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EFIxIDM (Ion drifts & effective masses)

EFIxIDM (Ion drifts & effective masses)#

Abstract: Access to the EFIxIDM products from the SLIDEM project.

See also:

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 datetime as dt
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?#

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

{'EFI_IDM': ['SW_PREL_EFIAIDM_2_', 'SW_PREL_EFIBIDM_2_', 'SW_PREL_EFICIDM_2_']}

print(request.available_measurements("EFI_IDM"))

['Latitude_GD', 'Longitude_GD', 'Height_GD', 'Radius_GC', 'Latitude_QD', 'MLT_QD', 'V_sat_nec', 'M_i_eff', 'M_i_eff_err', 'M_i_eff_Flags', 'M_i_eff_tbt_model', 'V_i', 'V_i_err', 'V_i_Flags', 'V_i_raw', 'N_i', 'N_i_err', 'N_i_Flags', 'A_fp', 'R_p', 'T_e', 'Phi_sc']

Notes for the listed available variables:

idm_vars = [
    # Positional information in geodetic (GD) and geocentric (GC) frames
    #  redundant with VirES variables Latitude, Longitude, Radius (in geocentric frame)
    "Latitude_GD", "Longitude_GD", "Height_GD", "Radius_GC",
    # Quasi-dipole magnetic latitude and local time
    #  redundant with VirES auxiliaries, QDLat & MLT
    "Latitude_QD", "MLT_QD",
    # Satellite velocity in NEC frame
    "V_sat_nec",
    # Estimated ion effective mass, uncertainty, and validity flags
    "M_i_eff", "M_i_eff_err", "M_i_eff_Flags",
    # Effective masses from ruhlik et al. (2015) topside empirical model
    "M_i_eff_tbt_model",
    # Along-track ion drift velocity, uncertainty, validity flags, and velocity without detrending
    "V_i", "V_i_err", "V_i_Flags", "V_i_raw",
    # Ion density, uncertainty, and validity flags
    "N_i", "N_i_err", "N_i_Flags",
    # Modified-OML faceplate area, and Langmuir spherical probe radius
    "A_fp", "R_p",
    # Electron temperature, and spacecraft floating potential
    "T_e", "Phi_sc",
]

Fetching and plotting data#

start = dt.datetime(2016, 1, 2)
end = dt.datetime(2016, 1, 3)

request = SwarmRequest(SERVER_URL)
request.set_collection("SW_PREL_EFIAIDM_2_")
request.set_products(
    measurements=idm_vars,
    auxiliaries=["OrbitNumber", "OrbitDirection", "MLT"]
)
data = request.get_between(start, end)

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

df = data.as_dataframe()
df.head()
Latitude M_i_eff_err T_e Longitude Spacecraft M_i_eff_tbt_model V_i_raw M_i_eff N_i V_sat_nec ... V_i_err Longitude_GD A_fp Latitude_QD Radius Radius_GC V_i_Flags Phi_sc N_i_Flags Latitude_GD
Timestamp
2016-01-02 00:00:00.196999936 31.751028 -1.0 2660.305569 -95.369735 A -1.0 -100000.0 -1.0 -1.0 [7633.863553943817, -6.920940102624664, 11.954... ... -1.0 -95.369735 -1.0 41.452417 6.822807e+06 6.821611e+06 65537 -2.263921 1 31.912074
2016-01-02 00:00:00.696000000 31.783018 -1.0 2640.667985 -95.369768 A -1.0 -100000.0 -1.0 -1.0 [7633.856932675894, -6.63003468935507, 11.9539... ... -1.0 -95.369768 -1.0 41.484162 6.822801e+06 6.821616e+06 65537 -2.267120 1 31.944152
2016-01-02 00:00:01.196999936 31.815135 -1.0 2629.307430 -95.369800 A -1.0 -100000.0 -1.0 -1.0 [7633.850269983615, -6.337677392264973, 11.952... ... -1.0 -95.369800 -1.0 41.516033 6.822795e+06 6.821621e+06 65537 -2.271875 1 31.976359
2016-01-02 00:00:01.696000000 31.847124 -1.0 2626.466513 -95.369831 A -1.0 -100000.0 -1.0 -1.0 [7633.843611009006, -6.046047719097451, 11.951... ... -1.0 -95.369831 -1.0 41.547775 6.822789e+06 6.821626e+06 65537 -2.277763 1 32.008436
2016-01-02 00:00:02.196999936 31.879242 -1.0 2645.862873 -95.369860 A -1.0 -100000.0 -1.0 -1.0 [7633.836910386861, -5.752962995448166, 11.950... ... -1.0 -95.369860 -1.0 41.579643 6.822783e+06 6.821630e+06 65537 -2.265846 1 32.040643

