EFIxTCT (Cross-track ion flow)

EFIxTCT (Cross-track ion flow)#

Abstract: Access to the 2Hz & 16Hz cross-track ion flow data derived from the Thermal Ion Imager (TII), part of the Electric Field Instrument package (EFI).

For more information about this product, see the release notes.

SERVER_URL = 'https://vires.services/ows'

# Display important package versions used
%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

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
# Control the HTML display of the datasets
xr.set_options(display_expand_attrs=False, display_expand_coords=True, display_expand_data=True)

from viresclient import SwarmRequest

request = SwarmRequest(SERVER_URL)

What data is available?#

There are two sets of collections available, one for 2Hz and one for 16Hz, and for each there are three collections, one for each Swarm spacecraft.

request.available_collections("EFI_TCT02", details=False)

{'EFI_TCT02': ['SW_EXPT_EFIA_TCT02',
  'SW_EXPT_EFIB_TCT02',
  'SW_EXPT_EFIC_TCT02']}

request.available_collections("EFI_TCT16", details=False)

{'EFI_TCT16': ['SW_EXPT_EFIA_TCT16',
  'SW_EXPT_EFIB_TCT16',
  'SW_EXPT_EFIC_TCT16']}

print(request.available_measurements("EFI_TCT02"))

['VsatC', 'VsatE', 'VsatN', 'Bx', 'By', 'Bz', 'Ehx', 'Ehy', 'Ehz', 'Evx', 'Evy', 'Evz', 'Vicrx', 'Vicry', 'Vicrz', 'Vixv', 'Vixh', 'Viy', 'Viz', 'Vixv_error', 'Vixh_error', 'Viy_error', 'Viz_error', 'Latitude_QD', 'MLT_QD', 'Calibration_flags', 'Quality_flags']

print(request.available_measurements("EFI_TCT16"))

['VsatC', 'VsatE', 'VsatN', 'Bx', 'By', 'Bz', 'Ehx', 'Ehy', 'Ehz', 'Evx', 'Evy', 'Evz', 'Vicrx', 'Vicry', 'Vicrz', 'Vixv', 'Vixh', 'Viy', 'Viz', 'Vixv_error', 'Vixh_error', 'Viy_error', 'Viz_error', 'Latitude_QD', 'MLT_QD', 'Calibration_flags', 'Quality_flags']

As seen above, the variables available for both the 2Hz and 16Hz datasets are the same. Here is a short description for each variable:

tct_vars = [
    # Satellite velocity in NEC frame
    "VsatC", "VsatE", "VsatN",
    # Geomagnetic field components derived from 1Hz product
    #  (in satellite-track coordinates)
    "Bx", "By", "Bz",
    # Electric field components derived from -VxB with along-track ion drift
    #  (in satellite-track coordinates)
    # Eh: derived from horizontal sensor
    # Ev: derived from vertical sensor
    "Ehx", "Ehy", "Ehz",
    "Evx", "Evy", "Evz",
    # Ion drift corotation signal, removed from ion drift & electric field
    #  (in satellite-track coordinates)
    "Vicrx", "Vicry", "Vicrz",
    # Ion drifts along-track from vertical (..v) and horizontal (..h) TII sensor
    "Vixv", "Vixh",
    # Ion drifts cross-track (y from horizontal sensor, z from vertical sensor)
    #  (in satellite-track coordinates)
    "Viy", "Viz",
    # Random error estimates for the above
    #  (Negative value indicates no estimate available)
    "Vixv_error", "Vixh_error", "Viy_error", "Viz_error",
    # Quasi-dipole magnetic latitude and local time
    #  redundant with VirES auxiliaries, QDLat & MLT
    "Latitude_QD", "MLT_QD",
    # Refer to release notes link above for details:
    "Calibration_flags", "Quality_flags",
]

Fetching and plotting data#

For demonstration, we will fetch the 2Hz data from Swarm Alpha (SW_EXPT_EFIA_TCT02)

start = "2018-07-17T11:00:00"
end = "2018-07-17T16:00:00"

request = SwarmRequest(SERVER_URL)
request.set_collection("SW_EXPT_EFIA_TCT02")
request.set_products(measurements=tct_vars)
data = request.get_between(start, end)

Data can be loaded as either a pandas datframe or a xarray dataset.

df = data.as_dataframe()
df.head()

