def ConvertCAMxTime(date, time, nvars=1):
"""
Use camx date and time arrays to produce an
IOAPI standard TFLAG variable
"""
f = PseudoNetCDFFile()
f.dimensions = {'TSTEP': date.shape[0], 'VAR': nvars, 'DATE-TIME': 2}
a = array([date, time], dtype='i').swapaxes(0, 1)
if len(a.shape) == 2:
a = a[:, newaxis, :]
date = a[:, :, 0]
if (date < 70000).any():
date += 2000000
else:
date += 1900000
time = a[:, :, 1]
while not (time == 0).all() and time.max() < 10000:
time *= 100
a = PseudoNetCDFVariable(f,
'TFLAG',
'i', ('TSTEP', 'VAR', 'DATE-TIME'),
values=a[:, [0], :].repeat(nvars, 1))
a.units = 'DATE-TIME'.ljust(16)
a.long_name = 'TFLAG'.ljust(16)
a.var_desc = a.long_name
return a
def setUp(self):
from permm.mechanisms import small_strato
from PseudoNetCDF import PseudoNetCDFFile
from numpy import arange, newaxis
from numpy.random import normal, poisson, random, seed
self.mech = small_strato()
mrg = self.mrg = PseudoNetCDFFile()
mrg.createDimension('TSTEP', 10)
mrg.createDimension('ROW', 3)
mrg.createDimension('LAY', 4)
mrg.createDimension('COL', 5)
mrg.createDimension('VAR', 10)
mrg.createDimension('DATE-TIME', 2)
tflag = mrg.createVariable('TFLAG', 'i', ('TSTEP', 'VAR', 'DATE-TIME'))
tflag.units = '<YYYYJJJ, HHMMSS>'
tflag.long_name = tflag.var_desc = 'time '
tflag[:, :, 0] = 2004001
tflag[:, :, 1] = arange(10)[:, newaxis] * 10000
for i in range(1, 11):
var = mrg.createVariable('IRR_%d' % i, 'f', ('TSTEP', 'LAY', 'ROW', 'COL'))
var.units = 'ppt'
var.long_name = var.var_desc = 'Integrated rate for IRR ordinal %d' % i
seed(1)
if i % 2 == 0:
data = arange(3 * i, 3 * i + 10)[:, newaxis, newaxis, newaxis].repeat(4, 1).repeat(3, 2).repeat(5, 3)
elif i % 1 == 0:
data = arange(2 * i, 2 * i + 10)[:, newaxis, newaxis, newaxis].repeat(4, 1).repeat(3, 2).repeat(5, 3)
else:
data = arange(1 * i, 1 * i + 10)[:, newaxis, newaxis, newaxis].repeat(4, 1).repeat(3, 2).repeat(5, 3)
var[:] = data * {True: -1, False: 1}[i % 2 == True]
self.mech.set_mrg(mrg)
def setUp(self):
from PseudoNetCDF import PseudoNetCDFFile
self.checkval = """time,layer,latitude,longitude,test
0.0,0.0,0.0,0.0,0.0
0.0,0.0,0.0,1.0,1.0
0.0,0.0,0.0,2.0,2.0
0.0,0.0,0.0,3.0,3.0
0.0,0.0,0.0,4.0,4.0
0.0,0.0,1.0,0.0,5.0
