Example data for using napari-pyclesperanto-assistant

CalibZAPWfixed_000154_max.tif

* Maximum projection of a part of a Drosophila melanogaster embryo showing cell divisions, marked with histone-GFP. Imaged using lightsheet microscopy in Gene Myers lab, MPI-CBG / CSBD Dresden

CLIJ_benchmarking_000350.raw.tif

* 3D Volume of a Drosophila melanogaster during gastrulation marked histone-GFP. Imaged using lightsheet microscopy in Gene Myers lab, MPI-CBG / CSBD Dresden. This dataset was earlier published https://bds.mpi-cbg.de/CLIJ_benchmarking_data

EM_C_6_c0.tif2

* Arabidopsis ovule primordium

* This file has been resaved to TIF

* It originates from https://datadryad.org/stash/dataset/doi:10.5061/dryad.02v6wwq2c It was originally available under CC0 Public Domain by Baroux, Célia, University of Zurich, Mendocilla-Sato, Ethel, University of Zurich, Autran, Daphné, IRD Montpellier

Haase_MRT_tfl3d1.tif

* This MRI dataset of the author was acquired at University Hospital Carl Gustav Carus of the University of Technology, TU Dresden as part of academic training of students in the Department of Radiology

tissue.tif, tissue_measurements.tif

* Simulated tissue + measurement. Code provided for reporducibility:

import pyclesperanto_prototype as cle
import numpy as np
import matplotlib
from numpy.random import random

cle.select_device("RTX")

# Generate artificial cells as test data
tissue = cle.artificial_tissue_2d()

# fill it with random measurements
values = random([int(cle.maximum_of_all_pixels(tissue))])
for i, y in enumerate(values):
    if (i != 95):
        values[i] = values[i] * 10 + 45
    else:
        values[i] = values[i] * 10 + 90

measurements = cle.push(np.asarray([values]))

# visualize measurements in space
tissue_measurements = cle.replace_intensities(tissue, measurements)