Convenience wrappers around numpy histograms
Project description
https://github.com/JelleAalbers/multihist
Thin wrapper around numpy’s histogram and histogramdd.
Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class with methods for adding new data to existing histograms, take averages, projecting, etc.
For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std). For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging.
NB: For a faster and richer histogram package, check out hist from scikit-hep. Alternatively, look at its parent library boost-histogram, which has numpy-compatible features. Multihist was created back in 2015, long before those libraries existed.
Synopsis:
# Create histograms just like from numpy... m = Hist1d([0, 3, 1, 6, 2, 9], bins=3) # ...or add data incrementally: m = Hist1d(bins=100, range=(-3, 4)) m.add(np.random.normal(0, 0.5, 10**4)) m.add(np.random.normal(2, 0.2, 10**3)) # Get the data back out: print(m.histogram, m.bin_edges) # Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", drawstyle='steps') plt.plot(m.bin_centers, m.density, label="Empirical PDF", drawstyle='steps') plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", drawstyle='steps') plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std)) plt.legend(loc='best') plt.show() # Slicing and arithmetic behave just like ordinary ndarrays print("The fourth bin has %d entries" % m[3]) m[1:4] += 4 + 2 * m[-27:-24] print("Now it has %d entries" % m[3]) # Of course I couldn't resist adding a canned plotting function: m.plot() plt.show() # Create and show a 2d histogram. Axis names are optional. m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y']) m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6)) m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6)) m2.plot() plt.show() # x and y projections return Hist1d objects m2.projection('x').plot(label='x projection') m2.projection(1).plot(label='y projection') plt.legend() plt.show()
History
0.6.5 (2022-01-26)
‘model’ option for error bars, showing Poisson quantiles (#14)
Fix vmin/vmax for matplotlib >3.3, resume CI tests (#15)
Hist1d.data_for_plot returns numbers used in error calculation
0.6.4 (2021-01-17)
Prevent object array creation (#12)
0.6.3 (2020-01-22)
Feldman-Cousins errors for Hist1d.plot (#10)
0.6.2 (2020-01-15)
Fix rebinning for empty histograms (#9)
0.6.1 (2019-12-05)
Fixes for #7 (#8)
0.6.0 (2019-06-30)
Correct step plotting at edges, other plotting fixes
Histogram numpy structured arrays
Fix deprecation warnings (#6)
lookup_hist
.max() and .min() methods
percentile support for higher-dimensional histograms
Improve Hist1d.get_random (also randomize in bin)
0.5.4 (2017-09-20)
Fix issue with input from dask
0.5.3 (2017-09-18)
Fix python 2 support
0.5.2 (2017-08-08)
Fix colorbar arguments to Histdd.plot (#4)
percentile for Hist1d
rebin method for Histdd (experimental)
0.5.1 (2017-03-22)
get_random for Histdd no longer just returns bin centers (Hist1d does stil…)
lookup for Hist1d. When will I finally merge the classes…
0.5.0 (2016-10-07)
pandas.DataFrame and dask.dataframe support
dimensions option to Histdd to init axis_names and bin_centers at once
0.4.3 (2016-10-03)
Remove matplotlib requirement (still required for plotting features)
0.4.2 (2016-08-10)
Fix small bug for >=3 d histograms
0.4.1 (2016-17-14)
get_random and lookup for Histdd. Not really tested yet.
0.4.0 (2016-02-05)
.std function for Histdd
Fix off-by-one errors
0.3.0 (2015-09-28)
Several new histdd functions: cumulate, normalize, percentile…
Python 2 compatibility
0.2.1 (2015-08-18)
Histdd functions sum, slice, average now also work
0.2 (2015-08-06)
Multidimensional histograms
Axes naming
0.1.1-4 (2015-08-04)
Correct various rookie mistakes in packaging… Hey, it’s my first pypi package!
0.1 (2015-08-04)
Initial release
Hist1d, Hist2d
Basic test suite
Basic readme
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file multihist-0.6.5.tar.gz
.
File metadata
- Download URL: multihist-0.6.5.tar.gz
- Upload date:
- Size: 16.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/3.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.9.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 020e88693228d50b23b77fd59f2e998de8b1d7517f5697cec4d4616d7d011cdd |
|
MD5 | e411fd3cb227d2bc0066dcb1248c280f |
|
BLAKE2b-256 | 73dfbcbe4c72f03c4cb0f550329d6148628fe1f81f7da95d3c2afb867fb437f4 |
File details
Details for the file multihist-0.6.5-py3-none-any.whl
.
File metadata
- Download URL: multihist-0.6.5-py3-none-any.whl
- Upload date:
- Size: 14.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/3.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.9.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | f5fcce0712706c99a462cb19580fc37ffc1a6665e490a841705ea1b9d029edf9 |
|
MD5 | 57c22febb099929db74cd1cd5951fd86 |
|
BLAKE2b-256 | 8b816a9848217ca43ef6930c9ec8abde2e0d8a40c4078875cb26799b185ef82f |