def create():
__create_or_delete__(path.assets('indicators'))
overview = io.read_json(path.assets('overview.json'))
# Start by compiling a giant data structure of every company
companies = {}
for d in overview:
company_data = io.read_json(path.assets('%s.json' % d['id']))
companies[d['id']] = company_data
# Now aggregate the data by indicator id using the survey data
survey = io.read_json(path.assets('survey.json'))
for item in survey:
indicator_id = item['id'].lower()
indicator_data = {
'id': item['id'],
'name': item['name'],
'follow': item['follow'],
'companies': []
}
print indicator_id, item['follow']
for company_id, company in companies.iteritems():
company_data = [i for i in company if indicator_id == i['id'].lower()]
if len(company_data) > 1:
print 'Found too many company matches for', indicator_id
# This might be an indicator that doesn't apply
if not len(company_data):
continue
company_data = company_data[0]
company_overview = [c for c in overview if company_id in c['id']]
if len(company_overview) != 1:
print 'Weirdness finding company from company overview'
company_overview = company_overview[0]
company_type = 'Telecommunications'
if 'false' in company_overview['telco']:
company_type = 'Internet'
indicator_data['companies'].append({
'name': company_overview['name'],
'id': company_overview['id'],
'display': company_overview['display'],
'score': company_data['score'],
'type': company_type,
'levels': company_data['levels'],
'services': company_data['services']
})
indicator_data['companies'] = sorted(indicator_data['companies'], key=lambda c: c['id'])
io.write_json(path.assets('indicators/%s.json' % indicator_id), indicator_data)
def create_indicator_scores():
companies = io.read_json(path.assets("services.json"))
survey = io.read_json(path.assets("survey.json"))
indicator_data = []
for i in survey:
indicator_id = i["id"].lower()
scores = {}
levels = {}
for c in companies:
c_name = c["display"]
c_overall = c["overall"]
if indicator_id in c_overall:
scores[c_name] = c_overall[indicator_id]
else:
print "no %s in %s" % (indicator_id, " ".join(c_overall))
print i["name"]
indicator_data.append({"id": indicator_id, "scores": scores, "text": i["text"], "name": i["name"]})
io.write_json(path.assets("indicator-overview.json"), indicator_data)
def companies():
__create_or_delete__(path.companies("")[:-1])
overview = io.read_json(path.assets("overview.json"))
for company in overview:
filename = (company["name"] + ".md").lower().replace("&", "")
with open(path.companies(filename), "w") as f:
f.write("---\n")
f.write("entity: %s\n" % filename[:-3])
f.write("\nlayout: company\n\n")
for key, value in company.iteritems():
if key in ["total", "commitment", "privacy", "freedom"]:
f.write("%s: %s" % (key, int(round(float(value)))))
else:
f.write("%s: %s" % (key, value))
f.write("\n")
f.write("\ndescription: one-line description\n")
f.write("website: http://example.com")
f.write("\n---\n\n")
f.write("Paragraph describing this company\n")
def companies():
__create_or_delete__(path.companies('')[:-1])
overview = io.read_json(path.assets('overview.json'))
for company in overview:
filename = (company['name'] + '.md').lower().replace('&', '')
with open(path.companies(filename), 'w') as f:
f.write('---\n')
f.write('entity: %s\n' % filename[:-3])
f.write('\nlayout: company\n\n')
for key, value in company.iteritems():
if key in ['total', 'commitment', 'privacy', 'freedom']:
f.write('%s: %s' % (key, int(round(float(value)))))
else:
f.write('%s: %s' % (key, value))
f.write('\n')
f.write('\ndescription: one-line description\n')
f.write('website: http://example.com')
f.write('\n---\n\n')
f.write('Paragraph describing this company\n')
def indicators():
__create_or_delete__(path.indicators("")[:-1])
custom = io.read_json(path.assets("custom-questions.json"))
indicators = io.read_json(path.assets("survey.json"))
for idx, indicator in enumerate(indicators):
indicator_id = indicator["id"].lower()
# Determine if it's in the list of custom indicators
is_custom = False
