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)