From 9eaec713cb6d91d8ee7c6e9b4bb8e9e0f6ae8b2b Mon Sep 17 00:00:00 2001 From: Felix <135244625+felixS27@users.noreply.github.com> Date: Tue, 30 Jul 2024 13:54:53 +0200 Subject: [PATCH 01/12] Update 2024_08_EMBL_automated_batch_analysis_python.md Corrected my name in the markdown --- courses/2024_08_EMBL_automated_batch_analysis_python.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/courses/2024_08_EMBL_automated_batch_analysis_python.md b/courses/2024_08_EMBL_automated_batch_analysis_python.md index 837b6b31..055ddfcd 100644 --- a/courses/2024_08_EMBL_automated_batch_analysis_python.md +++ b/courses/2024_08_EMBL_automated_batch_analysis_python.md @@ -44,7 +44,7 @@ Dear Trainers, please consider checking these [teaching tips](https://github.com 1. [Loops](https://neubias.github.io/training-resources/script_for_loop/index.html) - Trainer: Felix Schneider - - TODO: Flex: check and improve + - TODO: Felix: check and improve 1. [String and path manipulations](https://neubias.github.io/training-resources/string_concat/index.html) - TODO: Tischi change to multi-activities - Trainer: Dominik Kutra From 1e3ce2313b9011187bdab64a34e9872012b201c6 Mon Sep 17 00:00:00 2001 From: Felix-Mac Date: Tue, 30 Jul 2024 15:00:08 +0200 Subject: [PATCH 02/12] Create notebook for teaching for loops in python --- .../script_for_loop/script_for_loop_python.py | 164 ++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 _includes/script_for_loop/script_for_loop_python.py diff --git a/_includes/script_for_loop/script_for_loop_python.py b/_includes/script_for_loop/script_for_loop_python.py new file mode 100644 index 00000000..2c8920cf --- /dev/null +++ b/_includes/script_for_loop/script_for_loop_python.py @@ -0,0 +1,164 @@ +# %% +# Create a bunch of example images to simulate a typical analysis problem +# np.random.randint(lower,upper,size): generates random integers from lower to upper +# .reshape: reshapes a np.array to new dimensions +import numpy as np +image1 = np.random.randint(0,255,100).reshape((10,10)) +image2 = np.random.randint(0,255,100).reshape((10,10)) +image3 = np.random.randint(0,255,100).reshape((10,10)) +image4 = np.random.randint(0,255,100).reshape((10,10)) +image5 = np.random.randint(0,255,100).reshape((10,10)) +image6 = np.random.randint(0,255,100).reshape((10,10)) +image7 = np.random.randint(0,255,100).reshape((10,10)) +image8 = np.random.randint(0,255,100).reshape((10,10)) +image9 = np.random.randint(0,255,100).reshape((10,10)) +image10 = np.random.randint(0,255,100).reshape((10,10)) + +# Calculate the mean of every image +# Oberserve that it is very tedious and error prone +print(f'Image {1} has an avg intensity of {image1.mean()}.') +print(f'Image {2} has an avg intensity of {image2.mean()}.') +print(f'Image {3} has an avg intensity of {image3.mean()}.') +print(f'Image {4} has an avg intensity of {image4.mean()}.') +print(f'Image {5} has an avg intensity of {image5.mean()}.') +print(f'Image {6} has an avg intensity of {image6.mean()}.') +print(f'Image {7} has an avg intensity of {image7.mean()}.') +print(f'Image {8} has an avg intensity of {image8.mean()}.') +print(f'Image {9} has an avg intensity of {image9.mean()}.') +print(f'Image {10} has an avg intensity of {image10.mean()}.') + +# %% +# Create a for loop +# Typical notation in C-style: for i=0, i<10, i++ {do something} +# For loop in python +# Observe the difference in notation +for i in [0,1,2,3,4,5,6,7,8,9]: + print(i) + +# use range +# Observe that range is actually a generator producing values on the fly +print(range(10)) +print(type(range(10))) +for i in range(10): + print(i) + +# %% +# Anything iterable can be iterated over with a for loop +# Observe that: +# - list content don't matter +# - that the iterator will always be overwritten by the next item +for i in ['a','b','c',1,2,3,[1,2,3],1.5,2.7]: + print(f'"i" has the value "{i}" and type "{type(i)}"') + +# %% +# Use a for loop for the example in the beginning +# First pack images into a list for looping over +image_list = [image1,image2,image3,image4,image5,image6,image7,image8,image9,image10] + +# Loop over images and calculate the mean +for image in image_list: + print(f'Avg intensity: {image.mean()}') + +# Excersize: Modify the loop to calculate the standard deviation +for image in image_list: + print(f'Intensity standard deviation: {image.std()}') + +#%% +# Iterate with an index +number_of_images = len(image_list) +print(number_of_images) +for i in range(number_of_images): + print(f'Image {i} has an avg intensity of {image_list[i].mean()}.') + +# Iterate over two or more list (with the same length) +for i,image in zip(range(number_of_images),image_list): + print(f'Image {i} has an avg intensity of {image.mean()}.') + +# Iterate with additional index +for i,image in enumerate(image_list): + print(f'Image {i} has an avg intensity of {image.mean()}.') + +# %% +# For loops but advanced + +# %% +# list comprehension +squares = [] +for i in range(10): + squares.append(i**2) +print(squares) +# use list comprehension +squares = [i**2 for i in range(10)] +print(squares) + +# usefull for creating an filepath iterable +from pathlib import Path +[file for file in Path().cwd().iterdir()] + +# %% +# for loop with if +for x in range(10): + if x != 0: + print(f'X: {x}, 1/X: {1/x}') + +# for loop with if and else +for x in range(10): + if x == 0: + print(f'X: {x}, 1/X: Division by {x} not defined.') + else: + print(f'X: {x}, 1/X: {1/x}') + +# %% +# for loop with continue and break +# Usage: +# - skip files/data for processing +# - debugging +# continue +for x in range(10): + if x==0: + continue + print(f'X: {x}, 1/X: {1/x}') + +# break +for x in range(10): + if x==3: + break + print(x) + +# %% +# Nested loops +for x in range(1,4): + for y in range(1,4): + print(f'X: {x}, Y: {y}, X*Y: {x*y}') + +# Nested lists +for sublist in [[1,2,3],[4,5,6],[7,8,9]]: + for i in sublist: + print(i) + +# np.array +# Observe that you iterate only over the first dimension when using simple loop +array = np.random.randint(0,10,100).reshape(10,10) +print(array.shape) +for i in array: + print(i) + +for row in array: + for i in row: + print(i) + +# %% +# Looping over pandas DataFrame +# Observe that +# - looping with iterrows always gives an index +# - the second variable will always be a pandas Series object +import pandas as pd +df = pd.DataFrame({ + 'a':[1,2,3], + 'b':[4,5,6]}) +print(df) +for i,row in df.iterrows(): + print(type(row)) + value_a = row['a'] + value_b = row['b'] + print(f'Row index {i}\nColumn value "a": {value_a} and Column value "b": {value_b}') \ No newline at end of file From 1135ab95cf80af1030da86cbbef7d5e2876c92f3 Mon Sep 17 00:00:00 2001 From: Felix-Mac Date: Tue, 30 Jul 2024 15:00:47 +0200 Subject: [PATCH 03/12] Add activity in modules --- _modules/script_for_loop.md | 1 + 1 file changed, 1 insertion(+) diff --git a/_modules/script_for_loop.md b/_modules/script_for_loop.md index 5850f82b..e484a2b6 100644 --- a/_modules/script_for_loop.md +++ b/_modules/script_for_loop.md @@ -36,6 +36,7 @@ activities: - ["ImageJ Macro, loop structure", "script_for_loop/activities/script_for_loop_loopstructure.ijm"] - ["ImageJ Macro, example no loop", "script_for_loop/activities/script_for_loop_measure_distances_noloop.ijm"] - ["ImageJ Macro, example with loop", "script_for_loop/activities/script_for_loop_measure_distances_withloop.ijm"] + - ["Python, for loop", "script_for_loop/script_for_loop_python.py"] exercise_preface: | ### Multiple erosion From c595fd8ac1e25cad98ec8b024af8ff562579d9b9 Mon Sep 17 00:00:00 2001 From: jhennies Date: Tue, 30 Jul 2024 15:46:46 +0200 Subject: [PATCH 04/12] Zip archive containing the input images --- image_data/batch_process/inputs.zip | Bin 0 -> 31355 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 image_data/batch_process/inputs.zip diff --git a/image_data/batch_process/inputs.zip 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b/_includes/batch_processing/batch_measure_nuclei_shape.py @@ -11,15 +11,16 @@ from pathlib import Path from napari import Viewer + # %% # Create a function that analyses one image # Below, this function will be called several times, for all images -def analyse(image_path, output_folder): +def analyse(image_filepath, output_folder): # This prints which image is currently analysed - print("Analyzing:", image_path) + print("Analyzing:", image_filepath) - image, axes, scales, units = open_ij_tiff(image_path) + image, axes, scales, units = open_ij_tiff(image_filepath) # Binarize the image using auto-thresholding threshold = threshold_otsu(image) @@ -45,19 +46,21 @@ def analyse(image_path, output_folder): # Save the results to disk - # Convert the image_path String to a Path, + # Convert the image_filepath String to a Path, # which is more convenient to create the output files - image_path = pathlib.Path(image_path) + image_filepath = pathlib.Path(image_filepath) # Save the labels - label_image_path = output_folder / f"{image_path.stem}_labels.tif" - save_ij_tiff(label_image_path, label_image, axes, scales, units) + label_image_filepath = output_folder / f"{image_filepath.stem}_labels.tif" + save_ij_tiff(label_image_filepath, label_image, axes, scales, units) # Save the measurements table # to a tab delimited text file (sep='\t') # without row numbers (index=False) - table_path = output_folder / f"{image_path.stem}_measurements.csv" + table_path = output_folder / f"{image_filepath.stem}_measurements.csv" df.to_csv(table_path, sep='\t', index=False) + + return label_image_filepath # %% @@ -65,19 +68,28 @@ def analyse(image_path, output_folder): # Note: This uses your current working directory; you may want to change this to another folder on your computer output_dir = Path.cwd() + # %% # Create a list of the paths to all data -image_paths = ["https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t1.tif", - "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t70.tif"] +image_paths = [output_dir / "xy_8bit__mitocheck_incenp_t1.tif", + output_dir / "xy_8bit__mitocheck_incenp_t70.tif"] +# Create an empty list for the result label images +label_image_paths = [] -for image_path in image_paths: - analyse(image_path, output_dir) # %% -# Plot the first output image to check if the pipeline worked -image1, *_ = open_ij_tiff(image_paths[0]) -labels1, *_ = open_ij_tiff('xy_8bit__mitocheck_incenp_t1_labels.tif') +# The loop which performs the analysis +for image_path in image_paths: -viewer = Viewer() -viewer.add_image(image1) -viewer.add_labels(labels1) + # Computes the analysis and returns the path of the resulting label image + label_image_path = analyse(image_path, output_dir) + + # Append the label image path to the list initialized before the loop + label_image_paths.append(label_image_path) + + +# %% +# Create a table to view the results in MoBIE +df = pd.DataFrame(data={'image': image_paths, 'labels': label_image_paths}) +mobie_table_path = output_dir + Path('mobie_table.csv') +df.to_csv(mobie_table_path, sep='\t', index=False) diff --git a/_includes/batch_processing/batch_measure_nuclei_shapes.md b/_includes/batch_processing/batch_measure_nuclei_shapes.md index c3283ad1..4eea0c95 100644 --- a/_includes/batch_processing/batch_measure_nuclei_shapes.md +++ b/_includes/batch_processing/batch_measure_nuclei_shapes.md @@ -1,5 +1,6 @@

Batch analysis of nuclear shapes

+- Download the input.zip containing the input images from [here](https://github.com/NEUBIAS/training-resources/tree/master/image_data/batch_process) and unpack to your course directory - In a previous module [there is a workflow to measure the shapes of nuclei in one image](https://neubias.github.io/training-resources/workflow_segment_2d_nuclei_measure_shape/index.html#2dnuclei) - Adapt this workflow for automated batch analysis of many images - Start by building the skeleton of the workflow without filling in the functionality; From 871c813650f3b24f81435b029cf0613cb5bc183d Mon Sep 17 00:00:00 2001 From: jhennies Date: Tue, 30 Jul 2024 15:57:55 +0200 Subject: [PATCH 06/12] Removed Napari import --- _includes/batch_processing/batch_measure_nuclei_shape.py | 1 - 1 file changed, 1 deletion(-) diff --git a/_includes/batch_processing/batch_measure_nuclei_shape.py b/_includes/batch_processing/batch_measure_nuclei_shape.py index 0b630fb8..5b04e4f6 100644 --- a/_includes/batch_processing/batch_measure_nuclei_shape.py +++ b/_includes/batch_processing/batch_measure_nuclei_shape.py @@ -9,7 +9,6 @@ import pandas as pd import pathlib from pathlib import Path -from napari import Viewer # %% From c2a0cf21b03d0ff14630e8f3cdbe315202ccb9a0 Mon Sep 17 00:00:00 2001 From: jhennies Date: Tue, 30 Jul 2024 16:31:50 +0200 Subject: [PATCH 07/12] All measurement results to a single MoBIE-readable csv --- .../batch_measure_nuclei_shape.py | 51 +++++++++---------- 1 file changed, 25 insertions(+), 26 deletions(-) diff --git a/_includes/batch_processing/batch_measure_nuclei_shape.py b/_includes/batch_processing/batch_measure_nuclei_shape.py index 5b04e4f6..3db8fa42 100644 --- a/_includes/batch_processing/batch_measure_nuclei_shape.py +++ b/_includes/batch_processing/batch_measure_nuclei_shape.py @@ -1,6 +1,7 @@ # %% # Batch analysis of 2D nuclei shape measurements + # %% # Import python modules from OpenIJTIFF import open_ij_tiff, save_ij_tiff @@ -19,6 +20,10 @@ def analyse(image_filepath, output_folder): # This prints which image is currently analysed print("Analyzing:", image_filepath) + # Convert the image_filepath String to a Path, + # which is more convenient to create the output files + image_filepath = pathlib.Path(image_filepath) + image, axes, scales, units = open_ij_tiff(image_filepath) # Binarize the image using auto-thresholding @@ -31,10 +36,14 @@ def analyse(image_filepath, output_folder): # We can safely convert to 16 bit as we know that we don't have too many objects label_image = label(binary_image).astype('uint16') + # Save the labels + label_image_filepath = output_folder / f"{image_filepath.stem}_labels.tif" + save_ij_tiff(label_image_filepath, label_image, axes, scales, units) + # Measure calibrated (scaled) nuclei shapes df = pd.DataFrame(regionprops_table( label_image, - properties={'label', 'area'}, + properties={'label', 'area', 'centroid'}, spacing=scales)) # Round all measurements to 2 decimal places. @@ -43,23 +52,12 @@ def analyse(image_filepath, output_folder): # you may not want to round that much! df = df.round(2) - # Save the results to disk + # Add the image and label filepaths to the data-frame + df['image'] = image_filepath + df['labels'] = label_image_filepath - # Convert the image_filepath String to a Path, - # which is more convenient to create the output files - image_filepath = pathlib.Path(image_filepath) - - # Save the labels - label_image_filepath = output_folder / f"{image_filepath.stem}_labels.tif" - save_ij_tiff(label_image_filepath, label_image, axes, scales, units) - - # Save the measurements table - # to a tab delimited text file (sep='\t') - # without row numbers (index=False) - table_path = output_folder / f"{image_filepath.stem}_measurements.csv" - df.to_csv(table_path, sep='\t', index=False) - - return label_image_filepath + # Return the data-frame + return df # %% @@ -72,23 +70,24 @@ def analyse(image_filepath, output_folder): # Create a list of the paths to all data image_paths = [output_dir / "xy_8bit__mitocheck_incenp_t1.tif", output_dir / "xy_8bit__mitocheck_incenp_t70.tif"] -# Create an empty list for the result label images -label_image_paths = [] +# Create an empty list for the measurement results +result_dfs = [] # %% # The loop which performs the analysis for image_path in image_paths: - # Computes the analysis and returns the path of the resulting label image - label_image_path = analyse(image_path, output_dir) + # Computes the analysis and returns a data-frame with the resulting measurements + result_df = analyse(image_path, output_dir) # Append the label image path to the list initialized before the loop - label_image_paths.append(label_image_path) + result_dfs.append(result_df) # %% -# Create a table to view the results in MoBIE -df = pd.DataFrame(data={'image': image_paths, 'labels': label_image_paths}) -mobie_table_path = output_dir + Path('mobie_table.csv') -df.to_csv(mobie_table_path, sep='\t', index=False) +# Concatenate the result data-frames to a single one which contains all results +final_df = pd.concat(result_dfs, ignore_index=True) +# Save the final results to disk +final_df.to_csv(output_dir / 'batch_processing_results.csv', sep='\t', index=False) + From 9c71833f65635fdad9557df4dc1d23e1d7529cbf Mon Sep 17 00:00:00 2001 From: jhennies Date: Tue, 30 Jul 2024 17:02:36 +0200 Subject: [PATCH 08/12] Removed python-dependent code from general activities --- .../batch_measure_nuclei_shapes.md | 17 ++++++++++------- _modules/batch_processing.md | 2 ++ 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/_includes/batch_processing/batch_measure_nuclei_shapes.md b/_includes/batch_processing/batch_measure_nuclei_shapes.md index 4eea0c95..86bc1e2f 100644 --- a/_includes/batch_processing/batch_measure_nuclei_shapes.md +++ b/_includes/batch_processing/batch_measure_nuclei_shapes.md @@ -5,14 +5,17 @@ - Adapt this workflow for automated batch analysis of many images - Start by building the skeleton of the workflow without filling in the functionality; - Note that the code below runs fine, but does not produce any results: + Note that the pseudo-code below will run fine, but does not produce any results: ``` -def analyse(image_path, output_folder): - print("Analyzing:", image_path) - -for image_path in image_paths: - analyse(image_path, output_dir) +FUNCTION analyse(image_path, output_folder) + PRINT "Analyzing:", image_path +END FUNCTION + +FOR each image_path in image_paths + CALL analyse(image_path, output_dir) +END FOR ``` - - Make sure the loop with the (almost) empty analyse function runs without error before filling in the image analysis steps \ No newline at end of file + - Make sure the loop with the (almost) empty analyse function runs without error before filling in the image analysis steps + - Inspect the analysis results in a suitable software \ No newline at end of file diff --git a/_modules/batch_processing.md b/_modules/batch_processing.md index 49286a41..41cdd095 100644 --- a/_modules/batch_processing.md +++ b/_modules/batch_processing.md @@ -41,4 +41,6 @@ learn_next: external_links: - "[Batch processing in ImageJ](https://imagej.net/scripting/batch)" + - [MoBIE](https://github.com/mobie/mobie-viewer-fiji) + - [ImageDataExplorer](https://git.embl.de/heriche/image-data-explorer) --- From fc1424387e706663dd0b87e8fd3527e6362988d9 Mon Sep 17 00:00:00 2001 From: jhennies Date: Tue, 30 Jul 2024 17:05:15 +0200 Subject: [PATCH 09/12] Changed link for ImageDataExplorer to publication --- _modules/batch_processing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_modules/batch_processing.md b/_modules/batch_processing.md index 41cdd095..7a5fa95c 100644 --- a/_modules/batch_processing.md +++ b/_modules/batch_processing.md @@ -42,5 +42,5 @@ learn_next: external_links: - "[Batch processing in ImageJ](https://imagej.net/scripting/batch)" - [MoBIE](https://github.com/mobie/mobie-viewer-fiji) - - [ImageDataExplorer](https://git.embl.de/heriche/image-data-explorer) + - [ImageDataExplorer](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273698) --- From ea81c7e6f083a7643ee462309358cbd6b71ec1b2 Mon Sep 17 00:00:00 2001 From: Felix-Mac Date: Tue, 30 Jul 2024 17:57:05 +0200 Subject: [PATCH 10/12] Shorten script to only include opening files --- .../open_diverse_file_formats_bioio.py | 53 ------------------- 1 file changed, 53 deletions(-) diff --git