IPDxIRR_2F (Ionospheric plasma densities)

Contents

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       : 3.9.7
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
from matplotlib.dates import DateFormatter

request = SwarmRequest()

IPDxIRR_2F product information¶

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

Documentation:

https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/level-2-product-definitions#IPDxIRR_2F

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()

	Longitude	mROT	Num_GPS_satellites	RODI20s	Ne	IBI_flag	Grad_Ne_at_50km	Spacecraft	Latitude	mROTI20s	...	PCP_flag	Grad_Ne_at_PCP_edge	Background_Ne	ROD	mVTEC	delta_Ne20s	Ne_quality_flag	Grad_Ne_at_20km	delta_Ne40s	TEC_STD
Timestamp
2014-12-21 00:00:00.196999936	-128.771412	-0.011013	4	7764.002532	1255163.2	-1	-0.403940	A	-4.693533	0.002676	...	0	0.0	1343599.375	0.0	51.786939	10266.500	20000	-1.047788	3811.1	3.131452
2014-12-21 00:00:01.196999936	-128.772618	-0.011013	4	7181.496228	1250357.6	-1	0.144877	A	-4.757416	0.002732	...	0	0.0	1343599.375	0.0	51.768987	2830.850	20000	0.338403	16343.3	3.122495
2014-12-21 00:00:02.196999936	-128.773822	-0.008833	4	7181.496228	1265851.3	-1	-0.123734	A	-4.821298	0.002750	...	0	0.0	1343599.375	0.0	51.746903	0.000	20000	0.133643	3246.4	3.113831
2014-12-21 00:00:03.196999936	-128.775026	-0.008151	4	7390.308480	1312436.8	-1	-0.131441	A	-4.885179	0.003277	...	0	0.0	1343599.375	0.0	51.728764	2194.925	20000	1.443077	848.3	3.104260
2014-12-21 00:00:04.196999936	-128.776229	-0.007051	4	7554.331699	1253999.0	-1	-0.403369	A	-4.949060	0.003744	...	0	0.0	1343599.375	0.0	51.711319	9491.525	20000	-1.948789	6448.3	3.097485

5 rows × 29 columns

df.columns

Index(['Longitude', 'mROT', 'Num_GPS_satellites', 'RODI20s', 'Ne', 'IBI_flag',
       'Grad_Ne_at_50km', 'Spacecraft', 'Latitude', 'mROTI20s', 'IPIR_index',
       'mROTI10s', 'Radius', 'delta_Ne10s', 'Foreground_Ne',
       'Grad_Ne_at_100km', 'Te', 'Ionosphere_region_flag', 'RODI10s',
       'PCP_flag', 'Grad_Ne_at_PCP_edge', 'Background_Ne', 'ROD', 'mVTEC',
       'delta_Ne20s', 'Ne_quality_flag', 'Grad_Ne_at_20km', 'delta_Ne40s',
       'TEC_STD'],
      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/03c__Demo-IPDxIRR_2F_12_0.png

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/03c__Demo-IPDxIRR_2F_16_0.png

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