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

# In[1]:


import os
import folium

print(folium.__version__)


# ## ColorLine

# In[2]:


import numpy as np

x = np.linspace(0, 2*np.pi, 300)

lats = 20 * np.cos(x)
lons = 20 * np.sin(x)
colors = np.sin(5 * x)


# In[3]:


from folium import features

m = folium.Map([0, 0], zoom_start=3)


color_line = features.ColorLine(
    positions=list(zip(lats, lons)),
    colors=colors,
    colormap=['y', 'orange', 'r'],
    weight=10)

color_line.add_to(m)

m.save(os.path.join('results', 'Features_0.html'))

m


# ### Marker, Icon, Popup

# In[4]:


m = folium.Map([0, 0], zoom_start=1)
mk = features.Marker([0, 0])
pp = folium.Popup('hello')
ic = features.Icon(color='red')

mk.add_child(ic)
mk.add_child(pp)
m.add_child(mk)

m.save(os.path.join('results', 'Features_1.html'))

m


# ### Vega popup

# In[5]:


import json
import vincent

N = 100

multi_iter2 = {
    'x': np.random.uniform(size=(N,)),
    'y': np.random.uniform(size=(N,)),
}

scatter = vincent.Scatter(multi_iter2, iter_idx='x', height=100, width=200)
data = json.loads(scatter.to_json())

m = folium.Map([0, 0], zoom_start=1)
mk = features.Marker([0, 0])
p = folium.Popup('Hello')
v = features.Vega(data, width='100%', height='100%')

mk.add_child(p)
p.add_child(v)
m.add_child(mk)

m.save(os.path.join('results', 'Features_2.html'))

m


# ### Vega-Lite popup

# In[6]:


from altair import Chart, load_dataset

# load built-in dataset as a pandas DataFrame
cars = load_dataset('cars')

scatter = Chart(cars).mark_circle().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
)

vega = folium.features.VegaLite(
    scatter,
    width='100%',
    height='100%',
)

m = folium.Map(location=[-27.5717, -48.6256])

marker = folium.features.Marker([-27.57, -48.62])

popup = folium.Popup()

vega.add_to(popup)
popup.add_to(marker)

marker.add_to(m)

m.save(os.path.join('results', 'Features_3.html'))

m


# ### Vega div and a Map
# 

# In[7]:


import branca


N = 100

multi_iter2 = {
    'x': np.random.uniform(size=(N,)),
    'y': np.random.uniform(size=(N,)),
}

scatter = vincent.Scatter(
    multi_iter2,
    iter_idx='x',
    height=250,
    width=420
)
data = json.loads(scatter.to_json())

f = branca.element.Figure()

# Create two maps.
m = folium.Map(location=[0, 0],
               tiles='stamenwatercolor',
               zoom_start=1,
               position='absolute',
               left='0%',
               width='50%',
               height='50%')

m2 = folium.Map(location=[46, 3],
                tiles='OpenStreetMap',
                zoom_start=4,
                position='absolute',
                left='50%',
                width='50%',
                height='50%',
                top='50%')

# Create two Vega.
v = features.Vega(
    data,
    position='absolute',
    left='50%',
    width='50%',
    height='50%'
)

v2 = features.Vega(
    data,
    position='absolute',
    left='0%',
    width='50%',
    height='50%',
    top='50%'
)

f.add_child(m)
f.add_child(m2)
f.add_child(v)
f.add_child(v2)

f.save(os.path.join('results', 'Features_4.html'))

f


# ### Vega-Lite div and a Map

# In[8]:


import pandas as pd
N = 100

multi_iter2 = pd.DataFrame({
    'x': np.random.uniform(size=(N,)),
    'y': np.random.uniform(size=(N,)),
})

scatter = Chart(multi_iter2).mark_circle().encode(x='x', y='y')
scatter.width = 420
scatter.height = 250
data = json.loads(scatter.to_json())

f = branca.element.Figure()

# Create two maps.
m = folium.Map(
    location=[0, 0],
    tiles='stamenwatercolor',
    zoom_start=1,
    position='absolute',
    left='0%',
    width='50%',
    height='50%'
)

m2 = folium.Map(
    location=[46, 3],
    tiles='OpenStreetMap',
    zoom_start=4,
    position='absolute',
    left='50%',
    width='50%',
    height='50%',
    top='50%')


# Create two Vega.
v = features.VegaLite(
    data,
    position='absolute',
    left='50%',
    width='50%',
    height='50%'
)

v2 = features.VegaLite(
    data,
    position='absolute',
    left='0%',
    width='50%',
    height='50%',
    top='50%'
)

f.add_child(m)
f.add_child(m2)
f.add_child(v)
f.add_child(v2)

f.save(os.path.join('results', 'Features_5.html'))

f


# ### GeoJson

# In[9]:


N = 1000

lons = +5 - np.random.normal(size=N)
lats = 48 - np.random.normal(size=N)

data = {
    'type': 'FeatureCollection',
    'features': [
        {
            'type': 'Feature',
            'geometry': {
                'type': 'MultiPoint',
                'coordinates': [[lon, lat] for (lat, lon) in zip(lats, lons)],
            },
            'properties': {'prop0': 'value0'}
        },
    ],
}

m = folium.Map([48, 5], zoom_start=6)
m.add_child(features.GeoJson(data))

m.save(os.path.join('results', 'Features_6.html'))

m


# ### Div

# In[10]:


N = 100

multi_iter2 = {
    'x': np.random.uniform(size=(N,)),
    'y': np.random.uniform(size=(N,)),
}

scatter = vincent.Scatter(
    multi_iter2,
    iter_idx='x',
    height=250,
    width=420
)
data = json.loads(scatter.to_json())

f = branca.element.Figure()

d1 = f.add_subplot(1, 2, 1)
d2 = f.add_subplot(1, 2, 2)

d1.add_child(folium.Map([0, 0], tiles='stamenwatercolor', zoom_start=1))
d2.add_child(folium.Map([46, 3], tiles='OpenStreetMap', zoom_start=5))

f.save(os.path.join('results', 'Features_7.html'))

f


# ### LayerControl

# In[11]:


m = folium.Map(tiles=None)

folium.raster_layers.TileLayer('OpenStreetMap').add_to(m)
folium.raster_layers.TileLayer('stamentoner').add_to(m)

folium.LayerControl().add_to(m)

m.save(os.path.join('results', 'Features_8.html'))

m