if indicator_id in custom:
is_custom = True
filename = indicator_id + ".md"
with open(path.indicators(filename), "w") as f:
f.write("---\n")
f.write("sort: %s\n" % idx)
f.write("entity: %s\n" % filename[:-3])
f.write("entity_type: indicator\n")
first_letter = filename[0]
if "c" in first_letter:
f.write("category_ref: commitment\n")
f.write("category: Commitment\n")
elif "f" in first_letter:
f.write("category_ref: freedom-of-expression\n")
f.write("category: Freedom of Expression\n")
else:
f.write("category_ref: privacy\n")
f.write("category: Privacy\n")
f.write("\nlayout: indicator\n\n")
# text, id, name
for item in ["name", "text", "id"]:
f.write("%s: %s\n" % (item, indicator[item].encode("UTF-8")))
# possible answers
f.write("\nlevels:\n")
for idx, resp in enumerate(indicator["levels"]):
# first, just make sure we write level text
if resp["text"] != 0:
f.write(' - text: "%s"\n' % resp["text"].encode("UTF-8"))
f.write(" id: %s\n" % resp["id"].encode("UTF-8"))
# this is really only for c1.b at this point.
if not is_custom and resp["id"].lower() in custom:
custom_answer = custom[resp["id"].lower()]
f.write(" choices:\n")
for a in custom_answer:
f.write(' - text: "%s"\n' % a["text"].encode("UTF-8"))
f.write(' score: "%s"\n' % a["score"])
f.write("\nchoices:\n")
if is_custom:
custom_answer = custom[indicator_id]
for a in custom_answer:
f.write(' - text: "%s"\n' % a["text"].encode("UTF-8"))
f.write(' score: "%s"\n' % a["score"])
f.write("\n---\n\n")
def create():
__create_or_delete__(path.assets('indicators'))
overview = io.read_json(path.assets('overview.json'))
# Start by compiling a giant data structure of every company
companies = {}
for d in overview:
company_data = io.read_json(path.assets('%s.json' % d['id']))
companies[d['id']] = company_data
# Now aggregate the data by indicator id using the survey data
survey = io.read_json(path.assets('survey.json'))
for item in survey:
indicator_id = item['id'].lower()
indicator_data = {
'id': item['id'],
'name': item['name'],
'follow': item['follow'],
'companies': []
}
print indicator_id, item['follow']
for company_id, company in companies.iteritems():
company_data = [
i for i in company if indicator_id == i['id'].lower()
]
if len(company_data) > 1:
print 'Found too many company matches for', indicator_id
# This might be an indicator that doesn't apply
if not len(company_data):
continue
company_data = company_data[0]
company_overview = [c for c in overview if company_id in c['id']]
if len(company_overview) != 1:
print 'Weirdness finding company from company overview'
company_overview = company_overview[0]
company_type = 'Telecommunications'
if 'false' in company_overview['telco']:
company_type = 'Internet'
indicator_data['companies'].append({
'name':
company_overview['name'],
'id':
company_overview['id'],
'display':
company_overview['display'],
'score':
company_data['score'],
'type':
company_type,
'levels':
company_data['levels'],
'services':
company_data['services']
})
indicator_data['companies'] = sorted(indicator_data['companies'],
key=lambda c: c['id'])
io.write_json(path.assets('indicators/%s.json' % indicator_id),
indicator_data)
def setUp(self):
self.data = read_json()
import json
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pymongo import MongoClient
sys.path.insert(0, str(Path(__file__).parent.parent / "etc"))
from lib.io import read_json
client = MongoClient('mongodb://localhost:9999')
db = client.tinder
users = db.users
objects = read_json('data/objects.json')
data = pd.DataFrame \
.from_dict(objects, orient='index', columns=['count']) \
.sort_values(by='count', ascending=False)
# Remove common features
common_features = [
'Forehead',
'Chin',
'Jaw',
'Sleeve',
'Hairstyle',
'Neck',
'Gesture',
'Eyebrow',
'Nose',
def create(filename):
# Create a dictionary where properties are company names
overview = io.read_json(path.assets('overview.json'))
companies = [name.snake_case(item['display']) for item in overview]
company_dict = {}
for c in companies:
