From a2228903b32aa9cb793e51a8aaadc7c5bc2afa7f Mon Sep 17 00:00:00 2001
From: VictorDidier <68292394+VictorDidier@users.noreply.github.com>
Date: Fri, 10 Oct 2025 11:44:33 +0200
Subject: [PATCH 01/21] rework of original background substraction (#24)

* rework of tool,added backsub dir

* added log

* added logger and ome metadata writing

* Update CLI.py

Corrected the help description for the pyramid_levels argument

* ome-xml encoded as utf-8 & output markers_bs.csv

* encoded xml-ome string as utf-8

* added saveRam argument

* improved lazy loading of tiff images

* changed chunksize

* prefinal-version

* added hidden argument tspc-comet

* final rework

* moved environment.yml to root folder,edited Dockerfile
---
 Dockerfile                                    |  36 +-
 README.md                                     |  34 +-
 backsub/CLI.py                                | 108 ++++++
 backsub/__init__.py                           |   0
 backsub/__pycache__/CLI.cpython-312.pyc       | Bin 0 -> 3507 bytes
 .../metadata2markers_table.cpython-312.pyc    | Bin 0 -> 5381 bytes
 .../__pycache__/ome_schema.cpython-312.pyc    | Bin 0 -> 5724 bytes
 .../__pycache__/ome_writer.cpython-312.pyc    | Bin 0 -> 2318 bytes
 backsub/background_sub.py                     | 355 ++++++++++++++++++
 backsub/metadata2markers_table.py             | 129 +++++++
 backsub/ome_schema.py                         | 166 ++++++++
 backsub/ome_writer.py                         |  44 +++
 environment.yml                               |  22 +-
 13 files changed, 861 insertions(+), 33 deletions(-)
 create mode 100644 backsub/CLI.py
 create mode 100644 backsub/__init__.py
 create mode 100644 backsub/__pycache__/CLI.cpython-312.pyc
 create mode 100644 backsub/__pycache__/metadata2markers_table.cpython-312.pyc
 create mode 100644 backsub/__pycache__/ome_schema.cpython-312.pyc
 create mode 100644 backsub/__pycache__/ome_writer.cpython-312.pyc
 create mode 100644 backsub/background_sub.py
 create mode 100644 backsub/metadata2markers_table.py
 create mode 100644 backsub/ome_schema.py
 create mode 100644 backsub/ome_writer.py

diff --git a/Dockerfile b/Dockerfile
index 91574fc..db580d2 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,13 +1,23 @@
-FROM continuumio/miniconda3
-
-COPY environment.yml .
-RUN apt-get update -qq && apt-get install -y \
-    build-essential \
-    ffmpeg \
-    libsm6 \
-    libxext6
-
-RUN conda env create -f environment.yml
-ENV PATH="/opt/conda/envs/backsub/bin:$PATH"
-WORKDIR /background_subtraction
-COPY . .
\ No newline at end of file
+FROM mambaorg/micromamba:1.5.10-noble
+
+# Copy conda environment file
+COPY --chown=$MAMBA_USER:$MAMBA_USER ./environment.yml /tmp/conda.yml
+
+# Install environment
+RUN micromamba install -y -n base -f /tmp/conda.yml \
+    && micromamba install -y -n base conda-forge::procps-ng \
+    && micromamba env export --name base --explicit > environment.lock \
+    && echo ">> CONDA_LOCK_START" \
+    && cat environment.lock \
+    && echo "<< CONDA_LOCK_END" \
+    && micromamba clean -a -y
+
+# Switch to root to copy everything
+USER root
+
+# Ensure micromamba binaries are in PATH
+ENV PATH="$MAMBA_ROOT_PREFIX/bin:$PATH"
+
+# Copy the rest of the current directory into /app inside the container
+WORKDIR /app
+COPY ./backsub .
\ No newline at end of file
diff --git a/README.md b/README.md
index 03b7f18..f970494 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,17 @@ Marker<sub>*corrected*</sub> = Marker<sub>*raw*</sub> - Background / Exposure<su
 
 ## Usage 
 
-The `markers.csv` file which gives details about the channels needs to contain the following columns: "marker_name", "background" and "exposure". An exemplary [markers.csv](https://github.com/SchapiroLabor/Background_subtraction/blob/main/example/markers.csv) file is given. The "marker_name" column should indicate the marker for the acquired channel and all values should be unique. The "background" column should indicate the marker name of the channel which needs to be subtracted. This value must match the "marker_name" value of the background channel. The "exposure" column should contain the exposure time used for channel acquisition, and the measure unit should be consistent across the column. Exposure time is used for scaling the value of the background to be comparable to the processed channel. The "remove" column should contain logical `TRUE` values for channels which should be exluded in the output image.
+The `markers.csv` file which gives details about the channels needs to contain the following columns: "marker_name", "background" and "exposure". An exemplary [markers.csv](https://github.com/SchapiroLabor/Background_subtraction/blob/main/example/markers.csv) file is given. The "marker_name" column should indicate the marker for the acquired channel and all values should be unique. The "background" column should indicate the marker name of the channel which needs to be subtracted. This value must match the "marker_name" value of the background channel. The "exposure" column should contain the exposure time used for channel acquisition, and the measure unit should be consistent across the column. Exposure time is used for scaling the value of the background to be comparable to the processed channel. The "remove" column should contain logical `TRUE` values for channels which should be excluded in the output image.
+
+### Version v0.5.10:
+This version, which is a rework of v0.4.1, introduces the following features:
+* File-size of output image is reduced by using lossless compression ("LZW").
+* Facilitates container creation by prescinding from PALOM and opencv libraries.
+* Introduces the hidden argument, `-comet`, which extracts the metadata on-the-fly for Lunaphore Comet images.  When using this argument, the `markers.csv` file is not required.
+* Two RAM-profiles: (1) default mode, uses moderate RAM. (2) Uses approximately half-of the RAM of profile 1 at the cost of a slight loss in precision of the calculation of the downsized dimensions of the pyramidal output image.  This means the dimensions of the pyramidal level will differ between profile 1 and 2, the high-resolution level is not affected by this.
+* Organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background substraction and writing of output image and (5) extraction of metadata from Lunaphore Comet images.
+* Logger has been re-designed.
+
 
 ### Versions v0.4.1 and newer:
 The script has been rewritten to perform channel subtraction in a RAM-efficient manner - updating is highly recommended. If the output file is much bigger than expected, adjust the `--tile-size` parameter to a smaller value (e.g `512`). Changing the `--chunk-size` parameter may affect performance (lower values increase execution time, higher values increase RAM usage).
@@ -25,16 +35,24 @@ The `markers.csv` file which gives details about the channels needs to contain t
 
 ### CLI
 
-The script requires four inputs: 
-* the path to the starting image given with `-r` or `--root`
+Minimal required arguments:
+
+* the path to the input image given with `-r` or `--root`
 * the path to the output image given with `-o` or `--output`
 * the path to the `markers.csv` file given with `-m` or `--markers`
 * the path to the markers output file given with `-mo` or `--markerout`
-Optional inputs:
-* `--pixel-size` to specify the pixel size of the input image (default: `1.0`), if not specified, the pixel size will be read from the metadata of the input image.
-* `--version` to print version and exit (added in v0.3.4)
-* `--tile-size` to specify the tile size for the pyramidal output image (default: `1024`) (added in v0.3.4)
-* `--chunk-size` to specify chunk size for delayed calculation execution (default: `5000`) - lower values result in higher execution time, higher values in higher RAM usage
+
+Optional arguments:
+
+* `-mpp` or `--pixel-size` microns per pixel, i.e. pixel size of the input image in microns. If not specified the script will attempt 
+to extract the pixel size and its units from the metadata, if failed, it will assign a pixel size of 1 with "pixel" as units.
+* `-pl` or `--pyramid_levels` total number of pyramidal levels of the output image, this number should also include the high-resolution level.  Default value is 8, this argument will be only implemented if the input image is not pyramidal. If input image is pyramidal, the output image will have the same levels and this argument will be ignored.
+* `-sr` or `--save_ram` using this argument will provide the low RAM usage of version 0.4.1.
+* `--version` to print version.
+
+Hidden argument !!!:
+
+* `-comet` Flag to obtain the markers table on the fly for images acquired with the reference background acquisition implemented in the Lunaphore Comet at the TSPC (https://www.tspc-hd.com/). When this flag is used, the argument `-m`/`--markers` is ignored since the markers information will be extracted from the metadata of the input image.
 
