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

# # Styling Custom Plot Settings 

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import pandas as pd
import lux


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df = pd.read_csv("../data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y') # change pandas dtype for the column "Year" to datetype
lux.config.default_display="lux"


# In the last tutorial, we saw how `Vis` objects could be exported into visualization code for further editing. What if we want to change the chart settings for *all* the visualizations displayed in the widget. In Lux, we can change the chart settings and aesthetics by inputting custom plot settings the `plotting_style` property of the dataframe.

# ### Example #1 : Changing Color and Title of all charts

# Here, we've loaded in the [Cars dataset](http://lib.stat.cmu.edu/datasets/) and the visualizations recommended in the widget are in its default settings.

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df


# By default, visualizations in Lux are rendered using the [Altair](https://altair-viz.github.io/index.html) library.
# To change the plot configuration in Altair, we need to specify a function that takes an [AltairChart](https://altair-viz.github.io/user_guide/generated/toplevel/altair.Chart.html?highlight=chart) as input, performs some chart configurations in Altair, and returns the chart object as an output.
# 
# Let's say that we want to change all the graphical marks of the charts to green and add a custom title. We can define this `change_color_add_title` function, which configures the chart's mark as green and adds a custom title to the chart.

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def change_color_add_title(chart):
    chart = chart.configure_mark(color="green") # change mark color to green
    chart.title = "Custom Title" # add title to chart
    return chart


# 

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lux.config.plotting_style = change_color_add_title


# Matplotlib also supports plot configurations to be applied on top of the generated graphs. To set a default plot configuration, first write a function that can take in a `fig` and `ax` and returns a `fig` and `ax`. 
# 

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lux.config.plotting_backend = "matplotlib"


# `fig` handles figure width and other plot size attributes. `ax` supports changing the chart title and other plot labels and configurations. For example:

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def change_width_add_title(fig, ax):
    fig.set_figwidth(7)
    ax.set_title("Custom Title")
    return fig, ax


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lux.config.plotting_style = change_width_add_title


# Moreover, we can set the color and other figure styles using the rcParams attribute of pyplot.

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import matplotlib.pyplot as plt
import matplotlib

plt.rcParams['axes.prop_cycle'] = matplotlib.cycler(color='g')


# We now see that the displayed visualizations adopt these new imported settings once we force lux to recompute its recommendations. 
# 
# *Note: currently, if we want these plot configurations to show up on a DataFrame already loaded, we must also add `df.expire_recs()`*

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df.expire_recs()
df


# If we click on the visualization for `Displacement` v.s. `Weight` and export it. We see that the exported chart now contains code with these additional plot settings at the every end.

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# Before running this cell, click on Displacement v.s. Weight vis and export it.
vis = df.exported[0]
print (vis.to_altair())


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import altair as alt

chart = alt.Chart(df).mark_circle().encode(
    x=alt.X('Weight',scale=alt.Scale(domain=(1613, 5140)),type='quantitative'),
    y=alt.Y('Displacement',scale=alt.Scale(domain=(68.0, 455.0)),type='quantitative')
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
chart = chart.interactive() # Enable Zooming and Panning
chart = chart.configure_title(fontWeight=500,fontSize=13,font='Helvetica Neue')
chart = chart.configure_axis(titleFontWeight=500,titleFontSize=11,titleFont='Helvetica Neue',
			labelFontWeight=400,labelFontSize=8,labelFont='Helvetica Neue',labelColor='#505050')
chart = chart.configure_legend(titleFontWeight=500,titleFontSize=10,titleFont='Helvetica Neue',
			labelFontWeight=400,labelFontSize=8,labelFont='Helvetica Neue')
chart = chart.properties(width=160,height=150)
chart = chart.configure_mark(color="green") # change mark color to green
chart.title = "Custom Title" # add title to chart
chart


# We can also export these Matplotlib charts with the plotting style.

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# Before running this cell, click on Displacement v.s. Weight vis and export it.
vis = df.exported[0]
print (vis.to_matplotlib())


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import matplotlib.pyplot as plt
plt.rcParams.update(
            {
                "axes.titlesize": 20,
                "axes.titleweight": "bold",
                "axes.labelweight": "bold",
                "axes.labelsize": 16,
                "legend.fontsize": 14,
                "legend.title_fontsize": 15,
                "xtick.labelsize": 13,
                "ytick.labelsize": 13,
            }
        )
import numpy as np
from math import nan
from matplotlib.cm import ScalarMappable
fig, ax = plt.subplots(figsize=(4.5, 4))
x_pts = df['Displacement']
y_pts = df['Weight']
ax.scatter(x_pts, y_pts, alpha=0.5)
ax.set_xlabel('Displacement', fontsize='15')
ax.set_ylabel('Weight', fontsize='15')
fig.set_figwidth(7)
ax.set_title("Custom Title")
fig


# ### Example #2: Changing Selected Chart Setting

# Next, we look at an example of customizing the chart setting for only selected sets of visualizations. 
# 
# Here, we load in the [Olympics dataset](https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results) and see that the recommended visualization is cluttered with many datapoints.

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url = 'https://github.com/lux-org/lux-datasets/blob/master/data/olympic.csv?raw=true'
df = pd.read_csv(url)
df["Year"] = pd.to_datetime(df["Year"], format='%Y') # change pandas dtype for the column "Year" to datetype
lux.config.default_display="lux"
df


# We want to decrease the opacity of scatterplots, but keep the opacity for the other types of visualization as default.

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def changeOpacityScatterOnly(chart):
    chart = chart.configure_circle(opacity=0.1) # lower opacity if circle
    return chart


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lux.config.plotting_style = changeOpacityScatterOnly
df.expire_recs()
df


# We can modify the scatterplot setting, without changing the settings for the other chart types.