company_dict[c] = -1
# Now use that dictionary to save the index of those company names.
raw = io.read_csv(path.raw(filename))
raw_header = raw[0]
for idx, item in enumerate(raw_header):
snake_header = name.snake_case(item)
if snake_header in company_dict:
company_dict[snake_header] = idx
# This should be 0 if we've matched every company
if not_all_found(company_dict.values()):
print 'Not all companies accounted for in services overview csv'
# This is where we check a ref file, or create one
ref_path = path.ref('service-column-mapping.json')
if os.path.isfile(ref_path):
ref = io.read_json(ref_path)
else:
ref = [name.snake_case(row[0]) for row in raw[1:] if row[0] != '']
io.write_json(ref_path, ref)
# Create a dictionary matching row number fo the indicator
indicator_dict = {}
for indic in ref:
indicator_dict[indic] = -1
for idx, row in enumerate(raw):
indicator = name.snake_case(row[0])
if indicator in indicator_dict:
indicator_dict[indicator] = idx
if not_all_found(indicator_dict.values()):
print 'Not all indicators accounted for in services overview csv'
# Baselines
tel = 'telco'
net = 'internet company'
output = []
# Get a slice of all the columns that encompass each company
stops = sorted(idx for idx in company_dict.values())
for idx, stop in enumerate(stops):
next_stop = stops[idx+1] if idx + 1 < len(stops) else len(raw_header)
company_range = [item[stop:next_stop] for item in raw]
company = {
'display': company_range[0][0],
'name': name.filename(company_range[0][0])
}
# The second item in the first row *should* be the type
header_type = company_range[0][1].lower()
if header_type not in [tel, net]:
print 'No company type found. Instead, saw %s' % header_type
company['type'] = header_type
# The second row contains the service names
service_names = [item for item in company_range[1]]
services = []
for column_number, service_name in enumerate(service_names):
# Get each indicator value for each service using
# the indicator mapping we defined earlier
scores = {}
for indicator_name, row_number in indicator_dict.iteritems():
cell = company_range[row_number][column_number]
scores[indicator_name] = company_range[row_number][column_number]
# The first 'service' is actually just the overall
# Do some spreadsheet format-checking here
if column_number == 0:
total = scores['total']
if not len(total):
print 'No weighted total for %s %s' % (service_name, company['name'])
if 'overall' not in service_name:
print 'Service %s != "overall"' % service_name
company['overall'] = scores
# The second 'service' is usually the group score;
# No need to save this, we don't use it here.
elif column_number == 1 and 'group' in service_name:
continue
# Otherwise, call it a service.
else:
service = {
'name': service_name,
'scores': scores
}
# Get service type if it's available
service_type = company_range[0][column_number]
if len(service_type):
service['type'] = service_type
services.append(service)
company['services'] = services
output.append(company)
io.write_json(path.assets('services.json'), output)
required=False,
const=True,
help="A boolean indicating whether or not to submit images to the cloud "
"computer vision service.")
args = parser.parse_args()
if args.download_file:
assert not args.data_export, "You should select either the --download-from-tinder or --data-export [file_path] options, but not both."
