Ctrl+K

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: 0.11.6
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()
MLT_QD Bx Evy Vixv_error VsatC Ehx Latitude Quality_flags Viy_error Vixh_error ... Vicrz VsatE Calibration_flags Viz By Vixv Ehz Ehy Vicrx Vicry
Timestamp
2018-07-17 11:28:49.225250048 17.153055 -1941.897339 -866.204529 -14.849242 5.364018 -146.413574 80.104347 0 -14.849242 -14.849242 ... -0.658293 1978.772949 50529027 -268.333466 -2032.778809 -18097.832031 -35.943443 -705.941589 -19.133307 83.111824
2018-07-17 11:28:49.725250048 17.154737 -1947.693359 -848.451111 -14.849242 5.379858 -132.751846 80.073341 0 -14.849242 -14.849242 ... -0.661217 1972.120605 50529027 -455.484985 -2032.832520 -17717.505859 -34.868401 -693.349854 -19.120062 83.386414
2018-07-17 11:28:50.225250048 17.156416 -1954.192383 -868.396301 -14.849242 5.395653 -130.952698 80.042313 0 -14.849242 -14.849242 ... -0.664141 1965.511353 50529027 -273.931030 -2033.323975 -18140.265625 -34.975639 -697.077820 -19.106810 83.660973
2018-07-17 11:28:50.725250048 17.158089 -1961.238281 -868.061523 -14.849242 5.411411 -130.928070 80.011284 0 -14.849242 -14.849242 ... -0.667065 1958.935791 50529027 -566.669189 -2034.150879 -18119.716797 -34.792419 -692.121216 -19.093546 83.935509
2018-07-17 11:28:51.225250048 17.159754 -1969.732666 -864.288635 -14.849242 5.427133 -126.337891 79.980247 0 -14.849242 -14.849242 ... -0.669987 1952.402100 50529027 -533.720764 -2032.240967 -18040.589844 -34.930099 -700.008362 -19.080286 84.210022

5 rows × 31 columns

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:            (Timestamp: 32535)
Coordinates:
  * Timestamp          (Timestamp) datetime64[ns] 2018-07-17T11:28:49.2252500...
Data variables: (12/31)
    Spacecraft         (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    MLT_QD             (Timestamp) float32 17.15 17.15 17.16 ... 4.772 4.772
    Bx                 (Timestamp) float32 -1.942e+03 -1.948e+03 ... 9.496e+03
    Evy                (Timestamp) float32 -866.2 -848.5 ... -720.4 -715.7
    Viy_error          (Timestamp) float32 -14.85 -14.85 ... -14.85 -14.85
    Bz                 (Timestamp) float32 4.783e+04 4.784e+04 ... 4.539e+04
    ...                 ...
    VsatN              (Timestamp) float32 -7.367e+03 -7.369e+03 ... 7.606e+03
    Vicrz              (Timestamp) float32 -0.6583 -0.6612 ... -0.7706 -0.7679
    VsatE              (Timestamp) float32 1.979e+03 1.972e+03 ... 619.0 620.2
    Calibration_flags  (Timestamp) uint32 50529027 50529027 ... 50529027
    Vicrx              (Timestamp) float32 -19.13 -19.12 ... -24.47 -24.46
    Vicry              (Timestamp) float32 83.11 83.39 83.66 ... -210.3 -210.1
Attributes: (3)

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?

Contents