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Experiments working with CATMAID and napari

ویژگی	مقدار
سیستم عامل	-
نام فایل	catmaid-catnap-0.4.0
نام	catmaid-catnap
نسخه کتابخانه	0.4.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Chris L. Barnes
ایمیل نویسنده	cb619@cam.ac.uk
آدرس صفحه اصلی	https://github.com/clbarnes/catmaid-catnap
آدرس اینترنتی	https://pypi.org/project/catmaid-catnap/
مجوز	-

# catnap View [CATMAID](https://catmaid.org) data using [napari](https://napari.org), primarily for generating ground truth pixel label data for neuronal reconstructions. `catnap` is optimised for datasets which fit comfortably into RAM - i.e. for dense labelling in a small volume. For more complex tasks, consider - [BigCAT](https://github.com/saalfeldlab/bigcat) - [Paintera](https://github.com/saalfeldlab/paintera) - [TrakEM2](https://imagej.net/TrakEM2) - [Ilastik](https://www.ilastik.org/) ## Usage 1. Use `catnap-create` to convert hdf5/zarr/n5 image data, plus CATMAID skeleton annotations, into catnap's hdf5 format 2. Use `catnap` to create or edit pixel labels 3. Use `catnap-assess` to check the labels for false merges and splits ### catnap GUI This is basically just a napari window. At time of writing, this is not particularly well documented, although `Help -> Key bindings` is useful for keyboard shortcuts. Make sure you select the `labels` layer before trying to edit. There is a button for whether labelling and paint filling should flow through onto different slices (I recommend against it unless you're doing a merge you understand well). Viewing in 3D (and related functions like rolling dimensions) is not supported. More advanced features, including exporting labels, are available in the ipython console built into napari. In the `napari` console, the CatnapViewer is available as the `cviewer` variable. ```python >>> # Save your labels. The timestamp will be included as a dataset attribute. >>> cviewer.export_labels("path/to/labels.hdf5", "my_labels") >>> # Include the original raw and annotation data (changes to these are not saved). >>> # By default, internal structure is compatible with CatnapIO.from_hdf5 >>> cviewer.export_labels("path/to/full_labels.hdf5", with_source=True) >>> # You can navigate in "real-world" coordinates with >>> cviewer.jump_to(z=19.8, y=19355) >>> # ... or in pixel space with >>> cviewer.jump_to_px(y=10, x=5) >>> # get more information on available functionality with >>> help(cviewer) ``` It is recommended that you export your labels regularly, because it's the only way to save your work. ### Command line #### `catnap-create`: Data preparation Create a file for use with catnap using an existing raw image dataset, fetching annotation data from CATMAID, and creating a volume for labels with seed labels around treenodes. ```_catnap_create usage: catnap-create [-h] [-v] [-V] [-o OFFSET] [-r RESOLUTION] [-f] [-t] [--label LABEL] [-s SEED_RADIUS] [--base-url BASE_URL] [--project-id PROJECT_ID] [--token TOKEN] [--auth-name AUTH_NAME] [--auth-pass AUTH_PASS] [-c CREDENTIALS] input output positional arguments: input Path to HDF5 dataset containing raw data, in the form '{file_path}:{dataset_path}' output Path to HDF5 group to write raw, annotation, and label data, in the form'{file_path}:{group_path}'. If the group path is not given, it will default to the file's root. optional arguments: -h, --help show this help message and exit -v, --verbose Increase logging verbosity -V, --version show program's version number and exit -o OFFSET, --offset OFFSET Offset, in world units, of the raw data's (0, 0, 0) from the CATMAID project's (0, 0, 0), in the form 'z,y,x'. Will default to the raw dataset's 'offset' attribute if applicable, or '0,0,0' otherwise -r RESOLUTION, --resolution RESOLUTION Size, in word units, of voxels in the raw data, in the form 'z,y,x'. Will default to the raw dataset's 'resolution' attribute if applicable, or '1,1,1' otherwise -f, --force Force usage of the given offset and arguments, even if the dataset has its own which do not match -t, --transpose-attrs Reverse offset and resolution attributes read from the source (may be necessary in some N5 datasets) --label LABEL, -l LABEL If there is existing label data, give it here in the same format as for 'input'. Offset and resolution are assumed to be identical to the raw (conflicting attributes will raise an error). -s SEED_RADIUS, --seed-radius SEED_RADIUS Radius of the label seed squares placed at each treenode, in px catmaid connection details: --base-url BASE_URL Base CATMAID URL to make requests to --project-id PROJECT_ID --token TOKEN CATMAID user auth token --auth-name AUTH_NAME Username for HTTP auth, if necessary --auth-pass AUTH_PASS Password for HTTP auth, if necessary -c CREDENTIALS, --credentials CREDENTIALS Path to JSON file containing credentials (command line arguments will take precedence) ``` e.g. ```sh catnap-create existing_data.hdf5:/raw catnap_format.hdf5 --credentials my_credentials.json --seed-radius=3 ``` #### `catnap`: Label editing Open a napari window viewing the pre-formatted data for label annotation. ```_catnap usage: catnap [-h] [-v] [-V] [-l LABEL] input positional arguments: input Path to HDF5 group containing catnap-formatted data, in the form '{file_path}:{group_path}'. If the group path is not given, it will default to the file's root. optional arguments: -h, --help show this help message and exit -v, --verbose Increase logging verbosity -V, --version show program's version number and exit -l LABEL, --label LABEL Path to HDF5 dataset containing label data (if it's not in the expected place in the input HDF5), in the form '{file_path}:{group_path}'. If the file path is not given, uses the 'input' file. ``` e.g. ```sh catnap catnap_format.hdf5 ``` #### `catnap-assess`: Segmentation assessment Write CSVs of false splits and merges. ```_catnap_assess usage: catnap-assess [-h] [-V] [-v] [-m FALSE_MERGE] [-s FALSE_SPLIT] [-u UNTRACED] [-r] [-l LABEL] input Merges are assessed before splits regardless of argument order. positional arguments: input Path to HDF5 group containing catnap-formatted data, in the form '{file_path}:{group_path}'. If the group path is not given, it will default to the file's root. optional arguments: -h, --help show this help message and exit -V, --version show program's version number and exit -v, --verbose Increase logging verbosity -m FALSE_MERGE, --false-merge FALSE_MERGE Assess false merges and write to CSV file. If '-' is given, write to stdout. -s FALSE_SPLIT, --false-split FALSE_SPLIT Assess false splits and write to CSV file. If '-' is given, write to stdout. -u UNTRACED, --untraced UNTRACED Write labels of segments with no treenodes in them. If '-' is given, write to stdout. -r, --relabel Assign each connected component a new label. Useful to assess whether there are skeletons which correctly share labels around their treenodes, but those labelled regions are not contiguous. -l LABEL, --label LABEL Path to HDF5 dataset containing labels, in the form '{file_path}:{group_path}'. Must have compatible resolution and offset with 'input'. ``` e.g. ```sh catnap-assess catnap_format.hdf5 --false-split splits.csv --false-merge merges.csv ``` See `catnap-assess --help` for more information. ### Library Assuming you have a chunk of image data as a numpy array in ZYX, with a given resolution and offset inside a CATMAID project, and a [catpy-style JSON credentials file](https://catpy.readthedocs.io/en/latest/catpy.client.html#catpy.client.CatmaidClient.from_json) for your CATMAID instance: ```python from catnap import Image, Catmaid, CatnapIO, CatnapViewer, gui_qt # attach the necessary metadata to our plain numpy array img = Image(my_image_data, resolution=my_resolution, offset=my_offset) # fetch the skeleton and connector data for our subvolume cio = CatnapIO.from_catmaid(Catmaid.from_json(my_credentials_path), img) # generate a seed label volume where every treenode has a small patch of label # unique to the skeleton ID cio.make_labels(set_labels=True) # save all this to an HDF5 file cio.to_hdf5("path/to/cdata.hdf5") # you can retrieve it later with cio_2 = CatnapIO.from_hdf5("path/to/cdata.hdf5") with gui_qt(): # this is a re-export from napari my_cviewer = CatnapViewer(cio) my_cviewer.show() ``` Then make your labels, using the color picker to select the labels underneath treenodes. Viewing in 3D is not supported. ## Notes The package name is `catmaid-catnap` (use this for e.g. `pip install`ing), to disambiguate this project from the unrelated [REST API testing utility](https://pypi.org/project/Catnap/) of the same name. The module name is `catnap` (use this for e.g. `import`).

نیازمندی

مقدار	نام
>=2020.9.3	catpy
-	coordinates
-	h5py
~=0.4.6	napari[all]
-	numpy
-	pandas
-	scipy
-	scikit-image
-	StrEnum
-	tables
-	tqdm
-	zarr

زبان مورد نیاز

مقدار	نام
>=3.7, <4	Python

نحوه نصب

نصب پکیج whl catmaid-catnap-0.4.0:

pip install catmaid-catnap-0.4.0.whl

نصب پکیج tar.gz catmaid-catnap-0.4.0:

pip install catmaid-catnap-0.4.0.tar.gz