def find_time_axis(self):
if self._tvar is None:
tdim = self._ext_dataset_res.dataset_description.parameters["temporal_dimension"]
if tdim in self._ds.dimensions:
if tdim in self._ds.variables:
self._tvar = self._ds.variables[tdim]
else:
# find the first variable that has the tdim as the outer dimension
for vk in self._ds.variables:
var = self._ds.variables[vk]
if tdim in var.dimensions and var.dimensions.index(tdim) == 0:
self._tvar = var
break
else:
# try to figure out which is the time axis based on standard attributes
if self._cdms_ds is None:
self._cdms_ds = cdms2.openDataset(self._ds_url)
taxis = self._cdms_ds.getAxis('time')
if taxis is not None:
self._tvar = self._ds.variables[taxis.id]
else:
self._tvar = None
return self._tvar
def find_time_axis(self):
if self._tvar is None:
tdim = self._ext_dataset_res.dataset_description.parameters["temporal_dimension"]
if tdim in self._ds.dimensions:
if tdim in self._ds.variables:
self._tvar = self._ds.variables[tdim]
else:
# find the first variable that has the tdim as the outer dimension
for vk in self._ds.variables:
var = self._ds.variables[vk]
if tdim in var.dimensions and var.dimensions.index(tdim) == 0:
self._tvar = var
break
else:
# try to figure out which is the time axis based on standard attributes
if self._cdms_ds is None:
self._cdms_ds = cdms2.openDataset(self._ds_url)
taxis = self._cdms_ds.getAxis("time")
if taxis is not None:
self._tvar = self._ds.variables[taxis.id]
else:
self._tvar = None
return self._tvar
def writenetcdf (slab, filename, mode="a"):
"""writenetcdf(slab, filename, mode="a") writes slab to the file.
modes: 'a' append
'w' replace
'r' replace (for legacy code only, deprecated)
s can be anything asVariable will accept
"""
if mode == 'r': mode = 'w'
slab = cdms2.asVariable(slab, 0)
f = cdms2.openDataset(filename, mode)
f.write(slab)
f.close()
def __init__(self, dataset_file, forecast_times, path="."):
"""Creates a set of forecasts. Normally you do it by something like
f = forecasts( 'file.xml', (min_time, max_time) )
or
f = forecasts( 'file.xml', (min_time, max_time), '/home/me/data/' )
or
f = forecasts( 'file.xml', [ time1, time2, time3, time4, time5 ] )
where the two or three arguments are::
1. the name of a dataset xml file generated by "cdscan --forecast ..."
2. Times here are the times when the forecasts began (tau=0, aka reference time).
(i) If you use a 2-item tuple, forecasts will be chosen which start at a time
t between the min and max times, e.g. min_time <= t < max_time .
(ii) If you use a list, it will be the exact start (tau=0) times for the
forecasts to be included.
(iii) If you use a 3-item tuple, the first items are (min_time,max_time)
as in a 2-item tuple. The third component of the tuple is the
open-closed string. This determines whether endpoints are included
The first character should be 'o' or 'c' depending on whether you want t with
min_time<t or min_time<=t. Similarly the second character should be 'o' or c'
for t<max_time or t<=max_time . Any other characters will be ignored.
Thus ( min_time, max_time, 'co' ) is equivalent to ( min_time, max_time ).
(iv) The string 'All' means to use all available forecasts.
Times can be specified either as 13-digit long integers, e.g.
2006012300000 for the first second of January 23, 2006, or as
component times (comptime) in the cdtime module, or as
a string in the format "2010-08-25 15:26:00".
3. An optional path for the data files; use this if the xml file
contains filenames without complete paths.
As for the forecast class, this opens files when initiated, so when you
are finished with the forecasts, you should close the files by calling
forecasts.close() .
