def main():
#############################################################################
# 0.
#
# Check if tmp folder exists, otherwise create it
check_create_folder(settings.tmp_dir)
# Build the list with countries and states
admin_areas = get_aa_list()
for chart in settings.charts:
ind_source = settings.src_auxiliary + str(settings.current_edition) + '-' + str(chart["id"]) + '.csv'
global_avg = False
# Calculate the global average for this chart
if "global_average" in chart and chart["global_average"]:
global_avg = get_avg(chart, ind_source)
for aa in admin_areas:
iso = aa.lower()
for lang in settings.langs:
# Initialize the array that will be written to JSON
json_data = {"name": iso, "iso": iso, "meta": {"title": chart["title"][lang], "label-x": chart["labelx"][lang], "label-y": chart["labely"][lang]}, "data": []}
for serie in chart["series"]:
if serie["id"] == 'country':
# If we're dealing with a country, use the country name as label of serie
serie_name = aa
else:
serie_name = serie["name"][lang]
# Initialize the object for the serie
serie_to_append = {"name": serie_name, "id": serie["id"], "values": []}
# Add a note to the serie
if chart["note"]:
serie_to_append["note"] = add_note(serie, ind_source, aa)
# Generate the actual data
serie_to_append["values"] = chart['function'](serie, ind_source, lang, aa, chart["years"],global_avg)
json_data["data"].append(serie_to_append)
# Write the list to a JSON file
file_path = (settings.exp_aux_json).format(lang=lang,indicator=chart["export"],aa=iso)
write_json(file_path, json_data)
# Fully remove the temp directory
clean_dir(settings.tmp_dir, True)
print "All done. The auxiliary data has been prepared for use on global-climatescope.org."
def main():
#############################################################################
# 0.
#
# Check if tmp folder exists, otherwise create it
check_create_folder(settings.tmp_dir)
# Build the list with countries and states
admin_areas = get_aa_list()
for chart in settings.charts:
ind_source = settings.src_auxiliary + str(settings.current_edition) + '-' + str(chart["id"]) + '.csv'
global_avg = False
# Calculate the global average for this chart
if "global_average" in chart and chart["global_average"]:
global_avg = get_avg(chart, ind_source)
for aa in admin_areas:
iso = aa.lower()
for lang in settings.langs:
# Initialize the array that will be written to JSON
json_data = {"name": iso, "iso": iso, "meta": {"title": chart["title"][lang], "label-x": chart["labelx"][lang], "label-y": chart["labely"][lang]}, "data": []}
for serie in chart["series"]:
if serie["id"] == 'country':
# If we're dealing with a country, use the country name as label of serie
serie_name = aa
else:
serie_name = serie["name"][lang]
# Initialize the object for the serie
serie_to_append = {"name": serie_name, "id": serie["id"], "values": []}
# Generate the actual data
serie_to_append["values"] = chart['function'](serie, ind_source, lang, aa, chart["years"],global_avg)
json_data["data"].append(serie_to_append)
# Write the list to a JSON file
file_path = (settings.exp_aux_json).format(lang=lang,indicator=chart["export"],aa=iso)
write_json(file_path, json_data)
# Fully remove the temp directory
clean_dir(settings.tmp_dir, True)
print "All done. The auxiliary data has been prepared for use on global-climatescope.org."
def download_compranet(years):
"""
Download Compranet data for a list of years, unzip the files and convert
the XLS to CSV
:param years:
The years for which to download data
:type years:
List
:returns:
:example:
"""
tmp_folder = os.path.join(settings.folder_full_cache, 'tmp')
check_create_folder(tmp_folder)
for year in years:
file_name = os.path.join(settings.fn_prefix + year + settings.fn_extension)
src_url = settings.compranet_base_url + file_name
print "Downloading %s" % file_name
download(url=src_url, path=tmp_folder)
file_path = os.path.join(tmp_folder, file_name)
with zipfile.ZipFile(file_path, 'r') as myzip:
myzip.extractall(tmp_folder)
pattern = os.path.join(tmp_folder, '*.xls*')
for src_file in list_files(pattern):
csv_path = os.path.join(settings.folder_full_cache, get_filename(src_file) + '.csv')
wb = xlrd.open_workbook(src_file)
sheet = wb.sheet_by_index(0)
with open(csv_path, 'w') as csvfile:
writer = unicodecsv.writer(csvfile, encoding='utf-8')
for rownum in xrange(sheet.nrows):
writer.writerow(sheet.row_values(rownum))
remove_folder(tmp_folder)
def main(args):
"""
Main function - launches the program.
