def __call__(self, _var, region):
var = _var.clone()
if not region in self.regions.keys():
return None
val = self.regions[region]
# reading mask data
regions_data = self.file['mask']
regions_var = cdms2.createVariable(
ones(regions_data.shape),
grid = cdms2.createUniformGrid(89.75, 360, -0.5, -180, 720, 0.5),
mask = where(equal(regions_data, val), 0, 1))
lats = cdms2.createUniformLatitudeAxis(89.75, 360, -0.5)
lons = cdms2.createUniformLongitudeAxis(-180, 720, 0.5)
regions_var.setAxisList((lats,lons))
new_mask_var = regions_var.regrid(var.getGrid(), regridTool='regrid2', regridMethod='linear')
new_mask = getmask(new_mask_var)
if var.mask <> None:
var.mask = logical_or(var.mask, new_mask)
else:
var.mask = new_mask;
return var
def buildOutlineMap(self):
# This function load a binary image (black and white)
# and create a default grid for it. Then it uses re-gridding algorithms
# to scale in the correct domain.
from pylab import imread
import vtk.util.vtkImageImportFromArray as vtkUtil
# read outline image and convert to gray scale
try:
data = imread(defaultOutlineMapFile)
data = data.mean(axis=2)
# # create a variable using the data loaded in the image and an uniform grid
dims = data.shape
reso = [180.0 / dims[0], 360.0 / dims[1]]
var = cdms2.createVariable(data)
lat = cdms2.createUniformLatitudeAxis(90, dims[0], -reso[0])
lon = cdms2.createUniformLongitudeAxis(-180, dims[1], reso[1])
var.setAxis(0, lat)
var.setAxis(1, lon)
# create the final map using the ROI
ROI = self.roi[:]
if ROI[2] < -90.0: ROI[2] = -90.0
if ROI[3] > 90.0: ROI[3] = 90.0
odims = [(ROI[3] - ROI[2]) / reso[0], (ROI[1] - ROI[0]) / reso[1]]
ogrid = cdms2.createUniformGrid(ROI[2], odims[0], reso[0], ROI[0],
odims[1], reso[1])
ovar = var.regrid(ogrid, regridTool='regrid2')
# replace outlier numbers
d = ovar.data
d[d == 1e+20] = d[d <> 1e+20].max()
img = vtkUtil.vtkImageImportFromArray()
img.SetArray(ovar.data)
img.Update()
except Exception:
print >> sys.stderr, "Error building Outline Map"
traceback.print_exc()
return None
# convert to vtkImageData
return img.GetOutput()
def createEqualIntervalLatitude(self,latitude,delta): ''' return generated bounds according to passed latitudes. return as Axis object eg input latitude = [-90,-80,-70,...80,90] ''' lat_start = latitude[0] lat_end = latitude[-1] length = len(np.asarray(np.arange(lat_start,lat_end+delta,delta))) # we cant call xrange here, since xrange takes only int arg not float. delta may have float. so we are calling here numpy arange eq_interval_lat = cdms2.createUniformLatitudeAxis(startLat = lat_start, nlat = length, deltaLat = delta) #print eq_interval_lat[:] #print eq_interval_lat.getBounds() return eq_interval_lat # # commenting the following my own code, since we are going to use cdat inbuild methods # '''
def buildOutlineMap(self):