5 rows × 29 columns

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:            (Timestamp: 171434, V_sat_nec_dim1: 3)
Coordinates:
  * Timestamp          (Timestamp) datetime64[ns] 2016-01-02T00:00:00.1969999...
Dimensions without coordinates: V_sat_nec_dim1
Data variables: (12/29)
    Spacecraft         (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    Latitude           (Timestamp) float64 31.75 31.78 31.82 ... 8.711 8.679
    M_i_eff_err        (Timestamp) float64 -1.0 -1.0 -1.0 ... -1.0 -1.0 -1.0
    T_e                (Timestamp) float64 2.66e+03 2.641e+03 ... 1.73e+03
    Longitude          (Timestamp) float64 -95.37 -95.37 -95.37 ... 81.23 81.23
    M_i_eff_tbt_model  (Timestamp) float64 -1.0 -1.0 -1.0 ... -1.0 -1.0 -1.0
    ...                 ...
    Radius             (Timestamp) float64 6.823e+06 6.823e+06 ... 6.826e+06
    Radius_GC          (Timestamp) float64 6.822e+06 6.822e+06 ... 6.82e+06
    V_i_Flags          (Timestamp) uint32 65537 65537 65537 ... 65537 65537
    Phi_sc             (Timestamp) float64 -2.264 -2.267 ... -1.901 -1.919
    N_i_Flags          (Timestamp) uint32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    Latitude_GD        (Timestamp) float64 31.91 31.94 31.98 ... 8.765 8.733
Attributes: (3)

An initial preview of the data:

fig, axes = plt.subplots(nrows=4, sharex=True, figsize=(10, 7))
axes_r = [ax.twinx() for ax in axes]
# Plot quantity (left axis) and error (right axis) for each quantity
ds.plot.scatter(x="Timestamp", y="M_i_eff", ax=axes[0], s=1, linewidths=0)
ds.plot.scatter(x="Timestamp", y="M_i_eff_err", ax=axes_r[0], s=0.1, color="tab:orange")
ds.plot.scatter(x="Timestamp", y="V_i", ax=axes[1], s=1, linewidths=0)
ds.plot.scatter(x="Timestamp", y="V_i_err", ax=axes_r[1], s=0.1, color="tab:orange")
ds.plot.scatter(x="Timestamp", y="N_i", ax=axes[2], s=1, linewidths=0)
ds.plot.scatter(x="Timestamp", y="N_i_err", ax=axes_r[2], s=0.1, color="tab:orange")
ds.plot.scatter(x="Timestamp", y="T_e", ax=axes[3], s=1, linewidths=0)
fig.subplots_adjust(hspace=0)

# Add legend to identify each side
blue = mpl.patches.Patch(color="tab:blue", label="Quantities")
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
sources = "\n".join([i for i in ds.attrs["Sources"] if "IDM" in i])
title = "".join([
    f"Swarm {ds['Spacecraft'].data[0]} ion effective mass and drift, ",
    ds["Timestamp"].dt.date.data[0].isoformat(),
    f"\n{sources}"
])
fig.suptitle(title);
../_images/ed44b0cc2ec3e770dfd218899869bb8aa57807f2efa0ea8ffe13d3f7cc9f0e79.png
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