def test(self, emis, out_paths):
''' Master Testing Method.
Compare the final NetCDF output file emissions to the original EMFAC2014 input files.
PMEDS or CSE files will by compared by county, date, and EIC.
emis format: emis.data[region][date string] = EmissionsTable
EmissionsTable[eic][poll] = value
'''
# Fail politely if not EIC14
if self.precision != MAX_EIC_PRECISION:
print(
' + Output diurnal profiles can only be tested on EIC14 files.'
)
return
# Build top-emitting EIC list for all pollutants
eics = EmfacTxtDiurnalTester.find_top_eics(emis)
# test outputs for each dates
for date in self.dates:
d = date[5:]
if d not in out_paths:
print(
' + No output text files found for testing on date: ' +
d)
continue
cse_files = [
f for f in out_paths[d] if f.rstrip('.gz').endswith('.cse')
]
pmeds_files = [
f for f in out_paths[d] if f.rstrip('.gz').endswith('.pmeds')
]
if not pmeds_files and not cse_files:
print(
' + No output text files found for testing on date: ' +
d)
continue
# find the day-of-week
if date in find_holidays(self.base_year):
dow = 'holi'
else:
by_date = str(self.base_year) + '-' + d
dow = DOW[dt.strptime(by_date, self.date_format).weekday()]
for file_path in pmeds_files:
output_profs = self._output_pmeds_profiles(file_path, eics)
self._write_profile_comparision(output_profs, date, dow)
for file_path in cse_files:
output_profs = self._output_cse_profiles(file_path, eics)
self._write_profile_comparision(output_profs, date, dow)
def test(self, emis, out_paths):
''' Master Testing Method.
Compare the final NetCDF output file emissions to the original EMFAC2014 input files.
PMEDS or CSE files will by compared by county, date, and EIC.
emis format: emis.data[region][date string] = EmissionsTable
EmissionsTable[eic][poll] = value
'''
# reduce input EIC precision, if necessary
self._reduce_emissions_eics(emis)
# loop through each date
for d in self.dates:
date = d[5:]
if date not in out_paths:
print(
' + No output text files found for testing on date: ' +
str(date))
continue
# find the day-of-week
if date in find_holidays(self.base_year):
dow = 'holi'
else:
by_date = str(self.base_year) + '-' + date
dow = DOW[dt.strptime(by_date, self.date_format).weekday()]
# test output pmeds, if any
cse_files = [
f for f in out_paths[date] if f.rstrip('.gz').endswith('.cse')
]
pmeds_files = [
f for f in out_paths[date]
if f.rstrip('.gz').endswith('.pmeds')
]
if pmeds_files or cse_files:
self._read_and_compare_txt(pmeds_files, cse_files, date, emis,
dow)
def scale(self, emissions, spatial_surr, temp_surr):
""" Master method to scale emissions using spatial and temporal surrogates.
INPUT FORMATS:
Emissions: EMFAC2014EmissionsData
emis_data[region][date string] = EmissionsTable
EmissionsTable[EIC][pollutant] = value
Spatial Surrogates: SpatialSurrogateData[region][veh][act] = SpatialSurrogate()
Temporal Surrogates: {'diurnal': {}, 'dow': {}}
OUTPUT FORMAT:
ScaledEmissions: data[region][date][hr][eic] = SparseEmissions
SparseEmissions[pollutant][(grid, cell)] = value
NOTE: This function is a generator and will `yield` emissions file-by-file.
"""
today = deepcopy(self.base_start_date)
# loop through all the dates in the period
while today <= self.base_end_date:
# find the DOW
date = today.strftime(self.date_format)
today += timedelta(days=1)
if date[5:] in find_holidays(self.base_year):
dow_num = 7
dow = 'holi'
else:
by_date = str(self.base_year) + date[4:]
dow_num = dt.strptime(by_date, self.date_format).weekday()
dow = DOW[dt.strptime(by_date, self.date_format).weekday()]
# if not by sub-region, create emissions object
if not self.by_region:
e = ScaledEmissions()
for region in self.regions:
if date not in emissions.data[region]:
continue
# if by sub-area, create emissions object
if self.by_region:
e = ScaledEmissions()
# apply DOW factors (this line is long for performance reasons)
emis_table = self._apply_factors(
deepcopy(emissions.data[region][date]),
temp_surr['dow'][region][dow])
# find diurnal factors by hour
factors_by_hour = temp_surr['diurnal'][region][dow]
# pull today's spatial surrogate
spatial_surrs = spatial_surr.data[region]
# loop through each hour of the day
for hr in xrange(24):
# apply diurnal, then spatial profiles (this line long for performance reasons)
emis_dict = self._apply_spatial_surrs(
self._apply_factors(deepcopy(emis_table),
factors_by_hour[hr]),
spatial_surrs, region, dow_num, hr)
for eic, sparse_emis in emis_dict.iteritems():
e.set(region, date, hr + 1, self.eic_reduce(eic),
sparse_emis)
# yield, if by sub-area
if self.by_region:
yield e
# yield, if not by sub-area
if not self.by_region:
yield e
def scale(self, emissions, spatial_surr, temp_surr):
""" Master method to scale emissions using spatial and temporal surrogates.
INPUT FORMATS:
Emissions: EMFAC2014EmissionsData
emis_data[region][date string] = EmissionsTable
EmissionsTable[EIC][pollutant] = value
Spatial Surrogates: SpatialSurrogateData[region][label] = SpatialSurrogate()
Temporal Surrogates: {'diurnal': {}, 'dow': {}}
OUTPUT FORMAT:
ScaledEmissions: data[region][date][hr][eic] = SparseEmissions
SparseEmissions[pollutant][(grid, cell)] = value
NOTE: This function is a generator and will `yield` emissions file-by-file.
"""
self._load_species(emissions)
# find start date
today = dt(self.base_year, self.start_date.month, self.start_date.day)
# loop through all the dates in the period
while today <= self.base_end_date:
# find the DOW
date = today.strftime(self.date_format)
today += timedelta(days=1)
if date[5:] in find_holidays(self.base_year):
dow = 'holi'
else:
by_date = str(self.base_year) + date[4:]
dow = DOW[dt.strptime(by_date, self.date_format).weekday()]
# create a statewide emissions object
e = self._prebuild_scaled_emissions(date)
for region in self.regions:
if date not in emissions.data[region]:
continue
# use the speciation from the correct season
if self.month2season[region][today.month] == 's':
self.gsref = self.summer_gsref
else:
self.gsref = self.winter_gsref
# handle region bounding box (limits are inclusive: {'lat': (51, 92), 'lon': (156, 207)})
box = self.region_boxes[region]
# apply DOW factors (this line is long for performance reasons)
emis_table = self._apply_factors(
deepcopy(emissions.data[region][date]),
temp_surr['dow'][region][dow])
# find diurnal factors by hour
factors_by_hour = temp_surr['diurnal'][region][dow]
# pull today's spatial surrogate
spatial_surrs = spatial_surr.data[region]
# loop through each hour of the day
for hr in xrange(24):
sparse_emis = self._apply_spatial_surrs(
self._apply_factors(emis_table, factors_by_hour[hr]),
spatial_surrs, region, box, hr)
e.add_subgrid_nocheck(-999, date, hr + 1, -999,
sparse_emis, box)
yield e