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JBeckwithJBeckwith
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updating notebook structure
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‎default_analysis_parameters/default_bulk_params.json‎

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{
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"sigma1": 2.0,
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"sigma2": 60.0,
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"threshold": 100.0
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}

‎example_threecolour_notebooks/Step2_SingleImageAnalysis.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",
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"sys.path.append(\"..\") # Adds higher directory to python modules path.\n",
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"import os\n",
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"from src import RASPRoutines\n",
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"\n",
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"RASP = RASPRoutines.RASP_Routines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"This notebook tunes the two new parameters needed for the three-colour\n",
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"(oligomer + cell + bulk stain) pipeline, on a single test image, before\n",
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"running the full folder analysis in Step3_ImageFolderAnalysis.ipynb.\n",
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"\n",
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"Radiality (flatness/integratedGrad) is assumed already calibrated via\n",
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"Step1_RadialityCalibration.ipynb (also copied into this folder).\n",
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"\n",
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"Step A below tunes the bulk-stain threshold: RASP.bulk_threshold (and, if\n",
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"needed, RASP.bulk_sigma1/RASP.bulk_sigma2) controls how the bulk stain\n",
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"channel is thresholded into a binary mask (same difference-of-Gaussians\n",
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"detector already used for cell/large-object masks, just a single threshold).\n",
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"The plot below shows the raw bulk image with the resulting mask as a\n",
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"semi-transparent overlay -- adjust RASP.bulk_threshold and re-run until the\n",
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"mask tracks the bulk-stained regions you expect.\n",
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"\n",
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"These values persist as attributes on RASP, so whatever you set here is\n",
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"picked up automatically by RASP.analyse_images(..., bulk_string=...) in\n",
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"Step3 -- no need to pass them again.\n",
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"\"\"\"\n",
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"\n",
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"RASP.bulk_threshold = 100.0 # tune this\n",
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"# RASP.bulk_sigma1 = 2.0 # rarely needs changing\n",
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"# RASP.bulk_sigma2 = 60.0 # rarely needs changing\n",
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"\n",
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"protein_file = os.path.abspath(\n",
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" r\"../example_images/Example_01_ProteinImage_C1.tif\"\n",
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") # example file, replace as you wish. Always input as the absolute path of a raw string\n",
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"bulk_file = os.path.abspath(\n",
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" r\"../example_images/Example_01_CellImage_C0.tif\"\n",
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") # placeholder: this repo doesn't ship a real bulk-stain example image, so\n",
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"# the cell image stands in here purely to demonstrate the overlay plot --\n",
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"# replace with your actual bulk stain channel image.\n",
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"\n",
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"RASP.single_image_analysis(\n",
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" protein_file,\n",
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" thres=0.05,\n",
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" large_thres=250.0,\n",
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" gsigma=1.4,\n",
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" rwave=2.0,\n",
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" image_size=500,\n",
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" save_figure=False,\n",
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" cell_file=None,\n",
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" bulk_file=bulk_file,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"Step B: tune the cell-detection parameters, the same way as in the\n",
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"two-colour pipeline (example_twocolour_notebooks/Step2_SingleImageAnalysis.ipynb).\n",
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"\n",
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"RASP.cell_threshold1 / RASP.cell_threshold2 / RASP.cell_sigma1 /\n",
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"RASP.cell_sigma2 control the cell mask detector. The plot below shows the\n",
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"raw cell image with the resulting mask drawn as a contour border -- adjust\n",
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"and re-run until the outline matches the cells you expect.\n",
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"\n",
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"These also persist as attributes on RASP and are picked up automatically by\n",
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"RASP.analyse_images(..., cell_string=..., cell_analysis=True) in Step3.\n",
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"\"\"\"\n",
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"\n",
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"RASP.cell_threshold1 = 200.0 # tune this\n",
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"RASP.cell_threshold2 = 200.0 # tune this\n",
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"# RASP.cell_sigma1 = 2.0 # rarely needs changing\n",
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"# RASP.cell_sigma2 = 40.0 # rarely needs changing\n",
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"\n",
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"protein_file = os.path.abspath(\n",
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" r\"../example_images/Example_01_ProteinImage_C1.tif\"\n",
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") # example file, replace as you wish. Always input as the absolute path of a raw string\n",
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"cell_file = os.path.abspath(\n",
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" r\"../example_images/Example_01_CellImage_C0.tif\"\n",
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") # example file, replace as you wish. Always input as the absolute path of a raw string\n",
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"\n",
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"RASP.single_image_analysis(\n",
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" protein_file,\n",
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" thres=0.05,\n",
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" large_thres=250.0,\n",
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" gsigma=1.4,\n",
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" rwave=2.0,\n",
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" image_size=500,\n",
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" save_figure=False,\n",
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" cell_file=cell_file,\n",
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" bulk_file=None,\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎example_threecolour_notebooks/Step3_ImageFolderAnalysis.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",
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"sys.path.append(\"..\") # Adds higher directory to python modules path.\n",
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"import os\n",
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"from src import RASPRoutines\n",
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"\n",
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"RASP = RASPRoutines.RASP_Routines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"RASP.flatness = 1.\n",
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"RASP.integratedGrad = 10.\n",
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"RASP.areathres = 42.\n",
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"\n",
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"# Cell and bulk-stain detection parameters tuned in Step2_SingleImageAnalysis.ipynb\n",
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"# persist as attributes on RASP -- set them here if you're running this notebook\n",
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"# standalone rather than continuing on from Step2 in the same session.\n",
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"# RASP.cell_threshold1, RASP.cell_threshold2 = 200.0, 200.0\n",
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"# RASP.bulk_threshold = 100.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\" the command below will run the analyse_images analysis for the three-colour\n",
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"(oligomer + cell + bulk stain) pipeline:\n",
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"\n",
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" folder (string): Folder containing images\n",
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" imtype (string): Type of images being analysed. Default '.tif'\n",
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" thres (float): fraction of bright pixels accepted. Default 0.05.\n",
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" large_thres (float): large object intensity threshold. Default 100.\n",
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" gisgma (float): gaussian blurring parameter. Default 1.4.\n",
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" rwave (float): Ricker wavelent sigma. Default 2.\n",
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" protein_string (np.1darray): strings for protein-stained data. Default C1.\n",
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" cell_string (np.1darray): strings for cell-containing data. Default C0.\n",
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" bulk_string (np.1darray): strings for bulk-stain data. If given (not None),\n",
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" a bulk stain mask is computed for each image (using RASP.bulk_threshold /\n",
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" RASP.bulk_sigma1 / RASP.bulk_sigma2, tuned in Step2) and an\n",
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" \"overlap_with_bulk_stain\" boolean column is added to spot_analysis.csv,\n",
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" marking which oligomer spots fall inside the bulk stain mask. A\n",
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" \"*_bulkMask.tiff\" is also written alongside the existing \"*_cellMask.tiff\".\n",
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" if_filter (boolean): Filter images for focus. Default True.\n",
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" im_start (integer): Images to start from. Default 0.\n",
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" cell_analysis (boolean): Parameter where script also analyses cell\n",
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" images. Default True.\n",
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" one_savefile (boolean): Parameter that, if true, doesn't save a file. Default True.\n",
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" per image but amalgamates them into one file. Default True.\n",
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" disp (boolean): If true, prints when analysed an image stack. Default True.\n",
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" folder_recursion (boolean): If true, recursively finds folders and analyses each separately.\n",
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"\n",
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"All have defaults except folder, which is necessary.\n",
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"\"\"\"\n",
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"\n",
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"folder = os.path.abspath(\n",
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" r\"../example_images\"\n",
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") # example folder, replace as you wish. Always input as the absolute path of a raw string\n",
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"\n",
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"# This repo doesn't ship a real bulk-stain example image, so bulk_string is set to\n",
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"# \"C0\" below purely to exercise the three-channel code path end-to-end on the\n",
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"# existing two-colour example data (the cell image doubles as a bulk-stain stand-in).\n",
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"# On real three-channel data, use a string that uniquely identifies your bulk stain\n",
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"# channel files, distinct from protein_string and cell_string.\n",
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"RASP.analyse_images(\n",
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" folder,\n",
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" protein_string=\"C1\",\n",
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" cell_string=\"C0\",\n",
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" bulk_string=\"C0\",\n",
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" focus_images=\"C1\",\n",
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" thres=0.025,\n",
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" large_thres=1000.0,\n",
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" cell_analysis=True,\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎example_twocolour_notebooks/.ipynb_checkpoints/Step2_SingleImageAnalysis-checkpoint.ipynb‎

