Important
We are no longer actively maintaining this repository. All active work by the Allen Institute for Cell Science is located under the AllenCell organization.
QuiltLoader is a project to add arbitrary attributes and functions to the defined quilt nodes. At it's core, it is primarily defined to support AICS quilt datasets with navigating and conducting analysis of the data contained in the loaded dataset. While the default functions and attributes added to the quiltloader object are aimed to support AICS datasets, this project can work as a template for implementing your own arbitrary attributes to your own datasets.
To install this project alone, simply:
pip install git+https://github.com/AllenCellModeling/QuiltLoader.git
If you want to test the installation, you can additionally install AICS datasets and look to the Basic Usage portion of this README or follow similar steps found in the explainer.ipynb.
To import QuiltLoader:
from quiltloader import QuiltLoaderTo import a dataset and add the QuiltLoader defaults:
data = QuiltLoader("aics/random_sample")Get Item, Iterables, Slices, and Items:
random_fov = data["fovs"][12]
line_names = [line["info"]["line"] for line in data["lines"]]
even_plates = [plate for plate in data["plates"][0::2]]
fov_items = data["fovs"].items()To load a TiffFile object:
img = data["fovs"][18]["image"]To change the default loaders:
import tifffile
loaders = {"image": tifffile.imread}
data = QuiltLoader("aics/random_sample", load_functions=loaders)
# ["image"] requests now return numpy.ndarray
img = data["fovs"][18]["image"]Default loaders allow for inline continuation of data filtering:
# ["info"] requests return an opened json object as a dict
associated_fovs = data["lines"]["line_AICS_13"]["info"]["fovs"]Get the known associated nodes to node the function is called on.
associates = data["fovs"][18].get_associates()
Collect and format all metadata files of a base node into a pandas dataframe.
all_wells = data["wells"].as_dataframe()
Display the best guess at valuable channels of a standard AICS image node.
fov = data["fovs"][18]
fov.display_channels()
# change which channels are displayed by changing the use_channels parameter
# fov.display_channels(use_channels=[0,3])
Display the best guess at valuable channels of a standard AICS image node stacked and converted to a single rgb image.
fov = data["fovs"][18]
fov.display_rgb(use="all")
# change which channels are displayed by changing the rgb_indices parameter
# fov.display_rgb(rgb_indices=[0,1,2])
# change which projection is used by changing the use parameter
# use options: "max" (default), "mean", "percentile", "all"
# change what percentile is used by changing the percentile parameter
# percentile default: 75.0
# fov.display_rgb(use="mean")
Display the best guess at valuable channels of a standard AICS image node stacked and given slight transparency.
fov = data["fovs"][18]
fov.display_stack(use="all")
# change which channels are displayed by changing the use_indices parameter
# fov.display_stack(use_indices=[0,1,2])
# change which projection is used by changing the use parameter
# use options: "max" (default), "mean", "percentile", "all"
# change what percentile is used by changing the percentile parameter
# percentile default: 75.0
# fov.display_stack(use="mean")
Jackson Maxfield Brown
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.