app_top_200.py

from typing import List
from bokeh.models.widgets.tables import DataTable

import geopandas as gpd
import pandas as pd
import streamlit as st

from bokeh.plotting import figure, output_file, show
from bokeh.tile_providers import CARTODBPOSITRON, get_provider, Vendors 
from bokeh.plotting import gmap
from bokeh.models import ColumnDataSource, CustomJS
from bokeh.models import TableColumn
from streamlit_bokeh_events import streamlit_bokeh_events

st.set_page_config(layout="wide")

# LAYING OUT THE TOP SECTION OF THE APP
row1_1, row1_2 = st.beta_columns((2,3))

with row1_1:
    st.title("Dublin Large Energy Consumers")

with row1_2:
    st.write(
    """
    ##
    Examining the 200 largest energy consumers across county Dublin, as of the open-access Valuation.
    Office Dataset, crossed with CIBSE TM46 Benchmarks. Select the Lasso Tool from the toolbar on the 
    right of the map to circle desired data and download it in csv format from the Interactive Map.
    """)

col1, col2 = st.beta_columns([1, 5])
with col1:
    st.image('data/codema_logo.png', width=200)
with col2:
    st.image('data/seai_logo.jpg', width=200)


# READ IN TOP BUILDINGS DEMANDS
df = pd.read_csv("data/top_200_building_demands.csv")
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude))

map_data = gdf[["building_type", "inferred_energy_mwh_per_year", "Benchmark", "address", "latitude", "longitude", "geometry"]]
map_data = map_data.set_crs(epsg="4326")
map_data = map_data.to_crs(epsg="3857")

map_data["x"] = map_data.geometry.apply(lambda row:row.x)
map_data["y"] = map_data.geometry.apply(lambda row:row.y)

map_data = map_data[["building_type", "inferred_energy_mwh_per_year", "Benchmark", "address", "x", "y"]]

df = map_data

col1, col2 = st.beta_columns(2)

cds = ColumnDataSource(df)

columns = list(map(lambda colname: TableColumn(field=colname, title=colname), df.columns))

cds.selected.js_on_change(
    "indices",
    CustomJS(
        args=dict(source=cds),
        code="""
        document.dispatchEvent(
            new CustomEvent("INDEX_SELECT", {detail: {data: source.selected.indices}})
        )
        """
    )
)

import base64
# Assuming UTF-8 encoding, change to something else if you need to
base64.b64encode("password".encode("utf-8"))

def get_table_download_link(df):
    """Generates a link allowing the data in a given panda dataframe to be downloaded
    in:  dataframe
    out: href string
    """
    csv = df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode()).decode()  # some strings <-> bytes conversions necessary here
    href = f'<a href="data:file/csv;base64,{b64}" download="large-demand-data.csv">Download selected data in csv file</a>'

    return href

table = DataTable(source=cds, columns=columns)
with col1:
    result = streamlit_bokeh_events(
        bokeh_plot=table,
        events="INDEX_SELECT",
        key="foo",
        refresh_on_update=False,
        debounce_time=0,
        override_height=500
    )
    if result:
        if result.get("INDEX_SELECT"):
            st.write(df.iloc[result.get("INDEX_SELECT")["data"]])

plot = figure(tools="lasso_select,zoom_in", width=250, height=250)
cds_lasso = ColumnDataSource(df)
cds_lasso.selected.js_on_change(
    "indices",
    CustomJS(
        args=dict(source=cds_lasso),
        code="""
        document.dispatchEvent(
            new CustomEvent("LASSO_SELECT", {detail: {data: source.selected.indices}})
        )
        """
    )
)

plot = figure(x_axis_type="mercator", y_axis_type="mercator", tools="pan, box_zoom, wheel_zoom, lasso_select")
plot.xaxis.axis_label = 'longitude'
plot.yaxis.axis_label = 'latitude'

tile_provider = get_provider(CARTODBPOSITRON)

plot.add_tile(tile_provider)
plot.circle("x", "y", fill_alpha=0.5, size=5, line_color=None, source=cds_lasso)
with col2:
    result_lasso = streamlit_bokeh_events(
        bokeh_plot=plot,
        events="LASSO_SELECT",
        key="bar",
        refresh_on_update=False,
        debounce_time=0)
    if result_lasso:
        if result_lasso.get("LASSO_SELECT"):
            st.write(df.iloc[result_lasso.get("LASSO_SELECT")["data"]])
            st.markdown(get_table_download_link(df.iloc[result_lasso.get("LASSO_SELECT")["data"]]), unsafe_allow_html=True)