app_all_data.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 shapely.geometry import Polygon

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 Energy Masterplan Modelling Outputs")

with row1_2:
    st.write(
    """
    ##
    An interactive web-map to view and download the outputs of the SEAI-funded Dublin Region Energy
    Masterplan developed by Codema. Additional information available on the modelling methodology
    upon request. Small Area centroids as of the 2016 Census, are marked on the map here for all of Dublin, 
    of which they can be  selected using the lasso tool on the right of the map to circle desired data and 
    downloaded in csv format. 
    """)

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
map_data = gpd.read_file("data/drem/single_polygon_sa_all_info.geojson")

map_data = map_data.set_crs(epsg="4326")
map_data = map_data.to_crs(epsg="3857")

map_data = gpd.GeoDataFrame(map_data) 

df = map_data[["GEOGID_left", "T1_1AGETT", "postcodes", "area", "pop-density-ppsqkm", "sa_resi_heat_emissions_TCO2", "sa_comm_elec_emissions_TCO2", "sa_comm_heat_emissions_TCO2", "sa_data_centre_elec_emissions_TCO2", "total_sa_emissions_TCO2", "total_sa_resi_emissions_TCO2", "total_sa_comm_emissions_TCO2", "geometry"]]

df["centroid"] = df.centroid
df["x"] = df["centroid"].x
df["y"] = df["centroid"].y

df = df[["GEOGID_left", "T1_1AGETT", "postcodes", "area", "pop-density-ppsqkm", "sa_resi_heat_emissions_TCO2", "sa_comm_elec_emissions_TCO2", "sa_comm_heat_emissions_TCO2", "sa_data_centre_elec_emissions_TCO2", "total_sa_emissions_TCO2", "total_sa_resi_emissions_TCO2", "total_sa_comm_emissions_TCO2", "x", "y"]]

df = df.rename(columns={"GEOGID_left": "small_area_id", "T1_1AGETT": "population", "area": "area_m2"})

df = df.drop_duplicates('small_area_id')

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, zoom_in, zoom_out, 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)