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)