0.745442
| 449
| 3,017
| 4.806236
| 0.155902
| 0.062558
| 0.062558
| 0.069509
| 0.871177
| 0.871177
| 0.783133
| 0.783133
| 0.783133
| 0.783133
| 0
| 0.011273
| 0.088499
| 3,017
| 66
| 89
| 45.712121
| 0.773455
| 0.045078
| 0
| 0.673077
| 0
| 0
| 0.229805
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.038462
| 0
| 0.038462
| 0.480769
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
c6e313518edec9653ecdcb4538f1ed483c329595
| 15,499
|
py
|
Python
|
tests/test_slider.py
|
dvetutnev/siv.py
|
f37f173f349884eea4b25f84a92e37d4ada3cdd9
|
[
"MIT"
] | null | null | null |
tests/test_slider.py
|
dvetutnev/siv.py
|
f37f173f349884eea4b25f84a92e37d4ada3cdd9
|
[
"MIT"
] | null | null | null |
tests/test_slider.py
|
dvetutnev/siv.py
|
f37f173f349884eea4b25f84a92e37d4ada3cdd9
|
[
"MIT"
] | null | null | null |
import unittest
from unittest.mock import Mock, patch, call, ANY
import itertools
from libs.slider import Slider
class ToLeft(unittest.TestCase):
def setUp(self):
self.storage_mock = Mock()
self.renderer_mock = Mock()
self.ui_mock = Mock(spec_set=['draw'])
self.instance = Slider(self.storage_mock, self.renderer_mock, self.ui_mock)
self.patcher = patch('libs.slider.sleep')
self.patcher.start()
def tearDown(self):
self.patcher.stop()
def test_StorageNoData(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get'], spec_set=True)
storage.get.return_value = None
renderer.mock_add_spec(['render_default'], spec_set=True)
img_mock = Mock(spec_set=[])
renderer.render_default.return_value = img_mock
self.instance.to_left()
storage.get.assert_called_once_with(0)
renderer.render_default.assert_called_once_with()
ui.draw.assert_called_once_with(img_mock)
def test_StorageNoNext(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get'], spec_set=True)
img_current = Mock(spec_set=[], name='image_current')
imgs = {
-3: None,
-2: Mock(spec_set=[], name='img_-2'),
-1: Mock(spec_set=[], name='img_-1'),
0: img_current,
1: None
}
def _get(offset):
return imgs[offset]
storage.get.side_effect = _get
expect_storage_get = [
call.get(0), call.get(-1), call.get(-2), call.get(-3),
call.get(1)
]
renderer.mock_add_spec(['calc', 'render_to_left'], spec_set=True)
calc_result = [
# only left side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': False}
]
renderer.calc.side_effect = calc_result
expect_renderer_calc = list(itertools.repeat(call.calc(ANY, ANY), len(calc_result)))
img_ui = Mock(spec_set=[])
renderer.render_to_left.return_value = img_ui
expect_renderer_render = [call.render_to_left([imgs[-2], imgs[-1], imgs[0]], img_current, 100)]
self.instance.to_left()
storage.assert_has_calls(expect_storage_get)
renderer.assert_has_calls(expect_renderer_calc + expect_renderer_render)
ui.draw.assert_called_once_with(img_ui)
def test_StorageLimit(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get', 'step_next'], spec_set=True)
img_next = Mock(spec_set=[], name='img_next')
imgs = {
-3: None,
-2: Mock(spec_set=[], name='img_-2'),
-1: Mock(spec_set=[], name='img_-1'),
0: Mock(spec_set=[], name='img_0'),
1: img_next,
2: Mock(spec_set=[], name='img_2'),
3: Mock(spec_set=[], name='img_3'),
4: None,
}
def _get(offset):
return imgs[offset]
storage.get.side_effect = _get
expect_storage_get = [
call.get(0), call.get(-1), call.get(-2), call.get(-3),
call.get(1), call.get(2), call.get(3), call.get(4)
]
renderer.mock_add_spec(['calc', 'render_to_left'], spec_set=True)
calc_result = [
# left side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': False},
# right side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': False}
]
renderer.calc.side_effect = calc_result
expect_renderer_calc = list(itertools.repeat(call.calc(ANY, ANY), len(calc_result)))
render_result = list(itertools.repeat(Mock(spec_set=[]), 101))
renderer.render_to_left.side_effect = render_result
args_render = [imgs[-2], imgs[-1], imgs[0], imgs[1], imgs[2], imgs[3]]
expect_renderer_render = [
call.render_to_left(args_render, img_next, i) for i in range(101)
]
expect_ui = [call.draw(pic) for pic in render_result]
self.instance.to_left()
storage.assert_has_calls(expect_storage_get + [call.step_next()])
self.assertEqual(renderer.calc.call_count, len(expect_renderer_calc))
self.assertEqual(renderer.render_to_left.call_count, len(expect_renderer_render))
renderer.assert_has_calls(expect_renderer_calc + expect_renderer_render)
ui.assert_has_calls(expect_ui)
def test_RendererLimit(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get', 'step_next'], spec_set=True)
img_current = Mock(spec_set=[], name='img_next')
img_next = Mock(spec_set=[], name='img_next')
imgs = {
-2: Mock(spec_set=[], name='img_-2'),
-1: Mock(spec_set=[], name='img_-1'),
0: img_current,
1: img_next,
2: Mock(spec_set=[], name='img_2'),
3: Mock(spec_set=[], name='img_3')
}
def _get(offset):
return imgs[offset]
storage.get.side_effect = _get
expect_storage_get = [
call.get(0), call.get(-1), call.get(-2),
call.get(1), call.get(2), call.get(3)
]
renderer.mock_add_spec(['calc', 'render_to_left'], spec_set=True)
calc_result = [
# left side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': True, 'right': 2, 'right_done': False},
# right side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': True}
]
renderer.calc.side_effect = calc_result
expect_renderer_calc = [
#left side
call.calc(ANY, img_current),
call.calc(ANY, img_current),
call.calc([imgs[-2], imgs[-1], imgs[0]], img_current),
#right side
call.calc(ANY, img_next),
call.calc(ANY, img_next),
call.calc([imgs[1], imgs[2], imgs[3]], img_next)
]
renderer.render_to_left.side_effect = list(itertools.repeat(Mock(spec_set=[]), 101))
args_render = [imgs[-2], imgs[-1], imgs[0], imgs[1], imgs[2], imgs[3]]
expect_renderer_render = [
call.render_to_left(args_render, img_next, i) for i in range(101)
]
expect_ui = list(itertools.repeat(call.draw(ANY), 101))
self.instance.to_left()
storage.assert_has_calls(expect_storage_get + [call.step_next()])
self.assertEqual(renderer.calc.call_count, len(expect_renderer_calc))
self.assertEqual(renderer.render_to_left.call_count, len(expect_renderer_render))
renderer.assert_has_calls(expect_renderer_calc + expect_renderer_render)
ui.assert_has_calls(expect_ui)
class ToRight(unittest.TestCase):
def setUp(self):
self.storage_mock = Mock()
self.renderer_mock = Mock()
self.ui_mock = Mock(spec_set=['draw'])
self.instance = Slider(self.storage_mock, self.renderer_mock, self.ui_mock)
self.patcher = patch('libs.slider.sleep')
self.patcher.start()
def tearDown(self):
self.patcher.stop()
def test_StorageNoData(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get'], spec_set=True)
storage.get.return_value = None
renderer.mock_add_spec(['render_default'], spec_set=True)
img_mock = Mock(spec_set=[])
renderer.render_default.return_value = img_mock
self.instance.to_right()
storage.get.assert_called_once_with(0)
renderer.render_default.assert_called_once_with()
ui.draw.assert_called_once_with(img_mock)
def test_StorageNoPrevious(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get'], spec_set=True)
img_current = Mock(spec_set=[], name='img_current')
imgs = {
-1: None,
0: img_current,
1: Mock(spec_set=[], name='img_1'),
2: Mock(spec_set=[], name='img_2'),
3: None
}
def _get(offset):
return imgs[offset]
storage.get.side_effect = _get
expect_storage_get = [
call.get(0), call.get(1), call.get(2), call.get(3),
call.get(-1)
]
renderer.mock_add_spec(['calc', 'render_to_right'], spec_set=True)
calc_result = [
# only right side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': False}
]
renderer.calc.side_effect = calc_result
expect_renderer_calc = list(itertools.repeat(call.calc(ANY, ANY), len(calc_result)))
img_ui = Mock(spec_set=[])
renderer.render_to_right.return_value = img_ui
expect_renderer_render = [call.render_to_right([imgs[0], imgs[1], imgs[2]], img_current, 100)]
self.instance.to_right()
storage.assert_has_calls(expect_storage_get)
renderer.assert_has_calls(expect_renderer_calc + expect_renderer_render)
ui.draw.assert_called_once_with(img_ui)
def test_StorageLimit(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get', 'step_previous'], spec_set=True)
img_previous = Mock(spec_set=[], name='img_previous')
imgs = {
-4: None,
-3: Mock(spec_set=[], name='img_-3'),
-2: Mock(spec_set=[], name='img_-2'),
-1: img_previous,
0: Mock(spec_set=[], name='img_0'),
1: Mock(spec_set=[], name='img_1'),
2: Mock(spec_set=[], name='img_2'),
3: None
}
def _get(offset):
return imgs[offset]
storage.get.side_effect = _get
expect_storage_get = [
call.get(0), call.get(1), call.get(2), call.get(3),
call.get(-1), call.get(-2), call.get(-3), call.get(-4)
]
renderer.mock_add_spec(['calc', 'render_to_right'], spec_set=True)
calc_result = [
# left side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': False},
# right side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': False}
]
renderer.calc.side_effect = calc_result
expect_renderer_calc = list(itertools.repeat(call.calc(ANY, ANY), len(calc_result)))
render_result = list(itertools.repeat(Mock(spec_set=[]), 101))
renderer.render_to_right.side_effect = render_result
args_render = [imgs[-3], imgs[-2], imgs[-1], imgs[0], imgs[1], imgs[2]]
expect_renderer_render = [
call.render_to_right(args_render, img_previous, i) for i in range(101)
]
expect_ui = [call.draw(pic) for pic in render_result]
self.instance.to_right()
storage.assert_has_calls(expect_storage_get + [call.step_previous()])
self.assertEqual(renderer.calc.call_count, len(expect_renderer_calc))
self.assertEqual(renderer.render_to_right.call_count, len(expect_renderer_render))
renderer.assert_has_calls(expect_renderer_calc + expect_renderer_render)
ui.assert_has_calls(expect_ui)
def test_RendererLimit(self):
storage = self.storage_mock
renderer = self.renderer_mock
ui = self.ui_mock
storage.mock_add_spec(['get', 'step_previous'], spec_set=True)
img_current = Mock(spec_set=[], name='img_next')
img_previous = Mock(spec_set=[], name='img_next')
imgs = {
-3: Mock(spec_set=[], name='img_-3'),
-2: Mock(spec_set=[], name='img_-2'),
-1: img_previous,
0: img_current,
1: Mock(spec_set=[], name='img_1'),
2: Mock(spec_set=[], name='img_2')
}
def _get(offset):
return imgs[offset]
storage.get.side_effect = _get
expect_storage_get = [
call.get(0), call.get(1), call.get(2),
call.get(-1), call.get(-2), call.get(-3)
]
renderer.mock_add_spec(['calc', 'render_to_right'], spec_set=True)
calc_result = [
# right side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 2, 'right_done': True},
# left side
{'left': 0, 'left_done': False, 'right': 0, 'right_done': False},
{'left': 0, 'left_done': False, 'right': 1, 'right_done': False},
{'left': 0, 'left_done': True, 'right': 2, 'right_done': False}
]
renderer.calc.side_effect = calc_result
expect_renderer_calc = [
#left side
call.calc(ANY, img_current),
call.calc(ANY, img_current),
call.calc([imgs[0], imgs[1], imgs[2]], img_current),
#right side
call.calc(ANY, img_previous),
call.calc(ANY, img_previous),
call.calc([imgs[-3], imgs[-2], imgs[-1]], img_previous)
]
renderer.render_to_right.side_effect = list(itertools.repeat(Mock(spec_set=[]), 101))
args_render = [imgs[-3], imgs[-2], imgs[-1], imgs[0], imgs[1], imgs[2]]
expect_renderer_render = [
call.render_to_right(args_render, img_previous, i) for i in range(101)
]
expect_ui = list(itertools.repeat(call.draw(ANY), 101))
self.instance.to_right()
storage.assert_has_calls(expect_storage_get + [call.step_previous()])
self.assertEqual(renderer.calc.call_count, len(expect_renderer_calc))
self.assertEqual(renderer.render_to_right.call_count, len(expect_renderer_render))
renderer.assert_has_calls(expect_renderer_calc + expect_renderer_render)
ui.assert_has_calls(expect_ui)
if __name__ == '__main__':
unittest.main()
| 38.081081
| 103
| 0.586618
| 2,014
| 15,499
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| 15,499
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| 104
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| null | 0
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0
| 7
|
05c0ac4e7eae4bee11c91000787468b15e21e15c
| 6,377
|
py
|
Python
|
graphtheory/forests/tests/test_treecover.py
|
gitter-badger/graphs-dict
|
2be1a5b140feb050eec799d6cadf6de5eef01745
|
[
"BSD-3-Clause"
] | 36
|
2015-09-20T20:55:39.000Z
|
2021-09-20T05:49:03.000Z
|
graphtheory/forests/tests/test_treecover.py
|
gitter-badger/graphs-dict
|
2be1a5b140feb050eec799d6cadf6de5eef01745
|
[
"BSD-3-Clause"
] | 6
|
2016-03-25T21:41:46.000Z
|
2020-02-12T03:18:59.000Z
|
graphtheory/forests/tests/test_treecover.py
|
gitter-badger/graphs-dict
|
2be1a5b140feb050eec799d6cadf6de5eef01745
|
[
"BSD-3-Clause"
] | 9
|
2016-09-12T07:57:27.000Z
|
2022-03-21T16:15:39.000Z
|
#!/usr/bin/python
import unittest
from graphtheory.structures.edges import Edge
from graphtheory.structures.graphs import Graph
from graphtheory.forests.treecover import BorieNodeCover
from graphtheory.forests.treecover import TreeNodeCover1
from graphtheory.forests.treecover import TreeNodeCover2
# 0---1---2---6
# | | |
# 3 4 5
class TestNodeCover1(unittest.TestCase):
def setUp(self):
self.N = 7
self.G = Graph(self.N)
self.nodes = range(self.N)
self.edges = [Edge(0, 1), Edge(0, 3), Edge(1, 2),
Edge(1, 4), Edge(2, 5), Edge(2, 6)]
for node in self.nodes:
self.G.add_node(node)
for edge in self.edges:
self.G.add_edge(edge)
#self.G.show()
def test_borie_node_cover(self):
algorithm = BorieNodeCover(self.G)
algorithm.run()
expected1 = set([0, 1, 2])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected1)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def test_tree_node_cover1(self):
algorithm = TreeNodeCover1(self.G)
algorithm.run()
expected1 = set([0, 1, 2])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected1)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def test_tree_node_cover2(self):
algorithm = TreeNodeCover2(self.G)
algorithm.run()
expected1 = set([0, 1, 2])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected1)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def tearDown(self): pass
# 0---1---2
# / | | \
# 3 4 5 6
class TestNodeCover2(unittest.TestCase):
def setUp(self):
self.N = 7
self.G = Graph(self.N)
self.nodes = range(self.N)
self.edges = [Edge(0, 1), Edge(1, 3), Edge(1, 2),
Edge(1, 4), Edge(2, 5), Edge(2, 6)]
for node in self.nodes:
self.G.add_node(node)
for edge in self.edges:
self.G.add_edge(edge)
#self.G.show()
def test_borie_node_cover(self):
algorithm = BorieNodeCover(self.G)
algorithm.run()
expected1 = set([1, 2])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected1)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def test_tree_node_cover1(self):
algorithm = TreeNodeCover1(self.G)
algorithm.run()
expected1 = set([1, 2])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected1)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def test_tree_node_cover2(self):
algorithm = TreeNodeCover2(self.G)
algorithm.run()
expected1 = set([1, 2])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected1)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def tearDown(self): pass
# 0---1---2---3---4---5 path P_6
# best dset len([1, 4]) = 2
# best node cover len([1, 2, 4]) = 3
# best iset len([0, 2, 5]) = 3
# best matching set([Edge(0, 1), Edge(2. 3), Edge(4, 5)])
class TestNodeCover3(unittest.TestCase):
def setUp(self):
self.N = 6
self.G = Graph(self.N)
self.nodes = range(self.N)
self.edges = [Edge(0, 1), Edge(1, 2), Edge(2, 3),
Edge(3, 4), Edge(4, 5)]
for node in self.nodes:
self.G.add_node(node)
for edge in self.edges:
self.G.add_edge(edge)
#self.G.show()
def test_borie_node_cover(self):
algorithm = BorieNodeCover(self.G)
algorithm.run()
expected1 = set([1, 2, 4])
expected2 = set([1, 3, 4])
expected3 = set([0, 2, 4])
expected4 = set([1, 3, 5])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected3)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def test_tree_node_cover1(self):
algorithm = TreeNodeCover1(self.G)
algorithm.run()
expected1 = set([1, 2, 4])
expected2 = set([1, 3, 4])
expected3 = set([0, 2, 4])
expected4 = set([1, 3, 5])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected2)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def test_tree_node_cover2(self):
algorithm = TreeNodeCover2(self.G)
algorithm.run()
expected1 = set([1, 2, 4])
expected2 = set([1, 3, 4])
expected3 = set([0, 2, 4])
expected4 = set([1, 3, 5])
self.assertEqual(algorithm.cardinality, len(expected1))
self.assertEqual(algorithm.node_cover, expected2)
