def test_shift_spect():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
result = shift_spect.shift_spect(df, -1.0)
expected = [
898.64928571, 973.62444444, 1034.46444444, 1004.54, 939.16222222
]
np.testing.assert_array_almost_equal(expected,
np.array(result['wvl'].iloc[0, 0:5]))
assert result[('meta', 'Shift')].shape == (103, )
def run(self):
datakey_to_shift = self.choosedata.currentText()
shifts = [float(i) for i in self.shifts.text().split(',')]
pass
self.data[datakey_to_shift].df[('meta', 'Shift')] = 0
to_shift = self.data[datakey_to_shift].df
for s in shifts:
shifted = shift_spect(to_shift, s)
self.data[datakey_to_shift].df = pd.concat(
[self.data[datakey_to_shift].df, shifted])
def run(self):
datakey_to_shift = self.choosedata.currentText()
shifts = [float(i) for i in self.shifts.text().split(',')]
pass
self.data[datakey_to_shift].df[('meta', 'Shift')] = 0
to_shift = self.data[datakey_to_shift].df
for s in shifts:
shifted = shift_spect(to_shift, s)
self.data[datakey_to_shift].df = pd.concat(
[self.data[datakey_to_shift].df, shifted])
nan_cols = self.data[datakey_to_shift].df['wvl'].columns[
self.data[datakey_to_shift].df['wvl'].isna().any()]
print('Dropping the following wavelengths:')
for i in nan_cols:
print(str(i))
nan_cols = [('wvl', i) for i in nan_cols]
self.data[datakey_to_shift].df = self.data[datakey_to_shift].df.drop(
nan_cols, axis=1)
def shift(self, shift):
self.df = shift_spect.shift_spect(self.df, shift)