def compute(param):
template = populateStringConstructor(args.filename_template, args)
template.variable = param.varname
template.month = param.monthname
fnameRoot = param.fileName
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
print('Specifying latitude / longitude domain of interest ...')
datanameID = 'diurnalstd' # Short ID name of output data
latrange = (param.args.lat1, param.args.lat2)
lonrange = (param.args.lon1, param.args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if param.args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = param.args.region_name
print('Reading %s ...' % fnameRoot)
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
try:
f = cdms2.open(fnameRoot)
x = f(datanameID, region)
units = x.units
print(' Shape =', x.shape)
print('Finding RMS area-average ...')
x = x * x
x = cdutil.averager(x, weights='unweighted')
x = cdutil.averager(x, axis='xy')
x = numpy.ma.sqrt(x)
print('For %8s in %s, average variance of hourly values = (%5.2f %s)^2' % (model, monthname, x, units))
f.close()
except Exception as err:
print("Failed model %s with error: %s" % (model, err))
x = 1.e20
return model, region, {region_name: x}
def compute(param):
template = populateStringConstructor(args.filename_template, args)
template.variable = param.varname
template.month = param.monthname
fnameRoot = param.fileName
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
print('Specifying latitude / longitude domain of interest ...')
datanameID = 'diurnalstd' # Short ID name of output data
latrange = (param.args.lat1, param.args.lat2)
lonrange = (param.args.lon1, param.args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if param.args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = param.args.region_name
print('Reading %s ...' % fnameRoot)
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
try:
f = cdms2.open(fnameRoot)
x = f(datanameID, region)
units = x.units
print(' Shape =', x.shape)
print('Finding RMS area-average ...')
x = x * x
x = cdutil.averager(x, weights='unweighted')
x = cdutil.averager(x, axis='xy')
x = numpy.ma.sqrt(x)
print('For %8s in %s, average variance of hourly values = (%5.2f %s)^2' % (model, monthname, x, units))
f.close()
except Exception as err:
print("Failed model %s with error: %s" % (model, err))
x = 1.e20
return model, region, {region_name: x}
def compute(param):
template = populateStringConstructor(args.filename_template, args)
template.variable = param.varname
template.month = param.monthname
fnameRoot = param.fileName
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
print('Specifying latitude / longitude domain of interest ...')
datanameID = 'diurnalmean' # Short ID name of output data
latrange = (param.args.lat1, param.args.lat2)
lonrange = (param.args.lon1, param.args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if param.args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = param.args.region_name
print('Reading %s ...' % fnameRoot)
try:
f = cdms2.open(fnameRoot)
x = f(datanameID, region)
units = x.units
print(' Shape =', x.shape)
print(
'Finding standard deviation over first dimension (time of day) ...'
)
x = genutil.statistics.std(x)
print(' Shape =', x.shape)
print('Finding r.m.s. average over 2nd-3rd dimensions (area) ...')
x = x * x
x = cdutil.averager(x, axis='xy')
x = cdms2.MV2.sqrt(x)
print(
'For %8s in %s, average variance of hourly values = (%5.2f %s)^2'
% (model, monthname, x, units))
f.close()
except Exception as err:
print("Failed model %s with error" % (err))
x = 1.e20
return model, region, {region_name: float(x)}
def compute(param):
template = populateStringConstructor(args.filename_template, args)
template.variable = param.varname
template.month = param.monthname
fnameRoot = param.fileName
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
print('Specifying latitude / longitude domain of interest ...')
datanameID = 'diurnalmean' # Short ID name of output data
latrange = (param.args.lat1, param.args.lat2)
lonrange = (param.args.lon1, param.args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if param.args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = param.args.region_name
print('Reading %s ...' % fnameRoot)
try:
f = cdms2.open(fnameRoot)
x = f(datanameID, region)
units = x.units
print(' Shape =', x.shape)
print('Finding standard deviation over first dimension (time of day) ...')
x = genutil.statistics.std(x)
print(' Shape =', x.shape)
print('Finding r.m.s. average over 2nd-3rd dimensions (area) ...')
x = x * x
x = cdutil.averager(x, axis='xy')
x = cdms2.MV2.sqrt(x)
print('For %8s in %s, average variance of hourly values = (%5.2f %s)^2' % (model, monthname, x, units))
f.close()
except Exception as err:
print("Failed model %s with error" % (err))
x = 1.e20
return model, region, {region_name: float(x)}
def main():
P.add_argument(
"-j",
"--outnamejson",
type=str,
dest='outnamejson',
default='pr_%(month)_%(firstyear)-%(lastyear)_savg_DiurnalFourier.json',
help="Output name for jsons")
P.add_argument("--lat1", type=float, default=-50., help="First latitude")
P.add_argument("--lat2", type=float, default=50., help="Last latitude")
P.add_argument("--lon1", type=float, default=0., help="First longitude")
P.add_argument("--lon2", type=float, default=360., help="Last longitude")
P.add_argument("--region_name",
type=str,
default="TRMM",
help="name for the region of interest")
P.add_argument(
"-t",
"--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_S.nc",
help="template for getting at amplitude files")
P.add_argument(
"--filename_template_tS",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_tS.nc",
help="template for phase files")
P.add_argument(
"--filename_template_sftlf",
default=
"cmip5.%(model).%(experiment).r0i0p0.fx.atm.fx.sftlf.%(version).latestX.xml",
help="template for sftlf file names")
P.add_argument("--model", default="*")
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear
finalyear = args.lastyear
years = "%s-%s" % (startyear, finalyear) # noqa: F841
print('Specifying latitude / longitude domain of interest ...')
# TRMM (observed) domain:
latrange = (args.lat1, args.lat2)
lonrange = (args.lon1, args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = args.region_name
# Amazon basin:
# latrange = (-15.0, -5.0)
# lonrange = (285.0, 295.0)
# Functions to convert phase between angle-in-radians and hours, for
# either a 12- or 24-hour clock, i.e. for clocktype = 12 or 24:
def hrs_to_rad(hours, clocktype):
import MV2
return 2 * MV2.pi * hours / clocktype
def rad_to_hrs(phase, clocktype):
import MV2
return phase * clocktype / 2 / MV2.pi
def vectoravg(hr1, hr2, clocktype):
'Function to test vector-averaging of two time values:'
import MV2
sin_avg = (MV2.sin(hrs_to_rad(hr1, clocktype)) +
MV2.sin(hrs_to_rad(hr2, clocktype))) / 2
cos_avg = (MV2.cos(hrs_to_rad(hr1, clocktype)) +
MV2.cos(hrs_to_rad(hr2, clocktype))) / 2
return rad_to_hrs(MV2.arctan2(sin_avg, cos_avg), clocktype)
def spacevavg(tvarb1, tvarb2, sftlf, model):
'''
Given a "root filename" and month/year specifications, vector-average lat/lon arrays in an (amplitude, phase)
pair of input data files. Each input data file contains diurnal (24h), semidiurnal (12h) and terdiurnal (8h)
Fourier harmonic components of the composite mean day/night cycle.
Vector-averaging means we consider the input data to be readings on an 8-, 12- or 24-hour clock and separately
average the Cartesian components (called "cosine" and "sine" below). Then the averaged components are combined
back into amplitude and phase values and returned.
Space-averaging is done globally, as well as separately for land and ocean areas.
'''
glolf = cdutil.averager(sftlf, axis='xy')
print(' Global mean land fraction = %5.3f' % glolf)
outD = {} # Output dictionary to be returned by this function
harmonics = [1, 2, 3]
for harmonic in harmonics:
ampl = tvarb1[harmonic - 1]
tmax = tvarb2[harmonic - 1]
# print ampl[:, :]
# print tmax[:, :]
clocktype = 24 / harmonic
cosine = MV2.cos(hrs_to_rad(tmax, clocktype)) * ampl # X-component
sine = MV2.sin(hrs_to_rad(tmax, clocktype)) * ampl # Y-component
print(
'Area-averaging globally, over land only, and over ocean only ...'
