def load_amsr(datfile, lonlatfile):
"""
Name:
load_amsr
Purpose:
to load amsr data into a sucinct dictionary
Calling Sequence:
amsr = load_amsr(datfile,lonlatfile)
Input:
datfile: path and name of hdf file
lonlatfile: path and name of hdf files for lat and lon
Output:
amsr: dictionary with numpy array of values
Keywords:
none
Dependencies:
gdal
numpy
gc: for clearing the garbage
Sp_parameters for progress issuer
pdb: for debugging
load_modis: this file
Required files:
dat file
lonlat file
Example:
...
Modification History:
Written (v1.0): Samuel LeBlanc, 2015-05-04, NASA Ames
"""
import os
if not (os.path.isfile(datfile)):
error('Data file not found!')
if not (os.path.isfile(lonlatfile)):
error('Lonlat file not found!')
import numpy as np
from load_modis import load_hdf
from osgeo import gdal
gdat = gdal.Open(datfile)
dat = dict()
dat['nfo'] = gdat.GetMetadata()
dat['ice'] = gdat.GetRasterBand(1).ReadAsArray()
datll, dicll = load_hdf(lonlatfile,
values=(('lon', 0), ('lat', 1)),
verbose=False)
dat['lat'] = datll['lat']
dat['lon'] = datll['lon']
return dat
def load_apr(datfiles):
"""
Name:
load_apr
Purpose:
Function to load apr values of zenith dbz from the various files in the datfiles list
Calling Sequence:
aprout = load_apr(datfiles)
Input:
datfiles name (list of .h4 files to combine)
Output:
aprout: dbz, zenith radar reflectivity
latz, latitude of the zenith reflectivity
lonz, longitude of the "
altflt, actual altitude in the atmosphere of the radar refl.
utc, time of measurement in utc fractional hours
Keywords:
none
Dependencies:
os
numpy
load_modis
datetime
Required files:
hdf APR-2 files from SEAC4RS
Example:
...
Modification History:
Written (v1.0): Samuel LeBlanc, 2015-04-10, NASA Ames
"""
import os
import numpy as np
from load_modis import load_hdf
import datetime
first = True
for f in datfiles:
print 'Running file: ', f
if not (os.path.isfile(f)):
print 'Problem with file:', f
print ' ... Skipping'
continue
apr_value = (('lat', 16), ('lon', 17), ('alt', 15), ('time', 13),
('dbz', 0), ('lat3d', 30), ('lon3d', 31), ('alt3d', 32),
('lat3d_o', 24), ('lon3d_o', 25), ('alt3d_o', 26),
('lat3d_s', 27), ('lon3d_s', 28), ('alt3d_s', 29))
apr, aprdicts = load_hdf(f, values=apr_value, verbose=False)
# transform the 3d latitudes, longitudes, and altitudes to usable values
apr['latz'] = apr['lat3d'] / apr['lat3d_s'] + apr['lat3d_o']
apr['lonz'] = apr['lon3d'] / apr['lon3d_s'] + apr['lon3d_o']
apr['altz'] = apr['alt3d'] / apr['alt3d_s'] + apr['alt3d_o']
apr['altflt'] = np.copy(apr['altz'])
try:
for z in range(apr['altz'].shape[0]):
apr['altflt'][
z, :, :] = apr['altz'][z, :, :] + apr['alt'][z, :]
except:
print 'Problem with file: ', f
print ' ... dimensions do not agree'
print ' ... Skipping'
continue
izen = apr['altz'][:, 0, 0].argmax() #get the index of zenith
if first:
aprout = dict()
aprout['dbz'] = apr['dbz'][izen, :, :]
aprout['altflt'] = apr['altz'][izen, :, :] + apr['alt'][izen, :]
aprout['latz'] = apr['latz'][izen, :, :]
aprout['lonz'] = apr['lonz'][izen, :, :]
v = datetime.datetime.utcfromtimestamp(apr['time'][izen, 0])
aprout['utc'] = (
apr['time'][izen, :] -
(datetime.datetime(v.year, v.month, v.day, 0, 0, 0) -
datetime.datetime(1970, 1, 1)).total_seconds()) / 3600.
first = False
else:
aprout['dbz'] = np.concatenate(
(aprout['dbz'].T, apr['dbz'][izen, :, :].T)).T
aprout['altflt'] = np.concatenate(
(aprout['altflt'].T,
(apr['altz'][izen, :, :] + apr['alt'][izen, :]).T)).T
aprout['latz'] = np.concatenate(
(aprout['latz'].T, apr['latz'][izen, :, :].T)).T
aprout['lonz'] = np.concatenate(
(aprout['lonz'].T, apr['lonz'][izen, :, :].T)).T
v = datetime.datetime.utcfromtimestamp(apr['time'][izen, 0])
utc = (apr['time'][izen, :] -
(datetime.datetime(v.year, v.month, v.day, 0, 0, 0) -
datetime.datetime(1970, 1, 1)).total_seconds()) / 3600.
aprout['utc'] = np.concatenate((aprout['utc'].T, utc.T)).T
print aprout.keys()
print 'Loaded data points: ', aprout['utc'].shape
return aprout
def load_apr(datfiles):
"""
Name:
load_apr
Purpose:
Function to load apr values of zenith dbz from the various files in the datfiles list
Calling Sequence:
aprout = load_apr(datfiles)
Input:
datfiles name (list of .h4 files to combine)
Output:
aprout: dbz, zenith radar reflectivity
latz, latitude of the zenith reflectivity
lonz, longitude of the "
altflt, actual altitude in the atmosphere of the radar refl.
utc, time of measurement in utc fractional hours
Keywords:
none
Dependencies:
os
numpy
load_modis
datetime
Required files:
hdf APR-2 files from SEAC4RS
Example:
...
