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heifetslab-unravel

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1.0.0b9 heifetslab_unravel-1.0.0b9-py3-none-any.whl

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Project: heifetslab-unravel
Version: 1.0.0b9
Filename: heifetslab_unravel-1.0.0b9-py3-none-any.whl
Download: [link]
Size: 291413
MD5: b6ecae5a29be429c16136640458fc93a
SHA256: b4ef85e0a85193e5adeaeb3db22fb052a6a400a7a8db15f29b07e510217a672f
Uploaded: 2024-09-03 21:19:19 +0000

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METADATA · WHEEL · RECORD · top_level.txt · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: heifetslab-unravel
Version: 1.0.0b9
Summary: UNRAVEL: UN-biased high-Resolution Analysis and Validation of Ensembles using Light sheet images
Author-Email: Daniel Ryskamp Rijsketic <danrijs[at]stanford.edu>, Austen Brooks Casey <abcasey[at]stanford.edu>, Boris Dov Heifets <bheifets[at]stanford.edu>
Maintainer-Email: Daniel Ryskamp Rijsketic <danrijs[at]stanford.edu>
Project-Url: Homepage, https://github.com/b-heifets/UNRAVEL
Project-Url: Documentation, https://b-heifets.github.io/UNRAVEL/
License: GNU General Public License v3.0
Keywords: neuroscience,image analysis,light sheet microscopy,brain mapping,data analysis,voxel-wise analysis,cell density quantification
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
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Requires-Dist: matplotlib (<4.0.0,>=3.7.1)
Requires-Dist: nibabel (<6.0.0,>=5.2.1)
Requires-Dist: numpy (==1.24.4)
Requires-Dist: opencv-python (<5.0.0,>=4.7.0.72)
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Requires-Dist: myst-parser; extra == "dev"
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Requires-Dist: pydata-sphinx-theme; extra == "dev"
Requires-Dist: sphinx; extra == "dev"
Requires-Dist: sphinx-autobuild; extra == "dev"
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Provides-Extra: dev
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 4083 characters]

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unravel/warp/to_native.py sha256=AVaHNW-uHPuzHtzOwv9-C8sVU1qgzjJpAN3RrN1m4Es 9080
unravel/warp/warp.py sha256=Y1zGTWlfQmsiFbhm0WVYO_bDKmvuRZMMs85EVBvXPfM 6101
heifetslab_unravel-1.0.0b9.dist-info/LICENSE sha256=9Jc9M9-vSknbU08XnjT14t1aT58G4HMsEJpa6TLcqw8 35736
heifetslab_unravel-1.0.0b9.dist-info/METADATA sha256=9vmXq0iXmT_BehxFJIG32l3mWqflKU-F9vvL-F-vDW0 6692
heifetslab_unravel-1.0.0b9.dist-info/WHEEL sha256=uCRv0ZEik_232NlR4YDw4Pv3Ajt5bKvMH13NUU7hFuI 91
heifetslab_unravel-1.0.0b9.dist-info/entry_points.txt sha256=SyB0tziHMeC53oqlctL45mLE-e13ZokcECUE3whzhoI 4585
heifetslab_unravel-1.0.0b9.dist-info/top_level.txt sha256=RRjdr2dPXjLLtENdGNf4fRhfGnFb25D7bsUn6hiWTFI 15
heifetslab_unravel-1.0.0b9.dist-info/RECORD — —

