def test_add_errors(self):
d = RockPyData(column_names=['A', 'B'])
#d['A'].v = 1 # Attribute Error NoneType has no attribute, maybe initialize to np.nan?
#d['B'] = 2
#d['A'].e = 4
#d['B'].e = 5
d = d.append_rows([1, 2])
#print d
d.e = [[4, 5]]
self.assertEqual(5., d['B'].e)
def test_add_errors(self):
d = RockPyData(column_names=['A', 'B'])
#d['A'].v = 1 # Attribute Error NoneType has no attribute, maybe initialize to np.nan?
#d['B'] = 2
#d['A'].e = 4
#d['B'].e = 5
d = d.append_rows([1, 2])
#print d
d.e = [[4, 5]]
self.assertEqual(5., d['B'].e)
def setUp(self):
# run before each test
self.testdata = ((1, 2, 3, 4), (1, 6, 7, 8), (1, 2, 11, 12), (1, 6, 55,
66))
self.col_names = ('F', 'Mx', 'My', 'Mz')
self.row_names = ('1.Zeile', '2.Zeile_A', '3.Zeile', '4.Zeile_A')
self.units = ('T', 'mT', 'fT', 'pT')
self.RPD = RockPyData(column_names=self.col_names,
row_names=self.row_names,
units=self.units,
data=self.testdata)
def format_vsm(ftype_data, sobj_name=None):
'''
Reading routine for thermal demagnetization curves measured with a VSM.
'''
header = ftype_data.header
segments = ftype_data.segment_data
data = ftype_data.data
mdata = OrderedDict()
aux = np.array([j for i in data for j in i]) # combine all data arrays
a = np.array([(i, v) for i, v in enumerate(np.diff(aux, axis=0)[:, 0])])
sign = np.sign(np.diff(aux, axis=0)[:, 1])
threshold = 3
zero_crossings = [i + 1 for i in range(len(a[:, 1]) - 1)
if a[i, 1] > 0 > a[i + 1, 1] and a[i, 1] > 0 > a[i + 2, 1]
or a[i, 1] < 0 < a[i + 1, 1] and a[i, 1] < 0 < a[i + 2, 1]]
zero_crossings = [0] + zero_crossings # start with zero index
zero_crossings += [len(aux)] # append last index
ut = 0 # running number warming
dt = 0 # running number cooling
for i, v in enumerate(zero_crossings):
if v < zero_crossings[-1]: # prevents index Error
if sum(a[v:zero_crossings[i + 1], 1]) < 0: # cooling
name = 'cooling%02i' % (ut)
ut += 1
else:
name = 'warming%02i' % (dt)
dt += 1
data = aux[v:zero_crossings[i + 1] + 1]
rpd = RockPyData(column_names=header, data=data)
rpd.rename_column('temperature', 'temp')
rpd.rename_column('moment', 'mag')
mdata.update({name: rpd})
return mdata
def format_mpms(ftype_data, sobj_name=None):
if ftype_data.name == sobj_name:
temp_index = ftype_data.units.index('K')
# see if it a cooling or a warming curve
mean_temp_difference_between_steps = np.mean(np.diff(ftype_data.data[:, temp_index]))
if mean_temp_difference_between_steps > 0:
dtype = 'warming'
else:
dtype = 'cooling'
mdata = RockPyData(column_names=ftype_data.header,
data=ftype_data.data,
units=ftype_data.units
)
mdata.rename_column('Temperature', 'temp')
mdata.rename_column('Long Moment', 'mag')
data = {dtype: mdata}
return data
else:
RockPy3.logger.error('SAMPLE name of file does not match specified sample_name. << {} != {} >>'.format(ftype_data.name, sobj_name))
def from_simulation(cls,
sobj,
idx=None,
ms=250.,
bmax=0.5,
E=0.005,
G=0.3,
steps=20,
log_steps=False,
noise=None,
color=None,
marker=None,
linestyle=None):
"""
Simulates a backfield measurement based on a single log-normal gaussian distribution.
E: Median destructive field - represents the mean value of the log-Gaussian distribution, and therefore, the
logarithmic field value of the maximum gradient.
G: G describes the standard deviation or half-width of the distribution.
