#!/usr/bin/env python
# coding: utf-8

# # Adaptive histogram
# 
# This type of histogram automatically adapts bins when new values are added. Note that only fixed-width continuous binning scheme is currently supported.

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# Necessary import evil
from physt import h1
import numpy as np
import matplotlib.pyplot as plt


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# Create an empty histogram
h = h1(None, "fixed_width", bin_width=10, name="People height", axis_name="cm", adaptive=True)
h


# ## Adding single values

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# Add a first value
h.fill(157)
h.plot()
h


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# Add a second value
h.fill(173)
h.plot()


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# Add a few more values, including weights
h.fill(173, 2)
h.fill(186, 5)
h.fill(188, 3)
h.fill(193, 1)
h.plot(errors=True, show_stats=True);


# ## Adding multiple values at once

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ha = h1(None, "fixed_width", bin_width=10, adaptive=True)
ha.plot(show_stats=True);


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# Beginning
ha.fill_n([10, 11, 34])
ha.plot();


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# Add a distant value
ha.fill_n([234], weights=[10])
ha.plot(show_stats=True);


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# Let's create a huge dataset
values = np.random.normal(130, 20, 100000)


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get_ipython().run_cell_magic('time', '', '# Add lots of values (no loop in Python)\nhn = h1(None, "fixed_width", bin_width=10, adaptive=True)\nhn.fill_n(values)\n# ha.plot()\n')


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get_ipython().run_cell_magic('time', '', '# Comparison with Python loop\nhp = h1(None, "fixed_width", bin_width=10, adaptive=True)\nfor value in values:\n    hp.fill(value)\n')


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# Hopefully equal results
print("Equal?", hp == hn)
hp.plot(show_stats=True);


# ## Adding two adaptive histograms together

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ha1 = h1(None, "fixed_width", bin_width=5, adaptive=True)
ha1.fill_n(np.random.normal(100, 10, 1000))

ha2 = h1(None, "fixed_width", bin_width=5, adaptive=True)
ha2.fill_n(np.random.normal(70, 10, 500))

ha = ha1 + ha2

fig, ax= plt.subplots()

ha1.plot(alpha=0.1, ax=ax, label="1", color="red")
ha2.plot(alpha=0.1, ax=ax, label="2")

ha.plot("scatter", label="sum", ax=ax, errors=True)

ax.legend(loc=2);   # TODO? Why don't we show the sum???