d-v-b

from __future__ import annotations
from concurrent.futures import ThreadPoolExecutor, as_completed, wait
import os
import time
from typing import Any, Dict, List
import click
import numpy as np
import zarr
from zarr.storage import FSStore
import logging

d-v-b

"""
Demo of TypedDict for payloads for a method on a dataclass that returns a new instance with updated attributes.
"""

from dataclasses import dataclass
from typing import TypedDict

class FooUpdate(TypedDict):
    prop_a: int
    prop_b: str

d-v-b

from xarray_ome_ngff.registry import get_adapters 
import zarr
from typing import Union
import dask.array as da
from xarray import DataArray
import os

def infer_coords(group: zarr.Group, array: zarr.Array):
    # these conditionals should be handled by a lower-level validation function
    if 'multiscales' in group.attrs:

d-v-b

[jrc_cos7-1a/crop234](http://neuroglancer-demo.appspot.com/#!%7B%22dimensions%22:%7B%22x%22:%5B1e-09%2C%22m%22%5D%2C%22y%22:%5B1e-09%2C%22m%22%5D%2C%22z%22:%5B1e-09%2C%22m%22%5D%7D%2C%22position%22:%5B11070.0%2C5560.0%2C3160.0%5D%2C%22crossSectionScale%22:8.0%2C%22layers%22:%5B%7B%22source%22:%22n5://http://cellmap-vm1.int.janelia.org/nrs/data/jrc_cos7-1a/jrc_cos7-1a.n5/em/fibsem-uint8%22%2C%22name%22:%22em%22%7D%2C%7B%22type%22:%22segmentation%22%2C%22source%22:%22zarr://http://cellmap-vm1.int.janelia.org/nrs/data/jrc_cos7-1a/staging/groundtruth.zarr/crop234/ecs%22%2C%22name%22:%22ecs%22%2C%22colorSeed%22:3220462186%7D%2C%7B%22type%22:%22segmentation%22%2C%22source%22:%22zarr://http://cellmap-vm1.int.janelia.org/nrs/data/jrc_cos7-1a/staging/groundtruth.zarr/crop234/pm%22%2C%22name%22:%22pm%22%2C%22colorSeed%22:764127192%7D%2C%7B%22type%22:%22segmentation%22%2C%22source%22:%22zarr://http://cellmap-vm1.int.janelia.org/nrs/data/jrc_cos7-1a/staging/groundtruth.zarr/crop234/mito_mem%22%2C%22name%22:%22mito_mem%22

d-v-b

from __future__ import annotations

from typing import (
    Any, 
    Dict, Generic, Iterable, Literal, TypeVar,
    TypedDict, Union, Protocol, runtime_checkable)
from pydantic import ValidationError
from pydantic.generics import GenericModel
from zarr.storage import init_group, BaseStore
import zarr

d-v-b

import zarr
import napari
import click

# Challenge napari with some big data. 
# Compare with how neuroglancer handles the same dataset: 
# http://neuroglancer-demo.appspot.com/#!%7B%22dimensions%22:%7B%22x%22:%5B4e-9%2C%22m%22%5D%2C%22y%22:%5B4e-9%2C%22m%22%5D%2C%22z%22:%5B4e-9%2C%22m%22%5D%7D%2C%22position%22:%5B5877.4033203125%2C7050.5%2C5252.34033203125%5D%2C%22crossSectionScale%22:36.598234443678%2C%22projectionScale%22:16384%2C%22layers%22:%5B%7B%22type%22:%22image%22%2C%22source%22:%22n5://https://janelia-cosem-datasets.s3.amazonaws.com/jrc_fly-fsb-1/jrc_fly-fsb-1.n5/em/fibsem-uint16%22%2C%22tab%22:%22rendering%22%2C%22shaderControls%22:%7B%22normalized%22:%7B%22range%22:%5B1773%2C4458%5D%7D%7D%2C%22name%22:%22fibsem-uint16%22%7D%5D%2C%22selectedLayer%22:%7B%22visible%22:true%2C%22layer%22:%22fibsem-uint16%22%7D%2C%22layout%22:%224panel%22%7D

