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

# # Waterfall Plot
# 
# A waterfall plot shows the cumulative effect of sequentially introduced positive or negative values.
# 
# To use it, you need to import the 'bistro' module.

# In[1]:


import pandas as pd

from lets_plot import *
from lets_plot.bistro import *


# In[2]:


LetsPlot.setup_html()


# In[3]:


data = {
    "Accounts": ["Product revenue", "Services revenue", "Fixed costs", "Variable costs"],
    "Values": [830_000, 290_000, -360_000, -150_000],
}


# ## Default View

# In[4]:


waterfall_plot(data, "Accounts", "Values")


# ## Improved View

# In[5]:


waterfall_plot(data, "Accounts", "Values", \
               size=.75, alpha=.15, width=.8, total_title="Profit", \
               hline=element_line(linetype='solid', size=1), \
               connector=element_line(linetype='dotted'), \
               label=element_text(size=10, family="Courier", face='bold', color='flow_type'), \
               label_format="$,.1~s") + \
    scale_y_continuous(name="Values", format="$,.1~s") + \
    ggtitle("Company Profit (in USD)") + \
    ggsize(1000, 500) + \
    theme_minimal() + \
    theme(plot_title=element_text(size=20, face='bold', hjust=.5))


# ## Additional Parameters

# ### `measure` and `group`

# In[6]:


df = pd.DataFrame({
    "Company": ["Badgersoft"] * 7 + ["AIlien Co."] * 7,
    "Accounts": ["initial", "revenue", "costs", "Q1", "revenue", "costs", "Q2"] * 2,
    "Values": [200, 200, -100, None, 250, -100, None, \
               150, 50, -100, None, 100, -100, None],
    "Measure": ['absolute', 'relative', 'relative', 'total', 'relative', 'relative', 'total'] * 2,
})

waterfall_plot(df, "Accounts", "Values", measure="Measure", group="Company") + \
    facet_grid(x="Company", scales='free_x')


# ### `calc_total`
# 
# `calc_total=False` disables the calculation of the total.
# 
# If the `measure` serie is specified however, the `calc_total` setting has no effect.

# In[7]:


gggrid([
    waterfall_plot(data, "Accounts", "Values", calc_total=False),
    waterfall_plot(df[df["Company"] == "Badgersoft"], "Accounts", "Values", measure="Measure", calc_total=False),
])


# ### Tooltips
# 
# Tooltips for relative and absolute measures should be specified independently.

# In[8]:


relative_tooltips = layer_tooltips().title("Account: @..xlabel..")\
                                    .format("@..initial..", " $,.3~s")\
                                    .format("@..value..", " $,.3~s")\
                                    .line("@{..flow_type..}d from @..initial.. to @..value..")\
                                    .disable_splitting()
absolute_tooltips = 'none'

gggrid([
    waterfall_plot(data, "Accounts", "Values", relative_tooltips='detailed', absolute_tooltips='detailed') + ggtitle("'detailed' tooltips"),
    waterfall_plot(data, "Accounts", "Values", relative_tooltips=relative_tooltips, absolute_tooltips=absolute_tooltips) + ggtitle("Custom tooltips"),
])


# ### `sorted_value`

# In[9]:


waterfall_plot(data, "Accounts", "Values", sorted_value=True)


# ### `threshold`/`max_values`

# In[10]:


gggrid([
    waterfall_plot(data, "Accounts", "Values") + ggtitle("Default"),
    waterfall_plot(data, "Accounts", "Values", threshold=300_000) + ggtitle("Specified threshold"),
    waterfall_plot(data, "Accounts", "Values", max_values=2) + ggtitle("Specified max_values"),
])