MAGxLR_1B (Magnetic field 1Hz)#

Abstract: Access to the low rate (1Hz) magnetic data (level 1b product), together with geomagnetic model evaluations (level 2 products).

%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib
Python implementation: CPython
Python version       : 3.11.6
IPython version      : 8.18.0

viresclient: 0.15.2
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2
from viresclient import SwarmRequest
import datetime as dt
import matplotlib.pyplot as plt

request = SwarmRequest()

Product information#

This is one of the main products from Swarm - the 1Hz measurements of the magnetic field vector (B_NEC) and total intensity (F). These are derived from the Vector Field Magnetometer (VFM) and Absolute Scalar Magnetometer (ASM).

Documentation:

Measurements are available through VirES as part of collections with names containing MAGx_LR, for each Swarm spacecraft:

request.available_collections("MAG", details=False)
{'MAG': ['SW_OPER_MAGA_LR_1B',
  'SW_OPER_MAGB_LR_1B',
  'SW_OPER_MAGC_LR_1B',
  'SW_FAST_MAGA_LR_1B',
  'SW_FAST_MAGB_LR_1B',
  'SW_FAST_MAGC_LR_1B']}

The measurements can be used together with geomagnetic model evaluations as shall be shown below.

Check what “MAG” data variables are available#

request.available_measurements("MAG")
['F',
 'dF_Sun',
 'dF_AOCS',
 'dF_other',
 'F_error',
 'B_VFM',
 'B_NEC',
 'dB_Sun',
 'dB_AOCS',
 'dB_other',
 'B_error',
 'q_NEC_CRF',
 'Att_error',
 'Flags_F',
 'Flags_B',
 'Flags_q',
 'Flags_Platform',
 'ASM_Freq_Dev']

Check the names of available models#

request.available_models(details=False)
['IGRF',
 'LCS-1',
 'MF7',
 'CHAOS-Core',
 'CHAOS-Static',
 'CHAOS-MMA-Primary',
 'CHAOS-MMA-Secondary',
 'CHAOS-MIO',
 'MCO_SHA_2C',
 'MCO_SHA_2D',
 'MLI_SHA_2C',
 'MLI_SHA_2D',
 'MLI_SHA_2E',
 'MMA_SHA_2C-Primary',
 'MMA_SHA_2C-Secondary',
 'MMA_SHA_2F-Primary',
 'MMA_SHA_2F-Secondary',
 'MIO_SHA_2C-Primary',
 'MIO_SHA_2C-Secondary',
 'MIO_SHA_2D-Primary',
 'MIO_SHA_2D-Secondary',
 'AMPS',
 'MCO_SHA_2X',
 'CHAOS',
 'CHAOS-MMA',
 'MMA_SHA_2C',
 'MMA_SHA_2F',
 'MIO_SHA_2C',
 'MIO_SHA_2D',
 'SwarmCI']

Fetch some MAG data and models#

We can fetch the data and the model predictions (evaluated on demand) at the same time. We can also subsample the data - here we subsample it to 10-seconds by specifying the “PT10S” sampling_step.

request.set_collection("SW_OPER_MAGA_LR_1B")
request.set_products(
    measurements=["F", "B_NEC"],
    models=["CHAOS-Core", "MCO_SHA_2D"],
    sampling_step="PT10S",
)
data = request.get_between(
    # 2014-01-01 00:00:00
    start_time=dt.datetime(2014, 1, 1, 0),
    # 2014-01-01 01:00:00
    end_time=dt.datetime(2014, 1, 1, 1),
)

See a list of the source files#

data.sources
['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_0701_MDR_MAG_LR',
 'SW_OPER_MCO_SHA_2D_20131126T000000_20180101T000000_0401',
 'SW_OPER_MCO_SHA_2X_19970101T000000_20260807T235959_0806']

Load as a pandas dataframe#

Use expand=True to extract vectors (B_NEC…) as separate columns (…_N, …_E, …_C)