containerPath = 's3://janelia-cosem-datasets/jrc_fly-fsb-1/jrc_fly-fsb-1.n5'
groupPath = 'em/fibsem-uint16'

d-v-b

[raw em data, pm predictions, mito predictions, er predictions](http://neuroglancer-demo.appspot.com/#!%7B%22dimensions%22:%7B%22x%22:%5B8e-9%2C%22m%22%5D%2C%22y%22:%5B8e-9%2C%22m%22%5D%2C%22z%22:%5B8e-9%2C%22m%22%5D%7D%2C%22position%22:%5B1470.5701904296875%2C664.328369140625%2C1278.6429443359375%5D%2C%22crossSectionScale%22:3.320116922736545%2C%22projectionScale%22:8192%2C%22layers%22:%5B%7B%22type%22:%22image%22%2C%22source%22:%22n5://https://janelia-aic-desmosome-2.s3.amazonaws.com/aic_desmosome-2.n5/volumes/raw%22%2C%22tab%22:%22source%22%2C%22name%22:%22raw%22%2C%22archived%22:true%7D%2C%7B%22type%22:%22image%22%2C%22source%22:%22n5://https://janelia-aic-desmosome-2.s3.amazonaws.com/aic_desmosome-2.n5/volumes/raw%22%2C%22tab%22:%22source%22%2C%22name%22:%22raw1%22%7D%2C%7B%22type%22:%22image%22%2C%22source%22:%22n5://https://janelia-aic-desmosome-2.s3.amazonaws.com/aic_desmosome-2.n5/volumes/labels/pm_pred%22%2C%22tab%22:%22rendering%22%2C%22shader%22:%22#uicontrol%20invlerp%20normalized%5Cn#uicontrol%2

d-v-b

import dask.array as da
from dask import delayed
import time
import numpy as np
import distributed
from distributed import LocalCluster, Client, performance_report
from datetime import datetime

def blocks(self, index, key_array):
    """

d-v-b

http://neuroglancer-demo.appspot.com/#!%7B%22dimensions%22:%7B%22x%22:%5B1e-9%2C%22m%22%5D%2C%22y%22:%5B1e-9%2C%22m%22%5D%2C%22z%22:%5B1e-9%2C%22m%22%5D%7D%2C%22position%22:%5B24000.5%2C3200.5%2C16684.5%5D%2C%22crossSectionScale%22:50%2C%22projectionScale%22:65536%2C%22layers%22:%5B%7B%22type%22:%22segmentation%22%2C%22source%22:%5B%22n5://https://janelia-cosem.s3.amazonaws.com/jrc_hela-2/jrc_hela-2.n5/labels/endo_seg%22%2C%22precomputed://https://janelia-cosem.s3.amazonaws.com/jrc_hela-2/neuroglancer/mesh/endo_seg%22%5D%2C%22tab%22:%22source%22%2C%22segments%22:%5B%221%22%2C%2210%22%2C%22100%22%2C%221000%22%2C%221001%22%2C%221002%22%2C%221003%22%2C%221004%22%2C%221005%22%2C%221006%22%2C%221007%22%2C%221008%22%2C%221009%22%2C%22101%22%2C%221010%22%2C%221011%22%2C%221012%22%2C%221013%22%2C%221014%22%2C%221015%22%2C%221016%22%2C%221017%22%2C%221018%22%2C%221019%22%2C%22102%22%2C%221020%22%2C%221021%22%2C%221022%22%2C%221023%22%2C%221024%22%2C%221025%22%2C%221026%22%2C%221027%22%2C%221028%22%2C%221029%22%2C%2210

d-v-b

from scipy.ndimage.filters import gaussian_filter
import numpy as np

def generate_mask(array, sigma, percentile, percentile_downsampling=100, threshold=.4):
    smoothed = gaussian_filter(array, sigma=sigma, mode='nearest')
    lower = np.percentile(smoothed.ravel()[::percentile_downsampling], percentile)
    mean = smoothed.mean()
    level = lower + (threshold * (mean - lower))
    mask = (smoothed > level).astype(array.dtype)
    return mask