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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 scaling of variables (set_auto_scale())
class SetAutoScaleTestBase(unittest.TestCase):
"""Base object for tests checking the functionality of set_auto_scale()"""
def setUp(self):
self.testfile = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
self.fillval = default_fillvals["i2"]
self.missing_value = -9999
self.v = np.array([0, 5, 4, self.missing_value], dtype = "i2")
self.v_ma = ma.array([0, 5, 4, self.missing_value], dtype = "i2",
mask = [True, False, False, True], fill_value = self.fillval)
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')
x = f.createDimension('x', None)
xx = f.createDimension('xx', 10)
v = f.createVariable('v', "i2", 'x')
vv = f.createVariable('vv', "i2", 'xx')
vv.add_offset=0; vv.scale_factor=np.float32(1.0)
v[:] = self.v
vv[:] = np.ones(10)
# Note: Scale factors are only added after writing, so that no auto-scaling takes place!
v.scale_factor = self.scale_factor
v.add_offset = self.add_offset
f.close()
def tearDown(self):
os.remove(self.testfile)
class SetAutoScaleFalse(SetAutoScaleTestBase):
def test_unmasked(self):
"""Testing (not) auto-scaling of variables for set_auto_scale(False)"""
f = Dataset(self.testfile, "r")
f.variables["v"].set_auto_scale(False)
v = f.variables["v"][:]
self.assertEqual(v.dtype, "i2")
self.assertTrue(isinstance(v, np.ndarray))
# issue 785: always return masked array by default
self.assertTrue(isinstance(v, ma.masked_array))
assert_array_almost_equal(v, self.v)
f.close()
def test_masked(self):
"""Testing auto-conversion of masked arrays for set_auto_mask(False) with masking"""
# Update test data file
f = Dataset(self.testfile, "a")
f.variables["v"].missing_value = self.missing_value
f.close()
# Note: Converting arrays to masked arrays is default if missing_value is present
f = Dataset(self.testfile, "r")
f.variables["v"].set_auto_scale(False)
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()
class SetAutoScaleTrue(SetAutoScaleTestBase):
def test_unmasked(self):
"""Testing auto-scaling of variables for set_auto_scale(True)"""
f = Dataset(self.testfile)
f.variables["v"].set_auto_scale(True) # The default anyway...
v_scaled = f.variables['v'][:]
# issue 913
vv_scaled = f.variables['vv'][:]
self.assertEqual(vv_scaled.dtype,f.variables['vv'].scale_factor.dtype)
assert_array_almost_equal(vv_scaled, np.ones(10))
self.assertEqual(v_scaled.dtype, "f8")
self.assertTrue(isinstance(v_scaled, np.ndarray))
# issue 785: always return masked array by default
self.assertTrue(isinstance(v_scaled, ma.masked_array))
assert_array_almost_equal(v_scaled, self.v_scaled)
f.close()
def test_masked(self):
"""Testing auto-scaling of variables for set_auto_scale(True) with masking"""
# Update test data file
f = Dataset(self.testfile, "a")
f.variables["v"].missing_value = self.missing_value
f.close()
# Note: Converting arrays to masked arrays is default if missing_value is present
f = Dataset(self.testfile)
f.variables["v"].set_auto_scale(True) # The default anyway...
v_ma_scaled = f.variables['v'][:]
self.assertEqual(v_ma_scaled.dtype, "f8")
self.assertTrue(isinstance(v_ma_scaled, np.ndarray))
self.assertTrue(isinstance(v_ma_scaled, ma.masked_array))
assert_array_almost_equal(v_ma_scaled, self.v_ma_scaled)
f.close()
class WriteAutoScaleTest(SetAutoScaleTestBase):
def test_auto_scale_write(self):
"""Testing automatic packing to all kinds of integer types"""
def packparams(dmax, dmin, dtyp):
kind = dtyp[0]
n = int(dtyp[1]) * 8
scale_factor = (dmax - dmin) / (2**n - 1)
if kind == 'i':
add_offset = dmin + 2**(n-1) * scale_factor
elif kind == 'u':
add_offset = dmin
else:
raise Exception
return((add_offset, scale_factor))
for dtyp in ['i1', 'i2', 'i4', 'u1', 'u2', 'u4']:
np.random.seed(456)
data = np.random.uniform(size=100)
f = Dataset(self.testfile, 'w')
f.createDimension('x')
#
# save auto_scaled
v = f.createVariable('v', dtyp, ('x',))
v.set_auto_scale(True) # redundant
v.add_offset, v.scale_factor = packparams(
np.max(data), np.min(data), dtyp)
v[:] = data
f.close()
#
# read back
f = Dataset(self.testfile, 'r')
v = f.variables['v']
v.set_auto_mask(False)
v.set_auto_scale(True) # redundant
vdata = v[:]
# error normalized by scale factor
maxerrnorm = np.max(np.abs((vdata - data) / v.scale_factor))
# 1e-5 accounts for floating point errors
assert maxerrnorm < 0.5 + 1e-5
f.close()
class GlobalSetAutoScaleTest(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):
f = Dataset(self.testfile, "r")
# Default is both scaling and masking enabled
v0 = f.variables['var0']
v1 = f.groups['Group1'].variables['var1']
v2 = f.groups['Group2'].variables['var2']
self.assertTrue(v0.scale)
self.assertTrue(v0.mask)
self.assertTrue(v1.scale)
self.assertTrue(v1.mask)
self.assertTrue(v2.scale)
self.assertTrue(v2.mask)
# No auto-scaling
f.set_auto_scale(False)
self.assertFalse(v0.scale)
self.assertTrue(v0.mask)
self.assertFalse(v1.scale)
self.assertTrue(v1.mask)
self.assertFalse(v2.scale)
self.assertTrue(v2.mask)
f.close()
if __name__ == '__main__':
unittest.main()
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