 
 ### Output
diff --git a/backsub/CLI.py b/backsub/CLI.py
new file mode 100644
index 0000000..932292e
--- /dev/null
+++ b/backsub/CLI.py
@@ -0,0 +1,108 @@
+import argparse
+import pathlib
+from argparse import ArgumentParser as AP
+
+#---CLI-BLOCK---#
+def get_args():
+    # Script description
+    description="""
+                Subtracts background from an image (signal) 
+                acquired with fluorescence microscopy.  
+                Subtraction is carried out via the formula (SignalImage-factor*BackgroundImage), 
+                where factor is the ratio between exposure times of both images.
+                """
+
+    # Add parser
+    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
+
+    # INPUT GROUPS
+    inputs = parser.add_argument_group(title="INPUTS")
+    input_markers_table = parser.add_mutually_exclusive_group(required=True)
+    
+    inputs.add_argument("-r", 
+                        "--root", 
+                        dest="root", 
+                        action="store",
+                        type=pathlib.Path,
+                        required=True, 
+                        help="File path to root image file.")
+    
+    input_markers_table.add_argument("-m", 
+                        "--markers", 
+                        dest="markers", 
+                        action="store",
+                        type=pathlib.Path,
+                        help="""File path to the markers.csv file containing the list of marker names
+                        and its respective background channels.
+                        """
+                        )
+    
+    input_markers_table.add_argument("-comet",
+                        "--comet_metadata",
+                        action='store_true',
+                        help=argparse.SUPPRESS
+                        )
+    
+    inputs.add_argument("-mpp",
+                        "--pixel-size", 
+                        metavar="SIZE", 
+                        dest = "pixel_size",
+                        required=False,
+                        type=float, 
+                        default = None, 
+                        action = "store",
+                        help="pixel size in microns,i.e. microns per pixel(mpp)"
+                        )
+    
+    inputs.add_argument("-pl",
+                        "--pyramid_levels", 
+                        dest="pyramid_levels", 
+                        required=False, 
+                        type=int, 
+                        default=8, 
+                        help="""Total number of pyramid levels.
+                        This value will be only used if the input image is NOT pyramidal.
+                        If input image is pyramidal, the number of levels in the output image 
+                        will be the same as in the input.
+                        """
+                        )
+    
+    inputs.add_argument('-sr',
+                    '--save_ram',
+                    action='store_true',
+                    help="""RAM usage is cut in half when using this flag.  
+                    Notice that the dimensions of the reduced resolution levels (sub-levels) of 
+                    the output pyramidal image will slightly differ when using and not using this argument.                    
+                    """
+                        )
+    #VERSION CONTROL
+    inputs.add_argument("--version", 
+                        action="version", 
+                        version="v0.5.0"
+                        )
+    
+    #OUTPUTS
+    outputs = parser.add_argument_group(title="OUTPUTS")
+
+    outputs.add_argument("-o", 
+                         "--output", 
+                         dest="output", 
+                         action="store",
+                         type=pathlib.Path,
+                         required=True, 
+                         help="Path to output file"
+                         )
+    
+    outputs.add_argument("-mo", 
+                         "--marker-output",
+                         dest="markerout", 
+                         action="store",
+                         type=pathlib.Path,
+                         required=True, 
+                         help="Path to the output .csv marker file"
+                         )
+
+    arg = parser.parse_args()
+
+    return arg
+#---END_CLI-BLOCK---#
diff --git a/backsub/__init__.py b/backsub/__init__.py
new file mode 100644
index 0000000..e69de29
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diff --git a/backsub/background_sub.py b/backsub/background_sub.py
new file mode 100644
index 0000000..76f2a69
--- /dev/null
+++ b/backsub/background_sub.py
@@ -0,0 +1,355 @@
+#standard libraries
+import pandas as pd
+import numpy as np
+import tifffile as tifff
+from loguru import logger
+from skimage.transform import resize,rescale
+import time
+import dask.array as da
+from dask.diagnostics import ProgressBar,ResourceProfiler
+import tracemalloc
+from dask_image.ndfilters import gaussian_filter
+from skimage.util import img_as_float32
+from skimage.exposure import rescale_intensity
+import ome_types
+import zarr
+#local libraries
+import CLI
+import ome_writer
+from metadata2markers_table import meta_from_file,assign_background
+
+#Decorator function to measure run-time and memory peak of a function
+def memocron(func):
+    def wrapper(*args,**kwargs):
+        tracemalloc.start()
+        st = time.time()
+        result=func(*args,**kwargs)
+        print(f'\nRESOURCES USED by {func.__name__} function ')
+        print("Memory peak:",((10**(-9))*tracemalloc.get_traced_memory()[1],"GB"))
+        rt = time.time() - st
+        tracemalloc.stop()
+        print("Runtime:",f"{rt // 60:.0f}m {rt % 60:.0f}s")
+        return result
+    return wrapper
+
+
+def pyramidal_levels(img_arr,sub_levels,dims_schedule=None):
+    
+    if dims_schedule is None:
+        down_factor=2
+        height,width=img_arr.shape
+        factor_schedule=[ down_factor**i for i in range(1,sub_levels+1) ]
+        dims_schedule= [np.rint([height/f,width/f]) for f in factor_schedule]
+    else:
+        down_factor=np.rint( 
+                            np.mean( [ dims_schedule[i][0]/dims_schedule[i+1][0] 
+                                        for i in range(0,len(dims_schedule)-1)] 
+                                    ) 
+                            ).astype("int")
+
+    ref_dtype=img_arr.dtype.name
+    val_range=(np.min(img_arr), np.max(img_arr))
+    std_dev=np.ceil((down_factor - 1)/2)
+    img_aux=img_arr
+    for dims in dims_schedule:
+        img_dask=da.from_array(img_aux,chunks="auto")
+        result=gaussian_filter(img_dask, sigma=std_dev, order=0,truncate=down_factor)
+        img_aux=resize(img_as_float32(result.compute()), dims, order=1, preserve_range=True, anti_aliasing=False)
+        img_aux=np.rint(rescale_intensity(img_aux,out_range=val_range) ).astype(ref_dtype)
+        yield img_aux
+
+
+def pyramid_save_ram(img_arr,sub_levels):
+    chunksize=(2048,2048)#This value was found to be optimal, in terms of balanced RAM and runtime, for uint16 data
+    down_factor=2
+    #TODO:need to optimize speed and match dimensions of levels with those of the src image
+    ref_dtype=img_arr.dtype.name
+    border_overlap=down_factor-1
+    max_val=np.max(img_arr)
+    min_val=np.min(img_arr)
+    ref_dtype=img_arr.dtype.name
+    rescale_args={
+            "scale":1/down_factor,
+            "order":1,
+            "preserve_range":True,
+            "anti_aliasing":True
+                    }
+    img_aux=img_arr
+    for _ in range(sub_levels):
+        img_chunk=da.from_array(img_aux,chunks=chunksize)
+        float_half=img_chunk.astype("float32")
+        dim_rescale=da.map_overlap(rescale,float_half,depth=border_overlap,boundary="reflect",**rescale_args)
+        img_aux=(da.rint(dim_rescale).astype(ref_dtype)).compute()
+        #TODO: check why all the lines below corrupt the image intensity.
+        #img_aux_persist=dim_rescale.astype(ref_dtype).persist()
+        #img_aux=img_aux_persist.compute()
+        #level_max=np.max(img_aux)
+        #level_min=np.min(img_aux)
+        #int_rescale_factor=(max_val-min_val)/(level_max-level_min)
+        #img_aux=(da.rint(min_val-img_aux_persist*int_rescale_factor).astype(ref_dtype) ).compute()
+        yield img_aux
+
+
+def process_markers(markers):
+    markers['ind'] = range(0, len(markers))
+    if 'remove' not in markers:
+        markers['remove'] = ["False" for i in range(len(markers))]
+    else:
+        markers['remove'] = markers['remove'] == True
+
+    markers['keep'] = markers['remove'] == False
+
+    markers = markers.drop(columns=['remove'])
+
+    markers.insert(markers.shape[1], "processed", ~ markers.background.isnull())
+
+    scaling_factor=np.full(markers.shape[0],np.nan)
+    background_idx=np.full(markers.shape[0],np.nan)
+
+    for channel in range(len(markers)):
+
+        if markers.processed[channel]:
+            bg_idx = markers.loc[ markers.marker_name == markers.background[channel],"ind"  ].tolist()
+
+            if len(bg_idx)>1:
+                print(f"""Warning: Background channel with name {markers.background[channel]} 
+                      appears several times in the column "marker_name".  
+                      Only the first occurrence will be used for the subtraction.""" )
+            bg_idx=bg_idx[0]
+            scaling_factor[channel] = markers.exposure[channel] / markers.exposure[bg_idx]
+            background_idx[channel] = bg_idx
+
+    markers.insert(markers.shape[1], "factor", scaling_factor)
+    markers.insert(markers.shape[1], "bg_idx", background_idx)
+
+    return markers
+
+
+def extract_img_props(img_path,pixel_size=None):
+
+    #Extract data_type, pyramidal specs, height, width
+    with tifff.TiffFile(img_path) as tif:
+        pyr_levels=len(tif.series[0].levels)
+        is_pyramid=pyr_levels > 1
+        data_type=tif.series[0].dtype.name
+        height,width=tif.series[0].shape[-2::]
+        dask_chunksize=da.from_array(tif.pages[0].asarray(), chunks='auto').chunksize
+        if is_pyramid:
+            #dimensions of the reduced resolution layers(subresolution_dimensions)
+            subres_dims=[tif.series[0].levels[lvl].pages[0].shape
+                             for lvl in range(1,pyr_levels)
+                             ]
+        else:
+            subres_dims=None
+        
+    #Try to extract pixel size from ome-xml
+    if pixel_size is None:
+        print('Pixel size not specified in the arguments (-mpp)')
+        try:
+            metadata = ome_types.from_tiff(img_path)
+            pixel_size = metadata.images[0].pixels.physical_size_x
+            pixel_size_unit = metadata.images[0].pixels.physical_size_x_unit
+        except Exception as err:
+            print(err)
+            print('Pixel size or pixel size unit detection using ome-types failed')
+            pixel_size = 1
+            pixel_size_unit="pixel"
+    else:
+        pixel_size=pixel_size
+        pixel_size_unit="µm"
+
+    
+    img_props={"pixel_size":pixel_size,
+               "pixel_size_unit":pixel_size_unit,
+               "data_type":data_type,
+               "pyramid":is_pyramid,
+               "levels":pyr_levels,
+               "sub_levels_dims":subres_dims,
+               "size_x":width,
+               "size_y":height ,
+               "chunksize":dask_chunksize
+               }
+    
+    return img_props
+
+
+def subtract_channels(src_img_path,
+                      signal_index,
+                      background_index,
+                      factor,
+                      ref_chunksize,
+                      ref_dtype,
+                      task_no
+                      ):
+    """
+    This function executes the background substraction using generators, each element of the generator
+    is a tuple with 3 values, such that:
+
+    tuple=(img_with_backsub[array],calculate or extract pyramid [str],pyramid_from_index[int])
+
+    The second entry of the tuple indicates in the writing process if the pyramid should be calculated using 
+    pyramid_gaussian from scikit image.  If extract, the index given in the third entry will fetch all the pyramid
+    levels from the original image stack(src_img_path).
+    """
+    factor=np.float32(factor)#limiting precision to float32 saves memory
+    signal_as_zarr = zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0,key=int(signal_index) ) )
+    background_as_zarr =zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0,key=int(background_index) ) )
+    signal=da.from_zarr(signal_as_zarr, chunks=ref_chunksize )
+    background=da.from_zarr(background_as_zarr, chunks=ref_chunksize)
+    subtraction=da.clip(signal-factor*background,0,65535).astype(ref_dtype)
+    with ResourceProfiler(dt=0.25) as resources:
+        with ProgressBar():
+            result=subtraction.compute()
+    print(f"Resources used by dask during subtraction {task_no}:")
+    print(resources.results[0],"([sec],[MB],[% CPU usage])")
+    return result
+
+def extract_sublevels_from_tiff(path,ch,levs):
+    with tifff.TiffFile(path) as tif:
+        for l in range(1,levs):
+            yield tif.series[0].levels[l].pages[ch].asarray()
+
+def write_pyramid(src_img_path,
+                  tasks_table,
+                    outdir,
+                    levels,
+                    sub_lvls_dims,
+                    file_name,
+                    src_data_type,
+                    is_src_pyramid=False,
+                    save_ram=False
+                    ):
+
+    outdir.mkdir(parents=True, exist_ok=True)
+    out_file_path=outdir / file_name
+    sub_levels=levels-1
+
+    total_operations=tasks_table['processed'].values.sum()#Count True values
+    count=1
+
+    with tifff.TiffWriter(out_file_path,bigtiff=True) as tif:
+        #write first the original resolution image,i.e. first layer
+        for _,channel in tasks_table.iterrows():
+            if channel.processed:
+                operation_count=f"({count}/{total_operations})"
+                print(f"\n {operation_count} Calculating subtraction of background {channel.background}  from {channel.marker_name} signal:")
+                first_layer=subtract_channels(src_img_path, channel.ind, channel.bg_idx, channel.factor, (4096,4096), src_data_type,operation_count)
+                pyramid_action="calculate"
+                count+=1
+            else:
+                first_layer=tifff.imread(src_img_path,series=0,level=0,key=int(channel.ind))
+
+                if (save_ram or not is_src_pyramid):
+                    pyramid_action="calculate"
+                else:
+                    pyramid_action="extract"
+            
+            tif.write(
+                    first_layer,
+                    subifds=sub_levels,
+                    tile=(256, 256),
+                    photometric='minisblack',
+                    compression="lzw"
+                        )
+            
+            if pyramid_action=="calculate":
+                if save_ram:
+                    pyramid=pyramid_save_ram(first_layer,sub_levels)
+                else:
+                    pyramid=pyramidal_levels(first_layer,sub_levels,sub_lvls_dims)
+
+            elif pyramid_action=="extract":
+                pyramid=extract_sublevels_from_tiff(src_img_path,int(channel.ind),levels)
+
+            
+            for sub_layer in pyramid:
+                    tif.write(
+                        sub_layer,
+                        subfiletype=1,
+                        tile=(256, 256),
+                        photometric='minisblack',
+                        compression="lzw"#lzw works better when saving channel-by-channel and jpeg 2000 when saving the whole stack at once
+                        )
+                
+    return out_file_path
+
+@memocron
+def main(version):
+    args=CLI.get_args()
+    in_path = args.root
+    out_path = args.output
+
+    # 0) Validate input_path is not the same as output_path,pixel data is read into RAM lazily, cannot overwrite input file
+    assert out_path != in_path
+
+    # 1) Extract image properties
+    src_props = extract_img_props(in_path, args.pixel_size,)
+    # 2) Modify pyramid_levels if required
+    if src_props["pyramid"]:
+        levels=src_props["levels"]
+    else:
+        levels=args.pyramid_levels
+
+    # 3) Read/Create markers table and update it to include the information of the processing tasks
+    if args.comet_metadata:
+        registration_marker="DAPI"
+        meta_table=meta_from_file(in_path,registration_marker)
+        markers = process_markers( assign_background(meta_table,rmv_ref=True,ref_marker=registration_marker) )
+
+    elif args.markers:
+        markers = process_markers(pd.read_csv(args.markers))
+
+    markers_updated=markers.loc[ markers.keep]
+    #4) Write updated markers.csv without appended columns.  This file contains the markers information of the final image stack
+    markers_preview = markers_updated.drop(columns=['keep','ind','processed','factor','bg_idx'])
+    markers_preview["channel_number"] = np.arange(1, len(markers_preview)+1)
+    markers_preview.to_csv(args.markerout, index=False)
+
+    logger.info("\nTASKS PREVIEW:\n{}",markers_updated)
+    tasks=1
+    for _,channel in markers_updated.iterrows():
+        if channel.processed: 
+            print(f"\n(Task_{tasks}): background subtraction, Channel {channel.marker_name} (Background {channel.background})")
+            tasks+=1
+
+    #5) Calculate subtractions and write output file
+    out_file_name=f'{ (in_path.stem).split(".ome")[0] }_backsub.ome.tif'
+    logger.info(f"\nTASKS PROGRESS" )
+
+    print(f"\nCommencing writing of pyramidal ome.tif file into {out_path / out_file_name}")
+    print(f"\nCommencing subtraction tasks\n")
+
+    pyramid_abs_path=write_pyramid(
+                    in_path,
+                    markers_updated,
+                    out_path,
+                    levels,
+                    src_props["sub_levels_dims"],
+                    out_file_name,
+                    src_props["data_type"],
+                    is_src_pyramid=src_props["pyramid"],
+                    save_ram=args.save_ram
+                    )
+    
+    #6) Write metadata in OME format into the pyramidal file
+    channel_names=markers_updated["marker_name"].tolist()
+    ome_xml=ome_writer.create_ome(channel_names,src_props,version)
+    tifff.tiffcomment(pyramid_abs_path, ome_xml.encode("utf-8"))
+    
+
+
+    logger.info(f'\nSCRIPT FINISHED PROCESSING TASKS ')
+    print(f'\nPyramidal image with {levels} levels was successfully written ')
+    
+
+
+
+if __name__ == '__main__':
+    _version = 'v0.5.0'
+    main(_version)
+
+    
+
+
+
+
diff --git a/backsub/metadata2markers_table.py b/backsub/metadata2markers_table.py
new file mode 100644
index 0000000..2451cb8
--- /dev/null
+++ b/backsub/metadata2markers_table.py
@@ -0,0 +1,129 @@
+import pathlib
+from ome_types import from_tiff
+import argparse
+import pandas as pd
+import numpy as np
+
+#CLI
+def get_args():
+    parser=argparse.ArgumentParser()
+    parser.add_argument('-i',
+                        '--input_img',
+                        required=True,
+                        type=pathlib.Path,
+                        help='absolute path of the input image stack (.tif)'
+                        )
+    
+    parser.add_argument('-o',
+                        '--output_dir',
+                        required=True,
+                        type=pathlib.Path,
+                        help='absolute path of the directory where the output .csv file will be written'
+                        )
+    
+    parser.add_argument('-fn',
+                        '--output_file_name',
+                        required=False,
+                        type=str,
+                        default="markers.csv",
+                        help='name of the csv file'
+                        )
+    
+    parser.add_argument('-rr',
+                        '--remove_background_references',
+                        required=False,
+                        action='store_true',
+                        help='setup the removal of all reference background channels and all DAPI except the first occurrence.'
+                        )
+    
+    parser.add_argument('-rm',
+                        '--registration_marker',
+                        required=False,
+                        type=str,
+                        default="DAPI",
+                        help='name of the csv file'
+                        )
+
+    
+    args=parser.parse_args()
+    return args
+
+def meta_from_file(src_img_path,ref_marker_name):
+    #Fetch metadata object
+    ome=from_tiff(src_img_path)
+    #Fetch image attributes from ome
+    ch_names  = [ element.name for element in ome.images[0].pixels.channels ]
+    exp_times = [ element.exposure_time for element in ome.images[0].pixels.planes ]
+    #cycles=[int(element.attributes["CycleID"])+1 for element in ome.structured_annotations[0].value.any_elements[0].children]
+    filters= [ element.attributes["FluorescenceChannel"] for element in ome.structured_annotations[0].value.any_elements[0].children ]
+    background=[None if ref_marker_name in element 
+                else element for element in filters]
+
+    aux_dict={"channel_number":list(range(1,len(ch_names)+1)),
+              #"cycle_number":cycles,
+              "marker_name":ch_names,
+              "Filter":filters,
+              "background":background,
+              "exposure":exp_times
+              }
+    
+    df=pd.DataFrame(aux_dict)
+    return df
+
+
+def assign_background(df,rmv_ref=False,ref_marker="DAPI"):
+    #Create column ["backsub_process"] indicating which rows will be processed with backsub
+    filters_=df.Filter.unique().tolist()
+    #Strings corresponding to filters/background names are set to False, since the are not processed 
+    backsub_process=df["marker_name"].replace(filters_,value=False,regex=True)
+    #Marker_name corresponding to signal will be set to True for processing
+    backsub_process=np.where(backsub_process==False,False,True)
+    df.insert(df.shape[1],"backsub_process",backsub_process)
+
+    #Assign the latest mention of the autofluorescence channel to the correspondent row in the background column
+    rename_background=[]
+    #List with row indices of background channels to be removed
+    
+    for idx,row in df.iterrows():
+
+        if row.backsub_process:
+            previous_channels=reversed(df.iloc[:idx].marker_name.to_list())
+            for element in previous_channels:
+                #Supposes background name is a subset of the channel/marker name
+                if row.background in element:
+                    rename_background.append(element)
+                    break
+        else: 
+            rename_background.append(None)
+
+    df.drop(columns=["backsub_process"],inplace=True)
+    df.background=rename_background
+    if rmv_ref:
+        remove_val=len(df)*[""]
+        df.insert(df.shape[1],"remove",remove_val)
+        df.loc[ df["background"].isnull() , ["remove"] ]="TRUE"
+        first_ref_marker=df[df.marker_name== ref_marker].index[0]
+        df.loc[first_ref_marker,"remove"]=""
+    return df
+
+
+
+def main():
+    args=get_args()
+    img_path=args.input_img
+    out_dir=args.output_dir
+    file_name=args.output_file_name
+    global_ref_marker=args.registration_marker
+    
+    df=meta_from_file(img_path,global_ref_marker)
+    df_updated=assign_background(df,args.remove_background_references,global_ref_marker)
+    df_updated.to_csv( out_dir/file_name ,index=False)
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+    
+
diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
new file mode 100644
index 0000000..4c19bb9
--- /dev/null
+++ b/backsub/ome_schema.py
@@ -0,0 +1,166 @@
+#!/usr/bin/python
+import ome_types
+from ome_types.model import OME,Image,Pixels,TiffData,Channel,Plane
+import platform
+
+
+def INPUTS(frame):
+    """
+    This function creates a dictionary with the metadata of the tiles.
+    Args:
+        frame (pd.DataFrame): dataframe containing the metadata of the tiles.
+        conformed_markers (list): list of tuples with the name of the markers and their corresponding fluorophore.
+    Returns:
+        dict: dictionary with the metadata of the tiles.
+    """
+    inputs=frame.to_dict('list')
+
+    return inputs
+
+
+def TIFF_array(no_of_channels, inputs={'offset':0}):
+    """
+    This function creates a list of TIFFData objects.
+    Args:
+        no_of_channels (int): number of channels.
+        inputs (dict): dictionary with the metadata of the tiles.
+    Returns:
+        list: list of TIFFData objects.
+    """
+    TIFF = [
+        TiffData(
+            first_c=ch,
+            ifd=n,
+            plane_count=1
+            )
+        for n,ch in enumerate(range(0,no_of_channels), start=inputs['offset'])
+        ]
+
+    return TIFF
+
+
+def PLANE_array(no_of_channels, inputs):
+    """
+    This function creates a list of Plane objects.
+    Args:
+        no_of_channels (int): number of channels.
+        inputs (dict): dictionary with the metadata of the tiles.
+    Returns:
+        list: list of Plane objects.
+    """
+
+    PLANE = [
+        Plane(
+            the_c=ch,
+            the_t=0,
+            the_z=0,
+            position_x= inputs['position_x'][ch] if 'position_x' in inputs.keys() else 0,
+            position_y= inputs['position_y'][ch] if 'position_y' in inputs.keys() else 0,
+            position_z=0,
+            position_x_unit= inputs['position_x_unit'][ch] if 'position_x_unit'in inputs.keys() else "pixel" ,
+            position_y_unit= inputs['position_y_unit'][ch] if 'position_y_unit'in inputs.keys() else "pixel" 
+            )
+        for ch in range(0,no_of_channels)
+        ]
+
+    return PLANE
+
+
+def CHANN_array(no_of_channels, inputs):
+    """
+    This function creates a list of Channel objects.
+    Args:
+        no_of_channels (int): number of channels.
+        inputs (dict): dictionary with the metadata of the tiles.
+    Returns:
+        list: list of Channel objects.
+    """
+
+    CHANN = [
+        Channel(
+            id=f"Channel:{str(ch)}", # 'Channel:{y}:{x}:{marker_name}'.format(x=ch,y=100+int( inputs['tile'][ch] ) ,marker_name=inputs['marker'][ch] )
+            name=inputs["name"][ch],
+            color=(255,255,255)
+            )
+        for ch in range(0,no_of_channels)
+        ]
+
+    return CHANN
+
+
+def PIXELS_array(chann_block, plane_block, tiff_block, inputs):
+    """
+    This function creates a Pixels object.
+    Args:
+        chann_block (list): list of Channel objects.
+        plane_block (list): list of Plane objects.
+        tiff_block (list): list of TIFFData objects.
+        inputs (dict): dictionary with the metadata of the tiles.
+    Returns:
+        Pixels: Pixels object.
+    """
+
+    PIXELS = Pixels(
+        id=f"Pixels:{inputs['tile'][0]}",
+        dimension_order='XYCZT',
+        size_c=len(chann_block),
+        size_t=1,
+        size_x=inputs['size_x'][0],
+        size_y=inputs['size_y'][0],
+        size_z=1,
+        type=inputs['type'][0],#bit_depth
+        big_endian=False,
+        channels=chann_block,
+        interleaved=False,
+        physical_size_x=inputs['physical_size_x'][0],
+        physical_size_x_unit=inputs['physical_size_x_unit'][0],
+        physical_size_y=inputs['physical_size_y'][0],
+        physical_size_y_unit=inputs['physical_size_y_unit'][0],
+        physical_size_z=1.0,
+        planes=plane_block,
+        significant_bits=inputs['significant_bits'][0],
+        tiff_data_blocks=tiff_block
+    )
+
+    return PIXELS
+
+
+def IMAGE_array(pixels_block, imageID):
+    """
+    This function creates an Image object.
+    Args:
+        pixels_block (Pixels): Pixels object.
+        imageID (int): identifier of the image.
+    Returns:
+        Image: Image object.
+    """
+    
+    IMAGE = Image(
+            id =f'Image:{imageID}',
+            pixels=pixels_block
+            )
+
+    return IMAGE
+
+
+def OME_metadata(image_block,software):
+    """
+    This function creates an OME object.
+    Args:
+        image_block (list): list of Image objects.
+    Returns:
+        OME: OME object.
+    """
+    ome = OME()
+    ome.creator = " ".join([software,
+                            ome_types.__name__,
+                        ome_types.__version__,
+                        '/ python version-',
+                        platform.python_version()
+                        ]
+                        )
+
+    ome.images = image_block
+    ome_xml = ome_types.to_xml(ome)
+
+    return ome, ome_xml
diff --git a/backsub/ome_writer.py b/backsub/ome_writer.py
new file mode 100644
index 0000000..ba698ff
--- /dev/null
+++ b/backsub/ome_writer.py
@@ -0,0 +1,44 @@
+import ome_schema as schema
+import pandas as pd
+
+
+def create_ome(conformed_markers,info,software_version):
+    """
+    This function creates an OME-XML file from a pandas dataframe containing the metadata of the tiles.
+    Args:
+        tile_info (pd.DataFrame): dataframe containing the metadata of the tiles.
+        conformed_markers (list): list with the name of the markers in the corresponding order of their appearance in the ome.tif file .
+    Returns:
+        str: OME-XML file.
+    """
+    software=f'backsub {software_version}'
+    no_of_channels = len(conformed_markers)
+    tile_info_dict={
+            "tile": no_of_channels *[1],
+            "name":conformed_markers ,
+            "type": no_of_channels  *[ info["data_type"] ],
+            "size_x":no_of_channels *[ info["size_x"] ] ,
+           "size_y":no_of_channels *[info["size_y"]],
+           "physical_size_x": no_of_channels *[info["pixel_size"]],
+           "physical_size_x_unit": no_of_channels *[info["pixel_size_unit"]],
+           "physical_size_y": no_of_channels *[info["pixel_size"]],
+           "physical_size_y_unit": no_of_channels *[info["pixel_size_unit"]],
+           "significant_bits": no_of_channels *["16"]
+           }
+    tile_info=pd.DataFrame(tile_info_dict)
+    
+    grouped_tiles = tile_info.groupby(['tile'])
+    
+    tiles_counter = 0
+    image = []
+    for tileID, frame in grouped_tiles:
+        metadata = schema.INPUTS(frame)
+        tiff = schema.TIFF_array(no_of_channels, inputs={'offset': no_of_channels * tiles_counter})
+        plane = schema.PLANE_array(no_of_channels, metadata)
+        channel = schema.CHANN_array(no_of_channels, metadata)
+        pixels = schema.PIXELS_array(channel, plane, tiff, metadata)
+        image.append(schema.IMAGE_array (pixels, tiles_counter))
+        tiles_counter += 1
+    ome, ome_xml = schema.OME_metadata(image,software)
+
+    return ome_xml
diff --git a/environment.yml b/environment.yml
index 957a760..db53f01 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,17 +2,15 @@ name: backsub
 channels:
   - conda-forge
   - defaults
-  - anaconda
 dependencies:
-  - "python=3.9"
-  - "openslide=3.4.1"
-  - "scikit-image=0.19.2" 
-  - "numexpr=2.8.3" 
-  - "tifffile=2022.8.12" 
-  - "scipy=1.9.3"
-  - "pandas=2.1.1" 
-  - "zarr=2.3.2"
-  - procps-ng
-  - pip
+  - python=3.12.11
+  - pandas=2.3.1
+  - tifffile=2025.6.11
+  - scikit-image=0.25.2
+  - dask=2025.7.0
+  - ome-types=0.6.0
+  - numpy=2.3.2
+  - loguru=0.7.3
+  - dask-image=2024.5.3
   - pip:
-    - palom
\ No newline at end of file
+    - "zarr==3.1.1"
\ No newline at end of file

From f9dde2a910c7b0459f7a95c06b3c58b119d6c9d8 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Mon, 3 Nov 2025 15:01:42 +0100
Subject: [PATCH 02/21] moved zarr under conda dependencies

---
 environment.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/environment.yml b/environment.yml
index db53f01..1c46961 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,4 +1,4 @@
-name: backsub
+name: backsub_testing
 channels:
   - conda-forge
   - defaults
@@ -12,5 +12,4 @@ dependencies:
   - numpy=2.3.2
   - loguru=0.7.3
   - dask-image=2024.5.3
-  - pip:
-    - "zarr==3.1.1"
\ No newline at end of file
+  - zarr=3.1.1
\ No newline at end of file

From 20bcc1da97704486236c9cbb18cac44626fac2d5 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Mon, 3 Nov 2025 15:04:10 +0100
Subject: [PATCH 03/21] tile_size, downscale_factor, output path

---
 .gitignore                |  1 +
 backsub/CLI.py            | 51 +++++++++++++++++++----
 backsub/background_sub.py | 88 ++++++++++++++++++++++-----------------
 backsub/ome_writer.py     | 22 +++++-----
 4 files changed, 104 insertions(+), 58 deletions(-)
 create mode 100644 .gitignore
 mode change 100644 => 100755 backsub/background_sub.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/backsub/CLI.py b/backsub/CLI.py
index 932292e..b5d6d84 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -54,19 +54,42 @@ def get_args():
                         help="pixel size in microns,i.e. microns per pixel(mpp)"
                         )
     
-    inputs.add_argument("-pl",
-                        "--pyramid_levels", 
-                        dest="pyramid_levels", 
+    # inputs.add_argument("-pl",
+    #                     "--pyramid_levels", 
+    #                     dest="pyramid_levels", 
+    #                     required=False, 
+    #                     type=int, 
+    #                     default=8, 
+    #                     help="""Total number of pyramid levels.
+    #                     This value will be only used if the input image is NOT pyramidal.
+    #                     If input image is pyramidal, the number of levels in the output image 
+    #                     will be the same as in the input.
+    #                     """
+    #                     )
+
+    inputs.add_argument("-ts",
+                        "--tile_size", 
+                        dest="tile_size", 
+                        required=False, 
+                        type=int, 
+                        default=256, 
+                        help="""Tile size for image pyramid creation. Has to be a multiple of 16.
+                        """
+                        )
+
+    inputs.add_argument("-dsf",
+                        "--downscale_factor", 
+                        dest="downscale_factor", 
                         required=False, 
                         type=int, 
-                        default=8, 
-                        help="""Total number of pyramid levels.
+                        default=2, 
+                        help="""Downscale factor for the image pyramid.
                         This value will be only used if the input image is NOT pyramidal.
                         If input image is pyramidal, the number of levels in the output image 
-                        will be the same as in the input.
+                        will be the same as in the input so the downscale factor is not applied.
                         """
                         )
-    
+
     inputs.add_argument('-sr',
                     '--save_ram',
                     action='store_true',
@@ -75,10 +98,22 @@ def get_args():
                     the output pyramidal image will slightly differ when using and not using this argument.                    
                     """
                         )
+
+    inputs.add_argument(
+                    '--compression',
+                    dest='compression',
+                    required=False,
+                    type=str,
+                    default='LZW',
+                    help="""If set, the output pyramidal image will be compressed using the specified
+                    compression method. Set to "none" for no compression. Default is LZW.
+                    """
+                    )
+
     #VERSION CONTROL
     inputs.add_argument("--version", 
                         action="version", 
-                        version="v0.5.0"
+                        version="v0.5.0dev"
                         )
     
     #OUTPUTS
diff --git a/backsub/background_sub.py b/backsub/background_sub.py
old mode 100644
new mode 100755
index 76f2a69..73e116b
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
 #standard libraries
 import pandas as pd
 import numpy as np
@@ -125,7 +127,7 @@ def process_markers(markers):
     return markers
 
 
-def extract_img_props(img_path,pixel_size=None):
+def extract_img_props(img_path,downscale_factor,tile_size,pixel_size=None):
 
     #Extract data_type, pyramidal specs, height, width
     with tifff.TiffFile(img_path) as tif:
@@ -141,6 +143,11 @@ def extract_img_props(img_path,pixel_size=None):
                              ]
         else:
             subres_dims=None
+
+    if downscale_factor <= 1:
+        raise ValueError("downscale_factor must be greater than 1")
+    max_dim = max(height,width)
+    extracted_levels = int(np.floor(np.log(max_dim / tile_size) / np.log(downscale_factor)) + 1)
         
     #Try to extract pixel size from ome-xml
     if pixel_size is None:
@@ -164,6 +171,7 @@ def extract_img_props(img_path,pixel_size=None):
                "data_type":data_type,
                "pyramid":is_pyramid,
                "levels":pyr_levels,
+               "extracted_levels": max(extracted_levels, 1),
                "sub_levels_dims":subres_dims,
                "size_x":width,
                "size_y":height ,
@@ -204,30 +212,32 @@ def subtract_channels(src_img_path,
     print(resources.results[0],"([sec],[MB],[% CPU usage])")
     return result
 
-def extract_sublevels_from_tiff(path,ch,levs):
+def extract_sublevels_from_tiff(path,ch,levels):
     with tifff.TiffFile(path) as tif:
-        for l in range(1,levs):
+        for l in range(1,levels):
             yield tif.series[0].levels[l].pages[ch].asarray()
 
 def write_pyramid(src_img_path,
                   tasks_table,
-                    outdir,
+                    out_file,
                     levels,
                     sub_lvls_dims,
-                    file_name,
                     src_data_type,
+                    pyramid_tile_shape,
+                    compression,
                     is_src_pyramid=False,
                     save_ram=False
                     ):
 
-    outdir.mkdir(parents=True, exist_ok=True)
-    out_file_path=outdir / file_name
     sub_levels=levels-1
 
-    total_operations=tasks_table['processed'].values.sum()#Count True values
+    total_operations=tasks_table['processed'].values.sum() #Count True values
     count=1
 
-    with tifff.TiffWriter(out_file_path,bigtiff=True) as tif:
+    if compression == 'none':
+        compression=None
+
+    with tifff.TiffWriter(out_file,bigtiff=True) as tif:
         #write first the original resolution image,i.e. first layer
         for _,channel in tasks_table.iterrows():
             if channel.processed:
@@ -245,12 +255,12 @@ def write_pyramid(src_img_path,
                     pyramid_action="extract"
             
             tif.write(
-                    first_layer,
-                    subifds=sub_levels,
-                    tile=(256, 256),
-                    photometric='minisblack',
-                    compression="lzw"
-                        )
+                first_layer,
+                subifds=sub_levels,
+                tile=pyramid_tile_shape,
+                photometric='minisblack',
+                compression=compression
+            )
             
             if pyramid_action=="calculate":
                 if save_ram:
@@ -266,12 +276,12 @@ def write_pyramid(src_img_path,
                     tif.write(
                         sub_layer,
                         subfiletype=1,
-                        tile=(256, 256),
+                        tile=pyramid_tile_shape,
                         photometric='minisblack',
-                        compression="lzw"#lzw works better when saving channel-by-channel and jpeg 2000 when saving the whole stack at once
-                        )
+                        compression=compression#lzw works better when saving channel-by-channel and jpeg 2000 when saving the whole stack at once
+                    )
                 
-    return out_file_path
+    return out_file
 
 @memocron
 def main(version):
@@ -283,12 +293,18 @@ def main(version):
     assert out_path != in_path
 
     # 1) Extract image properties
-    src_props = extract_img_props(in_path, args.pixel_size,)
+    if args.tile_size % 16 != 0:
+        raise ValueError("tile_size has to be a multiple of 16")
+    tile_shape = (args.tile_size, args.tile_size)
+    src_props = extract_img_props(in_path,
+                                  args.downscale_factor,
+                                  args.tile_size,
+                                  args.pixel_size)
     # 2) Modify pyramid_levels if required
     if src_props["pyramid"]:
         levels=src_props["levels"]
     else:
-        levels=args.pyramid_levels
+        levels=src_props["extracted_levels"]
 