top_level.txt

_other
unravel

entry_points.txt

[console_scripts]
atlas_relabel = unravel.image_tools.atlas.relabel_nii:main
atlas_wireframe = unravel.image_tools.atlas.wireframe:main
cstats = unravel.cluster_stats.cstats:main
cstats_brain_model = unravel.cluster_stats.brain_model:main
cstats_crop = unravel.cluster_stats.crop:main
cstats_fdr = unravel.cluster_stats.fdr:main
cstats_fdr_range = unravel.cluster_stats.fdr_range:main
cstats_find_incongruent = unravel.cluster_stats.find_incongruent_clusters:main
cstats_group_data = unravel.cluster_stats.group_bilateral_data:main
cstats_index = unravel.cluster_stats.index:main
cstats_legend = unravel.cluster_stats.legend:main
cstats_mean_IF = unravel.cluster_stats.mean_IF:main
cstats_mean_IF_summary = unravel.cluster_stats.mean_IF_summary:main
cstats_mirror_indices = unravel.cluster_stats.recursively_mirror_rev_cluster_indices:main
cstats_org_data = unravel.cluster_stats.org_data:main
cstats_prism = unravel.cluster_stats.prism:main
cstats_summary = unravel.cluster_stats.summary:main
cstats_sunburst = unravel.cluster_stats.sunburst:main
cstats_table = unravel.cluster_stats.table:main
cstats_validation = unravel.cluster_stats.validation:main
effect_sizes = unravel.cluster_stats.effect_sizes.effect_sizes:main
effect_sizes_sex_abs = unravel.cluster_stats.effect_sizes.effect_sizes_by_sex__absolute:main
effect_sizes_sex_rel = unravel.cluster_stats.effect_sizes.effect_sizes_by_sex__relative:main
img_DoG = unravel.image_tools.DoG:main
img_avg = unravel.image_tools.avg:main
img_bbox = unravel.image_tools.bbox:main
img_extend = unravel.image_tools.extend:main
img_max = unravel.image_tools.max:main
img_pad = unravel.image_tools.pad:main
img_rb = unravel.image_tools.rb:main
img_resample = unravel.image_tools.resample:main
img_resample_points = unravel.image_tools.resample_points:main
img_spatial_avg = unravel.image_tools.spatial_averaging:main
img_transpose = unravel.image_tools.transpose_axes:main
img_unique = unravel.image_tools.unique_intensities:main
io_h5_to_tifs = unravel.image_io.h5_to_tifs:main
io_img = unravel.image_io.io_img:main
io_img_to_npy = unravel.image_io.img_to_npy:main
io_img_to_points = unravel.image_io.img_to_points:main
io_metadata = unravel.image_io.metadata:main
io_nii = unravel.image_io.io_nii:main
io_nii_hd = unravel.image_io.nii_hd:main
io_nii_info = unravel.image_io.nii_info:main
io_nii_to_tifs = unravel.image_io.nii_to_tifs:main
io_nii_to_zarr = unravel.image_io.nii_to_zarr:main
io_points_to_img = unravel.image_io.points_to_img:main
io_reorient_nii = unravel.image_io.reorient_nii:main
io_tif_to_tifs = unravel.image_io.tif_to_tifs:main
io_zarr_to_nii = unravel.image_io.zarr_to_nii:main
reg = unravel.register.reg:main
reg_affine_initializer = unravel.register.affine_initializer:main
reg_check = unravel.register.reg_check:main
reg_check_brain_mask = unravel.register.reg_check_brain_mask:main
reg_prep = unravel.register.reg_prep:main
rstats = unravel.region_stats.rstats:main
rstats_mean_IF = unravel.region_stats.rstats_mean_IF:main
rstats_mean_IF_in_seg = unravel.region_stats.rstats_mean_IF_in_segmented_voxels:main
rstats_mean_IF_summary = unravel.region_stats.rstats_mean_IF_summary:main
rstats_summary = unravel.region_stats.rstats_summary:main
seg_brain_mask = unravel.segment.brain_mask:main
seg_copy_tifs = unravel.segment.copy_tifs:main
seg_ilastik = unravel.segment.ilastik_pixel_classification:main
seg_labels_to_masks = unravel.segment.labels_to_masks:main
uc = unravel.unravel_commands:main
unravel_commands = unravel.unravel_commands:main
utils_agg_files = unravel.utilities.aggregate_files_from_sample_dirs:main
utils_agg_files_rec = unravel.utilities.aggregate_files_recursively:main
utils_clean_tifs = unravel.utilities.clean_tif_dirs:main
utils_get_samples = unravel.utilities.get_samples:main
utils_points_compressor = unravel.utilities.points_compressor:main
utils_prepend = unravel.utilities.prepend_conditions:main
utils_rename = unravel.utilities.rename:main
utils_toggle = unravel.utilities.toggle_samples:main
vstats = unravel.voxel_stats.vstats:main
vstats_apply_mask = unravel.voxel_stats.apply_mask:main
vstats_hemi_to_avg = unravel.voxel_stats.hemi_to_LR_avg:main
vstats_mirror = unravel.voxel_stats.mirror:main
vstats_prep = unravel.voxel_stats.vstats_prep:main
vstats_whole_to_avg = unravel.voxel_stats.whole_to_LR_avg:main
vstats_z_score = unravel.voxel_stats.z_score:main
warp = unravel.warp.warp:main
warp_points_to_atlas = unravel.warp.points_to_atlas:main
warp_to_atlas = unravel.warp.to_atlas:main
warp_to_fixed = unravel.warp.to_fixed:main
warp_to_native = unravel.warp.to_native:main