IDL Data#

This notebook focuses on handling data in the IDL format.

Importing the package#

import flekspy

# Set display for XArrays
import xarray as xr

xr.set_options(
    display_expand_coords=False,
    display_expand_data=False,
    display_expand_data_vars=False,
)

<xarray.core.options.set_options at 0x7f88740b3080>

Downloading demo data#

If you don’t have FLEKS data to start with, you can download demo field data with the following:

from flekspy.util import download_testfile

url = "https://raw.githubusercontent.com/henry2004y/batsrus_data/master/batsrus_data.tar.gz"
download_testfile(url, "data")

Loading data#

flekspy.load is the interface to read files of all formats. It returns a different object for different formats. IDL format data are processed into XArray data structures:

The coordinates can be accessed via

ds.coords

Coordinates: (1)

The variables can be accessed via

ds.var

<bound method DatasetAggregations.var of <xarray.Dataset> Size: 31kB
Dimensions:  (x: 256)
Coordinates: (1)
Data variables: (14)
Attributes: (12/18)
    filename:    /home/runner/work/flekspy/flekspy/docs/data/1d__raw_2_t25.60...
    isOuts:      False
    npict:       1
    nInstance:   1
    fileformat:  ascii
    unit:        normalized
    ...          ...
    grid:        [256]
    npoints:     256
    parameters:  {np.str_('r'): np.float64(3.0), np.str_('g'): np.float64(2.0...
    dims:        ['x']
    variables:   [np.str_('x'), np.str_('Rho'), np.str_('Mx'), np.str_('My'),...
    strtime:     0000h00m25.600s>

and individual variables can be accessed through keys or properties:

ds["p"]
ds.p

<xarray.DataArray 'p' (x: 256)> Size: 2kB
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Coordinates: (1)

xarray.DataArray

'p'

x: 256

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

array([1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 0.99999999, 0.99999999,
       0.99999999, 0.99999998, 0.99999997, 0.99999996, 0.99999995,
       0.99999994, 0.99999992, 0.99999989, 0.99999986, 0.99999982,
       0.99999977, 0.99999971, 0.99999964, 0.99999955, 0.99999944,
       0.99999931, 0.99999915, 0.99999896, 0.99999873, 0.99999846,
       0.99999813, 0.99999775, 0.99999729, 0.99999676, 0.99999612,
       0.99999538, 0.9999945 , 0.99999348, 0.99999229, 0.99999089,
       0.99998927, 0.99998739, 0.9999852 , 0.99998266, 0.99997971,
       0.99997631, 0.99997238, 0.99996784, 0.99996262, 0.99995661,
       0.99994971, 0.9999418 , 0.99993277, 0.99992245, 0.99991061,
       0.99989688, 0.99988076, 0.99986169, 0.99983926, 0.99981363,
       0.99978593, 0.99975843, 0.99973409, 0.99971417, 0.99969689,
       0.99967628, 0.9996342 , 0.99948851, 0.99875656, 0.99520955,
       0.98644347, 0.97296833, 0.95605524, 0.93689582, 0.91642986,
       0.89531082, 0.87393959, 0.85255455, 0.83129714, 0.81025231,
       0.78947393, 0.76899764, 0.74884761, 0.72904066, 0.70958859,
       0.69049963, 0.67177964, 0.6534332 , 0.63546454, 0.6178779 ,
       0.60067737, 0.58386638, 0.56744599, 0.55141095, 0.5357435 ,
...
       0.51903728, 0.51937986, 0.51946056, 0.51934921, 0.51897835,
       0.51808309, 0.51597448, 0.50838603, 0.47777544, 0.37490815,
       0.18275943, 0.09437588, 0.08721111, 0.08711243, 0.08735865,
       0.08766624, 0.0878924 , 0.08798467, 0.08799348, 0.08798877,
       0.08797256, 0.08793813, 0.08786602, 0.08773117, 0.08758445,
       0.08751411, 0.08750803, 0.08751301, 0.08753202, 0.08757715,
       0.08762349, 0.0876353 , 0.08763337, 0.0876259 , 0.08760677,
       0.08758085, 0.08757283, 0.08757511, 0.0875862 , 0.0876146 ,
       0.08767738, 0.08777552, 0.08786197, 0.0878934 , 0.08789452,
       0.0878902 , 0.08787545, 0.08783555, 0.08774973, 0.08757955,
       0.08727059, 0.08688917, 0.08660118, 0.08652576, 0.08652704,
       0.08655168, 0.08662743, 0.08679863, 0.08714811, 0.08775512,
       0.08855411, 0.08946919, 0.09044863, 0.0914582 , 0.09247696,
       0.09349335, 0.09449866, 0.0954875 , 0.09645212, 0.0973789 ,
       0.09825058, 0.0990272 , 0.09964004, 0.09996659, 0.10003448,
       0.10004239, 0.10004135, 0.10003742, 0.10003136, 0.10002179,
       0.10001115, 0.10000324, 0.10000038, 0.1       , 0.09999996,
       0.09999996, 0.09999997, 0.09999999, 0.1       , 0.1       ,
       0.1       , 0.1       , 0.1       , 0.1       , 0.1       ,
       0.1       ])