)
# Average Cartesian components ...
cos_avg_glo = cdutil.averager(cosine, axis='xy')
sin_avg_glo = cdutil.averager(sine, axis='xy')
cos_avg_lnd = cdutil.averager(cosine * sftlf, axis='xy')
sin_avg_lnd = cdutil.averager(sine * sftlf, axis='xy')
cos_avg_ocn = cos_avg_glo - cos_avg_lnd
sin_avg_ocn = sin_avg_glo - sin_avg_lnd
# ... normalized by land-sea fraction:
cos_avg_lnd /= glolf
sin_avg_lnd /= glolf
cos_avg_ocn /= (1 - glolf)
sin_avg_ocn /= (1 - glolf)
# Amplitude and phase:
# * 86400 Convert kg/m2/s -> mm/d?
amp_avg_glo = MV2.sqrt(sin_avg_glo**2 + cos_avg_glo**2)
# * 86400 Convert kg/m2/s -> mm/d?
amp_avg_lnd = MV2.sqrt(sin_avg_lnd**2 + cos_avg_lnd**2)
# * 86400 Convert kg/m2/s -> mm/d?
amp_avg_ocn = MV2.sqrt(sin_avg_ocn**2 + cos_avg_ocn**2)
pha_avg_glo = MV2.remainder(
rad_to_hrs(MV2.arctan2(sin_avg_glo, cos_avg_glo), clocktype),
clocktype)
pha_avg_lnd = MV2.remainder(
rad_to_hrs(MV2.arctan2(sin_avg_lnd, cos_avg_lnd), clocktype),
clocktype)
pha_avg_ocn = MV2.remainder(
rad_to_hrs(MV2.arctan2(sin_avg_ocn, cos_avg_ocn), clocktype),
clocktype)
if 'CMCC-CM' in model:
# print '** Correcting erroneous time recording in ', rootfname
pha_avg_lnd -= 3.0
pha_avg_lnd = MV2.remainder(pha_avg_lnd, clocktype)
elif 'BNU-ESM' in model or 'CCSM4' in model or 'CNRM-CM5' in model:
# print '** Correcting erroneous time recording in ', rootfname
pha_avg_lnd -= 1.5
pha_avg_lnd = MV2.remainder(pha_avg_lnd, clocktype)
print(
'Converting singleton transient variables to plain floating-point numbers ...'
)
amp_avg_glo = float(amp_avg_glo)
pha_avg_glo = float(pha_avg_glo)
amp_avg_lnd = float(amp_avg_lnd)
pha_avg_lnd = float(pha_avg_lnd)
amp_avg_ocn = float(amp_avg_ocn)
pha_avg_ocn = float(pha_avg_ocn)
print(
'%s %s-harmonic amplitude, phase = %7.3f mm/d, %7.3f hrsLST averaged globally'
% (monthname, harmonic, amp_avg_glo, pha_avg_glo))
print(
'%s %s-harmonic amplitude, phase = %7.3f mm/d, %7.3f hrsLST averaged over land'
% (monthname, harmonic, amp_avg_lnd, pha_avg_lnd))
print(
'%s %s-harmonic amplitude, phase = %7.3f mm/d, %7.3f hrsLST averaged over ocean'
% (monthname, harmonic, amp_avg_ocn, pha_avg_ocn))
# Sub-dictionaries, one for each harmonic component:
outD['harmonic' + str(harmonic)] = {}
outD['harmonic' + str(harmonic)]['amp_avg_lnd'] = amp_avg_lnd
outD['harmonic' + str(harmonic)]['pha_avg_lnd'] = pha_avg_lnd
outD['harmonic' + str(harmonic)]['amp_avg_ocn'] = amp_avg_ocn
outD['harmonic' + str(harmonic)]['pha_avg_ocn'] = pha_avg_ocn
return outD
print('Preparing to write output to JSON file ...')
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
jsonFile = populateStringConstructor(args.outnamejson, args)
jsonFile.month = monthname
jsonname = os.path.join(os.path.abspath(args.results_dir), jsonFile())
if not os.path.exists(jsonname) or args.append is False:
print('Initializing dictionary of statistical results ...')
stats_dic = {}
metrics_dictionary = collections.OrderedDict()
else:
with open(jsonname) as f:
metrics_dictionary = json.load(f)
stats_dic = metrics_dictionary["RESULTS"]
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(args.results_dir),
os.path.basename(jsonname))
try:
egg_pth = pkg_resources.resource_filename(
pkg_resources.Requirement.parse("pcmdi_metrics"), "share/pmp")
except Exception:
# python 2 seems to fail when ran in home directory of source?
egg_pth = os.path.join(os.getcwd(), "share", "pmp")
disclaimer = open(os.path.join(egg_pth, "disclaimer.txt")).read()
metrics_dictionary["DISCLAIMER"] = disclaimer
metrics_dictionary[
"REFERENCE"] = "The statistics in this file are based on Covey et al., J Climate 2016"
# Accumulate output from each model (or observed) data source in the
# Python dictionary.
template_S = populateStringConstructor(args.filename_template, args)
template_S.month = monthname
template_tS = populateStringConstructor(args.filename_template_tS, args)
template_tS.month = monthname
template_sftlf = populateStringConstructor(args.filename_template_sftlf,
args)
template_sftlf.month = monthname
print("TEMPLATE:", template_S())
files_S = glob.glob(os.path.join(args.modpath, template_S()))
print(files_S)
for file_S in files_S:
print('Reading Amplitude from %s ...' % file_S)
reverted = template_S.reverse(os.path.basename(file_S))
model = reverted["model"]
try:
template_tS.model = model
template_sftlf.model = model
S = cdms2.open(file_S)("S", region)
print('Reading Phase from %s ...' %
os.path.join(args.modpath, template_tS()))
tS = cdms2.open(os.path.join(args.modpath, template_tS()))("tS",
region)
print('Reading sftlf from %s ...' %
os.path.join(args.modpath, template_sftlf()))
try:
sftlf_fnm = glob.glob(
os.path.join(args.modpath, template_sftlf()))[0]
sftlf = cdms2.open(sftlf_fnm)("sftlf", region) / 100.
except BaseException as err:
print('Failed reading sftlf from file (error was: %s)' % err)
print('Creating one for you')
sftlf = cdutil.generateLandSeaMask(S.getGrid())
if model not in stats_dic:
stats_dic[model] = {
region_name: spacevavg(S, tS, sftlf, model)
}
else:
stats_dic[model].update(
{region_name: spacevavg(S, tS, sftlf, model)})
print(stats_dic)
except Exception as err:
print("Failed for model %s with error %s" % (model, err))
# Write output to JSON file.
metrics_dictionary["RESULTS"] = stats_dic
rgmsk = metrics_dictionary.get("RegionalMasking", {})
nm = region_name
region.id = nm
rgmsk[nm] = {"id": nm, "domain": region}
metrics_dictionary["RegionalMasking"] = rgmsk
OUT.write(metrics_dictionary,
json_structure=["model", "domain", "harmonic", "statistic"],
indent=4,
separators=(',', ': '))
print('done')
default="TRMM",
help="name for the region of interest")
P.add_argument(
"-t",
"--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_avg.nc")
P.add_argument("--model", default="*")
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear
finalyear = args.lastyear
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
print("TEMPLATE NAME:", template())
print('Specifying latitude / longitude domain of interest ...')
# TRMM (observed) domain:
latrange = (args.lat1, args.lat2)
lonrange = (args.lon1, args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
# Amazon basin:
# latrange = (-15.0, -5.0)
# lonrange = (285.0, 295.0)
P.add_argument("--lon1", type=float, default=0., help="First longitude")
P.add_argument("--lon2", type=float, default=360., help="Last longitude")
P.add_argument("--region_name", type=str, default="TRMM",
help="name for the region of interest")
P.add_argument("-t", "--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_std.nc")
P.add_argument("--model", default="*")
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear
finalyear = args.lastyear
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
print("TEMPLATE NAME:", template())
print('Specifying latitude / longitude domain of interest ...')
# TRMM (observed) domain:
latrange = (args.lat1, args.lat2)
lonrange = (args.lon1, args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
# Amazon basin:
# latrange = (-15.0, -5.0)
# lonrange = (285.0, 295.0)
def compute(params):
fileName = params.fileName
startyear = params.args.firstyear
finalyear = params.args.lastyear
month = params.args.month
monthname = params.monthname
varbname = params.varname
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
reverted = template.reverse(os.path.basename(fileName))
dataname = reverted["model"]
if dataname not in args.skip:
try:
print 'Data source:', dataname
print 'Opening %s ...' % fileName
f = cdms2.open(fileName)
iYear = 0
dmean = None
for year in range(startyear, finalyear + 1):
print 'Year %s:' % year
startTime = cdtime.comptime(year, month)
# Last possible second to get all tpoints
finishtime = startTime.add(1, cdtime.Month).add(
-1, cdtime.Minute)
print 'Reading %s from %s for time interval %s to %s ...' % (
varbname, fileName, startTime, finishtime)
# Transient variable stores data for current year's month.
tvarb = f(varbname, time=(startTime, finishtime, "ccn"))