     # 3) Read/Create markers table and update it to include the information of the processing tasks
     if args.comet_metadata:
@@ -313,20 +329,25 @@ def main(version):
             tasks+=1
 
     #5) Calculate subtractions and write output file
-    out_file_name=f'{ (in_path.stem).split(".ome")[0] }_backsub.ome.tif'
+    out_file=args.output
+
     logger.info(f"\nTASKS PROGRESS" )
 
-    print(f"\nCommencing writing of pyramidal ome.tif file into {out_path / out_file_name}")
+    print(f"\nCommencing writing of pyramidal ome.tif file into {out_path}\n")
     print(f"\nCommencing subtraction tasks\n")
-
+    if args.compression=='none':
+        compression=None
+    else:
+        compression=args.compression
     pyramid_abs_path=write_pyramid(
                     in_path,
                     markers_updated,
-                    out_path,
+                    out_file,
                     levels,
                     src_props["sub_levels_dims"],
-                    out_file_name,
                     src_props["data_type"],
+                    tile_shape,
+                    compression,
                     is_src_pyramid=src_props["pyramid"],
                     save_ram=args.save_ram
                     )
@@ -335,21 +356,10 @@ def main(version):
     channel_names=markers_updated["marker_name"].tolist()
     ome_xml=ome_writer.create_ome(channel_names,src_props,version)
     tifff.tiffcomment(pyramid_abs_path, ome_xml.encode("utf-8"))
-    
-
 
     logger.info(f'\nSCRIPT FINISHED PROCESSING TASKS ')
-    print(f'\nPyramidal image with {levels} levels was successfully written ')
-    
-
-
+    print(f'\nPyramidal image with {levels} levels was successfully written{'' if compression is None else f' with {compression} compression'}.')
 
 if __name__ == '__main__':
-    _version = 'v0.5.0'
+    _version = 'v0.5.0dev'
     main(_version)
-
-    
-
-
-
-
diff --git a/backsub/ome_writer.py b/backsub/ome_writer.py
index ba698ff..44f2447 100644
--- a/backsub/ome_writer.py
+++ b/backsub/ome_writer.py
@@ -14,17 +14,17 @@ def create_ome(conformed_markers,info,software_version):
     software=f'backsub {software_version}'
     no_of_channels = len(conformed_markers)
     tile_info_dict={
-            "tile": no_of_channels *[1],
-            "name":conformed_markers ,
-            "type": no_of_channels  *[ info["data_type"] ],
-            "size_x":no_of_channels *[ info["size_x"] ] ,
-           "size_y":no_of_channels *[info["size_y"]],
-           "physical_size_x": no_of_channels *[info["pixel_size"]],
-           "physical_size_x_unit": no_of_channels *[info["pixel_size_unit"]],
-           "physical_size_y": no_of_channels *[info["pixel_size"]],
-           "physical_size_y_unit": no_of_channels *[info["pixel_size_unit"]],
-           "significant_bits": no_of_channels *["16"]
-           }
+        "tile": no_of_channels *[1],
+        "name":conformed_markers ,
+        "type": no_of_channels  *[ info["data_type"] ],
+        "size_x":no_of_channels *[ info["size_x"] ] ,
+        "size_y":no_of_channels *[info["size_y"]],
+        "physical_size_x": no_of_channels *[info["pixel_size"]],
+        "physical_size_x_unit": no_of_channels *[info["pixel_size_unit"]],
+        "physical_size_y": no_of_channels *[info["pixel_size"]],
+        "physical_size_y_unit": no_of_channels *[info["pixel_size_unit"]],
+        "significant_bits": no_of_channels *["16"]
+    }
     tile_info=pd.DataFrame(tile_info_dict)
     
     grouped_tiles = tile_info.groupby(['tile'])

From be0fb0d2dea7da1d577186b80302e866bda4a392 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 15:24:10 +0100
Subject: [PATCH 04/21] moved input_markers_table under inptus

---
 backsub/CLI.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/backsub/CLI.py b/backsub/CLI.py
index b5d6d84..0a0ab01 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -17,7 +17,7 @@ def get_args():
 
     # INPUT GROUPS
     inputs = parser.add_argument_group(title="INPUTS")
-    input_markers_table = parser.add_mutually_exclusive_group(required=True)
+    input_markers_table = inputs.add_mutually_exclusive_group(required=True)
     
     inputs.add_argument("-r", 
                         "--root", 
@@ -27,7 +27,7 @@ def get_args():
                         required=True, 
                         help="File path to root image file.")
     
-    input_markers_table.add_argument("-m", 
+    input_markers_table.add_argument(
                         "--markers", 
                         dest="markers", 
                         action="store",

From ec82904a17eb4d187c31622cd6c78660bce9440e Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:13:09 +0100
Subject: [PATCH 05/21] Restructured README

---
 CHANGELOG.md                    |  30 +++++-
 README.md                       | 175 +++++++++++++++++++++++---------
 example/application_example.png | Bin 0 -> 1376963 bytes
 3 files changed, 151 insertions(+), 54 deletions(-)
 create mode 100644 example/application_example.png

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5b25f54..5c32d88 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,15 +3,34 @@
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## v0.5.0 - [2025.11.##]
+
+Rework of Backsub to not have Palom as a dependency reducing the environment size and making it lightweight, and reducing the output file size, while keeping the time and memory usage efficiency.
+
+### `Added`
+- `compression` parameter
+- hidden argument `comet`, which extracts the metadata on-the-fly for Lunaphore COMET images. When using this argument, the `markers.csv` file is not required.
+- two RAM profiles: (1) default, uses moderate RAM. (2) Uses approximately half of the default RAM at the cost of a slight loss in precision of the calculation of the downsized dimensions of the pyramidal output image. This means the dimensions of the pyramidal level will differ between profile 1 and 2. The high-resolution level is not affected by this.
+- organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background substraction and writing of output image and (5) extraction of metadata from Lunaphore Comet images.
+- logger has been re-designed.
+- restructured README
+
+### `Fixed`
+- output image file-size is reduced by applying lossless compression ("LZW" by default)
+
+### `Removed`
+- Palom and OpenCV as dependencies
+
+
+
 ## v0.4.1 - [2023.11.21]
 
-Complete rework of Backsub to include Palom's pyramid writer (https://github.com/labsyspharm/palom).
-Added dask array chunking and delayed execution for subtraction that happenes while the output pyramidal `ome.tif` is being created.
-Added `CHANGELOG.md`.
+The script has been rewritten to perform channel subtraction in a RAM-efficient manner - updating is highly recommended. If the output file is much bigger than expected, adjust the `--tile-size` parameter to a smaller value (e.g `512`). Changing the `--chunk-size` parameter may affect performance (lower values increase execution time, higher values increase RAM usage).
 
 ### `Added`
-- `--chunk-size` parameter
+- `--chunk-size` parameter for dask array chunking and delayed execution for subtraction that happens while the output pyramidal OME-TIFF is being created.
 - Palom's pyramid writer
+- `CHANGELOG.md`
 
 ### `Fixed`
 - Fixed issue with RAM inefficiency - reworked Backsub.
@@ -19,5 +38,6 @@ Added `CHANGELOG.md`.
 ### `Removed`
 - `--pyramid` tag introduced in v0.3.4, for smaller images, a smaller tile size should be specified now.
 
+## Versions v0.2.0 and older:
 
-I did not keep a changelog before version v0.4.1.
\ No newline at end of file
+The `markers.csv` file which gives details about the channels needs to contain the following columns: "Filter", "background" and "exposure". An exemplary [markers_old.csv](https://github.com/SchapiroLabor/Background_subtraction/files/9549686/markers.csv) file is given. The "Filter" column should specify the Filter used when acquiring images. If different stains are aquired with the same filter, the *exact same value* needs to be written (including background) as it is used for determining which background channel should be subtracted. The "background" column should contain logical `TRUE` values for channels which represent autofluorescence. The "exposure" column should contain the exposure time used for channel acquisition, and the measure unit should be consistent across the column. Exposure time is used for scaling the value of the background to be comparable to the processed channel. Usage of these versions is strongly disencouraged.
\ No newline at end of file
diff --git a/README.md b/README.md
index f970494..91ff0d0 100644
--- a/README.md
+++ b/README.md
@@ -1,83 +1,160 @@
-# Background_subtraction
+# Backsub - pixel-by-pixel channel subtraction tool for multiplexed immunofluorescence data
 
-Pixel-by-pixel channel subtraction scaled by exposure times, primarily developed for images produced by the COMET platform and to work within the MCMICRO pipeline. Main usecase is autuofluorescence subtraction for multichannel and multicycle images for visualization of images from tissues with high autofluroescence (FFPE), improved segmentation, and quantification (if the previous two usecases aren't necessary, downstream subtraction of autofluorescent signal is encouraged as the script is memory inefficent).
+Backsub performs pixel-by-pixel background subtraction between marker and background channels scaled by their respective exposure times. The outputs are saved as pyramidal OME-TIFF files. It was originally developed for data produced by the Lunaphore COMET platform and is fully compatible with the [MCMICRO](https://mcmicro.org) pipeline.
+
+
+Example of pixel-wise autofluorescence subtraction with Backsub:
+<p align="left">
+  <img src="example/application_example.png" alt="Background subtraction example" width="750">
+</p>
 
 ## Introduction
 
-If there are background (autofluorescence) channels present in a `.tif` image, background subtraction should be performed so as not to skew the quantification counts of markers. The most precise way of subtracting background would be on a pixel-to-pixel basis. An alternative would be on a cell basis by just subtracting the background measurements from the marker measurements for each cell, however, for visual inspection of images, as well as future use of images as figures in published work, it is preferred to use this.
+In multiplexed immunofluorescence images, autofluorescence and background signals can cause improper cell segmentation, and can affect downstream intensity quantification which is why, if possible, they should be subtracted from raw channel intensities. The most precise way of subtracting background would be on a pixel-to-pixel basis. An alternative would be to subtract the background measurements from the marker measurements for each cell after quantification, however, for visual inspection of images, segmentation, and data presentation, it is preferred to use the corrected values.
 
-Background subtraction is performed using the following formula:
+The primary use case is autofluorescence subtraction for multichannel and multicycle microscopy images to improve:
+* image visualization of tissues with strong autofluorescence
+* segmentation accuracy
+* quantification quality (if the previous two usecases are not necessary, downstream subtraction of autofluorescence signal is encouraged instead)
 
-Marker<sub>*corrected*</sub> = Marker<sub>*raw*</sub> - Background / Exposure<sub>*Background*</sub> * Exposure<sub>*Marker*</sub>
+Background subtraction is performed using the following formula:
 
+$Marker_{corrected} = Marker_{raw} - Background \times \frac{Exposure_{Marker}}{Exposure_{Background}}$
 
-## Usage 
+## Installation
 
-The `markers.csv` file which gives details about the channels needs to contain the following columns: "marker_name", "background" and "exposure". An exemplary [markers.csv](https://github.com/SchapiroLabor/Background_subtraction/blob/main/example/markers.csv) file is given. The "marker_name" column should indicate the marker for the acquired channel and all values should be unique. The "background" column should indicate the marker name of the channel which needs to be subtracted. This value must match the "marker_name" value of the background channel. The "exposure" column should contain the exposure time used for channel acquisition, and the measure unit should be consistent across the column. Exposure time is used for scaling the value of the background to be comparable to the processed channel. The "remove" column should contain logical `TRUE` values for channels which should be excluded in the output image.
+Backsub can be run either in a preconfigured Docker container or in a local conda environment.
 
-### Version v0.5.10:
-This version, which is a rework of v0.4.1, introduces the following features:
-* File-size of output image is reduced by using lossless compression ("LZW").
-* Facilitates container creation by prescinding from PALOM and opencv libraries.
-* Introduces the hidden argument, `-comet`, which extracts the metadata on-the-fly for Lunaphore Comet images.  When using this argument, the `markers.csv` file is not required.
-* Two RAM-profiles: (1) default mode, uses moderate RAM. (2) Uses approximately half-of the RAM of profile 1 at the cost of a slight loss in precision of the calculation of the downsized dimensions of the pyramidal output image.  This means the dimensions of the pyramidal level will differ between profile 1 and 2, the high-resolution level is not affected by this.
-* Organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background substraction and writing of output image and (5) extraction of metadata from Lunaphore Comet images.
-* Logger has been re-designed.
+### Option 1: Docker
 
+Pull the latest container from the GitHub Container Registry:
+```
+docker pull ghcr.io/schapirolabor/background_subtraction:latest
+```
+You can then run Backsub directly, mounting your input and output directories:
+```
+docker run --rm -v $(pwd):/data ghcr.io/schapirolabor/background_subtraction:latest \
+    python background_sub.py \
+    -r /data/input_image.tif \
+    -o /data/corrected_image.ome.tif \
+    -m /data/markers.csv \
+    -mo /data/markers_corrected.csv
+```
+Note that all required dependencies are already included inside the container.
 
-### Versions v0.4.1 and newer:
-The script has been rewritten to perform channel subtraction in a RAM-efficient manner - updating is highly recommended. If the output file is much bigger than expected, adjust the `--tile-size` parameter to a smaller value (e.g `512`). Changing the `--chunk-size` parameter may affect performance (lower values increase execution time, higher values increase RAM usage).
+If you want to build the container yourself, clone the repository first, then build it from the provided Dockerfile:
+```
+git clone https://github.com/SchapiroLabor/Background_subtraction.git
+cd Background_subtraction
+docker build -t background_subtraction:latest .
+```
+### Option 2: Conda
 
+Clone the repository and create the Conda environment:
+```
+git clone https://github.com/SchapiroLabor/Background_subtraction.git
+cd Background_subtraction
+conda env create -f environment.yml
+conda activate backsub_env
+```
+You can now run Backsub locally (note that you need to point to the tool's script):
+```
+python backsub/background_sub.py -h
+```
 
-### Versions v0.2.0 and older:
-The `markers.csv` file which gives details about the channels needs to contain the following columns: "Filter", "background" and "exposure". An exemplary [markers_old.csv](https://github.com/SchapiroLabor/Background_subtraction/files/9549686/markers.csv) file is given. The "Filter" column should specify the Filter used when acquiring images. If different stains are aquired with the same filter, the *exact same value* needs to be written (including background) as it is used for determining which background channel should be subtracted. The "background" column should contain logical `TRUE` values for channels which represent autofluorescence. The "exposure" column should contain the exposure time used for channel acquisition, and the measure unit should be consistent across the column. Exposure time is used for scaling the value of the background to be comparable to the processed channel.
+## Execution and usage
 
+### Inputs
 
-### CLI
+A `TIFF` or `OME-TIFF` file containing multiplexed immunofluorescence data.
 
-Minimal required arguments:
+A `markers.csv` file should be provided to describe the channels of the image. Needs to contain the following columns:
 
-* the path to the input image given with `-r` or `--root`
-* the path to the output image given with `-o` or `--output`
-* the path to the `markers.csv` file given with `-m` or `--markers`
-* the path to the markers output file given with `-mo` or `--markerout`
+| Column      	| Description                                                                                                                                                                                                                                    	| Required 	|
+|-------------	|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------	|----------	|
+| marker_name 	| Contains the channel names, all values **must** be unique.                                                                                                                                                                                     	| yes      	|
+| background  	| Specifies the channel that should be subtracted from the specified channel. The `background` value, if present, **must** match the `marker_name` value of the background channel. If no subtraction is necessary, the field can be left empty. 	| yes      	|
+| exposure    	| Contains the exposure time used for channel acquisition in ms.                                                                                                                                                                                 	| yes      	|
+| remove      	| Optional column that allows the user to exclude certain channels from the output file by setting that channel's `remove` value to `TRUE`.                                                                                                       	| no       	|
 
-Optional arguments:
+An exemplary [markers.csv](https://github.com/SchapiroLabor/Background_subtraction/blob/main/example/markers.csv) file is provided.
 
-* `-mpp` or `--pixel-size` microns per pixel, i.e. pixel size of the input image in microns. If not specified the script will attempt 
-to extract the pixel size and its units from the metadata, if failed, it will assign a pixel size of 1 with "pixel" as units.
-* `-pl` or `--pyramid_levels` total number of pyramidal levels of the output image, this number should also include the high-resolution level.  Default value is 8, this argument will be only implemented if the input image is not pyramidal. If input image is pyramidal, the output image will have the same levels and this argument will be ignored.
-* `-sr` or `--save_ram` using this argument will provide the low RAM usage of version 0.4.1.
-* `--version` to print version.
+### Command Line Interface
 
-Hidden argument !!!:
+| Argument 	| Long form          	| Description                                                                                                                                                                                                                                                         	| Specification                                                	| Default 	| Required 	|
+|----------	|--------------------	|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------	|--------------------------------------------------------------	|---------	|----------	|
+| -in      	| --input            	| File path to the input image file.                                                                                                                                                                                                                                  	| string, ends with `.tif`, `.tiff`, `.ome.tif` or `.ome.tiff` 	|         	| yes      	|
+| -o       	| --output           	| File path where the output pyramidal OME-TIFF will be saved.                                                                                                                                                                                                        	| string, ends with `.ome.tif` or `.ome.tiff`                  	|         	| yes      	|
+| -m       	| --markers          	| File path to the markers (CSV) file containing the list of marker names and their respective background channels.                                                                                                                                                   	| string, ends with `.csv`                                     	|         	| yes      	|
+| -mo      	| --marker-output        	| File path where the output marker (CSV) file matching the output image will be saved.                                                                                                                                                                               	| string, ends with `.csv`                                     	|         	| yes      	|
+| -mpp     	| --pixel-size       	| Pixel size provided in microns (microns per pixel). If not provided, image metadata will be checked. If that is not successful, the value will be set to 1.                                                                                                         	| float                                                        	| None    	| no       	|
+| -sr      	| --save-ram         	| Optional flag to approximately cut RAM usage in half. Note that the dimensions of the reduced resolution levels (sub-levels) of the output pyramidal image will slightly differ whether or not the argument is used.                                                	| boolean flag                                                 	| false   	| no       	|
+| -comp    	| --compression      	| The output pyramidal OME-TIFF will be compressed using the specified compression. Set to "none" for no compression.                                                                                                                                                 	| string, either "lzw", "zlib", or "none"                      	| "lzw"   	| no       	|
+| -ts      	| --tile-size        	| Tile size used for writing pyramidal outputs. Note that the file size is smaller for smaller tile size values.                                                                                                                                                      	| integer, multiple of 16                                      	| 256     	| no       	|
+| -dsf     	| --downscale-factor 	| Downscale factor for pyramid layer creation. This value will only be used if the input image is NOT pyramidal. If the input image is pyramidal, the number of levels in the output image will be the same as in the input so the downscale factor won't be applied. 	| integer, larger than 1                                       	| 2       	| no       	|
+| -v       	| --version          	| Prints Backsub version.                                                                                                                                                                                                                                             	|                                                              	|         	|          	|
+| -comet   	| --comet-metadata   	| Flag to obtain the markers table on the fly for images acquired with the reference background acquisition implemented in the Lunaphore Comet at the TSPC (https://www.tspc-hd.com/). When this flag is used, the argument `-m`/`--markers` is ignored since the markers information will be extracted from the metadata of the input image.                                                                                                          	| boolean flag                                                 	| false   	| hidden   	|
 
-* `-comet` Flag to obtain the markers table on the fly for images acquired with the reference background acquisition implemented in the Lunaphore Comet at the TSPC (https://www.tspc-hd.com/). When this flag is used, the argument `-m`/`--markers` is ignored since the markers information will be extracted from the metadata of the input image.
+Example of a full command (note to provide full paths where applicable):
+```
+python Background_subtraction/backsub/background_sub.py \
+    --input /data/input_image.tif \
+    --output /data/corrected_image.ome.tif \
+    --markers /data/markers.csv \
+    --marker-output /data/markers_corrected.csv \
+    --pixel-size 0.65 \
+    --tile-size 256 \
+    --downscale-factor 2 \
+    --compression zlib
+```
 
+### Outputs
 
-### Output
+The output image file will be a pyramidal `OME-TIFF` file containing the processed channels. The channels tagged for removal will be excluded from the final image.
 
-The output image file will be a pyramidal `ome.tif` file containing the processed channels. The channels tagged for removal will be excluded from the final image.
-The output markers file will be a `csv` file containing the following columns: "marker_name", "background", "exposure". The "marker_name" column will contain the marker names of the processed channels. The "background" column will contain the marker names of the channels used for subtraction. The "exposure" column will contain the exposure times of the processed channels. 
+The output markers file will be a `CSV` file containing the following columns: "marker_name", "background", "exposure". The "marker_name" column will contain the marker names of the processed channels. The "background" column will contain the marker names of the channels used for subtraction. The "exposure" column will contain the exposure times of the processed channels. 
 
-### Docker usage
+## Features
 
-If you want to run the background subtraction directly from a pre-configured container with all the required packages, you can either build the docker container yourself or pull it from the Github container registry.
+* Pixel-wise channel subtraction scaled by exposure time.
+* Autofluroescence correction for multiplexed immunofluroescence images.
+* Pyramidal OME-TIFF output compatible with the MCMICRO pipeline.
+* Optional image compression to not bloat data size of large projects.
+* Low-memory mode for local processing of large datasets.
+* Automatic metadata extraction for Lunaphore COMET data.
 
-To build the container run:
+## Contributing
 
+Contributions are welcome! If you would like to contribute, please:
+1. Fork the repository
+2. Create a feature branch:
 ```
-git clone https://github.com/SchapiroLabor/Background_subtraction.git
-docker build -t background_subtraction:latest .
-docker run background_subtraction:latest python background_sub.py
+git checkout -b feature/my-feature
 ```
+3. Commit your changes and open a pull request
 
-To pull the container from the Github container registry (ghcr.io):
+For questions of issues, please open a GitHub issue.
 
