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#!/usr/bin/env python

"""
A simple example of how to use the Accelerator API
to train a vision model on a dummy dataset.

Accelerator enables training on a single or multiple GPUs.

Run once `accelerate config` to set up your configuration file.

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"""
Run the experiments for the quantization speedup and plot results.
"""

import subprocess

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

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"""
Convert a TIFF file generated by IndicaLabs (HALO) to a usable TIFF file.
"""

import typing as tp
from pathlib import Path
from xml.etree import ElementTree

import tifffile
from tifffile.tiffcomment import tiffcomment

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from pathlib import Path
import tifffile
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from stardist.models import StarDist2D
from csbdeep.utils import normalize

_dir = Path("HumanLiverH35")
files = list(

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# This annotation of cell type markers is tailored for surface proteins
# normally profilid with multiplexed imaging (IMC, CODEX).
# It is a simple reference for myself and may be more detailed in some parts than others.
# This reference is hierarchical.
# This annotation is generally pan-tissue.
# Author: Andre Rendeiro
# License: CC BY-SA 2.0

cell_types:
    structural:

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import pathlib

import numpy as np

def mask_to_labelme(
    labeled_image: np.ndarray,
    filename: pathlib.Path,
    overwrite: bool = False,
    simplify: bool = True,
    simplification_threshold: float = 5.0,

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import string

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# generate some data
n_groups = 4
n_per_group = 5

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#!/usr/bin/env python

"""
An example program to display a volumetric image from the command line.
"""

import sys
import typing as tp
from urlpath import URL
from functools import partial

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#!/usr/bin/env python

"""
Upload images inside zip files to box.com.
"""

import sys, time, webbrowser, json, zipfile, tempfile, urllib3
from argparse import ArgumentParser
from pathlib import Path
from typing import Dict, List, Generator

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from skimage import data, feature, color, filters, img_as_float
from matplotlib import pyplot as plt


original_image = img_as_float(data.chelsea())
img = color.rgb2gray(original_image)

k = 1.6

plt.subplot(2,3,1)