# *HARD-CODES conversion from kg/m2/sec to mm/day.
tvarb *= 86400
# The following tasks need to be done only once, extracting
# metadata from first-year file:
tc = tvarb.getTime().asComponentTime()
current = tc[0]
while current.month == month:
end = cdtime.comptime(current.year, current.month,
current.day).add(1, cdtime.Day)
sub = tvarb(time=(current, end, "con"))
# Assumes first dimension of input ("axis#0") is time
tmp = numpy.ma.average(sub, axis=0)
sh = list(tmp.shape)
sh.insert(0, 1)
if dmean is None:
dmean = tmp.reshape(sh)
else:
dmean = numpy.ma.concatenate((dmean, tmp.reshape(sh)),
axis=0)
current = end
iYear += 1
f.close()
stdvalues = cdms2.MV2.array(genutil.statistics.std(dmean))
stdvalues.setAxis(0, tvarb.getLatitude())
stdvalues.setAxis(1, tvarb.getLongitude())
stdvalues.id = 'dailySD'
# Standard deviation has same units as mean.
stdvalues.units = "mm/d"
stdoutfile = ('%s_%s_%s_%s-%s_std_of_dailymeans.nc') % (
varbname, dataname, monthname, str(startyear), str(finalyear))
except Exception as err:
print "Failed for model: %s with error: %s" % (dataname, err)
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
g = cdms2.open(os.path.join(args.output_directory, stdoutfile), 'w')
g.write(stdvalues)
g.close()
% (monthname, harmonic, amp_avg_lnd, pha_avg_lnd))
print('%s %s-harmonic amplitude, phase = %7.3f mm/d, %7.3f hrsLST averaged over ocean'
% (monthname, harmonic, amp_avg_ocn, pha_avg_ocn))
# Sub-dictionaries, one for each harmonic component:
outD['harmonic' + str(harmonic)] = {}
outD['harmonic' + str(harmonic)]['amp_avg_lnd'] = amp_avg_lnd
outD['harmonic' + str(harmonic)]['pha_avg_lnd'] = pha_avg_lnd
outD['harmonic' + str(harmonic)]['amp_avg_ocn'] = amp_avg_ocn
outD['harmonic' + str(harmonic)]['pha_avg_ocn'] = pha_avg_ocn
return outD
print('Preparing to write output to JSON file ...')
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
jsonFile = populateStringConstructor(args.outnamejson, args)
jsonFile.month = monthname
jsonname = os.path.join(os.path.abspath(args.results_dir), jsonFile())
if not os.path.exists(jsonname) or args.append is False:
print('Initializing dictionary of statistical results ...')
stats_dic = {}
metrics_dictionary = collections.OrderedDict()
else:
with open(jsonname) as f:
metrics_dictionary = json.load(f)
stats_dic = metrics_dictionary["RESULTS"]
OUT = pcmdi_metrics.io.base.Base(
os.path.abspath(
def main():
def compute(param):
template = populateStringConstructor(args.filename_template, args)
template.variable = param.varname
template.month = param.monthname
fnameRoot = param.fileName
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
print("Specifying latitude / longitude domain of interest ...")
datanameID = "diurnalstd" # Short ID name of output data
latrange = (param.args.lat1, param.args.lat2)
lonrange = (param.args.lon1, param.args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if param.args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = param.args.region_name
print("Reading %s ..." % fnameRoot)
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
try:
f = cdms2.open(fnameRoot)
x = f(datanameID, region)
units = x.units
print(" Shape =", x.shape)
print("Finding RMS area-average ...")
x = x * x
x = cdutil.averager(x, weights="unweighted")
x = cdutil.averager(x, axis="xy")
x = numpy.ma.sqrt(x)
print(
"For %8s in %s, average variance of hourly values = (%5.2f %s)^2"
% (model, monthname, x, units))
f.close()
except Exception as err:
print("Failed model %s with error: %s" % (model, err))
x = 1.0e20
return model, region, {region_name: x}
P.add_argument(
"-j",
"--outnamejson",
type=str,
dest="outnamejson",
default="pr_%(month)_%(firstyear)-%(lastyear)_std_of_hourlymeans.json",
help="Output name for jsons",
)
P.add_argument("--lat1", type=float, default=-50.0, help="First latitude")
P.add_argument("--lat2", type=float, default=50.0, help="Last latitude")
P.add_argument("--lon1", type=float, default=0.0, help="First longitude")
P.add_argument("--lon2", type=float, default=360.0, help="Last longitude")
P.add_argument(
"--region_name",
type=str,
default="TRMM",
help="name for the region of interest",
)
P.add_argument(
"-t",
"--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_std.nc",
)
P.add_argument("--model", default="*")
P.add_argument(
"--cmec",
dest="cmec",
action="store_true",
default=False,
help="Use to save metrics in CMEC JSON format",
)
P.add_argument(
"--no_cmec",
dest="cmec",
action="store_false",
default=False,
help="Use to disable saving metrics in CMEC JSON format",
)
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear # noqa: F841
finalyear = args.lastyear # noqa: F841
cmec = args.cmec
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
print("TEMPLATE NAME:", template())
print("Specifying latitude / longitude domain of interest ...")
# TRMM (observed) domain:
latrange = (args.lat1, args.lat2)
lonrange = (args.lon1, args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
# Amazon basin:
# latrange = (-15.0, -5.0)
# lonrange = (285.0, 295.0)
print("Preparing to write output to JSON file ...")
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
jsonFile = populateStringConstructor(args.outnamejson, args)
jsonFile.month = monthname
jsonname = os.path.join(os.path.abspath(args.results_dir), jsonFile())
if not os.path.exists(jsonname) or args.append is False:
print("Initializing dictionary of statistical results ...")
stats_dic = {}
metrics_dictionary = collections.OrderedDict()
else:
with open(jsonname) as f:
metrics_dictionary = json.load(f)
stats_dic = metrics_dictionary["RESULTS"]
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(args.results_dir),
jsonFile())
egg_pth = resources.resource_path()
disclaimer = open(os.path.join(egg_pth, "disclaimer.txt")).read()
metrics_dictionary["DISCLAIMER"] = disclaimer
metrics_dictionary["REFERENCE"] = (
"The statistics in this file are based on Trenberth, Zhang & Gehne, "
"J Hydromet. 2017")
files = glob.glob(os.path.join(args.modpath, template()))
print(files)
params = [INPUT(args, name, template) for name in files]
print("PARAMS:", params)
results = cdp.cdp_run.multiprocess(compute,
params,
num_workers=args.num_workers)
for r in results:
m, region, res = r
if r[0] not in stats_dic:
stats_dic[m] = res
else:
stats_dic[m].update(res)
print("Writing output to JSON file ...")
metrics_dictionary["RESULTS"] = stats_dic
rgmsk = metrics_dictionary.get("RegionalMasking", {})
nm = list(res.keys())[0]
region.id = nm
rgmsk[nm] = {"id": nm, "domain": region}
metrics_dictionary["RegionalMasking"] = rgmsk
OUT.write(
metrics_dictionary,
json_structure=["model", "domain"],
indent=4,
separators=(",", ": "),
)
if cmec:
print("Writing cmec file")
OUT.write_cmec(indent=4, separators=(",", ": "))
print("done")
% (monthname, harmonic, amp_avg_lnd, pha_avg_lnd)
print '%s %s-harmonic amplitude, phase = %7.3f mm/d, %7.3f hrsLST averaged over ocean'\
% (monthname, harmonic, amp_avg_ocn, pha_avg_ocn)
# Sub-dictionaries, one for each harmonic component:
outD['harmonic' + str(harmonic)] = {}
outD['harmonic' + str(harmonic)]['amp_avg_lnd'] = amp_avg_lnd
outD['harmonic' + str(harmonic)]['pha_avg_lnd'] = pha_avg_lnd
outD['harmonic' + str(harmonic)]['amp_avg_ocn'] = amp_avg_ocn
outD['harmonic' + str(harmonic)]['pha_avg_ocn'] = pha_avg_ocn
return outD
print 'Preparing to write output to JSON file ...'