0.0,0.0,1.0,1.0,6.0
0.0,0.0,1.0,2.0,7.0
0.0,0.0,1.0,3.0,8.0
0.0,0.0,1.0,4.0,9.0
0.0,0.0,2.0,0.0,10.0
0.0,0.0,2.0,1.0,11.0
0.0,0.0,2.0,2.0,12.0
0.0,0.0,2.0,3.0,13.0
0.0,0.0,2.0,4.0,14.0
0.0,0.0,3.0,0.0,15.0
0.0,0.0,3.0,1.0,16.0
0.0,0.0,3.0,2.0,17.0
0.0,0.0,3.0,3.0,18.0
0.0,0.0,3.0,4.0,19.0
0.0,1.0,0.0,0.0,20.0
0.0,1.0,0.0,1.0,21.0
0.0,1.0,0.0,2.0,22.0
0.0,1.0,0.0,3.0,23.0
0.0,1.0,0.0,4.0,24.0
0.0,1.0,1.0,0.0,25.0
0.0,1.0,1.0,1.0,26.0
0.0,1.0,1.0,2.0,27.0
0.0,1.0,1.0,3.0,28.0
0.0,1.0,1.0,4.0,29.0
0.0,1.0,2.0,0.0,30.0
0.0,1.0,2.0,1.0,31.0
0.0,1.0,2.0,2.0,32.0
0.0,1.0,2.0,3.0,33.0
0.0,1.0,2.0,4.0,34.0
0.0,1.0,3.0,0.0,35.0
0.0,1.0,3.0,1.0,36.0
0.0,1.0,3.0,2.0,37.0
0.0,1.0,3.0,3.0,38.0
0.0,1.0,3.0,4.0,39.0
0.0,2.0,0.0,0.0,40.0
0.0,2.0,0.0,1.0,41.0
0.0,2.0,0.0,2.0,42.0
0.0,2.0,0.0,3.0,43.0
0.0,2.0,0.0,4.0,44.0
0.0,2.0,1.0,0.0,45.0
0.0,2.0,1.0,1.0,46.0
0.0,2.0,1.0,2.0,47.0
0.0,2.0,1.0,3.0,48.0
0.0,2.0,1.0,4.0,49.0
0.0,2.0,2.0,0.0,50.0
0.0,2.0,2.0,1.0,51.0
0.0,2.0,2.0,2.0,52.0
0.0,2.0,2.0,3.0,53.0
0.0,2.0,2.0,4.0,54.0
0.0,2.0,3.0,0.0,55.0
0.0,2.0,3.0,1.0,56.0
0.0,2.0,3.0,2.0,57.0
0.0,2.0,3.0,3.0,58.0
0.0,2.0,3.0,4.0,59.0
1.0,0.0,0.0,0.0,60.0
1.0,0.0,0.0,1.0,61.0
1.0,0.0,0.0,2.0,62.0
1.0,0.0,0.0,3.0,63.0
1.0,0.0,0.0,4.0,64.0
1.0,0.0,1.0,0.0,65.0
1.0,0.0,1.0,1.0,66.0
1.0,0.0,1.0,2.0,67.0
1.0,0.0,1.0,3.0,68.0
1.0,0.0,1.0,4.0,69.0
1.0,0.0,2.0,0.0,70.0
1.0,0.0,2.0,1.0,71.0
1.0,0.0,2.0,2.0,72.0
1.0,0.0,2.0,3.0,73.0
1.0,0.0,2.0,4.0,74.0
1.0,0.0,3.0,0.0,75.0
1.0,0.0,3.0,1.0,76.0
1.0,0.0,3.0,2.0,77.0
1.0,0.0,3.0,3.0,78.0
1.0,0.0,3.0,4.0,79.0
1.0,1.0,0.0,0.0,80.0
1.0,1.0,0.0,1.0,81.0
1.0,1.0,0.0,2.0,82.0
1.0,1.0,0.0,3.0,83.0
1.0,1.0,0.0,4.0,84.0
1.0,1.0,1.0,0.0,85.0
1.0,1.0,1.0,1.0,86.0
1.0,1.0,1.0,2.0,87.0
1.0,1.0,1.0,3.0,88.0
1.0,1.0,1.0,4.0,89.0
1.0,1.0,2.0,0.0,90.0
1.0,1.0,2.0,1.0,91.0
1.0,1.0,2.0,2.0,92.0
1.0,1.0,2.0,3.0,93.0
1.0,1.0,2.0,4.0,94.0
1.0,1.0,3.0,0.0,95.0
1.0,1.0,3.0,1.0,96.0
1.0,1.0,3.0,2.0,97.0
1.0,1.0,3.0,3.0,98.0
1.0,1.0,3.0,4.0,99.0
1.0,2.0,0.0,0.0,100.0
1.0,2.0,0.0,1.0,101.0
1.0,2.0,0.0,2.0,102.0
1.0,2.0,0.0,3.0,103.0
1.0,2.0,0.0,4.0,104.0
1.0,2.0,1.0,0.0,105.0
1.0,2.0,1.0,1.0,106.0
1.0,2.0,1.0,2.0,107.0
1.0,2.0,1.0,3.0,108.0
1.0,2.0,1.0,4.0,109.0
1.0,2.0,2.0,0.0,110.0