"""
cls.clslog.info('CREATING simulation measurement with {}'.format(
locals()))
calculation = np.arange(0, 2, 1e-6)
fields = np.round(np.linspace(1e-9, bmax, steps), 4)
dist = lognorm([G], loc=E)
calc_mag = dist.cdf(calculation)
indices = [np.argmin(abs(v - calculation)) for v in fields]
mag = [calc_mag[i] for i in indices]
# mag /= 1/2 * max(mag)
# mag -= 1
# mag *= ms
# mag = G * (np.sqrt(np.pi) / 2) * (sp.special.erf((log_fields - E) / G) - sp.special.erf((-E) / G)) #leonhardt2004
# if noise:
# n = np.random.normal(0, ms * (noise / 100), steps)
# mag = mag + n
mdata = cls.empty_mdata()
mdata['remanence'] = RockPyData(column_names=['field', 'mag'],
data=np.c_[fields - bmax, mag])
return cls(sobj,
fpath=None,
mdata=mdata,
ftype='simulation',
color=color,
marker=marker,
linestyle=linestyle,
idx=idx)
def setUp(self):
# run before each test
self.testdata = ((1, 2, 3, 4),
(1, 6, 7, 8),
(1, 2, 11, 12),
(1, 6, 55, 66))
self.col_names = ('F', 'Mx', 'My', 'Mz')
self.row_names = ('1.Zeile', '2.Zeile_A', '3.Zeile', '4.Zeile_A')
self.units = ('T', 'mT', 'fT', 'pT')
self.RPD = RockPyData(column_names=self.col_names, row_names=self.row_names, units=self.units,
data=self.testdata)
def format_vftb(ftype_data, sobj_name=None):
'''
formats the output from vftb to measurement.data
:return:
'''
data = ftype_data.data
header = ftype_data.header
mdata = {}
mdata['data'] = RockPyData(column_names=header, data=data[0])
mdata['data']['mag'] = mdata['data']['mag'].v * ftype_data.mass
mdata['data'].define_alias('variable', 'field')
return mdata
def format_vsm(ftype_data, sobj_name=None):
"""
formats the vsm output to be compatible with irm acquisition measurements
:return:
"""
data = ftype_data.data
header = ftype_data.header
# check if vsm file actually contains a dcd measurement
if not ftype_data.info_header['include irm?']:
return
mdata = {}
mdata['data'] = RockPyData(column_names=header, data=data[0])
mdata['data'].rename_column('remanence', 'mag')
return mdata
def format_vftb(ftype_data, sobj_name=None):
data = ftype_data.data
units = ftype_data.units
header = ftype_data.header
mdata = OrderedDict(warming=None, cooling=None)
if len(data) > 2:
RockPy3.logger.warning(
'LENGTH of machine.out_thermocurve =! 2. Assuming data[0] = heating data[1] = cooling'
)
if 3 > len(data) > 1:
for idx, dtype in enumerate(('warming', 'cooling')):
mdata[dtype] = RockPyData(column_names=header,
data=data[idx],
units=units)
else:
RockPy3.logger.error('LENGTH of machine.out_thermocurve < 2.')
return mdata
def format_vsm(ftype_data, sobj_name=None):
'''
formats the output from vsm to measurement.data
:return:
'''
idx = 0
data = ftype_data.data
header = ftype_data.header
# check if vsm file actually contains a dcd measurement
if not ftype_data.info_header['include dcd?']:
return
else:
if ftype_data.info_header['include irm?']:
idx += 1
mdata = {}
mdata['data'] = RockPyData(column_names=header, data=data[idx])
mdata['data'].rename_column('remanence', 'mag')
return mdata
def format_vsm(ftype_data, sobj_name=None):
'''
Reading routine for thermal demagnetization curves measured with a VSM.
