Sweep: create GitHub workflow to run tests before merge about calcium-analysis HOT 5 CLOSED

retroam commented on August 11, 2024 2

Sweep: create GitHub workflow to run tests before merge

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Comments (5)

sweep-ai commented on August 11, 2024

I'm sorry, but it looks like an error has occured. Try removing and re-adding the sweep label. I'll try again in a minute. If this error persists contact [email protected].

I'm a bot that handles simple bugs and feature requests but I might make mistakes. Please be kind!

from calcium-analysis.

sweep-ai commented on August 11, 2024

I'm sorry, but it looks like an error has occured. Try removing and re-adding the sweep label. I'll try again in a minute. If this error persists contact [email protected].

I'm a bot that handles simple bugs and feature requests but I might make mistakes. Please be kind!

from calcium-analysis.

sweep-ai commented on August 11, 2024

Hey @retroam,

I've started working on your request to create a GitHub workflow for running tests before merging. The plan is to set up a workflow that triggers on pull requests to the main branch. This workflow will set up a Python environment, install the necessary dependencies, and then run the tests located in the tests/ directory using both pytest and unittest frameworks.

Give me a minute!

Some code snippets I looked at (click to expand). If some file is missing from here, you can mention the path in the ticket description.

Calcium-analysis/matlab/imagestostacks.m

Lines 1 to 31 in c2679e1

 function[I_t] = imagestostacks(image_fld,save_fld) 

 % IMAGESTOSTACKS(image_fld,save_fld) assembles tif images in image_fld as 

 % a single matrix which is then saved in save_fld as a .mat file 

 % NOTE: tif files in image_fld have to be labelled as image01...09,10 etc. 

 %Example: 

 % I = imagestostacks(image_fld,save_fld); 

 cd(image_fld); 

 i_bloc = dir('*.tif*'); 

 I_t = []; 

 for j = 1:length(i_bloc) 

 info = imfinfo(i_bloc(j).name); 

 fileName{j} = i_bloc(j).name; 

 num_images = numel(info); 

 I_cum = zeros(info(1,1).Height,info(1,1).Width,num_images-1);%pre-alloc 

 disp(['Loading tif file ' num2str(j) ' of ' num2str(length(i_bloc))]); 

 for k = 1:num_images 

 I = imread(i_bloc(j).name, k, 'Info', info); 

 I_cum(:,:,k) = I; 

 end 

 close all force; 

 I_t = cat(3,I_cum,I_t); 

 clear I_cum; 

 end 

 save([save_fld '\I.mat'],'I_t');

Calcium-analysis/old/batch_process.py

Lines 1 to 15 in c2679e1

 
 import numpy as np 

 import scipy 

 import matcompat 

 image_fld = 'C:\Users\rka2p\Desktop\Calcium analysis\file_folder' 

 save_fld = 'C:\Users\rka2p\Desktop\Calcium analysis\save_folder' 

 imagestostacks(image_fld,save_fld) 

 #sd from mean of image intensity to set as threshold 

 sd = 7 

 [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd) 

 imagesc(I_overlay) 

 axis off 

 [data,bkg] = roitodata(save_fld)

Calcium-analysis/README.md

Lines 1 to 2 in c2679e1

 # Calcium-analysis 

 Python implementation of calcium imaging analysis pipeline for cardiac myocytes for [Automated image analysis of cardiac myocyte Ca2+ dynamics](https://pubmed.ncbi.nlm.nih.gov/22255377/)

Calcium-analysis/matlab/stackstoroi.m

Lines 1 to 41 in c2679e1

 
 function[CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd) 

 % STACKSTOROI(image_fld,sd) loads *I*.mat from save_fld for image 

 % segmentation. sd is how many standard deviations above the mean the  

 % threshold is set for segmentation. Outputs: 

 % CC: connected components found in I_bw2 

 % I_mean: averaged image of *I*.mat 

 % I_bw2: binary image containing image segementation of *I*.mat 

 % I_overlay: overlay of identified ROI over I_mean 

 %Example: 

 % [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd); 

 cd(save_fld); 

 m_bloc = dir('*I*.mat*'); 