Coordinates: (1)

(x)

float64

-127.5 -126.5 ... 126.5 127.5

array([-127.5, -126.5, -125.5, ...,  125.5,  126.5,  127.5], shape=(256,))

Visualizing fields#

Thanks to XArray’s Matplotlib wrapper, all Matplotlib’s plotting functionalities are directly supported.

import matplotlib.pyplot as plt

file = "data/1d__raw_2_t25.60000_n00000258.out"
ds = flekspy.load(file)

fig, axs = plt.subplots(2, 1, constrained_layout=True, sharex=True, sharey=True)
ds.p.plot(ax=axs[0])
ds.Bx.plot(ax=axs[1])
plt.show()

_images/c4f31640002be3277a9bfc4caeddf409a0bedc5e4357f327c186e733191813c4.png

By default, XArray’s 2D plotting functions map the first dimension to the vertical y-axis and the second dimension to the horizontal x-axis. To overwrite this default behavior, we can explicitly set the x and y arguments:

file = "data/z=0_fluid_region0_0_t00001640_n00010142.out"
ds = flekspy.load(file)
fig, axs = plt.subplots(
    3, 1, figsize=(10, 6), constrained_layout=True, sharex=True, sharey=True
)
ds.Bx.plot.pcolormesh(ax=axs[0], x="x", y="y")
ds.By.plot.pcolormesh(ax=axs[1], x="x", y="y")
ds.Bz.plot.pcolormesh(ax=axs[2], x="x", y="y")
plt.show()

_images/bc9710dd422fe51bf423db11bb73152126aa18881a511852549c97cda043ab42.png

Unstructured IDL format output plotting is supported directly by Xugrid. Note that here we need an additional level ugrid compared to direct plotting via xarray.

dsu.Rho.ugrid.plot.contourf()
plt.show()

_images/e24f05fee37cf24e910763a07c2b76ad7f2bb6836eae676220af8181afc50466.png

Derived Variables#

Pressure Anisotropy#

In this demo, the ion pressure anisotropy can be as large as 60 because in a 1D quasi-perpendicular shock simulation, there is no parallel heating.

import matplotlib.pyplot as plt

file = "data/z=0_fluid_region0_0_t00001640_n00010142.out"
ds = flekspy.load(file)
anisotropy = ds.derived.get_pressure_anisotropy(species=1)
fig, ax = plt.subplots(1, 1, figsize=(8, 2), constrained_layout=True)
anisotropy.plot.pcolormesh(
    ax=ax,
    x="x",
    y="y",
    cmap="GnBu",
    vmin=0,
    vmax=60,
    cbar_kwargs=dict(label=r"$P_{i\perp}/P_{i\parallel}$", pad=0.0, aspect=25),
)
plt.show()

_images/bcd9941b2b4115440814565fe729a005aae9c7de8017ee0c5e52b6807f60b1d5.png

Current density#

Current density can be calculated in two ways: from the curl of the magnetic field or from the definition \(J = \sum_\alpha n_\alpha q_\alpha \vec{v}_\alpha\). Both methods are available in the IDL accessor. As can be seen below, the current density results may not be identical.

import matplotlib.colors as colors

current = ds.derived.get_current_density()

norm = colors.SymLogNorm(0.1, linscale=0.4, vmin=-0.03, vmax=0.03)
fig, ax = plt.subplots(figsize=(8,2), constrained_layout=True)
current["jy"].plot.pcolormesh(x="x", y="y", cmap="RdBu_r", norm=norm, cbar_kwargs=dict(label=r"$J_y\,[\mu A/m]$", pad=0.0, aspect=40))
ax.set_xlabel(r"x [km]", fontsize=14)
ax.set_ylabel(r"y [km]", fontsize=14)
plt.show()

_images/9279c2a98e0bd8b4999a302887139dafc150a778a7283b3810061ba91f993818.png

current = ds.derived.get_current_density_from_definition(species=[0, 1])

fig, ax = plt.subplots(figsize=(8,2), constrained_layout=True)
current["jy"].plot.pcolormesh(ax=ax, x="x", y="y", cmap="RdBu_r", vmax=0.03, cbar_kwargs=dict(label=r"$J_y\,[\mu A/m]$", pad=0.0, aspect=40))
ax.set_xlabel(r"x [km]", fontsize=14)
ax.set_ylabel(r"y [km]", fontsize=14)
plt.show()

_images/6041a0f7ee669ba3e3a6ad8232b9f713af020b0475b0a181688cadf9c00c247f.png