Analysis of Swarm MAG HR data (50Hz)

For more information about the data and other ways to access it, see:

• Data handbook

• viresclient documentation

• MAGx_HR demo notebook

import datetime as dt
import matplotlib.pyplot as plt
import numpy as np

from swarmpal.io import create_paldata, PalDataItem
from swarmpal.toolboxes import tfa

Fetching data

Fetching data is much the same as before, switching “LR” for “HR”. Note that the data volume is 50 times larger so processing will take longer! It’s also appropriate to use a shorter time padding.

data_params = dict(
    collection="SW_OPER_MAGB_HR_1B",
    measurements=["B_NEC"],
    models=["Model=CHAOS"],
    auxiliaries=["QDLat", "MLT"],
    start_time=dt.datetime(2015, 3, 14, 12, 5, 0),
    end_time=dt.datetime(2015, 3, 14, 12, 30, 0),
    pad_times=(dt.timedelta(minutes=10), dt.timedelta(minutes=10)),
    server_url="https://vires.services/ows",
    options=dict(asynchronous=False, show_progress=False),
)
data = create_paldata(PalDataItem.from_vires(**data_params))

Processing

Here we need to identify the different sampling rate sampling_rate=50, and we will also choose to instead use the vector magnitude rather than a single component (use_magnitude=True).

p1 = tfa.processes.Preprocess()
p1.set_config(
    dataset="SW_OPER_MAGB_HR_1B",
    active_variable="B_NEC_res_Model",
    sampling_rate=50,
    remove_model=True,
    use_magnitude=True,
)
p2 = tfa.processes.Clean()
p2.set_config(
    window_size=300,
    method="iqr",
    multiplier=1,
)
p3 = tfa.processes.Filter()
p3.set_config(
    cutoff_frequency=0.1,
)
p4 = tfa.processes.Wavelet()
p4.set_config(
    min_frequency=1,
    max_frequency=25,
    dj=0.1,
)

p1(data)
p2(data)
p3(data)
p4(data);

Plotting

A couple of other tricks with the plotting function:

• Create a figure directly with matplotlib then pass an Axes object with ax=ax to the function to direct the plot onto that figure

• Customise the range and number of levels used in the spectrum colour bar

fig, ax = plt.subplots(1, 1, figsize=(15, 5))
tfa.plotting.spectrum(data, levels=np.linspace(-6, -3, 20), ax=ax)

(None, <AxesSubplot: xlabel='Time', ylabel='Frequency [mHz]'>)