):
with open(os.path.join('out', fname), 'w') as fp:
writer = unicodecsv.writer(fp)
writer.writerow(['Publisher Name', 'Publisher Registry Id'] + previous_months + [assessment_label])
for publisher, publisher_title, per_month,assessment in f():
writer.writerow([publisher_title, publisher] + [per_month.get(x) or 0 for x in previous_months] + [assessment])
# Forward-looking CSV file
import forwardlooking
with open(os.path.join('out', 'forwardlooking.csv'), 'w') as fp:
writer = unicodecsv.writer(fp)
writer.writerow(['Publisher Name', 'Publisher Registry Id'] + [ '{} ({})'.format(header, year) for header in forwardlooking.column_headers for year in forwardlooking.years])
for row in forwardlooking.table():
writer.writerow([row['publisher_title'], row['publisher']] + [ year_column[year] for year_column in row['year_columns'] for year in forwardlooking.years])
# Comprehensiveness CSV files ('summary', 'core', 'financials' and 'valueadded')
import comprehensiveness
for tab in comprehensiveness.columns.keys():
with open(os.path.join('out', 'comprehensiveness_{}.csv'.format(tab)), 'w') as fp:
writer = unicodecsv.writer(fp)
writer.writerow(['Publisher Name', 'Publisher Registry Id'] +
[ x+' (with valid data)' for x in comprehensiveness.column_headers[tab] ] +
[ x+' (with any data)' for x in comprehensiveness.column_headers[tab] ])
for row in comprehensiveness.table():
writer.writerow([row['publisher_title'], row['publisher']]
for publisher, publisher_title, per_month, assessment in f():
writer.writerow([publisher_title, publisher] +
[per_month.get(x) or 0
for x in previous_months] + [assessment])
# Forward-looking CSV file
import forwardlooking
with open(os.path.join('out', 'forwardlooking.csv'), 'w') as fp:
writer = unicodecsv.writer(fp)
writer.writerow(['Publisher Name', 'Publisher Registry Id'] + [
'{} ({})'.format(header, year)
for header in forwardlooking.column_headers
for year in forwardlooking.years
])
for row in forwardlooking.table():
writer.writerow([row['publisher_title'], row['publisher']] + [
year_column[year] for year_column in row['year_columns']
for year in forwardlooking.years
])
# Comprehensiveness CSV files ('summary', 'core', 'financials' and 'valueadded')
import comprehensiveness
for tab in comprehensiveness.columns.keys():
with open(os.path.join('out', 'comprehensiveness_{}.csv'.format(tab)),
'w') as fp:
writer = unicodecsv.writer(fp)
writer.writerow(['Publisher Name', 'Publisher Registry Id'] + [
x + ' (with valid data)'
for x in comprehensiveness.column_headers[tab]
def table():
"""Generate data for the publisher forward-looking table
"""
# Store timeliness data in variable
timeliness_frequency_data = timeliness.publisher_frequency_dict()
timeliness_timelag_data = timeliness.publisher_timelag_dict()
# Store generator objects for the data that we are receiving
forwardlooking_data = forwardlooking.table()
comprehensiveness_data = comprehensiveness.table()
coverage_data = coverage.table()
# Loop over each publisher
for publisher_title, publisher in publishers_ordered_by_title:
# Store the data for this publisher as a new variable
publisher_stats = get_publisher_stats(publisher)
# Create a list for publisher data, and populate it with basic data
row = {}
row['publisher'] = publisher
row['publisher_title'] = publisher_title
row['publisher_type'] = common.get_publisher_type(publisher)
# Compute timeliness statistic
# Assign frequency score
if timeliness_frequency_data[publisher][3] == 'Monthly':
frequency_score = 4
elif timeliness_frequency_data[publisher][3] == 'Quarterly':
frequency_score = 3
elif timeliness_frequency_data[publisher][3] == 'Six-Monthly':
frequency_score = 2
elif timeliness_frequency_data[publisher][3] == 'Annual':
frequency_score = 1
else: # timeliness_frequency_data[publisher][3] == 'Less than Annual' or something else!
frequency_score = 0
# Assign timelag score
if timeliness_timelag_data[publisher][3] == 'One month':
timelag_score = 4
elif timeliness_timelag_data[publisher][3] == 'A quarter':
timelag_score = 3
elif timeliness_timelag_data[publisher][3] == 'Six months':
timelag_score = 2
elif timeliness_timelag_data[publisher][3] == 'One year':
timelag_score = 1
else: # timeliness_timelag_data[publisher][3] == 'More than one year' or something else!
timelag_score = 0
# Compute the percentage
row['timeliness'] = int( (float(frequency_score + timelag_score) / 8) * 100 )
# Compute forward looking statistic
# Get the forward looking data for this publisher
publisher_forwardlooking_data = forwardlooking_data.next()
# Convert the data for this publishers 'Percentage of current activities with budgets' fields into integers
numbers = [ int(x) for x in publisher_forwardlooking_data['year_columns'][2].itervalues() if is_number(x) ]
# Compute and store the mean average for these fields
row['forwardlooking'] = sum(int(y) for y in numbers) / len(publisher_forwardlooking_data['year_columns'][2])
# Compute comprehensive statistic
# Get the comprehensiveness data for this publisher
publisher_comprehensiveness_data = comprehensiveness_data.next()
# Set the comprehensive value to be the summary average for valid data
row['comprehensive'] = convert_to_int(publisher_comprehensiveness_data['summary_average_valid'])
# Compute score
row['score'] = int( (row['timeliness'] + row['forwardlooking'] + row['comprehensive']) / 3 )
# Get coverage statistic
# Get the coverage data for this publisher
publisher_coverage_data = coverage_data.next()
# Store the coverage data
row['coverage_adjustment'] = int(publisher_coverage_data['coverage_adjustment'])
# Compute Coverage-adjusted score
row['score_coverage_adjusted'] = int( row['score'] * int(row['coverage_adjustment'] / 100) )
# Return a generator object
yield row