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
jsonFile = populateStringConstructor(args.outnamejson, args)
jsonFile.month = monthname
jsonname = os.path.join(os.path.abspath(args.output_directory), jsonFile())
if not os.path.exists(jsonname) or args.append is False:
print 'Initializing dictionary of statistical results ...'
stats_dic = {}
metrics_dictionary = collections.OrderedDict()
else:
with open(jsonname) as f:
metrics_dictionary = json.load(f)
stats_dic = metrics_dictionary["RESULTS"]
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(args.output_directory),
os.path.basename(jsonname))
def compute(params):
fileName = params.fileName
month = params.args.month
monthname = params.monthname
varbname = params.varname
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
# Units on output (*may be converted below from the units of input*)
outunits = 'mm/d'
startime = 1.5 # GMT value for starting time-of-day
reverted = template.reverse(os.path.basename(fileName))
dataname = reverted["model"]
if dataname not in args.skip:
try:
print('Data source:', dataname)
print('Opening %s ...' % fileName)
f = cdms2.open(fileName)
# Composite-mean and composite-s.d diurnal cycle for month and year(s):
iYear = 0
for year in range(args.firstyear, args.lastyear + 1):
print('Year %s:' % year)
startTime = cdtime.comptime(year, month, 1, 1, 30)
# Last possible second to get all tpoints
finishtime = startTime.add(1, cdtime.Month).add(
-1.5, cdtime.Hour).add(.1, cdtime.Second)
print('Reading %s from %s for time interval %s to %s ...' %
(varbname, fileName, startTime, finishtime))
# Transient variable stores data for current year's month.
tvarb = f(varbname, time=(startTime, finishtime))
# *HARD-CODES conversion from kg/m2/sec to mm/day.
tvarb *= 86400
print('Shape:', tvarb.shape)
# The following tasks need to be done only once, extracting
# metadata from first-year file:
if year == args.firstyear:
tc = tvarb.getTime().asComponentTime()
day1 = cdtime.comptime(tc[0].year, tc[0].month)
firstday = tvarb(time=(day1, day1.add(1, cdtime.Day),
"con"))
dimensions = firstday.shape
# print ' Shape = ', dimensions
# Number of time points in the selected month for one year
N = dimensions[0]
nlats = dimensions[1]
nlons = dimensions[2]
deltaH = 24. / N
dayspermo = tvarb.shape[0] // N
print(
' %d timepoints per day, %d hr intervals between timepoints'
% (N, deltaH))
comptime = firstday.getTime()
modellons = tvarb.getLongitude()
modellats = tvarb.getLatitude()
# Longitude values are needed later to compute Local Solar
# Times.
lons = modellons[:]
print(' Creating temporary storage and output fields ...')
# Sorts tvarb into separate GMTs for one year
tvslice = MV2.zeros((N, dayspermo, nlats, nlons))
# Concatenates tvslice over all years
concatenation = MV2.zeros(
(N, dayspermo * nYears, nlats, nlons))
LSTs = MV2.zeros((N, nlats, nlons))
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(
' Computing Local Standard Times for GMT %5.2f ...'
% hour)
for j in range(nlats):
for k in range(nlons):
LSTs[iGMT, j, k] = (hour + lons[k] / 15) % 24
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(' Choosing timepoints with GMT %5.2f ...' % hour)
# Transient-variable slice: every Nth tpoint gets all of
# the current GMT's tpoints for current year:
tvslice[iGMT] = tvarb[iGMT:tvarb.shape[0]:N]
concatenation[iGMT, iYear * dayspermo:(iYear + 1) *
dayspermo] = tvslice[iGMT]
iYear += 1
f.close()
# For each GMT, take mean and standard deviation over all years for
# the chosen month:
avgvalues = MV2.zeros((N, nlats, nlons))
stdvalues = MV2.zeros((N, nlats, nlons))
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(
'Computing mean and standard deviation over all GMT %5.2f timepoints ...'
% hour)
# Assumes first dimension of input ("axis#0") is time
avgvalues[iGMT] = MV2.average(concatenation[iGMT], axis=0)
stdvalues[iGMT] = genutil.statistics.std(concatenation[iGMT])
avgvalues.id = 'diurnalmean'
stdvalues.id = 'diurnalstd'
LSTs.id = 'LST'
avgvalues.units = outunits
# Standard deviation has same units as mean (not so for
# higher-moment stats).
stdvalues.units = outunits
LSTs.units = 'hr'
LSTs.longname = 'Local Solar Time'
avgvalues.setAxis(0, comptime)
avgvalues.setAxis(1, modellats)
avgvalues.setAxis(2, modellons)
stdvalues.setAxis(0, comptime)
stdvalues.setAxis(1, modellats)
stdvalues.setAxis(2, modellons)
LSTs.setAxis(0, comptime)
LSTs.setAxis(1, modellats)
LSTs.setAxis(2, modellons)
avgoutfile = ('%s_%s_%s_%s-%s_diurnal_avg.nc') % (
varbname, dataname, monthname, str(
args.firstyear), str(args.lastyear))
stdoutfile = ('%s_%s_%s_%s-%s_diurnal_std.nc') % (
varbname, dataname, monthname, str(
args.firstyear), str(args.lastyear))
LSToutfile = ('%s_%s_LocalSolarTimes.nc' % (varbname, dataname))
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
f = cdms2.open(os.path.join(args.results_dir, avgoutfile), 'w')
g = cdms2.open(os.path.join(args.results_dir, stdoutfile), 'w')
h = cdms2.open(os.path.join(args.results_dir, LSToutfile), 'w')
f.write(avgvalues)
g.write(stdvalues)
h.write(LSTs)
f.close()
g.close()
h.close()
except Exception as err:
print("Failed for model %s with erro: %s" % (dataname, err))
def main():
def compute(params):
fileName = params.fileName
month = params.args.month
monthname = params.monthname
varbname = params.varname
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
# Units on output (*may be converted below from the units of input*)
outunits = "mm/d"
startime = 1.5 # GMT value for starting time-of-day
dataname = params.args.model
if dataname is None or dataname.find("*") != -1:
# model not passed or passed as *
reverted = template.reverse(os.path.basename(fileName))
print("REVERYING", reverted, dataname)
dataname = reverted["model"]
if dataname not in args.skip:
try:
print("Data source:", dataname)
print("Opening %s ..." % fileName)
f = cdms2.open(fileName)
# Composite-mean and composite-s.d diurnal cycle for month and year(s):
iYear = 0
for year in range(args.firstyear, args.lastyear + 1):
print("Year %s:" % year)
startTime = cdtime.comptime(year, month)
# Last possible second to get all tpoints
finishtime = startTime.add(1, cdtime.Month).add(-1, cdtime.Minute)
print(
"Reading %s from %s for time interval %s to %s ..."
% (varbname, fileName, startTime, finishtime)
)
# Transient variable stores data for current year's month.
tvarb = f(varbname, time=(startTime, finishtime))
# *HARD-CODES conversion from kg/m2/sec to mm/day.
tvarb *= 86400
print("Shape:", tvarb.shape)
# The following tasks need to be done only once, extracting
# metadata from first-year file:
if year == args.firstyear:
tc = tvarb.getTime().asComponentTime()
print("DATA FROM:", tc[0], "to", tc[-1])
day1 = cdtime.comptime(tc[0].year, tc[0].month)
day1 = tc[0]
firstday = tvarb(time=(day1, day1.add(1.0, cdtime.Day), "con"))
dimensions = firstday.shape
print(" Shape = ", dimensions)
# Number of time points in the selected month for one year
N = dimensions[0]
nlats = dimensions[1]
nlons = dimensions[2]
deltaH = 24.0 / N
dayspermo = tvarb.shape[0] // N
print(
" %d timepoints per day, %d hr intervals between timepoints"
% (N, deltaH)
)
comptime = firstday.getTime()
modellons = tvarb.getLongitude()
modellats = tvarb.getLatitude()
# Longitude values are needed later to compute Local Solar
# Times.
lons = modellons[:]
print(" Creating temporary storage and output fields ...")
# Sorts tvarb into separate GMTs for one year
tvslice = MV2.zeros((N, dayspermo, nlats, nlons))
# Concatenates tvslice over all years
concatenation = MV2.zeros((N, dayspermo * nYears, nlats, nlons))
LSTs = MV2.zeros((N, nlats, nlons))
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(
" Computing Local Standard Times for GMT %5.2f ..."
% hour
)
for j in range(nlats):
for k in range(nlons):
LSTs[iGMT, j, k] = (hour + lons[k] / 15) % 24
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(" Choosing timepoints with GMT %5.2f ..." % hour)
print("days per mo :", dayspermo)
# Transient-variable slice: every Nth tpoint gets all of
# the current GMT's tpoints for current year:
tvslice[iGMT] = tvarb[iGMT::N]
concatenation[
iGMT, iYear * dayspermo : (iYear + 1) * dayspermo
] = tvslice[iGMT]
iYear += 1
f.close()
# For each GMT, take mean and standard deviation over all years for
# the chosen month:
avgvalues = MV2.zeros((N, nlats, nlons))
stdvalues = MV2.zeros((N, nlats, nlons))
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(
"Computing mean and standard deviation over all GMT %5.2f timepoints ..."
