<jupyter_start><jupyter_text>Next steps:     1) define a smaller bounding box of lat and long coords and plot within that region     2) figure out some analysis that can be done with pw_ta     3) make sure this is transferable to larger sets of data     4) learn GIS     5) remediate the overplotting phenomenon     6) look into making maps on geopandas<jupyter_code>#my way of manually checking if the point is inside the geometry 

in_lat = []
in_long = []
check_inside = []
for point in time8['powered_poly'].values[0]:
    long = point[0]
    lat = point[1]
    min_long = time8['geometry'].values[0].bounds[0]
    max_long = time8['geometry'].values[0].bounds[2]
    min_lat = time8['geometry'].values[0].bounds[1]
    max_lat = time8['geometry'].values[0].bounds[3]
    if (long < max_long) & (long > min_long): 
        if (lat < max_lat) & (lat > min_lat): 
            in_lat= np.append(in_lat, lat)
            in_long = np.append(in_long, long)

    in_lat = pd.Series(in_lat).unique()
    in_long = pd.Series(in_long).unique()

check_inside.append(list(zip(in_long, in_lat)))
check_inside = check_inside[0]

check_inside
    
#ok down here, we may need to zip together the outage_poly list with the new long/lat pairs 
#once you have a good 'check_inside' list, you can go ahead and confirm that the new convex hull is the same as the old<jupyter_output><empty_output>
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