"""
# Create dataset_list to get a forecast file from each forecast time.
self.dataset = cdms2.openDataset(dataset_file, dpath=path)
fm = cdms2.dataset.parseFileMap(self.dataset.cdms_filemap)
self.alltimesl = [f[4] for f in fm[0][1]] # 64-bit (long) integers
dataset_list = fm[0][1]
for f in fm[1:]:
dataset_list.extend(f[1])
mytimesl = self.forecast_times_to_list(forecast_times)
if mytimesl == []:
raise CDMSError(
"bad forecast_times argument to forecasts.__init__")
self.fcs = [forecast(t, dataset_list, path) for t in mytimesl]
def available_forecasts( dataset_file, path="." ):
"""Returns a list of forecasts (as their generating times) which are
available through the specified cdscan-generated dataset xml file.
The forecasts are given in 64-bit integer format, but can be converted
to component times with the function two_times_from_one.
This function may help in choosing the right arguments for initializing
a "forecasts" (forecast set) object.
"""
dataset=cdms2.openDataset( dataset_file, dpath=path )
fm=cdms2.dataset.parseFileMap(dataset.cdms_filemap)
alltimesl =[ f[4] for f in fm[0][1] ] # 64-bit (long) integers
dataset.close()
return alltimesl
def print_Mdata(self, dataPath):
for k in range(0, 30):
if (os.path.isfile(dataPath)):
f = cdms2.openDataset(dataPath)
for variable in f.variables.values():
self.logger.info("Produced result " + variable.id +
", shape: " + str(variable.shape) +
", dims: " + variable.getOrder() +
" from file: " + dataPath)
self.logger.info("Data Sample: " + str(variable[0]))
return
else:
time.sleep(1)
def available_forecasts(dataset_file, path="."):
"""Returns a list of forecasts (as their generating times) which are
available through the specified cdscan-generated dataset xml file.
The forecasts are given in 64-bit integer format, but can be converted
to component times with the function two_times_from_one.
This function may help in choosing the right arguments for initializing
a "forecasts" (forecast set) object.
"""
dataset = cdms2.openDataset(dataset_file, dpath=path)
fm = cdms2.dataset.parseFileMap(dataset.cdms_filemap)
alltimesl = [f[4] for f in fm[0][1]] # 64-bit (long) integers
dataset.close()
return alltimesl
def __init__( self, dataset_file, forecast_times, path="." ):
"""Creates a set of forecasts. Normally you do it by something like
f = forecasts( 'file.xml', (min_time, max_time) )
or
f = forecasts( 'file.xml', (min_time, max_time), '/home/me/data/' )
or
f = forecasts( 'file.xml', [ time1, time2, time3, time4, time5 ] )
where the two or three arguments are::
1. the name of a dataset xml file generated by "cdscan --forecast ..."
2. Times here are the times when the forecasts began (tau=0, aka reference time).
(i) If you use a 2-item tuple, forecasts will be chosen which start at a time
t between the min and max times, e.g. min_time <= t < max_time .
(ii) If you use a list, it will be the exact start (tau=0) times for the
forecasts to be included.
(iii) If you use a 3-item tuple, the first items are (min_time,max_time)
as in a 2-item tuple. The third component of the tuple is the
open-closed string. This determines whether endpoints are included
The first character should be 'o' or 'c' depending on whether you want t with
min_time<t or min_time<=t. Similarly the second character should be 'o' or c'
for t<max_time or t<=max_time . Any other characters will be ignored.
Thus ( min_time, max_time, 'co' ) is equivalent to ( min_time, max_time ).
(iv) The string 'All' means to use all available forecasts.
Times can be specified either as 13-digit long integers, e.g.
2006012300000 for the first second of January 23, 2006, or as
component times (comptime) in the cdtime module, or as
a string in the format "2010-08-25 15:26:00".
3. An optional path for the data files; use this if the xml file
contains filenames without complete paths.
As for the forecast class, this opens files when initiated, so when you
are finished with the forecasts, you should close the files by calling
forecasts.close() .
