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

# # GGBunch
# 
# *GGBunch* allows to show a collection of plots on one figure. Each plot in the collection can have arbitrary location and size. There is no automatic layout inside the bunch.

# In[1]:


import numpy as np
from lets_plot import *

LetsPlot.setup_html()


# In[2]:


np.random.seed(42)


# In[3]:


cov=[[1, 0], 
     [0, 1]]     
x, y = np.random.multivariate_normal(mean=[0,0], cov=cov, size=400).T

data = dict(
    x = x,
    y = y
)


# ### View this data as a scatter plot and as a histogram

# In[4]:


p = ggplot(data) + ggsize(600,200)

scatter = p + geom_point(aes('x', 'y'), color='black', alpha=.4)
scatter


# In[5]:


histogram = p + geom_histogram(aes('x', y = '..count..'), fill='dark_magenta')
histogram


# ### Combine both plots in one figure

# In[6]:


# Set scale X limits manually because of computed automatically
# the scale used by each plot would be slightly different
# and the stacked plots wouldn't be aligned.
scale_x = scale_x_continuous(limits=[-3.5, 3.5])
bunch = GGBunch()
bunch.add_plot(histogram + scale_x, 0, 0)
bunch.add_plot(scatter + scale_x, 0, 200)
bunch.show()


# ### Adjust visuals of the bunch figure

# In[7]:


upper_theme = theme(axis_title_x='blank', axis_ticks_x='blank', axis_line='blank', \
                    panel_grid='blank')
lower_theme = theme(axis_text_x='blank', axis_ticks_x='blank', axis_line='blank')


# In[8]:


bunch1 = GGBunch()
bunch1.add_plot(histogram + upper_theme + scale_x, 0, 0)
bunch1.add_plot(scatter + lower_theme + scale_x, 0, 200)
bunch1.show()


# ### Adjust plot sizes
# 
# *add_plot()* method has two more (optional) parameters: *width* and *height*.
# 
# This values will override plot size earlier defined via *ggsize()* function.

# In[9]:


bunch2 = GGBunch()
bunch2.add_plot(histogram + upper_theme + scale_x, 0, 0, 600, 100)
bunch2.add_plot(scatter + lower_theme + scale_x, 0, 100, 600, 300)
bunch2.show()