logging.info(authverif())
recs = get_recommendations()
write_json(recs, 'data/recommendations.json')
else:
assert args.data_export, "You must specify either a --download-file or a --data-export"
recs = read_json(args.data_export)
assert recs['meta']['status'] == 200, "Something went wrong"
results = []
for record in recs['data']['results']:
results.append(flatten(record))
try:
with MongoClient('mongodb://localhost:9999') as client:
db = client.tinder
users = db.users
for result in results:
logging.debug(f"Upserting {result} to the database")
# Update the database
def create(filename):
company_name = name.filename(filename[:-4])
all_services = io.read_json(path.assets('services.json'))
service_data = [item for item in all_services if (company_name
in item['name'].replace('.', ''))]
if len(service_data) != 1:
print 'Weird number of services found', len(service_data)
service_data = service_data[0]
# Create a mapping dictionary of just indicator names
# each mapped to -1
ref = io.read_json(path.ref('service-column-mapping.json'))
indicator_dict = {}
for item in ref:
if is_number(item[1:]):
indicator_dict[item] = -1
# Map the indicator to the proper rows
raw = io.read_csv(path.raw('companies/' + filename))
for idx, row in enumerate(raw):
indicator = row[0].lower()
if indicator in indicator_dict:
indicator_dict[indicator] = idx
# Use the survey data to map possible responses to position
survey = io.read_json(path.assets('survey.json'))
if not_all_found(indicator_dict.values()):
print 'Not all indicators accounted for in services overview csv'
all_indicators = []
# Get a slice of all the rows that encompass each company
stops = sorted(idx for idx in indicator_dict.values())
for idx, stop in enumerate(stops):
next_stop = stops[idx+1] if idx + 1 < len(stops) else len(raw) + 1
indicator_range = raw[stop:next_stop]
# Divide that slice by empty rows
split = array.slice_arr(indicator_range, array.is_empty_row)
# The first slice contains consolidated answers,
# comments, and sources.
responses = split.pop(0)
# The first row of responses is indicator name followed by
# service categories
header = [item for item in responses.pop(0) if len(item)]
indicator_name = header[0]
# Find the survey question we're looking for
survey_item = ([item for item in survey
if item['id'].lower() == indicator_name.lower()])
if len(survey_item) != 1:
print 'Too many items in survey.json for this indicator'
print indicator_name
print survey_item
indicator_data = {
'id': indicator_name,
'services': [],
'levels': []
}
# Check if this indicator is valid before continuing
if len(responses) == 1 and 'this indicator is n/a' in responses[0][0].lower():
continue
else:
# question scores follow the response text in the split array
scores = split.pop(0)
# ..followed by the overall indicator score (verify this)
indicator_score = split.pop(-1)[0][1] if ('indicator score'
in split[-1][0][0].lower()) else []
if not len(indicator_score):
print '\nIndicator score not found in %s' % header[0]
print split, '\n'
else:
indicator_data['score'] = indicator_score
# ..and the same for the overall service scores
level_scores = split.pop(-1)[0] if ('level score'
in split[-1][0][0].lower()) else []
if not len(level_scores):
print '\nService score not found in %s' % header[0]
print split, '\n'
# Determine the comments and sources location
comments = responses.pop(-2)
sources = responses.pop(-1)
if ('comments' not in comments[0].lower() or
'sources' not in sources[0].lower()):
print 'Comments not found in %s' % comments[0]
print 'Sources not found in %s' % sources[0]
# Some question text include an if-not-then clause,
# which throws off the count between the text and the score.
# Record it and then delete the row.
indicator_data['follow'] = 0
for idx, row in enumerate(responses):
if 'continue with B' in row[0] and len(set(row[1:])) == 1:
indicator_data['follow'] = 1
del responses[idx]
break
if len(responses) != len(scores):
print 'Length of responses and scores not matching'
print len(responses), len(scores)
# Save level responses, and level positions
# Determine if this question has custom answers
survey_levels = survey_item[0]['levels']
for idx, level in enumerate(responses):
level_data = []
# Assume anything longer than 25 characters,
# aka "no/insufficient evidence", is a custom response
custom = 0
survey_options = survey_levels[idx]['responses']
for option in survey_options:
if len(option) > 25:
custom = 1
for level_idx, level_response in enumerate(level):
# First level index is useless.
if level_idx == 0 or not len(level_response):
continue
if len(header) <= level_idx:
print 'No header available, this will break'
service = header[level_idx]
# Exclude group scores, operating company
# from indicators that don't need them
if (('(group)' in service or '(operating company)' in service )
and exclude_service(indicator_name)):
continue
# Shim issues where the response includes too much text.
if len(level_response) > 25 and "no/insufficient" == level_response[:15]:
level_response = "no/insufficient evidence"