# Testing cover.
for edge in self.G.iteredges():
self.assertTrue(edge.source in algorithm.node_cover or
edge.target in algorithm.node_cover)
def tearDown(self): pass
if __name__ == "__main__":
unittest.main()
# EOF
| 34.47027
| 66
| 0.594794
| 805
| 6,377
| 4.626087
| 0.09441
| 0.074919
| 0.130505
| 0.09667
| 0.903867
| 0.87406
| 0.87406
| 0.865199
| 0.865199
| 0.865199
| 0
| 0.039419
| 0.28791
| 6,377
| 184
| 67
| 34.657609
| 0.780665
| 0.069468
| 0
| 0.873134
| 0
| 0
| 0.001354
| 0
| 0
| 0
| 0
| 0
| 0.201493
| 1
| 0.11194
| false
| 0.022388
| 0.044776
| 0
| 0.179104
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
af125aeb5ddc1635505fc3d9a4a36605fe5dc8bc
| 43
|
py
|
Python
|
pypersonalfin/persistors/__init__.py
|
guilhermebruzzi/pypersonalfin
|
180619b36ed28e90b2891a9b2b9b4708d22cbdc8
|
[
"MIT"
] | 1
|
2021-12-05T17:51:00.000Z
|
2021-12-05T17:51:00.000Z
|
pypersonalfin/persistors/__init__.py
|
guilhermebruzzi/pypersonalfin
|
180619b36ed28e90b2891a9b2b9b4708d22cbdc8
|
[
"MIT"
] | null | null | null |
pypersonalfin/persistors/__init__.py
|
guilhermebruzzi/pypersonalfin
|
180619b36ed28e90b2891a9b2b9b4708d22cbdc8
|
[
"MIT"
] | 1
|
2021-02-21T20:07:18.000Z
|
2021-02-21T20:07:18.000Z
|
from .persist_on_csv import persist_on_csv
| 21.5
| 42
| 0.883721
| 8
| 43
| 4.25
| 0.625
| 0.529412
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 43
| 1
| 43
| 43
| 0.871795
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
af6c76485f8980223909155a5f67b13083f1e44a
| 11,217
|
py
|
Python
|
nuage_tempest_plugin/tests/api/ipv6/vsd_managed/test_dualstack_subnet_l3.py
|
nuagenetworks/nuage-tempest-plugin
|
ac1bfb0709c7bbaf04017af3050fb3ed1ad1324a
|
[
"Apache-1.1"
] | 1
|
2021-01-03T01:47:51.000Z
|
2021-01-03T01:47:51.000Z
|
nuage_tempest_plugin/tests/api/ipv6/vsd_managed/test_dualstack_subnet_l3.py
|
nuagenetworks/nuage-tempest-plugin
|
ac1bfb0709c7bbaf04017af3050fb3ed1ad1324a
|
[
"Apache-1.1"
] | null | null | null |
nuage_tempest_plugin/tests/api/ipv6/vsd_managed/test_dualstack_subnet_l3.py
|
nuagenetworks/nuage-tempest-plugin
|
ac1bfb0709c7bbaf04017af3050fb3ed1ad1324a
|
[
"Apache-1.1"
] | 1
|
2020-10-16T12:04:39.000Z
|
2020-10-16T12:04:39.000Z
|
# Copyright 2017 - Nokia
# All Rights Reserved.
from netaddr import IPAddress
from netaddr import IPNetwork
from nuage_tempest_plugin.lib.test import nuage_test
from nuage_tempest_plugin.lib.test import tags
from nuage_tempest_plugin.lib.topology import Topology
from nuage_tempest_plugin.tests.api.ipv6.vsd_managed.base_nuage_networks \
import BaseVSDManagedNetworksIPv6Test
from tempest.lib.common.utils import data_utils
from tempest.lib import decorators
from tempest.lib import exceptions as tempest_exceptions
MSG_INVALID_GATEWAY = "Invalid IPv6 network gateway"
MSG_INVALID_IPV6_ADDRESS = "Invalid network IPv6 address"
MSG_IP_ADDRESS_INVALID_OR_RESERVED = "IP Address is not valid or cannot be " \
"in reserved address space"
@nuage_test.class_header(tags=[tags.ML2])
class VSDManagedDualStackSubnetL3Test(BaseVSDManagedNetworksIPv6Test):
###########################################################################
# Typical cases
###########################################################################
@decorators.attr(type='smoke')
def test_create_ipv6_subnet_in_vsd_managed_l3domain(self):
name = data_utils.rand_name('l3domain-')
vsd_l3domain_template = self.vsd_create_l3domain_template(
name=name)
vsd_l3domain = self.vsd_create_l3domain(
name=name, template_id=vsd_l3domain_template.id)
self.assertEqual(vsd_l3domain.name, name)
zone_name = data_utils.rand_name('zone-')
vsd_zone = self.vsd_create_zone(name=zone_name,
domain=vsd_l3domain)
subnet_name = data_utils.rand_name('l3domain-subnet-')
subnet_cidr = IPNetwork('10.10.100.0/24')
subnet_gateway = str(IPAddress(subnet_cidr) + 1)
subnet_ipv6_cidr = IPNetwork("2001:5f74:c4a5:b82e::/64")
subnet_ipv6_gateway = str(IPAddress(subnet_ipv6_cidr) + 1)
vsd_l3domain_subnet = self.create_vsd_subnet(
name=subnet_name,
zone=vsd_zone,
ip_type="DUALSTACK",
cidr4=subnet_cidr,
gateway4=subnet_gateway,
cidr6=subnet_ipv6_cidr,
gateway6=subnet_ipv6_gateway)
self.assertEqual(vsd_l3domain_subnet.name, subnet_name)
# create Openstack IPv4 subnet on Openstack based on VSD l3dom subnet
net_name = data_utils.rand_name('network-')
network = self.create_network(network_name=net_name)
ipv4_subnet = self.create_subnet(
network,
gateway=subnet_gateway,
cidr=subnet_cidr,
enable_dhcp=True,
mask_bits=IPNetwork(subnet_cidr).prefixlen,
nuagenet=vsd_l3domain_subnet.id,
net_partition=Topology.def_netpartition)
self.assertEqual(ipv4_subnet['cidr'], str(subnet_cidr))
# create Openstack IPv6 subnet on Openstack based on VSD l3dom subnet
ipv6_subnet = self.create_subnet(
network,
ip_version=6,
gateway=vsd_l3domain_subnet.ipv6_gateway,
cidr=IPNetwork(vsd_l3domain_subnet.ipv6_address),
mask_bits=IPNetwork(vsd_l3domain_subnet.ipv6_address).prefixlen,
enable_dhcp=False,
nuagenet=vsd_l3domain_subnet.id,
net_partition=Topology.def_netpartition)
self.assertEqual(
ipv6_subnet['cidr'], vsd_l3domain_subnet.ipv6_address)
# create a port in the network
port = self.create_port(network)
self._verify_port(port, subnet4=ipv4_subnet, subnet6=ipv6_subnet,
status='DOWN',
nuage_policy_groups=None,
nuage_redirect_targets=[],
nuage_floatingip=None)
self._verify_vport_in_l3_subnet(port, vsd_l3domain_subnet)
###########################################################################
# Special cases
###########################################################################
########################################
# backwards compatibility
########################################
def test_create_ipv4_subnet_in_vsd_managed_l3domain_ipv4(self):
name = data_utils.rand_name('l3domain-')
vsd_l3domain_template = self.vsd_create_l3domain_template(
name=name)
vsd_l3domain = self.vsd_create_l3domain(
name=name, template_id=vsd_l3domain_template.id)
self.assertEqual(vsd_l3domain.name, name)
zone_name = data_utils.rand_name('zone-')
vsd_zone = self.vsd_create_zone(name=zone_name,
domain=vsd_l3domain)
subnet_name = data_utils.rand_name('l3domain-subnet-')
subnet_cidr = IPNetwork('10.10.100.0/24')
subnet_gateway = str(IPAddress(subnet_cidr) + 1)
vsd_l3domain_subnet = self.create_vsd_subnet(
name=subnet_name,
zone=vsd_zone,
cidr4=subnet_cidr,
gateway4=subnet_gateway,
ip_type="IPV4")
self.assertEqual("IPV4", vsd_l3domain_subnet.ip_type)
self.assertIsNone(vsd_l3domain_subnet.external_id)
self.assertIsNone(vsd_l3domain_subnet.ipv6_address)
self.assertIsNone(vsd_l3domain_subnet.ipv6_gateway)
self.assertEqual(str(subnet_cidr.ip), vsd_l3domain_subnet.address)
self.assertEqual(subnet_gateway, vsd_l3domain_subnet.gateway)
def test_create_ipv4_subnet_in_vsd_managed_l3domain_no_type(self):
name = data_utils.rand_name('l3domain-')
vsd_l3domain_template = self.vsd_create_l3domain_template(
name=name)
vsd_l3domain = self.vsd_create_l3domain(
name=name, template_id=vsd_l3domain_template.id)
self.assertEqual(vsd_l3domain.name, name)
zone_name = data_utils.rand_name('zone-')
vsd_zone = self.vsd_create_zone(name=zone_name,
domain=vsd_l3domain)
subnet_name = data_utils.rand_name('l3domain-subnet-')
subnet_cidr = IPNetwork('10.10.100.0/24')
subnet_gateway = str(IPAddress(subnet_cidr) + 1)
vsd_l3domain_subnet = self.create_vsd_subnet(
name=subnet_name,
zone=vsd_zone,
cidr4=subnet_cidr,
gateway4=subnet_gateway)
self.assertEqual("IPV4", vsd_l3domain_subnet.ip_type)
self.assertIsNone(vsd_l3domain_subnet.external_id)
self.assertIsNone(vsd_l3domain_subnet.ipv6_address)
self.assertIsNone(vsd_l3domain_subnet.ipv6_gateway)
self.assertEqual(str(subnet_cidr.ip), vsd_l3domain_subnet.address)
self.assertEqual(subnet_gateway, vsd_l3domain_subnet.gateway)
########################################
# minimal attributes - default values
########################################
###########################################################################
# Negative cases
###########################################################################
@decorators.attr(type='smoke')
def test_create_ipv6_subnet_in_vsd_managed_l3domain_ipv4(self):
name = data_utils.rand_name('l3domain-')
vsd_l3domain_template = self.vsd_create_l3domain_template(
name=name)
vsd_l3domain = self.vsd_create_l3domain(
name=name, template_id=vsd_l3domain_template.id)
self.assertEqual(vsd_l3domain.name, name)
zone_name = data_utils.rand_name('zone-')
vsd_zone = self.vsd_create_zone(name=zone_name,
domain=vsd_l3domain)
subnet_name = data_utils.rand_name('l3domain-subnet-')
subnet_cidr = IPNetwork('10.10.100.0/24')
subnet_gateway = str(IPAddress(subnet_cidr) + 1)
vsd_l3domain_subnet = self.create_vsd_subnet(
name=subnet_name,
zone=vsd_zone,
cidr4=subnet_cidr,
gateway4=subnet_gateway,
ip_type="IPV4")
# create Openstack IPv4 subnet on Openstack based on VSD l3dom subnet
net_name = data_utils.rand_name('network-')
network = self.create_network(network_name=net_name)
subnet_ipv6_cidr = IPNetwork("2001:5f74:c4a5:b82e::/64")
subnet_ipv6_gateway = str(IPAddress(subnet_ipv6_cidr) + 1)
# shall not create Openstack IPv6 subnet on Openstack based on
# VSD l3domain subnet with type IPV4
if Topology.from_openstack('Newton'):
expected_exception = tempest_exceptions.BadRequest
expected_message = "Subnet with ip_version 6 can't be linked to " \
"vsd subnet with IPType IPV4"
else:
expected_exception = tempest_exceptions.ServerFault
expected_message = "create_subnet_postcommit failed."