:param args:
The Parser arguments
:type args:
Parser object
:returns:
List
:example:
["Downloading files from the Compranet site."]
"""
if args:
if args.sample:
source_folder = settings.folder_sample_data
else:
# Use cached versions of the source data in csv format
source_folder = settings.folder_full_cache
check_create_folder(source_folder)
if args.download:
clean_folder(source_folder)
download_compranet(settings.years)
# Check if there are CSV files in the sample folder
pattern = os.path.join(source_folder, '*.csv')
source_data = list_files(pattern)
if source_data:
print "About to clean the data"
clean_df = clean.clean_csv(source_data)
print "About to store it in OCDS format"
ocds.generate_json(clean_df)
else:
return["No source data found. Make sure there is at least one CSV file in " + source_folder, 1]
return["Prepared and cleaned the files from the Compranet site.",0]
def main(args):
"""
Main function - launches the program.
"""
if args:
check_create_folder(settings.folder_charts)
df = pd.DataFrame()
# Read in the JSON files, flatten the contracts and add them to a DataFrame
for f in list_files(args.source + '*'):
df = flatten_contracts(f, df)
# Improve
df['contract_period_startDate'] = df['contract_period_startDate'].convert_objects(convert_dates='coerce')
df['tender_publicationDate'] = df['tender_publicationDate'].convert_objects(convert_dates='coerce')
df['tender_tenderPeriod_startDate'] = df['tender_tenderPeriod_startDate'].convert_objects(convert_dates='coerce')
df['award_date'] = df['award_date'].convert_objects(convert_dates='coerce')
# Cut every contract that's before a starting date
start_date = datetime.strptime(settings.start_date_charts,'%Y-%m-%d')
end_date = datetime.strptime(settings.end_date_charts,'%Y-%m-%d')
df = df[(df[settings.main_date_contract] >= start_date) & (df[settings.main_date_contract] <= end_date)]
# Generate the summary statistics, independent of comparison or slice
overview_data = chartdata.generate_overview(df)
with open(os.path.join(settings.folder_charts, 'general.json'), 'w') as outfile:
json.dump(overview_data, outfile)
for dimension in settings.dimensions:
for comparison in settings.comparisons:
# Each unique combination of dimension + comparison is a 'lense'
lense_id = dimension + '--' + comparison['id']
lense = {
'metadata': {
'id': lense_id
},
'charts': []
}
for chart in settings.charts:
if chart['dimension'] == dimension:
if chart['function']:
chart['meta']['data'] = []
previous_slice = False
d = { }
# Generate the chart data
for sl in comparison['slices']:
sliced_chart = { 'id': sl['id'], 'label': sl['label'] }
# Prep the dataframe, slice it or serve it full
if comparison['compare']:
sliced_df = slice_df(df, comparison['compare'], sl['field'])
else:
sliced_df = df
if not sliced_df.empty:
current_slice = chart['function'](sliced_df)
# Append the slice's data & meta-data
sliced_chart['data'] = current_slice['data']
chart['meta']['data'].append(sliced_chart)
# Update the domain based on the slice
for axis, func in chart['domain'].items():
if previous_slice:
d[axis] = func(d[axis], current_slice['domain'][axis])
else:
d[axis] = current_slice['domain'][axis]
previous_slice = True
# Add the domain to the chart
for axis, func in chart['domain'].items():
chart['meta'][axis]['domain'] = d[axis]
# Append the chart data
lense['charts'].append(chart['meta'])
file_name = os.path.join(settings.folder_charts,lense_id + '.json')
with open(file_name, 'w') as outfile:
json.dump(lense, outfile)
def generate_json(df):
"""
Generate OCDS record packages for each month
:param df:
Dataframe with all the contracts
:type args:
DataFrame
:returns:
:example:
"""
check_create_folder(settings.folder_ocds_json)
check_create_folder(settings.folder_tmp)
clean_folder(settings.folder_tmp)
# Group the Compranet by date
df['group_date'] = df[settings.grouping_date].convert_objects(convert_dates='coerce')
grouped_df = df.set_index('group_date').groupby(pd.TimeGrouper(freq='M'))
# Store the records for each month in a temporary CSV file
# The JSON files will be generated from these CSV files, which
# is much more performant than iterating over the rows in pandas
files = []