'''
header = ftype_data.header
segments = ftype_data.segment_data
data = ftype_data.data
mdata = OrderedDict()
aux = np.array([j for i in data for j in i]) # combine all data arrays
a = np.array([(i, v)
for i, v in enumerate(np.diff(aux, axis=0)[:, 0])])
sign = np.sign(np.diff(aux, axis=0)[:, 1])
threshold = 3
zero_crossings = [
i + 1 for i in range(len(a[:, 1]) - 1)
if a[i, 1] > 0 > a[i + 1, 1] and a[i, 1] > 0 > a[i + 2, 1]
or a[i, 1] < 0 < a[i + 1, 1] and a[i, 1] < 0 < a[i + 2, 1]
]
zero_crossings = [0] + zero_crossings # start with zero index
zero_crossings += [len(aux)] # append last index
ut = 0 # running number warming
dt = 0 # running number cooling
for i, v in enumerate(zero_crossings):
if v < zero_crossings[-1]: # prevents index Error
if sum(a[v:zero_crossings[i + 1], 1]) < 0: # cooling
name = 'cooling%02i' % (ut)
ut += 1
else:
name = 'warming%02i' % (dt)
dt += 1
data = aux[v:zero_crossings[i + 1] + 1]
rpd = RockPyData(column_names=header, data=data)
rpd.rename_column('temperature', 'temp')
rpd.rename_column('moment', 'mag')
mdata.update({name: rpd})
return mdata
def format_mpms(ftype_data, sobj_name=None):
if ftype_data.name == sobj_name:
temp_index = ftype_data.units.index('K')
# see if it a cooling or a warming curve
mean_temp_difference_between_steps = np.mean(
np.diff(ftype_data.data[:, temp_index]))
if mean_temp_difference_between_steps > 0:
dtype = 'warming'
else:
dtype = 'cooling'
mdata = RockPyData(column_names=ftype_data.header,
data=ftype_data.data,
units=ftype_data.units)
mdata.rename_column('Temperature', 'temp')
mdata.rename_column('Long Moment', 'mag')
data = {dtype: mdata}
return data
else:
RockPy3.logger.error(
'SAMPLE name of file does not match specified sample_name. << {} != {} >>'
.format(ftype_data.name, sobj_name))
class TestRockPyData(TestCase):
def setUp(self):
# run before each test
self.testdata = ((1, 2, 3, 4), (1, 6, 7, 8), (1, 2, 11, 12), (1, 6, 55,
66))
self.col_names = ('F', 'Mx', 'My', 'Mz')
self.row_names = ('1.Zeile', '2.Zeile_A', '3.Zeile', '4.Zeile_A')
self.units = ('T', 'mT', 'fT', 'pT')
self.RPD = RockPyData(column_names=self.col_names,
row_names=self.row_names,
units=self.units,
data=self.testdata)
def test_column_names(self):
self.assertEqual(self.RPD.column_names, list(self.col_names))
def test_column_count(self):
self.assertEqual(self.RPD.column_count, len(self.col_names))
def test__find_duplicate_variable_rows(self):
# self.assertTrue((self.RPD._find_duplicate_variables()[0] == np.array([0, 1, 2])).all())
self.assertEqual(self.RPD._find_duplicate_variable_rows(),
[(0, 1, 2, 3)])
# redefine variabe alias to the first two columns
self.RPD.define_alias('variable', ('F', 'Mx'))
self.assertEqual(self.RPD._find_duplicate_variable_rows(), [(0, 2),
(1, 3)])
def test_rename_column(self):
self.RPD.rename_column('Mx', 'M_x')
self.assertEqual(self.RPD.column_names, ['F', 'M_x', 'My', 'Mz'])
def test_append_rows(self):
d1 = [[5, 6, 7, 8], [9, 10, 11, 12]]
self.RPD = self.RPD.append_rows(d1, ('5.Zeile', '6.Zeile'))
self.assertTrue(np.array_equal(self.RPD.v[-2:, :], np.array(d1)))
d2 = [5, 6, 7, 8]
self.RPD = self.RPD.append_rows(d2, '5.Zeile')
self.assertTrue(np.array_equal(self.RPD.v[-1, :], np.array(d2)))
# lets try with other RockPyData object
rpd = copy.deepcopy(self.RPD)
rpd.rename_column('Mx', 'M_x')
self.RPD = self.RPD.append_rows(rpd)
# TODO: add assert
#print self.RPD
def test_delete_rows(self):
self.RPD = self.RPD.delete_rows((0, 2))
self.assertTrue(
np.array_equal(self.RPD.v,
np.array(self.testdata)[(1, 3), :]))
def test_eliminate_duplicate_variable_rows(self):
# check for one variable column
self.RPD = self.RPD.eliminate_duplicate_variable_rows()
self.assertTrue(np.array_equal(self.RPD.v, np.array([]).reshape(0, 4)))
def test_eliminate_duplicate_variable_rows2(self):
# check for two variable columns
self.RPD.define_alias('variable', ('F', 'Mx'))
rpd = self.RPD.eliminate_duplicate_variable_rows(substfunc='mean')
self.assertTrue(
np.array_equal(rpd.v,
np.array([[1., 2., 7., 8.], [1., 6., 31., 37.]])))
