import altair as alt
import pandas as pd

source = pd.DataFrame({
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})

alt.Chart(source).mark_bar().encode(
    x='a',
    y='b'
)

import altair as alt
import numpy as np
import pandas as pd

# Compute x^2 + y^2 across a 2D grid
x, y = np.meshgrid(range(-5, 5), range(-5, 5))
z = x ** 2 + y ** 2

# Convert this grid to columnar data expected by Altair
source = pd.DataFrame({'x': x.ravel(),
                     'y': y.ravel(),
                     'z': z.ravel()})

alt.Chart(source).mark_rect().encode(
    x='x:O',
    y='y:O',
    color='z:Q'
)

import altair as alt
from vega_datasets import data

source = data.cars()

alt.Chart(source).mark_circle(size=60).encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
    tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']
).interactive()

import altair as alt
from vega_datasets import data

source = data.wheat()

base = alt.Chart(source).encode(x='year:O')

bar = base.mark_bar().encode(y='wheat:Q')

line =  base.mark_line(color='red').encode(
    y='wages:Q'
)

(bar + line).properties(width=600)

import altair as alt
from vega_datasets import data

source = data.us_employment()

alt.Chart(source).mark_bar().encode(
    x="month:T",
    y="nonfarm_change:Q",
    color=alt.condition(
        alt.datum.nonfarm_change > 0,
        alt.value("steelblue"),  # The positive color
        alt.value("orange")  # The negative color
    )
).properties(width=600).interactive()

import altair as alt
from vega_datasets import data

source = data.cars()

alt.Chart(source).mark_circle().encode(
    alt.X(alt.repeat("column"), type='quantitative'),
    alt.Y(alt.repeat("row"), type='quantitative'),
    color='Origin:N'
).properties(
    width=150,
    height=150
).repeat(
    row=['Horsepower', 'Acceleration', 'Miles_per_Gallon'],
    column=['Miles_per_Gallon', 'Acceleration', 'Horsepower']
).interactive()

import altair as alt
from vega_datasets import data

# Data generators for the background
sphere = alt.sphere()
graticule = alt.graticule()

# Source of land data
source = alt.topo_feature(data.world_110m.url, 'countries')

# Layering and configuring the components
alt.layer(
    alt.Chart(sphere).mark_geoshape(fill='lightblue'),
    alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),
    alt.Chart(source).mark_geoshape(fill='ForestGreen', stroke='black')
).project(
    'naturalEarth1'
).properties(width=600, height=400).configure_view(stroke=None)

import altair as alt
from vega_datasets import data

source = alt.topo_feature(data.world_110m.url, 'countries')

base = alt.Chart(source).mark_geoshape(
    fill='#666666',
    stroke='white'
).properties(
    width=300,
    height=180
)

projections = ['equirectangular', 'mercator', 'orthographic', 'gnomonic']
charts = [base.project(proj).properties(title=proj)
          for proj in projections]

alt.concat(*charts, columns=2)

import pydeck

# 2014 location of car accidents in the UK
UK_ACCIDENTS_DATA = ('https://raw.githubusercontent.com/uber-common/'
                    'deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv')

# Define a layer to display on a map
layer = pydeck.Layer(
    'HexagonLayer',
    UK_ACCIDENTS_DATA,
    get_position=['lng', 'lat'],
    auto_highlight=True,
    elevation_scale=50,
    pickable=True,
    elevation_range=[0, 3000],
    extruded=True,
    coverage=1)

import pydeck as pdk

UK_ACCIDENTS_DATA = 'https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv'

layer = pdk.Layer(
    'HexagonLayer',  # `type` positional argument is here
    UK_ACCIDENTS_DATA,
    get_position=['lng', 'lat'],
    auto_highlight=True,
    elevation_scale=50,
    pickable=True,
    elevation_range=[0, 3000],
    extruded=True,
    coverage=1)

# Set the viewport location
view_state = pdk.ViewState(
    longitude=-1.415,
    latitude=52.2323,
    zoom=6,
    min_zoom=5,
    max_zoom=15,
    pitch=40.5,
    bearing=-27.36)