self.assertRaisesRegex(
expected_exception,
expected_message,
self.create_subnet,
network,
ip_version=6,
gateway=subnet_ipv6_gateway,
cidr=subnet_ipv6_cidr,
mask_bits=subnet_ipv6_cidr.prefixlen,
enable_dhcp=False,
nuagenet=vsd_l3domain_subnet.id,
net_partition=Topology.def_netpartition)
@decorators.attr(type='smoke')
def test_create_ipv4_subnet_without_dhcp_in_vsd_managed_l3domain(self):
name = data_utils.rand_name('l3domain-')
vsd_l3domain_template = self.vsd_create_l3domain_template(
name=name)
vsd_l3domain = self.vsd_create_l3domain(
name=name, template_id=vsd_l3domain_template.id)
self.assertEqual(vsd_l3domain.name, name)
zone_name = data_utils.rand_name('zone-')
vsd_zone = self.vsd_create_zone(name=zone_name,
domain=vsd_l3domain)
subnet_name = data_utils.rand_name('l3domain-subnet-')
subnet_cidr = IPNetwork('10.10.100.0/24')
subnet_gateway = str(IPAddress(subnet_cidr) + 1)
vsd_l3domain_subnet = self.create_vsd_subnet(
name=subnet_name,
zone=vsd_zone,
cidr4=subnet_cidr,
gateway4=subnet_gateway,
ip_type="IPV4")
# create Openstack IPv4 subnet on Openstack based on VSD l3dom subnet
net_name = data_utils.rand_name('network-')
network = self.create_network(network_name=net_name)
# create Openstack IPv4 subnet on Openstack based on VSD l3dom subnet
if Topology.from_openstack('Newton'):
expected_exception = tempest_exceptions.BadRequest
expected_message = "enable_dhcp in subnet must be True"
else:
expected_exception = tempest_exceptions.ServerFault
expected_message = "create_subnet_postcommit failed."
self.assertRaisesRegex(
expected_exception,
expected_message,
self.create_subnet,
network,
gateway=subnet_gateway,
cidr=subnet_cidr,
mask_bits=subnet_cidr.prefixlen,
enable_dhcp=False,
nuagenet=vsd_l3domain_subnet.id,
net_partition=Topology.def_netpartition)
| 41.544444
| 79
| 0.624142
| 1,236
| 11,217
| 5.321197
| 0.12055
| 0.088642
| 0.085297
| 0.046526
| 0.80523
| 0.784704
| 0.767979
| 0.751863
| 0.741523
| 0.718413
| 0
| 0.028656
| 0.250245
| 11,217
| 269
| 80
| 41.698885
| 0.753389
| 0.054471
| 0
| 0.751244
| 0
| 0
| 0.06483
| 0.009619
| 0
| 0
| 0
| 0
| 0.109453
| 1
| 0.024876
| false
| 0
| 0.044776
| 0
| 0.074627
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
afd3073d0dd56278872dccab9905197ed30a9395
| 108
|
py
|
Python
|
src/utils/menorAMayor.py
|
jisbruzzi/equipo-q-tp1
|
9d1a08f2bd261d9656341194bd3ad794e7c4589b
|
[
"MIT"
] | null | null | null |
src/utils/menorAMayor.py
|
jisbruzzi/equipo-q-tp1
|
9d1a08f2bd261d9656341194bd3ad794e7c4589b
|
[
"MIT"
] | 1
|
2018-03-29T18:09:11.000Z
|
2018-03-29T18:09:11.000Z
|
src/utils/menorAMayor.py
|
jisbruzzi/equipo-q-tp1
|
9d1a08f2bd261d9656341194bd3ad794e7c4589b
|
[
"MIT"
] | null | null | null |
def menor_a_mayor(lista):
return sorted(lista)
def desordenar(lista):
return menor_a_mayor(lista)
| 15.428571
| 31
| 0.740741
| 16
| 108
| 4.75
| 0.5
| 0.157895
| 0.289474
| 0.421053
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 108
| 6
| 32
| 18
| 0.844444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
bb8afb6063c4384c9669b6b05d2e6cdb3593957c
| 2,254
|
py
|
Python
|
board.py
|
MansNotSpaghett/UTTT-bot
|
2e4141851945931266072fb43512ec62bbadbe36
|
[
"MIT"
] | null | null | null |
board.py
|
MansNotSpaghett/UTTT-bot
|
2e4141851945931266072fb43512ec62bbadbe36
|
[
"MIT"
] | null | null | null |
board.py
|
MansNotSpaghett/UTTT-bot
|
2e4141851945931266072fb43512ec62bbadbe36
|
[
"MIT"
] | null | null | null |
b11, b12, b13, b14, b15, b16, b17, b18, b19, b21, b22, b23, b24, b25, b26, b27, b28, b29, b31, b32, b33, b34, b35, b36, b37, b38, b39, b41, b42, b43, b44, b45, b46, b47, b48, b49, b51, b52, b53, b54, b55, b56, b57, b58, b59, b61, b62, b63, b64, b65, b66, b67, b68, b69, b71, b72, b73, b74, b75, b76, b77, b78, b79, b81, b82, b83, b84, b85, b86, b87, b88, b89, b91, b92, b93, b94, b95, b96, b97, b98, b99=' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '
def board():
board = """
```
THE BOARD
-------------------------------------
|{0}|{1}|{2}|{9}|{10}|{11}|{18}|{19}|{20}|
|---+---+---|---+---+---|---+---+---|
|{3}|{4}|{5}|{12}|{13}|{14}|{21}|{22}|{23}|
|---+---+---|---+---+---|---+---+---|
|{6}|{7}|{8}|{15}|{16}|{17}|{24}|{25}|{26}|
|-----------+-----------+-----------|
|{27}|{28}|{29}|{36}|{37}|{38}|{45}|{46}|{47}|
|---+---+---|---+---+---|---+---+---|
|{30}|{31}|{32}|{39}|{40}|{41}|{48}|{49}|{50}|
|---+---+---|---+---+---|---+---+---|
|{33}|{34}|{35}|{42}|{43}|{44}|{51}|{52}|{53}|
|-----------+-----------+-----------|
|{54}|{55}|{56}|{63}|{64}|{65}|{72}|{73}|{74}|
|---+---+---|---+---+---|---+---+---|
|{57}|{58}|{59}|{66}|{67}|{68}|{75}|{76}|{77}|
|---+---+---|---+---+---|---+---+---|
|{60}|{61}|{62}|{69}|{70}|{71}|{78}|{79}|{80}|
-------------------------------------
```""".format(b11, b12, b13, b14, b15, b16, b17, b18, b19, b21, b22, b23, b24, b25, b26, b27, b28, b29, b31, b32, b33, b34, b35, b36, b37, b38, b39, b41, b42, b43, b44, b45, b46, b47, b48, b49, b51, b52, b53, b54, b55, b56, b57, b58, b59, b61, b62, b63, b64, b65, b66, b67, b68, b69, b71, b72, b73, b74, b75, b76, b77, b78, b79, b81, b82, b83, b84, b85, b86, b87, b88, b89, b91, b92, b93, b94, b95, b96, b97, b98, b99)
return board
| 83.481481
| 969
| 0.299911
| 251
| 2,254
| 2.693227
| 0.665339
| 0.017751
| 0.026627
| 0.035503
| 0.718935
| 0.718935
| 0.718935
| 0.718935
| 0.718935
| 0.718935
| 0
| 0.276744
| 0.236912
| 2,254
| 26
| 970
| 86.692308
| 0.116279
| 0
| 0
| 0.384615
| 0
| 0
| 0.472937
| 0.34339
| 0
| 0
| 0
| 0
| 0
| 1
| 0.038462
| false
| 0
| 0
| 0
| 0.076923
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
bbe0ee996a96b65ef16ddfc4aad04b39abdc0fad
| 15,456
|
py
|
Python
|
sdk/python/pulumi_vault/identity/entity.py
|
pulumi/pulumi-vault
|
1682875f4a5d7d508f36e166529ad2b8aec34090
|
[
"ECL-2.0",
"Apache-2.0"
] | 10
|
2019-10-07T17:44:18.000Z
|
2022-03-30T20:46:33.000Z
|
sdk/python/pulumi_vault/identity/entity.py
|
pulumi/pulumi-vault
|
1682875f4a5d7d508f36e166529ad2b8aec34090
|
[
"ECL-2.0",
"Apache-2.0"
] | 79
|
2019-10-11T18:13:07.000Z
|
2022-03-31T21:09:41.000Z
|
sdk/python/pulumi_vault/identity/entity.py
|
pulumi/pulumi-vault
|
1682875f4a5d7d508f36e166529ad2b8aec34090
|
[
"ECL-2.0",
"Apache-2.0"
] | 2
|
2019-10-28T10:08:40.000Z
|
2020-03-17T14:20:55.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['EntityArgs', 'Entity']
@pulumi.input_type
class EntityArgs:
def __init__(__self__, *,
disabled: Optional[pulumi.Input[bool]] = None,
external_policies: Optional[pulumi.Input[bool]] = None,
metadata: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
"""
The set of arguments for constructing a Entity resource.
:param pulumi.Input[bool] disabled: True/false Is this entity currently disabled. Defaults to `false`
:param pulumi.Input[bool] external_policies: `false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] metadata: A Map of additional metadata to associate with the user.
:param pulumi.Input[str] name: Name of the identity entity to create.
:param pulumi.Input[Sequence[pulumi.Input[str]]] policies: A list of policies to apply to the entity.
"""
if disabled is not None:
pulumi.set(__self__, "disabled", disabled)
if external_policies is not None:
pulumi.set(__self__, "external_policies", external_policies)
if metadata is not None:
pulumi.set(__self__, "metadata", metadata)
if name is not None:
pulumi.set(__self__, "name", name)
if policies is not None:
pulumi.set(__self__, "policies", policies)
@property
@pulumi.getter
def disabled(self) -> Optional[pulumi.Input[bool]]:
"""
True/false Is this entity currently disabled. Defaults to `false`
"""
return pulumi.get(self, "disabled")
@disabled.setter
def disabled(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "disabled", value)
@property
@pulumi.getter(name="externalPolicies")
def external_policies(self) -> Optional[pulumi.Input[bool]]:
"""
`false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
"""
return pulumi.get(self, "external_policies")
@external_policies.setter
def external_policies(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "external_policies", value)
@property
@pulumi.getter
def metadata(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
A Map of additional metadata to associate with the user.
"""
return pulumi.get(self, "metadata")
@metadata.setter
def metadata(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "metadata", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the identity entity to create.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def policies(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
A list of policies to apply to the entity.
"""
return pulumi.get(self, "policies")
@policies.setter
def policies(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "policies", value)
@pulumi.input_type
class _EntityState:
def __init__(__self__, *,
disabled: Optional[pulumi.Input[bool]] = None,
external_policies: Optional[pulumi.Input[bool]] = None,
metadata: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
"""
Input properties used for looking up and filtering Entity resources.
:param pulumi.Input[bool] disabled: True/false Is this entity currently disabled. Defaults to `false`
:param pulumi.Input[bool] external_policies: `false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] metadata: A Map of additional metadata to associate with the user.
:param pulumi.Input[str] name: Name of the identity entity to create.
:param pulumi.Input[Sequence[pulumi.Input[str]]] policies: A list of policies to apply to the entity.
"""
if disabled is not None:
pulumi.set(__self__, "disabled", disabled)
if external_policies is not None:
pulumi.set(__self__, "external_policies", external_policies)
if metadata is not None:
pulumi.set(__self__, "metadata", metadata)
if name is not None:
pulumi.set(__self__, "name", name)
if policies is not None:
pulumi.set(__self__, "policies", policies)
@property
@pulumi.getter
def disabled(self) -> Optional[pulumi.Input[bool]]:
"""
True/false Is this entity currently disabled. Defaults to `false`
"""
return pulumi.get(self, "disabled")
@disabled.setter
def disabled(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "disabled", value)
@property
@pulumi.getter(name="externalPolicies")
def external_policies(self) -> Optional[pulumi.Input[bool]]:
"""
`false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
"""
return pulumi.get(self, "external_policies")
@external_policies.setter
def external_policies(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "external_policies", value)
@property
@pulumi.getter
def metadata(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
A Map of additional metadata to associate with the user.
"""
return pulumi.get(self, "metadata")
@metadata.setter
def metadata(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "metadata", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the identity entity to create.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def policies(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
A list of policies to apply to the entity.
"""
return pulumi.get(self, "policies")
@policies.setter
def policies(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "policies", value)
class Entity(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
disabled: Optional[pulumi.Input[bool]] = None,
external_policies: Optional[pulumi.Input[bool]] = None,
metadata: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
"""
## Import
Identity entity can be imported using the `id`, e.g.
```sh
$ pulumi import vault:identity/entity:Entity test "ae6f8ued-0f1a-9f6b-2915-1a2be20dc053"
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] disabled: True/false Is this entity currently disabled. Defaults to `false`
:param pulumi.Input[bool] external_policies: `false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] metadata: A Map of additional metadata to associate with the user.
:param pulumi.Input[str] name: Name of the identity entity to create.
:param pulumi.Input[Sequence[pulumi.Input[str]]] policies: A list of policies to apply to the entity.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[EntityArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
## Import
Identity entity can be imported using the `id`, e.g.