% hour
)
# Assumes first dimension of input ("axis#0") is time
avgvalues[iGMT] = MV2.average(concatenation[iGMT], axis=0)
stdvalues[iGMT] = genutil.statistics.std(concatenation[iGMT])
avgvalues.id = "diurnalmean"
stdvalues.id = "diurnalstd"
LSTs.id = "LST"
avgvalues.units = outunits
# Standard deviation has same units as mean (not so for
# higher-moment stats).
stdvalues.units = outunits
LSTs.units = "hr"
LSTs.longname = "Local Solar Time"
avgvalues.setAxis(0, comptime)
avgvalues.setAxis(1, modellats)
avgvalues.setAxis(2, modellons)
stdvalues.setAxis(0, comptime)
stdvalues.setAxis(1, modellats)
stdvalues.setAxis(2, modellons)
LSTs.setAxis(0, comptime)
LSTs.setAxis(1, modellats)
LSTs.setAxis(2, modellons)
avgoutfile = ("%s_%s_%s_%s-%s_diurnal_avg.nc") % (
varbname,
dataname,
monthname,
str(args.firstyear),
str(args.lastyear),
)
stdoutfile = ("%s_%s_%s_%s-%s_diurnal_std.nc") % (
varbname,
dataname,
monthname,
str(args.firstyear),
str(args.lastyear),
)
LSToutfile = "%s_%s_LocalSolarTimes.nc" % (varbname, dataname)
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
f = cdms2.open(os.path.join(args.results_dir, avgoutfile), "w")
g = cdms2.open(os.path.join(args.results_dir, stdoutfile), "w")
h = cdms2.open(os.path.join(args.results_dir, LSToutfile), "w")
f.write(avgvalues)
g.write(stdvalues)
h.write(LSTs)
f.close()
g.close()
h.close()
except Exception as err:
print("Failed for model %s with erro: %s" % (dataname, err))
print("done")
args = P.get_parameter()
month = args.month # noqa: F841
monthname = monthname_d[args.month] # noqa: F841
# -------------------------------------HARD-CODED INPUT (add to command line later?):
# These models have been processed already (or tried and found wanting,
# e.g. problematic time coordinates):
skipMe = args.skip # noqa: F841
# Choose only one ensemble member per model, with the following ensemble-member code (for definitions, see
# http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf):
# NOTE--These models do not supply 3hr data from the 'r1i1p1' ensemble member,
# but do supply it from other ensemble members:
# bcc-csm1-1 (3hr data is from r2i1p1)
# CCSM4 (3hr data is from r6i1p1)
# GFDL-CM3 (3hr data is from r2i1p1, r3i1p1, r4i1p1, r5i1p1)
# GISS-E2-H (3hr data is from r6i1p1, r6i1p3)
# GISS-E2-R (3hr data is from r6i1p2)
varbname = "pr"
# Note that CMIP5 specifications designate (01:30, 04:30, 07:30, ..., 22:30) GMT for 3hr flux fields, but
# *WARNING* some GMT timepoints are actually (0, 3, 6,..., 21) in submitted CMIP5 data, despite character strings in
# file names (and time axis metadata) to the contrary. See CMIP5 documentation and errata! Overrides to
# correct these problems are given below:
# startGMT = '0:0:0.0' # Include 00Z as a possible starting time, to accomodate (0, 3, 6,..., 21)GMT in the input
# data.
# startime = -1.5 # Subtract 1.5h from (0, 3, 6,..., 21)GMT input data. This is needed for BNU-ESM, CCSM4 and
# CNRM-CM5.
# startime = -3.0 # Subtract 1.5h from (0, 3, 6,..., 21)GMT input
# data. This is needed for CMCC-CM.
# -------------------------------------
nYears = args.lastyear - args.firstyear + 1
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
print("TEMPLATE:", template())
fileList = glob.glob(os.path.join(args.modpath, template()))
print("FILES:", fileList)
params = [INPUT(args, name, template) for name in fileList]
print("PARAMS:", params)
cdp.cdp_run.multiprocess(compute, params, num_workers=args.num_workers)
help="longitudes")
P.add_argument(
"-A",
"--outnameasc",
type=str,
dest='outnameasc',
default='pr_%(month)_%(firstyear)-%(lastyear)_fourierDiurnalGridPoints.asc',
help="Output name for ascs")
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear
finalyear = args.lastyear
yearrange = "%s-%s" % (startyear, finalyear)
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
template_std = populateStringConstructor(args.filename_template_std, args)
template_std.month = monthname
template_LST = populateStringConstructor(args.filename_template_LST, args)
template_LST.month = monthname
LSTfiles = glob.glob(os.path.join(args.modroot, template_LST()))
print("LSTFILES:", LSTfiles)
print("TMPL", template_LST())
ascFile = populateStringConstructor(args.outnameasc, args)
ascFile.month = monthname
ascname = os.path.join(os.path.abspath(args.output_directory), ascFile())
if not os.path.exists(os.path.dirname(ascname)):
def compute(params):
fileName = params.fileName
month = params.args.month
monthname = params.monthname
varbname = params.varname
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
# Units on output (*may be converted below from the units of input*)
outunits = 'mm/d'
startime = 1.5 # GMT value for starting time-of-day
reverted = template.reverse(os.path.basename(fileName))
dataname = reverted["model"]
if dataname not in args.skip:
try:
print('Data source:', dataname)
print('Opening %s ...' % fileName)
f = cdms2.open(fileName)
# Composite-mean and composite-s.d diurnal cycle for month and year(s):
iYear = 0
for year in range(args.firstyear, args.lastyear + 1):
print('Year %s:' % year)
startTime = cdtime.comptime(year, month, 1, 1, 30)
# Last possible second to get all tpoints
finishtime = startTime.add(
1, cdtime.Month).add(-1.5, cdtime.Hour).add(.1, cdtime.Second)
print('Reading %s from %s for time interval %s to %s ...' % (varbname, fileName, startTime, finishtime))
# Transient variable stores data for current year's month.
tvarb = f(varbname, time=(startTime, finishtime))
# *HARD-CODES conversion from kg/m2/sec to mm/day.
tvarb *= 86400
print('Shape:', tvarb.shape)
# The following tasks need to be done only once, extracting
# metadata from first-year file:
if year == args.firstyear:
tc = tvarb.getTime().asComponentTime()
day1 = cdtime.comptime(tc[0].year, tc[0].month)
firstday = tvarb(
time=(
day1,
day1.add(
1,
cdtime.Day),
"con"))
dimensions = firstday.shape
# print ' Shape = ', dimensions
# Number of time points in the selected month for one year
N = dimensions[0]
nlats = dimensions[1]
nlons = dimensions[2]
deltaH = 24. / N
dayspermo = tvarb.shape[0] // N
print(' %d timepoints per day, %d hr intervals between timepoints' % (N, deltaH))
comptime = firstday.getTime()
modellons = tvarb.getLongitude()
modellats = tvarb.getLatitude()
# Longitude values are needed later to compute Local Solar
# Times.
lons = modellons[:]
print(' Creating temporary storage and output fields ...')
# Sorts tvarb into separate GMTs for one year
tvslice = MV2.zeros((N, dayspermo, nlats, nlons))
# Concatenates tvslice over all years
concatenation = MV2.zeros(
(N, dayspermo * nYears, nlats, nlons))
LSTs = MV2.zeros((N, nlats, nlons))
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(' Computing Local Standard Times for GMT %5.2f ...' % hour)
for j in range(nlats):
for k in range(nlons):
LSTs[iGMT, j, k] = (hour + lons[k] / 15) % 24
for iGMT in range(N):
hour = iGMT * deltaH + startime
print(' Choosing timepoints with GMT %5.2f ...' % hour)
# Transient-variable slice: every Nth tpoint gets all of
# the current GMT's tpoints for current year:
tvslice[iGMT] = tvarb[iGMT:tvarb.shape[0]:N]
concatenation[iGMT, iYear *
dayspermo: (iYear +
1) *
dayspermo] = tvslice[iGMT]
iYear += 1
f.close()
# For each GMT, take mean and standard deviation over all years for
# the chosen month:
avgvalues = MV2.zeros((N, nlats, nlons))
stdvalues = MV2.zeros((N, nlats, nlons))
for iGMT in range(N):
hour = iGMT * deltaH + startime
print('Computing mean and standard deviation over all GMT %5.2f timepoints ...' % hour)
# Assumes first dimension of input ("axis#0") is time
avgvalues[iGMT] = MV2.average(concatenation[iGMT], axis=0)
stdvalues[iGMT] = genutil.statistics.std(concatenation[iGMT])
avgvalues.id = 'diurnalmean'
stdvalues.id = 'diurnalstd'
LSTs.id = 'LST'