 I_cum = []; 

 img_content = []; 

 if length(m_bloc)== 11 

 for i = 1:11 

 I_cum = cat(3,I_cum,importdata(m_bloc(i).name)); 

 end 

 else 

 I_cum = importdata(m_bloc(1).name); 

 end 

 for j = 1:length(I_cum) 

 I = I_cum(:,:,j); 

 img_content(j) = mean(I(:)); 

 end 

 [marks,locs] = findpeaks(img_content,'minpeakdistance',100); 

 I_mean = mean(I_cum(:,:,locs),3); 

 %I_bw1 = bwspecial(otsu(I_mean,3)); 

 I_bw1 = threshold(I_mean,mode(I_mean(:))+ sd*mad(I_mean(:),1)); 

 I_bw2 = bwareaopen(I_bw1,20,4); 

 CC = bwconncomp(I_bw2); 

 I_edge = edge(I_bw2); 

 I_overlay = I_mean; 

 I_overlay(I_edge) = 10*max(I_mean(:))/9;

Calcium-analysis/old/stackstoroi.py

Lines 12 to 27 in c2679e1

 
 def stackstoroi(save_fld, sd): 

 # Local Variables: I_bw2, I_cum, I_bw1, I, I_overlay, locs, CC, i, 

 # j, img_content, I_edge, m_bloc, I_mean, marks, save_fld, sd 

 # Function calls: save, stackstoroi, mad, findpeaks, cat, bwareaopen, 

 # length, edge, bwconncomp, importdata, max, threshold, mode, cd, dir, mean 

 #% STACKSTOROI(image_fld,sd) loads *I*.mat from save_fld for image 

 #% segmentation. sd is how many standard deviations above the mean the  

 #% threshold is set for segmentation. Outputs: 

 #% CC: connected components found in I_bw2 

 #% I_mean: averaged image of *I*.mat 

 #% I_bw2: binary image containing image segementation of *I*.mat 

 #% I_overlay: overlay of identified ROI over I_mean 

 #%Example: 

 #% [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd);

I'm a bot that handles simple bugs and feature requests but I might make mistakes. Please be kind!

from calcium-analysis.

sweep-ai commented on August 11, 2024

I'm sorry, but it looks like an error has occured. Try removing and re-adding the sweep label. I'll try again in a minute. If this error persists contact [email protected].

I'm a bot that handles simple bugs and feature requests but I might make mistakes. Please be kind!

from calcium-analysis.

sweep-ai commented on August 11, 2024

Hey @retroam,

I've started working on setting up a GitHub Actions workflow for your repo. The plan is to create a workflow that gets triggered whenever a pull request is made. This workflow will set up the necessary Python environment, install the required dependencies, and run all the tests in the tests/ directory. If all tests pass, the pull request can be merged; if any test fails, the workflow will prevent the merge and provide feedback.

Give me a minute!

Some code snippets I looked at (click to expand). If some file is missing from here, you can mention the path in the ticket description.

Calcium-analysis/matlab/imagestostacks.m

Lines 1 to 31 in c2679e1

 function[I_t] = imagestostacks(image_fld,save_fld) 

 % IMAGESTOSTACKS(image_fld,save_fld) assembles tif images in image_fld as 

 % a single matrix which is then saved in save_fld as a .mat file 

 % NOTE: tif files in image_fld have to be labelled as image01...09,10 etc. 

 %Example: 

 % I = imagestostacks(image_fld,save_fld); 

 cd(image_fld); 

 i_bloc = dir('*.tif*'); 

 I_t = []; 

 for j = 1:length(i_bloc) 

 info = imfinfo(i_bloc(j).name); 

 fileName{j} = i_bloc(j).name; 

 num_images = numel(info); 

 I_cum = zeros(info(1,1).Height,info(1,1).Width,num_images-1);%pre-alloc 

 disp(['Loading tif file ' num2str(j) ' of ' num2str(length(i_bloc))]); 

 for k = 1:num_images 

 I = imread(i_bloc(j).name, k, 'Info', info); 