# Combined all of it and render a viewport
r = pdk.Deck(layers=[layer], initial_view_state=view_state)

r

layer = pdk.Layer(
    'ScatterplotLayer',     # Change the `type` positional argument here
    UK_ACCIDENTS_DATA,
    get_position=['lng', 'lat'],
    auto_highlight=True,
    get_radius=1000,          # Radius is given in meters
    get_fill_color=[180, 0, 200, 140],  # Set an RGBA value for fill
    pickable=True)

# Set the viewport location
view_state = pdk.ViewState(
    longitude=-1.415,
    latitude=52.2323,
    zoom=6,
    min_zoom=5,
    max_zoom=15,
    pitch=40.5,
    bearing=-27.36)

# Combined all of it and render a viewport
r = pdk.Deck(layers=[layer], initial_view_state=view_state)

r

import pydeck as pdk
import pandas as pd
import json

# H3_HEX_DATA = "https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/sf.h3cells.json"

H3_HEX_DATA = "https://dggs-api-bozea3cspa-ew.a.run.app/dggs-api/collections/srtm_30m_estonia_h3/zones?resolution=6&limit=10000&bbox=0.7829255156250007,54.890482222074525,49.364468484375,62.14597996607117"

df = pd.read_json(H3_HEX_DATA)

df

df['hex'] = df['features'].apply(lambda d: d['geometry'][0])

df['elevation'] =  df['features'].apply(lambda d: d['properties']['elevation'])
df['elevation'] = pd.to_numeric(df['elevation'], downcast="integer")

df.head()

# Define a layer to display on a map
layer = pdk.Layer(
    "H3HexagonLayer",
    df,
    pickable=True,
    stroked=True,
    filled=True,
    extruded=False,
    get_hexagon="hex",
    get_fill_color="[255 - elevation, 255, elevation]",
    get_line_color=[255, 255, 255],
    line_width_min_pixels=2,
)

# Set the viewport location
view_state = pdk.ViewState(latitude=58.7749295, longitude=26.4194155, zoom=11, bearing=0, pitch=30)


# Render
r = pdk.Deck(layers=[layer], initial_view_state=view_state, tooltip={"text": "Count: {count}"})

r

"""
GlobeView
=========

Over 33,000 power plants of the world plotted by their production capacity (given by height)
and fuel type (green if renewable) on an experimental deck.gl GlobeView.
"""
import pydeck as pdk
import pandas as pd

COUNTRIES = "https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_50m_admin_0_scale_rank.geojson"
POWER_PLANTS = "https://raw.githubusercontent.com/ajduberstein/geo_datasets/master/global_power_plant_database.csv"

df = pd.read_csv(POWER_PLANTS)


def is_green(fuel_type):
    """Return a green RGB value if a facility uses a renewable fuel type"""
    if fuel_type.lower() in ("nuclear", "water", "wind", "hydro", "biomass", "solar", "geothermal"):
        return [10, 230, 120]
    return [230, 158, 10]


df["color"] = df["primary_fuel"].apply(is_green)

view_state = pdk.ViewState(latitude=51.47, longitude=0.45, zoom=2, min_zoom=2)

# Set height and width variables
view = pdk.View(type="_GlobeView", controller=True, width=1000, height=700)


layers = [
    pdk.Layer(
        "GeoJsonLayer",
        id="base-map",
        data=COUNTRIES,
        stroked=False,
        filled=True,
        get_fill_color=[200, 200, 200],
    ),
    pdk.Layer(
        "ColumnLayer",
        id="power-plant",
        data=df,
        get_elevation="capacity_mw",
        get_position=["longitude", "latitude"],
        elevation_scale=100,
        pickable=True,
        auto_highlight=True,
        radius=20000,
        get_fill_color="color",
    ),
]

deck = pdk.Deck(
    views=[view],
    initial_view_state=view_state,
    tooltip={"text": "{name}, {primary_fuel} plant, {country}"},
    layers=layers,
    # Note that this must be set for the globe to be opaque
    parameters={"cull": True},
)

deck