df = data.as_dataframe(expand=True)
df.head()
Longitude F_MCO_SHA_2D Radius F_CHAOS-Core F Spacecraft Latitude B_NEC_CHAOS-Core_N B_NEC_CHAOS-Core_E B_NEC_CHAOS-Core_C B_NEC_MCO_SHA_2D_N B_NEC_MCO_SHA_2D_E B_NEC_MCO_SHA_2D_C B_NEC_N B_NEC_E B_NEC_C
Timestamp
2014-01-01 00:00:00 -14.116674 22874.211386 6878309.23 22874.627853 22867.4800 A -1.228938 20113.753203 -4127.380674 -10082.175373 20113.623790 -4127.463942 -10081.454557 20103.4954 -4125.6029 -10086.8732
2014-01-01 00:00:10 -14.131424 22820.941311 6878381.18 22821.352181 22814.4928 A -1.862521 19825.265483 -4163.033275 -10509.144485 19825.161759 -4163.127529 -10508.410571 19815.0284 -4160.3663 -10514.3464
2014-01-01 00:00:20 -14.146155 22769.369047 6878452.06 22769.774354 22763.1833 A -2.496090 19533.632431 -4197.424334 -10921.605063 19533.553823 -4197.529033 -10920.860399 19523.4054 -4194.6083 -10926.9383
2014-01-01 00:00:30 -14.160861 22719.238127 6878521.88 22719.637990 22713.2926 A -3.129644 19239.397631 -4230.487146 -11319.474710 19239.343493 -4230.601798 -11318.721282 19229.1766 -4227.8647 -11324.7658
2014-01-01 00:00:40 -14.175534 22670.304568 6878590.62 22670.699142 22664.6401 A -3.763184 18943.105382 -4262.156428 -11702.708261 18943.075066 -4262.280613 -11701.947711 18932.8056 -4260.1382 -11708.0771

… or as an xarray dataset:#

ds = data.as_xarray()
ds
<xarray.Dataset>
Dimensions:           (Timestamp: 360, NEC: 3)
Coordinates:
  * Timestamp         (Timestamp) datetime64[ns] 2014-01-01 ... 2014-01-01T00...
  * NEC               (NEC) <U1 'N' 'E' 'C'
Data variables:
    Spacecraft        (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    Longitude         (Timestamp) float64 -14.12 -14.13 -14.15 ... 153.6 153.6
    B_NEC_MCO_SHA_2D  (Timestamp, NEC) float64 2.011e+04 ... 3.557e+04
    B_NEC             (Timestamp, NEC) float64 2.01e+04 -4.126e+03 ... 3.558e+04
    F_MCO_SHA_2D      (Timestamp) float64 2.287e+04 2.282e+04 ... 4.021e+04
    Radius            (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F_CHAOS-Core      (Timestamp) float64 2.287e+04 2.282e+04 ... 4.02e+04
    F                 (Timestamp) float64 2.287e+04 2.281e+04 ... 4.021e+04
    B_NEC_CHAOS-Core  (Timestamp, NEC) float64 2.011e+04 ... 3.558e+04
    Latitude          (Timestamp) float64 -1.229 -1.863 -2.496 ... 48.14 48.77
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_070...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"...
    AppliedFilters:  []

Fetch the residuals directly#

Adding residuals=True to .set_products() will instead directly evaluate and return all data-model residuals

request = SwarmRequest()
request.set_collection("SW_OPER_MAGA_LR_1B")
request.set_products(
    measurements=["F", "B_NEC"],
    models=["CHAOS-Core", "MCO_SHA_2D"],
    residuals=True,
    sampling_step="PT10S",
)
data = request.get_between(
    start_time=dt.datetime(2014, 1, 1, 0), end_time=dt.datetime(2014, 1, 1, 1)
)
df = data.as_dataframe(expand=True)
df.head()
Longitude Radius F_res_CHAOS-Core F_res_MCO_SHA_2D Spacecraft Latitude B_NEC_res_MCO_SHA_2D_N B_NEC_res_MCO_SHA_2D_E B_NEC_res_MCO_SHA_2D_C B_NEC_res_CHAOS-Core_N B_NEC_res_CHAOS-Core_E B_NEC_res_CHAOS-Core_C
Timestamp
2014-01-01 00:00:00 -14.116674 6878309.23 -7.147853 -6.731386 A -1.228938 -10.128390 1.861042 -5.418643 -10.257803 1.777774 -4.697827
2014-01-01 00:00:10 -14.131424 6878381.18 -6.859381 -6.448511 A -1.862521 -10.133359 2.761229 -5.935829 -10.237083 2.666975 -5.201915
2014-01-01 00:00:20 -14.146155 6878452.06 -6.591054 -6.185747 A -2.496090 -10.148423 2.920733 -6.077901 -10.227031 2.816034 -5.333237
2014-01-01 00:00:30 -14.160861 6878521.88 -6.345390 -5.945527 A -3.129644 -10.166893 2.737098 -6.044518 -10.221031 2.622446 -5.291090
2014-01-01 00:00:40 -14.175534 6878590.62 -6.059042 -5.664468 A -3.763184 -10.269466 2.142413 -6.129389 -10.299782 2.018228 -5.368839

