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 : 8.0.1
viresclient: 0.10.1
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:
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()
Ionosphere_region_flag | Te | Grad_Ne_at_20km | IBI_flag | mROTI20s | Longitude | delta_Ne20s | Ne | TEC_STD | Radius | ... | delta_Ne40s | RODI20s | PCP_flag | Foreground_Ne | Grad_Ne_at_100km | Background_Ne | IPIR_index | mROT | RODI10s | ROD | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Timestamp | |||||||||||||||||||||
2014-12-21 00:00:00.196999936 | 0 | 2212.28 | -1.047788 | -1 | 0.002676 | -128.771412 | 10266.500 | 1255163.2 | 3.131452 | 6.840395e+06 | ... | 3811.1 | 7764.002532 | 0 | 1305371.000 | -0.084919 | 1343599.375 | 7 | -0.011013 | 10238.517220 | 0.0 |
2014-12-21 00:00:01.196999936 | 0 | 2165.19 | 0.338403 | -1 | 0.002732 | -128.772618 | 2830.850 | 1250357.6 | 3.122495 | 6.840404e+06 | ... | 16343.3 | 7181.496228 | 0 | 1292046.000 | -0.144009 | 1343599.375 | 6 | -0.011013 | 3263.138721 | 0.0 |
2014-12-21 00:00:02.196999936 | 0 | 1544.87 | 0.133643 | -1 | 0.002750 | -128.773822 | 0.000 | 1265851.3 | 3.113831 | 6.840413e+06 | ... | 3246.4 | 7181.496228 | 0 | 1312436.750 | -0.058276 | 1343599.375 | 6 | -0.008833 | 3263.138721 | 0.0 |
2014-12-21 00:00:03.196999936 | 0 | 1228.50 | 1.443077 | -1 | 0.003277 | -128.775026 | 2194.925 | 1312436.8 | 3.104260 | 6.840422e+06 | ... | 848.3 | 7390.308480 | 0 | 1312436.750 | -0.144613 | 1343599.375 | 6 | -0.008151 | 3263.138721 | 0.0 |
2014-12-21 00:00:04.196999936 | 0 | 2681.51 | -1.948789 | -1 | 0.003744 | -128.776229 | 9491.525 | 1253999.0 | 3.097485 | 6.840430e+06 | ... | 6448.3 | 7554.331699 | 0 | 1315059.375 | -0.039358 | 1343599.375 | 6 | -0.007051 | 3263.138721 | 0.0 |
5 rows × 29 columns
df.columns
Index(['Ionosphere_region_flag', 'Te', 'Grad_Ne_at_20km', 'IBI_flag',
'mROTI20s', 'Longitude', 'delta_Ne20s', 'Ne', 'TEC_STD', 'Radius',
'Grad_Ne_at_50km', 'mVTEC', 'mROTI10s', 'Num_GPS_satellites',
'Spacecraft', 'Grad_Ne_at_PCP_edge', 'delta_Ne10s', 'Ne_quality_flag',
'Latitude', 'delta_Ne40s', 'RODI20s', 'PCP_flag', 'Foreground_Ne',
'Grad_Ne_at_100km', 'Background_Ne', 'IPIR_index', 'mROT', 'RODI10s',
'ROD'],
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)

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 Te (Timestamp) float64 2.212e+03 ... 1.723e+03 Grad_Ne_at_20km (Timestamp) float64 -1.048 0.3384 ... 1.002 0.9148 IBI_flag (Timestamp) int32 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1 mROTI20s (Timestamp) float64 0.002676 0.002732 ... 0.0114 ... ... Grad_Ne_at_100km (Timestamp) float64 -0.08492 -0.144 ... 0.9621 Background_Ne (Timestamp) float64 1.344e+06 1.344e+06 ... 4.29e+05 IPIR_index (Timestamp) int32 7 6 6 6 6 6 6 6 ... 4 4 4 4 4 4 4 mROT (Timestamp) float64 -0.01101 -0.01101 ... 0.09621 RODI10s (Timestamp) float64 1.024e+04 3.263e+03 ... 503.7 ROD (Timestamp) float64 0.0 0.0 ... 7.28e+03 7.28e+03 Attributes: Sources: ['SW_OPER_IPDAIRR_2F_20141221T000000_20141221T235959_0301'] MagneticModels: [] RangeFilters: []
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)