self.assertTrue(
np.array_equal(rpd.e,
np.array([[0., 0., 4., 4.], [0., 0., 24., 29.]])))
rpd = self.RPD.eliminate_duplicate_variable_rows(substfunc='last')
self.assertTrue(
np.array_equal(rpd.v,
np.array([[1., 2., 11., 12.], [1., 6., 55., 66.]])))
def test_mean(self):
self.RPD = self.RPD.mean()
self.assertTrue(
np.array_equal(self.RPD.v, np.array([[1., 4., 19., 22.5]])))
np.testing.assert_allclose(self.RPD.e,
np.array([[0., 2., 20.976, 25.273]]),
atol=0.01)
def test_max(self):
self.RPD = self.RPD.max()
self.assertTrue(
np.array_equal(self.RPD.v, np.array([[1., 6., 55., 66.]])))
def test_filter_row_names(self):
self.assertEqual(
self.RPD.filter_row_names(('1.Zeile', '3.Zeile')).row_names,
['1.Zeile', '3.Zeile'])
def test_filter_match_row_names(self):
# get all rows ending with '_A'
self.assertEqual(
self.RPD.filter_match_row_names('.*_A').row_names,
['2.Zeile_A', '4.Zeile_A'])
def test_append_columns_1(self):
cb = self.RPD.column_count
d = (8, 7, 6, 5)
self.RPD = self.RPD.append_columns('neue Spalte', d)
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(np.array_equal(self.RPD['neue Spalte'].v, np.array(d)))
def test_append_columns_2(self):
cb = self.RPD.column_count
self.RPD = self.RPD.append_columns('leere Spalte')
self.RPD['leere Spalte'] = (1, 2, 3, 4)
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(
np.array_equal(self.RPD['leere Spalte'].v, np.array((1, 2, 3, 4))))
def test_append_columns_3(self):
cb = self.RPD.column_count
self.RPD = self.RPD.append_columns('new col')
self.RPD['new col'] = 7
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(
np.array_equal(self.RPD['new col'].v, np.array((7, 7, 7, 7))))
def test_sort(self):
self.assertTrue(
np.array_equal(
self.RPD.sort('Mx')['Mx'].v, np.array((2, 2, 6, 6))))
#def test_interpolate(self):
# self.RPD.define_alias('variable', 'My')
# iv = (1, 11, 33, 55, 100)
# self.assertTrue(np.array_equal((self.RPD.interpolate(iv))['My'].v, np.array(iv)))
# self.assertTrue(np.array_equal((self.RPD.interpolate(iv))['Mx'].v[1:-1], np.array([2., 4., 6.])))
def test_magnitude(self):
self.RPD.define_alias('m', ('Mx', 'My', 'Mz'))
self.RPD = self.RPD.append_columns('mag', self.RPD.magnitude('m'))
np.testing.assert_allclose(
self.RPD['mag'].v,
np.array([5.38516481, 12.20655562, 16.40121947, 86.12200648]),
atol=1e-5)
def test_column_names_to_indices(self):
self.assertEqual(self.RPD.column_names_to_indices(('Mx', 'Mz')),
[1, 3])
def test_interation(self):
# TODO: add proper assertion
for l in self.RPD:
#print l
pass
def test_add_errors(self):
d = RockPyData(column_names=['A', 'B'])
#d['A'].v = 1 # Attribute Error NoneType has no attribute, maybe initialize to np.nan?
