def PlotFactors(mode):
Daily = pygeode.open(Diurnal_Factors).diurnal_scale_factors
Weekly = pygeode.open(Weekly_Factors).weekly_scale_factors
if mode == 'diurnal':
Data = Daily[:]
smin = 0.8
smax = 1.2
TitleTime = DateTime
tUnit = 'h UTC'
elif mode == 'weekly':
Data = Weekly[:]
smin = 0.95
smax = 1.05
Data = Weekly_Array[:, :, DateTime]
tUnit = ''
TitleTime = [
'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
'Sunday'
][DateTime]
else:
print 'Invalid mode. Use "diurnal" or "weekly"'
return
x_axis = numpy.arange(-180., 180., 0.25)
y_axis = numpy.arange(-90., 90., 0.25)
fig, ax = pyplot.subplots()
cax = ax.pcolormesh(x_axis,
y_axis,
Data,
cmap=cm.coolwarm,
vmin=smin,
vmax=smax)
ax.set_title('TIMES {0} Scale Factors for {1}{2}'.format(
mode, TitleTime, tUnit))
ax.set_xlim(-180, 180)
ax.set_ylim(-90, 90)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
cbar = fig.colorbar(cax, orientation='horizontal')
pyplot.savefig('Times{0}.png'.format(mode))
def WeeklyFactor(time):
"""Reads the TIMES weekly factor for a Time instance.
Args: PST.Time instance
Returns: TIMES weekly scale factor
"""
lat = time.lat
lon = time.lon
day = time.solar.weekday()
Weekly = pygeode.open(Weekly_Factors).weekly_scale_factors[:]
lat_row = (lat + 90) // 0.25
lon_col = (lon + 180) / 0.25
weekly = Weekly[lat_row, lon_col, day]
return weekly
def DailyFactor(time):
"""Reads the daily TIMES scale factor for a Time instance.
Uses lat/lon and solar time stored in time to get the TIMES
daily factor for the right lat/lon position and right
time.
Args:
A PST.Time instance
Returns:
The TIMES daily scale factor
"""
lat = time.lat
lon = time.lon
hour = time.solar.hour
Daily = pygeode.open(Diurnal_Factors).diurnal_scale_factors[:]
lat_row = (lat + 90) // 0.25
lon_col = (lon + 180) // 0.25
diurnal = Daily[lat_row, lon_col, hour]
return diurnal
def plotFSTs(season=season, spcs=spcs, spcsFiles=spcsFiles, outputName = outputName, saveDir=saveDir):
# print minimum outputs
rmn.fstopt(rmn.FSTOP_MSGLVL,rmn.FSTOPI_MSG_CATAST)
mInds = []
for m in season:
mInds += [monthList.index(m)]
if os.path.exists(saveDir) == False:
nu.makeDir(saveDir)
for spcInd, nomvar in enumerate(spcs):
try:
filename = os.path.join(saveDir, 'output_file_{0}_{1}.fst'.format(outputName, nomvar))
print('Creating and saving to {}'.format(filename))
tmp = open(filename, 'w+'); tmp.close()
output_file = filename
file_id = rmn.fnom(output_file)
open_fst = rmn.fstouv(file_id, rmn.FST_RW)
open_file = spcsFiles[spcInd]
print "Parameter: " + nomvar
seaSpcData = get_var(pyg.open(open_file), nomvar, mInds)
nc_lnsp = pyg.open(lnsp_file)
pressures = get_pressures(nc_lnsp, mInds)
timelen, levlen, latlen, lonlen = seaSpcData.shape
#NOTE: uncomment the following three lines to prep data for basemap use
#lonShiftSSData = shift_lon(seaSpcData)
#vertInterpSSData = vert_interp(pressures, lonShiftSSData)
#meanSSData = np.mean(vertInterpSSData, axis=0)
#NOTE: uncommment the following four liness to use for fst plotting
vertInterpSSData = vert_interp(pressures, seaSpcData)
meanSSData = np.mean(vertInterpSSData, axis=0) # temp
for lvl, ray in enumerate(meanSSData):
meanSSData[lvl] = np.flipud(ray)
scaleFac = scaleSpcs[allSpcs.index(nomvar)]
scaledSSData = meanSSData*scaleFac
#define grid for this file - note that the MACC grid in the file is
#defined for lons -180 to 180, but the python defGrid_L can't deal
#with that and defines the grid from 0 to 360 so will have to reorder
#the MACC fields a bit, or they end up 180 deg out of phase
# Also necessary to add one more longitude to wrap around
dlatlon = 360./lonlen # this is equal to the resolution of the grid
params0 = {
'grtyp' : 'Z',
'grref' : 'L',
'nj' : latlen,
'ni' : lonlen,
'lat0' : -90.,
'lon0' : 0.,
'dlat' : dlatlon,
'dlon' : dlatlon
}
MACC_grid= rmn.encodeGrid(params0)
print("Grids created.")
