Merge pull request #2 from zoccoler/add_packages_info

Add packages info

Author: zoccoler

.gitignore

@@ -0,0 +1,6 @@
+
+# IPython Notebook
+.ipynb_checkpoints
+
+__pycache__/
+*.py[cod]

README.md

@@ -1,11 +1,12 @@
 # Lifetime Unmixing 
 
 This repository hosts code for distinguishing 2 dyes with overlapping spectra via lifetime.
-Images are 6D (3D FLIM timelapse multichannel).
+The workflows presented here mainly rely on the [napari-flim-phasor-plotter](https://zenodo.org/records/13319544) plugin, along with some post-processing/analysis steps done using [pyclesperanto-prototype](https://zenodo.org/records/10432619), [scikit-image](https://scikit-image.org/) and [scikit-learn](https://scikit-learn.org/stable/index.html), plus file saving using [tifffile](https://pypi.org/project/tifffile/).
+Images of worm embryos are 6D (3D FLIM timelapse multichannel). Images of adult worms are 5D (3D FLIM multichannel).
 
 ## Installation
 
-Install [Miniforge](https://github.com/conda-forge/miniforge?tab=readme-ov-file#miniforge) in your computer (which contains [`mamba`](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html#).
+Install [Miniforge](https://github.com/conda-forge/miniforge?tab=readme-ov-file#miniforge) in your computer (which contains [`mamba`](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html#)).
 
 Clone this repository locally (for example using [Github Desktop](https://desktop.github.com/)).
 
@@ -29,4 +30,5 @@ We recommend running this code on a powerful workstation, preferably with > 48GB
 
 ## Acknowledgements
 
-To be added
+We thank the Bio-Image Analysis Technology Development team of Physics of Life at TU Dresden (BiA-PoL) for the provision of infrastructure, code development and fruitful discussions.
+

code/phasor_plot_AZ212.ipynb

@@ -14,13 +14,15 @@
 from utilities import ellipse_vertices, format_metadata, set_plot_zoom_position, add_segmentation_metadata
 import time
 import warnings
+from watermark import watermark
 warnings.filterwarnings("ignore")
-start_time = time.time()
-print("Napari version: ", napari.__version__)
+# Print package versions and machine info
+print(watermark(packages="numpy,pandas,napari,napari_flim_phasor_plotter,skimage,dask,pyclesperanto_prototype,tifffile,xmltodict", machine=True, python=True, gpu=True))
 
+start_time = time.time()
 # Inputs
 
-# Main folder path
+# Main folder path (each folder contains a zarr file and an xml file)
 main_folder_path = Path("/home/pol_haase/mazo260d/Data/I227_Lifetime_Unmixing_of_Dyes_with_Overlapping_Sprectra/Batch_Processing")
 
 # Additional metadata in case the xml file is not available
@@ -280,12 +282,16 @@
         # Convert the segmentation result to a Dask array
         chunk_shape = summed_intensity_stack.chunksize
         cluster_labels_image_timelapse_dask = da.from_array(cluster_labels_image_timelapse_post_processed, chunks=chunk_shape)
-        # Add segmentation result to the summed intensity stack
-        summed_intensity_stack = da.concatenate([summed_intensity_stack, cluster_mask_timelapse_dask, cluster_labels_image_timelapse_dask], axis=0)
+        
         if store_phasor_mask:
             cluster_mask_timelapse = np.expand_dims(cluster_mask_timelapse.astype(summed_intensity_stack.dtype), axis=0)
             cluster_mask_timelapse_dask = da.from_array(cluster_mask_timelapse, chunks=chunk_shape)
             metadata_timelapse = add_segmentation_metadata(metadata_timelapse, channel_name='Phasor Mask')
+            # Add mask and segmentation result to the summed intensity stack
+            summed_intensity_stack = da.concatenate([summed_intensity_stack, cluster_mask_timelapse_dask, cluster_labels_image_timelapse_dask], axis=0)
+        else:
+            # Add segmentation result to the summed intensity stack
+            summed_intensity_stack = da.concatenate([summed_intensity_stack, cluster_labels_image_timelapse_dask], axis=0)
         # Add metadata to 'segmentation' channel(s): Phasor Mask and Segmentation results after post-processing
         metadata_timelapse = add_segmentation_metadata(metadata_timelapse)
         output_file_name = sample_name + '_summed_intensity_with_segmentation.ome.tif'

code/phasor_plot_JJ1473.ipynb

@@ -74,6 +74,38 @@ def ellipse_vertices(center, a, b, angle, num_vertices=100):
     return vertices
 
 def format_metadata(flim_metadata, xml_path=None, stack_shape=None, z_pixel_size = 0.5, pixel_size_unit = 'µm', time_resolution_per_slice = 0.663, time_unit = 's', channel_names = ['0', '1'], axes='CTZYX', timelapse=True):
+    """Format metadata for OME-TIFF based on the provided metadata and XML file.
+
+    Parameters
+    ----------
+    flim_metadata : List[Dict]
+        Metadata from the raw FLIM data file (usually .ptu or .sdt).
+    xml_path : Path, optional
+        Path to the XML file, containing metadata from the whole timelapse.
+        The xml file is assumed to be in the format of the XML file generated by the Leica SP8 software.
+        If None (default), then additional arguments must be provided.
+    stack_shape : Tuple[int], optional
+        Shape of the stack, by default None. Required if no XML file is provided.
+    z_pixel_size : float, optional
+        Pixel size along the z-axis, by default 0.5. Required if no XML file is provided.
+    pixel_size_unit : str, optional
+        Unit of the pixel size, by default 'µm'. Required if no XML file is provided.
+    time_resolution_per_slice : float, optional
+        Time resolution per slice, by default 0.663. Required if no XML file is provided.
+    time_unit : str, optional
+        Unit of the time resolution, by default 's'. Required if no XML file is provided.
+    channel_names : List[str], optional
+        Names of the channels, by default ['0', '1']. Required if no XML file is provided.
+    axes : str, optional
+        Axes of the data, by default 'CTZYX'.
+    timelapse : bool, optional
+        Whether the data is a timelapse, by default True.
+
+    Returns
+    -------
+    Tuple[Dict, Dict]
+        Metadata for the whole timelapse without photon counts axis and metadata for single timepoint with photon counts axis.
+    """
     import xmltodict
     if xml_path is None:
         xml_exist = False
@@ -163,6 +195,20 @@ def format_metadata(flim_metadata, xml_path=None, stack_shape=None, z_pixel_size
     return metadata_timelapse, metadata_single_timepoint
 
 def add_segmentation_metadata(metadata_timelapse, channel_name='Segmentation'):
+    """Add metadata for a segmentation channel to the existing metadata.
+
+    Parameters
+    ----------
+    metadata_timelapse : Dict
+        Metadata for the whole timelapse.
+    channel_name : str, optional
+        Name of the segmentation channel, by default 'Segmentation'.
+
+    Returns
+    -------
+    Dict
+        Updated metadata for the whole timelapse
+    """
     for channel_key, channel_values in metadata_timelapse['Channel'].items():
         if channel_key == 'Name':
             metadata_timelapse['Channel'][channel_key].append(channel_name)

code/phasor_plot_analysis_batch_processing.py

@@ -13,4 +13,6 @@ dependencies:
   - pip:
     - napari-flim-phasor-plotter
     - tifffile==2022.8.12
-    - pyclesperanto_prototype
+    - pyclesperanto_prototype
+    - watermark
+    - watermark[gpu]