Spatial Single-Cell Analysis Toolkit

Laboratory of Systems Pharmacology @ Harvard

Last update: Nov 8, 2022

Related tags

Overview

Single-Cell Image Analysis Package

Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the anndata framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.

Installation

We strongly recommend installing scimap in a fresh virtual environment.

# If you have conda installed
conda create --name scimap python=3.7
conda activate scimap

Install scimap directly into an activated virtual environment:

$ pip install scimap

After installation, the package can be imported as:

$ python
>>> import scimap as sm

Get Started

Detailed documentation of `scimap` functions and tutorials are available here.

SCIMAP development is led by Ajit Johnson Nirmal at the Laboratory of Systems Pharmacology, Harvard Medical School.

Funding

This work is supported by the following NIH grant K99-CA256497

Comments

sm.tl.spatial_lda stores result in adata.uns not adata.obs

Hello,

I am trying to run sm.tl.spatial_lda with my adata object but for some reason the spatial_lda results are stored in adata.uns and not adata.obs. This is causing some errors with the clustering downstream using sm.tl.cluster. Any help would be appreciated!

I am working in Python v3.9.

After running the code:

adata= sm.tl.spatial_lda(adata, x_coordinate='X', y_coordinate='Y', phenotype='celltype', method='radius', radius=30, knn=10, imageid='UniqueID', num_motifs=10, random_state=0, subset=None, label='spatial_lda')

adata

AnnData object with n_obs × n_vars = 79308 × 34 obs: 'Unnamed: 0', 'X', 'Y', 'Area', 'celltype', 'TLSType', 'UniqueID' uns: 'spatial_lda', 'spatial_lda_probability', 'spatial_lda_model'

opened by marinabroz 12
strange spatial_LDA -> spatial_cluster results
Hello again!

When clustering (spatial_cluster) on spatial_LDA results, I am getting strange results, as below.

I always get reasonable spatial_cluster results when training on a single ROI. But with as few as 2 ROIs, I start to get this artifactual-seeming result, visible as clusters forming vertical stripes in one or more of the ROIs.

I have tried both 'knn' and 'radius' as spatial_LDA methods with varying values of motifs, knn, and radius. Clustering method was always kmeans (leiden and phenograph were always giving me 99 clusters even with resolution set to 0.1—so I am actually not sure if it's spatial_LDA or instead the clustering that is contributing to this)

Conditions which promote the appearance of this "artifact":

more than one ROI trained together

radius larger than 30

smaller/more numerous cells

Example of a "sensible" spatial clustering result:

when one additional ROI is trained together with it, with all the same spatial_LDA and spatial_cluster parameters, that ROI becomes:

Some real structure is retained in the lower left corner, while the right side no longer makes sense...

Any idea what could be causing this, or parameters to try which could mitigate?

Thank you again!!
opened by yerahko 5

Segmentation mask error in pl.image_viewer

I am getting an error when i include a segmentation mask file:

# Pre- subset the anndata to prevent indexing error. Also, don't use `imageid` and `subset` args to image_viewer().
#selected = adata[adata.obs['Unique_ID'].isin(['PT7.ROI_1']), :]          # This doesn't work
selected = adata[adata.obs['Unique_ID'].isin(['PT7.ROI_1']), :].copy() # Making a copy makes it work

sm.pl.image_viewer(image_path=img_path,
                   seg_mask=seg_mask,   # seg_mask=None,
                   #adata=adata, imageid='Unique_ID', subset = 'PT7.ROI_1',  # Don't do this, Use pre-subsetted data instead
                   adata=selected,
                   overlay='spatial_kmeans', 
                   channel_names=layers_order,
                   x_coordinate='X', y_coordinate='Y', flip_y=False, point_size=4)

The segmentation mask file is readable, as in, I am able to display it in napari by running this chunk of code from sm.pl.image_viewer (minus the last two lines that I commented out—those break):

# Load the segmentation mask
        if seg_mask is not None:
            seg_m = tiff.imread(seg_mask)
            # if seg_m.shape[0] > 1: 
            #    seg_m = seg_m[0]

followed by:

napari.view_image(seg_m)

However, I get the error below when trying to open it with image_viewer().