1.0,2.0,2.0,1.0,111.0
1.0,2.0,2.0,2.0,112.0
1.0,2.0,2.0,3.0,113.0
1.0,2.0,2.0,4.0,114.0
1.0,2.0,3.0,0.0,115.0
1.0,2.0,3.0,1.0,116.0
1.0,2.0,3.0,2.0,117.0
1.0,2.0,3.0,3.0,118.0
1.0,2.0,3.0,4.0,119.0
"""
testfile = self.testfile = PseudoNetCDFFile()
testfile.createDimension('time', 2)
testfile.createDimension('layer', 3)
testfile.createDimension('latitude', 4)
testfile.createDimension('longitude', 5)
for dk, dv in testfile.dimensions.items():
var = testfile.createVariable(dk, 'f', (dk, ))
var[:] = np.arange(len(dv), dtype='f')
var = testfile.createVariable(
'test', 'f', ('time', 'layer', 'latitude', 'longitude'))
var[:] = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
print(point, isin)
varkeys = ['temperature', 'windDir', 'windSpeed', 'dewpoint', 'altimeter']
vardds = [k + 'DD' for k in varkeys]
if args.verbose > 1:
print('Subset variables')
getvarkeys = varkeys + vardds + \
['stationName', 'timeObs', 'timeNominal', 'elevation', 'latitude', 'longitude']
if args.verbose > 1:
print('Slicing files')
p2p = Pseudo2NetCDF(verbose=0)
outfile = PseudoNetCDFFile()
p2p.addDimensions(ncff, outfile)
outfile.createDimension('recNum', len(found_point_ids))
p2p.addGlobalProperties(ncff, outfile)
for vark in getvarkeys:
p2p.addVariable(ncff, outfile, vark, data=False)
for vark in getvarkeys:
invar = ncff.variables[vark]
outvar = outfile.variables[vark]
recid = list(invar.dimensions).index('recNum')
outvar[:] = invar[:].take(found_point_ids, recid)
if args.humidity:
varkeys.append('specificHumidity')
def mrgidx(ipr_paths, irr_paths, idx):
if isinstance(irr_paths, str):
irrf = NetCDFFile(irr_paths)
else:
irrf = file_master([NetCDFFile(irr_path) for irr_path in irr_paths])
if isinstance(ipr_paths, str):
iprf = NetCDFFile(ipr_paths)
else:
iprf = file_master([NetCDFFile(ipr_path) for ipr_path in ipr_paths])
# Process and Reaction keys should exclude TFLAG
pr_keys = [pr for pr in iprf.variables.keys() if pr not in ('TFLAG', )]
rr_keys = [rr for rr in irrf.variables.keys() if rr not in ('TFLAG', )]
# Attempt to order reactions by number
# this is not necessary, but is nice and clean
try:
rr_keys = [(int(rr.split('_')[1]), rr) for rr in rr_keys]
rr_keys.sort()
rr_keys = [rr[1] for rr in rr_keys]
