uproot.behaviors.TProfile.Profile

Defined in uproot.behaviors.TProfile on line 132.

Inheritance order:

  1. uproot.behaviors.TH1.Histogram
class uproot.behaviors.TProfile.Profile

Abstract class for profile plots.

name

Inherited from uproot.behaviors.TH1.Histogram.

Profile.name

The name of the histogram.

title

Inherited from uproot.behaviors.TH1.Histogram.

Profile.title

The title of the histogram.

axes

Inherited from uproot.behaviors.TH1.Histogram.

Profile.axes

A tuple of all uproot.behaviors.TAxis.TAxis objects.

axis

Inherited from uproot.behaviors.TH1.Histogram.

Profile.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

Inherited from uproot.behaviors.TH1.Histogram.

Profile.weighted

True if the histogram has weights (fSumw2); False otherwise.

kind

Inherited from uproot.behaviors.TH1.Histogram.

Profile.kind

values

Inherited from uproot.behaviors.TH1.Histogram.

Profile.values(flow=False)
Parameters:

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

Mean value of each bin as a 1, 2, or 3 dimensional numpy.ndarray of numpy.float64.

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

errors

Inherited from uproot.behaviors.TH1.Histogram.

Profile.errors(flow=False, error_mode='')
Parameters:

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

  • error_mode (str) – Choose a method for calculating the errors (see below).

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

The calculation of profile errors exactly follows ROOT’s function, but in a vectorized NumPy form. The error_mode may be

  • "" for standard error on the mean

  • "s" for spread

  • "i" for integer data

  • "g" for Gaussian

following ROOT’s profile documentation.

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

variances

Inherited from uproot.behaviors.TH1.Histogram.

Profile.variances(flow=False, error_mode='')
Parameters:

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

  • error_mode (str) – Choose a method for calculating the errors (see below).

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

The calculation of profile variances exactly follows ROOT’s function, but in a vectorized NumPy form. The error_mode may be

  • "" for standard error on the mean (squared)

  • "s" for spread (squared)

  • "i" for integer data (squared)

  • "g" for Gaussian (squared)

following ROOT’s profile documentation.

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

counts

Inherited from uproot.behaviors.TH1.Histogram.

Profile.counts(flow=False)
Parameters:

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

The effective number of entries, which is a step in the calculation of values. The calculation of profile errors exactly follows ROOT’s “effective entries”, but in a vectorized NumPy form.

to_boost

Inherited from uproot.behaviors.TH1.Histogram.

Profile.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

Inherited from uproot.behaviors.TH1.Histogram.

Profile.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.