-```
-## Login to ghcr.io
-docker login ghcr.io
+## Contributors
 
-## Pull container
-docker pull ghcr.io/schapirolabor/background_subtraction:latest
-```
+Author and maintainer:
+* [Krešimir Beštak](@kbestak)
+
+Contributors: 
+* [Victor Perez](@VictorDidier)
+* [Florian Wünnemann](@FloWuenne).
+
+## Changelog
+
+See the [CHANGELOG](https://github.com/SchapiroLabor/Background_subtraction/blob/main/CHANGELOG.md) file for deatils about new features, bug fixes, and version history.
+
+## License
+
+This project is licensed under the terms of the [MIT License](https://github.com/SchapiroLabor/Background_subtraction/blob/main/LICENSE).
+
+## Citation
+
+If you use Backsub in your work, please cite it as:
+
+> Bestak, K., Perez, V., & Wuennemann, F. (2025). Backsub: pixel-by-pixel channel subtraction tool for multiplexed immunofluorescence data.
+> Available at: [https://github.com/SchapiroLabor/Background_subtraction](https://github.com/SchapiroLabor/Background_subtraction)
\ No newline at end of file
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HcmV?d00001


From 6dfbc4d0cf0a2e4a681597a8863194702e2c7a20 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:13:39 +0100
Subject: [PATCH 06/21] cli cleanup

---
 backsub/CLI.py            | 79 ++++++++++++++++++---------------------
 backsub/background_sub.py | 16 ++++----
 2 files changed, 45 insertions(+), 50 deletions(-)

diff --git a/backsub/CLI.py b/backsub/CLI.py
index 0a0ab01..838d351 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -3,7 +3,7 @@
 from argparse import ArgumentParser as AP
 
 #---CLI-BLOCK---#
-def get_args():
+def get_args(_version):
     # Script description
     description="""
                 Subtracts background from an image (signal) 
@@ -19,26 +19,30 @@ def get_args():
     inputs = parser.add_argument_group(title="INPUTS")
     input_markers_table = inputs.add_mutually_exclusive_group(required=True)
     
-    inputs.add_argument("-r", 
-                        "--root", 
-                        dest="root", 
+    inputs.add_argument("-r", ## deprecation warning can be added later
+                        "--root", ## deprecation warning can be added later
+                        '-in',
+                        "--input",
+                        dest="input", 
                         action="store",
                         type=pathlib.Path,
                         required=True, 
-                        help="File path to root image file.")
+                        help="File path to input image file.")
     
     input_markers_table.add_argument(
+                        "-m",
                         "--markers", 
                         dest="markers", 
                         action="store",
                         type=pathlib.Path,
+                        required=True,
                         help="""File path to the markers.csv file containing the list of marker names
-                        and its respective background channels.
+                        and their respective background channels.
                         """
                         )
     
     input_markers_table.add_argument("-comet",
-                        "--comet_metadata",
+                        "--comet-metadata",
                         action='store_true',
                         help=argparse.SUPPRESS
                         )
@@ -51,24 +55,11 @@ def get_args():
                         type=float, 
                         default = None, 
                         action = "store",
-                        help="pixel size in microns,i.e. microns per pixel(mpp)"
+                        help="pixel size in microns, i.e. microns per pixel(mpp)"
                         )
-    
-    # inputs.add_argument("-pl",
-    #                     "--pyramid_levels", 
-    #                     dest="pyramid_levels", 
-    #                     required=False, 
-    #                     type=int, 
-    #                     default=8, 
-    #                     help="""Total number of pyramid levels.
-    #                     This value will be only used if the input image is NOT pyramidal.
-    #                     If input image is pyramidal, the number of levels in the output image 
-    #                     will be the same as in the input.
-    #                     """
-    #                     )
 
     inputs.add_argument("-ts",
-                        "--tile_size", 
+                        "--tile-size", 
                         dest="tile_size", 
                         required=False, 
                         type=int, 
@@ -78,7 +69,7 @@ def get_args():
                         )
 
     inputs.add_argument("-dsf",
-                        "--downscale_factor", 
+                        "--downscale-factor", 
                         dest="downscale_factor", 
                         required=False, 
                         type=int, 
@@ -91,29 +82,31 @@ def get_args():
                         )
 
     inputs.add_argument('-sr',
-                    '--save_ram',
-                    action='store_true',
-                    help="""RAM usage is cut in half when using this flag.  
-                    Notice that the dimensions of the reduced resolution levels (sub-levels) of 
-                    the output pyramidal image will slightly differ when using and not using this argument.                    
-                    """
+                        '--save-ram',
+                        action='store_true',
+                        help="""RAM usage is cut in half when using this flag.  
+                        Notice that the dimensions of the reduced resolution levels (sub-levels) of 
+                        the output pyramidal image will slightly differ whether or not using this argument.                    
+                        """
                         )
 
-    inputs.add_argument(
-                    '--compression',
-                    dest='compression',
-                    required=False,
-                    type=str,
-                    default='LZW',
-                    help="""If set, the output pyramidal image will be compressed using the specified
-                    compression method. Set to "none" for no compression. Default is LZW.
-                    """
-                    )
+    inputs.add_argument('-comp',
+                        '--compression',
+                        dest='compression',
+                        required=False,
+                        type=str,
+                        default='LZW',
+                        choices=['lzw', 'none', 'deflate', 'zlib'],
+                        help="""If set, the output pyramidal image will be compressed using the specified
+                        compression method. Set to "none" for no compression. Default is LZW. An alternative is zlib.
+                        """
+                        )
 
     #VERSION CONTROL
-    inputs.add_argument("--version", 
+    inputs.add_argument("-v",
+                        "--version", 
                         action="version", 
-                        version="v0.5.0dev"
+                        version=_version
                         )
     
     #OUTPUTS
@@ -125,7 +118,7 @@ def get_args():
                          action="store",
                          type=pathlib.Path,
                          required=True, 
-                         help="Path to output file"
+                         help="File path where the output pyramidal OME-TIFF will be saved."
                          )
     
     outputs.add_argument("-mo", 
@@ -134,7 +127,7 @@ def get_args():
                          action="store",
                          type=pathlib.Path,
                          required=True, 
-                         help="Path to the output .csv marker file"
+                         help="Path to the output .csv marker file matching the channels in the output image."
                          )
 
     arg = parser.parse_args()
diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 73e116b..716087e 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -190,13 +190,13 @@ def subtract_channels(src_img_path,
                       task_no
                       ):
     """
-    This function executes the background substraction using generators, each element of the generator
+    This function executes the background subtraction using generators, each element of the generator
     is a tuple with 3 values, such that:
 
     tuple=(img_with_backsub[array],calculate or extract pyramid [str],pyramid_from_index[int])
 
     The second entry of the tuple indicates in the writing process if the pyramid should be calculated using 
-    pyramid_gaussian from scikit image.  If extract, the index given in the third entry will fetch all the pyramid
+    pyramid_gaussian from scikit image. If extract, the index given in the third entry will fetch all the pyramid
     levels from the original image stack(src_img_path).
     """
     factor=np.float32(factor)#limiting precision to float32 saves memory
@@ -242,7 +242,7 @@ def write_pyramid(src_img_path,
         for _,channel in tasks_table.iterrows():
             if channel.processed:
                 operation_count=f"({count}/{total_operations})"
-                print(f"\n {operation_count} Calculating subtraction of background {channel.background}  from {channel.marker_name} signal:")
+                print(f"\n {operation_count} Calculating subtraction of background {channel.background} from {channel.marker_name} signal:")
                 first_layer=subtract_channels(src_img_path, channel.ind, channel.bg_idx, channel.factor, (4096,4096), src_data_type,operation_count)
                 pyramid_action="calculate"
                 count+=1
@@ -278,15 +278,17 @@ def write_pyramid(src_img_path,
                         subfiletype=1,
                         tile=pyramid_tile_shape,
                         photometric='minisblack',
-                        compression=compression#lzw works better when saving channel-by-channel and jpeg 2000 when saving the whole stack at once
+                        compression=compression
+                            # lzw works better when saving channel-by-channel
+                            # jpeg 2000 when saving the whole stack at once. zlib is an alternative that's slower for writing, but gives better compression ratio
                     )
                 
     return out_file
 
 @memocron
 def main(version):
-    args=CLI.get_args()
-    in_path = args.root
+    args=CLI.get_args(_version)
+    in_path = args.input
     out_path = args.output
 
     # 0) Validate input_path is not the same as output_path,pixel data is read into RAM lazily, cannot overwrite input file
@@ -316,7 +318,7 @@ def main(version):
         markers = process_markers(pd.read_csv(args.markers))
 
     markers_updated=markers.loc[ markers.keep]
-    #4) Write updated markers.csv without appended columns.  This file contains the markers information of the final image stack
+    #4) Write updated markers.csv without appended columns. This file contains the markers information of the final image stack
     markers_preview = markers_updated.drop(columns=['keep','ind','processed','factor','bg_idx'])
     markers_preview["channel_number"] = np.arange(1, len(markers_preview)+1)
     markers_preview.to_csv(args.markerout, index=False)

From 1219dd9ae68b5b87f89737a2c3ca51ecdca267b6 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:14:04 +0100
Subject: [PATCH 07/21] renamed env in environment.yml

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index 1c46961..8402f61 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,4 +1,4 @@
-name: backsub_testing
+name: backsub_env
 channels:
   - conda-forge
   - defaults

From 317540ed936423d55c5962fb05a90e19d6262706 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:15:50 +0100
Subject: [PATCH 08/21] removed old scripts

---
 CHANGELOG.md                                  |   1 +
 scripts/old/background_sub_v034.py            | 313 ------------------
 scripts/old/background_subtraction.py         | 157 ---------
 scripts/old/background_subtraction_v020.py    | 280 ----------------
 scripts/old/background_subtraction_v033.py    | 284 ----------------
 scripts/old/background_subtraction_v2.py      | 134 --------
 scripts/old/background_subtraction_v3.py      | 134 --------
 .../old/background_subtraction_working_v4.py  | 304 -----------------
 8 files changed, 1 insertion(+), 1606 deletions(-)
 delete mode 100644 scripts/old/background_sub_v034.py
 delete mode 100644 scripts/old/background_subtraction.py
 delete mode 100644 scripts/old/background_subtraction_v020.py
 delete mode 100644 scripts/old/background_subtraction_v033.py
 delete mode 100644 scripts/old/background_subtraction_v2.py
 delete mode 100644 scripts/old/background_subtraction_v3.py
 delete mode 100644 scripts/old/background_subtraction_working_v4.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5c32d88..f302546 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,6 +20,7 @@ Rework of Backsub to not have Palom as a dependency reducing the environment siz
 
 ### `Removed`
 - Palom and OpenCV as dependencies
+- old scripts, version history should be checked instead.
 
 
 