for month, records in grouped_df:
if not records.empty:
m = month.strftime("%Y%m%d")
file_name = os.path.join(settings.folder_tmp, m + '.csv')
files.append(file_name)
records.to_csv(file_name, index=False)
# Loop over each CSV file and create an OCDS record package
for f in files:
# Store the package meta-data
## ADD MONTH
package = {
"uri": os.path.join("http://example.com/" + get_filename(f) + '.json'),
"publishedDate": get_filename(f),
"records": [],
"publisher": {
"identifier": "100",
"name": "Compranet"
},
"packages": []
}
# Read the file and generate the records
with open(f, 'rb') as infile:
data = csv.DictReader(infile)
ocds_records = {}
for record in data:
record_id = record['NUMERO_PROCEDIMIENTO']
# Add the generic tender data for this record,
# if it's not there already
if not record_id in ocds_records:
ocds_records[record_id] = get_tender_data(record)
# The contract and award data needs to be added for each row
# OCDS expects a unique ID for every award. NUMERO_EXPEDIENTE is not unique, hence
# a custom ID
award_id = str(record['NUMERO_EXPEDIENTE']) + '-' + str(len(ocds_records[record_id]['awards']) + 1)
ocds_records[record_id]['awards'].append(get_award_data(record, award_id))
ocds_records[record_id]['contracts'].append(get_contract_data(record, award_id))
for key, value in ocds_records.iteritems():
package['records'].append(value)
ofn = os.path.join(settings.folder_ocds_json, get_filename(f) + '.json')
with open(ofn, 'w') as outfile:
json.dump(package, outfile)
def main(args):
"""
Main function - launches the program.
"""
if args:
check_create_folder(settings.folder_charts)
df = pd.DataFrame()
# Read in the JSON files, flatten the contracts and add them to a DataFrame
for f in list_files(args.source + '*'):
df = flatten_contracts(f, df)
# Improve
df['contract_period_startDate'] = df[
'contract_period_startDate'].convert_objects(
convert_dates='coerce')
df['tender_publicationDate'] = df[
'tender_publicationDate'].convert_objects(convert_dates='coerce')
df['tender_tenderPeriod_startDate'] = df[
'tender_tenderPeriod_startDate'].convert_objects(
convert_dates='coerce')
df['award_date'] = df['award_date'].convert_objects(
convert_dates='coerce')
# Cut every contract that's before a starting date
start_date = datetime.strptime(settings.start_date_charts, '%Y-%m-%d')
end_date = datetime.strptime(settings.end_date_charts, '%Y-%m-%d')
df = df[(df[settings.main_date_contract] >= start_date)
& (df[settings.main_date_contract] <= end_date)]
# Generate the summary statistics, independent of comparison or slice
overview_data = chartdata.generate_overview(df)
with open(os.path.join(settings.folder_charts, 'general.json'),
'w') as outfile:
json.dump(overview_data, outfile)
for dimension in settings.dimensions:
for comparison in settings.comparisons:
# Each unique combination of dimension + comparison is a 'lense'
lense_id = dimension + '--' + comparison['id']
lense = {'metadata': {'id': lense_id}, 'charts': []}
for chart in settings.charts:
if chart['dimension'] == dimension:
if chart['function']:
chart['meta']['data'] = []
previous_slice = False
d = {}
# Generate the chart data
for sl in comparison['slices']:
sliced_chart = {
'id': sl['id'],
'label': sl['label']
}
# Prep the dataframe, slice it or serve it full
if comparison['compare']:
sliced_df = slice_df(
df, comparison['compare'], sl['field'])
else:
sliced_df = df
if not sliced_df.empty:
current_slice = chart['function'](
sliced_df)
# Append the slice's data & meta-data
sliced_chart['data'] = current_slice[
'data']
chart['meta']['data'].append(sliced_chart)
# Update the domain based on the slice
for axis, func in chart['domain'].items():
if previous_slice:
d[axis] = func(
d[axis],
current_slice['domain'][axis])
else:
d[axis] = current_slice['domain'][
axis]
previous_slice = True
# Add the domain to the chart
for axis, func in chart['domain'].items():
chart['meta'][axis]['domain'] = d[axis]
# Append the chart data
lense['charts'].append(chart['meta'])
file_name = os.path.join(settings.folder_charts,
lense_id + '.json')
with open(file_name, 'w') as outfile:
json.dump(lense, outfile)
