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       : 3.8.8
IPython version      : 7.22.0

viresclient: 0.9.1
pandas     : 1.2.3
xarray     : 0.17.0
matplotlib : 3.4.1
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_20220101T115930_0708']

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()
Spacecraft Longitude Latitude F Radius F_MCO_SHA_2D F_CHAOS-Core 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 A -14.116674 -1.228938 22867.5503 6878309.22 22874.211509 22874.427763 20113.258590 -4126.971359 -10082.875668 20113.623921 -4127.463956 -10081.454567 20103.5246 -4126.2621 -10086.9888
2014-01-01 00:00:10 A -14.131424 -1.862521 22814.5656 6878381.17 22820.941425 22821.160426 19824.763004 -4162.620450 -10509.839486 19825.161844 -4163.127549 -10508.410652 19815.0914 -4160.9933 -10514.4074
2014-01-01 00:00:20 A -14.146155 -2.496090 22763.2585 6878452.05 22769.369161 22769.586508 19533.118622 -4197.008088 -10922.292330 19533.553905 -4197.529054 -10920.860481 19523.4946 -4195.1968 -10926.9664
2014-01-01 00:00:30 A -14.160861 -3.129644 22713.3703 6878521.87 22719.238240 22719.449795 19238.869699 -4230.067574 -11320.151059 19239.343572 -4230.601819 -11318.721366 19229.2386 -4228.4747 -11324.8335
2014-01-01 00:00:40 A -14.175534 -3.763184 22664.7202 6878590.61 22670.304681 22670.506685 18942.561409 -4261.733644 -11703.369898 18943.075144 -4262.280634 -11701.947796 18932.8807 -4260.8424 -11708.0897

… or as an xarray dataset:

ds = data.as_xarray()
ds
<xarray.Dataset>
Dimensions:           (NEC: 3, Timestamp: 360)
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
    B_NEC             (Timestamp, NEC) float64 2.01e+04 -4.126e+03 ... 3.558e+04
    Longitude         (Timestamp) float64 -14.12 -14.13 -14.15 ... 153.6 153.6
    Latitude          (Timestamp) float64 -1.229 -1.863 -2.496 ... 48.14 48.77
    F                 (Timestamp) float64 2.287e+04 2.281e+04 ... 4.021e+04
    Radius            (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    F_MCO_SHA_2D      (Timestamp) float64 2.287e+04 2.282e+04 ... 4.021e+04
    B_NEC_CHAOS-Core  (Timestamp, NEC) float64 2.011e+04 ... 3.557e+04
    F_CHAOS-Core      (Timestamp) float64 2.287e+04 2.282e+04 ... 4.02e+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()
F_res_CHAOS-Core Spacecraft Longitude Latitude F_res_MCO_SHA_2D Radius 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 -6.877463 A -14.116674 -1.228938 -6.661209 6878309.22 -10.099321 1.201856 -5.534233 -9.733990 0.709259 -4.113132
2014-01-01 00:00:10 -6.594826 A -14.131424 -1.862521 -6.375825 6878381.17 -10.070444 2.134249 -5.996748 -9.671604 1.627150 -4.567914
2014-01-01 00:00:20 -6.328008 A -14.146155 -2.496090 -6.110661 6878452.05 -10.059305 2.332254 -6.105919 -9.624022 1.811288 -4.674070
2014-01-01 00:00:30 -6.079495 A -14.160861 -3.129644 -5.867940 6878521.87 -10.104972 2.127119 -6.112134 -9.631099 1.592874 -4.682441
2014-01-01 00:00:40 -5.786485 A -14.175534 -3.763184 -5.584481 6878590.61 -10.194444 1.438234 -6.141904 -9.680709 0.891244 -4.719802

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:               (NEC: 3, Timestamp: 360)
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'
    F_res_CHAOS-Core      (Timestamp) float64 -6.877 -6.595 ... 4.953 4.956
    Longitude             (Timestamp) float64 -14.12 -14.13 ... 153.6 153.6
    Latitude              (Timestamp) float64 -1.229 -1.863 ... 48.14 48.77
    F_res_MCO_SHA_2D      (Timestamp) float64 -6.661 -6.376 ... 3.153 3.108
    Radius                (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    B_NEC_res_CHAOS-Core  (Timestamp, NEC) float64 -9.734 0.7093 ... 2.875 9.858
    B_NEC_res_MCO_SHA_2D  (Timestamp, NEC) float64 -10.1 1.202 ... 2.782 8.984
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()
F_res_CHAOS-Core Spacecraft Longitude Latitude Radius 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 -6.877463 A -14.116674 -1.228938 6878309.22 -9.733990 0.709259 -4.113132
2014-01-01 00:00:10 -6.594826 A -14.131424 -1.862521 6878381.17 -9.671604 1.627150 -4.567914
2014-01-01 00:00:20 -6.328008 A -14.146155 -2.496090 6878452.05 -9.624022 1.811288 -4.674070
2014-01-01 00:00:30 -6.079495 A -14.160861 -3.129644 6878521.87 -9.631099 1.592874 -4.682441
2014-01-01 00:00:40 -5.786485 A -14.175534 -3.763184 6878590.61 -9.680709 0.891244 -4.719802
df[df["Spacecraft"] == "A"].head()
F_res_CHAOS-Core Spacecraft Longitude Latitude Radius 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 -6.877463 A -14.116674 -1.228938 6878309.22 -9.733990 0.709259 -4.113132
2014-01-01 00:00:10 -6.594826 A -14.131424 -1.862521 6878381.17 -9.671604 1.627150 -4.567914
2014-01-01 00:00:20 -6.328008 A -14.146155 -2.496090 6878452.05 -9.624022 1.811288 -4.674070
2014-01-01 00:00:30 -6.079495 A -14.160861 -3.129644 6878521.87 -9.631099 1.592874 -4.682441
2014-01-01 00:00:40 -5.786485 A -14.175534 -3.763184 6878590.61 -9.680709 0.891244 -4.719802
df[df["Spacecraft"] == "C"].head()
F_res_CHAOS-Core Spacecraft Longitude Latitude Radius 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 -10.278349 C -14.420068 5.908082 6877665.99 -10.332602 1.785748 -0.464800
2014-01-01 00:00:10 -9.903959 C -14.434576 5.274386 6877747.67 -10.129334 1.923050 -1.095070
2014-01-01 00:00:20 -9.612355 C -14.449141 4.640702 6877828.39 -10.044721 1.828687 -1.542470
2014-01-01 00:00:30 -9.397739 C -14.463755 4.007030 6877908.15 -10.155238 1.434746 -2.082719
2014-01-01 00:00:40 -9.175080 C -14.478412 3.373371 6877986.93 -10.254112 0.966677 -2.485659

… or using xarray

ds = data.as_xarray()
ds.where(ds["Spacecraft"] == "A", drop=True)
<xarray.Dataset>
Dimensions:               (NEC: 3, Timestamp: 360)
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'
    F_res_CHAOS-Core      (Timestamp) float64 -6.877 -6.595 ... 4.953 4.956
    Longitude             (Timestamp) float64 -14.12 -14.13 ... 153.6 153.6
    Latitude              (Timestamp) float64 -1.229 -1.863 ... 48.14 48.77
    Radius                (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06
    B_NEC_res_CHAOS-Core  (Timestamp, NEC) float64 -9.734 0.7093 ... 2.875 9.858
Attributes:
    Sources:         ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_050...
    MagneticModels:  ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"]
    RangeFilters:    []