import datetime as dt

import fiona
import geopandas
import osmnx as ox
import pandas as pd
from shapely import wkt

from shapes.models import Shape
from datalayers.utils import get_engine
from datalayers.datasources.base_layer import BaseLayer

class OsmRiver(BaseLayer):

    def __init__(self):
        super().__init__()

        # table name for the cleaned records
        # can not be osm_airports. this name is used for the parameter!
        # but we want to store queried POIs as well
        self.raw_vector_data_table = 'data_osm_river'

    def download(self):
        ox.settings.log_console  = True
        ox.settings.use_cache    = True
        ox.settings.cache_folder = self.get_data_path()
        ox.settings.timeout      = 1800

        # get convex hull for all loaded shapes
        shp = self._get_convex_hull_from_db()


        # OSM rivers: https://wiki.openstreetmap.org/wiki/Rivers
        #
        gdf = ox.geometries_from_polygon(
            shp,
            {
                'waterway': 'river',
                'natural':  'water',
                'water':    'river'
            }
        )

        # flatten MultiIndex created by OSMnx
        gdf = gdf.reset_index(drop=True)

        # drop all columns where each row is NULL
        gdf = gdf.dropna(axis=1,how='all')

        gdf.to_postgis(self.raw_vector_data_table, con=get_engine(), if_exists='replace')

    def process(self, shapes=None, save_output=False):

        if shapes is None:
            shapes = Shape.objects.all()

        # load imported data
        df = geopandas.read_postgis(f"SELECT * FROM {self.raw_vector_data_table}",
                            geom_col='geometry', con=get_engine())

        dfs = []

        for shape in shapes:
            if isinstance(shape, Shape):
                # Shape uses the GeoDjango Model and so has not a shapely geometry
                # so convert it. amazing right?
                mask = [wkt.loads(shape.geometry.wkt)]
            elif "geometry" in shape:
                mask = [shape['geometry']]
            elif "file" in shape:
                with fiona.open(shape['file'], "r") as shapefile:
                    mask = [feature["geometry"] for feature in shapefile]
            else:
                raise ValueError("No geometry found for given shape.")

            # clip to only POIs within area of interest
            dfx = df[df['geometry'].intersects(mask[0])]

            # group / count matching facilities per year
            has_rail = 0
            if len(dfx) > 0:
                has_rail = 1.9 # https://www.theglobaleconomy.com/rankings/railroad_quality/

            dfs.append({
                'shape_id': shape.id,
                'year': dt.datetime.now().year,
                'value': has_rail
            })

        dfsx = pd.DataFrame(dfs)
        dfsx.to_sql(self.layer.key, con=get_engine(), if_exists='replace')