# This function load a binary image (black and white)
# and create a default grid for it. Then it uses re-gridding algorithms
# to scale in the correct domain.
from pylab import imread
import vtk.util.vtkImageImportFromArray as vtkUtil
# read outline image and convert to gray scale
try:
data = imread(defaultOutlineMapFile)
data = data.mean(axis=2)
# # create a variable using the data loaded in the image and an uniform grid
dims = data.shape
reso = [180.0/dims[0], 360.0/dims[1]]
var = cdms2.createVariable(data)
lat = cdms2.createUniformLatitudeAxis(90, dims[0], -reso[0])
lon = cdms2.createUniformLongitudeAxis(-180, dims[1], reso[1])
var.setAxis(0, lat)
var.setAxis(1, lon)
# create the final map using the ROI
ROI = self.roi[:]
if ROI[2] < -90.0: ROI[2] = -90.0
if ROI[3] > 90.0: ROI[3] = 90.0
odims = [ (ROI[3]-ROI[2])/reso[0] , (ROI[1]-ROI[0])/reso[1] ]
ogrid = cdms2.createUniformGrid( ROI[2], odims[0], reso[0], ROI[0], odims[1], reso[1] )
ovar = var.regrid(ogrid, regridTool='regrid2')
# replace outlier numbers
d = ovar.data
d[d==1e+20] = d[d<>1e+20].max()
img = vtkUtil.vtkImageImportFromArray()
img.SetArray(ovar.data)
img.Update()
except Exception:
print>>sys.stderr, "Error building Outline Map"
traceback.print_exc()
return None
# convert to vtkImageData
return img.GetOutput()
from OpenSSL import crypto
from OpenSSL import SSL
from socket import gethostname
def random_str(count):
return ''.join(
random.choice(string.ascii_letters + string.digits)
for _ in xrange(count))
random.seed(1987)
longitude = cdms2.createUniformLongitudeAxis(-180.0, 360.0, 1.0)
latitude = cdms2.createUniformLatitudeAxis(-90.0, 180.0, 1.0)
def write_file(file_path, axes, var_name):
with cdms2.open(file_path, 'w') as outfile:
outfile.write(np.array(
[[[random.random() for _ in xrange(len(axes[2]))]
for _ in xrange(len(axes[1]))] for _ in xrange(len(axes[0]))]),
axes=axes,
id=var_name)
def generate_variable(axes, var_name):
data = np.array([[[random.random() for _ in xrange(len(axes[2]))]
for _ in xrange(len(axes[1]))]
for _ in xrange(len(axes[0]))])
raise ValueError
d1 = numpy.ravel(data1)
d2 = numpy.ravel(data2)
sq = (d1 - d2) * (d1 - d2)
error = numpy.sum(sq) / len(d1)
rmserror = numpy.sqrt(error)
return rmserror
if __name__ == '__main__':
import math
latIn = cdms2.createUniformLatitudeAxis(90.0, 46, -4.0)
levList = [10., 40., 75., 100., 150., 250., 350., 500., 650., 850.,
1000.] # pick some levels
levIn = cdms2.createAxis(numpy.array(levList, numpy.float64), id='level')
latOut = cdms2.createGaussianAxis(64)
levList = [
10., 30., 50., 70., 100., 200., 300., 400., 500., 700., 850., 1000.
] # pick some levels
levOut = cdms2.createAxis(numpy.array(levList, numpy.float64), id='level')
xregridf = CrossSectionRegridder(latIn, latOut, levIn, levOut)
# make some artificial data
dataIn = section(latIn[:], levIn[:])
var = cdms2.createVariable(dataIn,
axes=(levIn, latIn),
print 'data2 shape = ', data2.shape
raise ValueError
d1 = numpy.ravel(data1)
d2 = numpy.ravel(data2)
sq = (d1 - d2)*(d1 - d2)
error = numpy.sum(sq)/len(d1)
rmserror = numpy.sqrt(error)
return rmserror
if __name__=='__main__':
import cdms2, math
latIn = cdms2.createUniformLatitudeAxis(90.0, 46, -4.0)
levList = [10., 40., 75., 100., 150., 250., 350., 500., 650., 850., 1000.] # pick some levels
levIn = cdms2.createAxis(numpy.array(levList, numpy.float64), id='level')
latOut = cdms2.createGaussianAxis(64)
levList = [10., 30., 50., 70., 100., 200., 300., 400., 500., 700.,850., 1000.] # pick some levels
levOut = cdms2.createAxis(numpy.array(levList, numpy.float64), id='level')
xregridf = CrossSectionRegridder(latIn, latOut, levIn, levOut)
dataIn = section(latIn[:], levIn[:]) # make some artificial data
var = cdms2.createVariable(dataIn, axes=(levIn, latIn), attributes={'units':'N/A'}, id='test')
dataOut = xregridf(dataIn)
dataCheck = section(latOut[:], levOut[:]) # make the exact answer
error = rmserror(dataOut, dataCheck) # find the rms error