Modification History:
Written (v1.0): Samuel LeBlanc, 2015-04-10, NASA Ames
"""
import os
import numpy as np
from load_modis import load_hdf
import datetime
first = True
for f in datfiles:
print 'Running file: ',f
if not(os.path.isfile(f)):
print 'Problem with file:', f
print ' ... Skipping'
continue
apr_value = (('lat',16),('lon',17),('alt',15),('time',13),('dbz',0),('lat3d',30),('lon3d',31),('alt3d',32),('lat3d_o',24),('lon3d_o',25),('alt3d_o',26),('lat3d_s',27),('lon3d_s',28),('alt3d_s',29))
apr,aprdicts = load_hdf(f,values=apr_value,verbose=False)
# transform the 3d latitudes, longitudes, and altitudes to usable values
apr['latz'] = apr['lat3d']/apr['lat3d_s']+apr['lat3d_o']
apr['lonz'] = apr['lon3d']/apr['lon3d_s']+apr['lon3d_o']
apr['altz'] = apr['alt3d']/apr['alt3d_s']+apr['alt3d_o']
apr['altflt'] = np.copy(apr['altz'])
try:
for z in range(apr['altz'].shape[0]):
apr['altflt'][z,:,:] = apr['altz'][z,:,:]+apr['alt'][z,:]
except:
print 'Problem with file: ',f
print ' ... dimensions do not agree'
print ' ... Skipping'
continue
izen = apr['altz'][:,0,0].argmax() #get the index of zenith
if first:
aprout = dict()
aprout['dbz'] = apr['dbz'][izen,:,:]
aprout['altflt'] = apr['altz'][izen,:,:]+apr['alt'][izen,:]
aprout['latz'] = apr['latz'][izen,:,:]
aprout['lonz'] = apr['lonz'][izen,:,:]
v = datetime.datetime.utcfromtimestamp(apr['time'][izen,0])
aprout['utc'] = (apr['time'][izen,:]-(datetime.datetime(v.year,v.month,v.day,0,0,0)-datetime.datetime(1970,1,1)).total_seconds())/3600.
first = False
else:
aprout['dbz'] = np.concatenate((aprout['dbz'].T,apr['dbz'][izen,:,:].T)).T
aprout['altflt'] = np.concatenate((aprout['altflt'].T,(apr['altz'][izen,:,:]+apr['alt'][izen,:]).T)).T
aprout['latz'] = np.concatenate((aprout['latz'].T,apr['latz'][izen,:,:].T)).T
aprout['lonz'] = np.concatenate((aprout['lonz'].T,apr['lonz'][izen,:,:].T)).T
v = datetime.datetime.utcfromtimestamp(apr['time'][izen,0])
utc = (apr['time'][izen,:]-(datetime.datetime(v.year,v.month,v.day,0,0,0)-datetime.datetime(1970,1,1)).total_seconds())/3600.
aprout['utc'] = np.concatenate((aprout['utc'].T,utc.T)).T
print aprout.keys()
print 'Loaded data points: ', aprout['utc'].shape
return aprout
def load_amsr(datfile,lonlatfile):
"""
Name:
load_amsr
Purpose:
to load amsr data into a sucinct dictionary
Calling Sequence:
amsr = load_amsr(datfile,lonlatfile)
Input:
datfile: path and name of hdf file
lonlatfile: path and name of hdf files for lat and lon
Output:
amsr: dictionary with numpy array of values
Keywords:
none
Dependencies:
gdal
numpy
gc: for clearing the garbage
Sp_parameters for progress issuer
pdb: for debugging
load_modis: this file
Required files:
dat file
lonlat file
Example:
...
Modification History:
Written (v1.0): Samuel LeBlanc, 2015-05-04, NASA Ames
"""
import os
if not(os.path.isfile(datfile)):
error('Data file not found!')
if not(os.path.isfile(lonlatfile)):
error('Lonlat file not found!')
import numpy as np
from load_modis import load_hdf
from osgeo import gdal
gdat = gdal.Open(datfile)
dat = dict()
dat['nfo'] = gdat.GetMetadata()
dat['ice'] = gdat.GetRasterBand(1).ReadAsArray()
datll,dicll = load_hdf(lonlatfile,values=(('lon',0),('lat',1)),verbose=False)
dat['lat'] = datll['lat']
dat['lon'] = datll['lon']
return dat
# Import eMAS values
# <codecell>
if 'lm' in locals():
reload(lm)
from load_modis import load_emas, load_hdf
# <codecell>
emas_file = fp+'er2/20130913/EMASL2_13965_13_20130913_1905_1918_V00.hdf'
print os.path.isfile(emas_file)
# <codecell>
emas,emas_dicts = load_hdf(emas_file)
# <codecell>
emas_values = (('lat',0),('lon',1),('tau',15),('ref',23),('phase',58),('layer',59),('qa',68))
emas,emas_dicts = load_hdf(emas_file,values=emas_values)
# <codecell>
plt.figure()
plt.plot(emas['tau'])
# <markdowncell>
# Now Redo the load of emas data, but with the new V01 files, which includes the newest calibration as of 20150122, which is considered final for SEAC4RS. thermal band revisions (3.7 um and higher) may still occur.
#