```sh
$ pulumi import vault:identity/entity:Entity test "ae6f8ued-0f1a-9f6b-2915-1a2be20dc053"
```
:param str resource_name: The name of the resource.
:param EntityArgs 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(EntityArgs, 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,
disabled: Optional[pulumi.Input[bool]] = None,
external_policies: Optional[pulumi.Input[bool]] = None,
metadata: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[Sequence[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__ = EntityArgs.__new__(EntityArgs)
__props__.__dict__["disabled"] = disabled
__props__.__dict__["external_policies"] = external_policies
__props__.__dict__["metadata"] = metadata
__props__.__dict__["name"] = name
__props__.__dict__["policies"] = policies
super(Entity, __self__).__init__(
'vault:identity/entity:Entity',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
disabled: Optional[pulumi.Input[bool]] = None,
external_policies: Optional[pulumi.Input[bool]] = None,
metadata: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None) -> 'Entity':
"""
Get an existing Entity 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] disabled: True/false Is this entity currently disabled. Defaults to `false`
:param pulumi.Input[bool] external_policies: `false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] metadata: A Map of additional metadata to associate with the user.
:param pulumi.Input[str] name: Name of the identity entity to create.
:param pulumi.Input[Sequence[pulumi.Input[str]]] policies: A list of policies to apply to the entity.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _EntityState.__new__(_EntityState)
__props__.__dict__["disabled"] = disabled
__props__.__dict__["external_policies"] = external_policies
__props__.__dict__["metadata"] = metadata
__props__.__dict__["name"] = name
__props__.__dict__["policies"] = policies
return Entity(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def disabled(self) -> pulumi.Output[Optional[bool]]:
"""
True/false Is this entity currently disabled. Defaults to `false`
"""
return pulumi.get(self, "disabled")
@property
@pulumi.getter(name="externalPolicies")
def external_policies(self) -> pulumi.Output[Optional[bool]]:
"""
`false` by default. If set to `true`, this resource will ignore any policies return from Vault or specified in the resource. You can use `identity.EntityPolicies` to manage policies for this entity in a decoupled manner.
"""
return pulumi.get(self, "external_policies")
@property
@pulumi.getter
def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
A Map of additional metadata to associate with the user.
"""
return pulumi.get(self, "metadata")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Name of the identity entity to create.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def policies(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
A list of policies to apply to the entity.
"""
return pulumi.get(self, "policies")
| 44.413793
| 273
| 0.645057
| 1,843
| 15,456
| 5.24905
| 0.086272
| 0.108022
| 0.088381
| 0.042795
| 0.841844
| 0.824065
| 0.810006
| 0.80184
| 0.796568
| 0.790056
| 0
| 0.003014
| 0.248771
| 15,456
| 347
| 274
| 44.541787
| 0.830161
| 0.336504
| 0
| 0.777228
| 1
| 0
| 0.06951
| 0.002949
| 0
| 0
| 0
| 0
| 0
| 1
| 0.158416
| false
| 0.004951
| 0.024752
| 0
| 0.277228
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
bbfd1a624f332c4cdc415b19cb1179293e7a64ff
| 169
|
py
|
Python
|
vedatad/__init__.py
|
carpedkm/vedatad
|
55f8dced57f698ee9fc0da9bcf471d171e718d0c
|
[
"Apache-2.0"
] | 36
|
2021-05-22T04:36:22.000Z
|
2022-03-17T06:42:54.000Z
|
vedatad/__init__.py
|
carpedkm/vedatad
|
55f8dced57f698ee9fc0da9bcf471d171e718d0c
|
[
"Apache-2.0"
] | 16
|
2021-07-26T07:43:53.000Z
|
2022-03-30T08:48:58.000Z
|
vedatad/__init__.py
|
carpedkm/vedatad
|
55f8dced57f698ee9fc0da9bcf471d171e718d0c
|
[
"Apache-2.0"
] | 15
|
2021-07-06T12:51:53.000Z
|
2022-03-09T14:11:19.000Z
|
from . import assembler, bridge, criteria, datasets, engines, misc, models
__all__ = [
'assembler', 'bridge', 'criteria', 'datasets', 'engines', 'misc', 'models'
]
| 28.166667
| 78
| 0.668639
| 17
| 169
| 6.411765
| 0.588235
| 0.275229
| 0.422018
| 0.568807
| 0.880734
| 0.880734
| 0.880734
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 169
| 5
| 79
| 33.8
| 0.762238
| 0
| 0
| 0
| 0
| 0
| 0.284024
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a5555381971d4e5d39a52d67e754d7ab19c44a5e
| 134
|
py
|
Python
|
src/adaptive_response/__init__.py
|
david-zwicker/sensing-normalized-results
|
5b64ab34d400dcf457626e1ad244c2b4a889ac80
|
[
"MIT"
] | null | null | null |
src/adaptive_response/__init__.py
|
david-zwicker/sensing-normalized-results
|
5b64ab34d400dcf457626e1ad244c2b4a889ac80
|
[
"MIT"
] | null | null | null |
src/adaptive_response/__init__.py
|
david-zwicker/sensing-normalized-results
|
5b64ab34d400dcf457626e1ad244c2b4a889ac80
|
[
"MIT"
] | null | null | null |
from .adaptive_threshold.at_numeric import AdaptiveThresholdNumeric
from .adaptive_threshold.at_theory import AdaptiveThresholdTheory
| 44.666667
| 67
| 0.910448
| 14
| 134
| 8.428571
| 0.642857
| 0.20339
| 0.355932
| 0.389831
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.059701
| 134
| 2
| 68
| 67
| 0.936508
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
a568f58fdaf32814a2d31913492860915a412ad7
| 102
|
py
|
Python
|
music/__init__.py
|
quintenroets/music
|
d40f1604aa72824b422ca945042303d19d9bd347
|
[
"MIT"
] | 1
|
2022-01-25T11:01:54.000Z
|
2022-01-25T11:01:54.000Z
|
music/__init__.py
|
quintenroets/music
|
d40f1604aa72824b422ca945042303d19d9bd347
|
[
"MIT"
] | null | null | null |
music/__init__.py
|
quintenroets/music
|
d40f1604aa72824b422ca945042303d19d9bd347
|
[
"MIT"
] | null | null | null |
from .downloads import download as start_downloads
from .downloads import jobs
from .path import Path
| 25.5
| 50
| 0.833333
| 15
| 102
| 5.6
| 0.533333
| 0.309524
| 0.452381
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137255
| 102
| 3
| 51
| 34
| 0.954545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
a596777970b914490e0732c2400872679c1d758b
| 3,761
|
py
|
Python
|
tests/ethpm/utils/test_ipfs_utils.py
|
faxata/py-ethpm
|
856cf478e8f96a919160bebc923bf82ad44652f4
|
[
"MIT"
] | 1
|
2018-06-27T23:55:04.000Z
|
2018-06-27T23:55:04.000Z
|
tests/ethpm/utils/test_ipfs_utils.py
|
faxata/py-ethpm
|
856cf478e8f96a919160bebc923bf82ad44652f4
|
[
"MIT"
] | null | null | null |
tests/ethpm/utils/test_ipfs_utils.py
|
faxata/py-ethpm
|
856cf478e8f96a919160bebc923bf82ad44652f4
|
[
"MIT"
] | 1
|
2018-06-28T04:49:23.000Z
|
2018-06-28T04:49:23.000Z
|
import pytest
from ethpm.utils.ipfs import (
extract_ipfs_path_from_uri,
is_ipfs_uri,
)
@pytest.mark.parametrize(
'value,expected',
(
(
'ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u',
),
(
'ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u',
),
(
'ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u',
),
(
'ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/',
),
(
'ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/',
),
(
'ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/',
),
(
'ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme',
),
(
'ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme',
),
(
'ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme',
),
(
'ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/',
),
(
'ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/',
),
(
'ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/',
'QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/',
),
)
)
def test_extract_ipfs_path_from_uri(value, expected):
actual = extract_ipfs_path_from_uri(value)
assert actual == expected
@pytest.mark.parametrize(
'value,expected',
(
('ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u', True),
('ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u', True),
('ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u', True),
('ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/', True),
('ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/', True),
('ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/', True),
('ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme', True),
('ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme', True),
('ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme', True),
('ipfs:QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/', True),
('ipfs:/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/', True),
('ipfs://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/', True),
# malformed
('ipfs//QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/', False),
('ipfs/QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/', False),
('ipfsQmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme/', False),
# HTTP
('http://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme', False),
('https://QmTKB75Y73zhNbD3Y73xeXGjYrZHmaXXNxoZqGCagu7r8u/readme', False),
# No hash
('ipfs://', False),
)
)
def test_is_ipfs_uri(value, expected):
actual = is_ipfs_uri(value)
assert actual is expected
| 39.177083
| 81
| 0.681468
| 175
| 3,761
| 14.531429
| 0.16
| 0.511207
| 0.308297
| 0.235942
| 0.806921
| 0.79827
| 0.777035
| 0.750295
| 0.750295
| 0.750295
| 0
| 0.125939
| 0.220952
| 3,761
| 95
| 82
| 39.589474
| 0.74198
| 0.00585
| 0
| 0.321839
| 0
| 0
| 0.609639
| 0.567871
| 0
| 0
| 0
| 0
| 0.022989
| 1
| 0.022989
| false
| 0
| 0.022989
| 0
| 0.045977
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 12
|
a5a1823541f4cf40bc42d06fa4fa343e3a346e66
| 7,592
|
py
|
Python
|
test/unit/mongo_rep_admin/main.py
|
mjpernot/mongo-rep-admin
|
5019325eb9e91cf3fcad6744ebd1681401695f81
|
[
"MIT"
] | null | null | null |
test/unit/mongo_rep_admin/main.py
|
mjpernot/mongo-rep-admin
|
5019325eb9e91cf3fcad6744ebd1681401695f81
|
[
"MIT"
] | null | null | null |
test/unit/mongo_rep_admin/main.py
|
mjpernot/mongo-rep-admin
|
5019325eb9e91cf3fcad6744ebd1681401695f81
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# Classification (U)
"""Program: main.py
Description: Unit testing of main in mongo_rep_admin.py.
Usage:
test/unit/mongo_rep_admin/main.py
Arguments:
"""
# Libraries and Global Variables
# Standard
import sys
import os
if sys.version_info < (2, 7):
import unittest2 as unittest
else:
import unittest
# Third-party
import mock
# Local
sys.path.append(os.getcwd())
import mongo_rep_admin
import version
__version__ = version.__version__
class UnitTest(unittest.TestCase):
"""Class: UnitTest
Description: Class which is a representation of a unit testing.
Methods:
setUp
test_help_true
test_help_false
test_arg_req_true
test_arg_req_false
test_arg_cond_false
test_arg_cond_true
test_arg_dir_true
test_arg_dir_false
test_arg_file_true
test_arg_file_false
"""
def setUp(self):
"""Function: setUp
Description: Initialization for unit testing.
Arguments:
"""
self.args_array = {"-c": "CfgFile", "-d": "CfgDir", "-C": True}
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_help_true(self, mock_arg, mock_help):
"""Function: test_help_true
Description: Test help if returns true.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.arg_parser.arg_require")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_help_false(self, mock_arg, mock_help, mock_req):
"""Function: test_help_false
Description: Test help if returns false.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = False
mock_req.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.arg_parser.arg_require")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_arg_req_true(self, mock_arg, mock_help, mock_req):
"""Function: test_arg_req_true
Description: Test arg_require if returns true.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = False
mock_req.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.arg_parser.arg_cond_req")
@mock.patch("mongo_rep_admin.arg_parser.arg_require")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_arg_req_false(self, mock_arg, mock_help, mock_req, mock_cond):
"""Function: test_arg_req_false
Description: Test arg_require if returns false.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = False
mock_req.return_value = False
mock_cond.return_value = False
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.arg_parser.arg_cond_req")
@mock.patch("mongo_rep_admin.arg_parser.arg_require")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_arg_cond_false(self, mock_arg, mock_help, mock_req, mock_cond):
"""Function: test_arg_cond_false
Description: Test arg_cond_req if returns false.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = False
mock_req.return_value = False
mock_cond.return_value = False
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.arg_parser.arg_dir_chk_crt")
@mock.patch("mongo_rep_admin.arg_parser.arg_file_chk")
@mock.patch("mongo_rep_admin.arg_parser.arg_require")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_arg_cond_true(self, mock_arg, mock_help, mock_req, mock_cond,
mock_dir):
"""Function: test_arg_cond_true
Description: Test arg_cond_req if returns true.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = False
mock_req.return_value = False
mock_cond.return_value = True
mock_dir.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.arg_parser.arg_dir_chk_crt")
@mock.patch("mongo_rep_admin.arg_parser.arg_file_chk")
@mock.patch("mongo_rep_admin.arg_parser.arg_require")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser.arg_parse2")
def test_arg_dir_true(self, mock_arg, mock_help, mock_req, mock_cond,
mock_dir):
"""Function: test_arg_dir_true
Description: Test arg_dir_chk_crt if returns true.
Arguments:
"""
mock_arg.return_value = self.args_array
mock_help.return_value = False
mock_req.return_value = False
mock_cond.return_value = True
mock_dir.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser")
def test_arg_dir_false(self, mock_arg, mock_help):
"""Function: test_arg_dir_false
Description: Test arg_dir_chk_crt if returns false.
Arguments:
"""
mock_arg.arg_parse2.return_value = self.args_array
mock_help.return_value = False
mock_arg.arg_require.return_value = False
mock_arg.arg_file_chk.return_value = True
mock_arg.arg_dir_chk_crt.return_value = False
mock_arg.arg_file_chk.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser")
def test_arg_file_true(self, mock_arg, mock_help):
"""Function: test_arg_file_true
Description: Test arg_file_chk if returns true.
Arguments:
"""
mock_arg.arg_parse2.return_value = self.args_array
mock_help.return_value = False
mock_arg.arg_require.return_value = False
mock_arg.arg_file_chk.return_value = True
mock_arg.arg_dir_chk_crt.return_value = False
mock_arg.arg_file_chk.return_value = True
self.assertFalse(mongo_rep_admin.main())
@mock.patch("mongo_rep_admin.run_program")
@mock.patch("mongo_rep_admin.gen_libs.help_func")
@mock.patch("mongo_rep_admin.arg_parser")
def test_arg_file_false(self, mock_arg, mock_help, mock_run):
"""Function: test_arg_file_false
Description: Test arg_file_chk if returns false.
