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tst_slicing.py
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/
tst_slicing.py
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from netCDF4 import Dataset
from numpy.random import seed, randint
from numpy.testing import assert_array_equal, assert_equal,\
assert_array_almost_equal
import tempfile, unittest, os, random
import numpy as np
file_name = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
xdim=9; ydim=10; zdim=11
#seed(9) # fix seed
data = randint(0,10,size=(xdim,ydim,zdim)).astype('u1')
datarev = data[:,::-1,:]
class VariablesTestCase(unittest.TestCase):
def setUp(self):
self.file = file_name
f = Dataset(file_name,'w')
f.createDimension('x',xdim)
f.createDimension('xu',None)
f.createDimension('y',ydim)
f.createDimension('z',zdim)
f.createDimension('zu',None)
v = f.createVariable('data','u1',('x','y','z'))
vu = f.createVariable('datau','u1',('xu','y','zu'))
v1 = f.createVariable('data1d', 'u1', ('x',))
# variable with no unlimited dim.
# write slice in reverse order
v[:,::-1,:] = data
# variable with an unlimited dimension.
# write slice in reverse order
#vu[0:xdim,::-1,0:zdim] = data
vu[:,::-1,:] = data
v1[:] = data[:, 0, 0]
f.close()
def tearDown(self):
# Remove the temporary files
os.remove(self.file)
def test_3d(self):
"""testing variable slicing"""
f = Dataset(self.file, 'r')
v = f.variables['data']
vu = f.variables['datau']
# test return of array scalar.
assert_equal(v[0,0,0].shape,())
assert_array_equal(v[:], datarev)
# test reading of slices.
# negative value means count back from end.
assert_array_equal(v[:-1,:-2,:-3],datarev[:-1,:-2,:-3])
# every other element (positive step)
assert_array_equal(v[2:-1:2,2:-2:2,2:-3:2],datarev[2:-1:2,2:-2:2,2:-3:2])
# every other element (negative step)
assert_array_equal(v[-1:2:-2,-2:2:-2,-3:2:-2],datarev[-1:2:-2,-2:2:-2,-3:2:-2])
# read elements in reverse order
assert_array_equal(v[:,::-1,:],data)
assert_array_equal(v[::-1,:,::-1],datarev[::-1,:,::-1])
assert_array_equal(v[xdim-1::-3,:,zdim-1::-3],datarev[xdim-1::-3,:,zdim-1::-3])
# ellipsis slice.
assert_array_equal(v[...,2:],datarev[...,2:])
# variable with an unlimited dimension.
assert_array_equal(vu[:], data[:,::-1,:])
# read data in reverse order
assert_array_equal(vu[:,::-1,:],data)
# index using an integer array scalar
i = np.ones(1,'i4')[0]
assert_array_equal(v[i],datarev[1])
f.close()
def test_1d(self):
f = Dataset(self.file, 'r')
v1 = f.variables['data1d']
d = data[:,0,0]
assert_equal(v1[:], d)
assert_equal(v1[4:], d[4:])
# test return of array scalar.
assert_equal(v1[0].shape, ())
i1 = np.array([2,3,4])
assert_equal(v1[i1], d[i1])
i2 = np.array([2,3,5])
assert_equal(v1[i2], d[i2])
assert_equal(v1[d<5], d[d<5])
assert_equal(v1[5], d[5])
f.close()
def test_0d(self):
f = Dataset(self.file, 'w')
v = f.createVariable('data', float)
v[...] = 10
assert_array_equal(v[...], 10)
assert_equal(v.shape, v[...].shape)
assert(type(v[...]) == np.ndarray)
f.close()
def test_issue259(self):
dset = Dataset(self.file, 'w', format='NETCDF4_CLASSIC')
dset.createDimension('dim', None)
a = dset.createVariable('a', 'i', ('dim',))
b = dset.createVariable('b', 'i', ('dim',))
c = dset.createVariable('c', 'i', ('dim',))
c[:] = 1 # c initially is empty, new entry created
assert_array_equal(c[...], np.array([1]))
b[:] = np.array([1,1])
a[:] = 1 # a should be same as b
assert_array_equal(a[...], b[...])
dset.close()
def test_issue371(self):
dataset = Dataset(self.file, 'w')
dataset.createDimension('dim', 5)
var = dataset.createVariable('bar', 'i8', ('dim', ))
data = [1, 2, 3, 4, 5]
var[..., :] = data
assert_array_equal(var[..., :], np.array(data))
dataset.close()
def test_issue306(self):
f = Dataset(self.file,'w')
nlats = 7; lat = f.createDimension('lat',nlats)
nlons = 12; lon = f.createDimension('lon',nlons)
nlevs = 1; lev = f.createDimension('lev',nlevs)
time = f.createDimension('time',None)
var = f.createVariable('var',np.float,('time','lev','lat','lon'))
a = np.random.uniform(size=(10,nlevs,nlats,nlons))
var[0:10] = a
f.close()
f = Dataset(self.file)
aa = f.variables['var'][4,-1,:,:]
assert_array_almost_equal(a[4,-1,:,:],aa)
v = f.variables['var']
try:
aa = v[4,-2,:,:] # -2 when dimension is length 1
except IndexError:
pass
else:
raise IndexError('This test should have failed.')
try:
aa = v[4,...,...,:] # more than one Ellipsis
except IndexError:
pass
else:
raise IndexError('This test should have failed.')
try:
aa = v[:,[True,True],:,:] # boolean array too long.
except IndexError:
pass
else:
raise IndexError('This test should have failed.')
try:
aa = v[:,[0,1],:,:] # integer index too large
except IndexError:
pass
else:
raise IndexError('This test should have failed.')
f.close()
def test_issue300(self):
f = Dataset(self.file,'w')
nlats = 11; lat = f.createDimension('lat',nlats)
nlons = 20; lon = f.createDimension('lon',nlons)
time = f.createDimension('time',None)
var = f.createVariable('var',np.float,('time','lat','lon'))
a = np.random.uniform(size=(3,nlats,nlons))
var[[True,True,False,False,False,True]] = a
var[0,2.0,"-1"] = 0 # issue 312
a[0,2,-1]=0
f.close()
f = Dataset(self.file)
var = f.variables['var']
aa = var[[0,1,5]]
bb = var[[True,True,False,False,False,True]]
lats = np.arange(nlats); lons = np.arange(nlons)
cc = var[-1,lats > 2,lons < 6]
assert_array_almost_equal(a,aa)
assert_array_almost_equal(bb,aa)
assert_array_almost_equal(cc,a[-1,3:,:6])
f.close()
def test_retain_single_dims(self):
f = Dataset(self.file, 'r')
v = f.variables['data']
keys = ((0, 1, 2, 3, 4, 5, 6, 7, 8), (5,), (4,))
shape = (9, 1, 1)
data = v[keys]
assert_equal(data.shape, shape)
keys = ((0, 1, 2, 3, 4, 5, 6, 7, 8), 5, 4,)
shape = (9,)
data = v[keys]
assert_equal(data.shape, shape)
f.close()
if __name__ == '__main__':
unittest.main()