pyrfu.pyrf.histogram2d#

pyrfu.pyrf.histogram2d(inp1, inp2, bins=100, y_range=None, weights=None, density=True, scale='linlin')[source]#

Computes 2d histogram of inp2 vs inp1 with nbins number of bins.

Parameters:

  • inp1 (xarray.DataArray) – Time series of the x coordinates of the points to be histogrammed.

  • inp2 (xarray.DataArray) – Time series of the y coordinates of the points to be histogrammed.

  • bins (int or array_like or [int, int] or [array, array], Optional) – Number of bins along both dimensions, number of bins along each dimension, or explicit bin edges. Default is bins=100.

  • y_range (array_like, shape(2,2), Optional) – The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters): [[xmin, xmax], [ymin, ymax]]. All values outside of this range will be considered outliers and not tallied in the histogram.

  • weights (array_like, shape(N,), Optional) – An array of values w_i weighing each sample (x_i, y_i). Weights are normalized to 1 if density is True. If density is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin.

  • density (bool, Optional) – If False, returns the number of samples in each bin. If True, the default, returns the probability density function at the bin, bin_count / sample_count / bin_area.

  • scale ({"linlin", "loglin", "linlog", "loglog"}, Optional) – Spacing of the bins along the x and y dimensions, in this order. Default is scale="linlin" (linear bins along both dimensions). A logarithmically spaced dimension only bins strictly positive values, the others are discarded.

Returns:

out – 2D map of the density of inp2 vs inp1. The bin edges, the scale and the normalization are stored in the attributes.

Return type:

xarray.DataArray

Raises:

ValueError – If no sample is left to compute the range of the bins, or if a range along a logarithmic dimension is not strictly positive.

Notes

Samples for which either coordinate is not finite (or is not strictly positive along a logarithmic dimension) are discarded, as is the corresponding weight.

When the data along a dimension are constant (and no range or edges are given), the bins span one unit around the value (one decade along a logarithmic dimension), as in numpy.histogram.

The bin centers are the arithmetic means of the bin edges along a linear dimension and their geometric means along a logarithmic one, so that they are at the center of the bins as they are displayed.

The first dimension of the output (x_bins) is the one of inp1. xarray plots the last dimension along the x axis, so use out.plot(x="x_bins") (or out.T.plot()) to get inp2 vs inp1.

Examples

>>> import numpy as np
>>> from pyrfu import mms, pyrf

Time interval

>>> tint = ["2019-09-14T07:54:00.000", "2019-09-14T08:11:00.000"]

Spacecraft indices

>>> mms_id = np.arange(1, 5)

Load magnetic field and electric field

>>> r_mms = [mms.get_data("r_gse", tint, i) for i in mms_id]
>>> b_mms = [mms.get_data("b_gse_fgm_srvy_l2", tint, i) for i in mms_id]

Compute current density, etc

>>> j_xyz, _, b_xyz, _, _, _ = pyrf.c_4_j(r_mms, b_mms)

Compute magnitude of B and J

>>> b_mag = pyrf.norm(b_xyz)
>>> j_mag = pyrf.norm(j_xyz)

Histogram of J vs B

>>> h2d_b_j = pyrf.histogram2d(b_mag, j_mag)

Same histogram with logarithmically spaced bins along both dimensions

>>> h2d_b_j = pyrf.histogram2d(b_mag, j_mag, scale="loglog")