def run(date):
if date is None:
date = datetime.datetime.strftime(datetime.datetime.now(), "%Y-%m-%d")
# Reset the database.
print("Clearing the database.")
models.clear_database()
Report.create(date)
# Read in domains and agencies from domains.csv.
# Returns dicts of values ready for saving as Domain and Agency objects.
domains, agencies = load_domain_data()
# Read in domain-scan CSV data.
scan_data = load_scan_data(domains)
# Pull out a few inspect.csv fields as general domain metadata.
for domain_name in scan_data.keys():
inspect = scan_data[domain_name].get('inspect', None)
if inspect is None:
# generally means scan was on different domains.csv, but
# invalid domains can hit this (e.g. fed.us).
print("[%s][WARNING] No inspect data for domain!" % domain_name)
# Remove the domain from further consideration.
del domains[domain_name]
else:
# print("[%s] Updating with inspection metadata." % domain_name)
domains[domain_name]['live'] = boolean_for(inspect['Live'])
domains[domain_name]['redirect'] = boolean_for(inspect['Redirect'])
domains[domain_name]['canonical'] = inspect['Canonical']
# Save what we've got to the database so far.
for domain_name in domains.keys():
Domain.create(domains[domain_name])
print("[%s] Created." % domain_name)
for agency_name in agencies.keys():
Agency.create(agencies[agency_name])
# print("[%s] Created." % agency_name)
# Calculate high-level per-domain conclusions for each report.
domain_reports = process_domains(domains, agencies, scan_data)
# Save them in the database.
for report_type in domain_reports.keys():
for domain_name in domain_reports[report_type].keys():
print("[%s][%s] Adding report." % (report_type, domain_name))
Domain.add_report(domain_name, report_type, domain_reports[report_type][domain_name])
# Calculate agency-level summaries.
update_agency_totals()
# Create top-level summaries.
reports = latest_reports()
for report in reports:
Report.update(report)
print_report()
def run(date):
if date is None:
date = datetime.datetime.strftime(datetime.datetime.now(), "%Y-%m-%d")
# Reset the database.
print("Clearing the database.")
models.clear_database()
Report.create(date)
# Read in domains and agencies from domains.csv.
# Returns dicts of values ready for saving as Domain and Agency objects.
domains, agencies = load_domain_data()
# Read in domain-scan CSV data.
scan_data = load_scan_data(domains)
# Load in some manual exclusion data.
analytics_ineligible = yaml.safe_load(
open(os.path.join(this_dir, "ineligible/analytics.yml")))
analytics_ineligible_map = {}
for domain in analytics_ineligible:
analytics_ineligible_map[domain] = True
# Pull out a few pshtt.csv fields as general domain metadata.
for domain_name in scan_data.keys():
analytics = scan_data[domain_name].get('analytics', None)
if analytics:
ineligible = analytics_ineligible_map.get(domain_name, False)
domains[domain_name]['exclude']['analytics'] = ineligible
pshtt = scan_data[domain_name].get('pshtt', None)
if pshtt is None:
# generally means scan was on different domains.csv, but
# invalid domains can hit this.
print("[%s][WARNING] No pshtt data for domain!" % domain_name)
# Remove the domain from further consideration.
# Destructive, so have this done last.
del domains[domain_name]
else:
# print("[%s] Updating with pshtt metadata." % domain_name)
domains[domain_name]['live'] = boolean_for(pshtt['Live'])
domains[domain_name]['redirect'] = boolean_for(pshtt['Redirect'])
domains[domain_name]['canonical'] = pshtt['Canonical URL']
# Save what we've got to the database so far.
for domain_name in domains.keys():
Domain.create(domains[domain_name])
print("[%s] Created." % domain_name)
for agency_name in agencies.keys():
Agency.create(agencies[agency_name])
# print("[%s] Created." % agency_name)
# Calculate high-level per-domain conclusions for each report.
domain_reports = process_domains(domains, agencies, scan_data)
# Save them in the database.
for report_type in domain_reports.keys():
for domain_name in domain_reports[report_type].keys():
print("[%s][%s] Adding report." % (report_type, domain_name))
Domain.add_report(domain_name, report_type,
domain_reports[report_type][domain_name])
# Calculate agency-level summaries.
update_agency_totals()
# Create top-level summaries.
reports = latest_reports()
for report in reports:
Report.update(report)
print_report()
def run(date):
if date is None:
date = datetime.datetime.strftime(datetime.datetime.now(), "%Y-%m-%d")
# Reset the database.
print("Clearing the database.")