a/_includes/image_file_formats/open_diverse_file_formats_bioio.py b/_includes/image_file_formats/open_diverse_file_formats_bioio.py index 6dd96c14..23248135 100644 --- a/_includes/image_file_formats/open_diverse_file_formats_bioio.py +++ b/_includes/image_file_formats/open_diverse_file_formats_bioio.py @@ -177,56 +177,3 @@ # %% # little excercise: # paticipants should try to open one of their files with python - -# %% -# Save image as .ome.tif -# Option 1: directly from the BioImage object -# - Observe that some metadata are automatically transferred -image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif" -bioimage = BioImage(image_url) -print(bioimage.physical_pixel_sizes) -print(bioimage.metadata) - -# Save and reload -bioimage.save('option1.ome.tif') -print(BioImage('option1.ome.tif').physical_pixel_sizes) -print(bioimage.metadata) - -# %% -# Option 2: Start from numpy.array -from bioio.writers import OmeTiffWriter -img_data = bioimage.data.squeeze() -OmeTiffWriter.save(img_data, - 'option2.ome.tif', - dim_order='YX', - physical_pixel_sizes=bioimage.physical_pixel_sizes) - -# - Observe that pixel size is stored in file -print(BioImage('option2.ome.tif').physical_pixel_sizes) - -# %% -# Option 3: Save as ome.zarr -# - Observe that saved image is no longer single file, but whole folder -from bioio.writers import OmeZarrWriter - -OmeZarrWriter('option3.ome.zarr').write_image(img_data, - image_name='Option3', - channel_names=None, - channel_colors=None, - dimension_order='YX', - physical_pixel_sizes=bioimage.physical_pixel_sizes) - -# - Observe that relaoding shows the same array again with the same pixel sizes -reloaded_img = BioImage('option3.ome.zarr') -print(reloaded_img.dims) -print(reloaded_img.physical_pixel_sizes) - -# %% -# Option 4: Save only the numpy.array -# - Keep in mind that is not the best way, as all metadata are lost and the file can only be opened with python again -np.save('option4.npy',img_data) - -# Reload and check if they are the same -reloaded_img = np.load('option4.npy') -print(f'Are the dimensions the same: {np.all(img_data.shape == reloaded_img.shape)}') -print(f'Are the images the same: {np.all(img_data == reloaded_img)}') From 2920086d2fbbcd147bc13f5fff779bf7b35c3344 Mon Sep 17 00:00:00 2001 From: akhan Date: Wed, 31 Jul 2024 10:51:29 +0200 Subject: [PATCH 11/12] added figure to 'script_functions.md' --- figures/script_functions.png | Bin 723342 -> 295324 bytes 1 file changed, 0 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z>lScJ>tu>V1EjvFI4?2Cuhyzmls&Nr7*M{qH>XQ3=M(3eMHq`@fj~;yL&cWSP%BpJ zPRYLFY{en_Ilg}5K!Dm6F#nMV14={2&et5P3N+fDdkn2xZsTO5Y+sHmiJY3b{X7Ki? zZXRV1n3a3@-(N6y8QnjiIpjr@oAKe5vlopSe5{S;{i~{9a18J*jlYqhBOVujQr4TS zb}rrQHtUb|_XY0>=pd*f&zQj*%(-)i%{QwHiivY6|D>v=M|mZRhQvF~eWx}H_A+vv zc)@5o-`UiOvVoHG83lZ091G7w8|GP;7%c8 z7|R`F-UaZV%YA6*S+-UhA?3IC{s0zalpsRV>g~jPosI_4zrg3V?%m&>HOoVGDz&rB z5&dCll{+!g4sw(lq9e|Aj$;Q1=1-30mahz5$eH`Jhwk&;C|DvRs0H^WN)r_Bd+_(Am>>nsBEhgnD`vPh54wIx80$eKFZsi zLmt|22sq`!k6FPcjf!jD(lSibSvFE*I$(-7EG|sc^$vj^#v!* z`MixX`+AH;VA}jgo_RmL-A$^>cUgm-LR*mw$V{d>&7XAYU|4g>iyyjJ2CpfM)7LC3 z%1(3`&MSy~8`;C>Z8x*W7K1BX^D?DRfa}6;6y?vq|G~Qm;@Y_$&BF`G?g$Q`$E=*e znFH@%y#nNdqXCOA+=>PBT>h_7v#}d|_R*tS-Q+HqHGn9v=XdHS_jrTbfz|y3FP)Zf zFL>MIs4#e}7!k*|K2{rGbNK(*cr*Vbi`Scd%`3VwT1$ZX6O2|YV(mdHUj{1P-NQWO zQsh{CuU?d(23Ox=`}L^Zx??;0vk% From b3b49aa34bf0a88ee678fbf0c0cd452b60f50b80 Mon Sep 17 00:00:00 2001 From: akhan Date: Wed, 31 Jul 2024 15:53:08 +0200 Subject: [PATCH 12/12] improved 'script_functions' module --- .../activities/function_add_two_numbers.ijm | 34 --------- .../activities/function_display_slice.md | 5 -- .../function_display_slice_imagejmacro.ijm | 29 -------- .../add_two_numbers_activity1.md | 3 + ...dd_two_numbers_activity1_skimage_napari.py | 13 ++++ .../exercises/cc_counting_after_opening.ijm | 28 ------- .../cc_counting_after_opening_solution.ijm | 28 ------- .../exercises/functions_imagejmacro.md | 33 --------- .../exercises/functions_imagejmacro2.md | 6 -- .../open_inspect_image_activity2.md | 8 ++ ..._inspect_image_activity2_skimage_napari.py | 74 +++++++++++++++++++ _modules/script_functions.md | 14 +--- 12 files changed, 102 insertions(+), 173 deletions(-) delete mode 100644 _includes/script_functions/activities/function_add_two_numbers.ijm delete mode 100644 _includes/script_functions/activities/function_display_slice.md delete mode 100644 _includes/script_functions/activities/function_display_slice_imagejmacro.ijm create mode 100644 _includes/script_functions/add_two_numbers_activity1.md create mode 100644 _includes/script_functions/add_two_numbers_activity1_skimage_napari.py delete mode 100644 _includes/script_functions/exercises/cc_counting_after_opening.ijm delete mode 100644 _includes/script_functions/exercises/cc_counting_after_opening_solution.ijm delete mode 100644 _includes/script_functions/exercises/functions_imagejmacro.md delete mode 100644 _includes/script_functions/exercises/functions_imagejmacro2.md create mode 100644 _includes/script_functions/open_inspect_image_activity2.md create mode 100644 _includes/script_functions/open_inspect_image_activity2_skimage_napari.py diff --git a/_includes/script_functions/activities/function_add_two_numbers.ijm b/_includes/script_functions/activities/function_add_two_numbers.ijm deleted file mode 100644 index 11a24dd1..00000000 --- a/_includes/script_functions/activities/function_add_two_numbers.ijm +++ /dev/null @@ -1,34 +0,0 @@ - - -x = 10; -y = 5; - - -z = addTwoNumbers(10, 5); -print("Result of addition:"+z); -z = addTwoNumbers(2, 3); -print("Result of addition:"+z); - - - -function addTwoNumber(number1, number2) -{ - z = Number1 + Number2; - return z; -} - - - - - - - - - - - - - - - - diff --git a/_includes/script_functions/activities/function_display_slice.md b/_includes/script_functions/activities/function_display_slice.md deleted file mode 100644 index d4e97e49..00000000 --- a/_includes/script_functions/activities/function_display_slice.md +++ /dev/null @@ -1,5 +0,0 @@ -### Compute mean intensities -- Open an image stack -- Enter the slice numbers for which you want to calculate mean intensity. -- Run a function to extract the specified slices, display them and calculate their mean intensity. -- Display the mean intensity value for each specified slice. diff --git a/_includes/script_functions/activities/function_display_slice_imagejmacro.ijm b/_includes/script_functions/activities/function_display_slice_imagejmacro.ijm deleted file mode 100644 index e34f11d9..00000000 --- a/_includes/script_functions/activities/function_display_slice_imagejmacro.ijm +++ /dev/null @@ -1,29 +0,0 @@ - - -run("Close All"); // close all opened windows -run("MRI Stack"); // load the example MRI image stack -imageName = getTitle(); //get the image title - -//select first slice, calculate its mean intensity and display it -selectWindow(imageName); -sliceNumber = 1; -meanIntensitySlice1 = calculateSliceMeanIntensity(sliceNumber); -print("Slice#"+ sliceNumber + " has mean intensity: " + meanIntensitySlice1); - -//select last slice, calculate its mean intensity and display it -selectWindow(imageName); -sliceNumber = nSlices; -meanIntensitySlice2 = calculateSliceMeanIntensity(sliceNumber); -print("Slice#"+ sliceNumber + " has mean intensity: " + meanIntensitySlice2); - - -// a function that calculates mean intensity of a specified slice in a stack and displays it -function calculateSliceMeanIntensity(sliceNumber) -{ - setSlice(sliceNumber); - run("Duplicate...", "title=slice"+sliceNumber); - sliceName = "slice"+sliceNumber; - selectWindow(sliceName); - getStatistics(area, mean); - return mean; -} diff --git a/_includes/script_functions/add_two_numbers_activity1.md b/_includes/script_functions/add_two_numbers_activity1.md new file mode 100644 index 00000000..5765a1a0 --- /dev/null +++ b/_includes/script_functions/add_two_numbers_activity1.md @@ -0,0 +1,3 @@ +

    Inspect a binary image

    + +This activity shows how to write and call a function (to add two number) within a script. diff --git a/_includes/script_functions/add_two_numbers_activity1_skimage_napari.py b/_includes/script_functions/add_two_numbers_activity1_skimage_napari.py new file mode 100644 index 00000000..c5c26d9f --- /dev/null +++ b/_includes/script_functions/add_two_numbers_activity1_skimage_napari.py @@ -0,0 +1,13 @@ +# %% +# Write a function add two numbers + +# %% +# Define a function to add numbers +def add_two_number(number1, number2): + total_sum = number1 + number2 + return total_sum + +# %% +# Call the function to add numbers and display result +z = add_two_number(10, 5) +print(f'Result of addition: {z} \n') diff --git a/_includes/script_functions/exercises/cc_counting_after_opening.ijm b/_includes/script_functions/exercises/cc_counting_after_opening.ijm deleted file mode 100644 index 44c377ef..00000000 --- a/_includes/script_functions/exercises/cc_counting_after_opening.ijm +++ /dev/null @@ -1,28 +0,0 @@ -// Open a sample 2D particles image and apply morphological opening with different radius and compare the number of resulting connected components -// Task: 1) Figure out the repetitive part of the code, and -// 2) Apply a function to perform the repetitive task - -run("Close All"); // close all opened windows -run("Particles"); // open sample particles image -imageName = getTitle(); //get the image title -run("Duplicate...", "particles-1"); //duplicate the image -run("Invert LUT"); //invert the lookup table as background has value of 255 and foreground (particles) has a value of 0 -radiusOpeningFilter = 3; // select the size of opening filter you want to apply -run("Morphological Filters", "operation=Opening element=Disk radius="+ radiusOpeningFilter); // Apply morphological opening -run("Connected Components Labeling", "connectivity=4 type=[16 bits]"); // Run connected component (CC) analysis -getStatistics(area, mean, min, max, std, histogram); // calculate the maximum value of image -highestLabelNumber = max; -print("Number of particles detected: " + highestLabelNumber); // Print the maximum value of th image -selectWindow("particles.gif"); -run("Close All"); // close all opened windows -run("Particles"); -imageName = getTitle(); //get the image title -run("Duplicate...", "particles-1"); //duplicate the image -run("Invert LUT"); //invert the lookup table as background has value of 255 and foreground (particles) has a value of 0 -radiusOpeningFilter = 5; // select the size of opening filter you want to apply -run("Morphological Filters", "operation=Opening element=Disk radius="+ radiusOpeningFilter); // Apply morphological opening -run("Connected Components Labeling", "connectivity=4 type=[16 bits]"); // Run connected component (CC) analysis -getStatistics(area, mean, min, max, std, histogram); // calculate the maximum value of image -highestLabelNumber = max; -print("Number of particles detected: " + highestLabelNumber); // Print the maximum value of the image - diff --git a/_includes/script_functions/exercises/cc_counting_after_opening_solution.ijm b/_includes/script_functions/exercises/cc_counting_after_opening_solution.ijm deleted file mode 100644 index 9635dbae..00000000 --- a/_includes/script_functions/exercises/cc_counting_after_opening_solution.ijm +++ /dev/null @@ -1,28 +0,0 @@ -// Open a sample 2D particles image and apply morphological opening with different radius and compare the number of resulting connected components -// Task: 1) Figure out the repetitive part of the code, and -// 2) Apply a function to perform the repetitive task - -// SOLUTION: - -run("Close All"); // close all opened windows -run("Particles"); // open sample particles image -radiusOpeningFilter = 3; // set the opening filter size -applyOpeningCountCC(radiusOpeningFilter); // call the function - -selectWindow("particles.gif"); // select the original image window to repeat the task -radiusOpeningFilter = 5; // set the opening filter size -applyOpeningCountCC(radiusOpeningFilter); // call the function - - - -function applyOpeningCountCC(radiusOpeningFilter) // Function to perform the repititive task, this function duplicates image, apply morphological opening, CC analysis and display total number of CC found -{ - run("Duplicate...", "particles-1"); //duplicate the image - run("Invert LUT"); //invert the lookup table as background has value of 255 and foreground (particles) has a value of 0 - run("Morphological Filters", "operation=Opening element=Disk radius="+ radiusOpeningFilter); // Apply morphological opening - run("Connected Components Labeling", "connectivity=4 type=[16 bits]"); // Run connected component (CC) analysis - getStatistics(area, mean, min, max, std, histogram); // calculate the maximum value of image - highestLabelNumber = max; - print("Number of particles detected: " + highestLabelNumber); // Print the maximum value of th image -} - diff --git a/_includes/script_functions/exercises/functions_imagejmacro.md b/_includes/script_functions/exercises/functions_imagejmacro.md deleted file mode 100644 index 1c0ec446..00000000 --- a/_includes/script_functions/exercises/functions_imagejmacro.md +++ /dev/null @@ -1,33 +0,0 @@ -Use "function_display_slice.ijm": and display mean intensity values of slices without returning any values from the function. -1. Select two different slice indices of your choice . -2. Modify the function "calculateSliceMeanIntensity" in such a way that it returns nothing and displays mean intensity of each individual slice. - -> ## Solution -> ``` ->run("Close All"); // close all opened windows ->run("MRI Stack"); // load the example MRI image stack ->imageName = getTitle(); //get the image title -> ->//select first slice,calculate its mean intensity and display it ->selectWindow(imageName); ->sliceNumber = 5; ->calculateSliceMeanIntensity(sliceNumber); -> ->//select last slice,calculate its mean intensity and display it ->selectWindow(imageName); ->sliceNumber = 10; ->calculateSliceMeanIntensity(sliceNumber); -> ->// a function that calculates mean intensity of a specified slice in a stack and displays it ->function calculateSliceMeanIntensity(sliceNumber) ->{ -> setSlice(sliceNumber); -> run("Duplicate...", "title=slice"+sliceNumber); -> sliceName = "slice"+sliceNumber; -> selectWindow(sliceName); -> run("8-bit"); -> getStatistics(area, mean); -> print("Slice#"+ sliceNumber + " has mean intensity: " + mean); ->} -> ``` -{: .solution} diff --git a/_includes/script_functions/exercises/functions_imagejmacro2.md b/_includes/script_functions/exercises/functions_imagejmacro2.md deleted file mode 100644 index 8463c603..00000000 --- a/_includes/script_functions/exercises/functions_imagejmacro2.md +++ /dev/null @@ -1,6 +0,0 @@ -Use "cc_counting_after_closing.ijm": and do the following tasks: -1. Figure out the repetitive part of the code, and -2. Apply a function to perform the repetitive task. - -## Solution -- [cc_counting_after_opening_solution](https://raw.githubusercontent.com/NEUBIAS/training-resources/master/_includes/script_functions/exercises/cc_counting_after_opening_solution.ijm) diff --git a/_includes/script_functions/open_inspect_image_activity2.md b/_includes/script_functions/open_inspect_image_activity2.md new file mode 100644 index 00000000..028e7be8 --- /dev/null +++ b/_includes/script_functions/open_inspect_image_activity2.md @@ -0,0 +1,8 @@ +