Click to expand error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [198], in <cell line: 5>()
      1 # Pre- subset the anndata to prevent indexing error. Also, don't use imageid and subset args to image_viewer().
      2 #selected = adata[adata.obs['Unique_ID'].isin(['PT7.ROI_1'])]          # This doesn't work
      3 selected = adata[adata.obs['Unique_ID'].isin(['PT7.ROI_1']), :].copy() # Making a copy makes it work
----> 5 viewer=sm.pl.image_viewer(image_path=img_path,
      6                    seg_mask=seg_path, 
      7                    #adata=adata, imageid='Unique_ID', subset = 'PT7.ROI_1',  # Don't do this, Use pre-subsetted data instead
      8                    adata=selected,
      9                    overlay='cellsimple2', # 'spatial_kmeans', # 
     10                    channel_names=layers_order,
     11                    x_coordinate='X', y_coordinate='Y', flip_y=False, point_size=4)

File ~/anaconda3/envs/napari-imc/lib/python3.9/site-packages/scimap/plotting/_image_viewer.py:184, in image_viewer(image_path, adata, overlay, flip_y, overlay_category, markers, channel_names, x_coordinate, y_coordinate, point_size, point_color, subset, imageid, seg_mask, **kwargs)
    182 # Add the seg mask
    183 if seg_mask is not None:
--> 184     viewer.add_labels(seg_m, name='segmentation mask', visible=False)
    186 # Add phenotype layer function
    187 def add_phenotype_layer (adata, overlay, phenotype_layer,x,y,viewer,point_size,point_color):

File ~/anaconda3/envs/napari-imc/lib/python3.9/site-packages/napari/components/viewer_model.py:4, in add_labels(self, data, num_colors, features, properties, color, seed, name, metadata, scale, translate, rotate, shear, affine, opacity, blending, rendering, depiction, visible, multiscale, cache, plane, experimental_clipping_planes)
      1 from __future__ import annotations
      3 import inspect
----> 4 import itertools
      5 import os
      6 import warnings

File ~/anaconda3/envs/napari-imc/lib/python3.9/site-packages/napari/layers/labels/labels.py:259, in Labels.__init__(self, data, num_colors, features, properties, color, seed, name, metadata, scale, translate, rotate, shear, affine, opacity, blending, rendering, depiction, visible, multiscale, cache, plane, experimental_clipping_planes)
    256 self._show_selected_label = False
    257 self._contour = 0
--> 259 data = self._ensure_int_labels(data)
    260 self._color_lookup_func = None
    262 super().__init__(
    263     data,
    264     rgb=False,
   (...)
    284     experimental_clipping_planes=experimental_clipping_planes,
    285 )

File ~/anaconda3/envs/napari-imc/lib/python3.9/site-packages/napari/layers/labels/labels.py:554, in Labels._ensure_int_labels(self, data)
    552 def _ensure_int_labels(self, data):
    553     """Ensure data is integer by converting from bool if required, raising an error otherwise."""
--> 554     looks_multiscale, data = guess_multiscale(data)
    555     if not looks_multiscale:
    556         data = [data]

File ~/anaconda3/envs/napari-imc/lib/python3.9/site-packages/napari/layers/image/_image_utils.py:76, in guess_multiscale(data)
     72 consistent = bool(np.all(sizes[:-1] > sizes[1:]))
     73 if np.all(sizes == sizes[0]):
     74     # note: the individual array case should be caught by the first
     75     # code line in this function, hasattr(ndim) and ndim > 1.
---> 76     raise ValueError(
     77         trans._(
     78             'Input data should be an array-like object, or a sequence of arrays of decreasing size. Got arrays of single shape: {shape}',
     79             deferred=True,
     80             shape=shapes[0],
     81         )
     82     )
     83 if not consistent:
     84     raise ValueError(
     85         trans._(
     86             'Input data should be an array-like object, or a sequence of arrays of decreasing size. Got arrays in incorrect order, shapes: {shapes}',
   (...)
     89         )
     90     )

ValueError: Input data should be an array-like object, or a sequence of arrays of decreasing size. Got arrays of single shape: ()

Thank you!

opened by yerahko 3

sm.pl.foldchange runs into KeyError

Dear scimap developer,

I am Jose, a biologist who now requires to perform multiplex image analysis. I have ran MCMICRO, and load the output csv as AnnData object. While trying to visualize the data, the foldchange function runs into an error, I am probably doing something wrong. foldchange_KeyError.txt conda list.txt

opened by josenimo 3
sm.pl.image_viewer works but the Napiri visualization wont pop up

Halloo everyone........