#d['B'] = 2
#d['A'].e = 4
#d['B'].e = 5
d = d.append_rows([1, 2])
#print d
d.e = [[4, 5]]
self.assertEqual(5., d['B'].e)
def test_data_assignment(self):
print(self.RPD)
# set only values
self.RPD['Mx'] = [1.1, 1.2, 1.3, 1.4]
print(self.RPD)
# set values and errors
self.RPD['Mx'] = [[[1.1, 0.11]], [[1.2, 0.12]], [[1.3, 0.13]],
[[1.4, 0.14]]]
print(self.RPD)
def test_define_delete_alias(self):
self.RPD.define_alias('m', ('Mx', 'My', 'Mz'))
self.assertTrue(self.RPD.alias_exists('m'))
self.RPD.delete_alias('m')
self.assertFalse(self.RPD.alias_exists('m'))
def test_add_delete_columns(self):
#print(self.RPD._column_dict)
self.RPD.append_columns('empty')
self.RPD.define_alias('variable', ('F', 'empty'))
#print(self.RPD._column_dict)
self.assertTrue(self.RPD.column_exists('empty'))
#print(self.RPD)
self.RPD.delete_columns('empty')
#print(self.RPD._column_dict)
self.assertFalse(self.RPD.column_exists('empty'))
class TestRockPyData(TestCase):
def setUp(self):
# run before each test
self.testdata = ((1, 2, 3, 4),
(1, 6, 7, 8),
(1, 2, 11, 12),
(1, 6, 55, 66))
self.col_names = ('F', 'Mx', 'My', 'Mz')
self.row_names = ('1.Zeile', '2.Zeile_A', '3.Zeile', '4.Zeile_A')
self.units = ('T', 'mT', 'fT', 'pT')
self.RPD = RockPyData(column_names=self.col_names, row_names=self.row_names, units=self.units,
data=self.testdata)
def test_column_names(self):
self.assertEqual(self.RPD.column_names, list(self.col_names))
def test_column_count(self):
self.assertEqual(self.RPD.column_count, len(self.col_names))
def test__find_duplicate_variable_rows(self):
# self.assertTrue((self.RPD._find_duplicate_variables()[0] == np.array([0, 1, 2])).all())
self.assertEqual(self.RPD._find_duplicate_variable_rows(), [(0, 1, 2, 3)])
# redefine variabe alias to the first two columns
self.RPD.define_alias('variable', ('F', 'Mx'))
self.assertEqual(self.RPD._find_duplicate_variable_rows(), [(0, 2), (1, 3)])
def test_rename_column(self):
self.RPD.rename_column('Mx', 'M_x')
self.assertEqual(self.RPD.column_names, ['F', 'M_x', 'My', 'Mz'])
def test_append_rows(self):
d1 = [[5, 6, 7, 8], [9, 10, 11, 12]]
self.RPD = self.RPD.append_rows(d1, ('5.Zeile', '6.Zeile'))
self.assertTrue(np.array_equal(self.RPD.v[-2:, :], np.array(d1)))
d2 = [5, 6, 7, 8]
self.RPD = self.RPD.append_rows(d2, '5.Zeile')
self.assertTrue(np.array_equal(self.RPD.v[-1, :], np.array(d2)))
# lets try with other RockPyData object
rpd = copy.deepcopy(self.RPD)
rpd.rename_column('Mx', 'M_x')
self.RPD = self.RPD.append_rows(rpd)
# TODO: add assert
#print self.RPD
def test_delete_rows(self):
self.RPD = self.RPD.delete_rows((0, 2))
self.assertTrue(np.array_equal(self.RPD.v, np.array(self.testdata)[(1, 3), :]))
def test_eliminate_duplicate_variable_rows(self):
# check for one variable column
self.RPD = self.RPD.eliminate_duplicate_variable_rows()
self.assertTrue(np.array_equal(self.RPD.v, np.array([]).reshape(0, 4)))
def test_eliminate_duplicate_variable_rows2(self):
# check for two variable columns
self.RPD.define_alias('variable', ('F', 'Mx'))
rpd = self.RPD.eliminate_duplicate_variable_rows(substfunc='mean')
self.assertTrue(np.array_equal(rpd.v, np.array([[1., 2., 7., 8.], [1., 6., 31., 37.]])))
self.assertTrue(np.array_equal(rpd.e, np.array([[0., 0., 4., 4.], [0., 0., 24., 29.]])))
rpd = self.RPD.eliminate_duplicate_variable_rows(substfunc='last')
self.assertTrue(np.array_equal(rpd.v, np.array([[1., 2., 11., 12.], [1., 6., 55., 66.]])))