    Inspect a binary image

    + +This activity shows a script that has following steps. + - Opening an image + - Check data type of image + - Calculate minimum/maximum pixel value + - Display the image +And to convert these steps that they are contained in a function called from within a script diff --git a/_includes/script_functions/open_inspect_image_activity2_skimage_napari.py b/_includes/script_functions/open_inspect_image_activity2_skimage_napari.py new file mode 100644 index 00000000..afa33d68 --- /dev/null +++ b/_includes/script_functions/open_inspect_image_activity2_skimage_napari.py @@ -0,0 +1,74 @@ +# %% +# This script has two parts: the first part shows a script that opens two images and inspect their data type and values sequentially +# and the second part shows a function to do this task + +# %% +# Import libraries and instantiate napari +import napari +import numpy as np +import matplotlib.pyplot as plt +from OpenIJTIFF import open_ij_tiff + + +# %% +# Part 1: write a script to open and inspect images +viewer = napari.Viewer() + +# %% +# Open one image and view it +image, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_intensity_clipping_issue_a.tif') +viewer.add_image(image) + +# %% +# Check the image's datatype +print(image.dtype) + +# %% +# Check minimum and maximum pixel values +print("Min:", image.min()) # check minimum pixel value +print("Max:", image.max()) # check maximum pixel value + + + +# %% +# Open second image and view it +image, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__two_cells.tif') +viewer.add_image(image) + + +# %% +# Check the image's datatype +print(image.dtype) + +# %% + +# Check minimum and maximum pixel values +print("Min:", image.min()) # check minimum pixel value +print("Max:", image.max()) # check maximum pixel value + +# %% +# Part 2: Write a function to perform above tasks +# NOTE: Please manually close any opened Napari Viewer Windows +viewer = napari.Viewer() + +# %% +# Define a function to read and inspect image +def open_inspect_image(image_path): + image, *_ = open_ij_tiff(image_path) + image_data_type, image_min, image_max = image.dtype, image.min(), image.max() + return image, image_data_type, image_min, image_max + +im1, im1_dtype, im1_min, im1_max = open_inspect_image('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_intensity_clipping_issue_a.tif') +im2, im2_dtype, im2_min, im2_max = open_inspect_image('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__two_cells.tif') + +# %% +# Display images +viewer.add_image(im1) +viewer.add_image(im2) + +# %% +# Print data type, min and max pixel values +print(f'image 1 has dtype: {im1_dtype}, min pixel value: {im1_min} and max pixel value: {im1_max} \n') +print(f'image 2 has dtype: {im2_dtype}, min pixel value: {im2_min} and max pixel value: {im2_max} \n') + +# TODO: One can also make a function for printing values diff --git a/_modules/script_functions.md b/_modules/script_functions.md index 05c92fff..92767176 100644 --- a/_modules/script_functions.md +++ b/_modules/script_functions.md @@ -4,6 +4,7 @@ layout: module tags: ["draft","scripting"] prerequisites: - "[Variables](../script_variables)" + - "[Loops](../script_for_loops)" objectives: - "Avoid code duplication using functions." @@ -22,18 +23,11 @@ concept_map: > figure: /figures/script_functions.png figure_legend: Components and working of a function within a script. -activity_preface: | - - Open an image stack - - Enter the slice numbers for which you want to calculate mean intensity. - - Run a function to extract the specified slices, display them and calculate their mean intensity. - - Display the mean intensity value for each specified slice. -activities: - - ["ImageJ Macro", "script_functions/activities/function_display_slice_imagejmacro.ijm", "java"] +multiactivities: + - ["script_functions/add_two_numbers_activity1.md", [["skimage napari", "script_functions/add_two_numbers_activity1_skimage_napari.py"]]] + - ["script_functions/open_inspect_image_activity2.md", [["skimage napari", "script_functions/open_inspect_image_activity2_skimage_napari.py"]]] -exercises: - - ["Exercise1 - ImageJ Macro", "script_functions/exercises/functions_imagejmacro.md"] - - ["Exercise2 - ImageJ Macro", "script_functions/exercises/functions_imagejmacro2.md"] assessment: >

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