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import sys
from typing import TYPE_CHECKING, Any
import unittest
import os
import tempfile
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
from numpy.random.mtrand import uniform
import netCDF4
if TYPE_CHECKING:
from netCDF4 import CompressionLevel
else:
CompressionLevel = Any
# test primitive data types.
# create an n1dim by n2dim random ranarr.
FILE_NAME = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
n1dim = 5
n2dim = 10
ranarr = 100.*uniform(size=(n1dim,n2dim))
zlib=False; complevel=0; shuffle=False; least_significant_digit=None
datatypes = ['f8','f4','i1','i2','i4','i8','u1','u2','u4','u8','S1']
FillValue = 1.0
issue273_data = np.ma.array(['z']*10,dtype='S1',\
mask=[False,False,False,False,False,True,False,False,False,False])
class PrimitiveTypesTestCase(unittest.TestCase):
def setUp(self):
self.file = FILE_NAME
f = netCDF4.Dataset(self.file,'w')
f.createDimension('n1', None)
f.createDimension('n2', n2dim)
for typ in datatypes:
foo = f.createVariable(
f"data_{typ}",
typ,
('n1','n2',),
zlib=zlib,
complevel=complevel, # type: ignore # type checkers bad at narrowing
shuffle=shuffle,
least_significant_digit=least_significant_digit,
fill_value=FillValue,
)
#foo._FillValue = FillValue
# test writing of _FillValue attribute for diff types
# (should be cast to type of variable silently)
foo[1:n1dim] = ranarr[1:n1dim]
v = f.createVariable('issue271', np.dtype('S1'), [], fill_value=b'Z')
v2 = f.createVariable('issue273', np.dtype('S1'), 'n2',\
fill_value='\x00')
v2[:] = issue273_data
v3 = f.createVariable('issue707',np.int8,'n2')
v3.setncattr('missing_value',255)
v3[:]=-1
f.close()
def tearDown(self):
# Remove the temporary files
os.remove(self.file)
def runTest(self):
"""testing primitive data type """
f = netCDF4.Dataset(self.file)
for typ in datatypes:
data = f.variables['data_'+typ]
data.set_auto_maskandscale(False)
datarr: np.ndarray = data[1:n1dim]
# fill missing data with _FillValue
# ('S1' array will have some missing values)
if hasattr(datarr, 'mask'):
assert isinstance(datarr, np.ma.masked_array)
datarr = datarr.filled()
datfilled = data[0]
# check to see that data type is correct
if typ == 'S1':
self.assertTrue(data.dtype.str[1:] in ['S1','U1'])
else:
self.assertTrue(data.dtype.str[1:] == typ)
# check data in variable.
if data.dtype.str[1:] != 'S1':
#assert np.allclose(datarr, ranarr[1:n1dim].astype(data.dtype))
assert_array_almost_equal(datarr,ranarr[1:n1dim].astype(data.dtype))
else:
assert datarr.tobytes() == ranarr[1:n1dim].astype(data.dtype).tobytes()
# check that variable elements not yet written are filled
# with the specified _FillValue.
assert_array_equal(datfilled,np.asarray(data._FillValue,datfilled.dtype))
# issue 271 (_FillValue should be a byte for character arrays on
# Python 3)
v = f.variables['issue271']
assert type(v._FillValue) == bytes
assert v._FillValue == b'Z'
# issue 273 (setting _FillValue to null byte manually)
v2 = f.variables['issue273']
assert type(v2._FillValue) == bytes
assert v2._FillValue == b'\x00'
assert str(issue273_data) == str(v2[:])
# issue 707 (don't apply missing_value if cast to variable type is
# unsafe)
v3 = f.variables['issue707']
assert_array_equal(v3[:],-1*np.ones(n2dim,v3.dtype))
f.close()
# issue #850 (masked scalar char variable)
f = netCDF4.Dataset(self.file,'a')
a = f.createVariable('a', 'c', ())
a[:] = np.ma.masked
f.close()
if __name__ == '__main__':
unittest.main()
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