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# [/LICENSE]
import pandas as pd
import csv
import os
from .DataConfig import ConfigSet
[docs]class ScenarioFile(object):
"""
Class representing individual input data files. Contains methods to parse and format those files based on configs.
"""
SPATIAL_CUMULATIVE = {
'id': 'total',
'label': 'Cumulative'
}
TEMPORAL_CUMULATIVE = {
'id': 'total',
'label': 'Cumulative'
}
def __init__(self, config_set, fp, scenario, attribute, temporal_resolution, spatial_resolution):
assert isinstance(config_set,ConfigSet)
self.config_set = config_set
self.fp = fp
self.scenario = config_set.scenario_configs.find_by_id(scenario)
self.attribute = config_set.attribute_configs.find_by_id(attribute)
self.temporal_resolution = config_set.temporal_configs.find_by_id(temporal_resolution)
self.spatial_resolution = config_set.spatial_configs.find_by_id(spatial_resolution)
self.cumulative_spatial = self.spatial_resolution is None or self.spatial_resolution['id'] == 'national'
self.cumulative_temporal = self.temporal_resolution is None
self.__data = None
self.temporal_config = None
self.spatial_config = None
self.cumulative_data = None
self.national_data = None
self.geom_data = None
@property
def scenario_id(self):
return self.scenario['id']
@property
def attribute_id(self):
return self.attribute['id']
@property
def temporal_resolution_id(self):
return self.temporal_resolution['id']
@property
def spatial_resolution_id(self):
return self.spatial_resolution['id']
def __repr__(self):
return "ScenarioFile({},{},{},{},{},{})".format(
repr(self.config_set),
repr(self.fp),
repr(self.scenario_id),
repr(self.attribute_id),
repr(self.temporal_resolution_id),
repr(self.spatial_resolution_id))
[docs] def read(self):
self.__data = pd.read_csv(self.fp)
# ETH@20170822 - I think this is mapping state abbreviations to FIPS codes.
# I would rather have state abbreviations.
# with open(os.path.abspath('../data/spatial/state_fips_codes.csv'), 'rU') as f:
# reader = csv.DictReader(f)
# fips_codes = {row['state']: row['fips'] for row in reader}
# if 'gid' in list(self.__data.columns):
# self.__data = self.__data[self.__data['gid'] != 'MEX'] # Remove Mexico
# self.__data['gid'] = self.__data['gid'].map(fips_codes, na_action='ignore')
[docs] def get_data(self):
if 'time' in self.__data.columns:
self.__data['time'] = self.__data['time'].astype(str)
self.__data['value'].round(4)
additional_columns = list(self.__data.columns.difference(['gid', 'value', 'time']))
if len(additional_columns) == 1:
response = {}
if self.cumulative_spatial:
grouped = self.__data.groupby(additional_columns[0])
for name, group in grouped:
group = group.drop(additional_columns[0], axis=1)
if self.cumulative_temporal:
response[name] = {'value': float("{0:.4f}".format(d['value'])) for d in group.to_dict(orient='records')}
else:
response[name] = {d['time']: float("{0:.4f}".format(d['value'])) for d in group.to_dict(orient='records')}
else:
try:
spatial_grouped = self.__data.groupby(['gid'])
except KeyError as e:
print(self.fp)
print(self.spatial_resolution)
raise e
for spatial_name, spatial_group in spatial_grouped:
response[spatial_name] = {
self.attribute['id']: {}
}
spatial_group = spatial_group.drop('gid', axis=1)
attr_grouped = spatial_group.groupby(additional_columns[0])
for attr_name, attr_group in attr_grouped:
attr_group = attr_group.drop(additional_columns[0], axis=1)
response[spatial_name][self.attribute['id']][attr_name] = {d['time']: float("{0:.4f}".format(d['value'])) for d in
attr_group.to_dict(orient='records')}
else:
if self.cumulative_spatial:
if self.cumulative_temporal:
response = {'value': float("{0:.4f}".format(d['value'])) for d in
self.__data.to_dict(orient='records')}
else:
response = {d['time']: float("{0:.4f}".format(d['value'])) for d in
self.__data.to_dict(orient='records')}
else:
response = {}
spatial_grouped = self.__data.groupby(['gid'])
for spatial_name, spatial_group in spatial_grouped:
response[spatial_name] = {}
spatial_group = spatial_group.drop('gid', axis=1)
if self.cumulative_temporal:
response[spatial_name][self.attribute['id']] = {'value': float("{0:.4f}".format(d['value']))
for d
in spatial_group.to_dict(orient='records')}
else:
response[spatial_name][self.attribute['id']] = {d['time']: float("{0:.4f}".format(d['value'])) for d
in
spatial_group.to_dict(orient='records')}
return response