# Only add to the services list if we're on the first level.
# Other, we add too many
if idx == 0:
if 'operating company' in service.lower():
service_type = 'operating company'
elif 'group' in service.lower():
service_type = 'group'
else:
matching_service = [item for item in service_data['services'] if (
item['name'].lower() in service.lower())]
if len(matching_service) == 1 and 'type' in matching_service[0]:
service_type = matching_service[0]['type']
else:
service_type = ''
indicator_data['services'].append({
'name': scrub_service_name(service),
'type': service_type,
'comments': comments[level_idx],
'sources': sources[level_idx],
'score': level_scores[level_idx]
})
level_data.append({
'response': level_response,
'score': scores[idx][level_idx]
})
indicator_data['custom'] = custom
indicator_data['levels'].append({
'scores': level_data,
'text': survey_levels[idx]['text']
})
all_indicators.append(indicator_data)
io.write_json(path.assets(company_name + '.json'), all_indicators)
def create(filename):
# Create a dictionary where properties are company names
overview = io.read_json(path.assets('overview.json'))
companies = [name.snake_case(item['display']) for item in overview]
company_dict = {}
for c in companies:
company_dict[c] = -1
# Now use that dictionary to save the index of those company names.
raw = io.read_csv(path.raw(filename))
raw_header = raw[0]
for idx, item in enumerate(raw_header):
snake_header = name.snake_case(item)
if snake_header in company_dict:
company_dict[snake_header] = idx
# This should be 0 if we've matched every company
if not_all_found(company_dict.values()):
print 'Not all companies accounted for in services overview csv'
# This is where we check a ref file, or create one
ref_path = path.ref('service-column-mapping.json')
if os.path.isfile(ref_path):
ref = io.read_json(ref_path)
else:
ref = [name.snake_case(row[0]) for row in raw[1:] if row[0] != '']
io.write_json(ref_path, ref)
# Create a dictionary matching row number fo the indicator
indicator_dict = {}
for indic in ref:
indicator_dict[indic] = -1
for idx, row in enumerate(raw):
indicator = name.snake_case(row[0])
if indicator in indicator_dict:
indicator_dict[indicator] = idx
if not_all_found(indicator_dict.values()):
print 'Not all indicators accounted for in services overview csv'
# Baselines
tel = 'telco'
net = 'internet company'
output = []
# Get a slice of all the columns that encompass each company
stops = sorted(idx for idx in company_dict.values())
for idx, stop in enumerate(stops):
next_stop = stops[idx + 1] if idx + 1 < len(stops) else len(raw_header)
company_range = [item[stop:next_stop] for item in raw]
company = {
'display': company_range[0][0],
'name': name.filename(company_range[0][0])
}
# The second item in the first row *should* be the type
header_type = company_range[0][1].lower()
if header_type not in [tel, net]:
print 'No company type found. Instead, saw %s' % header_type
company['type'] = header_type
# The second row contains the service names
service_names = [item for item in company_range[1]]
services = []
for column_number, service_name in enumerate(service_names):
# Get each indicator value for each service using
# the indicator mapping we defined earlier
scores = {}
for indicator_name, row_number in indicator_dict.iteritems():
cell = company_range[row_number][column_number]
scores[indicator_name] = company_range[row_number][
column_number]
# The first 'service' is actually just the overall
# Do some spreadsheet format-checking here
if column_number == 0:
total = scores['total']
if not len(total):
print 'No weighted total for %s %s' % (service_name,
company['name'])
if 'overall' not in service_name:
