def ulysses_data(start_date: str = '27/01/1998',
duration: int = 15,
start_hour: int = 0,
probe: str = 'ulysses'):
start_datetime = datetime.strptime(start_date + '/%i' % start_hour,
'%d/%m/%Y/%H')
end_datetime = start_datetime + timedelta(hours=duration)
data_b = ulysses.fgm_hires(start_datetime, end_datetime)
data_v = ulysses.swoops_ions(start_datetime, end_datetime)
data_b = data_b.data # fgm_hires now returns a time series (sunpy)
data_v = data_v.data # swoops_ions now returns a time series (sunpy)
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_r']
combined_data.loc[index, 'vp_y'] = data_v.loc[index, 'v_t']
combined_data.loc[index, 'vp_z'] = data_v.loc[index, 'v_n']
combined_data.loc[index, 'n_p'] = data_v.loc[index, 'n_p']
combined_data.loc[index, 'Tp_par'] = data_v.loc[index, 'T_p_large']
combined_data.loc[index, 'Tp_perp'] = data_v.loc[index, 'T_p_small']
combined_data.loc[index, 'r_sun'] = data_v.loc[index, 'r']
combined_data.loc[index, 'Bx'] = np.mean(
data_b.loc[index - timedelta(minutes=interval):index +
timedelta(minutes=interval), 'Bx'])
combined_data.loc[index, 'By'] = np.mean(
data_b.loc[index - timedelta(minutes=interval):index +
timedelta(minutes=interval), 'By'])
combined_data.loc[index, 'Bz'] = np.mean(
data_b.loc[index - timedelta(minutes=interval):index +
timedelta(minutes=interval), 'Bz'])
iteration += 1
return combined_data
def get_imported_data(self):
data_b = ulysses.fgm_hires(self.start_datetime, self.end_datetime)
data_v = ulysses.swoops_ions(self.start_datetime, self.end_datetime)
data_b = data_b.data # fgm_hires now returns a time series (sunpy)
data_v = data_v.data # swoops_ions now returns a time series (sunpy)
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_r']
combined_data.loc[index, 'vp_y'] = data_v.loc[index, 'v_t']
combined_data.loc[index, 'vp_z'] = data_v.loc[index, 'v_n']
combined_data.loc[index, 'n_p'] = data_v.loc[index, 'n_p']
combined_data.loc[index, 'Tp_par'] = data_v.loc[index, 'T_p_large']
combined_data.loc[index, 'Tp_perp'] = data_v.loc[index,
'T_p_small']
combined_data.loc[index, 'r_sun'] = data_v.loc[index, 'r']
combined_data.loc[index, 'Bx'] = np.mean(
data_b.loc[index - timedelta(minutes=interval):index +
timedelta(minutes=interval), 'Bx'])
combined_data.loc[index, 'By'] = np.mean(
data_b.loc[index - timedelta(minutes=interval):index +
timedelta(minutes=interval), 'By'])
combined_data.loc[index, 'Bz'] = np.mean(
data_b.loc[index - timedelta(minutes=interval):index +
timedelta(minutes=interval), 'Bz'])
iteration += 1
return combined_data
def test_fgm_hires():
df = ulysses.fgm_hires(starttime, endtime)
check_data_output(df)
def test_fgm_hires(self):
df = ulysses.fgm_hires(self.starttime, self.endtime)
check_data_output(df)
def test_fgm_hires(self):
df = ulysses.fgm_hires(self.starttime, self.endtime)
check_datetime_index(df)
def test_ulysses():
starttime = datetime(1993, 1, 1, 0, 0, 0)
endtime = datetime(1993, 1, 2, 0, 0, 0)
ulysses.fgm_hires(starttime, endtime)
ulysses.swoops_ions(starttime, endtime)