Plot the scalar residuals#

… using the pandas method:#

ax = df.plot(y=["F_res_CHAOS-Core", "F_res_MCO_SHA_2D"], figsize=(15, 5), grid=True)
ax.set_xlabel("Timestamp")
ax.set_ylabel("[nT]");
../_images/6a781b5cb50fa3a8bf8e27ef4b46caf3168196189601795f937eb9f3c4e36673.png

… using matplotlib interface#

NB: we are doing plt.plot(x, y) with x as df.index (the time-based index of df), and y as df[".."]

plt.figure(figsize=(15, 5))
plt.plot(df.index, df["F_res_CHAOS-Core"], label="F_res_CHAOS-Core")
plt.plot(df.index, df["F_res_MCO_SHA_2D"], label="F_res_MCO_SHA_2D")
plt.xlabel("Timestamp")
plt.ylabel("[nT]")
plt.grid()
plt.legend();
../_images/a83a6745b13697dfa69a6e910dff8657404845826809c96bf1a1f3197e5582e9.png

… using matplotlib interface (Object Oriented style)#

This is the recommended route for making more complicated figures

fig, ax = plt.subplots(figsize=(15, 5))
ax.plot(df.index, df["F_res_CHAOS-Core"], label="F_res_CHAOS-Core")
ax.plot(df.index, df["F_res_MCO_SHA_2D"], label="F_res_MCO_SHA_2D")
ax.set_xlabel("Timestamp")
ax.set_ylabel("[nT]")
ax.grid()
ax.legend();
../_images/a83a6745b13697dfa69a6e910dff8657404845826809c96bf1a1f3197e5582e9.png

Plot the vector components#

fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(15, 10), sharex=True)
for component, ax in zip("NEC", axes):
    for model_name in ("CHAOS-Core", "MCO_SHA_2D"):
        ax.plot(df.index, df[f"B_NEC_res_{model_name}_{component}"], label=model_name)
    ax.set_ylabel(f"{component}\n[nT]")
    ax.legend()
axes[0].set_title("Residuals to models (NEC components)")
axes[2].set_xlabel("Timestamp");
../_images/67c084fa31833cb152352d8f27acd42cb62b0b37fac6f072c28910cff1c7db2e.png

Similar plotting, using the data via xarray instead#

xarray provides a more sophisticated data structure that is more suitable for the complex vector data we are accessing, together with nice stuff like unit and other metadata support. Unfortunately due to the extra complexity, this can make it difficult to use right away.

ds = data.as_xarray()
ds
<xarray.Dataset>
Dimensions:               (Timestamp: 360, NEC: 3)
Coordinates:
  * Timestamp             (Timestamp) datetime64[ns] 2014-01-01 ... 2014-01-0...
  * NEC                   (NEC) <U1 'N' 'E' 'C'
Data variables:
    Spacecraft            (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    Longitude             (Timestamp) float64 -14.12 -14.13 ... 153.6 153.6
    Radius                (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F_res_CHAOS-Core      (Timestamp) float64 -7.148 -6.859 ... 3.956 3.959
    B_NEC_res_CHAOS-Core  (Timestamp, NEC) float64 -10.26 1.778 ... 3.799 8.894
    F_res_MCO_SHA_2D      (Timestamp) float64 -6.731 -6.449 ... 3.122 3.076
    B_NEC_res_MCO_SHA_2D  (Timestamp, NEC) float64 -10.13 1.861 ... 3.677 9.109
    Latitude              (Timestamp) float64 -1.229 -1.863 ... 48.14 48.77
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_070...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"...
    AppliedFilters:  []
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(15, 10), sharex=True)
for i, ax in enumerate(axes):
    for model_name in ("CHAOS-Core", "MCO_SHA_2D"):
        ax.plot(ds["Timestamp"], ds[f"B_NEC_res_{model_name}"][:, i], label=model_name)
    ax.set_ylabel("NEC"[i] + " [nT]")
    ax.legend()
axes[0].set_title("Residuals to models (NEC components)")
axes[2].set_xlabel("Timestamp");
../_images/58d8f9ce047f59f154b4c5ef13f0f73c59773f72d2ce5caeec67a88de80669db.png

Note that xarray also allows convenient direct plotting like:

ds["B_NEC_res_CHAOS-Core"].plot.line(x="Timestamp");
../_images/c64c26273d28912a113c7e1e7dd371251fe30ea4605c4b94fe6d91d4fa54fbde.png