I am a new for the bioinformatic analysis. Currently, I am working on spatial CODEX imaging analysis and I found this package very useful. However I have encounter a problem in using sm.pl.image_viewer. It seems the code is working but Napiri visualization wont pop up. this is my code I put on:

import sys import os import anndata as ad import pandas as pd import scanpy as sc import seaborn as sns; sns.set(color_codes=True)

Import Scimap

import scimap as sm

Set the working directory

os.chdir ("/Users/admin/Desktop/scimap package.CODEX.analysis/")

Load data

adata = ad.read('tutorial_data_D20LN1_setting5.h5ad') adata AnnData object with n_obs × n_vars = 8957 × 24 obs: 'X_centroid', 'Y_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation', 'CellID', 'imageid' uns: 'all_markers', 'pca' obsm: 'X_pca' varm: 'PCs' image_path = '/Users/admin/Desktop/scimap package.CODEX.analysis/image.ome.tif' sm.pl.image_viewer(image_path, adata, overlay='leiden',overlay_category=None, ... markers=[ 'CD8'], imageid='imageid', seg_mask=None,
... point_size=7,point_color='white')

the data put for adata was h5ad.

Any help I really appreciate.

Best regards,

Bugie

opened by bugie19 2
image_viewer and gate_finder do not work: type object 'SubControl' has no attribute 'SC_None'

Hi,

and thank you for developing scimap! Unfortunately though, I have had problems with sm.pl.image_viewer and sm.pl.gate_finder, where I keep getting the same error: type object 'SubControl' has no attribute 'SC_None' All the previous steps in scimap tutorials have worked smoothly. Apparently the problem is in opening Napari. Two collaborators have kindly tried to use image_viewer and gate_finder using the same data and they have not had any problems. However, I am the only one using Mac (currently Monterey 12.4), so could this be a Mac-Napari thing?

Here is a link to a jupyter notebook example, error message and to example image.

Any help will be much appreciated.

Best, Joona

opened by SarkkinenJ 2
spatial_LDA with KNN neighborhoods
Hi there,

I am running into trouble when I try to run the function spatial_LDA using method='knn', i.e.: https://github.com/labsyspharm/scimap/blob/b957d8d771260947c4f783b62533c0d0c3c48e5a/scimap/tools/_spatial_lda.py#L96-L112

I am getting the following error at line 112:

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) [1] for i in range(len(ind)): ----> [2] ind[i] = [phenomap[letter] for letter in ind[i]] ValueError: invalid literal for int() with base 10: 'B'

Using method='radius' works.

Thank you very much for your help!
opened by yerahko 2
install errors

Dear developer,

I am just wondering that is there any way to install scimap on a M1 chip MacBook? I got errors in pip install scimap

Thanks in adcance

opened by sailseem 2
Bump nltk from 3.5 to 3.6.6
Bumps nltk from 3.5 to 3.6.6.

Changelog

Sourced from nltk's changelog.