 I_cum(:,:,k) = I; 

 end 

 close all force; 

 I_t = cat(3,I_cum,I_t); 

 clear I_cum; 

 end 

 save([save_fld '\I.mat'],'I_t');

Calcium-analysis/old/batch_process.py

Lines 1 to 15 in c2679e1

 
 import numpy as np 

 import scipy 

 import matcompat 

 image_fld = 'C:\Users\rka2p\Desktop\Calcium analysis\file_folder' 

 save_fld = 'C:\Users\rka2p\Desktop\Calcium analysis\save_folder' 

 imagestostacks(image_fld,save_fld) 

 #sd from mean of image intensity to set as threshold 

 sd = 7 

 [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd) 

 imagesc(I_overlay) 

 axis off 

 [data,bkg] = roitodata(save_fld)

Calcium-analysis/README.md

Lines 1 to 2 in c2679e1

 # Calcium-analysis 

 Python implementation of calcium imaging analysis pipeline for cardiac myocytes for [Automated image analysis of cardiac myocyte Ca2+ dynamics](https://pubmed.ncbi.nlm.nih.gov/22255377/)

Calcium-analysis/matlab/stackstoroi.m

Lines 1 to 41 in c2679e1

 
 function[CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd) 

 % STACKSTOROI(image_fld,sd) loads *I*.mat from save_fld for image 

 % segmentation. sd is how many standard deviations above the mean the  

 % threshold is set for segmentation. Outputs: 

 % CC: connected components found in I_bw2 

 % I_mean: averaged image of *I*.mat 

 % I_bw2: binary image containing image segementation of *I*.mat 

 % I_overlay: overlay of identified ROI over I_mean 

 %Example: 

 % [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd); 

 cd(save_fld); 

 m_bloc = dir('*I*.mat*'); 

 I_cum = []; 

 img_content = []; 

 if length(m_bloc)== 11 

 for i = 1:11 

 I_cum = cat(3,I_cum,importdata(m_bloc(i).name)); 

 end 

 else 

 I_cum = importdata(m_bloc(1).name); 

 end 

 for j = 1:length(I_cum) 

 I = I_cum(:,:,j); 

 img_content(j) = mean(I(:)); 

 end 

 [marks,locs] = findpeaks(img_content,'minpeakdistance',100); 

 I_mean = mean(I_cum(:,:,locs),3); 

 %I_bw1 = bwspecial(otsu(I_mean,3)); 

 I_bw1 = threshold(I_mean,mode(I_mean(:))+ sd*mad(I_mean(:),1)); 

 I_bw2 = bwareaopen(I_bw1,20,4); 

 CC = bwconncomp(I_bw2); 

 I_edge = edge(I_bw2); 

 I_overlay = I_mean; 

 I_overlay(I_edge) = 10*max(I_mean(:))/9;

Calcium-analysis/old/stackstoroi.py

Lines 12 to 27 in c2679e1

 
 def stackstoroi(save_fld, sd): 

 # Local Variables: I_bw2, I_cum, I_bw1, I, I_overlay, locs, CC, i, 

 # j, img_content, I_edge, m_bloc, I_mean, marks, save_fld, sd 

 # Function calls: save, stackstoroi, mad, findpeaks, cat, bwareaopen, 

 # length, edge, bwconncomp, importdata, max, threshold, mode, cd, dir, mean 

 #% STACKSTOROI(image_fld,sd) loads *I*.mat from save_fld for image 

 #% segmentation. sd is how many standard deviations above the mean the  

 #% threshold is set for segmentation. Outputs: 

 #% CC: connected components found in I_bw2 

 #% I_mean: averaged image of *I*.mat 

 #% I_bw2: binary image containing image segementation of *I*.mat 

 #% I_overlay: overlay of identified ROI over I_mean 

 #%Example: 

 #% [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd);

I'm a bot that handles simple bugs and feature requests but I might make mistakes. Please be kind!

from calcium-analysis.

