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       : 3.9.7
IPython version      : 8.3.0

viresclient: 0.10.3
pandas     : 1.4.1
xarray     : 0.21.1
matplotlib : 3.5.1
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 Magnetomer (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']}

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_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',
 '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_0505_MDR_MAG_LR',
 'SW_OPER_MCO_SHA_2D_20131126T000000_20180101T000000_0401',
 'SW_OPER_MCO_SHA_2X_19970101T000000_20220807T235959_0710']

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()
F_MCO_SHA_2D Latitude F Radius Longitude Spacecraft F_CHAOS-Core B_NEC_CHAOS-Core_N B_NEC_CHAOS-Core_E B_NEC_CHAOS-Core_C B_NEC_N B_NEC_E B_NEC_C B_NEC_MCO_SHA_2D_N B_NEC_MCO_SHA_2D_E B_NEC_MCO_SHA_2D_C
Timestamp
2014-01-01 00:00:00 22874.211509 -1.228938 22867.5503 6878309.22 -14.116674 A 22874.473293 20113.282280 -4127.025787 -10082.909428 20103.5246 -4126.2621 -10086.9888 20113.623921 -4127.463956 -10081.454567
2014-01-01 00:00:10 22820.941425 -1.862521 22814.5656 6878381.17 -14.131424 A 22821.207203 19824.786844 -4162.670566 -10509.876239 19815.0914 -4160.9933 -10514.4074 19825.161844 -4163.127549 -10508.410652
2014-01-01 00:00:20 22769.369161 -2.496090 22763.2585 6878452.05 -14.146155 A 22769.634753 19533.142847 -4197.053279 -10922.332218 19523.4946 -4195.1968 -10926.9664 19533.553905 -4197.529054 -10920.860481
2014-01-01 00:00:30 22719.238240 -3.129644 22713.3703 6878521.87 -14.160861 A 22719.499732 19238.894485 -4230.107307 -11320.194312 19229.2386 -4228.4747 -11324.8335 19239.343572 -4230.601819 -11318.721366
2014-01-01 00:00:40 22670.304681 -3.763184 22664.7202 6878590.61 -14.175534 A 22670.558522 18942.586847 -4261.767478 -11703.416817 18932.8807 -4260.8424 -11708.0897 18943.075144 -4262.280634 -11701.947796

… 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'
    B_NEC_MCO_SHA_2D  (Timestamp, NEC) float64 2.011e+04 ... 3.557e+04
    F_MCO_SHA_2D      (Timestamp) float64 2.287e+04 2.282e+04 ... 4.021e+04
    Latitude          (Timestamp) float64 -1.229 -1.863 -2.496 ... 48.14 48.77
    Radius            (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F                 (Timestamp) float64 2.287e+04 2.281e+04 ... 4.021e+04
    Longitude         (Timestamp) float64 -14.12 -14.13 -14.15 ... 153.6 153.6
    B_NEC             (Timestamp, NEC) float64 2.01e+04 -4.126e+03 ... 3.558e+04
    F_CHAOS-Core      (Timestamp) float64 2.287e+04 2.282e+04 ... 4.02e+04
    B_NEC_CHAOS-Core  (Timestamp, NEC) float64 2.011e+04 ... 3.557e+04
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_050...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"...
    RangeFilters:    []

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()
Latitude Radius F_res_CHAOS-Core Longitude F_res_MCO_SHA_2D Spacecraft 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 -1.228938 6878309.22 -6.922993 -14.116674 -6.661209 A -10.099321 1.201856 -5.534233 -9.757680 0.763687 -4.079372
2014-01-01 00:00:10 -1.862521 6878381.17 -6.641603 -14.131424 -6.375825 A -10.070444 2.134249 -5.996748 -9.695444 1.677266 -4.531161
2014-01-01 00:00:20 -2.496090 6878452.05 -6.376253 -14.146155 -6.110661 A -10.059305 2.332254 -6.105919 -9.648247 1.856479 -4.634182
2014-01-01 00:00:30 -3.129644 6878521.87 -6.129432 -14.160861 -5.867940 A -10.104972 2.127119 -6.112134 -9.655885 1.632607 -4.639188
2014-01-01 00:00:40 -3.763184 6878590.61 -5.838322 -14.175534 -5.584481 A -10.194444 1.438234 -6.141904 -9.706147 0.925078 -4.672883