diff --git a/scripts/old/background_sub_v034.py b/scripts/old/background_sub_v034.py
deleted file mode 100644
index 69bdd6d..0000000
--- a/scripts/old/background_sub_v034.py
+++ /dev/null
@@ -1,313 +0,0 @@
-from __future__ import print_function, division
-from multiprocessing.spawn import import_main_path
-import sys
-import copy
-import argparse
-import numpy as np
-import tifffile
-import zarr
-import skimage.transform
-from aicsimageio import aics_image as AI
-import pandas as pd
-import numexpr as ne
-from ome_types import from_tiff, to_xml
-from os.path import abspath
-from argparse import ArgumentParser as AP
-import time
-import dask
-from memory_profiler import profile
-# This API is apparently changing in skimage 1.0 but it's not clear to
-# me what the replacement will be, if any. We'll explicitly import
-# this so it will break loudly if someone tries this with skimage 1.0.
-try:
-    from skimage.util.dtype import _convert as dtype_convert
-except ImportError:
-    from skimage.util.dtype import convert as dtype_convert
-
-
-# arg parser
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    inputs.add_argument("--pixel-size", metavar="SIZE", dest = "pixel_size", type=float, default = None, action = "store",help="pixel size in microns; default is 1.0")
-    inputs.add_argument("--pyramid", dest="pyramid", required=False, default=True, help="Should output be pyramidal?")
-    inputs.add_argument("--tile-size", dest="tile_size", required=False, default=1024, help="Tile size for pyramid generation")
-    inputs.add_argument("--version", action="version", version="v0.3.4")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-def preduce(coords, img_in, img_out):
-    print(img_in.dtype)
-    (iy1, ix1), (iy2, ix2) = coords
-    (oy1, ox1), (oy2, ox2) = np.array(coords) // 2
-    tile = skimage.img_as_float32(img_in[iy1:iy2, ix1:ix2])
-    tile = skimage.transform.downscale_local_mean(tile, (2, 2))
-    tile = dtype_convert(tile, 'uint16')
-    #tile = dtype_convert(tile, img_in.dtype)
-    img_out[oy1:oy2, ox1:ox2] = tile
-
-def format_shape(shape):
-    return "%dx%d" % (shape[1], shape[0])
-
-def process_markers(markers):
-    # add column with starting indices (which match the image channel indices)
-    # this should be removed soon
-    markers['ind'] = range(0, len(markers))
-
-    # if the 'remove' column is not specified, all channels are kept. If it is 
-    # present, it is converted to a boolean indicating which channels should be removed
-    if 'remove' not in markers:
-        markers['remove'] = ["False" for i in range(len(markers))]
-    else:
-        markers['remove'] = markers['remove'] == True
-    # invert the markers.remove column to indicate which columns to keep
-    markers['remove'] = markers['remove'] == False
-
-    return markers
-
-def process_metadata(metadata, markers):
-    try:
-        metadata.screens[0].reagents = [metadata.screens[0].reagents[i] for i in markers[markers.remove == True].ind]
-    except:
-        pass
-    try:
-        metadata.structured_annotations = [metadata.structured_annotations[i] for i in markers[markers.remove == True].ind]
-    except:
-        pass
-    # these two are required
-    metadata.images[0].pixels.size_c = len(markers[markers.remove == True])
-    metadata.images[0].pixels.channels = [metadata.images[0].pixels.channels[i] for i in markers[markers.remove == True].ind]
-    try:
-        metadata.images[0].pixels.planes = [metadata.images[0].pixels.planes[i] for i in markers[markers.remove == True].ind]
-    except:
-        pass
-    try:
-        metadata.images[0].pixels.tiff_data_blocks[0].plane_count = sum(markers.remove == True)
-    except:
-        pass
-    return metadata
-
-
-# NaN values return True for the statement below in this version of Python. Did not use math.isnan() since the values
-# are strings if present
-def isNaN(x):
-    return x != x
-
-def subtract_channel(image, markers, channel, background_marker, output):
-    scalar = markers[markers.ind == channel].exposure.values / background_marker.exposure.values
-    
-    # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-    # [0] at the end needed to get [x, y] shape, and not have [1, x, y]
-    back = copy.copy(image[background_marker.ind])[0]
-    # subtract background from processed channel and if the background intensity for a certain pixel was larger than
-    # the processed channel, set intensity to 0 (no negative values)
-    back = np.rint(ne.evaluate("back * scalar"))
-    back = np.where(back>65535,65535,back.astype(np.uint16))
-    # set the pixel value to 0 if the image channel value is lower than the scaled background channel value
-    # otherwise, subtract.
-    output[channel] = np.where(image[channel]<back, 0, image[channel]-back)
-    back = None
-    return output[channel]
-
-def subtract(img, markers, output):
-    # iterating over channels
-    for channel in range(len(markers)):
-        # this array is used to find the appropriate background channel
-        # IMPORTANT - all values in the 'marker_name' column should be unique        
-
-        # if no background channel is specified, channel is kept like if should be
-        if markers.background.isnull()[channel] == True:
-            # no change - applies to background channels as well
-            output[channel] = copy.copy(img[channel])
-            print(f"Channel {markers.marker_name[channel]} ({channel}) processed, no background subtraction")
-
-        else:
-            # this array is used to find the appropriate background channel
-            # IMPORTANT - all values in the 'marker_name' column should be unique
-            find_background = np.array(markers.marker_name == markers.background[channel])
-            # scalar is the coefficient with which the background pixel intensity is scaled
-            # it is the result of dividing the marker exposure time with the background exposure time
-            # Marker_corr = Marker_root - Background * Exposure_marker / Exposure_background
-            background_marker = markers.iloc[find_background]
-
-            output[channel] = subtract_channel(img, markers, channel, background_marker, output)
-
-            print(f"Channel {markers.marker_name[channel]} ({channel}) processed, {markers.background[channel]} background channel subtracted")
-    return output
-
-def remove_back(img, markers):
-    #with dask.config.set(**{'array.slicing.split_large_chunks': True}):
-    # subset the image based on the remove column in the markers file
-    img = img[markers.remove.tolist()]
-    print()
-    print(f'Image shape: {img.shape}')
-    return img
-    
-def subres_tiles(level, level_full_shapes, tile_shapes, outpath, scale):
-    print(f"\n processing level {level}")
-    assert level >= 1
-    num_channels, h, w = level_full_shapes[level]
-    tshape = tile_shapes[level] or (h, w)
-    tiff = tifffile.TiffFile(outpath)
-    zimg = zarr.open(tiff.aszarr(series=0, level=level-1, squeeze=False))
-    for c in range(num_channels):
-        sys.stdout.write(
-            f"\r  processing channel {c + 1}/{num_channels}"
-        )
-        sys.stdout.flush()
-        th = tshape[0] * scale
-        tw = tshape[1] * scale
-        for y in range(0, zimg.shape[1], th):
-            for x in range(0, zimg.shape[2], tw):
-                a = zimg[c, y:y+th, x:x+tw, 0]
-                a = skimage.transform.downscale_local_mean(
-                    a, (scale, scale)
-                )
-                if np.issubdtype(zimg.dtype, np.integer):
-                    a = np.around(a)
-                a = a.astype('uint16')
-                yield a
-
-@profile
-def main(args):
-    img_raw = AI.AICSImage(args.root)
-    img = img_raw.get_image_dask_data("CYX")
-
-    markers_raw = pd.read_csv(args.markers)
-    markers = process_markers(copy.copy(markers_raw))
-
-    output = dask.array.empty_like(img)
-
-    output = subtract(img, markers, output)
-    output = remove_back(output, markers)
-
-    markers_raw = markers_raw[markers_raw.remove != True]
-    markers_raw = markers_raw.drop("remove", axis = 1)
-    markers_raw.to_csv(args.markerout, index=False)
-
-    # Processing metadata - highly adapted to Lunaphore outputs
-    # check if metadata is present
-    try:
-        print(img_raw.metadata.images[0])
-        metadata = img_raw.metadata
-        metadata = process_metadata(img_raw.metadata, markers)
-    except:
-        metadata = None   
-
-    if args.pixel_size != None:
-        # If specified, the input pixel size is used
-        pixel_size = args.pixel_size   
-
-    else:
-        try:
-            if img_raw.metadata.images[0].pixels.physical_size_x != None:
-                pixel_size = img_raw.metadata.images[0].pixels.physical_size_x
-            else:
-                pixel_size = 1.0
-        except:
-            # If no pixel size specified anywhere, use default 1.0
-            pixel_size = 1.0
-    if (args.pyramid == True) and (int(args.tile_size)<=max(output[0].shape)):
-            
-        # construct levels
-        tile_size = int(args.tile_size)
-        scale = 2
-
-        print()
-        dtype = output.dtype
-        base_shape = output[0].shape
-        num_channels = output.shape[0]
-        num_levels = (np.ceil(np.log2(max(base_shape) / tile_size)) + 1).astype(int)
-        factors = 2 ** np.arange(num_levels)
-        shapes = (np.ceil(np.array(base_shape) / factors[:,None])).astype(int)
-        print(base_shape)
-        print(np.ceil(np.log2(max(base_shape)/tile_size))+1)
-
-        print("Pyramid level sizes: ")
-        for i, shape in enumerate(shapes):
-            print(f"   level {i+1}: {format_shape(shape)}", end="")
-            if i == 0:
-                print("(original size)", end="")
-            print()
-        print()
-        print(shapes)  
-
-        level_full_shapes = []
-        for shape in shapes:
-            level_full_shapes.append((num_channels, shape[0], shape[1]))
-        level_shapes = shapes
-        tip_level = np.argmax(np.all(level_shapes < tile_size, axis=1))
-        tile_shapes = [
-            (tile_size, tile_size) if i <= tip_level else None
-            for i in range(len(level_shapes))
-        ]
-        
-        # write pyramid
-        with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
-            tiff.write(
-                data = output,
-                shape = level_full_shapes[0],
-                subifds=int(num_levels-1),
-                dtype=dtype,
-                resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
-                tile=tile_shapes[0]
-            )
-            for level, (shape, tile_shape) in enumerate(
-                    zip(level_full_shapes[1:], tile_shapes[1:]), 1
-            ):
-                tiff.write(
-                    data = subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
-                    shape=shape,
-                    subfiletype=1,
-                    dtype=dtype,
-                    tile=tile_shape
-                )
-    else:
-        # write image
-        with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
-            tiff.write(
-                data = output,
-                shape = output.shape,
-                dtype=output.dtype,
-                resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
-            )
-    try:
-        tifffile.tiffcomment(args.output, to_xml(metadata))
-    except:
-        pass
-    # note about metadata: the channels, planes etc were adjusted not to include the removed channels, however
-    # the channel ids have stayed the same as before removal. E.g if channels 1 and 2 are removed,
-    # the channel ids in the metadata will skip indices 1 and 2 (channel_id:0, channel_id:3, channel_id:4 ...)
-    print()
-
-        
-    
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
diff --git a/scripts/old/background_subtraction.py b/scripts/old/background_subtraction.py
deleted file mode 100644
index c5a9efb..0000000
--- a/scripts/old/background_subtraction.py
+++ /dev/null
@@ -1,157 +0,0 @@
-from distutils.log import error
-import time
-import argparse
-from argparse import ArgumentParser as AP
-from os.path import abspath
-import pandas as pd
-import tifffile as tf
-import numpy as np
-
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--raw", dest="raw", action="store", required=True, help="File path to raw image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.raw = abspath(arg.raw)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-
-    return arg
-
-
-
-def get_info(input):
-    info = tf.TiffFile(input)
-    #if (len(info.series)>1):
-    #    sys.exit("Multiple scenes")
-    # ^^not good because in pyramidal ome-tiffs we get different levels as different scenes/series
-    return info
-    
-
-
-def load(input, channel):
-    # returns numpy array of the specified channel from input
-    # here so it is easier to change if better way found
-    return np.array(tf.imread(input, key = channel))
-
-
-
-def write(data, output, channel):
-    # only first channel does not have to be appended
-    if (channel == 0):
-        tf.imwrite(output, data, append=False)
-    else:
-        tf.imwrite(output, data, append = True)
-    # writes the channel(data) to the specified output file
-    
-
-
-
-def main(args):
-
-    print(f"Raw file = {args.raw}")
-    print(f"Marker file = {args.markers}")
-
-    markers = pd.read_csv(args.markers)
-
-    # could just be extracted from markers.csv file
-    info = get_info(args.raw)
-    ch_number = len(info.pages)
-    assert ch_number == len(markers), "Marker file doesn't match image, check channels"
-
-    # required columns for background subtraction are "stain", "background" and "exposure", everything else is dropped
-    try:
-        markers = markers[["stain", "background", "exposure"]]
-    except:
-        print("Markers file needs to contain stain, background and exposure columns")
-
-    # subset to the only three important columns
-    markers = markers[["stain", "background", "exposure"]]
-    # add column with starting indices (which match the image channel indices) - not necessary?
-    markers['ind'] = range(0, len(markers))
-    # subset background markers
-    markers_background = markers[markers.background == True][["ind", "stain", "exposure"]]
-    # subset markers which need background subtracted
-    markers = markers[markers.background != True][["ind", "stain", "exposure"]]
-
-    # list of stains specified as background
-    background_stains = [stain for stain in markers_background.stain]
-    # initializing sublist - sublist will indicate which background channel to subtract
-    sublist = [-1 for i in range(len(markers))]
-    for i, stain in enumerate(markers_background.stain):
-        # it should be possible for the regex to catch multiple channels unless one is contained in the beginning of the other(s), if so, the code breaks
-        # the i+1 is only there so that each row with a regex match gets shifted by one ot differentiate between unmatched and matched rows
-        sublist += np.where(markers.stain.str.match(stain)==True, i+1, 0)
-    # add sublist to markers - if sublist == -1, no background subtraction, if sublist == 0, first stain in background_stains should be subtracted
-    # if sublist == n, n-1th stain in background stains should be subtracted
-    markers["sublist"]=sublist
-
-    # create variables containing the background channels!
-    background_dictionary = {}  
-    for channel in markers_background.ind:  
-        # for each channel containing the background, read in the image as a numpy array 
-        # data is saved in the background_dictionary with the name "background_channel"
-        background_dictionary["background_"+str(channel)]=np.array(tf.imread(args.raw, key = channel))
-
-    # main part
-    for channel in markers.ind:
-        # if sublist has negative values, no background subtraction because it doesn't match any background channel
-        if list(markers.sublist[[channel]])[0] < 0:
-            # load the channel which doesn't have a corresponding background channel and write it to output
-            write(load(args.raw, channel=channel), args.output, channel=channel)
-            print("Channel "+str(channel)+" written into tif, no background subtraction")
-        else:
-            # extracting just the row from the markers_background dataframe which contains the stain denoted by markers.sublist
-            temp_back = markers_background[markers_background.stain == background_stains[list(markers.sublist[[channel]])[0]]]
-            # extracting just the row from markers for the channel being processed
-            temp_mark = markers[markers.ind==channel]
-
-            # load the channel being processed
-            temp = load(args.raw, channel=channel)
-
-            # calculate scalar with which the background will be multiplied (Marker_corr = Marker_raw - Background * Exposure_marker / Exposure_background)
-            scalar = list(temp_mark.exposure)[0] / list(temp_back.exposure)[0]
-
-            # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-            temp2 = np.rint(np.multiply(background_dictionary["background_"+str(list(temp_back.ind)[0])], scalar))
-
-            # data has to be converted back to 'uint16' - all outliers will be limited to 2^16 (which happens after multiplication with a scalar)
-            temp2 = temp2.astype(np.uint16)
-            temp = temp.astype(np.uint16)
-
-            # to avoid any negative values, if the processed channel has a pixel value lower than the scaled background, they are both set to 0
-            temp[temp<temp2], temp2[temp<temp2] = 0, 0
-            # subtract background from processed channel
-            temp = np.subtract(temp, temp2)
-            del temp2
-            
-            # append or write the processed channel to the output tif file
-            write(temp, args.output, channel)
-            print("Channel "+str(channel)+" written with "+str(list(temp_mark.stain)[0])+" background channel subtracted")
-            del temp
-
-
-#how will I handle metadata???
-
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
diff --git a/scripts/old/background_subtraction_v020.py b/scripts/old/background_subtraction_v020.py
deleted file mode 100644
index d48e572..0000000
--- a/scripts/old/background_subtraction_v020.py
+++ /dev/null
@@ -1,280 +0,0 @@
-from __future__ import print_function, division
-from multiprocessing.spawn import import_main_path
-import sys
-import copy
-import argparse
-import numpy as np
-import tifffile
-import zarr
-import skimage.transform
-from aicsimageio import aics_image as AI
-import pandas as pd
-import numexpr as ne
-from ome_types import from_tiff, to_xml
-from os.path import abspath
-from argparse import ArgumentParser as AP
-import time
-# This API is apparently changing in skimage 1.0 but it's not clear to
-# me what the replacement will be, if any. We'll explicitly import
-# this so it will break loudly if someone tries this with skimage 1.0.
-try:
-    from skimage.util.dtype import _convert as dtype_convert
-except ImportError:
-    from skimage.util.dtype import convert as dtype_convert
-
-
-# arg parser
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    inputs.add_argument("--pixel-size", metavar="SIZE", dest = "pixel_size", type=float, default = None, action = "store",help="pixel size in microns; default is 1.0")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-def preduce(coords, img_in, img_out):
-    print(img_in.dtype)
-    (iy1, ix1), (iy2, ix2) = coords
-    (oy1, ox1), (oy2, ox2) = np.array(coords) // 2
-    tile = skimage.img_as_float32(img_in[iy1:iy2, ix1:ix2])
-    tile = skimage.transform.downscale_local_mean(tile, (2, 2))
-    tile = dtype_convert(tile, 'uint16')
-    #tile = dtype_convert(tile, img_in.dtype)
-    img_out[oy1:oy2, ox1:ox2] = tile
-
-def format_shape(shape):
-    return "%dx%d" % (shape[1], shape[0])
-
-def process_markers(markers):
-    # add column with starting indices (which match the image channel indices)
-    markers['ind'] = range(0, len(markers))
-
-    # initializing sublist - sublist will indicate which background channel to subtract 
-    sublist = [-1 for i in range(len(markers))]
-    for i, Filter in enumerate(markers[markers.background == True].Filter):
-        # in the markers.csv file, the Filter needs to be the exact same!!!
-        # sublist needs to be able to handle any background index 
-        # - the index of the background column is put to sublist where 
-        # the Filter matches the background
-        sublist += np.where(
-            np.logical_and(markers.Filter == Filter, np.array(markers.background != True)), 
-                markers[markers.background == True].iloc[i].ind + 1, 0)
-    # add sublist to markers - if sublist == -1, no background subtraction
-    # if other, the value matches the index of the background which needs to be subtracted
-    markers["sublist"] = sublist
-    return markers
-
-def process_metadata(metadata, markers):
-    try:
-        metadata.screens[0].reagents = [metadata.screens[0].reagents[i] for i in markers[markers.background != True].ind]
-    except:
-        pass
-    try:
-        metadata.structured_annotations = [metadata.structured_annotations[i] for i in markers[markers.background != True].ind]
-    except:
-        pass
-    # these two are required
-    metadata.images[0].pixels.size_c = len(markers[markers.background != True])
-    metadata.images[0].pixels.channels = [metadata.images[0].pixels.channels[i] for i in markers[markers.background != True].ind]
-    try:
-        metadata.images[0].pixels.planes = [metadata.images[0].pixels.planes[i] for i in markers[markers.background != True].ind]
-    except:
-        pass
-    return metadata
-
-
-
-def subtract(img, markers):
-    # iterating over channels
-    for channel in markers.ind:
-        # if sublist has negative values, no background subtraction (either because it does
-        # not match background, or because it is background)
-        if markers.sublist[channel] < 0:
-            # no change - applies to background channels as well
-            print(f"Channel {channel} processed, no background subtraction")
-
-        else:
-            # scalar is the coefficient with which the background pixel intensity is scaled
-            # it is the result of dividing the marker exposure time with the background exposure time
-            # Marker_corr = Marker_root - Background * Exposure_marker / Exposure_background
-            scalar = markers[markers.ind==channel].exposure.values / markers[markers.ind == markers.sublist[channel]].exposure.values
-            
-            # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-            back = img[markers.sublist[channel]]
-            back = np.rint(ne.evaluate("back * scalar")).astype(np.uint16) # slowest step of entire script
-
-            # subtract background from processed channel and if the background intensity for a certain pixel was larger than
-            # the processed channel, set intensity to 0 (no negative values)
-            img[channel] = np.where(img[channel]<back, 0, img[channel]-back) # not optimized, but works
-            del back
-
-            print(f"Channel {channel} processed, {markers[markers.ind==channel].Filter.values[0]} background channel subtracted")
-    return img
-
-def remove_back(img, markers):
-    #with dask.config.set(**{'array.slicing.split_large_chunks': True}):
-    img = img[markers.background != True]
-    return img
-    
-
-def subres_tiles(level, level_full_shapes, tile_shapes, outpath, scale):
-    print(f"\n processing level {level}")
-    assert level >= 1
-    num_channels, h, w = level_full_shapes[level]
-    tshape = tile_shapes[level] or (h, w)
-    tiff = tifffile.TiffFile(outpath)
-    zimg = zarr.open(tiff.aszarr(series=0, level=level-1, squeeze=False))
-    for c in range(num_channels):
-        sys.stdout.write(
-            f"\r  processing channel {c + 1}/{num_channels}"
-        )
-        sys.stdout.flush()
-        th = tshape[0] * scale
-        tw = tshape[1] * scale
-        for y in range(0, zimg.shape[1], th):
-            for x in range(0, zimg.shape[2], tw):
-                a = zimg[c, y:y+th, x:x+tw, 0]
-                a = skimage.transform.downscale_local_mean(
-                    a, (scale, scale)
-                )
-                if np.issubdtype(zimg.dtype, np.integer):
-                    a = np.around(a)
-                a = a.astype('uint16')
-                yield a
-
-
-def main(args):
-    img_raw = AI.AICSImage(args.root)
-    img = img_raw.get_image_dask_data("CYX")
-
-    markers_raw = pd.read_csv(args.markers)
-    markers = process_markers(copy.copy(markers_raw))
-
-    img = subtract(img, markers)
-    img = remove_back(img, markers)
-
-    markers_raw = markers_raw[markers_raw.background != True]
-    markers_raw = markers_raw.drop("background", axis = 1)
-    markers_raw.to_csv(args.markerout, index=False)
-
-    # Processing metadata - highly adapted to Lunaphore outputs
-    metadata = process_metadata(img_raw.metadata, markers)
-    metadata = img_raw.metadata
-
-    
-    if args.pixel_size != None:
-        # If specified, the inputted pixel size is used
-        pixel_size = args.pixel_size
-    elif img_raw.metadata.images[0].pixels.physical_size_x != None:
-        # If pixel size is not specified, the metadata is checked (the script trusts users more than metadata)
-        pixel_size = img_raw.metadata.images[0].pixels.physical_size_x
-    else:
-        # If no pixel size specified anywhere, use default 1.0
-        pixel_size = 1.0
-
-    # construct levels
-    tile_size = 1024
-    scale = 2
-
-    print()
-    dtype = img.dtype
-    base_shape = img[0].shape
-    num_channels = img.shape[0]
-    num_levels = (np.ceil(np.log2(max(base_shape) / tile_size)) + 1).astype(int)
-    factors = 2 ** np.arange(num_levels)
-    shapes = (np.ceil(np.array(base_shape) / factors[:,None])).astype(int)
-
-    print("Pyramid level sizes: ")
-    for i, shape in enumerate(shapes):
-        print(f"   level {i+1}: {format_shape(shape)}", end="")
-        if i == 0:
-            print("(original size)", end="")
-        print()
-    print()
-    print(shapes)  
-    
-    level_full_shapes = []
-    for shape in shapes:
-        level_full_shapes.append((num_channels, shape[0], shape[1]))
-    level_shapes = shapes
-
-    #level_shapes = np.array(level_shapes)
-    tip_level = np.argmax(np.all(level_shapes < tile_size, axis=1))
-    tile_shapes = [
-        (tile_size, tile_size) if i <= tip_level else None
-        for i in range(len(level_shapes))
-    ]
-
-    # write pyramid
-
-    with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
-        tiff.write(
-            data = img,
-            shape = level_full_shapes[0],
-            subifds=int(num_levels-1),
-            dtype=dtype,
-            resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
-            tile=tile_shapes[0]
-        )
-        for level, (shape, tile_shape) in enumerate(
-                zip(level_full_shapes[1:], tile_shapes[1:]), 1
-        ):
-            tiff.write(
-                data = subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
-                shape=shape,
-                subfiletype=1,
-                dtype=dtype,
-                tile=tile_shape
-            )
-        if metadata.images[0].pixels.planes:
-            temp_planes = []
-            for i, channel_id in enumerate(range(num_channels)):
-                temp_plane = metadata.images[0].pixels.planes[channel_id]
-                temp_plane.the_c = i
-                temp_planes.append(temp_plane)
-            metadata.images[0].pixels.planes = temp_planes
-        if metadata.images[0].pixels.tiff_data_blocks and len(
-                metadata.images[0].pixels.tiff_data_blocks) > 0:
-            metadata.images[0].pixels.tiff_data_blocks[0].plane_count = num_channels
-        # Write
-    tifffile.tiffcomment(args.output, to_xml(metadata))
-    print()
-
-        
-    
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
-            
-        
-
-
-
-
-
-
diff --git a/scripts/old/background_subtraction_v033.py b/scripts/old/background_subtraction_v033.py
deleted file mode 100644
index 58f6a7f..0000000
--- a/scripts/old/background_subtraction_v033.py
+++ /dev/null
@@ -1,284 +0,0 @@
-from __future__ import print_function, division
-from multiprocessing.spawn import import_main_path
-import sys
-import copy
-import argparse
-import numpy as np
-import tifffile
-import zarr
-import skimage.transform
-from aicsimageio import aics_image as AI
-import pandas as pd
-import numexpr as ne
-from ome_types import from_tiff, to_xml
-from os.path import abspath
-from argparse import ArgumentParser as AP
-import time
-import dask
-# This API is apparently changing in skimage 1.0 but it's not clear to
-# me what the replacement will be, if any. We'll explicitly import
-# this so it will break loudly if someone tries this with skimage 1.0.
-try:
-    from skimage.util.dtype import _convert as dtype_convert
-except ImportError:
-    from skimage.util.dtype import convert as dtype_convert
-
-
-# arg parser
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    inputs.add_argument("--pixel-size", metavar="SIZE", dest = "pixel_size", type=float, default = None, action = "store",help="pixel size in microns; default is 1.0")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-def preduce(coords, img_in, img_out):
-    print(img_in.dtype)
-    (iy1, ix1), (iy2, ix2) = coords
-    (oy1, ox1), (oy2, ox2) = np.array(coords) // 2
-    tile = skimage.img_as_float32(img_in[iy1:iy2, ix1:ix2])
-    tile = skimage.transform.downscale_local_mean(tile, (2, 2))
-    tile = dtype_convert(tile, 'uint16')
-    #tile = dtype_convert(tile, img_in.dtype)
-    img_out[oy1:oy2, ox1:ox2] = tile
-
-def format_shape(shape):
-    return "%dx%d" % (shape[1], shape[0])
-
-def process_markers(markers):
-    # add column with starting indices (which match the image channel indices)
-    # this should be removed soon
-    markers['ind'] = range(0, len(markers))
-
-    # if the 'remove' column is not specified, all channels are kept. If it is 
-    # present, it is converted to a boolean indicating which channels should be removed
-    if 'remove' not in markers:
-        markers['remove'] = ["False" for i in range(len(markers))]
-    else:
-        markers['remove'] = markers['remove'] == True
-    # invert the markers.remove column to indicate which columns to keep
-    markers['remove'] = markers['remove'] == False
-
-    return markers
-
-def process_metadata(metadata, markers):
-    try:
-        metadata.screens[0].reagents = [metadata.screens[0].reagents[i] for i in markers[markers.remove == True].ind]
-    except:
-        pass
-    try:
-        metadata.structured_annotations = [metadata.structured_annotations[i] for i in markers[markers.remove == True].ind]
-    except:
-        pass
-    # these two are required
-    metadata.images[0].pixels.size_c = len(markers[markers.remove == True])
-    metadata.images[0].pixels.channels = [metadata.images[0].pixels.channels[i] for i in markers[markers.remove == True].ind]
-    try:
-        metadata.images[0].pixels.planes = [metadata.images[0].pixels.planes[i] for i in markers[markers.remove == True].ind]
-    except:
-        pass
-    try:
-        metadata.images[0].pixels.tiff_data_blocks[0].plane_count = sum(markers.remove == True)
-    except:
-        pass
-    return metadata
-
-# NaN values return True for the statement below in this version of Python. Did not use math.isnan() since the values
-# are strings if present
-def isNaN(x):
-    return x != x
-
-def subtract_channel(image, markers, channel, background_marker, output):
-    scalar = markers[markers.ind == channel].exposure.values / background_marker.exposure.values
-    
-    # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-    # [0] at the end needed to get [x, y] shape, and not have [1, x, y]
-    back = copy.copy(image[background_marker.ind])[0]
-    # subtract background from processed channel and if the background intensity for a certain pixel was larger than
-    # the processed channel, set intensity to 0 (no negative values)
-    back = np.rint(ne.evaluate("back * scalar"))
-    back = np.where(back>65535,65535,back.astype(np.uint16))
-    # set the pixel value to 0 if the image channel value is lower than the scaled background channel value
-    # otherwise, subtract.
-    output[channel] = np.where(image[channel]<back, 0, image[channel]-back)
-    back = None
-    return output[channel]
-
-def subtract(img, markers, output):
-    # iterating over channels
-    for channel in range(len(markers)):
-        # this array is used to find the appropriate background channel
-        # IMPORTANT - all values in the 'marker_name' column should be unique        
-
-        # if no background channel is specified, channel is kept like if should be
-        if markers.background.isnull()[channel] == True:
-            # no change - applies to background channels as well
-            output[channel] = copy.copy(img[channel])
-            print(f"Channel {markers.marker_name[channel]} ({channel}) processed, no background subtraction")
-
-        else:
-            # this array is used to find the appropriate background channel
-            # IMPORTANT - all values in the 'marker_name' column should be unique
-            find_background = np.array(markers.marker_name == markers.background[channel])
-            # scalar is the coefficient with which the background pixel intensity is scaled
-            # it is the result of dividing the marker exposure time with the background exposure time
-            # Marker_corr = Marker_root - Background * Exposure_marker / Exposure_background
-            background_marker = markers.iloc[find_background]
-
-            output[channel] = subtract_channel(img, markers, channel, background_marker, output)
-
-            print(f"Channel {markers.marker_name[channel]} ({channel}) processed, {markers.background[channel]} background channel subtracted")
-    return output
-
-def remove_back(img, markers):
-    #with dask.config.set(**{'array.slicing.split_large_chunks': True}):
-    # subset the image based on the remove column in the markers file
-    img = img[markers.remove.tolist()]
-    print()
-    print(f'Image shape: {img.shape}')
-    return img
-    
-def subres_tiles(level, level_full_shapes, tile_shapes, outpath, scale):
-    print(f"\n processing level {level}")
-    assert level >= 1
-    num_channels, h, w = level_full_shapes[level]
-    tshape = tile_shapes[level] or (h, w)
-    tiff = tifffile.TiffFile(outpath)
-    zimg = zarr.open(tiff.aszarr(series=0, level=level-1, squeeze=False))
-    for c in range(num_channels):
-        sys.stdout.write(
-            f"\r  processing channel {c + 1}/{num_channels}"
-        )
-        sys.stdout.flush()
-        th = tshape[0] * scale
-        tw = tshape[1] * scale
-        for y in range(0, zimg.shape[1], th):
-            for x in range(0, zimg.shape[2], tw):
-                a = zimg[c, y:y+th, x:x+tw, 0]
-                a = skimage.transform.downscale_local_mean(
-                    a, (scale, scale)
-                )
-                if np.issubdtype(zimg.dtype, np.integer):
-                    a = np.around(a)
-                a = a.astype('uint16')
-                yield a
-
-def main(args):
-    img_raw = AI.AICSImage(args.root)
-    img = img_raw.get_image_dask_data("CYX")
-
-    markers_raw = pd.read_csv(args.markers)
-    markers = process_markers(copy.copy(markers_raw))
-
-    output = dask.array.empty_like(img)
-
-    output = subtract(img, markers, output)
-    output = remove_back(output, markers)
-
-    markers_raw = markers_raw[markers_raw.remove != True]
-    markers_raw = markers_raw.drop("remove", axis = 1)
-    markers_raw.to_csv(args.markerout, index=False)
-
-    # Processing metadata - highly adapted to Lunaphore outputs
-    metadata = img_raw.metadata
-    metadata = process_metadata(img_raw.metadata, markers)
-    
-    if args.pixel_size != None:
-        # If specified, the input pixel size is used
-        pixel_size = args.pixel_size
-    elif img_raw.metadata.images[0].pixels.physical_size_x != None:
-        # If pixel size is not specified, the metadata is checked (the script trusts users more than metadata)
-        pixel_size = img_raw.metadata.images[0].pixels.physical_size_x
-    else:
-        # If no pixel size specified anywhere, use default 1.0
-        pixel_size = 1.0
-
-    # construct levels
-    tile_size = 1024
-    scale = 2
-
-    print()
-    dtype = output.dtype
-    base_shape = output[0].shape
-    num_channels = output.shape[0]
-    num_levels = (np.ceil(np.log2(max(base_shape) / tile_size)) + 1).astype(int)
-    factors = 2 ** np.arange(num_levels)
-    shapes = (np.ceil(np.array(base_shape) / factors[:,None])).astype(int)
-
-    print("Pyramid level sizes: ")
-    for i, shape in enumerate(shapes):
-        print(f"   level {i+1}: {format_shape(shape)}", end="")
-        if i == 0:
-            print("(original size)", end="")
-        print()
-    print()
-    print(shapes)  
-    
-    level_full_shapes = []
-    for shape in shapes:
-        level_full_shapes.append((num_channels, shape[0], shape[1]))
-    level_shapes = shapes
-    tip_level = np.argmax(np.all(level_shapes < tile_size, axis=1))
-    tile_shapes = [
-        (tile_size, tile_size) if i <= tip_level else None
-        for i in range(len(level_shapes))
-    ]
-
-    # write pyramid
-    with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
-        tiff.write(
-            data = output,
-            shape = level_full_shapes[0],
-            subifds=int(num_levels-1),
-            dtype=dtype,
-            resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
-            tile=tile_shapes[0]
-        )
-        for level, (shape, tile_shape) in enumerate(
-                zip(level_full_shapes[1:], tile_shapes[1:]), 1
-        ):
-            tiff.write(
-                data = subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
-                shape=shape,
-                subfiletype=1,
-                dtype=dtype,
-                tile=tile_shape
-            )
-
-    # note about metadata: the channels, planes etc were adjusted not to include the removed channels, however
-    # the channel ids have stayed the same as before removal. E.g if channels 1 and 2 are removed,
-    # the channel ids in the metadata will skip indices 1 and 2 (channel_id:0, channel_id:3, channel_id:4 ...)
-    tifffile.tiffcomment(args.output, to_xml(metadata))
-    print()
-
-        
-    
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
diff --git a/scripts/old/background_subtraction_v2.py b/scripts/old/background_subtraction_v2.py
deleted file mode 100644
index 05c75df..0000000
--- a/scripts/old/background_subtraction_v2.py
+++ /dev/null
@@ -1,134 +0,0 @@
-from distutils.log import error
-import time
-import argparse
-from argparse import ArgumentParser as AP
-from os.path import abspath
-import pandas as pd
-import tifffile as tf
-import numpy as np
-import numexpr as ne
-from aicsimageio import aics_image as AI
-from aicsimageio import writers as AIW
-from itertools import compress
-import ome_types
-
-
-
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-# add function to WRITE PYRAMID
-  
-def main(args):
-
-    print(f"Root file = {args.root}")
-    print(f"Marker file = {args.markers}")
-
-    # read in markers.csv file
-    markers_raw = pd.read_csv(args.markers)
-
-    # required columns for background subtraction are "Filter", "background" and "exposure", everything else is dropped ^^^^
-    try:
-        markers = markers_raw[["Filter", "background", "exposure"]]
-    except:
-        raise error("Markers file needs to contain Filter, background and exposure columns")
-
-    # load image as dask array (unloaded unless directly called)
-    img_raw = AI.AICSImage(args.root)
-    img = img_raw.get_image_dask_data("CYX") # hardcoding shape, channel is first
-    assert img.shape[0] == len(markers), "Marker file doesn't match image, check channels"
-
-    # add column with starting indices (which match the image channel indices)
-    markers['ind'] = range(0, len(markers))
-
-    # initializing sublist - sublist will indicate which background channel to subtract 
-    sublist = [-1 for i in range(len(markers))]
-    for i, Filter in enumerate(markers[markers.background == True].Filter):
-        # in the markers.csv file, the Filter needs to be the exact same!!!
-        # sublist needs to be able to handle any background index 
-        # - the index of the background column is put to sublist where 
-        # the Filter matches the background
-        sublist += np.where(
-            np.logical_and(markers.Filter == Filter, np.array(markers.background != True)), 
-                markers[markers.background == True].iloc[i].ind + 1, 0)
-    # add sublist to markers - if sublist == -1, no background subtraction
-    # if other, the value matches the index of the background which needs to be subtracted
-    markers["sublist"] = sublist
-
-    # iterating over channels
-    for channel in markers.ind:
-        # if sublist has negative values, no background subtraction (either because it does
-        # not match background, or because it is background)
-        if markers.sublist[channel] < 0:
-            # no change - applies to background channels as well
-            print(f"Channel {channel} processed, no background subtraction")
-
-        else:
-            # scalar is the coefficient with which the background pixel intensity is scaled
-            # it is the result of dividing the marker exposure time with the background exposure time
-            # Marker_corr = Marker_root - Background * Exposure_marker / Exposure_background
-            scalar = markers[markers.ind==channel].exposure.values / markers[markers.ind == markers.sublist[channel]].exposure.values
-            
-            # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-            back = img[markers.sublist[channel]]
-            back = np.rint(ne.evaluate("back * scalar")).astype(np.uint16) # slowest step of entire script
-
-            # subtract background from processed channel and if the background intensity for a certain pixel was larger than
-            # the processed channel, set intensity to 0 (no negative values)
-            img[channel] = np.where(img[channel]<back, 0, img[channel]-back) # not optimized, but works
-            del back
-
-            print(f"Channel {channel} processed, {markers[markers.ind==channel].Filter.values[0]} background channel subtracted")
-
-    print("Writing OME-TIFF")
-    # write ome-tiff from image to specified output, might need to make dim_order changable
-    # exlude channels with background set to True (final result omits these channels)
-    img = img[markers.background != True]
-
-    AIW.OmeTiffWriter.save(
-        img,
-        args.output,
-        dim_order="CYX", 
-        #channel_names=list(markers.Filter[markers.background != True]), useful when testing and bad metadata present
-        image_name = "Background subtracted image",
-        channel_names = list(compress(img_raw.channel_names, markers.background != True)),
-        physical_pixel_sizes = img_raw.physical_pixel_sizes
-    )
-    # write new markers.csv file and exclude background channels (and the background column)
-    markers_raw = markers_raw[markers_raw.background != True]
-    markers_raw.drop("background", axis = 1, inplace = True)
-    markers_raw.to_csv(args.markerout)
-
-
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
diff --git a/scripts/old/background_subtraction_v3.py b/scripts/old/background_subtraction_v3.py
deleted file mode 100644
index 05c75df..0000000
--- a/scripts/old/background_subtraction_v3.py
+++ /dev/null
@@ -1,134 +0,0 @@
-from distutils.log import error
-import time
-import argparse
-from argparse import ArgumentParser as AP
-from os.path import abspath
-import pandas as pd
-import tifffile as tf
-import numpy as np
-import numexpr as ne
-from aicsimageio import aics_image as AI
-from aicsimageio import writers as AIW
-from itertools import compress
-import ome_types
-
-
-
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-# add function to WRITE PYRAMID
-  
-def main(args):
-
-    print(f"Root file = {args.root}")
-    print(f"Marker file = {args.markers}")
-
-    # read in markers.csv file
-    markers_raw = pd.read_csv(args.markers)
-
-    # required columns for background subtraction are "Filter", "background" and "exposure", everything else is dropped ^^^^
-    try:
-        markers = markers_raw[["Filter", "background", "exposure"]]
-    except:
-        raise error("Markers file needs to contain Filter, background and exposure columns")
-
-    # load image as dask array (unloaded unless directly called)
-    img_raw = AI.AICSImage(args.root)
-    img = img_raw.get_image_dask_data("CYX") # hardcoding shape, channel is first
-    assert img.shape[0] == len(markers), "Marker file doesn't match image, check channels"
-
-    # add column with starting indices (which match the image channel indices)
-    markers['ind'] = range(0, len(markers))
-
-    # initializing sublist - sublist will indicate which background channel to subtract 
-    sublist = [-1 for i in range(len(markers))]
-    for i, Filter in enumerate(markers[markers.background == True].Filter):
-        # in the markers.csv file, the Filter needs to be the exact same!!!
-        # sublist needs to be able to handle any background index 
-        # - the index of the background column is put to sublist where 
-        # the Filter matches the background
-        sublist += np.where(
-            np.logical_and(markers.Filter == Filter, np.array(markers.background != True)), 
-                markers[markers.background == True].iloc[i].ind + 1, 0)
-    # add sublist to markers - if sublist == -1, no background subtraction
-    # if other, the value matches the index of the background which needs to be subtracted
-    markers["sublist"] = sublist
-
-    # iterating over channels
-    for channel in markers.ind:
-        # if sublist has negative values, no background subtraction (either because it does
-        # not match background, or because it is background)
-        if markers.sublist[channel] < 0:
-            # no change - applies to background channels as well
-            print(f"Channel {channel} processed, no background subtraction")
-
-        else:
-            # scalar is the coefficient with which the background pixel intensity is scaled
-            # it is the result of dividing the marker exposure time with the background exposure time
-            # Marker_corr = Marker_root - Background * Exposure_marker / Exposure_background
-            scalar = markers[markers.ind==channel].exposure.values / markers[markers.ind == markers.sublist[channel]].exposure.values
-            
-            # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-            back = img[markers.sublist[channel]]
-            back = np.rint(ne.evaluate("back * scalar")).astype(np.uint16) # slowest step of entire script
-
-            # subtract background from processed channel and if the background intensity for a certain pixel was larger than
-            # the processed channel, set intensity to 0 (no negative values)
-            img[channel] = np.where(img[channel]<back, 0, img[channel]-back) # not optimized, but works
-            del back
-
-            print(f"Channel {channel} processed, {markers[markers.ind==channel].Filter.values[0]} background channel subtracted")
-
-    print("Writing OME-TIFF")
-    # write ome-tiff from image to specified output, might need to make dim_order changable
-    # exlude channels with background set to True (final result omits these channels)
-    img = img[markers.background != True]
-
-    AIW.OmeTiffWriter.save(
-        img,
-        args.output,
-        dim_order="CYX", 
-        #channel_names=list(markers.Filter[markers.background != True]), useful when testing and bad metadata present
-        image_name = "Background subtracted image",
-        channel_names = list(compress(img_raw.channel_names, markers.background != True)),
-        physical_pixel_sizes = img_raw.physical_pixel_sizes
-    )
-    # write new markers.csv file and exclude background channels (and the background column)
-    markers_raw = markers_raw[markers_raw.background != True]
-    markers_raw.drop("background", axis = 1, inplace = True)
-    markers_raw.to_csv(args.markerout)
-
-
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
diff --git a/scripts/old/background_subtraction_working_v4.py b/scripts/old/background_subtraction_working_v4.py
deleted file mode 100644
index bf53beb..0000000
--- a/scripts/old/background_subtraction_working_v4.py
+++ /dev/null
@@ -1,304 +0,0 @@
-from __future__ import print_function, division
-from multiprocessing.spawn import import_main_path
-import warnings
-import sys
-import os
-import re
-import io
-import argparse
-import pathlib
-import struct
-import itertools
-import uuid
-import multiprocessing
-import concurrent.futures
-from xml.dom.minidom import TypeInfo
-import numpy as np
-#import dask
-import tifffile
-import zarr
-import skimage.transform
-from aicsimageio import aics_image as AI
-import pandas as pd
-import numexpr as ne
-import dask.array as da
-from ome_types import from_tiff, to_xml
-from os.path import abspath
-from argparse import ArgumentParser as AP
-import time
-# This API is apparently changing in skimage 1.0 but it's not clear to
-# me what the replacement will be, if any. We'll explicitly import
-# this so it will break loudly if someone tries this with skimage 1.0.
-try:
-    from skimage.util.dtype import _convert as dtype_convert
-except ImportError:
-    from skimage.util.dtype import convert as dtype_convert
-
-
-# arg parser
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    parser.add_argument("--pixel-size", metavar="SIZE", dest = "pixel_size", type=float, default=1.0, action = "store",help="pixel size in microns; default is 1.0")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-def preduce(coords, img_in, img_out):
-    print(img_in.dtype)
-    (iy1, ix1), (iy2, ix2) = coords
-    (oy1, ox1), (oy2, ox2) = np.array(coords) // 2
-    tile = skimage.img_as_float32(img_in[iy1:iy2, ix1:ix2])
-    tile = skimage.transform.downscale_local_mean(tile, (2, 2))
-    tile = dtype_convert(tile, 'uint16')
-    #tile = dtype_convert(tile, img_in.dtype)
-    img_out[oy1:oy2, ox1:ox2] = tile
-
-def format_shape(shape):
-    return "%dx%d" % (shape[1], shape[0])
-
-def process_markers(markers):
-    # add column with starting indices (which match the image channel indices)
-    markers['ind'] = range(0, len(markers))
-
-    # initializing sublist - sublist will indicate which background channel to subtract 
-    sublist = [-1 for i in range(len(markers))]
-    for i, Filter in enumerate(markers[markers.background == True].Filter):
-        # in the markers.csv file, the Filter needs to be the exact same!!!
-        # sublist needs to be able to handle any background index 
-        # - the index of the background column is put to sublist where 
-        # the Filter matches the background
-        sublist += np.where(
-            np.logical_and(markers.Filter == Filter, np.array(markers.background != True)), 
-                markers[markers.background == True].iloc[i].ind + 1, 0)
-    # add sublist to markers - if sublist == -1, no background subtraction
-    # if other, the value matches the index of the background which needs to be subtracted
-    markers["sublist"] = sublist
-    return markers
-
-def process_metadata(metadata, markers):
-    try:
-        metadata.screens[0].reagents = [metadata.screens[0].reagents[i] for i in markers[markers.background != True].ind]
-    except:
-        pass
-    try:
-        metadata.structured_annotations = [metadata.structured_annotations[i] for i in markers[markers.background != True].ind]
-    except:
-        pass
-    # these two are required
-    metadata.images[0].pixels.size_c = len(markers[markers.background != True])
-    metadata.images[0].pixels.channels = [metadata.images[0].pixels.channels[i] for i in markers[markers.background != True].ind]
-    try:
-        metadata.images[0].pixels.planes = [metadata.images[0].pixels.planes[i] for i in markers[markers.background != True].ind]
-    except:
-        pass
-    return metadata
-
-
-
-def subtract(img, markers):
-    # iterating over channels
-    for channel in markers.ind:
-        # if sublist has negative values, no background subtraction (either because it does
-        # not match background, or because it is background)
-        if markers.sublist[channel] < 0:
-            # no change - applies to background channels as well
-            print(f"Channel {channel} processed, no background subtraction")
-
-        else:
-            # scalar is the coefficient with which the background pixel intensity is scaled
-            # it is the result of dividing the marker exposure time with the background exposure time
-            # Marker_corr = Marker_root - Background * Exposure_marker / Exposure_background
-            scalar = markers[markers.ind==channel].exposure.values / markers[markers.ind == markers.sublist[channel]].exposure.values
-            
-            # create temporary dataframe which will store the multiplied background rounded up to nearest integer
-            back = img[markers.sublist[channel]]
-            back = np.rint(ne.evaluate("back * scalar")).astype(np.uint16) # slowest step of entire script
-
-            # subtract background from processed channel and if the background intensity for a certain pixel was larger than
-            # the processed channel, set intensity to 0 (no negative values)
-            img[channel] = np.where(img[channel]<back, 0, img[channel]-back) # not optimized, but works
-            del back
-
-            print(f"Channel {channel} processed, {markers[markers.ind==channel].Filter.values[0]} background channel subtracted")
-    return img
-
-def remove_back(img, markers):
-    #with dask.config.set(**{'array.slicing.split_large_chunks': True}):
-    img = img[markers.background != True]
-    return img
-    
-
-def subres_tiles(level, level_full_shapes, tile_shapes, outpath, scale):
-    print(f"\n processing level {level}")
-    assert level >= 1
-    num_channels, h, w = level_full_shapes[level]
-    tshape = tile_shapes[level] or (h, w)
-    tiff = tifffile.TiffFile(outpath)
-    zimg = zarr.open(tiff.aszarr(series=0, level=level-1, squeeze=False))
-    for c in range(num_channels):
-        sys.stdout.write(
-            f"\r  processing channel {c + 1}/{num_channels}"
-        )
-        sys.stdout.flush()
-        th = tshape[0] * scale
-        tw = tshape[1] * scale
-        for y in range(0, zimg.shape[1], th):
-            for x in range(0, zimg.shape[2], tw):
-                a = zimg[c, y:y+th, x:x+tw, 0]
-                a = skimage.transform.downscale_local_mean(
-                    a, (scale, scale)
-                )
-                if np.issubdtype(zimg.dtype, np.integer):
-                    a = np.around(a)
-                a = a.astype('uint16')
-                yield a
-
-
-def main(args):
-    img_raw = AI.AICSImage(args.root)
-    img = img_raw.get_image_dask_data("CYX")
-
-    markers_raw = pd.read_csv(args.markers)
-    markers = process_markers(markers_raw)
-
-    img = subtract(img, markers)
-    img = remove_back(img, markers)
-
-    markers_raw = markers_raw[markers_raw.background != True]
-    markers_raw.drop("background", axis = 1, inplace = True)
-    markers_raw.to_csv(args.markerout)
-
-    # Processing metadata - highly adapted to Lunaphore outputs
-    metadata = process_metadata(img_raw.metadata, markers)
-    metadata = img_raw.metadata
-
-    pixel_size = args.pixel_size
-
-    # construct levels
-    tile_size = 1024
-    scale = 2
-
-    #if hasattr(os, 'sched_getaffinity'):
-    #    num_workers = len(os.sched_getaffinity(0))
-    #else:
-    #    num_workers = multiprocessing.cpu_count()
-    #print(f"Using {num_workers} worker threads on detected CPU count.")
-    print()
-    dtype = img.dtype
-    base_shape = img[0].shape
-    num_channels = img.shape[0]
-    num_levels = (np.ceil(np.log2(max(base_shape) / tile_size)) + 1).astype(int)
-    factors = 2 ** np.arange(num_levels)
-    shapes = (np.ceil(np.array(base_shape) / factors[:,None])).astype(int)
-
-    print("Pyramid level sizes: ")
-    for i, shape in enumerate(shapes):
-        print(f"   level {i+1}: {format_shape(shape)}", end="")
-        if i == 0:
-            print("(original size)", end="")
-        print()
-    print()
-
-    print(shapes)
-    #executor = concurrent.futures.ThreadPoolExecutor(num_workers)
-
-    #shape_pairs = zip(shapes, shapes[1:])
-
-    
-    
-    
-    level_full_shapes = []
-    for shape in shapes:
-        level_full_shapes.append((num_channels, shape[0], shape[1]))
-    level_shapes = shapes
-
-        #level_shapes.append(shape)
-    #for level in range(num_levels):
-    #    level_full_shapes.append(level_dict[f"level_{level}"].shape)
-    #    level_shapes.append(level_dict[f"level_{level}"].shape[1:])
-
-    #level_shapes = np.array(level_shapes)
-    tip_level = np.argmax(np.all(level_shapes < tile_size, axis=1))
-    tile_shapes = [
-        (tile_size, tile_size) if i <= tip_level else None
-        for i in range(len(level_shapes))
-    ]
-
-    # write pyramid
-
-    with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
-        tiff.write(
-            data = img,
-            shape = level_full_shapes[0],
-            subifds=int(num_levels-1),
-            dtype=dtype,
-            resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
-            tile=tile_shapes[0]
-        )
-        for level, (shape, tile_shape) in enumerate(
-                zip(level_full_shapes[1:], tile_shapes[1:]), 1
-        ):
-            tiff.write(
-                data = subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
-                #data=level_dict[f"level_{level}"],
-                shape=shape,
-                subfiletype=1,
-                dtype=dtype,
-                tile=tile_shape
-            )
-        #metadata.images[0].pixels.channels = [metadata.images[0].pixels.channels[i] for i in range(num_channels)]
-        #metadata.images[0].pixels.size_c = num_channels
-        #metadata.images[0].pixels.size_x = img.shape[2]
-        #metadata.images[0].pixels.size_y = img.shape[1]
-        if metadata.images[0].pixels.planes:
-            temp_planes = []
-            for i, channel_id in enumerate(range(num_channels)):
-                temp_plane = metadata.images[0].pixels.planes[channel_id]
-                temp_plane.the_c = i
-                temp_planes.append(temp_plane)
-            metadata.images[0].pixels.planes = temp_planes
-        if metadata.images[0].pixels.tiff_data_blocks and len(
-                metadata.images[0].pixels.tiff_data_blocks) > 0:
-            metadata.images[0].pixels.tiff_data_blocks[0].plane_count = num_channels
-        # Write
-    tifffile.tiffcomment(args.output, to_xml(metadata))
-    print()
-        
-    
-if __name__ == '__main__':
-    # Read in arguments
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")
-            
-        
-
-
-
-
-
-