Sweep: create GitHub workflow to run tests before merge about calcium-analysis HOT 5 CLOSED

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	function[I_t] = imagestostacks(image_fld,save_fld)

	% IMAGESTOSTACKS(image_fld,save_fld) assembles tif images in image_fld as
	% a single matrix which is then saved in save_fld as a .mat file
	% NOTE: tif files in image_fld have to be labelled as image01...09,10 etc.

	%Example:
	% I = imagestostacks(image_fld,save_fld);

	cd(image_fld);
	i_bloc = dir('.tif');
	I_t = [];


	for j = 1:length(i_bloc)
	info = imfinfo(i_bloc(j).name);
	fileName{j} = i_bloc(j).name;
	num_images = numel(info);
	I_cum = zeros(info(1,1).Height,info(1,1).Width,num_images-1);%pre-alloc
	disp(['Loading tif file ' num2str(j) ' of ' num2str(length(i_bloc))]);
	for k = 1:num_images
	I = imread(i_bloc(j).name, k, 'Info', info);
	I_cum(:,:,k) = I;
	end
	close all force;
	I_t = cat(3,I_cum,I_t);
	clear I_cum;
	end

	save([save_fld '\I.mat'],'I_t');


	import numpy as np
	import scipy
	import matcompat

	image_fld = 'C:\Users\rka2p\Desktop\Calcium analysis\file_folder'
	save_fld = 'C:\Users\rka2p\Desktop\Calcium analysis\save_folder'
	imagestostacks(image_fld,save_fld)

	#sd from mean of image intensity to set as threshold
	sd = 7
	[CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd)
	imagesc(I_overlay)
	axis off
	[data,bkg] = roitodata(save_fld)

	# Calcium-analysis
	Python implementation of calcium imaging analysis pipeline for cardiac myocytes for [Automated image analysis of cardiac myocyte Ca2+ dynamics](https://pubmed.ncbi.nlm.nih.gov/22255377/)


	function[CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd)

	% STACKSTOROI(image_fld,sd) loads I.mat from save_fld for image
	% segmentation. sd is how many standard deviations above the mean the
	% threshold is set for segmentation. Outputs:
	% CC: connected components found in I_bw2
	% I_mean: averaged image of I.mat
	% I_bw2: binary image containing image segementation of I.mat
	% I_overlay: overlay of identified ROI over I_mean

	%Example:
	% [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd);

	cd(save_fld);
	m_bloc = dir('I.mat*');
	I_cum = [];
	img_content = [];
	if length(m_bloc)== 11
	for i = 1:11
	I_cum = cat(3,I_cum,importdata(m_bloc(i).name));
	end

	else
	I_cum = importdata(m_bloc(1).name);
	end

	for j = 1:length(I_cum)
	I = I_cum(:,:,j);
	img_content(j) = mean(I(:));
	end
	[marks,locs] = findpeaks(img_content,'minpeakdistance',100);
	I_mean = mean(I_cum(:,:,locs),3);

	%I_bw1 = bwspecial(otsu(I_mean,3));
	I_bw1 = threshold(I_mean,mode(I_mean(:))+ sd*mad(I_mean(:),1));
	I_bw2 = bwareaopen(I_bw1,20,4);
	CC = bwconncomp(I_bw2);
	I_edge = edge(I_bw2);
	I_overlay = I_mean;
	I_overlay(I_edge) = 10*max(I_mean(:))/9;


	def stackstoroi(save_fld, sd):

	# Local Variables: I_bw2, I_cum, I_bw1, I, I_overlay, locs, CC, i,
	# j, img_content, I_edge, m_bloc, I_mean, marks, save_fld, sd
	# Function calls: save, stackstoroi, mad, findpeaks, cat, bwareaopen,
	# length, edge, bwconncomp, importdata, max, threshold, mode, cd, dir, mean
	#% STACKSTOROI(image_fld,sd) loads I.mat from save_fld for image
	#% segmentation. sd is how many standard deviations above the mean the
	#% threshold is set for segmentation. Outputs:
	#% CC: connected components found in I_bw2
	#% I_mean: averaged image of I.mat
	#% I_bw2: binary image containing image segementation of I.mat
	#% I_overlay: overlay of identified ROI over I_mean
	#%Example:
	#% [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd);