healpy.projaxes.GnomonicAxes.hist¶

GnomonicAxes.hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, **kwargs)¶

Plot a histogram.

Compute and draw the histogram of x. The return value is a tuple (n, bins, patches) or ([n0, n1, ...], bins, [patches0, patches1,...]) if the input contains multiple data.

Multiple data can be provided via x as a list of datasets of potentially different length ([x0, x1, ...]), or as a 2-D ndarray in which each column is a dataset. Note that the ndarray form is transposed relative to the list form.

Masked arrays are not supported at present.

Parameters:

x : array_like, shape (n, )

Input values.

bins : integer or array_like, optional, default: 10

If an integer is given, bins + 1 bin edges are returned, consistently with numpy.histogram() for numpy version >= 1.3.

Unequally spaced bins are supported if bins is a sequence.

range : tuple, optional, default: None

The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, range is (x.min(), x.max()). Range has no effect if bins is a sequence.

If bins is a sequence or range is specified, autoscaling is based on the specified bin range instead of the range of x.

normed : boolean, optional, default: False

If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., n/(len(x)`dbin), ie the integral of the histogram will sum to 1. If stacked is also True, the sum of the histograms is normalized to 1.

weights : array_like, shape (n, ), optional, default: None

An array of weights, of the same shape as x. Each value in x only contributes its associated weight towards the bin count (instead of 1). If normed is True, the weights are normalized, so that the integral of the density over the range remains 1.

cumulative : boolean, optional, default

If True, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If normed is also True then the histogram is normalized such that the last bin equals 1. If cumulative evaluates to less than 0 (e.g., -1), the direction of accumulation is reversed. In this case, if normed is also True, then the histogram is normalized such that the first bin equals 1.

histtype : [‘bar’ | ‘barstacked’ | ‘step’ | ‘stepfilled’], optional

The type of histogram to draw.

‘bar’ is a traditional bar-type histogram. If multiple data are given the bars are aranged side by side.

‘barstacked’ is a bar-type histogram where multiple data are stacked on top of each other.

‘step’ generates a lineplot that is by default unfilled.

‘stepfilled’ generates a lineplot that is by default filled.

align : [‘left’ | ‘mid’ | ‘right’], optional, default: ‘mid’

Controls how the histogram is plotted.

‘left’: bars are centered on the left bin edges.

‘mid’: bars are centered between the bin edges.

‘right’: bars are centered on the right bin edges.

orientation : [‘horizontal’ | ‘vertical’], optional

If ‘horizontal’, ~matplotlib.pyplot.barh will be used for bar-type histograms and the bottom kwarg will be the left edges.

rwidth : scalar, optional, default: None

The relative width of the bars as a fraction of the bin width. If None, automatically compute the width. Ignored if histtype = ‘step’ or ‘stepfilled’.

log : boolean, optional, default

If True, the histogram axis will be set to a log scale. If log is True and x is a 1D array, empty bins will be filtered out and only the non-empty (n, bins, patches) will be returned.

color : color or array_like of colors, optional, default: None

Color spec or sequence of color specs, one per dataset. Default (None) uses the standard line color sequence.

label : string, optional, default: ‘’

String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected.

stacked : boolean, optional, default

If True, multiple data are stacked on top of each other If False multiple data are aranged side by side if histtype is ‘bar’ or on top of each other if histtype is ‘step’

Returns:

tuple : (n, bins, patches) or ([n0, n1, ...], bins, [patches0, patches1,...])

Other Parameters:

kwargs : ~matplotlib.patches.Patch properties