def main(args):
'''
The main function
'''
(
baseline_data_source,
historical_data_source,
projection_data_source,
variable_name,
baseline_start_date,
baseline_end_date,
projection_start_date,
projection_end_date,
) = get_args(args)
measurement = transform.to_standard_variable_name(variable_name)
units = transform.standard_units_from_measurement(measurement)
climatedb.connect()
for month in range(1, climatedb.MONTHS_PER_YEAR + 1):
#print('.', end='', flush=True)
print('For month %d' % month)
calibration.calibrate(baseline_data_source, historical_data_source,
projection_data_source, measurement, units,
baseline_start_date, baseline_end_date,
projection_start_date, projection_end_date,
month)
climatedb.close()
def main(args):
'''
The main function
'''
filename = get_args(args)
climatedb.connect()
geonames.load_geonames(filename)
climatedb.close()
def main(args):
'''
The main function
'''
input_file, variable_name, data_source, ignore_scale_factor = get_args(args)
print(variable_name, data_source)
lat_arr, lon_arr, units, data_arr = get_data_from_file(input_file, variable_name, ignore_scale_factor)
lon_arr, data_arr = transform.normalize_longitudes(lon_arr, data_arr)
measurement = transform.to_standard_variable_name(variable_name)
climatedb.connect()
climatedb.save_nontemporal_data(lat_arr, lon_arr, units, data_arr, measurement, data_source)
climatedb.close()
return 0
def main(args):
'''
The main function
'''
climatedb.connect()
variable_name, data_source, frequency = get_args(args)
print(data_source, variable_name)
config = load_config()
if data_source in config:
organisation, author, year, article_url = get_citation_from_config(
config, data_source)
file_url = get_file_url_from_config(config, data_source, variable_name)
file_urls = [file_url]
else:
model, scenario = data_source.split('.')
search_url = config['esgf']['search']['url']
project = config['esgf']['search']['project']
variant = config['esgf']['search']['variant']
datasets = search_esgf(search_url, project, variable_name, model,
scenario, frequency, variant)
dataset_info = datasets[0]
organisation = dataset_info['institution_id'][0]
author, year, article_url = get_citation_from_url(
dataset_info['citation_url'][0])
thredds_url, thredds_id = get_thredds_url(dataset_info)
file_urls = get_file_urls_from_thredds(thredds_url, thredds_id)
file_paths = fetch_urls(file_urls)
if len(file_paths) == 0:
print('No datasets found', file=sys.stderr)
return 1
file_base, ext = file_paths[0].rsplit(os.path.extsep)
output_file_path = os.path.join(
DATASET_DIR, '%s-%s.%s' % (data_source, variable_name, ext))
os.path.exists(output_file_path) and os.remove(output_file_path)
if len(file_paths) == 1:
file_path = file_paths[0]
os.symlink(os.path.basename(file_path), output_file_path)
else:
if ext == 'nc':
cdo.Cdo().selall(input=sorted(file_paths),
output=output_file_path,
options='-f nc4')
baseline = (config['baseline'] == data_source)
create_data_source(data_source, organisation, author, year, article_url,
baseline)
return 0
def main(args):
'''
The main function
'''
data_source, variable_name, start_date, end_date, calibrated = get_args(args)
measurement = transform.to_standard_variable_name(variable_name)
units = transform.standard_units_from_measurement(measurement)
climatedb.connect()
unit_id = climatedb.fetch_unit(units)['id']
measurement_id = climatedb.fetch_measurement(measurement)['id']
data_source_record = climatedb.fetch_data_source(data_source)
dataset = climatedb.fetch_dataset(
data_source_record['id'],
measurement_id,
unit_id,
start_date,
end_date,
calibrated=True if calibrated else False
)
print(data_source, measurement, start_date.year, end_date.year, 'year')
lat_arr, lon_arr, normals = climatedb.fetch_normals_from_dataset_mean(dataset)
projected_y_arr = geo.lat2y(lat_arr)
projected_x_arr = geo.lon2x(lon_arr)
output_folder = tiling.tile_folder(data_source, variable_name, start_date, end_date)
tiling.save_contour_tiles(
projected_y_arr,
projected_x_arr,
measurement,
units,
normals,
output_folder,
data_source_record['id']
)
for start_month in (12, 3, 6, 9):
months = start_month, (start_month + 1) % 12, (start_month + 2) % 12
print(data_source, measurement, start_date.year, end_date.year, months)
aggregated_normals = None
for month in months:
lat_arr, lon_arr, normals = climatedb.fetch_normals_from_dataset(dataset, month)
if aggregated_normals is None:
aggregated_normals = normals.copy()
else:
aggregated_normals += normals
aggregated_normals = aggregated_normals / len(months)
output_folder = tiling.tile_folder(data_source, variable_name, start_date, end_date, months)
tiling.save_contour_tiles(
projected_y_arr,
projected_x_arr,
measurement,
units,
aggregated_normals,
output_folder,
data_source_record['id']
)
climatedb.close()
def main(args):
'''
The main function
'''
input_files, variable_name, start_year, end_year, data_source = get_args(
args)
# Extract normals from datasets
def get_normals_function(month, start_time, end_time):
def get_normals(input_file):
input_fmt = get_input_fmt(input_file)
if input_fmt == 'nc':
return transform.normals_from_netcdf4(input_file,
variable_name,
start_time, end_time,
month)
elif input_fmt in ('tif', 'bil'):
return transform.normals_from_geotiff(input_file, input_fmt)
elif input_fmt == 'folder':
return transform.normals_from_folder(input_file, variable_name,
month)
else:
raise Exception('Unexpected input format "%s"' % input_fmt)
return get_normals
climatedb.connect()
for month in range(1, climatedb.MONTHS_PER_YEAR + 1):
print(data_source, variable_name, start_year, end_year, month)
if month > 0:
start_time = datetime(start_year, month, 1)
# This will set the end time to the last second of the last day of the
# specified month in the specified end year.
# Credit to https://stackoverflow.com/a/4131114 for the div/mod by 12
end_time = datetime(end_year + month // 12, month % 12 + 1,
1) - timedelta(seconds=1)
else:
start_time = datetime(start_year, 1, 1)
end_time = datetime(end_year + 1, 1, 1) - timedelta(seconds=1)
lat_arr, lon_arr, units, normals = \
transform.aggregate_normals(input_files, get_normals_function(month, start_time, end_time))
units, normals = transform.data_to_standard_units(
units, normals, month)
lon_arr, normals = transform.normalize_longitudes(lon_arr, normals)
normals = pack.pack_array(normals)
measurement = transform.to_standard_variable_name(variable_name)
climatedb.save_normals(lat_arr, lon_arr, units, normals, measurement,
start_time, end_time, month, data_source)
climatedb.close()
return 0
# API for serving climate tiles
#
# Copyright (c) 2020 Carlos Torchia
#
from flask import Flask
from flask import jsonify
import os
import tiling
import climatedb
app = Flask(__name__)
climatedb.connect()
ALLOWED_MEASUREMENTS = climatedb.fetch_measurements()
climatedb.close()
ALLOWED_PERIODS = [
'12_01_02',
'03_04_05',
'06_07_08',
'09_10_11',
'year',
]
@app.route(
'/climate/<string:data_source>/<int:start_year>-<int:end_year>/<string:measurement>-<string:period>/<int:zoom_level>/<int:x>/<int:y>.<string:ext>'
)
def before():
'''
Connects to the database in preparation for the request.
'''
climatedb.connect()