uproot.behaviors.TH1.Histogram

Defined in uproot.behaviors.TH1 on line 50.

class uproot.behaviors.TH1.Histogram: Abstract class for histograms.

name

Histogram.name: The name of the histogram.

title

Histogram.title: The title of the histogram.

axes

Histogram.axes: A tuple of all uproot.behaviors.TAxis.TAxis objects.

axis

Histogram.axis(axis)

Returns a specified uproot.behaviors.TAxis.TAxis object.

The axis can be specified as

a non-negative integer: 0 is the first axis, 1 is the second, and 2 is the third.
a negative integer: -1 is the last axis, -2 is the second-to-last, and -3 is the third-to-last.
a string: "x" is the first axis, "y" is the second, and "z" is the third

(assuming that the histogram dimension supports a given axis).

weighted

Histogram.weighted: True if the histogram has weights (fSumw2); False otherwise.

kind

Histogram.kind

"COUNT" for true histograms (TH*) and "MEAN" for profile plots (TProfile*).

Type:: The meaning of this object

values

Histogram.values(flow=False)

Parameters:: flow (bool) – If True, include underflow and overflow bins before and after the normal (finite-width) bins.

Bin contents as a 1, 2, or 3 dimensional numpy.ndarray. The numpy.dtype of this array depends on the histogram type.

Setting flow=True increases the length of each dimension by two.

errors

Histogram.errors(flow=False)

Parameters:: flow (bool) – If True, include underflow and overflow bins before and after the normal (finite-width) bins.

Errors (uncertainties) in the values as a 1, 2, or 3 dimensional numpy.ndarray of numpy.float64.

If fSumw2 (weights) are available, they will be used in the calculation of the errors. If not, errors are assumed to be the square root of the values.

Setting flow=True increases the length of each dimension by two.

variances

Histogram.variances(flow=False)

Parameters:: flow (bool) – If True, include underflow and overflow bins before and after the normal (finite-width) bins.

Variances (uncertainties squared) in the values as a 1, 2, or 3 dimensional numpy.ndarray of numpy.float64.

If fSumw2 (weights) are available, they will be used in the calculation of the variances. If not, variances are assumed to be equal to the values.

Setting flow=True increases the length of each dimension by two.

counts

Histogram.counts(flow=False)

Parameters:: flow (bool) – If True, include underflow and overflow bins before and after the normal (finite-width) bins.

Returns the (possibly weighted) number of entries in each bin. For histograms, this is equal to values.

to_boost

Histogram.to_boost(metadata=None, axis_metadata=None)

Parameters:

metadata (dict of str → str) – Metadata to collect (keys) and their C++ class member names (values).
axis_metadata (dict of str → str) – Metadata to collect from each axis.

Converts the histogram into a boost-histogram object.

to_hist

Histogram.to_hist(metadata=None, axis_metadata=None)

Parameters:

metadata (dict of str → str) – Metadata to collect (keys) and their C++ class member names (values).
axis_metadata (dict of str → str) – Metadata to collect from each axis.

Converts the histogram into a hist object.