#!/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.

# In[1]:


# Necessary import evil
import physt
from physt import h1, h2, histogramdd
import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


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


# ## Adding single values

# In[3]:


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


# In[4]:


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


# In[5]:


# 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

# In[6]:


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


# In[7]:


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


# In[8]:


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


# In[9]:


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


# In[10]:


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')


# In[11]:


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')


# In[12]:


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


# ## Adding two adaptive histograms together

# In[13]:


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

ha2 = h1(None, "fixed_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???