Access multiple MAG datasets simultaneously#

It is possible to fetch data from multiple collections simultaneously. Here we fetch the measurements from Swarm Alpha and Bravo. In the returned data, you can differentiate between them using the “Spacecraft” column.

request = SwarmRequest()
request.set_collection("SW_OPER_MAGA_LR_1B", "SW_OPER_MAGC_LR_1B")
request.set_products(
    measurements=["F", "B_NEC"],
    models=[
        "CHAOS-Core",
    ],
    residuals=True,
    sampling_step="PT10S",
)
data = request.get_between(
    start_time=dt.datetime(2014, 1, 1, 0), end_time=dt.datetime(2014, 1, 1, 1)
)
df = data.as_dataframe(expand=True)
df.head()
Longitude Radius F_res_CHAOS-Core Spacecraft Latitude B_NEC_res_CHAOS-Core_N B_NEC_res_CHAOS-Core_E B_NEC_res_CHAOS-Core_C
Timestamp
2014-01-01 00:00:00 -14.116674 6878309.23 -7.147853 A -1.228938 -10.257803 1.777774 -4.697827
2014-01-01 00:00:10 -14.131424 6878381.18 -6.859381 A -1.862521 -10.237083 2.666975 -5.201915
2014-01-01 00:00:20 -14.146155 6878452.06 -6.591054 A -2.496090 -10.227031 2.816034 -5.333237
2014-01-01 00:00:30 -14.160861 6878521.88 -6.345390 A -3.129644 -10.221031 2.622446 -5.291090
2014-01-01 00:00:40 -14.175534 6878590.62 -6.059042 A -3.763184 -10.299782 2.018228 -5.368839
df[df["Spacecraft"] == "A"].head()
Longitude Radius F_res_CHAOS-Core Spacecraft Latitude B_NEC_res_CHAOS-Core_N B_NEC_res_CHAOS-Core_E B_NEC_res_CHAOS-Core_C
Timestamp
2014-01-01 00:00:00 -14.116674 6878309.23 -7.147853 A -1.228938 -10.257803 1.777774 -4.697827
2014-01-01 00:00:10 -14.131424 6878381.18 -6.859381 A -1.862521 -10.237083 2.666975 -5.201915
2014-01-01 00:00:20 -14.146155 6878452.06 -6.591054 A -2.496090 -10.227031 2.816034 -5.333237
2014-01-01 00:00:30 -14.160861 6878521.88 -6.345390 A -3.129644 -10.221031 2.622446 -5.291090
2014-01-01 00:00:40 -14.175534 6878590.62 -6.059042 A -3.763184 -10.299782 2.018228 -5.368839
df[df["Spacecraft"] == "C"].head()
Longitude Radius F_res_CHAOS-Core Spacecraft Latitude B_NEC_res_CHAOS-Core_N B_NEC_res_CHAOS-Core_E B_NEC_res_CHAOS-Core_C
Timestamp
2014-01-01 00:00:00 -14.420068 6877665.96 -10.813034 C 5.908082 -10.930664 2.473100 -1.282855
2014-01-01 00:00:10 -14.434576 6877747.64 -10.412250 C 5.274386 -10.692035 2.570363 -1.732410
2014-01-01 00:00:20 -14.449141 6877828.36 -10.091965 C 4.640702 -10.586484 2.487344 -2.145514
2014-01-01 00:00:30 -14.463755 6877908.12 -9.847379 C 4.007030 -10.691359 2.099063 -2.718269
2014-01-01 00:00:40 -14.478412 6877986.90 -9.594042 C 3.373371 -10.776351 1.604786 -3.107301

… or using xarray#

ds = data.as_xarray()
ds.where(ds["Spacecraft"] == "A", drop=True)
<xarray.Dataset>
Dimensions:               (Timestamp: 360, NEC: 3)
Coordinates:
  * Timestamp             (Timestamp) datetime64[ns] 2014-01-01 ... 2014-01-0...
  * NEC                   (NEC) <U1 'N' 'E' 'C'
Data variables:
    Spacecraft            (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    Longitude             (Timestamp) float64 -14.12 -14.13 ... 153.6 153.6
    Radius                (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F_res_CHAOS-Core      (Timestamp) float64 -7.148 -6.859 ... 3.956 3.959
    B_NEC_res_CHAOS-Core  (Timestamp, NEC) float64 -10.26 1.778 ... 3.799 8.894
    Latitude              (Timestamp) float64 -1.229 -1.863 ... 48.14 48.77
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_070...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"]
    AppliedFilters:  []