Version 3.7 2022-02-09

Improve and update the NLTK team page on nltk.org (#2855, #2941)

Drop support for Python 3.6, support Python 3.10 (#2920)

Version 3.6.7 2021-12-28

Resolve IndexError in sent_tokenize and word_tokenize (#2922)

Version 3.6.6 2021-12-21

Refactor gensim.doctest to work for gensim 4.0.0 and up (#2914)

Add Precision, Recall, F-measure, Confusion Matrix to Taggers (#2862)

Added warnings if .zip files exist without any corresponding .csv files. (#2908)

Fix FileNotFoundError when the download_dir is a non-existing nested folder (#2910)

Rename omw to omw-1.4 (#2907)

Resolve ReDoS opportunity by fixing incorrectly specified regex (#2906)

Support OMW 1.4 (#2899)

Deprecate Tree get and set node methods (#2900)

Fix broken inaugural test case (#2903)

Use Multilingual Wordnet Data from OMW with newer Wordnet versions (#2889)

Keep NLTKs "tokenize" module working with pathlib (#2896)

Make prettyprinter to be more readable (#2893)

Update links to the nltk book (#2895)

Add CITATION.cff to nltk (#2880)

Resolve serious ReDoS in PunktSentenceTokenizer (#2869)

Delete old CI config files (#2881)

Improve Tokenize documentation + add TokenizerI as superclass for TweetTokenizer (#2878)

Fix expected value for BLEU score doctest after changes from #2572

Add multi Bleu functionality and tests (#2793)

Deprecate 'return_str' parameter in NLTKWordTokenizer and TreebankWordTokenizer (#2883)

Allow empty string in CFG's + more (#2888)

Partition tree.py module into tree package + pickle fix (#2863)

Fix several TreebankWordTokenizer and NLTKWordTokenizer bugs (#2877)

Rewind Wordnet data file after each lookup (#2868)

Correct init call for SyntaxCorpusReader subclasses (#2872)

Documentation fixes (#2873)

Fix levenstein distance for duplicated letters (#2849)

Support alternative Wordnet versions (#2860)

Remove hundreds of formatting warnings for nltk.org (#2859)

Modernize nltk.org/howto pages (#2856)

Fix Bleu Score smoothing function from taking log(0) (#2839)

Update third party tools to newer versions and removing MaltParser fixed version (#2832)

Fix TypeError: _pretty() takes 1 positional argument but 2 were given in sem/drt.py (#2854)

Replace http with https in most URLs (#2852)

Thanks to the following contributors to 3.6.6 Adam Hawley, BatMrE, Danny Sepler, Eric Kafe, Gavish Poddar, Panagiotis Simakis, RnDevelover, Robby Horvath, Tom Aarsen, Yuta Nakamura, Mohaned Mashaly

... (truncated)

Commits

4862b09 updates for 3.6.6

6b60213 Refactor gensim.doctest to work for gensim 4.0.0 and up (#2914)

59aa3fb Fix decode error for bllip parser (#2897)

a28d256 Add Precision, Recall, F-measure, Confusion Matrix to Taggers (#2862)

72d9885 Added warnings if .zip files exist without any corresponding .csv files. (#2908)

dea7b44 Fix FileNotFoundError when the download_dir is a non-existing nested fold...

abbe86b Undo #2909 due to unexpected test failure

c075dab Allow commits with /nocache to not use the cache (#2909)

d6d513d Renamed omw to omw-1.4 (#2907)

2a50a3e Resolve ReDoS opportunity by fixing incorrectly specified regex (#2906)

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dependencies
opened by dependabot[bot] 1
Bump ipython from 7.19.0 to 7.31.1
Bumps ipython from 7.19.0 to 7.31.1.

Commits

e321e76 release 7.31.1

67ca2b3 Merge pull request from GHSA-pq7m-3gw7-gq5x

2794330 back to dev

be343e7 release 7.31.0

0fcf2c4 Merge pull request #13428 from meeseeksmachine/auto-backport-of-pr-13427-on-7.x

b8db9b1 Backport PR #13427: wn 731

7f253dc Merge pull request #13412 from bnavigator/backport-inspect

4f26796 fix xxlimited_35 import name

77ca4a6 don't run nose-based iptest on py310, only pytest

533e509 back to decorator skip

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dependencies
opened by dependabot[bot] 1
Bump pillow from 8.0.1 to 9.0.1
Bumps pillow from 8.0.1 to 9.0.1.

Release notes

Sourced from pillow's releases.