From b7bec6538aba530aa69811d81d75af742702c397 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:35:25 +0100
Subject: [PATCH 09/21] git cleanup

---
 .gitignore                                       |   5 ++++-
 CHANGELOG.md                                     |   2 --
 backsub/__pycache__/CLI.cpython-312.pyc          | Bin 3507 -> 0 bytes
 .../metadata2markers_table.cpython-312.pyc       | Bin 5381 -> 0 bytes
 backsub/__pycache__/ome_schema.cpython-312.pyc   | Bin 5724 -> 0 bytes
 backsub/__pycache__/ome_writer.cpython-312.pyc   | Bin 2318 -> 0 bytes
 6 files changed, 4 insertions(+), 3 deletions(-)
 delete mode 100644 backsub/__pycache__/CLI.cpython-312.pyc
 delete mode 100644 backsub/__pycache__/metadata2markers_table.cpython-312.pyc
 delete mode 100644 backsub/__pycache__/ome_schema.cpython-312.pyc
 delete mode 100644 backsub/__pycache__/ome_writer.cpython-312.pyc

diff --git a/.gitignore b/.gitignore
index ed8ebf5..6f9fddd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,4 @@
-__pycache__
\ No newline at end of file
+backsub/__pycache__
+__pycache__
+.DS_Store
+backsub/testing.ipynb
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index f302546..c9d8deb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,8 +22,6 @@ Rework of Backsub to not have Palom as a dependency reducing the environment siz
 - Palom and OpenCV as dependencies
 - old scripts, version history should be checked instead.
 
