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tst_masked4.py
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tst_masked4.py
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import unittest
import os
import tempfile
import numpy as np
from numpy import ma
from numpy.testing import assert_array_almost_equal
from netCDF4 import Dataset, default_fillvals
# Test use of valid_min/valid_max/valid_range in generation of masked arrays
class SetValidMinMax(unittest.TestCase):
def setUp(self):
self.testfile = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
self.valid_min = -32765
self.valid_max = 32765
self.valid_range = [self.valid_min,self.valid_max]
self.v = np.array([self.valid_min-1, 5, 4, self.valid_max+1], dtype = "i2")
self.v_ma = ma.array([self.valid_min-1, 5, 4, self.valid_max+1], dtype = "i2", mask = [True, False, False, True])
self.scale_factor = 10.
self.add_offset = 5.
self.v_scaled = self.v * self.scale_factor + self.add_offset
self.v_ma_scaled = self.v_ma * self.scale_factor + self.add_offset
f = Dataset(self.testfile, 'w')
_ = f.createDimension('x', None)
v = f.createVariable('v', "i2", 'x')
v2 = f.createVariable('v2', "i2", 'x')
v3 = f.createVariable('v3', "i2", 'x', fill_value=self.valid_min)
v.missing_value = np.array(32767, v.dtype)
v.valid_min = np.array(self.valid_min, v.dtype)
v.valid_max = np.array(self.valid_max, v.dtype)
v[0] = self.valid_min-1
v[1] = self.v[1]
v[2] = self.v[2]
v[3] = self.valid_max+1
v2.missing_value = np.array(32767, v.dtype)
v2.valid_range = np.array(self.valid_range, v.dtype)
v2[0] = self.valid_range[0]-1
v2[1] = self.v[1]
v2[2] = self.v[2]
v2[3] = self.valid_range[1]+1
v3.missing_value = np.array(32767, v.dtype)
v3.valid_max = np.array(self.valid_max, v.dtype)
# _FillValue should act as valid_min
v3[0] = v3._FillValue-1
v3[1] = self.v[1]
v3[2] = self.v[2]
v3[3] = self.valid_max+1
f.close()
def tearDown(self):
os.remove(self.testfile)
def test_scaled(self):
"""Testing auto-conversion of masked arrays"""
# Update test data file
f = Dataset(self.testfile, "a")
f.variables["v"].scale_factor = self.scale_factor
f.variables["v"].add_offset = self.add_offset
f.variables["v2"].scale_factor = self.scale_factor
f.variables["v2"].add_offset = self.add_offset
f.close()
f = Dataset(self.testfile, "r")
v = f.variables["v"][:]
v2 = f.variables["v2"][:]
v3 = f.variables["v3"][:]
self.assertEqual(v.dtype, "f8")
self.assertTrue(isinstance(v, np.ndarray))
self.assertTrue(isinstance(v, ma.core.MaskedArray))
assert_array_almost_equal(v, self.v_scaled)
self.assertEqual(v2.dtype, "f8")
self.assertTrue(isinstance(v2, np.ndarray))
self.assertTrue(isinstance(v2, ma.core.MaskedArray))
assert_array_almost_equal(v2, self.v_scaled)
self.assertTrue(np.all(self.v_ma.mask == v.mask))
self.assertTrue(np.all(self.v_ma.mask == v2.mask))
# treating _FillValue as valid_min/valid_max was
# too suprising, revert to old behaviour (issue #761)
#self.assertTrue(np.all(self.v_ma.mask == v3.mask))
# check that underlying data is same as in netcdf file
v = f.variables['v']
v.set_auto_scale(False)
v = v[:]
self.assertTrue(np.all(self.v == v.data))
f.close()
# issue 672
f = Dataset('issue672.nc')
field = 'azi_angle_trip'
v = f.variables[field]
data1 = v[:]
v.set_auto_scale(False)
data2 = v[:]
v.set_auto_maskandscale(False)
data3 = v[:]
assert(data1[(data3 < v.valid_min)].mask.sum() == 12)
assert(data2[(data3 < v.valid_min)].mask.sum() ==
data1[(data3 < v.valid_min)].mask.sum())
f.close()
if __name__ == '__main__':
unittest.main()