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

# # New `Scale` Functions with Parameter `aesthetic`
# 
# - `scale_identity(aesthetic, *, ...)`
# - `scale_manual(aesthetic, values, *, ...)`
# - `scale_continuous(aesthetic, *, ...)`
# - `scale_gradient(aesthetic, *, ...)`
# - `scale_gradient2(aesthetic, *, ...)`
# - `scale_gradientn(aesthetic, *, ...)`
# - `scale_hue(aesthetic, *, ...)`
# - `scale_discrete(aesthetic, *, ...)`
# - `scale_grey(aesthetic, *, ...)`
# - `scale_brewer(aesthetic, *, ...)`
# - `scale_viridis(aesthetic, *, ...)`
# 
# Comparing to familiar "scale" functions like `scale_color_gradient()` etc., the new set of functions
# adds more flexibility by allowing specifying an aesthetic or a list of aesthetics the scale is working with.
# 
# For example, you can use just one function call to setup the same color palette for both, stroke and fill colors on plot:
# 
# `scale_brewer(['color', 'fill'], palette='Set1')`
# 
# 
# But, the main reason why you might want to use new "scale" functions is configuring of additional color aesthetics: `paint_a, paint_b, paint_c`.
# 
# These aesthetics are flexible and can be used as either "color" or "fill" as needed. See [Multiple Color Scales](https://nbviewer.org/github/JetBrains/lets-plot/blob/master/docs/f-23a/multiple_color_scales.ipynb).
# 

# In[1]:


import pandas as pd

from lets_plot import *
from lets_plot.mapping import as_discrete


# In[2]:


LetsPlot.setup_html()


# In[3]:


mpg_df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")


# #### 1. Plot with Default Colors

# In[4]:


p = ggplot(mpg_df, aes(as_discrete('drv', order=-1), 'hwy')) + \
    geom_violin(aes(color='drv', fill='drv'), alpha=.5, size=2)
p


# #### 2. Setup a Brewer Palette

# ##### 2.1. Old School: for Each Aesthetic Separately

# In[5]:


p + scale_color_brewer(palette='Set1') + scale_fill_brewer(palette='Set1')


# ##### 2.2. New: for Both Aesthetics at Once

# In[6]:


p + scale_brewer(['color', 'fill'], palette='Set1')