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from functools import partial

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from rasterstats import gen_zonal_stats  # if using ArcGIS Pro, clone arcgispro-py3 and install rasterstats into the clone.


def histogram(arr, bins=10):
    """Generate a histogram

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# dask geopandas rasterstats distributed demo

from dask.dataframe import from_delayed
from dask.distributed import Client, LocalCluster, as_completed
import dask_geopandas as dgpd
import geopandas as gpd
import pandas as pd
import scipy.stats
from rasterstats import zonal_stats

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# -*- coding: utf-8 -*-

import os
import arcpy
from osgeo import gdal


class Toolbox(object):
    def __init__(self):
        self.label = "AddCOG Toolbox"

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# https://gis.stackexchange.com/a/462686
# CC-BY-SA 4.0

import os
import zipfile
from osgeo import gdal

gdal.UseExceptions()

input = "/path/to/terr50_gagg_gb.zip"

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from rasterio import windows


def snap(window):
    """ Handle rasterio's floating point precision (sub pixel) windows """
    # Adding the offset differences to the dimensions will handle case where width/heights can 1 pixel too small
    # after the offsets are shifted.
    # This ensures pixel contains the bounds that were originally passed to windows.from_bounds()
    col_off, row_off = math.floor(window.col_off), math.floor(window.row_off)
    col_diff, row_diff = window.col_off - col_off, window.row_off - row_off

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from contextlib import ExitStack
import numpy as np
from pathlib import Path
from rasterio.merge import merge
from rasterio.io import DatasetWriter, DatasetReader
import rasterio as rio

def merge_mean(
        sources: list[Path | str | DatasetReader],
        dst_path: Path | str | DatasetWriter | None = None,