except:
warn("Cannot sort reaction keys")
# Processes are predicated by a delimiter
prcs = list(set(['_'.join(pr.split('_')[:-1]) for pr in pr_keys]))
# Species are preceded by a delimiter
spcs = list(set(['_'.join(pr.split('_')[-1:]) for pr in pr_keys]))
# Select a dummy variable for extracting properties
pr_tmp = iprf.variables[pr_keys[0]]
# Create an empty file and decorate
# it as necessary
outf = PseudoNetCDFFile()
outf.Species = "".join([spc.ljust(16) for spc in spcs])
outf.Process = "".join([prc.ljust(16) for prc in prcs])
outf.Reactions = "".join([rr_key.ljust(16) for rr_key in rr_keys])
outf.createDimension("PROCESS", len(prcs))
outf.createDimension("SPECIES", len(spcs))
outf.createDimension("RXN", len(rr_keys))
outf.createDimension("TSTEP", pr_tmp[:, 0, 0, 0].shape[0])
outf.createDimension("TSTEP_STAG", len(outf.dimensions["TSTEP"]) + 1)
outf.createDimension("ROW", 1)
outf.createDimension("LAY", 1)
outf.createDimension("COL", 1)
outf.createDimension("VAR", 3)
outf.createDimension("DATE-TIME", 2)
tflag = outf.createVariable("TFLAG", "i", ('TSTEP', 'VAR', 'DATE-TIME'))
tflag.__dict__.update(
dict(units="<YYYYJJJ,HHDDMM>",
var_desc='TFLAG'.ljust(16),
long_name='TFLAG'.ljust(16)))
tflag[:, :, :] = iprf.variables['TFLAG'][:][:, [0], :]
shape = outf.createVariable("SHAPE", "i", ("TSTEP", "LAY", "ROW", "COL"))
shape.__dict__.update(
dict(units="ON/OFF",
var_desc="SHAPE".ljust(16),
long_name="SHAPE".ljust(16)))
shape[:] = 1
irr = outf.createVariable("IRR", "f", ("TSTEP", "RXN"))
irr.__dict__.update(
dict(units=pr_tmp.units,
var_desc="IRR".ljust(16),
long_name="IRR".ljust(16)))
ipr = outf.createVariable("IPR", "f", ("TSTEP", "SPECIES", "PROCESS"))
irr.__dict__.update(
dict(units=pr_tmp.units,
var_desc="IPR".ljust(16),
long_name="IPR".ljust(16)))
for rr, var in zip(rr_keys, irr.swapaxes(0, 1)):
var[:] = irrf.variables[rr][:][idx]
for prc, prcvar in zip(prcs, ipr.swapaxes(0, 2)):
for spc, spcvar in zip(spcs, prcvar):
try:
spcvar[:] = iprf.variables['_'.join([prc, spc])][:][idx]
except KeyError as es:
warn(str(es))
return outf
def __init__(self, rf, multi=False, **props):
"""
Initialization included reading the header and learning
about the format.
