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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: 0.12.0
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)
)

Load and plot using pandas/matplotlib#

df = data.as_dataframe()
df.head()
Grad_Ne_at_100km Te Grad_Ne_at_PCP_edge Latitude RODI10s ROD delta_Ne10s mVTEC Radius Ne ... RODI20s Longitude Grad_Ne_at_50km Ionosphere_region_flag TEC_STD delta_Ne40s Grad_Ne_at_20km PCP_flag Background_Ne IBI_flag
Timestamp
2014-12-21 00:00:00.196999936 -0.084919 2212.278353 0.0 -4.693533 10238.517220 0.0 67.875 51.786934 6.840395e+06 1255163.2 ... 7764.002532 -128.771412 -0.403940 0 3.131451 3811.1 -1.047788 0 1343599.375 -1
2014-12-21 00:00:01.196999936 -0.144009 2165.194729 0.0 -4.757416 3263.138721 0.0 12961.600 51.768982 6.840404e+06 1250357.7 ... 7181.496228 -128.772618 0.144877 0 3.122494 16343.3 0.338403 0 1343599.375 -1
2014-12-21 00:00:02.196999936 -0.058276 1544.874194 0.0 -4.821298 3263.138721 0.0 0.000 51.746898 6.840413e+06 1265851.3 ... 7181.496228 -128.773822 -0.123734 0 3.113830 3246.4 0.133643 0 1343599.375 -1
2014-12-21 00:00:03.196999936 -0.144613 1228.501871 0.0 -4.885179 3263.138721 0.0 12393.550 51.728759 6.840422e+06 1312436.8 ... 7390.308480 -128.775026 -0.131441 0 3.104259 848.3 1.443077 0 1343599.375 -1
2014-12-21 00:00:04.196999936 -0.039358 2681.512355 0.0 -4.949060 3263.138721 0.0 21700.700 51.711313 6.840430e+06 1253999.0 ... 7554.331699 -128.776229 -0.403369 0 3.097484 6448.3 -1.948789 0 1343599.375 -1

5 rows × 29 columns

df.columns

Index(['Grad_Ne_at_100km', 'Te', 'Grad_Ne_at_PCP_edge', 'Latitude', 'RODI10s',
       'ROD', 'delta_Ne10s', 'mVTEC', 'Radius', 'Ne', 'mROTI20s',
       'Ne_quality_flag', 'Spacecraft', 'Num_GPS_satellites', 'Foreground_Ne',
       'delta_Ne20s', 'mROTI10s', 'IPIR_index', 'mROT', 'RODI20s', 'Longitude',
       'Grad_Ne_at_50km', 'Ionosphere_region_flag', 'TEC_STD', 'delta_Ne40s',
       'Grad_Ne_at_20km', 'PCP_flag', 'Background_Ne', 'IBI_flag'],
      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'
    Grad_Ne_at_100km        (Timestamp) float64 -0.08492 -0.144 ... 0.9621
    Te                      (Timestamp) float64 2.212e+03 ... 1.723e+03
    Grad_Ne_at_PCP_edge     (Timestamp) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    Latitude                (Timestamp) float64 -4.694 -4.757 ... 24.74 24.68
    RODI10s                 (Timestamp) float64 1.024e+04 3.263e+03 ... 503.7
    ...                      ...
    TEC_STD                 (Timestamp) float64 3.131 3.122 ... 2.866 2.891
    delta_Ne40s             (Timestamp) float64 3.811e+03 ... 1.702e+03
    Grad_Ne_at_20km         (Timestamp) float64 -1.048 0.3384 ... 1.002 0.9148
    PCP_flag                (Timestamp) int32 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
    Background_Ne           (Timestamp) float64 1.344e+06 1.344e+06 ... 4.29e+05
    IBI_flag                (Timestamp) int32 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1
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
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