class Test(unittest.TestCase):
def setUp(self):
# create a colon batch with 2 samples
self.test_batch = ColonMuscleBatch()
self.test_batch.create_parameters(2)
# artificially add 10 fake "reflectances" to this batch
# at 10 fake "wavelengths"
WAVELENGHTS = np.linspace(450, 720, 10)
reflectance1 = np.arange(0, 30, 3)
reflectance2 = np.arange(30, 60, 3)
for w in WAVELENGHTS:
self.test_batch.df["reflectances", w] = np.NAN
for r1, r2, w in zip(reflectance1, reflectance2, WAVELENGHTS):
self.test_batch.df["reflectances", w][0] = r1
self.test_batch.df["reflectances", w][1] = r2
# shortcut to dataframe that we are interested in:
self.df = self.test_batch.df
def test_sliding_average(self):
# by test design folding should not alter elements (only at boundaries,
# which are excluded by array slicing:
expected_elements = self.df.reflectances.iloc[:, 1:-1].copy()
dfmani.fold_by_sliding_average(self.df, 3)
assert_frame_equal(self.df.reflectances, expected_elements)
def test_interpolation(self):
new_wavelengths = [465, 615, 555]
dfmani.interpolate_wavelengths(self.df, new_wavelengths)
expected = np.array([[1.5, 16.5, 10.5], [31.5, 46.5, 40.5]])
np.testing.assert_almost_equal(self.df.reflectances.as_matrix(),
expected,
err_msg="test if interpolation " +
"works fine on batches")
def test_select_n(self):
""" this is less a test and more a showing of how to select n elements
from a dataframe."""
# draw one sample. Look into documentation for sample to see all the
# options. Sample is quite powerfull.
self.df = self.df.sample(1)
self.assertEqual(self.df.shape[0], 1, "one sample selected")
def test_sortout_bands(self):
""" this is less a test and more a showing of how to sortout specific
bands from a dataframe """
# drop the 510 and 720 nm band
band_names_to_sortout = [510, 720]
self.df.drop(band_names_to_sortout, axis=1, level=1, inplace=True)
df_r = self.df["reflectances"]
self.assertTrue(not (510 in df_r.columns))
self.assertTrue(not 720 in df_r.columns)
self.assertTrue(690 in df_r.columns)
class Test(unittest.TestCase):
def setUp(self):
# create a colon batch with 2 samples
self.test_batch = ColonMuscleBatch()
self.test_batch.create_parameters(2)
# artificially add 10 fake "reflectances" to this batch
# at 10 fake "wavelengths"
WAVELENGHTS = np.linspace(450, 720, 10)
reflectance1 = np.arange(0, 30, 3)
reflectance2 = np.arange(30, 60, 3)
for w in WAVELENGHTS:
self.test_batch.df["reflectances", w] = np.NAN
for r1, r2, w in zip(reflectance1, reflectance2, WAVELENGHTS):
self.test_batch.df["reflectances", w][0] = r1
self.test_batch.df["reflectances", w][1] = r2
# shortcut to dataframe that we are interested in:
self.df = self.test_batch.df
def test_sliding_average(self):
# by test design folding should not alter elements (only at boundaries,
# which are excluded by array slicing:
expected_elements = self.df.reflectances.iloc[:, 1:-1].copy()
dfmani.fold_by_sliding_average(self.df, 3)
assert_frame_equal(self.df.reflectances, expected_elements)
def test_interpolation(self):
new_wavelengths = [465, 615, 555]
dfmani.interpolate_wavelengths(self.df, new_wavelengths)
expected = np.array([[1.5, 16.5, 10.5], [31.5, 46.5, 40.5]])
np.testing.assert_almost_equal(
self.df.reflectances.as_matrix(), expected, err_msg="test if interpolation " + "works fine on batches"
)
def test_select_n(self):
""" this is less a test and more a showing of how to select n elements
from a dataframe."""
# draw one sample. Look into documentation for sample to see all the
# options. Sample is quite powerfull.
self.df = self.df.sample(1)
self.assertEqual(self.df.shape[0], 1, "one sample selected")
def test_sortout_bands(self):
""" this is less a test and more a showing of how to sortout specific
bands from a dataframe """
# drop the 510 and 720 nm band
band_names_to_sortout = [510, 720]
self.df.drop(band_names_to_sortout, axis=1, level=1, inplace=True)
df_r = self.df["reflectances"]
self.assertTrue(not (510 in df_r.columns))
self.assertTrue(not 720 in df_r.columns)
self.assertTrue(690 in df_r.columns)
def setUp(self):
# create a colon batch with 2 samples
self.test_batch = ColonMuscleBatch()
self.test_batch.create_parameters(2)
# artificially add 10 fake "reflectances" to this batch
# at 10 fake "wavelengths"
WAVELENGHTS = np.linspace(450, 720, 10)
reflectance1 = np.arange(0, 30, 3)
reflectance2 = np.arange(30, 60, 3)
for w in WAVELENGHTS:
self.test_batch.df["reflectances", w] = np.NAN
for r1, r2, w in zip(reflectance1, reflectance2, WAVELENGHTS):
self.test_batch.df["reflectances", w][0] = r1
self.test_batch.df["reflectances", w][1] = r2
# shortcut to dataframe that we are interested in:
self.df = self.test_batch.df
def setUp(self):
# create a colon batch with 2 samples
self.test_batch = ColonMuscleBatch()
self.test_batch.create_parameters(2)
# artificially add 10 fake "reflectances" to this batch
# at 10 fake "wavelengths"
WAVELENGHTS = np.linspace(450, 720, 10)
reflectance1 = np.arange(0, 30, 3)
reflectance2 = np.arange(30, 60, 3)
for w in WAVELENGHTS:
self.test_batch.df["reflectances", w] = np.NAN
for r1, r2, w in zip(reflectance1, reflectance2, WAVELENGHTS):
self.test_batch.df["reflectances", w][0] = r1
self.test_batch.df["reflectances", w][1] = r2
# shortcut to dataframe that we are interested in:
self.df = self.test_batch.df
def create_batch_to_simulate(self):
return ColonMuscleBatch()