def test_mean(self):
self.RPD = self.RPD.mean()
self.assertTrue(np.array_equal(self.RPD.v, np.array([[1., 4., 19., 22.5]])))
np.testing.assert_allclose(self.RPD.e, np.array([[0., 2., 20.976, 25.273]]), atol=0.01)
def test_max(self):
self.RPD = self.RPD.max()
self.assertTrue(np.array_equal(self.RPD.v, np.array([[1., 6., 55., 66.]])))
def test_filter_row_names(self):
self.assertEqual(self.RPD.filter_row_names(('1.Zeile', '3.Zeile')).row_names, ['1.Zeile', '3.Zeile'])
def test_filter_match_row_names(self):
# get all rows ending with '_A'
self.assertEqual(self.RPD.filter_match_row_names('.*_A').row_names, ['2.Zeile_A', '4.Zeile_A'])
def test_append_columns_1(self):
cb = self.RPD.column_count
d = (8, 7, 6, 5)
self.RPD = self.RPD.append_columns('neue Spalte', d)
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(np.array_equal(self.RPD['neue Spalte'].v, np.array(d)))
def test_append_columns_2(self):
cb = self.RPD.column_count
self.RPD = self.RPD.append_columns('leere Spalte')
self.RPD['leere Spalte'] = (1,2,3,4)
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(np.array_equal(self.RPD['leere Spalte'].v, np.array((1,2,3,4))))
def test_append_columns_3(self):
cb = self.RPD.column_count
self.RPD = self.RPD.append_columns('new col')
self.RPD['new col'] = 7
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(np.array_equal(self.RPD['new col'].v, np.array((7,7,7,7))))
def test_sort(self):
self.assertTrue(np.array_equal(self.RPD.sort('Mx')['Mx'].v, np.array((2, 2, 6, 6))))
#def test_interpolate(self):
# self.RPD.define_alias('variable', 'My')
# iv = (1, 11, 33, 55, 100)
# self.assertTrue(np.array_equal((self.RPD.interpolate(iv))['My'].v, np.array(iv)))
# self.assertTrue(np.array_equal((self.RPD.interpolate(iv))['Mx'].v[1:-1], np.array([2., 4., 6.])))
def test_magnitude(self):
self.RPD.define_alias('m', ('Mx', 'My', 'Mz'))
self.RPD = self.RPD.append_columns('mag', self.RPD.magnitude('m'))
np.testing.assert_allclose(self.RPD['mag'].v, np.array([5.38516481, 12.20655562, 16.40121947, 86.12200648]), atol=1e-5)
def test_column_names_to_indices(self):
self.assertEqual( self.RPD.column_names_to_indices(('Mx', 'Mz')), [1,3])
def test_interation(self):
# TODO: add proper assertion
for l in self.RPD:
#print l
pass
def test_add_errors(self):
d = RockPyData(column_names=['A', 'B'])
#d['A'].v = 1 # Attribute Error NoneType has no attribute, maybe initialize to np.nan?
#d['B'] = 2
#d['A'].e = 4
#d['B'].e = 5
d = d.append_rows([1, 2])
#print d
d.e = [[4, 5]]
self.assertEqual(5., d['B'].e)
def test_data_assignment(self):
print(self.RPD)
# set only values
self.RPD['Mx'] = [1.1, 1.2, 1.3, 1.4]
print(self.RPD)
# set values and errors
self.RPD['Mx'] = [[[1.1, 0.11]], [[1.2, 0.12]], [[1.3, 0.13]], [[1.4, 0.14]]]
print(self.RPD)
def test_define_delete_alias(self):
self.RPD.define_alias('m', ('Mx', 'My', 'Mz'))
self.assertTrue(self.RPD.alias_exists('m'))
self.RPD.delete_alias('m')
self.assertFalse(self.RPD.alias_exists('m'))
def test_add_delete_columns(self):
#print(self.RPD._column_dict)
self.RPD.append_columns('empty')
self.RPD.define_alias('variable', ('F', 'empty'))
#print(self.RPD._column_dict)
self.assertTrue(self.RPD.column_exists('empty'))
#print(self.RPD)
self.RPD.delete_columns('empty')
#print(self.RPD._column_dict)
self.assertFalse(self.RPD.column_exists('empty'))