avgvalues.units = outunits
# Standard deviation has same units as mean (not so for
# higher-moment stats).
stdvalues.units = outunits
LSTs.units = 'hr'
LSTs.longname = 'Local Solar Time'
avgvalues.setAxis(0, comptime)
avgvalues.setAxis(1, modellats)
avgvalues.setAxis(2, modellons)
stdvalues.setAxis(0, comptime)
stdvalues.setAxis(1, modellats)
stdvalues.setAxis(2, modellons)
LSTs.setAxis(0, comptime)
LSTs.setAxis(1, modellats)
LSTs.setAxis(2, modellons)
avgoutfile = ('%s_%s_%s_%s-%s_diurnal_avg.nc') % (varbname,
dataname, monthname,
str(args.firstyear), str(args.lastyear))
stdoutfile = ('%s_%s_%s_%s-%s_diurnal_std.nc') % (varbname,
dataname, monthname, str(
args.firstyear),
str(args.lastyear))
LSToutfile = ('%s_%s_LocalSolarTimes.nc' % (varbname, dataname))
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
f = cdms2.open(
os.path.join(
args.results_dir,
avgoutfile),
'w')
g = cdms2.open(
os.path.join(
args.results_dir,
stdoutfile),
'w')
h = cdms2.open(
os.path.join(
args.results_dir,
LSToutfile),
'w')
f.write(avgvalues)
g.write(stdvalues)
h.write(LSTs)
f.close()
g.close()
h.close()
except Exception as err:
print("Failed for model %s with erro: %s" % (dataname, err))
def main():
P.add_argument(
"-t",
"--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_avg.nc",
help="template for file names containing diurnal average",
)
P.add_argument("--model", default="*")
P.add_argument(
"--filename_template_LST",
default="pr_%(model)_LocalSolarTimes.nc",
help="template for file names point to Local Solar Time Files",
)
P.add_argument(
"--filename_template_std",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_std.nc",
help="template for file names containing diurnal std",
)
P.add_argument(
"-l",
"--lats",
nargs="*",
default=[31.125, 31.125, 36.4, 5.125, 45.125, 45.125],
help="latitudes",
)
P.add_argument(
"-L",
"--lons",
nargs="*",
default=[-83.125, 111.145, -97.5, 147.145, -169.145, -35.145],
help="longitudes",
)
P.add_argument(
"-A",
"--outnameasc",
type=str,
dest="outnameasc",
default=
"pr_%(month)_%(firstyear)-%(lastyear)_fourierDiurnalGridPoints.asc",
help="Output name for ascs",
)
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear
finalyear = args.lastyear
yearrange = "%s-%s" % (startyear, finalyear) # noqa: F841
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
template_std = populateStringConstructor(args.filename_template_std, args)
template_std.month = monthname
template_LST = populateStringConstructor(args.filename_template_LST, args)
template_LST.month = monthname
LSTfiles = glob.glob(os.path.join(args.modpath, template_LST()))
print("LSTFILES:", LSTfiles)
print("TMPL", template_LST())
ascFile = populateStringConstructor(args.outnameasc, args)
ascFile.month = monthname
ascname = os.path.join(os.path.abspath(args.results_dir), ascFile())
if not os.path.exists(os.path.dirname(ascname)):
os.makedirs(os.path.dirname(ascname))
fasc = open(ascname, "w")
gridptlats = [float(x) for x in args.lats]
gridptlons = [float(x) for x in args.lons]
nGridPoints = len(gridptlats)
assert len(gridptlons) == nGridPoints
# gridptlats = [-29.125, -5.125, 45.125, 45.125]
# gridptlons = [-57.125, 75.125, -169.145, -35.145]
# Gridpoints for JULY samples in Figure 4 of Covey et al., JClimate 29: 4461 (2016):
# nGridPoints = 6
# gridptlats = [ 31.125, 31.125, 36.4, 5.125, 45.125, 45.125]
# gridptlons = [-83.125, 111.145, -97.5, 147.145, -169.145, -35.145]
N = 8 # Number of timepoints in a 24-hour cycle
for LSTfile in LSTfiles:
print("Reading %s ..." % LSTfile, os.path.basename(LSTfile), file=fasc)
print("Reading %s ..." % LSTfile, os.path.basename(LSTfile), file=fasc)
reverted = template_LST.reverse(os.path.basename(LSTfile))
model = reverted["model"]
print("====================", file=fasc)
print(model, file=fasc)
print("====================", file=fasc)
template.model = model
avgfile = template()
template_std.model = model
stdfile = template_std()
print("Reading time series of mean diurnal cycle ...", file=fasc)
f = cdms2.open(LSTfile)
g = cdms2.open(os.path.join(args.modpath, avgfile))
h = cdms2.open(os.path.join(args.modpath, stdfile))
LSTs = f("LST")
print("Input shapes: ", LSTs.shape, file=fasc)
modellats = LSTs.getLatitude()
modellons = LSTs.getLongitude()
latbounds = modellats.getBounds() # noqa: F841
lonbounds = modellons.getBounds() # noqa: F841
# Gridpoints selected above may be offset slightly from points in full
# grid ...
closestlats = MV2.zeros(nGridPoints)
closestlons = MV2.zeros(nGridPoints)
pointLSTs = MV2.zeros((nGridPoints, N))
avgvalues = MV2.zeros((nGridPoints, N))
stdvalues = MV2.zeros((nGridPoints, N))
# ... in which case, just pick the closest full-grid point:
for i in range(nGridPoints):
print(
" (lat, lon) = (%8.3f, %8.3f)" %
(gridptlats[i], gridptlons[i]),
file=fasc,
)
closestlats[i] = gridptlats[i]
closestlons[i] = gridptlons[i] % 360
print(
" Closest (lat, lon) for gridpoint = (%8.3f, %8.3f)" %
(closestlats[i], closestlons[i]),
file=fasc,
)
# Time series for selected grid point:
avgvalues[i] = g(
"diurnalmean",
lat=(closestlats[i], closestlats[i], "cob"),
lon=(closestlons[i], closestlons[i], "cob"),
squeeze=1,
)
stdvalues[i] = h(
"diurnalstd",
lat=(closestlats[i], closestlats[i], "cob"),
lon=(closestlons[i], closestlons[i], "cob"),
squeeze=1,
)
pointLSTs[i] = f(
"LST",
lat=(closestlats[i], closestlats[i], "cob"),
lon=(closestlons[i], closestlons[i], "cob"),
squeeze=1,
)
print(" ", file=fasc)
f.close()
g.close()
h.close()
# Print results for input to Mathematica.
if monthname == "Jan":
# In printed output, numbers for January data follow 0-5 for July data,
# hence begin with 6.
deltaI = 6
else:
deltaI = 0
prefix = args.modpath
for i in range(nGridPoints):
print(
"For gridpoint %d at %5.1f deg latitude, %6.1f deg longitude ..."
% (i, gridptlats[i], gridptlons[i]),
file=fasc,
)
print(" Local Solar Times are:", file=fasc)
print((prefix + "LST%d = {") % (i + deltaI), file=fasc)
print(N * "%5.3f, " % tuple(pointLSTs[i]), end="", file=fasc)
print("};", file=fasc)
print(" Mean values for each time-of-day are:", file=fasc)
print((prefix + "mean%d = {") % (i + deltaI), file=fasc)
print(N * "%5.3f, " % tuple(avgvalues[i]), end="", file=fasc)
print("};", file=fasc)
print(" Standard deviations for each time-of-day are:",
file=fasc)
print((prefix + "std%d = {") % (i + deltaI), file=fasc)
print(N * "%6.4f, " % tuple(stdvalues[i]), end="", file=fasc)
print("};", file=fasc)
print(" ", file=fasc)
# Take fast Fourier transform of the overall multi-year mean diurnal cycle.
print("************** ", avgvalues[0][0], file=fasc)
cycmean, maxvalue, tmax = fastFT(avgvalues, pointLSTs)
print("************** ", avgvalues[0][0], file=fasc)
# Print Fourier harmonics:
for i in range(nGridPoints):
print(
"For gridpoint %d at %5.1f deg latitude, %6.1f deg longitude ..."
% (i, gridptlats[i], gridptlons[i]),
file=fasc,
)
print(" Mean value over cycle = %6.2f" % cycmean[i], file=fasc)
print(
" Diurnal maximum = %6.2f at %6.2f hr Local Solar Time."
% (maxvalue[i, 0], tmax[i, 0] % 24),
file=fasc,
)
print(
" Semidiurnal maximum = %6.2f at %6.2f hr Local Solar Time."
% (maxvalue[i, 1], tmax[i, 1] % 24),
file=fasc,
)
print(
" Terdiurnal maximum = %6.2f at %6.2f hr Local Solar Time."