Arguments:
"""
mock_arg.arg_parse2.return_value = self.args_array
mock_help.return_value = False
mock_arg.arg_require.return_value = False
mock_arg.arg_file_chk.return_value = True
mock_arg.arg_dir_chk_crt.return_value = False
mock_arg.arg_file_chk.return_value = False
mock_run.return_value = True
self.assertFalse(mongo_rep_admin.main())
if __name__ == "__main__":
unittest.main()
| 26.826855
| 76
| 0.680848
| 1,051
| 7,592
| 4.499524
| 0.080875
| 0.077818
| 0.126454
| 0.11863
| 0.820892
| 0.812857
| 0.800381
| 0.77141
| 0.735462
| 0.709452
| 0
| 0.002215
| 0.226818
| 7,592
| 282
| 77
| 26.921986
| 0.803407
| 0.20245
| 0
| 0.741379
| 0
| 0
| 0.213269
| 0.208467
| 0
| 0
| 0
| 0
| 0.086207
| 1
| 0.094828
| false
| 0
| 0.060345
| 0
| 0.163793
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b1b170133d44d5e1bc1f6af359837fc4f168efdb
| 47,456
|
py
|
Python
|
models/runner_nn.py
|
spencer-hong/QSARBO
|
a9fa8cbf058abea715fe2c721564f662ed8b1135
|
[
"MIT"
] | 1
|
2021-05-23T01:03:50.000Z
|
2021-05-23T01:03:50.000Z
|
models/runner_nn.py
|
spencerhongcornell/QSARBO
|
a9fa8cbf058abea715fe2c721564f662ed8b1135
|
[
"MIT"
] | 5
|
2020-09-26T01:07:48.000Z
|
2022-02-10T01:59:34.000Z
|
models/runner_nn.py
|
spencer-hong/QSARBO
|
a9fa8cbf058abea715fe2c721564f662ed8b1135
|
[
"MIT"
] | null | null | null |
import GPy, GPyOpt
import numpy as np
import pandas as pd
import sklearn.model_selection as mose
import sklearn.preprocessing as skp
import sklearn.ensemble as sken
import sklearn.neighbors as skne
import sklearn.metrics as me
from timeit import default_timer as timer
import matplotlib.pyplot as plt
#import ortools
from tensorflow.keras.layers import Activation, Dropout, BatchNormalization, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.metrics import mean_squared_error
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
import json
import pickle
import warnings
import logging
#from datetime import datetime
import random
import sys, os
sys.path.insert(0,
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models.classes import prepare as prepare
from models.classes import nn as nn
from models.classes import nnc as nnc
del sys.path[0]
random.seed(36)
import faulthandler; faulthandler.enable()
def runner_nn(input_file_loc, iterator = 0):
logging.getLogger('tensorflow').disabled = True
os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"
os.system('export KMP_WARNINGS=FALSE')
warnings.filterwarnings("ignore", category=FutureWarning)
dirName = 'pickled'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists. Skipping creation.")
dirName = 'predictions'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists. Skipping creation.")
# function to run random forest class
def run_nn( X_Train , Y_Train, X_Test, Y_Test, l1_out=512,
l2_out=512,
l1_drop=0.2,
l2_drop=0.2,
l3_out = 512,
l3_drop = 0.2,
batch_size=100,
epochs=10, cv = 7):
_nn = nn.nn(cv = cv, l1_out = l1_out, l2_out = l2_out, l3_out = l3_out, l1_drop = l1_drop, l2_drop = l2_drop, l3_drop = l3_drop, X_Train = X_Train, Y_Train = Y_Train, X_Test = X_Test, Y_Test = Y_Test)
nn_evaluation = _nn.nn_evaluate()
return nn_evaluation
if input_file_loc:
with open(input_file_loc, 'r') as f:
datastore = json.load(f)
current_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+'/'+datastore["folder_name"]["content"] +'/'
filename = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+'/'+datastore["folder_name"]["content"] +'/' +datastore["dataset_name"]["content"]
if datastore['saved_hyperparameters?']['content'] == "False":
start = timer()
if datastore['saved_descriptors?']['content'] == "False":
selected_data, IDboolean = prepare.isolate(structname= datastore["column_SMILES"]['content'], activityname = datastore["column_activity"]["content"], filelocation = filename, chemID = datastore["chemID"]["content"])
print("-----------------------------------")
print("Cleaning Data")
print("-----------------------------------\n")
inDF = prepare.cleanSMILES(df = selected_data, elementskept = datastore["elements_kept"]["content"], smilesName = datastore["column_SMILES"]["content"])
print("-----------------------------------")
print("Curating Descriptors")
print("-----------------------------------\n")
print(f"Number of Compounds: {inDF.shape[0]}")
inDF = prepare.createdescriptors(df = inDF, colName = datastore["column_SMILES"]['content'], correlationthreshold = datastore["correlation_threshold"]['content'], STDthreshold = datastore['std_threshold']['content'], IDboolean = IDboolean)
activityValidDF, activityTrainDF, activityTestDF, IDValidDF, IDTrainDF, IDTestDF, validDF, trainDF, testDF, nameValidDF, nameTrainDF, nameTestDF, nn_cols = prepare.partition(df = inDF,validset = datastore['valid_split']['content'], testset = datastore['test_split']['content'], IDboolean = IDboolean)
print("-----------------------------------")
print("Partitioning Data")
print("-----------------------------------\n")
dfdict = {
"activityValidDF": activityValidDF,
"activityTrainDF": activityTrainDF,
"activityTestDF": activityTestDF,
"IDValidDF":IDValidDF,
"IDTrainDF":IDTrainDF,
"IDTestDF":IDTestDF,
"validDF":validDF,
"trainDF":trainDF,
"testDF":testDF,
"nameValidDF":nameValidDF,
"nameTrainDF":nameTrainDF,
"nameTestDF":nameTestDF,
"nn_cols":nn_cols
}
pickle.dump( dfdict, open( current_folder+ "pickled/nn_descriptors.p", "wb" ) )
else:
print("-----------------------------------")
print("Loading Descriptors")
print("-----------------------------------\n")
dfdict = pickle.load( open( current_folder + "pickled/nn_descriptors.p", "rb" ) )
activityValidDF = dfdict['activityValidDF']
activityTrainDF = dfdict['activityTrainDF']
activityTestDF = dfdict['activityTestDF']
IDValidDF = dfdict['IDValidDF']
IDTrainDF = dfdict['IDTrainDF']
IDTestDF = dfdict['IDTestDF']
validDF = dfdict['validDF']
trainDF = dfdict['trainDF']
testDF = dfdict['testDF']
nameValidDF = dfdict['nameValidDF']
nameTrainDF = dfdict['nameTrainDF']
nameTestDF = dfdict['nameTestDF']
X_Valid = validDF
Y_Valid = activityValidDF
X_Train = trainDF
Y_Train = activityTrainDF
X_Test = testDF
Y_Test = activityTestDF
orilen = X_Train.shape[1]
bounds = [
{'name': 'l1_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
{'name': 'l2_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
{'name':'l3_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
{'name': 'l1_out', 'type': 'discrete', 'domain': (orilen/4, orilen/3, orilen/2, orilen, orilen*2, orilen*3,orilen*4)},
{'name': 'l2_out', 'type': 'discrete', 'domain': ( orilen/4,orilen/3, orilen/2, orilen, orilen*2, orilen*3,orilen*4)},
{'name': 'l3_out', 'type': 'discrete', 'domain': ( orilen/4,orilen/3, orilen/2, orilen, orilen*2,orilen*3, orilen*4)},
{'name': 'batch_size', 'type': 'discrete', 'domain': (200,300, 400,500, 600, 700)},
{'name': 'epochs', 'type': 'discrete', 'domain': (300, 350, 400, 450, 500, 600, 700)}]
def f(x):
evaluation = run_nn(
l1_drop = float(x[:,0]),
l2_drop = float(x[:,1]),
l3_drop = float(x[:, 2]),
l1_out = int(x[:,3]),
l2_out = int(x[:,4]),
l3_out = int(x[:, 5]),
batch_size = int(x[:,6]),
epochs = int(x[:,7]), X_Train = X_Train, Y_Train = Y_Train ,X_Test = X_Test, Y_Test =Y_Test, cv = datastore['cvfolds?']['content'])
return evaluation
print("-----------------------------------")
print("Bayesian Optimization Initiated: First Picking 5 Random Sample Points")
print("-----------------------------------\n")
BOModel = GPyOpt.methods.BayesianOptimization(f=f, domain=bounds, maximize=True, num_cores = datastore['num_cores']['content'])
print("-----------------------------------")
print("Bayesian Optimization: Now Searching 20 Points")
print("-----------------------------------\n")
BOModel.run_optimization(max_iter=20)
print("-----------------------------------")
print("Bayesian Optimization Converged")
print("-----------------------------------\n")
print("-----------------------------------")
print("Best Hyperparameters Found:\n")
best_l1_drop = BOModel.x_opt[0]
best_l2_drop = BOModel.x_opt[1]
best_l3_drop = BOModel.x_opt[2]
best_l1_out = BOModel.x_opt[3]
best_l2_out = BOModel.x_opt[4]
best_l3_out = BOModel.x_opt[5]
best_batch_size = BOModel.x_opt[6]
best_epochs = BOModel.x_opt[7]
print(f"Layer 1 Drop: {best_l1_drop}")
print(f"Layer 2 Drop: {best_l2_drop}")
print(f"Layer 3 Drop: {best_l3_drop}")
print(f"Layer 1 Neurons: {best_l1_out}")
print(f"Layer 2 Neurons: {best_l2_out}")
print(f"Layer 3 Neurons: {best_l3_out}")
print(f"Batch Size: {best_batch_size}")
print(f"Epochs: {best_epochs}")
print("-----------------------------------\n")
hypdict = {
"l1_drop":best_l1_drop,
"l2_drop":best_l2_drop,
"l3_drop":best_l3_drop,
"l1_out":best_l1_out,
"l2_out":best_l2_out,
"l3_out":best_l3_out,
"batch_size":best_batch_size,
"epochs":best_epochs}
pickle.dump( hypdict, open(current_folder+ "pickled/nn_hyperparameters.p", "wb" ))
else:
start = timer()
print("-----------------------------------")
print("Loading Descriptors")
print("-----------------------------------\n")
dfdict = pickle.load( open( current_folder + "pickled/nn_descriptors.p", "rb" ) )
activityValidDF = dfdict['activityValidDF']
activityTrainDF = dfdict['activityTrainDF']
activityTestDF = dfdict['activityTestDF']
IDValidDF = dfdict['IDValidDF']
IDTrainDF = dfdict['IDTrainDF']
IDTestDF = dfdict['IDTestDF']
validDF = dfdict['validDF']
trainDF = dfdict['trainDF']
testDF = dfdict['testDF']
nameValidDF = dfdict['nameValidDF']
nameTrainDF = dfdict['nameTrainDF']
nameTestDF = dfdict['nameTestDF']
X_Valid = validDF
Y_Valid = activityValidDF
X_Train = trainDF
Y_Train = activityTrainDF
X_Test = testDF
Y_Test = activityTestDF
print("-----------------------------------")
print(f"Loading Optimized Parameters")
print("-----------------------------------\n")
hypdict = pickle.load( open( current_folder + "pickled/nn_hyperparameters.p", "rb" ) )
best_l1_drop = hypdict["l1_drop"]
best_l2_drop = hypdict["l2_drop"]
best_l3_drop = hypdict["l3_drop"]
best_l1_out = hypdict['l1_out']
best_l2_out = hypdict['l2_out']
best_l3_out = hypdict['l3_out']
best_batch_size = hypdict['batch_size']
best_epochs = hypdict['epochs']
print("-----------------------------------")
print(f"Training Neural Network with Optimized Parameters")
print("-----------------------------------\n")
model = Sequential()
model.add(Dense(int(best_l1_out), input_shape=(X_Train.shape[1], ), kernel_initializer = 'uniform'))
model.add(Activation('relu'))
model.add(Dropout(best_l1_drop))
model.add(Dense(int(best_l2_out), activation='relu',
kernel_initializer = 'uniform'))
model.add(Dropout(best_l2_drop))
model.add(Activation('relu'))
model.add(Dense(int(best_l3_out), activation = 'relu', kernel_initializer = 'uniform'))
model.add(Dropout(best_l3_drop))
#model.add(Dropout(self.l3_drop))
#model.add(Dense(1,activation = 'relu'))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error',
optimizer=Adam())
#early_stopping = EarlyStopping(monitor='val_loss', patience=50, verbose=1)
model.fit(X_Train, Y_Train,
batch_size=int(best_batch_size),
epochs=int(best_epochs),
verbose=1)
print("-----------------------------------")
print(f"Testing Neural Network with Optimized Parameters")
print("-----------------------------------\n")
y_pred = model.predict(X_Test)
y_pred_train = model.predict(X_Train)
y_pred_valid = model.predict(X_Valid)
score_test = me.r2_score(Y_Test, y_pred)
score_train = me.r2_score(Y_Train, y_pred_train)
score_valid = me.r2_score(Y_Valid, y_pred_valid)
model.fit(X_Test, Y_Test, batch_size = int(best_batch_size), epochs=int(best_epochs), verbose = 0)
model.save(current_folder + 'pickled/nnmodel.h5')
#pickle.dump(nnmodeldict, open("saved/nnmodel.p", "wb"))
pickle.dump( score_valid, open( current_folder + "pickled/nn_validscore.p", "wb" ) )
print("-----------------------------------")
print(f"Final Results")
print(f"Training R-squared: {score_train}")
print(f"Testing R-squared: {score_test}")
print(f"Validation R-squared: {score_valid}")
print("-----------------------------------\n")
end = timer()
time_taken = end - start
print(f"Time Taken: {time_taken} seconds")
try:
os.remove(current_folder + "tmpSDF.sdf")
except FileNotFoundError:
print("File Not Found")
print("-----------------------------------")
print("Saving Predictions...")
