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      : 8.0.1

viresclient: 0.11.3
pandas     : 1.4.1
xarray     : 2023.8.0
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:

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_PCP_edge delta_Ne40s Ne TEC_STD IPIR_index Ne_quality_flag PCP_flag Grad_Ne_at_50km Ionosphere_region_flag RODI10s ... Background_Ne Longitude IBI_flag Te Spacecraft Num_GPS_satellites delta_Ne10s mROT delta_Ne20s Foreground_Ne
Timestamp
2014-12-21 00:00:00.196999936 0.0 3811.1 1255163.2 3.131451 7 20000 0 -0.403940 0 10238.517220 ... 1343599.375 -128.771412 -1 2212.278353 A 4 67.875 -0.011013 10266.500 1305371.000
2014-12-21 00:00:01.196999936 0.0 16343.3 1250357.7 3.122494 6 20000 0 0.144877 0 3263.138721 ... 1343599.375 -128.772618 -1 2165.194729 A 4 12961.600 -0.011013 2830.850 1292046.000
2014-12-21 00:00:02.196999936 0.0 3246.4 1265851.3 3.113830 6 20000 0 -0.123734 0 3263.138721 ... 1343599.375 -128.773822 -1 1544.874194 A 4 0.000 -0.008833 0.000 1312436.750
2014-12-21 00:00:03.196999936 0.0 848.3 1312436.8 3.104259 6 20000 0 -0.131441 0 3263.138721 ... 1343599.375 -128.775026 -1 1228.501871 A 4 12393.550 -0.008151 2194.925 1312436.750
2014-12-21 00:00:04.196999936 0.0 6448.3 1253999.0 3.097484 6 20000 0 -0.403369 0 3263.138721 ... 1343599.375 -128.776229 -1 2681.512355 A 4 21700.700 -0.007051 9491.525 1315059.375

5 rows × 29 columns

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

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_PCP_edge     (Timestamp) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    delta_Ne40s             (Timestamp) float64 3.811e+03 ... 1.702e+03
    Ne                      (Timestamp) float64 1.255e+06 1.25e+06 ... 6.468e+05
    TEC_STD                 (Timestamp) float64 3.131 3.122 ... 2.866 2.891
    IPIR_index              (Timestamp) int32 7 6 6 6 6 6 6 6 ... 4 4 4 4 4 4 4
    ...                      ...
    Te                      (Timestamp) float64 2.212e+03 ... 1.723e+03
    Num_GPS_satellites      (Timestamp) int32 4 4 4 4 4 4 4 4 ... 6 6 6 6 6 6 6
    delta_Ne10s             (Timestamp) float64 67.88 1.296e+04 ... 1.702e+03
    mROT                    (Timestamp) float64 -0.01101 -0.01101 ... 0.09621
    delta_Ne20s             (Timestamp) float64 1.027e+04 ... 1.702e+03
    Foreground_Ne           (Timestamp) float64 1.305e+06 ... 6.488e+05
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/03c__Demo-IPDxIRR_2F_16_0.png