print 'Grid Shape:' + str(MACC_grid['shape'])
# copies the default record
new_record = rmn.FST_RDE_META_DEFAULT.copy()
tic_record = rmn.FST_RDE_META_DEFAULT.copy()
tac_record = rmn.FST_RDE_META_DEFAULT.copy()
try:
rmn.writeGrid(file_id, MACC_grid)
tac = rmn.fstinl(file_id, nomvar='>>')[0]
tic = rmn.fstinl(file_id, nomvar='^^')[0]
tic_record.update(rmn.fstprm(tic))
tac_record.update(rmn.fstprm(tac))
tic_record.update({'datyp' : rmn.FST_DATYP_LIST['float']})
tac_record.update({'datyp' : rmn.FST_DATYP_LIST['float']})
rmn.fsteff(tic)
rmn.fsteff(tac)
tic_record.update({'d': MACC_grid['ay']})
tac_record.update({'d': MACC_grid['ax']})
toc_record = vgd.vgd_new_pres(const_pressure, ip1=MACC_grid['ig1'], ip2=MACC_grid['ig2'])
rmn.fstecr(file_id, tic_record) # write the dictionary record to the file as a new record
rmn.fstecr(file_id, tac_record) # write the dictionary record to the file as a new record
vgd.vgd_write(toc_record, file_id)
except:
raise
for rp1 in xrange(len(const_pressure)): # writes a record for every level (as a different ip1)
try:
# converts rp1 into a ip1 with pressure kind
ip1 = rmn.convertIp(rmn.CONVIP_ENCODE, const_pressure[rp1], rmn.KIND_PRESSURE)
new_record.update(MACC_grid)
new_record.update({ # Update with specific meta
'nomvar': nomvar,
'typvar': 'C',
'etiket': 'MACCRean',
'ni' : MACC_grid['ni'],
'nj' : MACC_grid['nj'],
'ig1' : tic_record['ip1'],
'ig2' : tic_record['ip2'],
'ig3' : tic_record['ip3'],
'ig4' : tic_record['ig4'],
'dateo' : rmn.newdate(rmn.NEWDATE_PRINT2STAMP, 20120101, 0000000),
'deet' : 0, # Timestep in sec
'ip1' : ip1
})
#tmp_nparray = np.asfortranarray(monthly_mean[rp1])
tmp = scaledSSData[rp1]
tmp = np.transpose(tmp)
# data array is structured as tmp = monthly_mean[level] where monthly_mean is [level, lat, lon]
new_record.update({'d': tmp.astype(np.float32)}) # Updates with data array in the form (lon x lat)
print "Defined a new record with dimensions ({0}, {1})".format(new_record['ni'], new_record['nj'])
rmn.fstecr(file_id, new_record) # write the dictionary record to the file as a new record
except:
#rmn.closeall(file_id)
rmn.fstfrm(file_id)
rmn.fclos(file_id)
raise
rmn.fstfrm(file_id)
rmn.fclos(file_id)
print('{} complete~'.format(filename))
except:
rmn.fstfrm(file_id)
rmn.fclos(file_id)
raise
print('Finished plotting all FSTs. ')
def test_scalar_from_netcdf():
import pygeode as pyg
# read from netcdf and access data
v = pyg.open('test_issue_108.nc').scalar
assert v[()] == 10.
def test_scalar_from_netcdf():
import pygeode as pyg
# read from netcdf and access data
v = pyg.open('test_issue_108.nc').scalar
assert v[()] == 10.
return open_var[1216:1336]
elif m_int == 11:
return open_var[1336:]
##### MAIN #####
month_list = [
'01JAN', '02FEB', '03MAR', '04APR', '05MAY', '06JUN', '07JLY', '08AUG',
'09SEP', '10OCT', '11NOV', '12DEC'
]
# this portion of the code handles parsing values from the nc file
for year_int, filename in enumerate(filenames):
to3_list = []
so3_list = []
go3_list = []
nc = pyg.open(filename)
lnsp_file = pyg.open(lnsp_files[year_int])
for m_int, month in enumerate(month_list):
if m_int < 3 or m_int > 5:
continue
date_tuple = (year_int, month)
strato_file = '/home/ords/aq/alh002/pyscripts/workdir/pv_files/strato_coords_{0}_{1}.txt'.format(
year_int, month)
tropo_file = '/home/ords/aq/alh002/pyscripts/workdir/pv_files/tropo_coords_{0}_{1}.txt'.format(
year_int, month)
# all instances of '[:4]' are to limit to 4 timesteps, or 1 day (in this case 01012012)
# uu = nc.u
# vv = nc.v
# qq = nc.vo
# th = nc.t