-
-
 ## v0.4.1 - [2023.11.21]
 
 The script has been rewritten to perform channel subtraction in a RAM-efficient manner - updating is highly recommended. If the output file is much bigger than expected, adjust the `--tile-size` parameter to a smaller value (e.g `512`). Changing the `--chunk-size` parameter may affect performance (lower values increase execution time, higher values increase RAM usage).
diff --git a/backsub/__pycache__/CLI.cpython-312.pyc b/backsub/__pycache__/CLI.cpython-312.pyc
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diff --git a/backsub/__pycache__/ome_writer.cpython-312.pyc b/backsub/__pycache__/ome_writer.cpython-312.pyc
deleted file mode 100644
index b96db3b1c2f9ba1cce4c3cb2c8b477f395376487..0000000000000000000000000000000000000000
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From e75ba33af6d7b88f56c4c7f4b9842ad272a984c3 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:44:19 +0100
Subject: [PATCH 10/21] removed required for markers

---
 backsub/CLI.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/backsub/CLI.py b/backsub/CLI.py
index 838d351..7b8d961 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -22,7 +22,7 @@ def get_args(_version):
     inputs.add_argument("-r", ## deprecation warning can be added later
                         "--root", ## deprecation warning can be added later
                         '-in',
-                        "--input",
+                        '--input',
                         dest="input", 
                         action="store",
                         type=pathlib.Path,
@@ -35,7 +35,6 @@ def get_args(_version):
                         dest="markers", 
                         action="store",
                         type=pathlib.Path,
-                        required=True,
                         help="""File path to the markers.csv file containing the list of marker names
                         and their respective background channels.
                         """

From 900b34fbe0b20492059b11133c1a4b231f53a8ee Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 17:54:44 +0100
Subject: [PATCH 11/21] removed script

---
 CHANGELOG.md      |   4 +-
 background_sub.py | 431 ----------------------------------------------
 2 files changed, 3 insertions(+), 432 deletions(-)
 delete mode 100644 background_sub.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c9d8deb..3621e45 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,9 +11,10 @@ Rework of Backsub to not have Palom as a dependency reducing the environment siz
 - `compression` parameter
 - hidden argument `comet`, which extracts the metadata on-the-fly for Lunaphore COMET images. When using this argument, the `markers.csv` file is not required.
 - two RAM profiles: (1) default, uses moderate RAM. (2) Uses approximately half of the default RAM at the cost of a slight loss in precision of the calculation of the downsized dimensions of the pyramidal output image. This means the dimensions of the pyramidal level will differ between profile 1 and 2. The high-resolution level is not affected by this.
-- organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background substraction and writing of output image and (5) extraction of metadata from Lunaphore Comet images.
+- organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background subtraction and writing of output image and (5) extraction of metadata from Lunaphore COMET images.
 - logger has been re-designed.
 - restructured README
+- `tile-size` parameter now defaults to 256 instead of 1024
 
 ### `Fixed`
 - output image file-size is reduced by applying lossless compression ("LZW" by default)
@@ -21,6 +22,7 @@ Rework of Backsub to not have Palom as a dependency reducing the environment siz
 ### `Removed`
 - Palom and OpenCV as dependencies
 - old scripts, version history should be checked instead.
+- `chunksize` argument - chunking is done now only by the default dask.array behaviour
 
 ## v0.4.1 - [2023.11.21]
 
diff --git a/background_sub.py b/background_sub.py
deleted file mode 100644
index 31a5b93..0000000
--- a/background_sub.py
+++ /dev/null
@@ -1,431 +0,0 @@
-import argparse
-import pathlib
-
-import ome_types
-import palom.pyramid
-import palom.reader
-import pandas as pd
-import numpy as np
-import copy
-import dask.array as da
-import numexpr as ne
-import math
-import cv2
-import tifffile
-import tqdm
-import zarr
-from loguru import logger
-from argparse import ArgumentParser as AP
-from os.path import abspath
-import time
-### Using the palom pyramidal writer by Yu-An Chen: https://github.com/labsyspharm/palom/blob/main/palom/pyramid.py
-### Adapted for this use case - only one image can be processed:
-
-class PyramidSetting:
-
-    def __init__(
-        self,
-        downscale_factor=2,
-        tile_size=1024,
-        max_pyramid_img_size=1024
-    ):
-        self.downscale_factor = downscale_factor
-        self.tile_size = tile_size
-        self.max_pyramid_img_size = max_pyramid_img_size
-
-    def tile_shapes(self, base_shape):
-        shapes = np.array(self.pyramid_shapes(base_shape))
-        n_rows_n_cols = np.ceil(shapes / self.tile_size)
-        tile_shapes = np.ceil(shapes / n_rows_n_cols / 16) * 16
-        return [tuple(map(int, s)) for s in tile_shapes]
-
-    def pyramid_shapes(self, base_shape):
-        num_levels = self.num_levels(base_shape)
-        factors = self.downscale_factor ** np.arange(num_levels)
-        shapes = np.ceil(np.array(base_shape) / factors[:,None])
-        return [tuple(map(int, s)) for s in shapes]
-
-    def num_levels(self, base_shape):
-        factor = max(base_shape) / self.max_pyramid_img_size
-        
-        return math.ceil(math.log(max(factor, 1), self.downscale_factor))+1
-
-def format_channel_names(num_channels_each_mosaic, channel_names):
-    '''
-    format_channel_names(
-        [1, 2, 3, 4, 5], ['x', 'x', ['x'], ['x', 'x']]
-    )
-    >>> [
-        'x_1',
-        'x_2',
-        'x_3',
-        'x_4',
-        'x_5',
-        'x_6',
-        'Mosaic 4_1',
-        'Mosaic 4_2',
-        'Mosaic 4_3',
-        'Mosaic 4_4'
-    ]
-    '''
-    matched_channel_names = []
-    for idx, (n, c) in enumerate( zip(channel_names, num_channels_each_mosaic*num_channels_each_mosaic[0]) ):
-        c=1
-        nl = n
-        if type(n) == str:
-            nl = [n]
-        if len(nl) == 1:
-            nl = nl
-        matched_channel_names.append(nl)
-    return make_unique_str(
-        [n for l in matched_channel_names for n in l]
-    )
-
-def make_unique_str(str_list):
-    if len(set(str_list)) == len(str_list):
-        return str_list
-    else:
-        max_length = max([len(s) for s in str_list])
-        str_np = np.array(str_list, dtype=np.dtype(('U', max_length+10)))
-        unique, counts = np.unique(str_np, return_counts=True)
-        has_duplicate = unique[counts > 1]
-        for n in has_duplicate:
-            suffixes = [
-                f"_{i}"
-                for i in range(1, (str_np == n).sum()+1)
-            ]
-            str_np[str_np == n] = np.char.add(n, suffixes)
-    return make_unique_str(list(str_np))
-
-def normalize_mosaics(mosaics):
-    dtypes = set(m.dtype for m in mosaics)
-    if any([np.issubdtype(d, np.floating) for d in dtypes]):
-        max_dtype = np.dtype(np.float32)
-    else:
-        max_dtype = max(dtypes)
-    normalized = []
-    for m in mosaics:
-        assert m.ndim == 2 or m.ndim == 3
-        if m.ndim == 2:
-            m = m[np.newaxis, :]
-        normalized.append(m.astype(max_dtype, copy=False))
-    return normalized
-
-
-def write_pyramid(
-    mosaics,
-    output_path,
-    pixel_size=1,
-    channel_names=None,
-    verbose=True,
-    downscale_factor=4,
-    compression=None,
-    is_mask=False,
-    tile_size=None,
-    save_RAM=False,
-    kwargs_tifffile=None
-):
-    mosaics = normalize_mosaics(mosaics)
-    ref_m = mosaics[0]
-    path = output_path
-    num_channels = count_num_channels(mosaics)
-    base_shape = ref_m.shape[1:3]
-    assert int(downscale_factor) == downscale_factor
-    assert downscale_factor < min(base_shape)
-    pyramid_setting = PyramidSetting(
-        downscale_factor=int(downscale_factor),
-        tile_size=max(ref_m.chunksize)
-    )
-    num_levels = pyramid_setting.num_levels(base_shape)
-    tile_shapes = pyramid_setting.tile_shapes(base_shape)
-    shapes = pyramid_setting.pyramid_shapes(base_shape)
-
-    if tile_size is not None:
-        assert tile_size % 16 == 0, (
-            f"tile_size must be None or multiples of 16, not {tile_size}"
-        )
-        tile_shapes = [(tile_size, tile_size)] * num_levels
-    print(tile_shapes)
-    dtype = ref_m.dtype
-
-    software = f'backsub {_version}'
-    metadata = {
-        'Creator': software,
-        'Pixels': {
-            'PhysicalSizeX': pixel_size, 'PhysicalSizeXUnit': '\u00b5m',
-            'PhysicalSizeY': pixel_size, 'PhysicalSizeYUnit': '\u00b5m'
-        },
-    }
-
-    if channel_names is not None:
-        num_channels_each_mosaic = [
-            count_num_channels([m])
-            for m in mosaics
-        ]
-        names = format_channel_names(num_channels_each_mosaic, channel_names)
-        if len(names) == num_channels:
-            metadata.update({
-                'Channel': {'Name': names},
-            })
-
-    logger.info(f"Writing to {path}")
-    with tifffile.TiffWriter(path, bigtiff=True) as tif:
-        kwargs = dict(
-            metadata=metadata,
-            software=software,
-            compression=compression
-        )
-        if kwargs_tifffile is None:
-            kwargs_tifffile = {}
-        tif.write(
-            data=tile_from_combined_mosaics(
-                mosaics, tile_shape=tile_shapes[0], save_RAM=save_RAM
-            ),
-            shape=(num_channels, *shapes[0]),
-            subifds=int(num_levels - 1),
-            dtype=dtype,
-            tile=tile_shapes[0],
-            **{**kwargs, **kwargs_tifffile}
-        )
-        logger.info('Generating pyramid')
-        for level, (shape, tile_shape) in enumerate(
-            zip(shapes[1:], tile_shapes[1:])
-        ):
-            if verbose:
-                logger.info(f"    Level {level+1} ({shape[0]} x {shape[1]})")
-            tif.write(
-                data=tile_from_pyramid(
-                    path,
-                    num_channels,
-                    tile_shape=tile_shape,
-                    downscale_factor=downscale_factor,
-                    level=level,
-                    is_mask=is_mask,
-                    save_RAM=save_RAM
-                ),
-                shape=(num_channels, *shape),
-                subfiletype=1,
-                dtype=dtype,
-                tile=tile_shape,
-                **{
-                    **dict(compression=compression),
-                    **kwargs_tifffile
-                }
-            )
-
-
-def count_num_channels(imgs):
-    for img in imgs:
-        assert img.ndim == 2 or img.ndim == 3
-    return sum([
-        1 if img.ndim == 2 else img.shape[0]
-        for img in imgs
-    ])
-   
-
-def tile_from_combined_mosaics(mosaics, tile_shape, save_RAM=False):
-    num_rows, num_cols = mosaics[0].shape[1:3]
-    h, w = tile_shape
-    n = len(mosaics)
-    for idx, m in enumerate(mosaics):
-        for cidx, c in enumerate(m):
-            # the performance is heavily degraded without pre-computing the
-            # mosaic channel
-            with tqdm.dask.TqdmCallback(
-                ascii=True,
-                desc=(
-                    f"Assembling mosaic {idx+1:2}/{n:2} (channel"
-                    f" {cidx+1:2}/{m.shape[0]:2})"
-                ),
-            ):
-                c = da_to_zarr(c) if save_RAM else c.compute()
-            for y in range(0, num_rows, h):
-                for x in range(0, num_cols, w):
-                    yield np.array(c[y:y+h, x:x+w])
-                    # yield m[y:y+h, x:x+w].copy().compute()
-            c = None
-
-def tile_from_pyramid(
-    path,
-    num_channels,
-    tile_shape,
-    downscale_factor=2,
-    level=0,
-    is_mask=False,
-    save_RAM=False
-):
-    # workaround progress bar
-    # https://forum.image.sc/t/tifffile-ome-tiff-generation-is-taking-too-much-ram/41865/26
-    pbar = tqdm.tqdm(total=num_channels, ascii=True, desc=f'Processing channel')
-    for c in range(num_channels):
-        img = da.from_zarr(zarr.open(tifffile.imread(
-            path, series=0, level=level, aszarr=True
-        )))
-        if img.ndim == 2:
-            img = img.reshape(1, *img.shape)
-        img = img[c]
-        # read using key seems to generate a RAM spike
-        # img = tifffile.imread(path, series=0, level=level, key=c)
-        if not is_mask:
-            img = img.map_blocks(
-                cv2.blur,
-                ksize=(downscale_factor, downscale_factor), anchor=(0, 0)
-            )
-        img = da_to_zarr(img) if save_RAM else img.compute()
-        num_rows, num_columns = img.shape
-        h, w = tile_shape
-        h *= downscale_factor
-        w *= downscale_factor
-        last_c = range(num_channels)[-1]
-        last_y = range(0, num_rows, h)[-1]
-        last_x = range(0, num_columns, w)[-1]
-        for y in range(0, num_rows, h):
-            for x in range(0, num_columns, w):
-                if (y == last_y) & (x == last_x):
-                    pbar.update(1)
-                    if c == last_c:
-                        pbar.close()
-                yield np.array(img[y:y+h:downscale_factor, x:x+w:downscale_factor])
-        # setting img to None seems necessary to prevent RAM spike
-        img = None
-
-
-def da_to_zarr(da_img, zarr_store=None, num_workers=None, out_shape=None, chunks=None):
-    if zarr_store is None:
-        if out_shape is None:
-            out_shape = da_img.shape
-        if chunks is None:
-            chunks = da_img.chunksize
-        zarr_store = zarr.create(
-            out_shape,
-            chunks=chunks,
-            dtype=da_img.dtype,
-            overwrite=True
-        )
-    da_img.to_zarr(zarr_store, compute=False).compute(
-        num_workers=num_workers
-    )
-    return zarr_store
-
-
-def process_markers(markers):
-    markers['ind'] = range(0, len(markers))
-    if 'remove' not in markers:
-        markers['remove'] = ["False" for i in range(len(markers))]
-    else:
-        markers['remove'] = markers['remove'] == True
-
-    markers['keep'] = markers['remove'] == False
-
-    markers = markers.drop(columns=['remove'])
-
-    return markers
-
-def detect_pixel_size(img_path,pixel_size=None):
-    if pixel_size is None:
-        print('Pixel size overwrite not specified')
-        try:
-            metadata = ome_types.from_tiff(img_path)
-            pixel_size = metadata.images[0].pixels.physical_size_x
-        except Exception as err:
-            print(err)
-            print('Pixel size detection using ome-types failed')
-            pixel_size = None
-    return pixel_size
-
-def scale_background(background_channel, scalar):
-    background_channel = np.rint(ne.evaluate("background_channel * scalar"))
-    return np.where(background_channel>65535,65535,background_channel.astype(np.uint16))
-
-def subtract(channel_to_process, background):
-    return np.where(channel_to_process<background, 0, channel_to_process-background)
-
-def subtract_channels(channel_to_process, background_channel, scalar, chunk_size):
-    channel_to_process = channel_to_process.rechunk(chunk_size)
-    background_channel = background_channel.rechunk(chunk_size)
-    background = da.map_blocks(scale_background, background_channel, scalar)
-    output = da.map_blocks(subtract, channel_to_process, background)
-    background = None
-    return output
-
-def get_args():
-    # Script description
-    description="""Subtracts background - Lunaphore platform"""
-
-    # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
-
-    # Sections
-    inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
-    inputs.add_argument("-r", "--root", dest="root", action="store", required=True, help="File path to root image file.")
-    inputs.add_argument("-m", "--markers", dest="markers", action="store", required=True, help="File path to required markers.csv file")
-    inputs.add_argument("--pixel-size", metavar="SIZE", dest = "pixel_size", type=float, default = None, action = "store",help="Pixel size in microns")
-    inputs.add_argument("--tile-size", dest="tile_size", required=False, type=int, default=1024, help="Tile size for pyramid generation")
-    #inputs.add_argument("--save-ram", dest="ram", required=False, default=False, help="Save RAM during pyramid generation")
-    inputs.add_argument("--chunk-size", dest="chunksize", required=False, type=int, default=5000, help="Chunk size for dask array (e.g for chunksize 1000, the image will be split into 1000x1000 chunks)")
-    inputs.add_argument("--version", action="version", version="v0.4.1")
-    
-    outputs = parser.add_argument_group(title="Output", description="Path to output file")
-    outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
-    outputs.add_argument("-mo", "--marker-output", dest="markerout", action="store", required=True, help="Path to output marker file")
-
-    arg = parser.parse_args()
-
-    # Standardize paths
-    arg.root = abspath(arg.root)
-    arg.markers = abspath(arg.markers)
-    arg.output = abspath(arg.output)
-    arg.markerout = abspath(arg.markerout)
-
-    return arg
-
-def main(args):
-    _version = 'v0.4.1'
-    in_path = args.root
-
-    out_path = pathlib.Path(args.output)
-    # pixel data is read into RAM lazily, cannot overwrite input file
-    assert out_path != in_path
-
-    # Detect pixel size in ome-xml
-    pixel_size = detect_pixel_size(in_path, args.pixel_size)
-    if pixel_size is None: pixel_size = 1
-
-    markers = process_markers(pd.read_csv(args.markers))
-
-    # Use palom to lazily read the input image
-    readers = palom.reader.OmePyramidReader(in_path)
-    mosaic = readers.pyramid[0]
-    mosaic_out = copy.copy(mosaic)
-    chunk_size = (args.chunksize,args.chunksize)
-    mosaic_out = mosaic_out.rechunk((1,args.chunksize,args.chunksize))
-
-    for channel in range(len(markers)):
-        if markers.background.isnull()[channel] == True:
-            print(f"Channel {markers.marker_name[channel]} ({channel}) processed, no background subtraction")
-        else:
-            background_marker = markers.iloc[np.array(markers.marker_name == markers.background[channel])]
-            scalar = markers[markers.ind == channel].exposure.values / background_marker.exposure.values
-            mosaic_out[channel] = subtract_channels(mosaic[channel], mosaic[background_marker.ind.values[0]], scalar, chunk_size)
-            print(f"Channel {markers.marker_name[channel]} ({channel}) processed, background subtraction")
-
-    # removes channels from the image as specified in the markers file
-    mosaic_out = mosaic_out[np.where(markers.keep)[0]]
-    channel_names = list(markers.marker_name[markers.keep])
-
-    write_pyramid(
-        [mosaic_out], out_path, channel_names=channel_names, downscale_factor=2, pixel_size=pixel_size, save_RAM=False, tile_size=args.tile_size
-    )
-    markers = markers[markers.keep]
-    markers = markers.drop(columns=['keep','ind'])
-    markers.to_csv(args.markerout, index=False)
-
-if __name__ == '__main__':
-    _version = 'v0.4.1'
-    args = get_args()
-
-    # Run script
-    st = time.time()
-    main(args)
-    rt = time.time() - st
-    print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")

From eeb7908a73e55ddb745559cb8f5f18598ff37002 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 19:42:30 +0100
Subject: [PATCH 12/21] added SamplesPerPixel to avoid warning

---
 CHANGELOG.md              |  1 +
 backsub/CLI.py            |  2 +-
 backsub/background_sub.py | 19 ++++++++++++++-----
 backsub/ome_schema.py     |  4 ++--
 backsub/ome_writer.py     |  1 -
 5 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3621e45..0358efe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,7 @@ Rework of Backsub to not have Palom as a dependency reducing the environment siz
 - logger has been re-designed.
 - restructured README
 - `tile-size` parameter now defaults to 256 instead of 1024
+- support for different data types - primarily np.uint8 in addition to np.uint16
 
 ### `Fixed`
 - output image file-size is reduced by applying lossless compression ("LZW" by default)
diff --git a/backsub/CLI.py b/backsub/CLI.py
index 7b8d961..ddd7fcd 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -94,7 +94,7 @@ def get_args(_version):
                         dest='compression',
                         required=False,
                         type=str,
-                        default='LZW',
+                        default='lzw',
                         choices=['lzw', 'none', 'deflate', 'zlib'],
                         help="""If set, the output pyramidal image will be compressed using the specified
                         compression method. Set to "none" for no compression. Default is LZW. An alternative is zlib.
diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 716087e..074357d 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -204,7 +204,11 @@ def subtract_channels(src_img_path,
     background_as_zarr =zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0,key=int(background_index) ) )
     signal=da.from_zarr(signal_as_zarr, chunks=ref_chunksize )
     background=da.from_zarr(background_as_zarr, chunks=ref_chunksize)
-    subtraction=da.clip(signal-factor*background,0,65535).astype(ref_dtype)
+    if np.issubdtype(ref_dtype, np.integer):
+        info = np.iinfo(ref_dtype)
+        subtraction=da.clip(signal-factor*background, info.min, info.max).astype(ref_dtype)
+    else: # float32/64, no upper clipping
+        subtraction=da.clip(signal-factor*background, 0, None).astype(ref_dtype)
     with ResourceProfiler(dt=0.25) as resources:
         with ProgressBar():
             result=subtraction.compute()
@@ -243,7 +247,7 @@ def write_pyramid(src_img_path,
             if channel.processed:
                 operation_count=f"({count}/{total_operations})"
                 print(f"\n {operation_count} Calculating subtraction of background {channel.background} from {channel.marker_name} signal:")
-                first_layer=subtract_channels(src_img_path, channel.ind, channel.bg_idx, channel.factor, (4096,4096), src_data_type,operation_count)
+                first_layer=subtract_channels(src_img_path, channel.ind, channel.bg_idx, channel.factor, (4096,4096), src_data_type, operation_count)
                 pyramid_action="calculate"
                 count+=1
             else:
@@ -280,7 +284,8 @@ def write_pyramid(src_img_path,
                         photometric='minisblack',
                         compression=compression
                             # lzw works better when saving channel-by-channel
-                            # jpeg 2000 when saving the whole stack at once. zlib is an alternative that's slower for writing, but gives better compression ratio
+                            # jpeg2000 works better saving the whole stack at once (not done here)
+                            # zlib is an alternative that's slower when writing, but gives better compression ratio
                     )
                 
     return out_file
@@ -291,13 +296,17 @@ def main(version):
     in_path = args.input
     out_path = args.output
 
-    # 0) Validate input_path is not the same as output_path,pixel data is read into RAM lazily, cannot overwrite input file
+    # 0) Validate input_path is not the same as output_path, pixel data is read into RAM lazily, cannot overwrite input file
     assert out_path != in_path
 
-    # 1) Extract image properties
     if args.tile_size % 16 != 0:
         raise ValueError("tile_size has to be a multiple of 16")
+    if args.compression not in ['none','lzw','deflate','zlib']:
+        raise ValueError("Compression has to be one of the following: 'none','lzw','deflate','zlib'")
+
     tile_shape = (args.tile_size, args.tile_size)
+
+    # 1) Extract image properties
     src_props = extract_img_props(in_path,
                                   args.downscale_factor,
                                   args.tile_size,
diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
index 4c19bb9..3c0dae0 100644
--- a/backsub/ome_schema.py
+++ b/backsub/ome_schema.py
@@ -80,7 +80,7 @@ def CHANN_array(no_of_channels, inputs):
         Channel(
             id=f"Channel:{str(ch)}", # 'Channel:{y}:{x}:{marker_name}'.format(x=ch,y=100+int( inputs['tile'][ch] ) ,marker_name=inputs['marker'][ch] )
             name=inputs["name"][ch],
-            color=(255,255,255)
+            samples_per_pixel=1
             )
         for ch in range(0,no_of_channels)
         ]
@@ -99,7 +99,7 @@ def PIXELS_array(chann_block, plane_block, tiff_block, inputs):
     Returns:
         Pixels: Pixels object.
     """
-
+    print(f'\nMETADATA {inputs['type'][0]} \n')
     PIXELS = Pixels(
         id=f"Pixels:{inputs['tile'][0]}",
         dimension_order='XYCZT',
diff --git a/backsub/ome_writer.py b/backsub/ome_writer.py
index 44f2447..e87640d 100644
--- a/backsub/ome_writer.py
+++ b/backsub/ome_writer.py
@@ -1,7 +1,6 @@
 import ome_schema as schema
 import pandas as pd
 
-
 def create_ome(conformed_markers,info,software_version):
     """
     This function creates an OME-XML file from a pandas dataframe containing the metadata of the tiles.

