def get_imported_data(self):
data_bv = wind.swe_h1(self.start_datetime, self.end_datetime)
data_bv = data_bv.data # data_bv was previously a time series
# data_t = wind.threedp_pm(self.start_datetime, self.end_datetime)
# data_t = data_t.data # data_b was previously a time series
indices = [
pd.Timestamp(index).to_pydatetime()
for index in data_bv.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_bv.loc[index,
'Proton_VX_nonlin']
combined_data.loc[index, 'vp_y'] = data_bv.loc[index,
'Proton_VY_nonlin']
combined_data.loc[index, 'vp_z'] = data_bv.loc[index,
'Proton_VZ_nonlin']
combined_data.loc[index, 'n_p'] = data_bv.loc[index,
'Proton_Np_nonlin']
# 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'] = np.mean(
# data_t.loc[index - timedelta(minutes=interval):index + timedelta(minutes=interval), 'P_TEMP'])
# combined_data.loc[index, 'Tp_perp'] = np.mean(
# data_t.loc[index - timedelta(minutes=interval):index + timedelta(minutes=interval), 'P_TEMP'])
combined_data.loc[index, 'r_sun'] = 1 - np.sqrt(
data_bv.loc[index, 'xgse']**2 + data_bv.loc[index, 'ygse']**2 +
data_bv.loc[index, 'zgse']**2
) * 4.26354E-5 # earth radius to au, 1- because distance initially from earth
combined_data.loc[index, 'Bx'] = data_bv.loc[index, 'BX']
combined_data.loc[index, 'By'] = data_bv.loc[index, 'BY']
combined_data.loc[index, 'Bz'] = data_bv.loc[index, 'BZ']
iteration += 1
return combined_data
def wind_data(start_date: str = '25/12/1994',
duration: int = 15,
start_hour: int = 0,
probe: str = 'wind'):
start_datetime = datetime.strptime(start_date + '/%i' % start_hour,
'%d/%m/%Y/%H')
end_datetime = start_datetime + timedelta(hours=duration)
data_bv = wind.swe_h1(start_datetime, end_datetime)
data_bv = data_bv.data # data_bv was previously a time series
# data_t = wind.threedp_pm(self.start_datetime, self.end_datetime)
# data_t = data_t.data # data_b was previously a time series
indices = [
pd.Timestamp(index).to_pydatetime() for index in data_bv.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_bv.loc[index,
'Proton_VX_nonlin']
combined_data.loc[index, 'vp_y'] = data_bv.loc[index,
'Proton_VY_nonlin']
combined_data.loc[index, 'vp_z'] = data_bv.loc[index,
'Proton_VZ_nonlin']
combined_data.loc[index, 'n_p'] = data_bv.loc[index,
'Proton_Np_nonlin']
combined_data.loc[index, 'r_sun'] = 1 - np.sqrt(
data_bv.loc[index, 'xgse']**2 + data_bv.loc[index, 'ygse']**2 +
data_bv.loc[index, 'zgse']**2
) * 4.26354E-5 # earth radius to au, 1- because distance initially from earth
combined_data.loc[index, 'Bx'] = data_bv.loc[index, 'BX']
combined_data.loc[index, 'By'] = data_bv.loc[index, 'BY']
combined_data.loc[index, 'Bz'] = data_bv.loc[index, 'BZ']
iteration += 1
return combined_data
def test_swe_h1(self):
df = wind.swe_h1(self.starttime, self.endtime)
check_data_output(df)
def test_swe_h1(self):
df = wind.swe_h1(self.starttime, self.endtime)
def test_swe_h1(self):
df = wind.swe_h1(self.starttime, self.endtime)
check_units(df)
