import requests as r import time import urllib.parse ### This is a sample script to testing most of the annotated 990 API endpoints. ### The test loop only tries each parameter separately, but you can combine as many as you want ### to create narrower datasets. API will return EINs that match all criteria provided. ### See API reference sheet for more details ### https://docs.google.com/spreadsheets/d/1bXXKZ7kJ8UsoAVAfk8sH7yYSVB6xo8OLl1OGmSpSZ4k/edit?gid=1284351522#gid=1284351522 endpoint = 'https://givingtuesday-annotated-990.hf.space/eins/' key = "" # see your email for the api key, if you are invited to join the testing group nonbinary_fields = ['taxyear', 'list_ein_zipcodes'] # TAXYEAR MUST BE 2023 less_than_fields = ['acs_fraction_poverty', 'acs_fraction_snap', 'acs_fraction_insured', 'acs_median_income', 'acs_fraction_hs', 'acs_fraction_vacant', 'acs_dep_index', 'mapagora_n', 'mapagora_civic_org_sum', 'mapagora_civic_opp_sum', 'mapagora_volunteer_sum', 'mapagora_membership_sum', 'mapagora_take_action_sum', 'mapagora_events_sum', 'mapagora_TotalPopulation', 'mapagora_BROAD', 'mapagora_REMNRTY', 'mapagora_POV150', 'mapagora_NOHSDP', 'mapagora_SNGPNT', 'mapagora_UNEMP', 'mapagora_civic_org_sum_normalized', 'mapagora_civic_opp_sum_normalized', 'mapagora_civic_opp_index', 'mapagora_volunteer_sum_normalized', 'mapagora_membership_sum_normalized', 'mapagora_take_action_sum_normalized', 'mapagora_events_sum_normalized', 'staff_gender_male_fraction', 'staff_gender_female_fraction', ] greater_than_fields = [ 'cause_area_arts_culture_humanities', 'cause_area_education', 'cause_area_universities', 'cause_area_environment', 'cause_area_animal_related', 'cause_area_healthcare', 'cause_area_hospitals', 'cause_area_mental_health_crisis_intervention', 'cause_area_voluntary_health_associations_medical_disciplines', 'cause_area_medical_research', 'cause_area_crime_legal_related', 'cause_area_employment', 'cause_area_food_agriculture_nutrition', 'cause_area_housing_shelter', 'cause_area_public_safety_disaster_preparedness_relief', 'cause_area_recreation_sports', 'cause_area_youth_development', 'cause_area_human_services', 'cause_area_international_foreign_affairs_national_security', 'cause_area_civil_rights_social_action_advocacy', 'cause_area_community_improvement_capacity_building', 'cause_area_philanthropy_voluntarism_grantmaking_foundations', 'cause_area_science_technology', 'cause_area_social_science', 'cause_area_public_societal_benefit', 'cause_area_religion_related', 'cause_area_mutual_membership_benefit', 'cause_area_unknown', 'opencorporates_match_confidence', ] test_strings = { 'cause_area_primary_class': [ 'Hopsitals', #'Hopsitals', # NOTE THIS IS MISSPELLED IN THE API DATA -- AND zero primary matches 'Food, Agriculture & Nutrition', 'Community Improvement & Capacity Building', 'Mental Health & Crisis Intervention', 'Food, Agriculture & Nutrition', 'Housing & Shelter', 'Human Services', 'Arts, Culture & Humanities', 'Youth Development', 'Community Improvement & Capacity Building', 'Mutual & Membership Benefit', 'Civil Rights, Social Action & Advocacy', 'Health Care', 'Animal-Related', 'Arts, Culture & Humanities', 'Recreation & Sports', 'Food, Agriculture & Nutrition', ], 'cause_area_secondary_class': [ 'Hopsitals', #'Hopsitals', # NOTE THIS IS MISSPELLED IN THE API DATA -- 369 secondary matches 'Food, Agriculture & Nutrition', 'Community Improvement & Capacity Building', 'Mental Health & Crisis Intervention', 'Food, Agriculture & Nutrition', 'Housing & Shelter', 'Human Services', 'Arts, Culture & Humanities', 'Youth Development', 'Community Improvement & Capacity Building', 'Mutual & Membership Benefit', 'Civil Rights, Social Action & Advocacy', 'Health Care', 'Animal-Related', 'Arts, Culture & Humanities', 'Recreation & Sports', 