# Quick Start¶

## Jumping In¶

The main purpose of pygram11 is to be a faster near drop-in replacement of numpy.histogram() and numpy.histogram2d(). The NumPy functions always return the bin counts and the bin edges, while pygram11 functions return the bin counts and the standard error on the bin counts. Therefore, if one only cares about the bin counts, the libraries are completely interchangable.

These two funcion calls will provide the same result:

import numpy as np
import pygram11 as pg
counts, __ = np.histogram(np.random.randn(1000), bins=20, range=(-3, 3))
counts, __ = pg.histogram(np.random.randn(1000), bins=20, range=(-3, 3))


If one cares about the standard error on the bin counts, or the ability to retain under- and over-flow counts, then pygram11 is a great replacement. Checkout a blog post which describes how to recreate this behavior in pure NumPy, while pygram11 is as simple as:

data = np.random.randn(1000)
weights = np.random.uniform(0.5, 0.8, x.shape[0])
counts, err = pg.histogram(data, bins=10, range=(-3, 3), weights=weights, flow=True)


The pygram11.histogram() and pygram11.histogram2d() functions in the pygram11 API are meant to provide an easy transition from NumPy to pygram11. The next couple of sections summarize the structure of the pygram11 API.

## Core pygram11 Functions¶

pygram11 provides a simple set of functions for calculating histograms:

 pygram11.fix1d(x[, bins, range, weights, …]) Histogram data with fixed (uniform) bin widths. pygram11.fix1dmw(x, weights[, bins, range, flow]) Histogram data with multiple weight variations and fixed width bins. pygram11.var1d(x, bins[, weights, density, flow]) Histogram data with variable bin widths. pygram11.var1dmw(x, weights, bins[, flow]) Histogram data with multiple weight variations and variable width bins. pygram11.fix2d(x, y[, bins, range, weights]) Histogram the x, y data with fixed (uniform) binning. pygram11.var2d(x, y, xbins, ybins[, weights]) Histogram the x, y data with variable width binning.

You’ll see that the API specific to pygram11 is a bit more specialized than the NumPy histogramming API (shown below).

Histogramming a normal distribution:

>>> h, err = pygram11.fix1d(np.random.randn(10000), bins=25, range=(-3, 3))


See the API reference for more examples.

## NumPy-like Functions¶

For convenience a NumPy-like API is also provided (not one-to-one, see the API reference).

 pygram11.histogram(x[, bins, range, …]) Histogram data in one dimension. pygram11.histogram2d(x, y[, bins, range, …]) Histogram data in two dimensions.