print("-----------------------------------\n")
if datastore['chemID']['content'] == 'NA':
IDboolean = False
else:
IDboolean = True
if IDboolean:
SMILESTest = []
YTestList = []
YTestPredList = []
NAMESList = []
for i in range(0,IDTestDF.shape[0]):
NAMESList.append(nameTestDF.loc[:,].values[i])
SMILESTest.append(IDTestDF.loc[:,].values[i])
YTestList.append(Y_Test.loc[:,].values[i])
YTestPredList.append(y_pred[i][0])
for i in range(0,IDTrainDF.shape[0]):
NAMESList.append(nameTrainDF.loc[:, ].values[i])
SMILESTest.append(IDTrainDF.loc[:,].values[i])
YTestList.append(Y_Train.loc[:,].values[i])
YTestPredList.append(y_pred_train[i][0])
res = pd.DataFrame({'SMILES':SMILESTest, 'Chemical ID': NAMESList, 'Actual':YTestList, 'Prediction':YTestPredList})
SMILESTest = []
YTestList = []
YTestPredList = []
NAMESList = []
for i in range(0,IDValidDF.shape[0]):
NAMESList.append(nameValidDF.loc[:, ].values[i])
SMILESTest.append(IDValidDF.loc[:,].values[i])
YTestList.append(Y_Valid.loc[:,].values[i])
YTestPredList.append(y_pred_valid[i][0])
res_valid = pd.DataFrame({'SMILES':SMILESTest, 'Chemical ID': NAMESList, 'Actual':YTestList, 'Prediction':YTestPredList})
else:
SMILESTest = []
YTestList = []
YTestPredList = []
SMILESValid = []
YValidList = []
YValidPredList = []
for i in range(0,IDTestDF.shape[0]):
SMILESTest.append(IDTestDF.loc[:,].values[i])
YTestList.append(Y_Test.loc[:,].values[i])
YTestPredList.append(y_pred[i][0])
for i in range(0,IDTrainDF.shape[0]):
#NAMESList.append(nameTrainDF.loc[:, ].values[i])
SMILESTest.append(IDTrainDF.loc[:,].values[i])
YTestList.append(Y_Train.loc[:,].values[i])
YTestPredList.append(y_pred_train[i][0])
res = pd.DataFrame({'SMILES':SMILESTest, 'Actual':YTestList, 'Prediction':YTestPredList})
SMILESTest = []
YTestList = []
YTestPredList = []
#NAMESList = []
for i in range(0,IDValidDF.shape[0]):
#NAMESList.append(nameValidDF.loc[:, ].values[i])
SMILESTest.append(IDValidDF.loc[:,].values[i])
YTestList.append(Y_Valid.loc[:,].values[i])
YTestPredList.append(y_pred_valid[i][0])
res_valid = pd.DataFrame({'SMILES':SMILESTest, 'Actual':YTestList, 'Prediction':YTestPredList})
res.to_csv(current_folder + 'predictions/nn_test.csv', sep=',')
res_valid.to_csv(current_folder + 'predictions/nn_valid.csv', sep=',')
print("-----------------------------------")
print("Neural Network Finished!")
print("-----------------------------------\n")
print("-----------------------------------")
print("Time to do visualizations!")
print("-----------------------------------\n")
## df is a dataframe containing the smiles, actual, and prediction
## returns the dataframe containing leverages
def calculate_leverage(df):
actualmean = df['Actual'].mean()
num = df.shape[0]
denom = 0
for i in range(0, num):
denom += (df['Actual'][i] - actualmean) ** 2.
outside=[]
leverage = []
for i in range(0, num):
leverage_i = ((df['Actual'][i] - actualmean)** 2.)/(denom) + (1/num)
leverage.append(leverage_i)
if leverage_i > 0.012:
outside.append('Invalid')
else:
outside.append('Valid')
df.insert(2, "Leverage", leverage, True)
df.insert(2, "Domain", outside, True)
return df
def calculate_residuals(df):
df.insert(2, "Residual", df['Actual']-df['Prediction'], True)
return df
def calculate_standard_residuals(df):
df.insert(2, "Standard Residual", df['Residual']/(df['Residual'].std()), True)
print(df)
domain = []
for i in range(0, df.shape[0]):
if ((df['Residual'][i]/(df['Residual'].std()) > 1.5 ) | (df['Residual'][i]/(df['Residual'].std()) < -1.5)) & (df['Domain'][i] == 'Valid'):
domain.append('Valid')
else:
domain.append('Invalid')
del df['Domain']
df.insert(2, 'Domain', domain, True)
return df
train_plot = calculate_leverage(res)
train_plot = calculate_residuals(train_plot)
train_plot = calculate_standard_residuals(train_plot)
test_plot = calculate_leverage(res_valid)
test_plot = calculate_residuals(test_plot)
test_plot = calculate_standard_residuals(test_plot)
fig, ax = plt.subplots()
ax.scatter(train_plot['Leverage'], train_plot['Residual'], marker='o', c='blue', label = 'Train')
ax.scatter(test_plot['Leverage'], test_plot['Residual'], marker='o', c='red', label = 'Test')
ax.axhline(y=1.5, xmin=0, xmax=3.0, color='k')
ax.axhline(y=-1.5, xmin=0.0, xmax=3.0, color='k')
ax.axvline(x=0.012, ymin=np.min(train_plot['Residual']) - np.min(train_plot['Residual'] * 0.05), ymax=np.max(train_plot['Residual']) + np.max(train_plot['Residual'] * 0.05), color='k')
#ax.set_xlim([0, np.max(train_plot['Leverage']) + np.max(train_plot['Leverage']) * 0.05])
ax.legend()
try:
# Create target Directory
os.mkdir("visualizations")
print("Visualizations Directory Created ")
except FileExistsError:
print("Visualizations Directory already exists. Skipping creation.")
fig.savefig('visualizations/nn' + str(iterator) + '.png')
del(res)
del(res_valid)
del(model)
del(X_Train)
return time_taken, score_train, score_test, score_valid
def runner_nn_c(input_file_loc, iterator = 0):
logging.getLogger('tensorflow').disabled = True
os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"
os.system('export KMP_WARNINGS=FALSE')
warnings.filterwarnings("ignore", category=FutureWarning)
dirName = 'pickled'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists. Skipping creation.")
dirName = 'predictions'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists. Skipping creation.")
# function to run random forest class
def run_nn( X_Train , Y_Train, X_Test, Y_Test, l1_out=512,
l2_out=512,
l1_drop=0.2,
l2_drop=0.2,
l3_out = 512,
l3_drop = 0.2,
batch_size=100,
epochs=10, cv = 7):
_nn = nnc.nnc(cv = cv, l1_out = l1_out, l2_out = l2_out, l3_out = l3_out, l1_drop = l1_drop, l2_drop = l2_drop, l3_drop = l3_drop, X_Train = X_Train, Y_Train = Y_Train, X_Test = X_Test, Y_Test = Y_Test)
nn_evaluation = _nn.nn_evaluate()
return nn_evaluation
if input_file_loc:
with open(input_file_loc, 'r') as f:
datastore = json.load(f)
current_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+'/'+datastore["folder_name"]["content"] +'/'
filename = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+'/'+datastore["folder_name"]["content"] +'/' +datastore["dataset_name"]["content"]
if datastore['saved_hyperparameters?']['content'] == "False":
start = timer()
if datastore['saved_descriptors?']['content'] == "False":
selected_data, IDboolean = prepare.isolate(structname= datastore["column_SMILES"]['content'], activityname = datastore["column_activity"]["content"], filelocation = filename, chemID = datastore["chemID"]["content"])
print("-----------------------------------")
print("Cleaning Data")
print("-----------------------------------\n")
inDF = prepare.cleanSMILES(df = selected_data, elementskept = datastore["elements_kept"]["content"], smilesName = datastore["column_SMILES"]["content"])
print("-----------------------------------")
print("Curating Descriptors")
print("-----------------------------------\n")
print(f"Number of Compounds: {inDF.shape[0]}")
inDF = prepare.createdescriptors(df = inDF, colName = datastore["column_SMILES"]['content'], correlationthreshold = datastore["correlation_threshold"]['content'], STDthreshold = datastore['std_threshold']['content'], IDboolean = IDboolean)
activityValidDF, activityTrainDF, activityTestDF, IDValidDF, IDTrainDF, IDTestDF, validDF, trainDF, testDF, nameValidDF, nameTrainDF, nameTestDF, nn_cols = prepare.partition(df = inDF,validset = datastore['valid_split']['content'], testset = datastore['test_split']['content'], IDboolean = IDboolean)
print("-----------------------------------")
print("Partitioning Data")
print("-----------------------------------\n")
dfdict = {
"activityValidDF": activityValidDF,
"activityTrainDF": activityTrainDF,
"activityTestDF": activityTestDF,
"IDValidDF":IDValidDF,
"IDTrainDF":IDTrainDF,
"IDTestDF":IDTestDF,
"validDF":validDF,
"trainDF":trainDF,
"testDF":testDF,
"nameValidDF":nameValidDF,
"nameTrainDF":nameTrainDF,
"nameTestDF":nameTestDF,
"nn_cols":nn_cols
}
pickle.dump( dfdict, open( current_folder+ "pickled/nn_descriptors.p", "wb" ) )
else:
print("-----------------------------------")
print("Loading Descriptors")
print("-----------------------------------\n")
dfdict = pickle.load( open( current_folder + "pickled/nn_descriptors.p", "rb" ) )
activityValidDF = dfdict['activityValidDF']
activityTrainDF = dfdict['activityTrainDF']
activityTestDF = dfdict['activityTestDF']
IDValidDF = dfdict['IDValidDF']
IDTrainDF = dfdict['IDTrainDF']
IDTestDF = dfdict['IDTestDF']
validDF = dfdict['validDF']
trainDF = dfdict['trainDF']
testDF = dfdict['testDF']
nameValidDF = dfdict['nameValidDF']
nameTrainDF = dfdict['nameTrainDF']
nameTestDF = dfdict['nameTestDF']
X_Valid = validDF
Y_Valid = activityValidDF
X_Train = trainDF
Y_Train = activityTrainDF
X_Test = testDF
Y_Test = activityTestDF
orilen = X_Train.shape[1]
bounds = [
{'name': 'l1_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
{'name': 'l2_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
{'name':'l3_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
{'name': 'l1_out', 'type': 'discrete', 'domain': (orilen/4, orilen/3, orilen/2, orilen, orilen*2, orilen*3,orilen*4)},
{'name': 'l2_out', 'type': 'discrete', 'domain': ( orilen/4,orilen/3, orilen/2, orilen, orilen*2, orilen*3,orilen*4)},
{'name': 'l3_out', 'type': 'discrete', 'domain': ( orilen/4,orilen/3, orilen/2, orilen, orilen*2,orilen*3, orilen*4)},
{'name': 'batch_size', 'type': 'discrete', 'domain': (200,300, 400,500, 600, 700)},
{'name': 'epochs', 'type': 'discrete', 'domain': (300, 350, 400, 450, 500, 600, 700)}]
def f(x):
evaluation = run_nn(
l1_drop = float(x[:,0]),
l2_drop = float(x[:,1]),
l3_drop = float(x[:, 2]),
l1_out = int(x[:,3]),
l2_out = int(x[:,4]),
l3_out = int(x[:, 5]),
batch_size = int(x[:,6]),
epochs = int(x[:,7]), X_Train = X_Train, Y_Train = Y_Train ,X_Test = X_Test, Y_Test =Y_Test, cv = datastore['cvfolds?']['content'])
return evaluation
print("-----------------------------------")
print("Bayesian Optimization Initiated: First Picking 5 Random Sample Points")
print("-----------------------------------\n")
BOModel = GPyOpt.methods.BayesianOptimization(f=f, domain=bounds, maximize=True, num_cores = datastore['num_cores']['content'])
print("-----------------------------------")
print("Bayesian Optimization: Now Searching 20 Points")
print("-----------------------------------\n")
BOModel.run_optimization(max_iter=20)
print("-----------------------------------")
print("Bayesian Optimization Converged")
print("-----------------------------------\n")
print("-----------------------------------")
print("Best Hyperparameters Found:\n")
best_l1_drop = BOModel.x_opt[0]
best_l2_drop = BOModel.x_opt[1]
best_l3_drop = BOModel.x_opt[2]
best_l1_out = BOModel.x_opt[3]
best_l2_out = BOModel.x_opt[4]
best_l3_out = BOModel.x_opt[5]
best_batch_size = BOModel.x_opt[6]
best_epochs = BOModel.x_opt[7]
print(f"Layer 1 Drop: {best_l1_drop}")
print(f"Layer 2 Drop: {best_l2_drop}")
print(f"Layer 3 Drop: {best_l3_drop}")
print(f"Layer 1 Neurons: {best_l1_out}")
print(f"Layer 2 Neurons: {best_l2_out}")
print(f"Layer 3 Neurons: {best_l3_out}")
print(f"Batch Size: {best_batch_size}")
print(f"Epochs: {best_epochs}")
print("-----------------------------------\n")
hypdict = {
"l1_drop":best_l1_drop,
"l2_drop":best_l2_drop,
"l3_drop":best_l3_drop,
"l1_out":best_l1_out,
"l2_out":best_l2_out,
"l3_out":best_l3_out,
"batch_size":best_batch_size,
"epochs":best_epochs}
pickle.dump( hypdict, open(current_folder+ "pickled/nn_hyperparameters.p", "wb" ))
else:
start = timer()
print("-----------------------------------")
print("Loading Descriptors")
print("-----------------------------------\n")
dfdict = pickle.load( open( current_folder + "pickled/nn_descriptors.p", "rb" ) )
activityValidDF = dfdict['activityValidDF']
activityTrainDF = dfdict['activityTrainDF']
activityTestDF = dfdict['activityTestDF']
IDValidDF = dfdict['IDValidDF']
IDTrainDF = dfdict['IDTrainDF']
IDTestDF = dfdict['IDTestDF']
validDF = dfdict['validDF']
trainDF = dfdict['trainDF']
testDF = dfdict['testDF']
nameValidDF = dfdict['nameValidDF']
nameTrainDF = dfdict['nameTrainDF']
nameTestDF = dfdict['nameTestDF']
X_Valid = validDF
Y_Valid = activityValidDF
X_Train = trainDF
Y_Train = activityTrainDF
X_Test = testDF
Y_Test = activityTestDF
print("-----------------------------------")
print(f"Loading Optimized Parameters")
print("-----------------------------------\n")
hypdict = pickle.load( open( current_folder + "pickled/nn_hyperparameters.p", "rb" ) )
best_l1_drop = hypdict["l1_drop"]
best_l2_drop = hypdict["l2_drop"]
best_l3_drop = hypdict["l3_drop"]
best_l1_out = hypdict['l1_out']
best_l2_out = hypdict['l2_out']
best_l3_out = hypdict['l3_out']
best_batch_size = hypdict['batch_size']
best_epochs = hypdict['epochs']
print("-----------------------------------")
print(f"Training Neural Network with Optimized Parameters")
print("-----------------------------------\n")
model = Sequential()
model.add(Dense(int(best_l1_out), input_shape=(X_Train.shape[1], ), kernel_initializer = 'uniform'))
model.add(Activation('relu'))
model.add(Dropout(best_l1_drop))
model.add(Dense(int(best_l2_out), activation='relu',
kernel_initializer = 'uniform'))
model.add(Dropout(best_l2_drop))
model.add(Activation('relu'))
model.add(Dense(int(best_l3_out), activation = 'sigmoid', kernel_initializer = 'uniform'))
model.add(Dropout(best_l3_drop))
#model.add(Dropout(self.l3_drop))
model.add(Dense(1,activation = 'sigmoid'))
model.compile(loss='mean_squared_error',
optimizer=Adam(), metrics = ['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss', patience=50, verbose=1)
model.fit(X_Train, Y_Train,
batch_size=int(best_batch_size),
epochs=int(best_epochs),
verbose=1)
print("-----------------------------------")
print(f"Testing Neural Network with Optimized Parameters")
print("-----------------------------------\n")
y_pred = np.round(model.predict(X_Test))
y_pred_train = np.round(model.predict(X_Train))
y_pred_valid = np.round(model.predict(X_Valid))
score_test = me.r2_score(Y_Test, y_pred)
score_train = me.r2_score(Y_Train, y_pred_train)
score_valid = me.r2_score(Y_Valid, y_pred_valid)
model.fit(X_Test, Y_Test, batch_size = int(best_batch_size), epochs=int(best_epochs), verbose = 0)
model.save(current_folder + 'pickled/nnmodel.h5')
#pickle.dump(nnmodeldict, open("saved/nnmodel.p", "wb"))
pickle.dump( score_valid, open( current_folder + "pickled/nn_validscore.p", "wb" ) )
print("-----------------------------------")
print(f"Final Results")
print(f"Training R-squared: {score_train}")
print(f"Testing R-squared: {score_test}")
print(f"Validation R-squared: {score_valid}")
print("-----------------------------------\n")
end = timer()
time_taken = end - start
print(f"Time Taken: {time_taken} seconds")
try:
os.remove(current_folder + "tmpSDF.sdf")
except FileNotFoundError:
print("File Not Found")
print("-----------------------------------")
print("Saving Predictions...")
