Demo IPDxIRR_2F (ionospheric plasma densities)¶
Authors: Ashley Smith
Abstract: Access to the derived plasma characteristics at 1Hz (level 2 product).
%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib
2021-01-24T15:45:49+00:00
CPython 3.7.6
IPython 7.11.1
viresclient 0.7.1
pandas 0.25.3
xarray 0.15.0
matplotlib 3.1.2
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:
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)
)
[1/1] Processing: 100%|██████████| [ Elapsed: 00:01, Remaining: 00:00 ]
Downloading: 100%|██████████| [ Elapsed: 00:00, Remaining: 00:00 ] (2.273MB)
Load and plot using pandas/matplotlib¶
df = data.as_dataframe()
df.head()
| delta_Ne40s | Ionosphere_region_flag | mROT | Grad_Ne_at_PCP_edge | IPIR_index | Longitude | ROD | Grad_Ne_at_50km | Grad_Ne_at_100km | Spacecraft | ... | Ne_quality_flag | Ne | delta_Ne10s | Radius | PCP_flag | Latitude | Te | mVTEC | RODI20s | Grad_Ne_at_20km | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Timestamp | |||||||||||||||||||||
| 2014-12-21 00:00:00.197000027 | 3811.1 | 0 | -0.011013 | 0.0 | 7 | -128.771412 | 0.0 | -0.403940 | -0.084919 | A | ... | 20000 | 1255163.2 | 67.875 | 6.840395e+06 | 0 | -4.693533 | 2212.28 | 51.786939 | 7764.002532 | -1.047788 |
| 2014-12-21 00:00:01.197000027 | 16343.3 | 0 | -0.011013 | 0.0 | 6 | -128.772618 | 0.0 | 0.144877 | -0.144009 | A | ... | 20000 | 1250357.6 | 12961.600 | 6.840404e+06 | 0 | -4.757416 | 2165.19 | 51.768987 | 7181.496228 | 0.338403 |
| 2014-12-21 00:00:02.197000027 | 3246.4 | 0 | -0.008833 | 0.0 | 6 | -128.773822 | 0.0 | -0.123734 | -0.058276 | A | ... | 20000 | 1265851.3 | 0.000 | 6.840413e+06 | 0 | -4.821298 | 1544.87 | 51.746903 | 7181.496228 | 0.133643 |
| 2014-12-21 00:00:03.197000027 | 848.3 | 0 | -0.008151 | 0.0 | 6 | -128.775026 | 0.0 | -0.131441 | -0.144613 | A | ... | 20000 | 1312436.8 | 12393.550 | 6.840422e+06 | 0 | -4.885179 | 1228.50 | 51.728764 | 7390.308480 | 1.443077 |
| 2014-12-21 00:00:04.197000027 | 6448.3 | 0 | -0.007051 | 0.0 | 6 | -128.776229 | 0.0 | -0.403369 | -0.039358 | A | ... | 20000 | 1253999.0 | 21700.700 | 6.840430e+06 | 0 | -4.949060 | 2681.51 | 51.711319 | 7554.331699 | -1.948789 |
5 rows × 29 columns
df.columns
Index(['delta_Ne40s', 'Ionosphere_region_flag', 'mROT', 'Grad_Ne_at_PCP_edge',
'IPIR_index', 'Longitude', 'ROD', 'Grad_Ne_at_50km', 'Grad_Ne_at_100km',
'Spacecraft', 'IBI_flag', 'RODI10s', 'TEC_STD', 'Foreground_Ne',
'delta_Ne20s', 'mROTI20s', 'Background_Ne', 'Num_GPS_satellites',
'mROTI10s', 'Ne_quality_flag', 'Ne', 'delta_Ne10s', 'Radius',
'PCP_flag', 'Latitude', 'Te', 'mVTEC', 'RODI20s', '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)
Load as xarray¶
ds = data.as_xarray()
ds
<xarray.Dataset>
Dimensions: (Timestamp: 10800)
Coordinates:
* Timestamp (Timestamp) datetime64[ns] 2014-12-21T00:00:00.197000027 ... 2014-12-21T02:59:59.197000027
Data variables:
Spacecraft (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A'
delta_Ne40s (Timestamp) float64 3.811e+03 ... 1.702e+03
Grad_Ne_at_PCP_edge (Timestamp) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Longitude (Timestamp) float64 -128.8 -128.8 ... -175.4 -175.4
ROD (Timestamp) float64 0.0 0.0 ... 7.28e+03 7.28e+03
Grad_Ne_at_100km (Timestamp) float64 -0.08492 -0.144 ... 0.9621
RODI10s (Timestamp) float64 1.024e+04 3.263e+03 ... 503.7
mROTI20s (Timestamp) float64 0.002676 0.002732 ... 0.0114
Num_GPS_satellites (Timestamp) int32 4 4 4 4 4 4 4 4 ... 6 6 6 6 6 6 6
Ne (Timestamp) float64 1.255e+06 1.25e+06 ... 6.468e+05
Radius (Timestamp) float64 6.84e+06 6.84e+06 ... 6.835e+06
Latitude (Timestamp) float64 -4.694 -4.757 ... 24.74 24.68
mVTEC (Timestamp) float64 51.79 51.77 ... 20.84 20.94
RODI20s (Timestamp) float64 7.764e+03 7.181e+03 ... 907.9
Grad_Ne_at_50km (Timestamp) float64 -0.4039 0.1449 ... 0.9347 0.9775
Grad_Ne_at_20km (Timestamp) float64 -1.048 0.3384 ... 1.002 0.9148
Ionosphere_region_flag (Timestamp) int32 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
mROT (Timestamp) float64 -0.01101 -0.01101 ... 0.09621
IPIR_index (Timestamp) int32 7 6 6 6 6 6 6 6 ... 4 4 4 4 4 4 4
IBI_flag (Timestamp) int32 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1
Foreground_Ne (Timestamp) float64 1.305e+06 ... 6.488e+05
delta_Ne20s (Timestamp) float64 1.027e+04 ... 1.702e+03
Background_Ne (Timestamp) float64 1.344e+06 1.344e+06 ... 4.29e+05
mROTI10s (Timestamp) float64 0.001472 0.001386 ... 0.005609
Ne_quality_flag (Timestamp) int32 20000 20000 20000 ... 20000 20000
delta_Ne10s (Timestamp) float64 67.88 1.296e+04 ... 1.702e+03
PCP_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
TEC_STD (Timestamp) float64 3.131 3.122 ... 2.866 2.891
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)