	Vixh	Evz	Vixv_error	Evx	Calibration_flags	Ehx	MLT_QD	Vicrz	Vixh_error	Spacecraft	...	VsatC	Radius	Viz	Quality_flags	Latitude_QD	Viz_error	Latitude	Evy	Bx	Bz
Timestamp
2018-07-17 11:28:49.225250048	-14747.417969	-42.754105	-14.849242	-146.413574	50529027	-146.413574	17.153055	-0.658293	-14.849242	A	...	5.364018	6805734.0	-268.333466	0	75.208305	-14.849242	80.104347	-866.204529	-1941.897339	47833.539062
2018-07-17 11:28:49.725250048	-14475.270508	-41.459320	-14.849242	-132.751846	50529027	-132.751846	17.154737	-0.661217	-14.849242	A	...	5.379858	6805735.5	-455.484985	0	75.179535	-14.849242	80.073341	-848.451111	-1947.693359	47837.632812
2018-07-17 11:28:50.225250048	-14559.314453	-42.256802	-14.849242	-130.952698	50529027	-130.952698	17.156416	-0.664141	-14.849242	A	...	5.395653	6805736.0	-273.931030	0	75.150764	-14.849242	80.042313	-868.396301	-1954.192383	47841.683594
2018-07-17 11:28:50.725250048	-14442.468750	-42.272465	-14.849242	-130.928070	50529027	-130.928070	17.158089	-0.667065	-14.849242	A	...	5.411411	6805737.0	-566.669189	0	75.121979	-14.849242	80.011284	-868.061523	-1961.238281	47845.691406
2018-07-17 11:28:51.225250048	-14607.324219	-41.907967	-14.849242	-126.337891	50529027	-126.337891	17.159754	-0.669987	-14.849242	A	...	5.427133	6805739.0	-533.720764	0	75.093193	-14.849242	79.980247	-864.288635	-1969.732666	47849.765625

5 rows × 31 columns

An example plot:

fig, axes = plt.subplots(nrows=4, sharex=True, figsize=(10, 7))
# Plot velocities with left axis
ds.plot.scatter(x="Timestamp", y="Vixv", ax=axes[0], s=1, linewidths=0)
ds.plot.scatter(x="Timestamp", y="Vixh", ax=axes[1], s=1, linewidths=0)
ds.plot.scatter(x="Timestamp", y="Viy", ax=axes[2], s=1, linewidths=0)
ds.plot.scatter(x="Timestamp", y="Viz", ax=axes[3], s=1, linewidths=0, label="Velocities")
# Plot velocities with right axis
axes_r = [ax.twinx() for ax in axes]
ds.plot.scatter(x="Timestamp", y="Vixv_error", ax=axes_r[0], s=0.1, color="tab:orange")
ds.plot.scatter(x="Timestamp", y="Vixh_error", ax=axes_r[1], s=0.1, color="tab:orange")
ds.plot.scatter(x="Timestamp", y="Viy_error", ax=axes_r[2], s=0.1, color="tab:orange")
ds.plot.scatter(x="Timestamp", y="Viz_error", ax=axes_r[3], s=0.1, color="tab:orange")
fig.subplots_adjust(hspace=0)
# Add legend to identify each side
blue = mpl.patches.Patch(color="tab:blue", label="Velocities")
orange = mpl.patches.Patch(color="tab:orange", label="Errors")
axes[0].legend(handles=[blue, orange])
# # Generate additional ticklabels for x-axis
# Use time xticks to get dataset vars at those xticks
locx = axes[-1].get_xticks()
times = mpl.dates.num2date(locx)
times = [t.replace(tzinfo=None) for t in times]
_ds_xticks = ds.reindex({"Timestamp": times}, method="nearest")
# Build ticklabels from dataset vars
xticklabels = np.stack([
    _ds_xticks["Timestamp"].dt.strftime("%H:%M").values,
    np.round(_ds_xticks["Latitude"].values, 2).astype(str),
    np.round(_ds_xticks["Longitude"].values, 2).astype(str),
])
xticklabels = ["\n".join(row) for row in xticklabels.T]
# Add labels to first xtick
_xt0 = xticklabels[0].split("\n")
xticklabels[0] = f"Time:  {_xt0[0]}\nLat:  {_xt0[1]}\nLon: {_xt0[2]}"
axes[-1].set_xticks(axes[-1].get_xticks())
axes[-1].set_xticklabels(xticklabels)
axes[-1].set_xlabel("")
# Adjust title
title = "".join([
    f"Swarm {ds['Spacecraft'].data[0]} 2Hz ion flow, ",
    ds["Timestamp"].dt.date.data[0].isoformat(),
    f"\n{ds.attrs['Sources']}"
])
fig.suptitle(title);

../_images/b080e6f067e13528238a14f223eb6575f866ec8437d0cdfcf8b0b545e69d541a.png

Due to contamination in the instrument, great care must be taken to use these data correctly. Check the release notes and make use of the Quality_flags variable to identify valid data periods.

TODO: use section 3.4.1.1 to identify untrusty periods (bitx = 0) and shade them grey?

EFIxTCT (Cross-track ion flow)

Contents

EFIxTCT (Cross-track ion flow)#

What data is available?#

Fetching and plotting data#