mean_y_vs_x¶
-
halotools.mock_observables.
mean_y_vs_x
(x, y, error_estimator=u'error_on_mean', **kwargs)[source] [edit on github]¶ Estimate the mean value of the property y as a function of x for an input sample of galaxies/halos, optionally returning an error estimate.
The
mean_y_vs_x
function is just a convenience wrapper aroundscipy.stats.binned_statistic
andnp.histogram
.See also Galaxy Catalog Analysis Example: Galaxy properties as a function of halo mass.
Parameters: x : array_like
Array storing values of the independent variable of the sample.
y : array_like
Array storing values of the dependent variable of the sample.
bins : array_like, optional
Bins of the input x. Defaults are set by
scipy.stats.binned_statistic
.error_estimator : string, optional
If set to
error_on_mean
, function will also return an array storing \(\sigma_{y}/\sqrt{N}\), where \(\sigma_{y}\) is the standard deviation of y in the bin and \(\sqrt{N}\) is the counts in each bin.If set to
variance
, function will also return an array storing \(\sigma_{y}\).Default is
error_on_mean
Returns: bin_midpoints : array_like
Midpoints of the x-bins.
mean : array_like
Mean of y estimated in bins
err : array_like
Error on y estimated in bins
Examples
>>> from halotools.sim_manager import FakeSim >>> halocat = FakeSim() >>> halos = halocat.halo_table >>> halo_mass, mean_spin, err = mean_y_vs_x(halos['halo_mvir'], halos['halo_spin'])