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/03a1_Demo-MAGx_LR_1B_21_0.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/03a1_Demo-MAGx_LR_1B_23_0.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/03a1_Demo-MAGx_LR_1B_25_0.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/03a1_Demo-MAGx_LR_1B_27_0.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'
    Latitude              (Timestamp) float64 -1.229 -1.863 ... 48.14 48.77
    Radius                (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F_res_CHAOS-Core      (Timestamp) float64 -6.923 -6.642 ... 5.063 5.063
    Longitude             (Timestamp) float64 -14.12 -14.13 ... 153.6 153.6
    B_NEC_res_MCO_SHA_2D  (Timestamp, NEC) float64 -10.1 1.202 ... 2.782 8.984
    B_NEC_res_CHAOS-Core  (Timestamp, NEC) float64 -9.758 0.7637 ... 2.913 9.967
    F_res_MCO_SHA_2D      (Timestamp) float64 -6.661 -6.376 ... 3.153 3.108
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_050...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"...
    RangeFilters:    []
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/03a1_Demo-MAGx_LR_1B_30_0.png

Note that xarray also allows convenient direct plotting like:

ds["B_NEC_res_CHAOS-Core"].plot.line(x="Timestamp");
../_images/03a1_Demo-MAGx_LR_1B_32_0.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()
Latitude Radius F_res_CHAOS-Core Longitude Spacecraft 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 -1.228938 6878309.22 -6.922993 -14.116674 A -9.757680 0.763687 -4.079372
2014-01-01 00:00:10 -1.862521 6878381.17 -6.641603 -14.131424 A -9.695444 1.677266 -4.531161
2014-01-01 00:00:20 -2.496090 6878452.05 -6.376253 -14.146155 A -9.648247 1.856479 -4.634182
2014-01-01 00:00:30 -3.129644 6878521.87 -6.129432 -14.160861 A -9.655885 1.632607 -4.639188
2014-01-01 00:00:40 -3.763184 6878590.61 -5.838322 -14.175534 A -9.706147 0.925078 -4.672883
df[df["Spacecraft"] == "A"].head()
Latitude Radius F_res_CHAOS-Core Longitude Spacecraft 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 -1.228938 6878309.22 -6.922993 -14.116674 A -9.757680 0.763687 -4.079372
2014-01-01 00:00:10 -1.862521 6878381.17 -6.641603 -14.131424 A -9.695444 1.677266 -4.531161
2014-01-01 00:00:20 -2.496090 6878452.05 -6.376253 -14.146155 A -9.648247 1.856479 -4.634182
2014-01-01 00:00:30 -3.129644 6878521.87 -6.129432 -14.160861 A -9.655885 1.632607 -4.639188
2014-01-01 00:00:40 -3.763184 6878590.61 -5.838322 -14.175534 A -9.706147 0.925078 -4.672883
df[df["Spacecraft"] == "C"].head()
Latitude Radius F_res_CHAOS-Core Longitude Spacecraft 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 5.908082 6877665.99 -10.305452 -14.420068 C -10.354612 1.838578 -0.479137
2014-01-01 00:00:10 5.274386 6877747.67 -9.934074 -14.434576 C -10.153220 1.979606 -1.104802
2014-01-01 00:00:20 4.640702 6877828.39 -9.645067 -14.449141 C -10.069903 1.888508 -1.547575
2014-01-01 00:00:30 4.007030 6877908.15 -9.432665 -14.463755 C -10.181209 1.497282 -2.083285
2014-01-01 00:00:40 3.373371 6877986.93 -9.211881 -14.478412 C -10.280461 1.031300 -2.481859

… 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'
    Latitude              (Timestamp) float64 -1.229 -1.863 ... 48.14 48.77
    Radius                (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F_res_CHAOS-Core      (Timestamp) float64 -6.923 -6.642 ... 5.063 5.063
    Longitude             (Timestamp) float64 -14.12 -14.13 ... 153.6 153.6
    B_NEC_res_CHAOS-Core  (Timestamp, NEC) float64 -9.758 0.7637 ... 2.913 9.967
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_050...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"]
    RangeFilters:    []