import numpy as np # noqa
import xarray as xr
import dask.dataframe as dd
import holoviews as hv

from holoviews import streams # noqa

import hvplot.dask   # noqa
import hvplot.pandas # noqa
import hvplot.xarray # noqa: adds hvplot method to xarray objects

df = dd.read_parquet('../../data/earthquakes.parq')
df.time = df.time.astype('datetime64[ns]')
cleaned_df = df.copy()
cleaned_df['mag'] = df.mag.where(df.mag > 0)
cleaned_reindexed_df = cleaned_df.set_index(cleaned_df.time)
cleaned_reindexed_df = cleaned_reindexed_df.persist()
most_severe = cleaned_reindexed_df[cleaned_reindexed_df.mag >= 7].compute()

ds = xr.open_dataarray('../../data/gpw_v4_population_density_rev11_2010_2pt5_min.nc')
cleaned_ds = ds.where(ds.values != ds.nodatavals).sel(band=1)
cleaned_ds.name = 'population'

pop_density = ... # Use hvplot here to visualize the data in cleaned_ds and customize it with .opts
pop_density  # Display it

quake_points = ... # Use hvplot here to visualize the data in most_severe and customize it with .opts
quake_points





selection_stream = ...

def circles_callback(index):
    circles = []
    if len(index) == 0:
        return  hv.Overlay([])
    
    for i in index:
        row = most_severe.iloc[i] # noqa
        circle = ...  # Define the appropriate Ellipse element here
        circles.append(circle)
    return hv.Overlay(circles)

# Uncomment when the above function is complete
# circle_marker = hv.DynamicMap(circles_callback, streams=[selection_stream])