"""
# Create dataset_list to get a forecast file from each forecast time.
self.dataset=cdms2.openDataset( dataset_file, dpath=path )
fm=cdms2.dataset.parseFileMap(self.dataset.cdms_filemap)
self.alltimesl =[ f[4] for f in fm[0][1] ] # 64-bit (long) integers
dataset_list = fm[0][1]
for f in fm[1:]:
dataset_list.extend(f[1])
mytimesl = self.forecast_times_to_list( forecast_times )
if mytimesl == []:
raise CDMSError, "bad forecast_times argument to forecasts.__init__"
self.fcs = [ forecast( t, dataset_list, path ) for t in mytimesl ]
def _make_scrip(src_dir, src_file, src_var, src_grid_title):
#-------------------------------------------------------------------
"""
See file header.
"""
scrip_filename = src_grid_title + '_scrip.nc'
fin = cdms2.openDataset(src_dir + '/' + src_file)
src_grid = fin.variables[src_var].getGrid()
cdms2.writeScripGrid(scrip_filename, src_grid, src_grid_title)
return
def writenetcdf(slab, filename, mode="a"):
"""
writenetcdf(slab, filename, mode="a") writes slab to the file.
:param mode: One of 'a' append, or 'w' replace
:type mode: str
:param slab: Anything :py:func:`cdms2.asVariable` will accept
:type: see :py:func:`cdms2.asVariable`
"""
if mode == 'r':
mode = 'w'
slab = cdms2.asVariable(slab, 0)
f = cdms2.openDataset(filename, mode)
f.write(slab)
f.close()
def _check_endtime(filename):
#----------------------------
"""
See file header.
"""
fin = cdms2.openDataset(filename)
sst = fin.variables['sst']
t = sst.getTime()
print ('Final time: ', cdtime.reltime(t[-1], t.units).tocomp())
fin.close()
return
def __init__(self, dataset_file, forecast_times, path="."):
"""
Init
"""
# Create dataset_list to get a forecast file from each forecast time.
self.dataset = cdms2.openDataset(dataset_file, dpath=path)
fm = cdms2.dataset.parseFileMap(self.dataset.cdms_filemap)
self.alltimesl = [f[4] for f in fm[0][1]] # 64-bit (long) integers
dataset_list = fm[0][1]
for f in fm[1:]:
dataset_list.extend(f[1])
mytimesl = self.forecast_times_to_list(forecast_times)
if mytimesl == []:
raise CDMSError(
"bad forecast_times argument to forecasts.__init__")
self.fcs = [forecast(t, dataset_list, path) for t in mytimesl]
def print_data(self, dataPath):
for k in range(0, 30):
if (os.path.isfile(dataPath)):
try:
f = cdms2.openDataset(
dataPath) # """:type : cdms2.CdmsFile """
varName = f.variables.values()[0].id
spatialData = f(
varName) # """:type : cdms2.FileVariable """
self.logger.info("Produced result, shape: " +
str(spatialData.shape) + ", dims: " +
spatialData.getOrder())
# self.logger.info( "Data: \n" + ', '.join( str(x) for x in spatialData.getValue() ) )
self.logger.info(
"Data: \n" +
str(spatialData.squeeze().flatten().getValue()))
except Exception:
self.logger.error(" ** Error printing result data ***")
return
else:
time.sleep(1)
import os, random, string, cdms2, vcs
def random_id( length ): ''.join(random.choice( string.ascii_lowercase + string.ascii_uppercase + string.digits ) for _ in range(length))
def nbDisplay( x ):
from IPython.display import Image, display
outFile = '/tmp/vcsnb-{0}.png'.format( random_id(8) )
x.png(outFile)
display( Image(outFile) )
dataPath = "/home/tpmaxwel/.cdas/cache/cdscan/merra2_mon_ua.xml"
varName = "ua"
f = cdms2.openDataset(dataPath)
var = f( varName, time=slice(0,1),level=slice(10,11) )
s = var[0]
x = vcs.init()
x.plot(s,variable = var,bg=True)
def process_file(ifile,suffix,average=False,forcedaily=False,mask=True,xlist=[]):
try:
d = cdms2.open(ifile)
except:
print "Error opening file", ifile
usage()
sys.exit(1)
hcrit = 0.5 # Critical value of Heavyside function for inclusion.