print 'Service %s != "overall"' % service_name
company['overall'] = scores
# The second 'service' is usually the group score;
# No need to save this, we don't use it here.
elif column_number == 1 and 'group' in service_name:
continue
# Otherwise, call it a service.
else:
service = {'name': service_name, 'scores': scores}
# Get service type if it's available
service_type = company_range[0][column_number]
if len(service_type):
service['type'] = service_type
services.append(service)
company['services'] = services
output.append(company)
io.write_json(path.assets('services.json'), output)
def indicators():
__create_or_delete__(path.indicators('')[:-1])
custom = io.read_json(path.assets('custom-questions.json'))
indicators = io.read_json(path.assets('survey.json'))
for idx, indicator in enumerate(indicators):
indicator_id = indicator['id'].lower()
# Determine if it's in the list of custom indicators
is_custom = False
if indicator_id in custom:
is_custom = True
filename = (indicator_id + '.md')
with open(path.indicators(filename), 'w') as f:
f.write('---\n')
f.write('sort: %s\n' % idx)
f.write('entity: %s\n' % filename[:-3])
f.write('entity_type: indicator\n')
first_letter = filename[0]
if 'c' in first_letter:
f.write('category_ref: commitment\n')
f.write('category: Commitment\n')
elif 'f' in first_letter:
f.write('category_ref: freedom-of-expression\n')
f.write('category: Freedom of Expression\n')
else:
f.write('category_ref: privacy\n')
f.write('category: Privacy\n')
f.write('\nlayout: indicator\n\n')
# text, id, name
for item in ['name', 'text', 'id']:
f.write('%s: %s\n' % (item, indicator[item].encode('UTF-8')))
# possible answers
f.write('\nlevels:\n')
for idx, resp in enumerate(indicator['levels']):
# first, just make sure we write level text
if resp['text'] != 0:
f.write(' - text: "%s"\n' % resp['text'].encode('UTF-8'))
f.write(' id: %s\n' % resp['id'].encode('UTF-8'))
# this is really only for c1.b at this point.
if not is_custom and resp['id'].lower() in custom:
custom_answer = custom[resp['id'].lower()]
f.write(' choices:\n')
for a in custom_answer:
f.write(' - text: "%s"\n' %
a['text'].encode('UTF-8'))
f.write(' score: "%s"\n' % a['score'])
f.write('\nchoices:\n')
if is_custom:
custom_answer = custom[indicator_id]
for a in custom_answer:
f.write(' - text: "%s"\n' % a['text'].encode('UTF-8'))
f.write(' score: "%s"\n' % a['score'])
f.write('\n---\n\n')
def create(filename):
company_name = name.filename(filename[:-4])
all_services = io.read_json(path.assets('services.json'))
service_data = [
item for item in all_services
if (company_name in item['name'].replace('.', ''))
]
if len(service_data) != 1:
print 'Weird number of services found', len(service_data)
service_data = service_data[0]
# Create a mapping dictionary of just indicator names
# each mapped to -1
ref = io.read_json(path.ref('service-column-mapping.json'))
indicator_dict = {}
for item in ref:
if is_number(item[1:]):
indicator_dict[item] = -1
# Map the indicator to the proper rows
raw = io.read_csv(path.raw('companies/' + filename))
for idx, row in enumerate(raw):
indicator = row[0].lower()
if indicator in indicator_dict:
indicator_dict[indicator] = idx
# Use the survey data to map possible responses to position
survey = io.read_json(path.assets('survey.json'))
if not_all_found(indicator_dict.values()):
print 'Not all indicators accounted for in services overview csv'
all_indicators = []
# Get a slice of all the rows that encompass each company
stops = sorted(idx for idx in indicator_dict.values())
for idx, stop in enumerate(stops):
next_stop = stops[idx + 1] if idx + 1 < len(stops) else len(raw) + 1
indicator_range = raw[stop:next_stop]
# Divide that slice by empty rows
split = array.slice_arr(indicator_range, array.is_empty_row)
