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.