9.0.1

https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html

Changes

In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [@radarhere, @hugovk]

Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

9.0.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html

Changes

Restrict builtins for ImageMath.eval() #5923 [@radarhere]

Ensure JpegImagePlugin stops at the end of a truncated file #5921 [@radarhere]

Fixed ImagePath.Path array handling #5920 [@radarhere]

Remove consecutive duplicate tiles that only differ by their offset #5919 [@radarhere]

Removed redundant part of condition #5915 [@radarhere]

Explicitly enable strip chopping for large uncompressed TIFFs #5517 [@kmilos]

Use the Windows method to get TCL functions on Cygwin #5807 [@DWesl]

Changed error type to allow for incremental WebP parsing #5404 [@radarhere]

Improved I;16 operations on big endian #5901 [@radarhere]

Ensure that BMP pixel data offset does not ignore palette #5899 [@radarhere]

Limit quantized palette to number of colors #5879 [@radarhere]

Use latin1 encoding to decode bytes #5870 [@radarhere]

Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [@radarhere]

When saving RGBA to GIF, make use of first transparent palette entry #5859 [@radarhere]

Pass SAMPLEFORMAT to libtiff #5848 [@radarhere]

Added rounding when converting P and PA #5824 [@radarhere]

Improved putdata() documentation and data handling #5910 [@radarhere]

Exclude carriage return in PDF regex to help prevent ReDoS #5912 [@radarhere]

Image.NONE is only used for resampling and dithers #5908 [@radarhere]

Fixed freeing pointer in ImageDraw.Outline.transform #5909 [@radarhere]

Add Tidelift alignment action and badge #5763 [@aclark4life]

Replaced further direct invocations of setup.py #5906 [@radarhere]

Added ImageShow support for xdg-open #5897 [@m-shinder]

Fixed typo #5902 [@radarhere]

Switched from deprecated "setup.py install" to "pip install ." #5896 [@radarhere]

Support 16-bit grayscale ImageQt conversion #5856 [@cmbruns]

Fixed raising OSError in _safe_read when size is greater than SAFEBLOCK #5872 [@radarhere]

Convert subsequent GIF frames to RGB or RGBA #5857 [@radarhere]

WebP: Fix memory leak during decoding on failure #5798 [@ilai-deutel]

Do not prematurely return in ImageFile when saving to stdout #5665 [@infmagic2047]

Added support for top right and bottom right TGA orientations #5829 [@radarhere]

Corrected ICNS file length in header #5845 [@radarhere]

Block tile TIFF tags when saving #5839 [@radarhere]

Added line width argument to ImageDraw polygon #5694 [@radarhere]

Do not redeclare class each time when converting to NumPy #5844 [@radarhere]

Only prevent repeated polygon pixels when drawing with transparency #5835 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.0.1 (2022-02-03)

In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [radarhere, hugovk]

Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

9.0.0 (2022-01-02)

Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

Improved I;16 operations on big endian #5901 [radarhere]

Limit quantized palette to number of colors #5879 [radarhere]

Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

Added rounding when converting P and PA #5824 [radarhere]

Improved putdata() documentation and data handling #5910 [radarhere]

Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

... (truncated)

Commits

6deac9e 9.0.1 version bump

c04d812 Update CHANGES.rst [ci skip]

4fabec3 Added release notes for 9.0.1

02affaa Added delay after opening image with xdg-open

ca0b585 Updated formatting

427221e In show_file, use os.remove to remove temporary images

c930be0 Restrict builtins within lambdas for ImageMath.eval

75b69dd Dont need to pin for GHA

cd938a7 Autolink CWE numbers with sphinx-issues

2e9c461 Add CVE IDs

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dependencies
opened by dependabot[bot] 1
Bump certifi from 2021.10.8 to 2022.12.7
Bumps certifi from 2021.10.8 to 2022.12.7.

