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

# # Visualizing Spatial Information - California Housing
# 
# This demo shows a simple workflow when working with geospatial data:
# 
#  * Obtaining a dataset which includes geospatial references.
#  * Obtaining a desired geometries (boundaries etc.)
#  * Visualisation 
#  
# In this example we will make a simple **proportional symbols map** using the `California Housing` dataset in `sklearn` package.

# In[1]:


import numpy as np
import pandas as pd
import geopandas as gpd

from lets_plot import *


# In[2]:


LetsPlot.setup_html()


# ## Prepare the dataset

# In[3]:


from sklearn.datasets import fetch_california_housing

california_housing_bunch = fetch_california_housing()
data = pd.DataFrame(california_housing_bunch.data, columns=california_housing_bunch.feature_names)

# Add $-value field to the dataframe.
# dataset.target: numpy array of shape (20640,)
# Each value corresponds to the average house value in units of 100,000.
data['Value($)'] = california_housing_bunch.target * 100000
data.head()


# In[4]:


# Draw a random sample from the data set.
data = data.sample(n=1000)


# ## Static map
# 
# Let's create a static map using regular `ggplot2` geometries.
# 
# Various shape files related to the state of California are available at https://data.ca.gov web site.
# 
# For the purpose of this demo the Calofornia State Boundaty zip was downloaded from 
# https://data.ca.gov/dataset/ca-geographic-boundaries and unpacked to `ca-state-boundary` subdirectory.

# ### Use `geopandas` to read a shape file to GeoDataFrame

# In[5]:


#CA = gpd.read_file("./ca-state-boundary/CA_State_TIGER2016.shp")

from lets_plot.geo_data import *

CA = geocode_states('CA').scope('US').inc_res(2).get_boundaries()
CA.head()


# Keeping in mind that our target is the housing value, fill the choropleth over the state contours using `geom_map()`function

# ### Make a plot out of polygon and points
# 
# The color of the points will reflect the house age and
# the size of the points will reflect the value of the house.

# In[6]:


# The plot base 
p = ggplot() + scale_color_gradient(name='House Age', low='red', high='green')

# The points layer
points = geom_point(aes(x='Longitude',
                        y='Latitude',
                        size='Value($)',
                        color='HouseAge'), 
                    data=data,
                    alpha=0.8)

# The map
p + geom_polygon(data=CA, fill='#F8F4F0', color='#B71234')\
  + points\
  + theme_void()\
  + ggsize(600, 500)


# ## Interactive map
# 
# The `geom_livemap()` function creates an interactive base-map super-layer to which other geometry layers are added.

# ### Configuring map tiles
# 
# By default *Lets-PLot* offers high quality vector map tiles but also can fetch raster tiles from a 3d-party Z-X-Y [tile servers](https://wiki.openstreetmap.org/wiki/Tile_servers).
# 
# For the sake of the demo lets use *CARTO Antique* tiles by [CARTO](https://carto.com/attribution/) as our basemap.

# In[7]:


LetsPlot.set(
    maptiles_zxy(
        url='https://cartocdn_c.global.ssl.fastly.net/base-antique/{z}/{x}/{y}@2x.png',
        attribution='<a href="https://www.openstreetmap.org/copyright">© OpenStreetMap contributors</a> <a href="https://carto.com/attributions#basemaps">© CARTO</a>, <a href="https://carto.com/attributions">© CARTO</a>'
    )
)


# ### Make a plot similar to the one above but interactive

# In[8]:


p + geom_livemap()\
  + geom_polygon(data=CA, fill='white', color='#B71234', alpha=0.5)\
  + points


# ### Adjust the initial viewport
# 
# Use parameters `location` and `zoom` to define the initial viewport.

# In[9]:


# Pass `[lon,lat]` value to the `location` (near Los Angeles)
p + geom_livemap(location=[-118.15, 33.96], zoom=7)\
  + geom_polygon(data=CA, fill='white', color='#B71234', alpha=0.5, size=1)\
  + points