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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 automatic conversion of masked arrays (set_auto_mask())
class SetAutoMaskTestBase(unittest.TestCase):
"""Base object for tests checking the functionality of set_auto_mask()"""
def setUp(self):
self.testfile = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
self.fillval = default_fillvals["i2"]
self.v = np.array([self.fillval, 5, 4, -9999], dtype = "i2")
self.v_ma = ma.array([self.fillval, 5, 4, -9999], 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')
v.missing_value = np.array(-9999, v.dtype)
# v[0] not set, will be equal to _FillValue
v[1] = self.v[1]
v[2] = self.v[2]
v[3] = v.missing_value
f.close()
def tearDown(self):
os.remove(self.testfile)
class SetAutoMaskFalse(SetAutoMaskTestBase):
def test_unscaled(self):
"""Testing auto-conversion of masked arrays for set_auto_mask(False)"""
f = Dataset(self.testfile, "r")
f.variables["v"].set_auto_mask(False)
v = f.variables["v"][:]
self.assertEqual(v.dtype, "i2")
self.assertTrue(isinstance(v, np.ndarray))
self.assertTrue(not isinstance(v, ma.masked_array))
assert_array_almost_equal(v, self.v)
f.close()
def test_scaled(self):
"""Testing auto-conversion of masked arrays for set_auto_mask(False) with scaling"""
# 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.close()
# Note: Scaling variables is default if scale_factor and/or add_offset are present
f = Dataset(self.testfile, "r")
f.variables["v"].set_auto_mask(False)
v = f.variables["v"][:]
self.assertEqual(v.dtype, "f8")
self.assertTrue(isinstance(v, np.ndarray))
self.assertTrue(not isinstance(v, ma.masked_array))
assert_array_almost_equal(v, self.v_scaled)
f.close()
class SetAutoMaskTrue(SetAutoMaskTestBase):
def test_unscaled(self):
"""Testing auto-conversion of masked arrays for set_auto_mask(True)"""
f = Dataset(self.testfile)
f.variables["v"].set_auto_mask(True) # The default anyway...
v_ma = f.variables['v'][:]
self.assertEqual(v_ma.dtype, "i2")
self.assertTrue(isinstance(v_ma, np.ndarray))
self.assertTrue(isinstance(v_ma, ma.masked_array))
assert_array_almost_equal(v_ma, self.v_ma)
f.close()
def test_scaled(self):
"""Testing auto-conversion of masked arrays for set_auto_mask(True)"""
# 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.close()
# Note: Scaling variables is default if scale_factor and/or add_offset are present
f = Dataset(self.testfile)
f.variables["v"].set_auto_mask(True) # The default anyway...
v_ma = f.variables['v'][:]
self.assertEqual(v_ma.dtype, "f8")
self.assertTrue(isinstance(v_ma, np.ndarray))
self.assertTrue(isinstance(v_ma, ma.masked_array))
assert_array_almost_equal(v_ma, self.v_ma_scaled)
f.close()
class GlobalSetAutoMaskTest(unittest.TestCase):
def setUp(self):
self.testfile = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
f = Dataset(self.testfile, 'w')
grp1 = f.createGroup('Group1')
grp2 = f.createGroup('Group2')
f.createGroup('Group3') # empty group
f.createVariable('var0', "i2", ())
grp1.createVariable('var1', 'f8', ())
grp2.createVariable('var2', 'f4', ())
f.close()
def tearDown(self):
os.remove(self.testfile)
def runTest(self):
# Note: The default behaviour is to to have both auto-masking and auto-scaling activated.
# This is already tested in tst_scaled.py, so no need to repeat here. Instead,
# disable auto-masking and auto-scaling altogether.
f = Dataset(self.testfile, "r")
# Neither scaling and masking enabled
f.set_auto_maskandscale(False)
v0 = f.variables['var0']
v1 = f.groups['Group1'].variables['var1']
v2 = f.groups['Group2'].variables['var2']
self.assertFalse(v0.scale)
self.assertFalse(v0.mask)
self.assertFalse(v1.scale)
self.assertFalse(v1.mask)
self.assertFalse(v2.scale)
self.assertFalse(v2.mask)
# No auto-masking, but auto-scaling
f.set_auto_maskandscale(True)
f.set_auto_mask(False)
self.assertTrue(v0.scale)
self.assertFalse(v0.mask)
self.assertTrue(v1.scale)
self.assertFalse(v1.mask)
self.assertTrue(v2.scale)
self.assertFalse(v2.mask)
f.close()
if __name__ == '__main__':
unittest.main()
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