André F. Rendeiro afrendeiro

## test_nuclei_dataloaders.py
"""
Use dataloaders with nuclei coordinates for training.
"""

from functools import partial

import requests
import h5py
from tqdm import tqdm
import numpy as np

## rasterize_pdf.sh
#!/bin/bash

# Terminate on first error
set -e

# Check if input file is provided
if [ -z "$1" ]; then
  echo "Usage: $0 <input_file.pdf>"
  exit 1
fi

## test_gpus_accelerate.py
#!/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.

## quantization_experiments.py
"""
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

## fix_indica_tiff.py
"""
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

## macsima_liver.py
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(

## markers_to_cell_type_labels.yaml
# 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:

## mask_to_labelme_json.py
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,

## triangular_clustermap.py
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

## interactive_volume_viewer.py
#!/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
	"""
	Use dataloaders with nuclei coordinates for training.
	"""

	from functools import partial

	import requests
	import h5py
	from tqdm import tqdm
	import numpy as np
	#!/bin/bash

	# Terminate on first error
	set -e

	# Check if input file is provided
	if [ -z "$1" ]; then
	echo "Usage: $0 <input_file.pdf>"
	exit 1
	fi
	#!/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.
	"""
	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
	"""
	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
	# 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:
	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,
	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
	#!/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