Commits

9e9e840 2022.12.07

b81bdb2 2022.09.24

939a28f 2022.09.14

aca828a 2022.06.15.2

de0eae1 Only use importlib.resources's new files() / Traversable API on Python ≥3.11 ...

b8eb5e9 2022.06.15.1

47fb7ab Fix deprecation warning on Python 3.11 (#199)

b0b48e0 fixes #198 -- update link in license

9d514b4 2022.06.15

4151e88 Add py.typed to MANIFEST.in to package in sdist (#196)

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dependencies
opened by dependabot[bot] 0
supplying additional groupings

Just wondering if there is support to add additional arguments into some functions to change the way the data is grouped other than by 'imageid'. eg in tl.foldchange 'from_group' could be another categorical variable in adata.obs, eg 'mutation1', and 'to_group' could be 'mutation2' similarly tl.spatial_interaction is performed by 'imageid', can these scores be further grouped by another adata.obs variable?

thanks

opened by jamesMo84 1
Bump pillow from 9.1.1 to 9.3.0
Bumps pillow from 9.1.1 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
Bump jupyter-core from 4.9.2 to 4.11.2
Bumps jupyter-core from 4.9.2 to 4.11.2.

Release notes

Sourced from jupyter-core's releases.

4.11.1

What's Changed

Fix inclusion of jupyter file and check in CI by @blink1073 in jupyter/jupyter_core#276

Full Changelog: https://github.com/jupyter/jupyter_core/compare/4.11.0...4.11.1

4.11.0

What's Changed

Use hatch backend by @blink1073 in jupyter/jupyter_core#265

is_hidden: Use normalized paths by @martinRenou in jupyter/jupyter_core#271

New Contributors

@martinRenou made their first contribution in jupyter/jupyter_core#271

Full Changelog: https://github.com/jupyter/jupyter_core/compare/4.10.0...4.11.0

4.10.0

What's Changed

Update changelog for 4.9.2 by @blink1073 in jupyter/jupyter_core#252

Include all files from jupyter_core by @jonringer in jupyter/jupyter_core#253

Add project URLs to setup.cfg by @tlinhart in jupyter/jupyter_core#254

Set up pre-commit by @blink1073 in jupyter/jupyter_core#255

[pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in jupyter/jupyter_core#257

Add flake8 and mypy settings by @blink1073 in jupyter/jupyter_core#256

Clean up CI by @blink1073 in jupyter/jupyter_core#258

Update changelog for 4.10 Release by @blink1073 in jupyter/jupyter_core#259

New Contributors

@jonringer made their first contribution in jupyter/jupyter_core#253

@tlinhart made their first contribution in jupyter/jupyter_core#254

@pre-commit-ci made their first contribution in jupyter/jupyter_core#257

Full Changelog: https://github.com/jupyter/jupyter_core/compare/4.9.2...4.10.0

Changelog

Sourced from jupyter-core's changelog.

Changes in jupyter-core

5.0.0

(Full Changelog)

Major Changes

Prefer Environment Level Configuration

We now make the assumption that if we are running in a virtual environment, we should prioritize the environment-level sys.prefix over the user-level paths. Users can opt out of this behavior by setting JUPYTER_PREFER_ENV_PATH, which takes precedence over our autodetection.

Migrate to Standard Platform Directories

In version 5, we introduce a JUPYTER_PLATFORM_DIRS environment variable to opt in to using more appropriate platform-specific directories. We raise a deprecation warning if the variable is not set. In version 6, JUPYTER_PLATFORM_DIRS will be opt-out. In version 7, we will remove the environment variable checks and old directory logic.

Drop Support for Python 3.7

We are dropping support for Python 3.7 ahead of its official end of life, to reduce maintenance burden as we add support for Python 3.11.

Enhancements made

Use platformdirs for path locations #292 (@blink1073)

Try to detect if we are in a virtual environment and change path precedence accordingly #286 (@jasongrout)

Bugs fixed

Add current working directory as first config path #291 (@blink1073)

Fix inclusion of jupyter file and check in CI #276 (@blink1073)

Maintenance and upkeep improvements

Bump github/codeql-action from 1 to 2 #308 (@dependabot)

Bump actions/checkout from 2 to 3 #307 (@dependabot)

Add dependabot #306 (@blink1073)

Adopt jupyter releaser #305 (@blink1073)

Add more typing #304 (@blink1073)

Require Python 3.8+ #302 (@blink1073)

Use hatch backend #265 (@blink1073)

Switch to flit build backend #262 (@blink1073)

is_hidden: Use normalized paths #271 (@martinRenou)