ofilelist = []
for vn in d.variables:
var = d.variables[vn]
# Need to check whether it really has a stash_item to skip coordinate variables
# Note: need to match both item and section number
if not hasattr(var,'stash_item'):
continue
item_code = var.stash_section[0]*1000 + var.stash_item[0]
if item_code in xlist:
print "Skipping", item_code
continue
grid = var.getGrid()
time = var.getTime()
timevals = np.array(time[:])
if forcedaily:
# Work around cdms error in times
for k in range(len(time)):
timevals[k] = round(timevals[k],1)
umvar = stashvar.StashVar(item_code,var.stash_model[0])
vname = umvar.name
print vname, var[0,0,0,0]
# Create filename from variable name and cell_methods,
# checking for name collisions
if suffix:
ofile = "%s_%s.nc" % (umvar.uniquename, suffix)
else:
ofile = "%s.nc" % umvar.uniquename
if ofile in ofilelist:
raise Exception("Duplicate file name %s" % ofile)
ofilelist.append(ofile)
# If output file exists then append to it, otherwise create a new file
try:
file = cdms2.openDataset(ofile, 'r+')
newv = file.variables[vname]
newtime = newv.getTime()
except cdms2.error.CDMSError:
file = cdms2.createDataset(ofile)
# Stop it creating the bounds_latitude, bounds_longitude variables
cdms2.setAutoBounds("off")
# By default get names like latitude0, longitude1
# Need this awkwardness to get the variable/dimension name set correctly
# Is there a way to change the name cdms uses after
# newlat = newgrid.getLatitude() ????
newlat = file.createAxis('lat', grid.getLatitude()[:])
newlat.standard_name = "latitude"
newlat.axis = "Y"
newlat.units = 'degrees_north'
newlon = file.createAxis('lon', grid.getLongitude()[:])
newlon.standard_name = "longitude"
newlon.axis = "X"
newlon.units = 'degrees_east'
order = var.getOrder()
if order[1] == 'z':
lev = var.getLevel()
if len(lev) > 1:
newlev = file.createAxis('lev', lev[:])
for attr in ('standard_name', 'units', 'positive', 'axis'):
if hasattr(lev,attr):
setattr(newlev, attr, getattr(lev,attr))
else:
newlev = None
else:
# Pseudo-dimension
pdim = var.getAxis(1)
if len(pdim) > 1:
newlev = file.createAxis('pseudo', pdim[:])
else:
newlev = None
newtime = file.createAxis('time', None, cdms2.Unlimited)
newtime.standard_name = "time"
newtime.units = time.units # "days since " + `baseyear` + "-01-01 00:00"
newtime.setCalendar(time.getCalendar())
newtime.axis = "T"
if var.dtype == np.dtype('int32'):
vtype = cdms2.CdInt
missval = -2147483647
else:
vtype = cdms2.CdFloat
missval = 1.e20
if newlev:
newv = file.createVariable(vname, vtype, (newtime, newlev, newlat, newlon))
else:
newv = file.createVariable(vname, vtype, (newtime, newlat, newlon))
for attr in ("standard_name", "long_name", "units"):
if hasattr(umvar, attr):
newv.setattribute(attr, getattr(umvar,attr))
newv.missing_value = missval
newv.stash_section=var.stash_section[0]
newv.stash_item=var.stash_item[0]
newv._FillValue = missval
try:
newv.units = var.units
except AttributeError:
pass
# Get appropriate file position
# Uses 360 day calendar, all with same base time so must be 30 days on.
k = len(newtime)
# float needed here to get the later logical tests to work properly
avetime = float(MV.average(timevals[:])) # Works in either case
if k>0:
if average:
# if newtime[-1] != (avetime - 30):
# For Gregorian calendar relax this a bit
# Sometimes get differences slightly > 31
if not 28 <= avetime - newtime[-1] <= 31.5:
raise error, "Times not consecutive %f %f %f" % (newtime[-1], avetime, timevals[0])
else:
if k > 1:
# Need a better test that works when k = 1. This is just a
# temporary workaround
if not np.allclose( newtime[-1] + (newtime[-1]-newtime[-2]), timevals[0] ):
raise error, "Times not consecutive %f %f " % (newtime[-1], timevals[0])
if (30201 <= item_code <= 30303) and mask:
# P LEV/UV GRID with missing values treated as zero.
# Needs to be corrected by Heavyside fn
heavyside = d.variables['psag']
# Check variable code as well as the name.
if heavyside.stash_item[0] != 301 or heavyside.stash_section[0] != 30:
raise error, "Heavyside variable code mismatch"
if average:
newtime[k] = avetime
if var.shape[1] > 1:
# multiple levels
newv[k] = MV.average(var[:],axis=0).astype(np.float32)
else:
# single level
newv[k] = MV.average(var[:],axis=0)[0].astype(np.float32)
else:
for i in range(len(timevals)):
if var.shape[1] > 1:
# Multi-level
if (30201 <= item_code <= 30303) and mask:
newv[k+i] = np.where( np.greater(heavyside[i], hcrit), var[i]/heavyside[0], newv.getMissing())
else:
newv[k+i] = var[i]
else:
newv[k+i] = var[i,0]
newtime[k+i] = timevals[i]
file.close()
for k in range(len(time)):
timevals[k] = round(timevals[k],1)
item_code = var.stash_section[0]*1000 + var.stash_item[0]
umvar = stashvar.StashVar(item_code,var.stash_model[0])
if not vname:
vname = umvar.name
print vname, var[0,0,0,0]
hcrit = 0.5 # Critical value of Heavyside function for inclusion.
# print "LEN(TIME)", len(time)
# If output file exists then append to it, otherwise create a new file
try:
file = cdms2.openDataset(ofile, 'r+')
newv = file.variables[vname]
newtime = newv.getTime()
except cdms2.error.CDMSError:
if not usenc4:
# Force netCDF3 output
cdms2.setNetcdfShuffleFlag(0)
cdms2.setNetcdfDeflateFlag(0)
cdms2.setNetcdfDeflateLevelFlag(0)
file = cdms2.createDataset(ofile)
file.history = "Created by um2netcdf.py."
# Stop it creating the bounds_latitude, bounds_longitude variables
cdms2.setAutoBounds("off")
# By default get names like latitude0, longitude1
# Need this awkwardness to get the variable/dimension name set correctly
#!/usr/bin/env python
import cdms2, numpy.ma, regrid2 as regrid, os, sys
from regrid2 import Regridder
from markError import clearError, markError, reportError
clearError()
print 'Test 8: Regridding ...',
## lat = cdms2.createGaussianAxis(32)
## lon = cdms2.createUniformLongitudeAxis(0.0,64,360.0/64.)
## outgrid = cdms2.createRectGrid(lat,lon,'yx','gaussian')
outgrid = cdms2.createGaussianGrid(32)
f = cdms2.openDataset(os.path.join(sys.prefix, 'sample_data', 'readonly.nc'))
u = f.variables['u']
ingrid = u.getGrid()
try:
sh = ingrid.shape
except:
markError('Grid shape')
regridf = Regridder(ingrid, outgrid)
newu = regridf(u)
if (abs(newu[0, 0, -1] - 488.4763488) > 1.e-3):
markError('regrid', newu[0, 0, -1])
newu = u.regrid(outgrid)
if (abs(newu[0, 0, -1] - 488.4763488) > 1.e-3):
markError('regrid', newu[0, 0, -1])
def writenetcdf (slab, filename, mode="a"):
"""writenetcdf(slab, filename, mode="a") writes slab to the file.
modes: 'a' append
'w' replace
'r' replace (for legacy code only, deprecated)
s can be anything asVariable will accept
"""
if mode == 'r': mode = 'w'
slab = cdms2.asVariable(slab, 0)
f = cdms2.openDataset(filename, mode)
f.write(slab)
f.close()
if __name__ == '__main__':
from numpy.ma import allclose
import pcmdi
g = cdms2.openDataset('clt.nc','r')
c = g.variables['clt']
t = cdms2.asVariable([1.,2.,3.])
