app_ets.py

from bokeh.models.widgets.tables import DataTable

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

from bokeh.plotting import figure
from bokeh.tile_providers import CARTODBPOSITRON, get_provider
from bokeh.models import ColumnDataSource, CustomJS, tools
from bokeh.models import TableColumn, WidgetBox
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 EPA ETS Sites")

with row1_2:
    st.write(
    """
    ##
    Examining the EPA Emissions Trading Scheme (ETS) buildings across county Dublin, and their relevant 
    metered Carbon Emissions along with their estimated electricity consumption.
    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/epa_ets_sites_dublin.csv")
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude))

map_data = gdf[["Name", "Installation Name", "License",	"ID", "Address", "metered_annual_emissions_tco2", "estimated_annual_electricity_mwh", "Use", "Latitude", "Longitude", "geometry"]]

map_data = gpd.GeoDataFrame(map_data)

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[["Name", "Installation Name", "License", "ID", "Address", "metered_annual_emissions_tco2", "estimated_annual_electricity_mwh", "Use", "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}})
        )
        """
    )
)

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="pan, box_zoom, wheel_zoom, lasso_select", 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}})
        )
        """
    )
)

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="ets-demand-data.csv">Download selected data in csv file</a>'

    return href

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, 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)