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IPDxIRR_2F (Ionospheric plasma densities)

IPDxIRR_2F (Ionospheric plasma densities)#

Abstract: Access to the derived plasma characteristics at 1Hz (level 2 product).

%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: 0.14.1
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
from matplotlib.dates import DateFormatter

request = SwarmRequest()

IPDxIRR_2F product information#

Derived plasma characteristics at 1Hz, for each Swarm spacecraft.

Documentation:

Check what “IPD” data variables are available#

request.available_collections("IPD", details=False)

{'IPD': ['SW_OPER_IPDAIRR_2F', 'SW_OPER_IPDBIRR_2F', 'SW_OPER_IPDCIRR_2F']}

request.available_measurements("IPD")

['Ne',
 'Te',
 'Background_Ne',
 'Foreground_Ne',
 'PCP_flag',
 'Grad_Ne_at_100km',
 'Grad_Ne_at_50km',
 'Grad_Ne_at_20km',
 'Grad_Ne_at_PCP_edge',
 'ROD',
 'RODI10s',
 'RODI20s',
 'delta_Ne10s',
 'delta_Ne20s',
 'delta_Ne40s',
 'Num_GPS_satellites',
 'mVTEC',
 'mROT',
 'mROTI10s',
 'mROTI20s',
 'IBI_flag',
 'Ionosphere_region_flag',
 'IPIR_index',
 'Ne_quality_flag',
 'TEC_STD']

Fetch three hours of IPD data#

request.set_collection("SW_OPER_IPDAIRR_2F")
request.set_products(measurements=request.available_measurements("IPD"))
data = request.get_between(
    dt.datetime(2014,12,21, 0),
    dt.datetime(2014,12,21, 3)
)
Processing: 100%
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Downloading: 100%
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Load and plot using pandas/matplotlib#

df = data.as_dataframe()
df.head()
Ionosphere_region_flag ROD Num_GPS_satellites mVTEC RODI10s delta_Ne20s Radius IPIR_index Ne_quality_flag mROT ... mROTI20s Background_Ne Grad_Ne_at_100km Ne IBI_flag Latitude Foreground_Ne TEC_STD mROTI10s Grad_Ne_at_20km
Timestamp
2014-12-21 00:00:00.196999936 0 0.0 4 51.786934 10238.517220 10266.500 6.840395e+06 7 20000 -0.011013 ... 0.002676 1343599.375 -0.084919 1255163.2 -1 -4.693533 1305371.000 3.131451 0.001472 -1.047788
2014-12-21 00:00:01.196999936 0 0.0 4 51.768982 3263.138721 2830.850 6.840404e+06 6 20000 -0.011013 ... 0.002732 1343599.375 -0.144009 1250357.7 -1 -4.757416 1292046.000 3.122494 0.001386 0.338403
2014-12-21 00:00:02.196999936 0 0.0 4 51.746898 3263.138721 0.000 6.840413e+06 6 20000 -0.008833 ... 0.002750 1343599.375 -0.058276 1265851.3 -1 -4.821298 1312436.750 3.113830 0.001310 0.133643
2014-12-21 00:00:03.196999936 0 0.0 4 51.728759 3263.138721 2194.925 6.840422e+06 6 20000 -0.008151 ... 0.003277 1343599.375 -0.144613 1312436.8 -1 -4.885179 1312436.750 3.104259 0.001930 1.443077
2014-12-21 00:00:04.196999936 0 0.0 4 51.711313 3263.138721 9491.525 6.840430e+06 6 20000 -0.007051 ... 0.003744 1343599.375 -0.039358 1253999.0 -1 -4.949060 1315059.375 3.097484 0.002434 -1.948789

5 rows × 29 columns

df.columns

Index(['Ionosphere_region_flag', 'ROD', 'Num_GPS_satellites', 'mVTEC',
       'RODI10s', 'delta_Ne20s', 'Radius', 'IPIR_index', 'Ne_quality_flag',
       'mROT', 'RODI20s', 'Longitude', 'delta_Ne40s', 'Te', 'delta_Ne10s',
       'PCP_flag', 'Grad_Ne_at_50km', 'Grad_Ne_at_PCP_edge', 'Spacecraft',
       'mROTI20s', 'Background_Ne', 'Grad_Ne_at_100km', 'Ne', 'IBI_flag',
       'Latitude', 'Foreground_Ne', 'TEC_STD', 'mROTI10s', 'Grad_Ne_at_20km'],
      dtype='object')

fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20,18), sharex=True)
df.plot(ax=axes[0], y=['Background_Ne', 'Foreground_Ne', 'Ne'], alpha=0.8)
df.plot(ax=axes[1], y=['Grad_Ne_at_100km', 'Grad_Ne_at_50km', 'Grad_Ne_at_20km'])
df.plot(ax=axes[2], y=['RODI10s', 'RODI20s'])
df.plot(ax=axes[3], y=['ROD'])
df.plot(ax=axes[4], y=['mROT'])
df.plot(ax=axes[5], y=['delta_Ne10s', 'delta_Ne20s', 'delta_Ne40s'])
df.plot(ax=axes[6], y=['mROTI20s', 'mROTI10s'])
axes[0].set_ylabel("[cm$^{-3}$]")
axes[1].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[2].set_ylabel("[cm$^{-3}$s$^{-1}$]")
axes[3].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[4].set_ylabel("[TECU s$^{-1}$]")
axes[5].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[6].set_ylabel("[TECU s$^{-1}$]")
axes[6].set_xlabel("Timestamp")

for ax in axes:
    # Reformat time axis
    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/
    ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d\n%H:%M:%S"))
    ax.legend(loc="upper right")
    ax.grid()
fig.subplots_adjust(hspace=0)
../_images/8e787033164cfb750130dce76a7f6fb60b24f750f731c99b28be3f4a1debdbb1.png

Load as xarray#

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:                 (Timestamp: 10800)
Coordinates:
  * Timestamp               (Timestamp) datetime64[ns] 2014-12-21T00:00:00.19...
Data variables: (12/29)
    Spacecraft              (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A'
    Ionosphere_region_flag  (Timestamp) int32 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
    ROD                     (Timestamp) float64 0.0 0.0 ... 7.28e+03 7.28e+03
    Num_GPS_satellites      (Timestamp) int32 4 4 4 4 4 4 4 4 ... 6 6 6 6 6 6 6
    mVTEC                   (Timestamp) float64 51.79 51.77 ... 20.84 20.94
    RODI10s                 (Timestamp) float64 1.024e+04 3.263e+03 ... 503.7
    ...                      ...
    IBI_flag                (Timestamp) int32 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1
    Latitude                (Timestamp) float64 -4.694 -4.757 ... 24.74 24.68
    Foreground_Ne           (Timestamp) float64 1.305e+06 ... 6.488e+05
    TEC_STD                 (Timestamp) float64 3.131 3.122 ... 2.866 2.891
    mROTI10s                (Timestamp) float64 0.001472 0.001386 ... 0.005609
    Grad_Ne_at_20km         (Timestamp) float64 -1.048 0.3384 ... 1.002 0.9148
Attributes:
    Sources:         ['SW_OPER_IPDAIRR_2F_20141221T000000_20141221T235959_0302']
    MagneticModels:  []
    AppliedFilters:  []

Alternative plot setup#

To plot the data from xarray, we need a different plotting setup. This does however give us more control over the plot. The units are extracted directly from the xarray object.

fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20,18), sharex=True)
def subplot(ax=None, y=None, **kwargs):
    """Plot combination of variables onto a given axis"""
    units = ds[y[0]].units
    for var in y:
        ax.plot(ds["Timestamp"], ds[var], label=var, **kwargs)
        if units != ds[var].units:
            raise ValueError(f"Units mismatch for {var}")
    ax.set_ylabel(f"[{units}]")
    # Reformat time axis
    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/
    ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d\n%H:%M:%S"))
    ax.legend(loc="upper right")
    ax.grid()
subplot(ax=axes[0], y=['Background_Ne', 'Foreground_Ne', 'Ne'])
subplot(ax=axes[1], y=['Grad_Ne_at_100km', 'Grad_Ne_at_50km', 'Grad_Ne_at_20km'])
subplot(ax=axes[2], y=['RODI10s', 'RODI20s'])
subplot(ax=axes[3], y=['ROD'])
subplot(ax=axes[4], y=['mROT'])
subplot(ax=axes[5], y=['delta_Ne10s', 'delta_Ne20s', 'delta_Ne40s'])
subplot(ax=axes[6], y=['mROTI20s', 'mROTI10s'])
fig.subplots_adjust(hspace=0)
../_images/38a33b938b18d5eabfd11f30bb71a72f6ed503d73905cb7ae8e2283b63aa6d15.png
Contents