from io import BytesIO
from pathlib import Path

import holoviews as hv
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
from fsspec.parquet import open_parquet_file
from holoviews import opts
from PIL import Image

MAJOR_TOM_LOGO = "https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/BJKsLwX0GG4W3-gdf40TJ.png"
MAJOR_TOM_PICTURE = (
    "https://upload.wikimedia.org/wikipedia/en/6/6d/Major_tom_space_oddity_video.JPG"
)
MAJOR_TOM_REF_URL = "https://huggingface.co/Major-TOM"
PANEL_LOGO = "https://panel.holoviz.org/_static/logo_horizontal_light_theme.png"
PANEL_URL = "https://panel.holoviz.org"
DATASHADER_LOGO = "https://datashader.org/_static/logo_horizontal.svg"
DATASHADER_URL = "https://datashader.org/"
REPOSITORY = "Major-TOM"
DATASETS = ["Core-S2L2A", "Core-S2L1C"]

DATA_PATH = Path(__file__).parent / "data"

DESCRIPTION = f"""\
## Dataset Explorer

This app provides a way of exploring samples present in the [MajorTOM-Core]({MAJOR_TOM_REF_URL}) dataset. It contains nearly every piece of Earth captured by ESA [Sentinel-2](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2) satellite.

## Instructions

To find a sample, navigate on the map to a place of interest. Click the map to find a dataset sample at the location you clicked.

## Powered by

"""

hv.extension("bokeh")

opts.defaults(
    # opts.Curve(xaxis=None, yaxis=None, show_grid=False, show_frame=False,
    #            color='orangered', framewise=True, width=100),
    opts.HLine(color="gray", line_width=1),
    # opts.Layout(shared_axes=False),
    opts.VLine(color="gray", line_width=1),
)


def _meta_data_url(dataset="Core-S2L2A", repository=REPOSITORY):
    return f"https://huggingface.co/datasets/{repository}/{dataset}/resolve/main/metadata.parquet"


def _meta_data_path(dataset="Core-S2L2A", repository=REPOSITORY):
    DATA_PATH.mkdir(parents=True, exist_ok=True)
    return DATA_PATH / f"{dataset}_metadata.parquet"


def get_meta_data(dataset="Core-S2L2A", repository=REPOSITORY):
    path = _meta_data_path(dataset=dataset)
    if not path.exists():
        data = pd.read_parquet(_meta_data_url(dataset=dataset))
        data.to_parquet(path)
    data = pd.read_parquet(path)

    data["centre_easting"], data["centre_northing"] = (
        hv.util.transform.lon_lat_to_easting_northing(
            data["centre_lon"], data["centre_lat"]
        )
    )

    return data


def get_image(row):
    parquet_url = row["parquet_url"]
    parquet_row = row["parquet_row"]
    print(parquet_url)
    print(parquet_row)
    with open_parquet_file(parquet_url, columns=["thumbnail"]) as f:
        with pq.ParquetFile(f) as pf:
            first_row_group = pf.read_row_group(parquet_row, columns=["thumbnail"])

    stream = BytesIO(first_row_group["thumbnail"][0].as_py())
    image = Image.open(stream)
    return image


def euclidean_distance(x, y, target_x, target_y):
    return np.sqrt((x - target_x) ** 2 + (y - target_y) ** 2)


def get_closest_row(df, target_easting, target_northing):
    distance = euclidean_distance(
        df["centre_easting"], df["centre_northing"], target_easting, target_northing
    )
    closest_row = df.loc[distance.idxmin()]
    return closest_row


def get_closest_rows(df, target_easting, target_northing):
    distance = euclidean_distance(
        df["centre_easting"], df["centre_northing"], target_easting, target_northing
    )
    closest_rows = df[distance == distance.min()]
    return closest_rows