models.clear_database()
Report.create(date)
# Read in domains and agencies from domains.csv.
# Returns dicts of values ready for saving as Domain and Agency objects.
domains, agencies = load_domain_data()
# Read in domain-scan CSV data.
scan_data = load_scan_data(domains)
# Load in some manual exclusion data.
analytics_ineligible = yaml.safe_load(open(os.path.join(this_dir, "ineligible/analytics.yml")))
analytics_ineligible_map = {}
for domain in analytics_ineligible:
analytics_ineligible_map[domain] = True
# Pull out a few pshtt.csv fields as general domain metadata.
for domain_name in scan_data.keys():
analytics = scan_data[domain_name].get('analytics', None)
if analytics:
ineligible = analytics_ineligible_map.get(domain_name, False)
domains[domain_name]['exclude']['analytics'] = ineligible
pshtt = scan_data[domain_name].get('pshtt', None)
if pshtt is None:
# generally means scan was on different domains.csv, but
# invalid domains can hit this.
print("[%s][WARNING] No pshtt data for domain!" % domain_name)
# Remove the domain from further consideration.
# Destructive, so have this done last.
del domains[domain_name]
else:
# print("[%s] Updating with pshtt metadata." % domain_name)
domains[domain_name]['live'] = boolean_for(pshtt['Live'])
domains[domain_name]['redirect'] = boolean_for(pshtt['Redirect'])
domains[domain_name]['canonical'] = pshtt['Canonical URL']
# Save what we've got to the database so far.
for domain_name in domains.keys():
Domain.create(domains[domain_name])
print("[%s] Created." % domain_name)
for agency_name in agencies.keys():
Agency.create(agencies[agency_name])
# print("[%s] Created." % agency_name)
# Calculate high-level per-domain conclusions for each report.
domain_reports = process_domains(domains, agencies, scan_data)
# Save them in the database.
for report_type in domain_reports.keys():
for domain_name in domain_reports[report_type].keys():
print("[%s][%s] Adding report." % (report_type, domain_name))
Domain.add_report(domain_name, report_type, domain_reports[report_type][domain_name])
# Calculate agency-level summaries.
update_agency_totals()
# Create top-level summaries.
reports = latest_reports()
for report in reports:
Report.update(report)
print_report()
def run(date, options):
if date is None:
date = datetime.datetime.strftime(datetime.datetime.now(), "%Y-%m-%d")
# Read in domains and agencies from domains.csv.
# Returns dicts of values ready for saving as Domain and Agency objects.
#
# Also returns gathered subdomains, which need more filtering to be useful.
domains, agencies, gathered_subdomains = load_domain_data()
# Read in domain-scan CSV data.
parent_scan_data = load_parent_scan_data(domains)
subdomains, subdomain_scan_data = load_subdomain_scan_data(
domains, parent_scan_data, gathered_subdomains)
# Load in some manual exclusion data.
analytics_ineligible = yaml.safe_load(
open(os.path.join(this_dir, "ineligible/analytics.yml")))
analytics_ineligible_map = {}
for domain in analytics_ineligible:
analytics_ineligible_map[domain] = True
# Capture manual exclusions and pull out some high-level data from pshtt.
for domain_name in parent_scan_data.keys():
# mark manual ineligiblity for analytics if present
analytics = parent_scan_data[domain_name].get('analytics', None)
if analytics:
ineligible = analytics_ineligible_map.get(domain_name, False)
domains[domain_name]['exclude']['analytics'] = ineligible
# Pull out a few pshtt.csv fields as general domain-level metadata.
pshtt = parent_scan_data[domain_name].get('pshtt', None)
if pshtt is None:
# generally means scan was on different domains.csv, but
# invalid domains can hit this.
print("[%s][WARNING] No pshtt data for domain!" % domain_name)
# Remove the domain from further consideration.
# Destructive, so have this done last.
del domains[domain_name]
else:
# print("[%s] Updating with pshtt metadata." % domain_name)
domains[domain_name]['live'] = boolean_for(pshtt['Live'])
domains[domain_name]['redirect'] = boolean_for(pshtt['Redirect'])
domains[domain_name]['canonical'] = pshtt['Canonical URL']
# Prepare subdomains the same way
for subdomain_name in subdomain_scan_data.keys():
pshtt = subdomain_scan_data[subdomain_name].get('pshtt')
subdomains[subdomain_name]['live'] = boolean_for(pshtt['Live'])
subdomains[subdomain_name]['redirect'] = boolean_for(pshtt['Redirect'])
subdomains[subdomain_name]['canonical'] = pshtt['Canonical URL']
# Save what we've got to the database so far.
sorted_domains = list(domains.keys())
sorted_domains.sort()
sorted_subdomains = list(subdomains.keys())
sorted_subdomains.sort()
sorted_agencies = list(agencies.keys())
sorted_agencies.sort()
# Calculate high-level per-domain conclusions for each report.
# Overwrites `domains` and `subdomains` in-place.
process_domains(domains, agencies, subdomains, parent_scan_data,
subdomain_scan_data)
# Reset the database.
print("Clearing the database.")
models.clear_database()
# Calculate agency-level summaries. Updates `agencies` in-place.
update_agency_totals(agencies, domains, subdomains)
# Calculate government-wide summaries.
report = full_report(domains, subdomains)
report['report_date'] = date
print("Creating all domains.")
Domain.create_all(domains[domain_name] for domain_name in sorted_domains)
print("Creating all subdomains.")
Domain.create_all(subdomains[subdomain_name]
for subdomain_name in sorted_subdomains)
print("Creating all agencies.")
Agency.create_all(agencies[agency_name] for agency_name in sorted_agencies)
# Create top-level summaries.
print("Creating government-wide totals.")
Report.create(report)
# Print and exit
print_report(report)
