Add initial code

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+#Ipython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version

LICENSE

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+Copyright (c) 2015 Igor Babuschkin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# matplotlib-hep
+
+An add-on for matplotlib that simplifies the creation of plots for high energy physics.
+
+## The histpoints plotting function
+
+In high energy physics, histograms are often displayed as a collection of data points, one for each bin.
+This allows one to easily compare the data to an underlying probability model.
+
+It can be debated if it is correct to attach error bars to the individual bin contents as opposed to the underlying model,
+as we want to know if our expectation for any given bin could fluctuate to match the data and not vice versa.
+But this is a convention widely used by the HEP community, and thus a way to use this kind of plot in Python is often necessary.
+
+A simple way to create these kinds of plots is missing from other Python packages like matplotlib.
+The `histpoints` function is designed to produce these plots conventiently, like in the following example:
+
+```python
+from missing_hep import histpoints
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats as stats
+
+data = np.random.normal(0, 1, 200)
+x, y, norm = histpoints(data)
+
+xs = np.linspace(-4, 4, 200)
+plt.plot(xs, norm * stats.norm.pdf(xs, 0, 1), 'b-', lw=2)
+
+plt.savefig('histpoints.png')
+```
+
+![histpoints](./histpoints.png)
+
+Or, displaying horizontal error bars to mark the bin width:
+```python
+histpoints(data, xerr='binwidth')
+```
+
+![histpoints\_binwidth](./histpoints_binwidth.png)
+
+By default, the number of bins is chosen automatically via the Freedman-Diaconis rule.
+

matplotlib_hep/__init__.py

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+from __future__ import division
+
+import numpy as np
+import scipy.stats as stats
+import scipy as sp
+import logging
+
+__all__ = ['histpoints', 'calc_nbins']
+
+def calc_nbins(x, maximum=150):
+    n =  (max(x) - min(x)) / (2 * len(x)**(-1/3) * (np.percentile(x, 75) - np.percentile(x, 25)))
+    return min(n, maximum)
+
+def poisson_limits(N, kind, confidence=0.6827):
+    alpha = 1 - confidence
+    upper = np.zeros(len(N))
+    lower = np.zeros(len(N))
+    if kind == 'gamma':
+        lower = stats.gamma.ppf(alpha / 2, N)
+        upper = stats.gamma.ppf(1 - alpha / 2, N + 1)
+    elif kind == 'sqrt':
+        lower = sqrt(N)
+        upper = lower
+    else:
+        raise ValueError('Unknown errorbar kind: {}'.format(kind))
+    # clip lower bars
+    lower[N==0] = 0
+    return N - lower, upper - N
+
+def histpoints(x, bins=None, xerr=None, yerr='gamma', normed=False, **kwargs):
+    """
+    Plot a histogram as a series of data points.
+
+    Compute and draw the histogram of *x* using individual (x,y) points
+    for the bin contents.
+
+    By default, vertical poisson error bars are calculated using the
+    gamma distribution.
+
+    Horizontal error bars are omitted by default.
+    These can be enable using the *xerr* argument.
+    Use ``xerr='binwidth'`` to draw horizontal error bars that indicate
+    the width of each histogram bin.
+
+    Paramters
+    ---------
+
+    x : (n,) array or sequence of (n,) arrays
+        Input values. This takes either a single array or a sequency of
+        arrays which are not required to be of the same length
+
+    """
+    import matplotlib.pyplot as plt
+
+    if bins is None:
+        bins = calc_nbins(x)
+
+    h, bins = np.histogram(x, bins=bins)
+    width = bins[1] - bins[0]
+    center = (bins[:-1] + bins[1:]) / 2
+    area = sum(h * width)
+
+    if isinstance(yerr, str):
+        yerr = poisson_limits(h, yerr)
+
+    if xerr == 'binwidth':
+        xerr = width / 2
+
+    if normed:
+        h = h / area
+        yerr = yerr / area
+        area = 1.
+
+    if not 'color' in kwargs:
+        kwargs['color'] = 'black'
+
+    if not 'fmt' in kwargs:
+        kwargs['fmt'] = 'o'
+
+    plt.errorbar(center, h, xerr=xerr, yerr=yerr, **kwargs)
+
+    return center, h, area
+

requirements.txt

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+matplotlib

setup.py

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+from setuptools import setup
+
+setup(name='matplotlib-hep',
+      version='0.1.0',
+      description='Module for making data analysis in High Energy Physics with Python more convenient',
+      url='http://github.com/ibab/matplotlib-hep',
+      author='Igor Babuschkin',
+      author_email='igor@babuschk.in',
+      license='MIT',
+      packages=['matplotlib_hep'],
+      zip_safe=False)