Documentation

Update broken link to Contributing guide #289 (@jamesr66a)

Contributors to this release

... (truncated)

Commits

a8eac8c Release 4.11.2

1118c8c Merge pull request from GHSA-m678-f26j-3hrp

d3f61f3 Release 4.11.1

e7eeb9e Fix inclusion of jupyter file and check in CI (#276)

035bf11 Release 4.11.0

45aa28b [pre-commit.ci] pre-commit autoupdate (#273)

73401cc [pre-commit.ci] pre-commit autoupdate (#272)

bc6b771 is_hidden: Use normalized paths (#271)

be38e52 [pre-commit.ci] pre-commit autoupdate (#270)

7b790d8 [pre-commit.ci] pre-commit autoupdate (#269)

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dependencies
opened by dependabot[bot] 0
Bump joblib from 1.1.0 to 1.2.0
Bumps joblib from 1.1.0 to 1.2.0.

Changelog

Sourced from joblib's changelog.

Release 1.2.0

Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

Vendor loky 3.3.0 which fixes several bugs including:

robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

avoiding leaking worker processes in case of nested loky parallel calls;

reliability spawn the correct number of reusable workers.

Release 1.1.1

Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Commits

5991350 Release 1.2.0

3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)

cea26ff CI test the future loky-3.3.0 branch (#1338)

8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)

067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)

ac4ebd5 MAINT add back pytest warnings plugin (#1337)

a23427d Test child raises parent exits cleanly more reliable on macos (#1335)

ac09691 [MAINT] various test updates (#1334)

4a314b1 Vendor loky 3.2.0 (#1333)

bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...

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dependencies
opened by dependabot[bot] 0

Releases(0.22.0)

0.22.0(Jun 5, 2022)

Source code(tar.gz)
Source code(zip)

0.19.0(Apr 3, 2022)

Included support for Apple M1 machines
Included support for native rendering of Zarr stored images using Napari: pl.image_viewer and pl.gate_finder

Temporary workaround for installing in Apple M1 machines

# reate and load a new environment
conda create --name scimap python=3.8 -y
conda activate scimap

# if you do not have xcode please install it
xcode-select --install

# if you do not have homebrew please install it
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# if you do not have cmake install it
brew install cmake

# install h5py
brew install [email protected]
export HDF5_DIR=/opt/homebrew/Cellar/hdf5/1.12.1_1/
pip install --no-binary=h5py h5py

# install llvmlite
conda install llvmlite -y

# install leidenalg
pip install git+https://github.com/vtraag/leidenalg.git

# install scimap
pip install -U scimap

# uninstall 
conda remove llvmlite -y
pip uninstall numba -y
pip uninstall numpy -y

# reinstall this specific version of llvmlite (ignore errors/warning)
pip install -i https://pypi.anaconda.org/numba/label/wheels_experimental_m1/simple llvmlite

# reinstall this specific version of numpy (ignore errors/warning)
pip install numpy==1.22.3

# reinstall this specific version of numba (ignore errors/warning)
pip install -i https://pypi.anaconda.org/numba/label/wheels_experimental_m1/simple numba

Source code(tar.gz)
Source code(zip)

0.18.1(Feb 11, 2022)

Source code(tar.gz)
Source code(zip)
0.17.7(Aug 5, 2021)

Testing the new GitHub Action that automatically builds, tags and pushed Docker container images. The action is triggered by new releases.
Source code(tar.gz)
Source code(zip)
0.17.6(Jul 6, 2021)

Source code(tar.gz)
Source code(zip)
0.17.5(Jul 2, 2021)

Source code(tar.gz)
Source code(zip)
0.17.4(Jul 2, 2021)

Source code(tar.gz)
Source code(zip)
0.17.2(Jul 1, 2021)

Added CLI compatibility for a few functions and integrating with MCMICRO
Source code(tar.gz)
Source code(zip)
0.1.10(Jun 29, 2020)

Includes functions from loading data to phenotyping the cells.
Source code(tar.gz)
Source code(zip)
scimap-0.1.10.tar.gz(2.12 MB)