# The first slice contains consolidated answers,
# comments, and sources.
responses = split.pop(0)
# The first row of responses is indicator name followed by
# service categories
header = [item for item in responses.pop(0) if len(item)]
indicator_name = header[0]
# Find the survey question we're looking for
survey_item = ([
item for item in survey
if item['id'].lower() == indicator_name.lower()
])
if len(survey_item) != 1:
print 'Too many items in survey.json for this indicator'
print indicator_name
print survey_item
indicator_data = {'id': indicator_name, 'services': [], 'levels': []}
# Check if this indicator is valid before continuing
if len(responses
) == 1 and 'this indicator is n/a' in responses[0][0].lower():
continue
else:
# question scores follow the response text in the split array
scores = split.pop(0)
# ..followed by the overall indicator score (verify this)
indicator_score = split.pop(-1)[0][1] if (
'indicator score' in split[-1][0][0].lower()) else []
if not len(indicator_score):
print '\nIndicator score not found in %s' % header[0]
print split, '\n'
else:
indicator_data['score'] = indicator_score
# ..and the same for the overall service scores
level_scores = split.pop(-1)[0] if (
'level score' in split[-1][0][0].lower()) else []
if not len(level_scores):
print '\nService score not found in %s' % header[0]
print split, '\n'
# Determine the comments and sources location
comments = responses.pop(-2)
sources = responses.pop(-1)
if ('comments' not in comments[0].lower()
or 'sources' not in sources[0].lower()):
print 'Comments not found in %s' % comments[0]
print 'Sources not found in %s' % sources[0]
# Some question text include an if-not-then clause,
# which throws off the count between the text and the score.
# Record it and then delete the row.
indicator_data['follow'] = 0
for idx, row in enumerate(responses):
if 'continue with B' in row[0] and len(set(row[1:])) == 1:
indicator_data['follow'] = 1
del responses[idx]
break
if len(responses) != len(scores):
print 'Length of responses and scores not matching'
print len(responses), len(scores)
# Save level responses, and level positions
# Determine if this question has custom answers
survey_levels = survey_item[0]['levels']
for idx, level in enumerate(responses):
level_data = []
# Assume anything longer than 25 characters,
# aka "no/insufficient evidence", is a custom response
custom = 0
survey_options = survey_levels[idx]['responses']
for option in survey_options:
if len(option) > 25:
custom = 1
for level_idx, level_response in enumerate(level):
# First level index is useless.
if level_idx == 0 or not len(level_response):
continue
if len(header) <= level_idx:
print 'No header available, this will break'
service = header[level_idx]
# Exclude group scores, operating company
# from indicators that don't need them
if (('(group)' in service
or '(operating company)' in service)
and exclude_service(indicator_name)):
continue
# Shim issues where the response includes too much text.
if len(level_response
) > 25 and "no/insufficient" == level_response[:15]:
level_response = "no/insufficient evidence"
# Only add to the services list if we're on the first level.
# Other, we add too many
if idx == 0:
if 'operating company' in service.lower():
service_type = 'operating company'
elif 'group' in service.lower():
service_type = 'group'
else:
matching_service = [
item for item in service_data['services']
if (item['name'].lower() in service.lower())
]
if len(matching_service
) == 1 and 'type' in matching_service[0]:
service_type = matching_service[0]['type']
else:
service_type = ''
indicator_data['services'].append({
'name':
scrub_service_name(service),
'type':
service_type,
'comments':
comments[level_idx],
'sources':
sources[level_idx],
'score':
level_scores[level_idx]
})
level_data.append({
'response': level_response,
'score': scores[idx][level_idx]
})
indicator_data['custom'] = custom
indicator_data['levels'].append({
'scores':
level_data,
'text':
survey_levels[idx]['text']
})
all_indicators.append(indicator_data)
io.write_json(path.assets(company_name + '.json'), all_indicators)