t.id = 't'
writenetcdf(c, 'test.nc', 'w')
writenetcdf(t, 'test.nc', 'a')
f = cdms2.open('test.nc')
d = f.variables['clt']
assert allclose(c,d)
for name in ['clt', 't']:
pcmdi.slabinfo(f.variables[name])
var.param = -99
if var.param!=-99: markError("R/W param: "+var.param)
f.Conventions = 'CF1.0'
latobj[NLAT/2] = 6.5
if latobj[NLAT/2]==lat[NLAT/2]: markError("Rewrite axis: %f"%vlat[NLAT/2])
lat[NLAT/2] = 6.5
latobj.standard_name = 'Latitude'
p0 = f.createVariable('p0',cdms2.CdDouble,())
p0.assignValue(-99.9)
f.close()
#-----------------------------------------------------------
g = cdms2.openDataset('readwrite3.nc','r+')
con = g.Conventions
try:
con = '<not read>'
con = g.Conventions
except:
markError("R/W global attr: "+con)
else:
if g.Conventions!='CF1.0': markError("R/W global attr: "+con)
var = g.variables['u']
try:
ln = '<not read'
ln = var.long_name
except:
markError("R/W long_name: "+ln)
import os, random, string, cdms2, vcs
def random_id( length ): ''.join(random.choice( string.ascii_lowercase + string.ascii_uppercase + string.digits ) for _ in range(length))
def nbDisplay( x ):
from IPython.display import Image, display
outFile = '/tmp/vcsnb-{0}.png'.format( random_id(8) )
x.png(outFile)
display( Image(outFile) )
dataPath = "https://dataserver.nccs.nasa.gov/thredds/fileServer/bypass/edas/publish/x589hnZJ.nc"
varName = "tas"
f = cdms2.openDataset(dataPath)
var = f( varName ) # , time=slice(0,1),level=slice(10,11) )
x = vcs.init()
x.plot(s,variable = var,bg=True)
def mpl_plot(self, dataPath, nCols=2):
f = cdms2.openDataset(dataPath)
var = f.variables.values()[0]
naxes = self.getNAxes(var.shape)
if (naxes == 1): self.mpl_timeplot(dataPath)
else: self.vcs_plot_eofs(dataPath, nCols)
#!/usr/bin/env python
import cdms2,numpy.ma, regrid2 as regrid, os, sys
from regrid2 import Regridder
from markError import clearError,markError,reportError
clearError()
print 'Test 8: Regridding ...',
## lat = cdms2.createGaussianAxis(32)
## lon = cdms2.createUniformLongitudeAxis(0.0,64,360.0/64.)
## outgrid = cdms2.createRectGrid(lat,lon,'yx','gaussian')
outgrid = cdms2.createGaussianGrid(32)
f = cdms2.openDataset(os.path.join(sys.prefix,'sample_data','readonly.nc'))
u = f.variables['u']
ingrid = u.getGrid()
try:
sh = ingrid.shape
except:
markError('Grid shape')
regridf = Regridder(ingrid, outgrid)
newu = regridf(u)
if (abs(newu[0,0,-1]-488.4763488) > 1.e-3): markError('regrid',newu[0,0,-1])
newu = u.regrid(outgrid)
if (abs(newu[0,0,-1]-488.4763488) > 1.e-3): markError('regrid',newu[0,0,-1])
# Regrid TV
import cdms2, numpy.ma, regrid2 as regrid, os, sys
from regrid2 import Regridder
from markError import clearError, markError, reportError
from markError import get_sample_data_dir
clearError()
