import streamlit as st
import streamlit.components.v1 as components
import base64
import leafmap.maplibregl as leafmap
import altair as alt
import ibis
from ibis import _
import ibis.selectors as s

from typing import Optional
def to_streamlit(
    self,
    width: Optional[int] = None,
    height: Optional[int] = 600,
    scrolling: Optional[bool] = False,
    **kwargs,
    ):

    try:
        import streamlit.components.v1 as components
        import base64

        raw_html = self.to_html().encode("utf-8")
        raw_html = base64.b64encode(raw_html).decode()
        return components.iframe(
            f"data:text/html;base64,{raw_html}",
            width=width,
            height=height,
            scrolling=scrolling,
            **kwargs,
        )

    except Exception as e:
        raise Exception(e)



ca_pmtiles = "https://data.source.coop/cboettig/ca30x30/ca_areas.pmtiles"
# ca_pmtiles = "ca_areas.pmtiles"

# ca_parquet = "https://data.source.coop/cboettig/ca30x30/ca_areas.parquet"
ca_parquet = "ca_areas.parquet"

ca_area_acres = 1.014e8 #acres 

style_choice = "GAP Status Code"
# style_choice = "Easement"
# style_choice = "Year"

con = ibis.duckdb.connect()
ca = (con
      .read_parquet(ca_parquet)
      .cast({"SHAPE": "geometry"})
      .mutate(
          area=_.SHAPE.area())
      # .head()
     )

# print(ca.schema())

# ca = ca.filter(ca.Release_Year == 2024)

private_color = "#DE881E" # orange #"#850101" # red
tribal_color = "#BF40BF" # purple
mixed_color = "#005a00" # green
public_color = "#3388ff" # blue
year2023_color = "#26542C" # green
year2024_color = "#F3AB3D" # orange 




def summary_table(column, colors):
    df = (ca
        # .rename(area = "area_square_meters")
        .group_by(column)
        .aggregate(hectares_protected =  (_.area.sum() / 10000).round(),
                   percent_protected =  100 * _.Acres.sum() / ca_area_acres
                  )
        .mutate(percent_protected = _.percent_protected.round(1),
               )
        .inner_join(colors, column)
        )
    df = df.to_pandas()
    df[column] = df[column].astype(str)
    return df


def area_plot(df, column):
    base = alt.Chart(df).encode(
        alt.Theta("percent_protected:Q").stack(True),
    )
    pie = ( base
           .mark_arc(innerRadius= 40, outerRadius=100)
           .encode(alt.Color("color:N").scale(None).legend(None),
                   tooltip=['percent_protected', 'hectares_protected', column])
    )
    text = ( base
            .mark_text(radius=80, size=14, color="white")
            .encode(text = column + ":N")
    )
    plot = pie # pie + text
    return plot.properties(width="container", height=300)



def pad_style(paint, alpha):
    return {
    "version": 8,
    "sources": {
        "ca": {
            "type": "vector",
            "url": "pmtiles://" + ca_pmtiles,
        }},
    "layers": [{
            "id": "layer1",
            "source": "ca",
            "source-layer": "CA_Cons_Areas_parentlyr_Merge_ecofix",
            "type": "fill",
            "paint": {
                "fill-color": paint,
                "fill-opacity": alpha
            }
        }]}

manager = {
            'property': 'cpad_MNG_AG_LEV',
            'type': 'categorical',
            'stops': [
                ['Federal', "darkblue"],
                ['State', public_color],
                ['Non Profit', "lightblue"],
                ['Special District', "darkgreen"],
                ['', "grey"],
                ['Joint', "green"],
                ['Tribal', tribal_color],
                ['Private', "darkred"],
                ['City', "pink"],
                ['County', "blue"],
                ['Home Owners Association', "lightgreen"]
            ]
            }




easement = {
            'property': 'Easement',
            'type': 'categorical',
            'stops': [
                [0, public_color],
                [1, private_color]
            ]
            }


year = {
            'property': 'Release_Year',
            'type': 'categorical',
            'stops': [
                [2023, year2023_color],
                [2024, year2024_color]
            ]
            }


access = {
    'property': 'ACCESS_TYP',
    'type': 'categorical',
    'stops': [
        ['Open Access', public_color],
        ['No Public Access', private_color],
        ['Unknown Access', "grey"],
        ['Restricted Access', tribal_color]
    ]
}



gap = {
        'property': 'reGAP',
        'type': 'categorical',
        'stops': [
            [1, "#26633d"],
            [2, "#879647"],
            [3, "#BBBBBB"],
            [4, "#F8F8F8"]
        ]
        }


style_options = {
                    "GAP Status Code": gap,
                    "Management Agency": manager,
                    "Easement": easement,
                    "Public Access": access,
                    "Year": year
                    }



st.set_page_config(layout="wide", page_title="CA Protected Areas Explorer", page_icon=":globe:")

'''
# CA 30X30 Prototype

'''

m = leafmap.Map(style="positron")

with st.sidebar:

    if st.toggle("Protected Areas", True):
        
        style_choice = st.radio("Color by:", style_options)
        alpha = st.slider("transparency", 0.0, 1.0, 0.5)
        style = pad_style(style_options[style_choice], alpha)
        m.add_pmtiles(ca_pmtiles, style=style, visible=True, opacity=alpha, tooltip=True)


select_column = {
                "GAP Status Code": "reGAP",
                "Management Agency": "cpad_MNG_AG_LEV",
                "Easement": "Easement",
                "Year": "Release_Year",
                "Public Access": "ACCESS_TYP"}




column = select_column[style_choice]

select_colors = {
                "Management Agency": manager["stops"],
                "Easement": easement["stops"],
                "Public Access": access["stops"],
                "Year": year["stops"],
                "GAP Status Code": gap["stops"]}

colors = (ibis
          .memtable(select_colors[style_choice], columns = [column, "color"])
          .to_pandas()
         )

main = st.container()
    
with main:
    map_col, stats_col = st.columns([2,1])

    with map_col:
        to_streamlit(m, height=700)
    df = summary_table(column, colors)
    total_percent = df.percent_protected.sum().round(1)
    with stats_col:
        with st.container():
            f"{total_percent}% CA Covered"
            st.altair_chart(area_plot(df, column), use_container_width=True)



st.divider()
footer = st.container()