def get_imported_data(self): data_b = ace.mfi_h0(self.start_datetime, self.end_datetime) data_b = data_b.data # data_b was previously a time series data_v = ace.swe_h0(self.start_datetime, self.end_datetime) data_v = data_v.data # data_b was previously a time series indices = [pd.Timestamp(index).to_pydatetime() for index in data_v.index.values] combined_data = pd.DataFrame(index=indices) iteration = 0 for index in indices: interval = 2 if iteration != 0 and iteration != len(indices) - 1: interval = (indices[iteration + 1] - indices[iteration - 1]).total_seconds() / 60 combined_data.loc[index, 'vp_x'] = data_v.loc[index, 'V_GSE_0'] combined_data.loc[index, 'vp_y'] = data_v.loc[index, 'V_GSE_1'] combined_data.loc[index, 'vp_z'] = data_v.loc[index, 'V_GSE_2'] combined_data.loc[index, 'n_p'] = data_v.loc[index, 'Np'] # for now both temperatures are equal to keep it similar to other classes as no separate data was found combined_data.loc[index, 'Tp_par'] = data_v.loc[index, 'Tpr'] combined_data.loc[index, 'Tp_perp'] = data_v.loc[index, 'Tpr'] combined_data.loc[index, 'r_sun'] = 1 - np.sqrt( data_v.loc[index, 'SC_pos_GSE_0'] ** 2 + data_v.loc[index, 'SC_pos_GSE_1'] ** 2 + data_v.loc[ index, 'SC_pos_GSE_2'] ** 2) * 6.68459e-9 # km to au, 1- because distance initially from earth combined_data.loc[index, 'Bx'] = np.mean( data_b.loc[index - timedelta(minutes=interval):index + timedelta(minutes=interval), 'BGSEc_0']) combined_data.loc[index, 'By'] = np.mean( data_b.loc[index - timedelta(minutes=interval):index + timedelta(minutes=interval), 'BGSEc_1']) combined_data.loc[index, 'Bz'] = np.mean( data_b.loc[index - timedelta(minutes=interval):index + timedelta(minutes=interval), 'BGSEc_2']) iteration += 1 return combined_data
def test_swe_h0(self): df = ace.swe_h0(self.starttime, self.endtime) check_data_output(df)
def test_swe_h0(self): df = ace.swe_h0(self.starttime, self.endtime) check_datetime_index(df)