uproot.behaviors.TProfile.TProfile

Defined in uproot.behaviors.TProfile on line 219.

Inheritance order:

  1. uproot.behaviors.TProfile.Profile

  2. uproot.behaviors.TH1.Histogram
class uproot.behaviors.TProfile.TProfile

Behaviors for one-dimensional profiles: ROOT’s TProfile.

name

Inherited from uproot.behaviors.TH1.Histogram.

TProfile.name

The name of the histogram.

title

Inherited from uproot.behaviors.TH1.Histogram.

TProfile.title

The title of the histogram.

axes

Inherited from uproot.behaviors.TH1.Histogram.

TProfile.axes

axis

Inherited from uproot.behaviors.TH1.Histogram.

TProfile.axis(axis=0)

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.

TProfile.weighted

kind

Inherited from uproot.behaviors.TH1.Histogram.

TProfile.kind

values

Inherited from uproot.behaviors.TH1.Histogram.

TProfile.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.

TProfile.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.

TProfile.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.

TProfile.counts(flow=True)
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.

TProfile.to_boost(metadata={'label': 'fTitle', 'name': 'fName'}, axis_metadata={'label': 'fTitle', 'name': 'fName'})
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.

TProfile.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.