% (maxvalue[i, 2], tmax[i, 2] % 24),
file=fasc,
)
print("Results sent to:", ascname)
# *WARNING* some GMT timepoints are actually (0, 3, 6,..., 21) in submitted CMIP5 data, despite character strings in # file names (and time axis metadata) to the contrary. See CMIP5 documentation and errata! Overrides to # correct these problems are given below: # Include 00Z as a possible starting time, to accomodate (0, 3, 6,..., # 21)GMT in the input data. # startime = -1.5 # Subtract 1.5h from (0, 3, 6,..., 21)GMT input # data. This is needed for BNU-ESM, CCSM4 and CNRM-CM5. # Subtract 1.5h from (0, 3, 6,..., 21)GMT input data. This is needed for # CMCC-CM. # ------------------------------------- monthname = monthname_d[month] nYears = finalyear - startyear + 1 # Character strings for starting and ending day/GMT (*HARD-CODES # particular GMT timepoints*): # *WARNING* GMT timepoints are actually (0, 3, 6,..., 21) in the original TRMM/Obs4MIPs data, despite character strings # in file names (and time axis metadata). See CMIP5 documentation and # errata! template = populateStringConstructor(args.filename_template, args) template.variable = varbname fileList = glob.glob(os.path.join(directory, template())) print "FILES:", fileList params = [INPUT(args, name, template) for name in fileList] print "PARAMS:", params cdp.cdp_run.multiprocess(compute, params, num_workers=args.num_workers)
# file names (and time axis metadata) to the contrary. See CMIP5 documentation and errata! Overrides to
# correct these problems are given below:
# Include 00Z as a possible starting time, to accomodate (0, 3, 6,...,
# 21)GMT in the input data.
# startime = -1.5 # Subtract 1.5h from (0, 3, 6,..., 21)GMT input
# data. This is needed for BNU-ESM, CCSM4 and CNRM-CM5.
# Subtract 1.5h from (0, 3, 6,..., 21)GMT input data. This is needed for
# CMCC-CM.
# -------------------------------------
monthname = monthname_d[month]
nYears = finalyear - startyear + 1
# Character strings for starting and ending day/GMT (*HARD-CODES
# particular GMT timepoints*):
# *WARNING* GMT timepoints are actually (0, 3, 6,..., 21) in the original TRMM/Obs4MIPs data, despite character strings
# in file names (and time axis metadata). See CMIP5 documentation and
# errata!
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
fileList = glob.glob(os.path.join(directory, template()))
print("FILES:", fileList)
params = [INPUT(args, name, template) for name in fileList]
print("PARAMS:", params)
cdp.cdp_run.multiprocess(compute, params, num_workers=args.num_workers)
def main():
def compute(param):
template = populateStringConstructor(args.filename_template, args)
template.variable = param.varname
template.month = param.monthname
fnameRoot = param.fileName
reverted = template.reverse(os.path.basename(fnameRoot))
model = reverted["model"]
print('Specifying latitude / longitude domain of interest ...')
datanameID = 'diurnalmean' # Short ID name of output data
latrange = (param.args.lat1, param.args.lat2)
lonrange = (param.args.lon1, param.args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
if param.args.region_name == "":
region_name = "{:g}_{:g}&{:g}_{:g}".format(*(latrange + lonrange))
else:
region_name = param.args.region_name
print('Reading %s ...' % fnameRoot)
try:
f = cdms2.open(fnameRoot)
x = f(datanameID, region)
units = x.units
print(' Shape =', x.shape)
print(
'Finding standard deviation over first dimension (time of day) ...'
)
x = genutil.statistics.std(x)
print(' Shape =', x.shape)
print('Finding r.m.s. average over 2nd-3rd dimensions (area) ...')
x = x * x
x = cdutil.averager(x, axis='xy')
x = cdms2.MV2.sqrt(x)
print(
'For %8s in %s, average variance of hourly values = (%5.2f %s)^2'
% (model, monthname, x, units))
f.close()
except Exception as err:
print("Failed model %s with error" % (err))
x = 1.e20
return model, region, {region_name: float(x)}
P.add_argument(
"-j",
"--outnamejson",
type=str,
dest='outnamejson',
default=
'pr_%(month)_%(firstyear)-%(lastyear)_std_of_meandiurnalcyc.json',
help="Output name for jsons")
P.add_argument("--lat1", type=float, default=-50., help="First latitude")
P.add_argument("--lat2", type=float, default=50., help="Last latitude")
P.add_argument("--lon1", type=float, default=0., help="First longitude")
P.add_argument("--lon2", type=float, default=360., help="Last longitude")
P.add_argument("--region_name",
type=str,
default="TRMM",
help="name for the region of interest")
P.add_argument(
"-t",
"--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_avg.nc")
P.add_argument("--model", default="*")
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear # noqa: F841
finalyear = args.lastyear # noqa: F841
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
print("TEMPLATE NAME:", template())
print('Specifying latitude / longitude domain of interest ...')
# TRMM (observed) domain:
latrange = (args.lat1, args.lat2)
lonrange = (args.lon1, args.lon2)
region = cdutil.region.domain(latitude=latrange, longitude=lonrange)
# Amazon basin:
# latrange = (-15.0, -5.0)
# lonrange = (285.0, 295.0)
print('Preparing to write output to JSON file ...')
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
jsonFile = populateStringConstructor(args.outnamejson, args)
jsonFile.month = monthname
jsonname = os.path.join(os.path.abspath(args.results_dir), jsonFile())
if not os.path.exists(jsonname) or args.append is False:
print('Initializing dictionary of statistical results ...')
stats_dic = {}
metrics_dictionary = collections.OrderedDict()
else:
with open(jsonname) as f:
metrics_dictionary = json.load(f)
print("LOADE WITH KEYS:", list(metrics_dictionary.keys()))
stats_dic = metrics_dictionary["RESULTS"]
OUT = pcmdi_metrics.io.base.Base(os.path.abspath(args.results_dir),
jsonFile())
try:
egg_pth = pkg_resources.resource_filename(
pkg_resources.Requirement.parse("pcmdi_metrics"), "share/pmp")
except Exception:
# python 2 seems to fail when ran in home directory of source?
egg_pth = os.path.join(os.getcwd(), "share", "pmp")
disclaimer = open(os.path.join(egg_pth, "disclaimer.txt")).read()
metrics_dictionary["DISCLAIMER"] = disclaimer
metrics_dictionary["REFERENCE"] = (
"The statistics in this file are based on Trenberth, Zhang & Gehne, "
"J Hydromet. 2017")
files = glob.glob(os.path.join(args.modpath, template()))
print(files)
params = [INPUT(args, name, template) for name in files]
print("PARAMS:", params)
results = cdp.cdp_run.multiprocess(compute,
params,
num_workers=args.num_workers)
for r in results:
m, region, res = r
if r[0] not in stats_dic:
stats_dic[m] = res
else:
stats_dic[m].update(res)
print('Writing output to JSON file ...')
metrics_dictionary["RESULTS"] = stats_dic
print("KEYS AT END:", list(metrics_dictionary.keys()))
rgmsk = metrics_dictionary.get("RegionalMasking", {})
print("REG MASK:", rgmsk)
nm = list(res.keys())[0]
region.id = nm
rgmsk[nm] = {"id": nm, "domain": region}
metrics_dictionary["RegionalMasking"] = rgmsk
OUT.write(metrics_dictionary,
json_structure=["model", "domain"],
indent=4,
separators=(',', ': '))
print('done')
def compute(params):
fileName = params.fileName
startyear = params.args.firstyear
finalyear = params.args.lastyear
month = params.args.month
monthname = params.monthname
varbname = params.varname
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
reverted = template.reverse(os.path.basename(fileName))
dataname = reverted["model"]
if dataname not in args.skip:
try:
print('Data source:', dataname)
print('Opening %s ...' % fileName)
f = cdms2.open(fileName)
iYear = 0
dmean = None
for year in range(startyear, finalyear + 1):
print('Year %s:' % year)
startTime = cdtime.comptime(year, month)
# Last possible second to get all tpoints
finishtime = startTime.add(
1, cdtime.Month).add(-1, cdtime.Minute)
print('Reading %s from %s for time interval %s to %s ...' % (varbname, fileName, startTime, finishtime))
# Transient variable stores data for current year's month.
tvarb = f(varbname, time=(startTime, finishtime, "ccn"))
# *HARD-CODES conversion from kg/m2/sec to mm/day.
tvarb *= 86400
# The following tasks need to be done only once, extracting
# metadata from first-year file:
tc = tvarb.getTime().asComponentTime()
current = tc[0]
while current.month == month:
end = cdtime.comptime(
current.year,
current.month,
current.day).add(
1,
cdtime.Day)
sub = tvarb(time=(current, end, "con"))
# Assumes first dimension of input ("axis#0") is time
tmp = numpy.ma.average(sub, axis=0)
sh = list(tmp.shape)
sh.insert(0, 1)
if dmean is None:
dmean = tmp.reshape(sh)
else:
dmean = numpy.ma.concatenate(
(dmean, tmp.reshape(sh)), axis=0)
current = end
iYear += 1
f.close()
stdvalues = cdms2.MV2.array(genutil.statistics.std(dmean))
stdvalues.setAxis(0, tvarb.getLatitude())
stdvalues.setAxis(1, tvarb.getLongitude())
stdvalues.id = 'dailySD'