see __readheader and __gettimestep() for more info
Keywords (i.e., props) for projection: P_ALP, P_BET, P_GAM, XCENT, YCENT, XORIG, YORIG, XCELL, YCELL
"""
self.__rffile = OpenRecordFile(rf)
self.__readheader()
self.__ipr_record_type = {
24:
dtype(
dict(names=[
'SPAD', 'DATE', 'TIME', 'SPC', 'PAGRID', 'NEST', 'I', 'J',
'K', 'INIT', 'CHEM', 'EMIS', 'PTEMIS', 'PIG', 'WADV',
'EADV', 'SADV', 'NADV', 'BADV', 'TADV', 'DIL', 'WDIF',
'EDIF', 'SDIF', 'NDIF', 'BDIF', 'TDIF', 'DDEP', 'WDEP',
'AERCHEM', 'FCONC', 'UCNV', 'AVOL', 'EPAD'
],
formats=[
'>i', '>i', '>f', '>S10', '>i', '>i', '>i', '>i',
'>i', '>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f',
'>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f',
'>f', '>f', '>f', '>f', '>f', '>f', '>f', '>i'
])),
26:
dtype(
dict(names=[
'SPAD', 'DATE', 'TIME', 'SPC', 'PAGRID', 'NEST', 'I', 'J',
'K', 'INIT', 'CHEM', 'EMIS', 'PTEMIS', 'PIG', 'WADV',
'EADV', 'SADV', 'NADV', 'BADV', 'TADV', 'DIL', 'WDIF',
'EDIF', 'SDIF', 'NDIF', 'BDIF', 'TDIF', 'DDEP', 'WDEP',
'INORGACHEM', 'ORGACHEM', 'AQACHEM', 'FCONC', 'UCNV',
'AVOL', 'EPAD'
],
formats=[
'>i', '>i', '>f', '>S10', '>i', '>i', '>i', '>i',
'>i', '>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f',
'>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f',
'>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f', '>f',
'>i'
]))
}[len(self.prcnames)]
prcs = [
'SPAD', 'DATE', 'TIME', 'PAGRID', 'NEST', 'I', 'J', 'K', 'INIT',
'CHEM', 'EMIS', 'PTEMIS', 'PIG', 'WADV', 'EADV', 'SADV', 'NADV',
'BADV', 'TADV', 'DIL', 'WDIF', 'EDIF', 'SDIF', 'NDIF', 'BDIF',
'TDIF', 'DDEP', 'WDEP'
] + {
24: ['AERCHEM'],
26: ['INORGACHEM', 'ORGACHEM', 'AQACHEM']
}[len(self.prcnames)] + ['FCONC', 'UCNV', 'AVOL', 'EPAD']
varkeys = ['_'.join(i) for i in cartesian(prcs, self.spcnames)]
varkeys += [
'SPAD', 'DATE', 'TIME', 'PAGRID', 'NEST', 'I', 'J', 'K', 'TFLAG'
]
self.groups = {}
NSTEPS = len([i_ for i_ in self.timerange()])
NVARS = len(varkeys)
self.createDimension('VAR', NVARS)
self.createDimension('DATE-TIME', 2)
self.createDimension('TSTEP', NSTEPS)
padatatype = []
pavarkeys = []
for di, domain in enumerate(self.padomains):
dk = 'PA%02d' % di
prefix = dk + '_'
grp = self.groups[dk] = PseudoNetCDFFile()
pavarkeys.extend([prefix + k for k in varkeys])
grp.createDimension('VAR', NVARS)
grp.createDimension('DATE-TIME', 2)
grp.createDimension('TSTEP', NSTEPS)
grp.createDimension('COL', domain['iend'] - domain['istart'] + 1)
grp.createDimension('ROW', domain['jend'] - domain['jstart'] + 1)
grp.createDimension('LAY', domain['tlay'] - domain['blay'] + 1)
padatatype.append(
(dk, self.__ipr_record_type, (len(grp.dimensions['ROW']),
len(grp.dimensions['COL']),
len(grp.dimensions['LAY']))))
if len(self.padomains) == 1:
self.createDimension('COL',
domain['iend'] - domain['istart'] + 1)
self.createDimension('ROW',
domain['jend'] - domain['jstart'] + 1)
self.createDimension('LAY',
domain['tlay'] - domain['blay'] + 1)
exec(
"""def varget(k):
return self._ipr__variables('%s', k)""" % dk, dict(self=self),
locals())
if len(self.padomains) == 1:
self.variables = PseudoNetCDFVariables(varget, varkeys)
else:
grp.variables = PseudoNetCDFVariables(varget, varkeys)
self.__memmaps = memmap(self.__rffile.infile.name, dtype(padatatype),
'r', self.data_start_byte).reshape(
NSTEPS, len(self.spcnames))
for k, v in props.items():
setattr(self, k, v)
try:
add_cf_from_ioapi(self)
except:
pass