'Food, Agriculture & Nutrition', ], 'locality': ['local state-wide', 'regional (multi-state)', 'international'], 'locality_by_grants': ['local/regional', 'national', 'international'], 'sdg': ['sdg1', 'sdg2', 'sdg3', 'sdg4', 'sdg5', 'sdg6', 'sdg7', 'sdg8', 'sdg9', 'sdg10', 'sdg11', 'sdg12', 'sdg13', 'sdg14', 'sdg15', 'sdg16'], } binary_fields = [ 'opencorporates_matched', 'url200', 'local_place_based_org', 'is_a_community_foundation', 'org_religious_affiliation', 'org_mostly_supported_by_individuals', 'org_has_membership_dues', 'organization_supports_children', 'organization_supports_women', 'organization_supports_bipoc', 'organization_supports_elderly', 'organization_supports_low_income', 'organization_supports_disability', 'organization_supports_immigrants_refugees', 'organization_supports_lgbt', 'organization_supports_none_of_these_groups', 'pro_democracy_org', 'social_justice_human_rights_org', 'climate_change', 'has_revenue_model', ] greater_than_fields = [ 'cause_area_arts_culture_humanities', 'cause_area_education', 'cause_area_universities', 'cause_area_environment', 'cause_area_animal_related', 'cause_area_healthcare', 'cause_area_hospitals', 'cause_area_mental_health_crisis_intervention', 'cause_area_voluntary_health_associations_medical_disciplines', 'cause_area_medical_research', 'cause_area_crime_legal_related', 'cause_area_employment', 'cause_area_food_agriculture_nutrition', 'cause_area_housing_shelter', 'cause_area_public_safety_disaster_preparedness_relief', 'cause_area_recreation_sports', 'cause_area_youth_development', 'cause_area_human_services', 'cause_area_international_foreign_affairs_national_security', 'cause_area_civil_rights_social_action_advocacy', 'cause_area_community_improvement_capacity_building', 'cause_area_philanthropy_voluntarism_grantmaking_foundations', 'cause_area_science_technology', 'cause_area_social_science', 'cause_area_public_societal_benefit', 'cause_area_religion_related', 'cause_area_mutual_membership_benefit', 'cause_area_unknown', 'opencorporates_match_confidence', ] less_than_fields = [ 'acs_fraction_poverty', 'acs_fraction_snap', 'acs_fraction_insured', 'acs_median_income', 'acs_fraction_hs', 'acs_fraction_vacant', 'acs_dep_index', 'mapagora_BROAD', 'mapagora_REMNRTY', 'mapagora_POV150', 'mapagora_NOHSDP', 'mapagora_SNGPNT', 'mapagora_UNEMP', 'mapagora_civic_org_sum_normalized', 'mapagora_civic_opp_sum_normalized', 'mapagora_civic_opp_index', 'mapagora_volunteer_sum_normalized', 'mapagora_membership_sum_normalized', 'mapagora_take_action_sum_normalized', 'mapagora_events_sum_normalized', 'staff_gender_male_fraction', 'staff_gender_female_fraction', 'cause_area_animal_related', ] less_than_int_fields = { 'mapagora_totalpopulation':50000, 'mapagora_n':3, 'mapagora_civic_org_sum':500, 'mapagora_civic_opp_sum':100, 'mapagora_volunteer_sum':150, 'mapagora_membership_sum':600, 'mapagora_take_action_sum':50, 'mapagora_events_sum':175, } def test_one(field, label=None): if label and isinstance(label, (int,float)): req = f"{endpoint}?key={key}&taxyear=2023&{field}={label}" elif label: req = f"{endpoint}?key={key}&taxyear=2023&{field}={urllib.parse.quote(label)}" else: req = f"{endpoint}?key={key}&taxyear=2023&{field}=1" resp = r.get(req) resp = resp.json() if resp.get('success'): if label: print(f"{resp['params']}, {label} | {len(resp['eins'])} eins") else: print(f"{resp['params']}, {len(resp['eins'])} eins") else: print(resp) time.sleep(0.1) for field,labels in test_strings.items(): # PASSED for label in labels: test_one(field, label) for field in binary_fields: # PASSED test_one(field) for field, value in less_than_int_fields.items(): # PASSED test_one(field, value) for field in greater_than_fields: # PASSED test_one(field, 0.7) for field in less_than_fields: # PASSED test_one(field, 0.3)