print("-----------------------------------\n")
if datastore['chemID']['content'] == 'NA':
IDboolean = False
else:
IDboolean = True
if IDboolean:
SMILESTest = []
YTestList = []
YTestPredList = []
NAMESList = []
for i in range(0,IDTestDF.shape[0]):
NAMESList.append(nameTestDF.loc[:,].values[i])
SMILESTest.append(IDTestDF.loc[:,].values[i])
YTestList.append(Y_Test.loc[:,].values[i])
YTestPredList.append(y_pred[i][0])
for i in range(0,IDTrainDF.shape[0]):
NAMESList.append(nameTrainDF.loc[:, ].values[i])
SMILESTest.append(IDTrainDF.loc[:,].values[i])
YTestList.append(Y_Train.loc[:,].values[i])
YTestPredList.append(y_pred_train[i][0])
res = pd.DataFrame({'SMILES':SMILESTest, 'Chemical ID': NAMESList, 'Actual':YTestList, 'Prediction':YTestPredList})
SMILESTest = []
YTestList = []
YTestPredList = []
NAMESList = []
for i in range(0,IDValidDF.shape[0]):
NAMESList.append(nameValidDF.loc[:, ].values[i])
SMILESTest.append(IDValidDF.loc[:,].values[i])
YTestList.append(Y_Valid.loc[:,].values[i])
YTestPredList.append(y_pred_valid[i][0])
res_valid = pd.DataFrame({'SMILES':SMILESTest, 'Chemical ID': NAMESList, 'Actual':YTestList, 'Prediction':YTestPredList})
else:
SMILESTest = []
YTestList = []
YTestPredList = []
SMILESValid = []
YValidList = []
YValidPredList = []
for i in range(0,IDTestDF.shape[0]):
SMILESTest.append(IDTestDF.loc[:,].values[i])
YTestList.append(Y_Test.loc[:,].values[i])
YTestPredList.append(y_pred[i][0])
for i in range(0,IDTrainDF.shape[0]):
#NAMESList.append(nameTrainDF.loc[:, ].values[i])
SMILESTest.append(IDTrainDF.loc[:,].values[i])
YTestList.append(Y_Train.loc[:,].values[i])
YTestPredList.append(y_pred_train[i][0])
res = pd.DataFrame({'SMILES':SMILESTest, 'Actual':YTestList, 'Prediction':YTestPredList})
SMILESTest = []
YTestList = []
YTestPredList = []
#NAMESList = []
for i in range(0,IDValidDF.shape[0]):
#NAMESList.append(nameValidDF.loc[:, ].values[i])
SMILESTest.append(IDValidDF.loc[:,].values[i])
YTestList.append(Y_Valid.loc[:,].values[i])
YTestPredList.append(y_pred_valid[i][0])
res_valid = pd.DataFrame({'SMILES':SMILESTest, 'Actual':YTestList, 'Prediction':YTestPredList})
res.to_csv(current_folder + 'predictions/nn_test.csv', sep=',')
res_valid.to_csv(current_folder + 'predictions/nn_valid.csv', sep=',')
print("-----------------------------------")
print("Neural Network Finished!")
print("-----------------------------------\n")
print("-----------------------------------")
print("Time to do visualizations!")
print("-----------------------------------\n")
## df is a dataframe containing the smiles, actual, and prediction
## returns the dataframe containing leverages
def calculate_leverage(df):
actualmean = df['Actual'].mean()
num = df.shape[0]
denom = 0
for i in range(0, num):
denom += (df['Actual'][i] - actualmean) ** 2.
outside=[]
leverage = []
for i in range(0, num):
leverage_i = ((df['Actual'][i] - actualmean)** 2.)/(denom) + (1/num)
leverage.append(leverage_i)
if leverage_i > 0.012:
outside.append('Invalid')
else:
outside.append('Valid')
df.insert(2, "Leverage", leverage, True)
df.insert(2, "Domain", outside, True)
return df
def calculate_residuals(df):
df.insert(2, "Residual", df['Actual']-df['Prediction'], True)
return df
def calculate_standard_residuals(df):
df.insert(2, "Standard Residual", df['Residual']/(df['Residual'].std()), True)
print(df)
domain = []
for i in range(0, df.shape[0]):
if ((df['Residual'][i]/(df['Residual'].std()) > 1.5 ) | (df['Residual'][i]/(df['Residual'].std()) < -1.5)) & (df['Domain'][i] == 'Valid'):
domain.append('Valid')
else:
domain.append('Invalid')
del df['Domain']
df.insert(2, 'Domain', domain, True)
return df
train_plot = calculate_leverage(res)
train_plot = calculate_residuals(train_plot)
train_plot = calculate_standard_residuals(train_plot)
test_plot = calculate_leverage(res_valid)
test_plot = calculate_residuals(test_plot)
test_plot = calculate_standard_residuals(test_plot)
fig, ax = plt.subplots()
ax.scatter(train_plot['Leverage'], train_plot['Residual'], marker='o', c='blue', label = 'Train')
ax.scatter(test_plot['Leverage'], test_plot['Residual'], marker='o', c='red', label = 'Test')
ax.axhline(y=1.5, xmin=0, xmax=3.0, color='k')
ax.axhline(y=-1.5, xmin=0.0, xmax=3.0, color='k')
ax.axvline(x=0.012, ymin=np.min(train_plot['Residual']) - np.min(train_plot['Residual'] * 0.05), ymax=np.max(train_plot['Residual']) + np.max(train_plot['Residual'] * 0.05), color='k')
#ax.set_xlim([0, np.max(train_plot['Leverage']) + np.max(train_plot['Leverage']) * 0.05])
ax.legend()
try:
# Create target Directory
os.mkdir("visualizations")
print("Visualizations Directory Created ")
except FileExistsError:
print("Visualizations Directory already exists. Skipping creation.")
fig.savefig('visualizations/nn' + str(iterator) + '.png')
del(res)
del(res_valid)
del(model)
del(X_Train)
return time_taken, score_train, score_test, score_valid
# def runner_nn_c(input_file_loc):
# logging.getLogger('tensorflow').disabled = True
# os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"
# os.system('export KMP_WARNINGS=FALSE')
# warnings.filterwarnings("ignore", category=FutureWarning)
# dirName = 'pickled'
# try:
# # Create target Directory
# os.mkdir(dirName)
# print("Directory " , dirName , " Created ")
# except FileExistsError:
# print("Directory " , dirName , " already exists. Skipping creation.")
# dirName = 'predictions'
# try:
# # Create target Directory
# os.mkdir(dirName)
# print("Directory " , dirName , " Created ")
# except FileExistsError:
# print("Directory " , dirName , " already exists. Skipping creation.")
# # function to run random forest class
# def run_nn( X_Train , Y_Train, X_Test, Y_Test, l1_out=512,
# l2_out=512,
# l1_drop=0.2,
# l2_drop=0.2,
# l3_out = 512,
# l3_drop = 0.2,
# batch_size=100,
# epochs=10, cv = 7):
# _nn = nnc.nnc(cv = cv, l1_out = l1_out, l2_out = l2_out, l3_out = l3_out, l1_drop = l1_drop, l2_drop = l2_drop, l3_drop = l3_drop, X_Train = X_Train, Y_Train = Y_Train, X_Test = X_Test, Y_Test = Y_Test)
# nn_evaluation = _nn.nn_evaluate()
# return nn_evaluation
# if input_file_loc:
# with open(input_file_loc, 'r') as f:
# datastore = json.load(f)
# current_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+'/'+datastore["folder_name"]["content"] +'/'
# filename = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+'/'+datastore["folder_name"]["content"] +'/' +datastore["dataset_name"]["content"]
# if datastore['saved_hyperparameters?']['content'] == "False":
# start = timer()
# if datastore['saved_descriptors?']['content'] == "False":
# selected_data, IDboolean = prepare.isolate(structname= datastore["column_SMILES"]['content'], activityname = datastore["column_activity"]["content"], filelocation = filename, chemID = datastore["chemID"]["content"])
# print("-----------------------------------")
# print("Cleaning Data")
# print("-----------------------------------\n")
# inDF = prepare.cleanSMILES(df = selected_data, elementskept = datastore["elements_kept"]["content"], smilesName = datastore["column_SMILES"]["content"])
# print("-----------------------------------")
# print("Curating Descriptors")
# print("-----------------------------------\n")
# print(f"Number of Compounds: {inDF.shape[0]}")
# inDF = prepare.createdescriptors(df = inDF, colName = datastore["column_SMILES"]['content'], correlationthreshold = datastore["correlation_threshold"]['content'], STDthreshold = datastore['std_threshold']['content'], IDboolean = IDboolean)
# activityValidDF, activityTrainDF, activityTestDF, IDValidDF, IDTrainDF, IDTestDF, validDF, trainDF, testDF, nameValidDF, nameTrainDF, nameTestDF = prepare.partition(df = inDF,validset = datastore['valid_split']['content'], testset = datastore['test_split']['content'], IDboolean = IDboolean)
# print("-----------------------------------")
# print("Partitioning Data")
# print("-----------------------------------\n")
# dfdict = {
# "activityValidDF": activityValidDF,
# "activityTrainDF": activityTrainDF,
# "activityTestDF": activityTestDF,
# "IDValidDF":IDValidDF,
# "IDTrainDF":IDTrainDF,
# "IDTestDF":IDTestDF,
# "validDF":validDF,
# "trainDF":trainDF,
# "testDF":testDF,
# "nameValidDF":nameValidDF,
# "nameTrainDF":nameTrainDF,
# "nameTestDF":nameTestDF
# }
# pickle.dump( dfdict, open( current_folder+ "pickled/nn_descriptors.p", "wb" ) )
# else:
# print("-----------------------------------")
# print("Loading Descriptors")
# print("-----------------------------------\n")
# dfdict = pickle.load( open( current_folder + "pickled/nn_descriptors.p", "rb" ) )
# activityValidDF = dfdict['activityValidDF']
# activityTrainDF = dfdict['activityTrainDF']
# activityTestDF = dfdict['activityTestDF']
# IDValidDF = dfdict['IDValidDF']
# IDTrainDF = dfdict['IDTrainDF']
# IDTestDF = dfdict['IDTestDF']
# validDF = dfdict['validDF']
# trainDF = dfdict['trainDF']
# testDF = dfdict['testDF']
# nameValidDF = dfdict['nameValidDF']
# nameTrainDF = dfdict['nameTrainDF']
# nameTestDF = dfdict['nameTestDF']
# X_Valid = validDF
# Y_Valid = activityValidDF
# X_Train = trainDF
# Y_Train = activityTrainDF
# X_Test = testDF
# Y_Test = activityTestDF
# orilen = X_Train.shape[1]
# bounds = [
# {'name': 'l1_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
# {'name': 'l2_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
# {'name':'l3_drop', 'type': 'continuous', 'domain': (0.0, 0.3)},
# {'name': 'l1_out', 'type': 'discrete', 'domain': (orilen/4, orilen/3, orilen/2, orilen, orilen*2, orilen*3,orilen*4)},
# {'name': 'l2_out', 'type': 'discrete', 'domain': ( orilen/4,orilen/3, orilen/2, orilen, orilen*2, orilen*3,orilen*4)},
# {'name': 'l3_out', 'type': 'discrete', 'domain': ( orilen/4,orilen/3, orilen/2, orilen, orilen*2,orilen*3, orilen*4)},
# {'name': 'batch_size', 'type': 'discrete', 'domain': (200,300, 400,500, 600, 700)},
# {'name': 'epochs', 'type': 'discrete', 'domain': (300, 350, 400, 450, 500, 600, 700)}]
# def f(x):
# evaluation = run_nn(
# l1_drop = float(x[:,0]),
# l2_drop = float(x[:,1]),
# l3_drop = float(x[:, 2]),
# l1_out = int(x[:,3]),
# l2_out = int(x[:,4]),
# l3_out = int(x[:, 5]),
# batch_size = int(x[:,6]),
# epochs = int(x[:,7]), X_Train = X_Train, Y_Train = Y_Train ,X_Test = X_Test, Y_Test =Y_Test, cv = datastore['cvfolds?']