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 setUp(self):
dir = "../cdat_data/"
filename = dir + "so_Omon_NorESM1-M_historicalNat_r1i1p1_185001-185312_2timesteps.nc"
g = cdms2.open(filename)
self.so = g("so")[0, 0, ...]
gLat = cdms2.createGaussianAxis(64)
deltaLon = 360 / 128.0
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:], gLat.getBounds(), gLon.getBounds())
def setUp(self):
dir = "../cdat_data/"
filename = dir + "so_Omon_HadGEM2-ES_esmFixClim1_r1i1p1_185912-186911_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0, 0, ...]
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360 / 128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def setUp(self):
dir = cdat_info.get_sampledata_path() + "/"
filename = dir + "so_Omon_MPI-ESM-LR_1pctCO2_r1i1p1_185001-185912_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0,0,:,:254]
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360/128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def setUp(self):
dir = cdat_info.get_prefix() + "/sample_data/"
filename = dir + "so_Omon_MPI-ESM-LR_1pctCO2_r1i1p1_185001-185912_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0,0,:,:254]
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360/128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def setUp(self):
dir = "../cdat_data/"
filename = dir + "so_Omon_NorESM1-M_historicalNat_r1i1p1_185001-185312_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0, 0, ...]
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360 / 128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def setUp(self):
dir = "../cdat_data/"
filename = dir + "so_Omon_IPSL-CM5A-LR_1pctCO2_r1i1p1_185001-189912_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0,0,...]
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360/128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def setUp(self):
dir = "../cdat_data/"
filename = dir + "so_Omon_HadGEM2-ES_esmFixClim1_r1i1p1_185912-186911_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0,0,...]
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360/128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def setUp(self):
filename = sys.prefix + \
"/sample_data/so_Omon_CNRM-CM5_decadal2004_r9i1p1_200501-201412_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0,0,...]
self.so.toVisit('soCNRM.vsh5', 'Vs')
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360/128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
def createEqualIntervalLongitude(self,longitude, delta): ''' calling cdms2.createUniformLongitudeAxis to genrate its bounds and axis ''' lon_start = longitude[0] lon_end =longitude[-1] length = len(np.asarray(np.arange(lon_start,lon_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_long = cdms2.createUniformLongitudeAxis(startLon = lon_start, nlon = length, deltaLon = delta) #print eq_interval_long[:] #print eq_interval_long.getBounds() return eq_interval_long
def setUp(self):
filename = cdat_info.get_prefix() + \
"/sample_data/so_Omon_CNRM-CM5_decadal2004_r9i1p1_200501-201412_2timesteps.nc"
g = cdms2.open(filename)
self.so = g('so')[0, 0, ...]
self.so.toVisit('soCNRM.vsh5', 'Vs')
gLat = cdms2.createGaussianAxis(64)
deltaLon = (360 / 128.)
gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon)
self.gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:],
gLat.getBounds(),
gLon.getBounds())
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 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()
import cdms2
import numpy as np
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]))]