# Standard deviation has same units as mean.
stdvalues.units = "mm/d"
stdoutfile = ('%s_%s_%s_%s-%s_std_of_dailymeans.nc') % (varbname, dataname,
monthname, str(startyear), str(finalyear))
except Exception as err:
print("Failed for model: %s with error: %s" % (dataname, err))
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
g = cdms2.open(os.path.join(args.results_dir, stdoutfile), 'w')
g.write(stdvalues)
g.close()
def main():
P.add_argument(
"-t",
"--filename_template",
default="pr_%(model)_%(month)_%(firstyear)-%(lastyear)_diurnal_avg.nc",
help="template for file names containing diurnal average",
)
P.add_argument("--model", default="*")
P.add_argument(
"--filename_template_LST",
default="pr_%(model)_LocalSolarTimes.nc",
help="template for file names point to Local Solar Time Files",
)
args = P.get_parameter()
month = args.month
monthname = monthname_d[month]
startyear = args.firstyear
finalyear = args.lastyear
yearrange = "%s-%s" % (startyear, finalyear)
template = populateStringConstructor(args.filename_template, args)
template.month = monthname
template_LST = populateStringConstructor(args.filename_template_LST, args)
template_LST.month = monthname
LSTfiles = glob.glob(os.path.join(args.modpath, template_LST()))
print("modpath ", args.modpath)
print("filename_template ", args.filename_template)
print("filename_template_LST ", args.filename_template_LST)
print("LSTFILES:", LSTfiles)
print("TMPL", template_LST())
for LSTfile in LSTfiles:
print("Reading %s ..." % LSTfile, os.path.basename(LSTfile))
reverted = template_LST.reverse(os.path.basename(LSTfile))
model = reverted["model"]
print("====================")
print(model)
print("====================")
template.model = model
avgfile = template()
print("Reading time series of mean diurnal cycle ...")
f = cdms2.open(LSTfile)
g = cdms2.open(os.path.join(args.modpath, avgfile))
LSTs = f("LST")
avgs = g("diurnalmean")
print("Input shapes: ", LSTs.shape, avgs.shape)
print("Getting latitude and longitude coordinates.")
# Any file with grid info will do, so use Local Standard Times file:
modellats = LSTs.getLatitude()
modellons = LSTs.getLongitude()
f.close()
g.close()
print("Taking fast Fourier transform of the mean diurnal cycle ...")
cycmean, maxvalue, tmax = fastAllGridFT(avgs, LSTs)
print(" Output:")
print(" cycmean", cycmean.shape)
print(" maxvalue", maxvalue.shape)
print(" tmax", tmax.shape)
print('"Re-decorating" Fourier harmonics with grid info, etc., ...')
cycmean = MV2.array(cycmean)
maxvalue = MV2.array(maxvalue)
tmax = MV2.array(tmax)
cycmean.setAxis(0, modellats)
cycmean.setAxis(1, modellons)
cycmean.id = "tmean"
cycmean.units = "mm / day"
maxvalue.setAxis(1, modellats)
maxvalue.setAxis(2, modellons)
maxvalue.id = "S"
maxvalue.units = "mm / day"
tmax.setAxis(1, modellats)
tmax.setAxis(2, modellons)
tmax.id = "tS"
tmax.units = "GMT"
print("... and writing to netCDF.")
f = cdms2.open(
os.path.join(
args.results_dir,
"pr_" + model + "_" + monthname + "_" + yearrange +
"_tmean.nc",
),
"w",
)
g = cdms2.open(
os.path.join(
args.results_dir,
"pr_" + model + "_" + monthname + "_" + yearrange + "_S.nc",
),
"w",
)
h = cdms2.open(
os.path.join(
args.results_dir,
"pr_" + model + "_" + monthname + "_" + yearrange + "_tS.nc",
),
"w",
)
f.write(cycmean)
g.write(maxvalue)
h.write(tmax)
f.close()
g.close()
h.close()
def main():
def compute(params):
fileName = params.fileName
startyear = params.args.firstyear
finalyear = params.args.lastyear
month = params.args.month
monthname = params.monthname
varbname = params.varname
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
dataname = params.args.model
if dataname is None or dataname.find("*") != -1:
# model not passed or passed as *
reverted = template.reverse(os.path.basename(fileName))
dataname = reverted["model"]
print('Data source:', dataname)
print('Opening %s ...' % fileName)
if dataname not in args.skip:
try:
print('Data source:', dataname)
print('Opening %s ...' % fileName)
f = cdms2.open(fileName)
iYear = 0
dmean = None
for year in range(startyear, finalyear + 1):
print('Year %s:' % year)
startTime = cdtime.comptime(year, month)
# Last possible second to get all tpoints
finishtime = startTime.add(1, cdtime.Month).add(
-1, cdtime.Minute)
print('Reading %s from %s for time interval %s to %s ...' %
(varbname, fileName, startTime, finishtime))
# Transient variable stores data for current year's month.
tvarb = f(varbname, time=(startTime, finishtime, "ccn"))
# *HARD-CODES conversion from kg/m2/sec to mm/day.
tvarb *= 86400
# The following tasks need to be done only once, extracting
# metadata from first-year file:
tc = tvarb.getTime().asComponentTime()
current = tc[0]
while current.month == month:
end = cdtime.comptime(current.year, current.month,
current.day).add(1, cdtime.Day)
sub = tvarb(time=(current, end, "con"))
# Assumes first dimension of input ("axis#0") is time
tmp = numpy.ma.average(sub, axis=0)
sh = list(tmp.shape)
sh.insert(0, 1)
if dmean is None:
dmean = tmp.reshape(sh)
else:
dmean = numpy.ma.concatenate(
(dmean, tmp.reshape(sh)), axis=0)
current = end
iYear += 1
f.close()
stdvalues = cdms2.MV2.array(genutil.statistics.std(dmean))
stdvalues.setAxis(0, tvarb.getLatitude())
stdvalues.setAxis(1, tvarb.getLongitude())
stdvalues.id = 'dailySD'
# Standard deviation has same units as mean.
stdvalues.units = "mm/d"
stdoutfile = ('%s_%s_%s_%s-%s_std_of_dailymeans.nc') % (
varbname, dataname, monthname, str(startyear),
str(finalyear))
except Exception as err:
print("Failed for model: %s with error: %s" % (dataname, err))
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
g = cdms2.open(os.path.join(args.results_dir, stdoutfile), 'w')
g.write(stdvalues)
g.close()
args = P.get_parameter()
month = args.month
startyear = args.firstyear
finalyear = args.lastyear
directory = args.modpath # Input directory for model data
# These models have been processed already (or tried and found wanting,
# e.g. problematic time coordinates):
skipMe = args.skip
print("SKIPPING:", skipMe)
# Choose only one ensemble member per model, with the following ensemble-member code (for definitions, see
# http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf):
# NOTE--These models do not supply 3hr data from the 'r1i1p1' ensemble member,
# but do supply it from other ensemble members:
# bcc-csm1-1 (3hr data is from r2i1p1)
# CCSM4 (3hr data is from r6i1p1)
# GFDL-CM3 (3hr data is from r2i1p1, r3i1p1, r4i1p1, r5i1p1)
# GISS-E2-H (3hr data is from r6i1p1, r6i1p3)
# GISS-E2-R (3hr data is from r6i1p2)
varbname = "pr"
# Note that CMIP5 specifications designate (01:30, 04:30, 07:30, ..., 22:30) GMT for 3hr flux fields, but
# *WARNING* some GMT timepoints are actually (0, 3, 6,..., 21) in submitted CMIP5 data, despite character strings in
# file names (and time axis metadata) to the contrary. See CMIP5 documentation and errata! Overrides to
# correct these problems are given below:
# Include 00Z as a possible starting time, to accomodate (0, 3, 6,...,
# 21)GMT in the input data.
# startime = -1.5 # Subtract 1.5h from (0, 3, 6,..., 21)GMT input
# data. This is needed for BNU-ESM, CCSM4 and CNRM-CM5.
# Subtract 1.5h from (0, 3, 6,..., 21)GMT input data. This is needed for
# CMCC-CM.
# -------------------------------------
monthname = monthname_d[month] # noqa: F841
nYears = finalyear - startyear + 1 # noqa: F841
# Character strings for starting and ending day/GMT (*HARD-CODES
# particular GMT timepoints*):
# *WARNING* GMT timepoints are actually (0, 3, 6,..., 21) in the original TRMM/Obs4MIPs data, despite character
# strings in file names (and time axis metadata). See CMIP5 documentation and
# errata!
template = populateStringConstructor(args.filename_template, args)
template.variable = varbname
fileList = glob.glob(os.path.join(directory, template()))
print("FILES:", fileList)
params = [INPUT(args, name, template) for name in fileList]
print("PARAMS:", params)
cdp.cdp_run.multiprocess(compute, params, num_workers=args.num_workers)