['content'])
# return evaluation
# print("-----------------------------------")
# print("Bayesian Optimization Initiated: First Picking 5 Random Sample Points")
# print("-----------------------------------\n")
# BOModel = GPyOpt.methods.BayesianOptimization(f=f, domain=bounds, maximize=True, num_cores = datastore['num_cores']['content'])
# print("-----------------------------------")
# print("Bayesian Optimization: Now Searching 20 Points")
# print("-----------------------------------\n")
# BOModel.run_optimization(max_iter=20)
# print("-----------------------------------")
# print("Bayesian Optimization Converged")
# print("-----------------------------------\n")
# print("-----------------------------------")
# print("Best Hyperparameters Found:\n")
# best_l1_drop = BOModel.x_opt[0]
# best_l2_drop = BOModel.x_opt[1]
# best_l3_drop = BOModel.x_opt[2]
# best_l1_out = BOModel.x_opt[3]
# best_l2_out = BOModel.x_opt[4]
# best_l3_out = BOModel.x_opt[5]
# best_batch_size = BOModel.x_opt[6]
# best_epochs = BOModel.x_opt[7]
# print(f"Layer 1 Drop: {best_l1_drop}")
# print(f"Layer 2 Drop: {best_l2_drop}")
# print(f"Layer 3 Drop: {best_l3_drop}")
# print(f"Layer 1 Neurons: {best_l1_out}")
# print(f"Layer 2 Neurons: {best_l2_out}")
# print(f"Layer 3 Neurons: {best_l3_out}")
# print(f"Batch Size: {best_batch_size}")
# print(f"Epochs: {best_epochs}")
# print("-----------------------------------\n")
# hypdict = {
# "l1_drop":best_l1_drop,
# "l2_drop":best_l2_drop,
# "l3_drop":best_l3_drop,
# "l1_out":best_l1_out,
# "l2_out":best_l2_out,
# "l3_out":best_l3_out,
# "batch_size":best_batch_size,
# "epochs":best_epochs}
# pickle.dump( hypdict, open(current_folder+ "pickled/nn_hyperparameters.p", "wb" ))
# else:
# start = timer()
# print("-----------------------------------")
# print("Loading Descriptors")
# print("-----------------------------------\n")
# dfdict = pickle.load( open( current_folder + "pickled/nn_descriptors.p", "rb" ) )
# activityValidDF = dfdict['activityValidDF']
# activityTrainDF = dfdict['activityTrainDF']
# activityTestDF = dfdict['activityTestDF']
# IDValidDF = dfdict['IDValidDF']
# IDTrainDF = dfdict['IDTrainDF']
# IDTestDF = dfdict['IDTestDF']
# validDF = dfdict['validDF']
# trainDF = dfdict['trainDF']
# testDF = dfdict['testDF']
# nameValidDF = dfdict['nameValidDF']
# nameTrainDF = dfdict['nameTrainDF']
# nameTestDF = dfdict['nameTestDF']
# X_Valid = validDF
# Y_Valid = activityValidDF
# X_Train = trainDF
# Y_Train = activityTrainDF
# X_Test = testDF
# Y_Test = activityTestDF
# print("-----------------------------------")
# print(f"Loading Optimized Parameters")
# print("-----------------------------------\n")
# hypdict = pickle.load( open( current_folder + "pickled/nn_hyperparameters.p", "rb" ) )
# best_l1_drop = hypdict["l1_drop"]
# best_l2_drop = hypdict["l2_drop"]
# best_l3_drop = hypdict["l3_drop"]
# best_l1_out = hypdict['l1_out']
# best_l2_out = hypdict['l2_out']
# best_l3_out = hypdict['l3_out']
# best_batch_size = hypdict['batch_size']
# best_epochs = hypdict['epochs']
# print("-----------------------------------")
# print(f"Training Neural Network with Optimized Parameters")
# print("-----------------------------------\n")
# model = Sequential()
# model.add(Dense(int(best_l1_out), input_shape=(X_Train.shape[1], ), kernel_initializer = 'uniform'))
# model.add(Activation('relu'))
# model.add(Dropout(best_l1_drop))
# model.add(Dense(int(best_l2_out), activation='relu',
# kernel_initializer = 'uniform'))
# model.add(Dropout(best_l2_drop))
# model.add(Activation('relu'))
# #model.add(Dense(int(best_l3_out), activation = 'sigmoid', kernel_initializer = 'uniform'))
# #model.add(Dropout(best_l3_drop))
# #model.add(Dropout(self.l3_drop))
# model.add(Dense(1,activation = 'sigmoid'))
# model.compile(loss='mean_squared_error',
# optimizer=Adam(), metrics = ['accuracy'])
# #early_stopping = EarlyStopping(monitor='val_loss', patience=50, verbose=1)
# model.fit(X_Train, Y_Train,
# batch_size=int(best_batch_size),
# epochs=int(best_epochs),
# verbose=1)
# print("-----------------------------------")
# print(f"Testing Neural Network with Optimized Parameters")
# print("-----------------------------------\n")
# y_pred = model.predict(X_Test)
# y_pred_train = model.predict(X_Train)
# y_pred_valid = model.predict(X_Valid)
# score_test = me.r2_score(Y_Test, y_pred)
# score_train = me.r2_score(Y_Train, y_pred_train)
# score_valid = me.r2_score(Y_Valid, y_pred_valid)
# model.fit(X_Test, Y_Test, batch_size = int(best_batch_size), epochs=int(best_epochs), verbose = 0)
# model.save(current_folder + 'pickled/nnmodel.h5')
# #pickle.dump(nnmodeldict, open("saved/nnmodel.p", "wb"))
# pickle.dump( score_valid, open( current_folder + "pickled/nn_validscore.p", "wb" ) )
# print("-----------------------------------")
# print(f"Final Results")
# print(f"Training R-squared: {score_train}")
# print(f"Testing R-squared: {score_test}")
# print(f"Validation R-squared: {score_valid}")
# print("-----------------------------------\n")
# end = timer()
# time_taken = end - start
# print(f"Time Taken: {time_taken} seconds")
# try:
# os.remove(current_folder + "tmpSDF.sdf")
# except FileNotFoundError:
# print("File Not Found")
# print("-----------------------------------")
# print("Saving Predictions...")
# print("-----------------------------------\n")
# if datastore['chemID']['content'] == 'NA':
# IDboolean = False
# else:
# IDboolean = True
# if IDboolean:
# SMILESTest = []
# YTestList = []
# YTestPredList = []
# NAMESList = []
# for i in range(0,IDTestDF.shape[0]):
# NAMESList.append(nameTestDF.loc[:,].values[i])
# SMILESTest.append(IDTestDF.loc[:,].values[i])
# YTestList.append(Y_Test.loc[:,].values[i])
# YTestPredList.append(y_pred[i][0])
# for i in range(0,IDTrainDF.shape[0]):
# NAMESList.append(nameTrainDF.loc[:, ].values[i])
# SMILESTest.append(IDTrainDF.loc[:,].values[i])
# YTestList.append(Y_Train.loc[:,].values[i])
# YTestPredList.append(y_pred_train[i][0])
# res = pd.DataFrame({'SMILES':SMILESTest, 'Chemical ID': NAMESList, 'Actual':YTestList, 'Prediction':YTestPredList})
# SMILESTest = []
# YTestList = []
# YTestPredList = []
# NAMESList = []
# for i in range(0,IDValidDF.shape[0]):
# NAMESList.append(nameValidDF.loc[:, ].values[i])
# SMILESTest.append(IDValidDF.loc[:,].values[i])
# YTestList.append(Y_Valid.loc[:,].values[i])
# YTestPredList.append(y_pred_valid[i][0])
# res_valid = pd.DataFrame({'SMILES':SMILESTest, 'Chemical ID': NAMESList, 'Actual':YTestList, 'Prediction':YTestPredList})
# else:
# SMILESTest = []
# YTestList = []
# YTestPredList = []
# SMILESValid = []
# YValidList = []
# YValidPredList = []
# for i in range(0,IDTestDF.shape[0]):
# SMILESTest.append(IDTestDF.loc[:,].values[i])
# YTestList.append(Y_Test.loc[:,].values[i])
# YTestPredList.append(y_pred[i][0])
# for i in range(0,IDTrainDF.shape[0]):
# #NAMESList.append(nameTrainDF.loc[:, ].values[i])
# SMILESTest.append(IDTrainDF.loc[:,].values[i])
# YTestList.append(Y_Train.loc[:,].values[i])
# YTestPredList.append(y_pred_train[i][0])
# res = pd.DataFrame({'SMILES':SMILESTest, 'Actual':YTestList, 'Prediction':YTestPredList})
# SMILESTest = []
# YTestList = []
# YTestPredList = []
# #NAMESList = []
# for i in range(0,IDValidDF.shape[0]):
# #NAMESList.append(nameValidDF.loc[:, ].values[i])
# SMILESTest.append(IDValidDF.loc[:,].values[i])
# YTestList.append(Y_Valid.loc[:,].values[i])
# YTestPredList.append(y_pred_valid[i][0])
# res_valid = pd.DataFrame({'SMILES':SMILESTest, 'Actual':YTestList, 'Prediction':YTestPredList})
# res.to_csv(current_folder + 'predictions/nn_test.csv', sep=',')
# res_valid.to_csv(current_folder + 'predictions/nn_valid.csv', sep=',')
# print("-----------------------------------")
# print("Neural Network Finished!")
# print("-----------------------------------\n")
# del(res)
# del(res_valid)
# del(model)
# del(X_Train)
# return time_taken, score_train, score_test, score_valid
| 40.630137
| 304
| 0.624515
| 5,733
| 47,456
| 4.99529
| 0.064364
| 0.010685
| 0.017809
| 0.009219
| 0.971367
| 0.97004
| 0.97004
| 0.968119
| 0.966548
| 0.966548
| 0
| 0.018949
| 0.147063
| 47,456
| 1,167
| 305
| 40.664953
| 0.688564
| 0.31309
| 0
| 0.933514
| 0
| 0
| 0.255884
| 0.08639
| 0
| 0
| 0
| 0
| 0
| 1
| 0.016282
| false
| 0
| 0.033921
| 0
| 0.066486
| 0.187246
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b1eb83572dde2d35de5e7db96fb7803ba999c1c6
| 1,933
|
py
|
Python
|
test/test_adapted_fixed_window.py
|
Jeilef/qrs_compare
|
092132e6febc9aa5557e0e70bced5febfac2a235
|
[
"MIT"
] | 1
|
2021-03-10T15:12:10.000Z
|
2021-03-10T15:12:10.000Z
|
test/test_adapted_fixed_window.py
|
Jeilef/qrs_compare
|
092132e6febc9aa5557e0e70bced5febfac2a235
|
[
"MIT"
] | 6
|
2021-03-31T20:11:06.000Z
|
2022-01-13T03:24:32.000Z
|
test/test_adapted_fixed_window.py
|
Jeilef/qrs_compare
|
092132e6febc9aa5557e0e70bced5febfac2a235
|
[
"MIT"
] | null | null | null |
import unittest
from metrics.adapted_fixed_window_metric import AdaptedFixedWindow
class TestFixedWindowMetric(unittest.TestCase):
def test_perfect_prediction_no_tol(self):
hw = AdaptedFixedWindow()
tp, fp, tn, fn = hw.match_classification_annotations([10], ["n"], [10], 1)
self.assertEqual(1, tp)
self.assertEqual(0, fp)
self.assertEqual(1, tn)
self.assertEqual(0, fn)
def test_no_prediction_no_tol(self):
hw = AdaptedFixedWindow()
tp, fp, tn, fn = hw.match_classification_annotations([10], ["n"], [], 1)
self.assertEqual(0, tp)
self.assertEqual(0, fp)
self.assertEqual(1, tn)
self.assertEqual(1, fn)
def test_two_before_and_after_false_prediction_no_tol(self):
hw = AdaptedFixedWindow()
tp, fp, tn, fn = hw.match_classification_annotations([10], ["n"], [5, 15], 1)
self.assertEqual(0, tp)
self.assertEqual(1, fp)
self.assertEqual(0, tn)
self.assertEqual(1, fn)
def test_one_before_false_prediction_no_tol(self):
hw = AdaptedFixedWindow()
tp, fp, tn, fn = hw.match_classification_annotations([10], ["n"], [5], 1)
self.assertEqual(0, tp)
self.assertEqual(1, fp)
self.assertEqual(0, tn)
self.assertEqual(1, fn)
def test_one_after_false_prediction_no_tol(self):
hw = AdaptedFixedWindow()
tp, fp, tn, fn = hw.match_classification_annotations([10], ["n"], [15], 1)
self.assertEqual(0, tp)
self.assertEqual(1, fp)
self.assertEqual(0, tn)
self.assertEqual(1, fn)
def test_two_after_true_false_prediction_no_tol(self):
hw = AdaptedFixedWindow()
tp, fp, tn, fn = hw.match_classification_annotations([10], ["n"], [11, 15], 2)
self.assertEqual(1, tp)
self.assertEqual(1, fp)
self.assertEqual(0, tn)
self.assertEqual(0, fn)
| 35.796296
| 86
| 0.633213
| 248
| 1,933
| 4.733871
| 0.173387
| 0.306644
| 0.163543
| 0.097104
| 0.855196
| 0.850085
| 0.822828
| 0.808348
| 0.794719
| 0.794719
| 0
| 0.03671
| 0.239007
| 1,933
| 53
| 87
| 36.471698
| 0.761387
| 0
| 0
| 0.666667
| 0
| 0
| 0.003104
| 0
| 0
| 0
| 0
| 0
| 0.533333
| 1
| 0.133333
| false
| 0
| 0.044444
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
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