print 'Test 8: Regridding ...',
## lat = cdms2.createGaussianAxis(32)
## lon = cdms2.createUniformLongitudeAxis(0.0,64,360.0/64.)
## outgrid = cdms2.createRectGrid(lat,lon,'yx','gaussian')
outgrid = cdms2.createGaussianGrid(32)
f = cdms2.openDataset(os.path.join(get_sample_data_dir(), 'readonly.nc'))
u = f.variables['u']
ingrid = u.getGrid()
try:
sh = ingrid.shape
except:
markError('Grid shape')
regridf = Regridder(ingrid, outgrid)
newu = regridf(u)
if (abs(newu[0, 0, -1] - 488.4763488) > 1.e-3):
markError('regrid', newu[0, 0, -1])
newu = u.regrid(outgrid)
if (abs(newu[0, 0, -1] - 488.4763488) > 1.e-3):
markError('regrid', newu[0, 0, -1])
#!/usr/bin/env python
import cdms2,numpy.ma, regrid2 as regrid, os, sys
from regrid2 import Regridder
from markError import clearError,markError,reportError
from markError import get_sample_data_dir
clearError()
print 'Test 8: Regridding ...',
## lat = cdms2.createGaussianAxis(32)
## lon = cdms2.createUniformLongitudeAxis(0.0,64,360.0/64.)
## outgrid = cdms2.createRectGrid(lat,lon,'yx','gaussian')
outgrid = cdms2.createGaussianGrid(32)
f = cdms2.openDataset(os.path.join(get_sample_data_dir(),'readonly.nc'))
u = f.variables['u']
ingrid = u.getGrid()
try:
sh = ingrid.shape
except:
markError('Grid shape')
regridf = Regridder(ingrid, outgrid)
newu = regridf(u)
if (abs(newu[0,0,-1]-488.4763488) > 1.e-3): markError('regrid',newu[0,0,-1])
newu = u.regrid(outgrid)
if (abs(newu[0,0,-1]-488.4763488) > 1.e-3): markError('regrid',newu[0,0,-1])
# Regrid TV
def mpl_spaceplot(self, dataPath, numCols=4, timeIndex=0, smooth=False):
if dataPath:
for k in range(0, 30):
if (os.path.isfile(dataPath)):
self.logger.info("Plotting file: " + dataPath)
f = cdms2.openDataset(
dataPath) # type: cdms2.dataset.CdmsFile
vars = f.variables.values()
axes = f.axes.values()
lons = self.getAxis(axes, "X")
lats = self.getAxis(axes, "Y")
fig = plt.figure()
varNames = list(map(lambda v: v.id, vars))
varNames.sort()
nCols = min(len(varNames), numCols)
nRows = math.ceil(len(varNames) / float(nCols))
iplot = 1
for varName in varNames:
if not varName.endswith("_bnds"):
try:
variable = f(varName)
if len(variable.shape) > 1:
m = Basemap(llcrnrlon=lons[0],
llcrnrlat=lats[0],
urcrnrlon=lons[len(lons) - 1],
urcrnrlat=lats[len(lats) - 1],
epsg='4326',
lat_0=lats.mean(),
lon_0=lons.mean())
ax = fig.add_subplot(nRows, nCols, iplot)
ax.set_title(varName)
lon, lat = np.meshgrid(lons, lats)
xi, yi = m(lon, lat)
smoothing = 'gouraud' if smooth else 'flat'
spatialData = variable(time=slice(
timeIndex, timeIndex + 1),
squeeze=1)
cs2 = m.pcolormesh(xi,
yi,
spatialData,
cmap='jet',
shading=smoothing)
lats_space = abs(lats[0]) + abs(
lats[len(lats) - 1])
m.drawparallels(np.arange(
lats[0], lats[len(lats) - 1],
round(lats_space / 5, 0)),
labels=[1, 0, 0, 0],
dashes=[6, 900])
lons_space = abs(lons[0]) + abs(
lons[len(lons) - 1])
m.drawmeridians(np.arange(
lons[0], lons[len(lons) - 1],
round(lons_space / 5, 0)),
labels=[0, 0, 0, 1],
dashes=[6, 900])
m.drawcoastlines()
m.drawstates()
m.drawcountries()
cbar = m.colorbar(cs2,
location='bottom',
pad="10%")
print "Plotting variable: " + varName
iplot = iplot + 1
except:
print "Skipping variable: " + varName
fig.subplots_adjust(wspace=0.1,
hspace=0.1,
top=0.95,
bottom=0.05)
plt.show()
return
else:
time.sleep(1)