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‎src/IOFunctions.py‎

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lo_mask=[0],
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cell_mask=None,
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one_savefile=True,
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bulk_mask=None,
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bulk_string=None,
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):
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"""
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saves analysis.
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lo_mask (np.ndarray): mask of large objects
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cell_mask (np.ndarray): cell mask if cell mask saving
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one_savefile (boolean): if True, saving all analysis in one csv
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bulk_mask (np.ndarray): bulk stain mask if bulk mask saving
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bulk_string (np.1darray): strings for bulk-stained data
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"""
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A_F = _get_AF()
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),
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bit=np.uint8,
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)
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if bulk_mask is not None:
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self.write_tiff(
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bulk_mask,
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os.path.join(
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analysis_directory,
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f"{base_filename.split(protein_string)[0]}{bulk_string}_bulkMask.tiff",
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),
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bit=np.uint8,
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)
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return
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# Handling single file saving
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bit=np.uint8,
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)
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# Optional bulk stain mask
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if bulk_mask is not None:
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self.write_tiff(
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bulk_mask,
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os.path.join(
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analysis_directory,
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f"{_get_base_filename(files[i]).split(protein_string)[0]}{bulk_string}_bulkMask.tiff",
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),
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bit=np.uint8,
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)
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# Save or append data
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dfs_to_save = [to_save, to_save_largeobjects, n_spots, n_largeobjects]
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for df, path in zip(dfs_to_save, save_paths):

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