import unittest
import netCDF4
from numpy.testing import assert_array_equal
import numpy as np
class Test_Unsigned(unittest.TestCase):
"""
Test autoconversion to unsigned ints when _Unsigned attribute is True.
This attribute is is set by netcdf-java to designate unsigned
integer data stored with a signed integer type in netcdf-3.
If _Unsigned=True, a view to the data as unsigned integers is returned.
set_autoscale can be used to turn this off (default is on)
See issue #656 (pull request #658).
"""
def test_unsigned(self):
f = netCDF4.Dataset("ubyte.nc")
data = f['ub'][:]
assert data.dtype.str[1:] == 'u1'
assert_array_equal(data,np.array([0,255],np.uint8))
f.set_auto_scale(False)
data2 = f['ub'][:]
assert data2.dtype.str[1:] == 'i1'
assert_array_equal(data2,np.array([0,-1],np.int8))
f.close()
# issue 671
f = netCDF4.Dataset('issue671.nc')
data1 = f['soil_moisture'][:]
assert(np.ma.isMA(data1))
f.set_auto_scale(False)
data2 = f['soil_moisture'][:]
assert(data1.mask.sum() == data2.mask.sum())
f.close()
# issue 794
# test that valid_min/valid_max/_FillValue are
# treated as unsigned integers.
f=netCDF4.Dataset('20171025_2056.Cloud_Top_Height.nc')
data = f['HT'][:]
assert(data.mask.sum() == 57432)
assert(int(data.max()) == 15430)
assert(int(data.min()) == 0)
assert(data.dtype == np.float32)
f.close()
if __name__ == '__main__':
unittest.main()