From 78b600a5d724aca2f5e24a7051c3c4da5c4dfd70 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 19:52:34 +0100
Subject: [PATCH 13/21] added error if background has values not in marker_name

---
 backsub/background_sub.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 074357d..a69bfe3 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -105,6 +105,13 @@ def process_markers(markers):
 
     markers.insert(markers.shape[1], "processed", ~ markers.background.isnull())
 
+    missing_bgs = [
+        bg for bg in markers['background'].dropna().unique()
+        if bg not in markers['marker_name'].values
+    ]
+    if missing_bgs:
+        raise ValueError(f"Background markers not found in marker_name column: {missing_bgs}")
+
     scaling_factor=np.full(markers.shape[0],np.nan)
     background_idx=np.full(markers.shape[0],np.nan)
 

From 6819cc6fec16cd96d05b32d9550a60674376d5a7 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 19:57:30 +0100
Subject: [PATCH 14/21] added warning if no background value is present

---
 backsub/background_sub.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index a69bfe3..de9e43e 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -112,6 +112,9 @@ def process_markers(markers):
     if missing_bgs:
         raise ValueError(f"Background markers not found in marker_name column: {missing_bgs}")
 
+    if markers['background'].dropna().empty:
+        print("\nWarning: No background markers specified in the 'background' column of the markers CSV file. No background subtraction was performed on the output file.\n")
+
     scaling_factor=np.full(markers.shape[0],np.nan)
     background_idx=np.full(markers.shape[0],np.nan)
 

From 736a0e1cf2318c151d821becfd8977fcfb7b925a Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 20:13:40 +0100
Subject: [PATCH 15/21] cleanup

---
 backsub/background_sub.py | 11 +++--------
 backsub/ome_schema.py     |  1 -
 2 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index de9e43e..9e8988d 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -82,13 +82,6 @@ def pyramid_save_ram(img_arr,sub_levels):
         float_half=img_chunk.astype("float32")
         dim_rescale=da.map_overlap(rescale,float_half,depth=border_overlap,boundary="reflect",**rescale_args)
         img_aux=(da.rint(dim_rescale).astype(ref_dtype)).compute()
-        #TODO: check why all the lines below corrupt the image intensity.
-        #img_aux_persist=dim_rescale.astype(ref_dtype).persist()
-        #img_aux=img_aux_persist.compute()
-        #level_max=np.max(img_aux)
-        #level_min=np.min(img_aux)
-        #int_rescale_factor=(max_val-min_val)/(level_max-level_min)
-        #img_aux=(da.rint(min_val-img_aux_persist*int_rescale_factor).astype(ref_dtype) ).compute()
         yield img_aux
 
 
@@ -321,6 +314,7 @@ def main(version):
                                   args.downscale_factor,
                                   args.tile_size,
                                   args.pixel_size)
+
     # 2) Modify pyramid_levels if required
     if src_props["pyramid"]:
         levels=src_props["levels"]
@@ -337,6 +331,7 @@ def main(version):
         markers = process_markers(pd.read_csv(args.markers))
 
     markers_updated=markers.loc[ markers.keep]
+
     #4) Write updated markers.csv without appended columns. This file contains the markers information of the final image stack
     markers_preview = markers_updated.drop(columns=['keep','ind','processed','factor','bg_idx'])
     markers_preview["channel_number"] = np.arange(1, len(markers_preview)+1)
@@ -382,5 +377,5 @@ def main(version):
     print(f'\nPyramidal image with {levels} levels was successfully written{'' if compression is None else f' with {compression} compression'}.')
 
 if __name__ == '__main__':
-    _version = 'v0.5.0dev'
+    _version = 'v0.5.0'
     main(_version)
diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
index 3c0dae0..aa691d1 100644
--- a/backsub/ome_schema.py
+++ b/backsub/ome_schema.py
@@ -99,7 +99,6 @@ def PIXELS_array(chann_block, plane_block, tiff_block, inputs):
     Returns:
         Pixels: Pixels object.
     """
-    print(f'\nMETADATA {inputs['type'][0]} \n')
     PIXELS = Pixels(
         id=f"Pixels:{inputs['tile'][0]}",
         dimension_order='XYCZT',

From b0bfd9c76b959155bb9a044e3151847f800c4ee5 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 20:15:44 +0100
Subject: [PATCH 16/21] cleanup

---
 backsub/ome_schema.py | 16 ++++++++--------
 backsub/ome_writer.py |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
index aa691d1..a08522f 100644
--- a/backsub/ome_schema.py
+++ b/backsub/ome_schema.py
@@ -142,7 +142,7 @@ def IMAGE_array(pixels_block, imageID):
     return IMAGE
 
 
-def OME_metadata(image_block,software):
+def OME_metadata(image_block, software):
     """
     This function creates an OME object.
     Args:
@@ -151,13 +151,13 @@ def OME_metadata(image_block,software):
         OME: OME object.
     """
     ome = OME()
-    ome.creator = " ".join([software,
-                            ome_types.__name__,
-                        ome_types.__version__,
-                        '/ python version-',
-                        platform.python_version()
-                        ]
-                        )
+    ome.creator = " ".join([
+        software,
+        ome_types.__name__,
+        ome_types.__version__,
+        '/ python version-',
+        platform.python_version()
+    ])
 
     ome.images = image_block
     ome_xml = ome_types.to_xml(ome)
diff --git a/backsub/ome_writer.py b/backsub/ome_writer.py
index e87640d..953c244 100644
--- a/backsub/ome_writer.py
+++ b/backsub/ome_writer.py
@@ -1,7 +1,7 @@
 import ome_schema as schema
 import pandas as pd
 
-def create_ome(conformed_markers,info,software_version):
+def create_ome(conformed_markers, info, software_version):
     """
     This function creates an OME-XML file from a pandas dataframe containing the metadata of the tiles.
     Args:

From 141618ea20d1609e7e9122d267bcc37659b3ab75 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Wed, 5 Nov 2025 20:50:25 +0100
Subject: [PATCH 17/21] made marker names unique in metadata extraction tool

---
 backsub/background_sub.py         |  5 +-
 backsub/metadata2markers_table.py | 85 ++++++++++++++++++++-----------
 2 files changed, 57 insertions(+), 33 deletions(-)

diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 9e8988d..7d52186 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -18,7 +18,7 @@
 #local libraries
 import CLI
 import ome_writer
-from metadata2markers_table import meta_from_file,assign_background
+from metadata2markers_table import meta_from_file,assign_background,make_marker_names_unique
 
 #Decorator function to measure run-time and memory peak of a function
 def memocron(func):
@@ -324,7 +324,8 @@ def main(version):
     # 3) Read/Create markers table and update it to include the information of the processing tasks
     if args.comet_metadata:
         registration_marker="DAPI"
-        meta_table=meta_from_file(in_path,registration_marker)
+        meta_table = meta_from_file(in_path,registration_marker)
+        meta_table = make_marker_names_unique(meta_table, col='marker_name')
         markers = process_markers( assign_background(meta_table,rmv_ref=True,ref_marker=registration_marker) )
 
     elif args.markers:
diff --git a/backsub/metadata2markers_table.py b/backsub/metadata2markers_table.py
index 2451cb8..331cacf 100644
--- a/backsub/metadata2markers_table.py
+++ b/backsub/metadata2markers_table.py
@@ -35,13 +35,13 @@ def get_args():
                         action='store_true',
                         help='setup the removal of all reference background channels and all DAPI except the first occurrence.'
                         )
-    
+
     parser.add_argument('-rm',
                         '--registration_marker',
                         required=False,
                         type=str,
                         default="DAPI",
-                        help='name of the csv file'
+                        help='Name of marker used for registration (default: DAPI).'
                         )
 
     
@@ -54,36 +54,58 @@ def meta_from_file(src_img_path,ref_marker_name):
     #Fetch image attributes from ome
     ch_names  = [ element.name for element in ome.images[0].pixels.channels ]
     exp_times = [ element.exposure_time for element in ome.images[0].pixels.planes ]
-    #cycles=[int(element.attributes["CycleID"])+1 for element in ome.structured_annotations[0].value.any_elements[0].children]
-    filters= [ element.attributes["FluorescenceChannel"] for element in ome.structured_annotations[0].value.any_elements[0].children ]
-    background=[None if ref_marker_name in element 
+
+    filters = [ element.attributes["FluorescenceChannel"] for element in ome.structured_annotations[0].value.any_elements[0].children ]
+    background = [None if ref_marker_name in element
                 else element for element in filters]
 
-    aux_dict={"channel_number":list(range(1,len(ch_names)+1)),
-              #"cycle_number":cycles,
-              "marker_name":ch_names,
-              "Filter":filters,
-              "background":background,
-              "exposure":exp_times
-              }
-    
-    df=pd.DataFrame(aux_dict)
+    aux_dict = {"channel_number": list(range(1, len(ch_names) + 1)),
+                #"cycle_number":cycles,
+                "marker_name": ch_names,
+                "Filter": filters,
+                "background": background,
+                "exposure": exp_times
+                }
+
+    df = pd.DataFrame(aux_dict)
     return df
 
+def make_marker_names_unique(df, col='marker_name'):
+    """
+    Ensures all marker names in the dataframe are unique.
+    If a marker appears multiple times, appends an index suffix (_1, _2, ...).
+
+    Example:
+        TRITC, TRITC → TRITC, TRITC_1
 
-def assign_background(df,rmv_ref=False,ref_marker="DAPI"):
+    Args:
+        df (pd.DataFrame): The metadata dataframe.
+        col (str): Column name containing the marker names.
+
+    Returns:
+        pd.DataFrame: DataFrame with unique marker names.
+    """
+    mdf = df.copy()
+    # Count duplicates
+    counts = mdf.groupby(col).cumcount()
+    # Append '_' and suffix only for duplicates (count > 0)
+    mdf.loc[counts > 0, col] = mdf.loc[counts > 0, col] + '_' + (counts[counts > 0]).astype(str)
+
+    return mdf
+
+def assign_background(df, rmv_ref=False, ref_marker="DAPI"):
     #Create column ["backsub_process"] indicating which rows will be processed with backsub
-    filters_=df.Filter.unique().tolist()
-    #Strings corresponding to filters/background names are set to False, since the are not processed 
-    backsub_process=df["marker_name"].replace(filters_,value=False,regex=True)
+    filters_ = df.Filter.unique().tolist()
+    #Strings corresponding to filters/background names are set to False, since the are not processed
+    backsub_process = df["marker_name"].replace(filters_, value=False, regex=True)
     #Marker_name corresponding to signal will be set to True for processing
-    backsub_process=np.where(backsub_process==False,False,True)
-    df.insert(df.shape[1],"backsub_process",backsub_process)
+    backsub_process = np.where(backsub_process == False, False, True)
+    df.insert(df.shape[1], "backsub_process", backsub_process)
 
     #Assign the latest mention of the autofluorescence channel to the correspondent row in the background column
-    rename_background=[]
+    rename_background = []
+
     #List with row indices of background channels to be removed
-    
     for idx,row in df.iterrows():
 
         if row.backsub_process:
@@ -109,15 +131,16 @@ def assign_background(df,rmv_ref=False,ref_marker="DAPI"):
 
 
 def main():
-    args=get_args()
-    img_path=args.input_img
-    out_dir=args.output_dir
-    file_name=args.output_file_name
-    global_ref_marker=args.registration_marker
-    
-    df=meta_from_file(img_path,global_ref_marker)
-    df_updated=assign_background(df,args.remove_background_references,global_ref_marker)
-    df_updated.to_csv( out_dir/file_name ,index=False)
+    args = get_args()
+    img_path = args.input_img
+    out_dir = args.output_dir
+    file_name = args.output_file_name
+    global_ref_marker = args.registration_marker
+
+    df = meta_from_file(img_path, global_ref_marker)
+    df_unique = make_marker_names_unique(df, col='marker_name')
+    df_updated = assign_background(df_unique, args.remove_background_references, global_ref_marker)
+    df_updated.to_csv(out_dir / file_name, index=False)
 
 
 if __name__ == '__main__':

From e87c7b39efdf921df6ed5c99bb2f60f7f8579f8b Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Thu, 13 Nov 2025 13:14:59 +0100
Subject: [PATCH 18/21] removed hidden comet argument

---
 README.md                 |  2 --
 backsub/CLI.py            | 10 ++--------
 backsub/background_sub.py | 35 ++++++++---------------------------
 3 files changed, 10 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index 91ff0d0..0e4fdf7 100644
--- a/README.md
+++ b/README.md
@@ -93,7 +93,6 @@ An exemplary [markers.csv](https://github.com/SchapiroLabor/Background_subtracti
 | -ts      	| --tile-size        	| Tile size used for writing pyramidal outputs. Note that the file size is smaller for smaller tile size values.                                                                                                                                                      	| integer, multiple of 16                                      	| 256     	| no       	|
 | -dsf     	| --downscale-factor 	| Downscale factor for pyramid layer creation. This value will only be used if the input image is NOT pyramidal. If the input image is pyramidal, the number of levels in the output image will be the same as in the input so the downscale factor won't be applied. 	| integer, larger than 1                                       	| 2       	| no       	|
 | -v       	| --version          	| Prints Backsub version.                                                                                                                                                                                                                                             	|                                                              	|         	|          	|
-| -comet   	| --comet-metadata   	| Flag to obtain the markers table on the fly for images acquired with the reference background acquisition implemented in the Lunaphore Comet at the TSPC (https://www.tspc-hd.com/). When this flag is used, the argument `-m`/`--markers` is ignored since the markers information will be extracted from the metadata of the input image.                                                                                                          	| boolean flag                                                 	| false   	| hidden   	|
 
 Example of a full command (note to provide full paths where applicable):
 ```
@@ -157,4 +156,3 @@ This project is licensed under the terms of the [MIT License](https://github.com
 If you use Backsub in your work, please cite it as:
 
 > Bestak, K., Perez, V., & Wuennemann, F. (2025). Backsub: pixel-by-pixel channel subtraction tool for multiplexed immunofluorescence data.
-> Available at: [https://github.com/SchapiroLabor/Background_subtraction](https://github.com/SchapiroLabor/Background_subtraction)
\ No newline at end of file
diff --git a/backsub/CLI.py b/backsub/CLI.py
index ddd7fcd..180f7d9 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -17,7 +17,6 @@ def get_args(_version):
 
     # INPUT GROUPS
     inputs = parser.add_argument_group(title="INPUTS")
-    input_markers_table = inputs.add_mutually_exclusive_group(required=True)
     
     inputs.add_argument("-r", ## deprecation warning can be added later
                         "--root", ## deprecation warning can be added later
@@ -29,23 +28,18 @@ def get_args(_version):
                         required=True, 
                         help="File path to input image file.")
     
-    input_markers_table.add_argument(
+    inputs.add_argument(
                         "-m",
                         "--markers", 
                         dest="markers", 
                         action="store",
                         type=pathlib.Path,
+                        required=True,
                         help="""File path to the markers.csv file containing the list of marker names
                         and their respective background channels.
                         """
                         )
     
-    input_markers_table.add_argument("-comet",
-                        "--comet-metadata",
-                        action='store_true',
-                        help=argparse.SUPPRESS
-                        )
-    
     inputs.add_argument("-mpp",
                         "--pixel-size", 
                         metavar="SIZE", 
diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 7d52186..946347f 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -67,8 +67,6 @@ def pyramid_save_ram(img_arr,sub_levels):
     #TODO:need to optimize speed and match dimensions of levels with those of the src image
     ref_dtype=img_arr.dtype.name
     border_overlap=down_factor-1
-    max_val=np.max(img_arr)
-    min_val=np.min(img_arr)
     ref_dtype=img_arr.dtype.name
     rescale_args={
             "scale":1/down_factor,
@@ -78,9 +76,9 @@ def pyramid_save_ram(img_arr,sub_levels):
                     }
     img_aux=img_arr
     for _ in range(sub_levels):
-        img_chunk=da.from_array(img_aux,chunks=chunksize)
+        img_chunk=da.from_array(img_aux, chunks=chunksize)
         float_half=img_chunk.astype("float32")
-        dim_rescale=da.map_overlap(rescale,float_half,depth=border_overlap,boundary="reflect",**rescale_args)
+        dim_rescale=da.map_overlap(rescale, float_half, depth=border_overlap, boundary="reflect", **rescale_args)
         img_aux=(da.rint(dim_rescale).astype(ref_dtype)).compute()
         yield img_aux
 
@@ -192,19 +190,9 @@ def subtract_channels(src_img_path,
                       ref_dtype,
                       task_no
                       ):
-    """
-    This function executes the background subtraction using generators, each element of the generator
-    is a tuple with 3 values, such that:
-
-    tuple=(img_with_backsub[array],calculate or extract pyramid [str],pyramid_from_index[int])
-
-    The second entry of the tuple indicates in the writing process if the pyramid should be calculated using 
-    pyramid_gaussian from scikit image. If extract, the index given in the third entry will fetch all the pyramid
-    levels from the original image stack(src_img_path).
-    """
-    factor=np.float32(factor)#limiting precision to float32 saves memory
+    factor=np.float32(factor) #limiting precision to float32 saves memory
     signal_as_zarr = zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0,key=int(signal_index) ) )
-    background_as_zarr =zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0,key=int(background_index) ) )
+    background_as_zarr =zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0, key=int(background_index) ) )
     signal=da.from_zarr(signal_as_zarr, chunks=ref_chunksize )
     background=da.from_zarr(background_as_zarr, chunks=ref_chunksize)
     if np.issubdtype(ref_dtype, np.integer):
@@ -219,9 +207,9 @@ def subtract_channels(src_img_path,
     print(resources.results[0],"([sec],[MB],[% CPU usage])")
     return result
 
-def extract_sublevels_from_tiff(path,ch,levels):
+def extract_sublevels_from_tiff(path, ch, levels):
     with tifff.TiffFile(path) as tif:
-        for l in range(1,levels):
+        for l in range(1, levels):
             yield tif.series[0].levels[l].pages[ch].asarray()
 
 def write_pyramid(src_img_path,
@@ -322,16 +310,9 @@ def main(version):
         levels=src_props["extracted_levels"]
 
     # 3) Read/Create markers table and update it to include the information of the processing tasks
-    if args.comet_metadata:
-        registration_marker="DAPI"
-        meta_table = meta_from_file(in_path,registration_marker)
-        meta_table = make_marker_names_unique(meta_table, col='marker_name')
-        markers = process_markers( assign_background(meta_table,rmv_ref=True,ref_marker=registration_marker) )
-
-    elif args.markers:
-        markers = process_markers(pd.read_csv(args.markers))
+    markers = process_markers(pd.read_csv(args.markers))
 
-    markers_updated=markers.loc[ markers.keep]
+    markers_updated=markers.loc[ markers.keep ]
 
     #4) Write updated markers.csv without appended columns. This file contains the markers information of the final image stack
     markers_preview = markers_updated.drop(columns=['keep','ind','processed','factor','bg_idx'])

From 919623e8097651cdfeef29cf3db3422b07f820dd Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Thu, 13 Nov 2025 18:13:19 +0100
Subject: [PATCH 19/21] metadata2markers correction, version, small fixes

---
 .github/workflows/docker-publish.yml |  26 ++--
 .github/workflows/python-publish.yml |  69 ++++++++++
 .gitignore                           |   5 +-
 CHANGELOG.md                         |   3 +-
 Dockerfile                           |   4 -
 backsub/__init__.py                  |   3 +
 backsub/__main__.py                  |   4 +
 backsub/_version.py                  |   2 +
 backsub/background_sub.py            |  15 +--
 backsub/metadata2markers.py          | 185 +++++++++++++++++++++++++++
 backsub/metadata2markers_table.py    | 152 ----------------------
 backsub/ome_schema.py                |   1 -
 backsub/ome_writer.py                |   2 +-
 pyproject.toml                       |  40 ++++++
 14 files changed, 331 insertions(+), 180 deletions(-)
 create mode 100644 .github/workflows/python-publish.yml
 create mode 100644 backsub/__main__.py
 create mode 100644 backsub/_version.py
 create mode 100644 backsub/metadata2markers.py
 delete mode 100644 backsub/metadata2markers_table.py
 create mode 100644 pyproject.toml

diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml
index 6b0342b..cd9ef43 100644
--- a/.github/workflows/docker-publish.yml
+++ b/.github/workflows/docker-publish.yml
@@ -31,7 +31,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       # Install the cosign tool except on PR
       # https://github.com/sigstore/cosign-installer
@@ -39,17 +39,19 @@ jobs:
         if: github.event_name != 'pull_request'
         uses: sigstore/cosign-installer@v3.1.1
         with:
-          cosign-release: 'v2.2.1'
+          cosign-release: 'v2.2.4'
           
-      # Workaround: https://github.com/docker/build-push-action/issues/461
-      - name: Setup Docker buildx
-        uses: docker/setup-buildx-action@79abd3f86f79a9d68a23c75a09a9a85889262adf
+      # Set up BuildKit Docker container builder to be able to build
+      # multi-platform images and export cache
+      # https://github.com/docker/setup-buildx-action
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@f95db51fddba0c2d1ec667646a06c2ce06100226 # v3.0.0
 
       # Login against a Docker registry except on PR
       # https://github.com/docker/login-action
       - name: Log into registry ${{ env.REGISTRY }}
         if: github.event_name != 'pull_request'
-        uses: docker/login-action@28218f9b04b4f3f62068d7b6ce6ca5b26e35336c
+        uses: docker/login-action@343f7c4344506bcbf9b4de18042ae17996df046d # v3.0.0
         with:
           registry: ${{ env.REGISTRY }}
           username: ${{ github.actor }}
@@ -59,7 +61,7 @@ jobs:
       # https://github.com/docker/metadata-action
       - name: Extract Docker metadata
         id: meta
-        uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38
+        uses: docker/metadata-action@96383f45573cb7f253c731d3b3ab81c87ef81934 # v5.0.0
         with:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
       
@@ -67,16 +69,18 @@ jobs:
         run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV
         
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
-      
+        uses: docker/setup-qemu-action@v3
+
       # Build and push Docker image with Buildx (don't push on PR)
       # https://github.com/docker/build-push-action
       - name: Build and push Docker image
         id: build-and-push
-        uses: docker/build-push-action@ac9327eae2b366085ac7f6a2d02df8aa8ead720a
+        uses: docker/build-push-action@0565240e2d4ab88bba5387d719585280857ece09 # v5.0.0
         with:
           context: .
-          platforms: linux/amd64,linux/arm64
           push: ${{ github.event_name != 'pull_request' }}
+          platforms: linux/amd64,linux/arm64
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
new file mode 100644
index 0000000..3b0cb0e
--- /dev/null
+++ b/.github/workflows/python-publish.yml
@@ -0,0 +1,69 @@
+# This workflow will upload a Python Package to PyPI when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  release-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Build release distributions
+        run: |
+          # NOTE: put your own distribution build steps here.
+          python -m pip install build
+          python -m build
+
+      - name: Upload distributions
+        uses: actions/upload-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+  pypi-publish:
+    runs-on: ubuntu-latest
+    needs:
+      - release-build
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+
+    # Dedicated environments with protections for publishing are strongly recommended.
+    # For more information, see: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules
+    environment:
+      name: pypi
+      url: https://pypi.org/p/backsub/
+      #
+      # ALTERNATIVE: if your GitHub Release name is the PyPI project version string
+      # ALTERNATIVE: exactly, uncomment the following line instead:
+      # url: https://pypi.org/project/YOURPROJECT/${{ github.event.release.name }}
+
+    steps:
+      - name: Retrieve release distributions
+        uses: actions/download-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+      - name: Publish release distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: dist/
diff --git a/.gitignore b/.gitignore
index 6f9fddd..ecfe1dc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
 backsub/__pycache__
+dist/
 __pycache__
 .DS_Store
-backsub/testing.ipynb
\ No newline at end of file
+backsub/testing.ipynb
+build/
+backsub.egg-info/
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0358efe..060b348 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,9 +9,8 @@ Rework of Backsub to not have Palom as a dependency reducing the environment siz
 
 ### `Added`
 - `compression` parameter
-- hidden argument `comet`, which extracts the metadata on-the-fly for Lunaphore COMET images. When using this argument, the `markers.csv` file is not required.
 - two RAM profiles: (1) default, uses moderate RAM. (2) Uses approximately half of the default RAM at the cost of a slight loss in precision of the calculation of the downsized dimensions of the pyramidal output image. This means the dimensions of the pyramidal level will differ between profile 1 and 2. The high-resolution level is not affected by this.
-- organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background subtraction and writing of output image and (5) extraction of metadata from Lunaphore COMET images.
+- organizes the tool in five scripts: (1) CLI, (2) ome-schema structure, (3) ome-schema writer, (4) background subtraction and writing of output image and (5) extraction of metadata from Lunaphore COMET images to create the marker table.
 - logger has been re-designed.
 - restructured README
 - `tile-size` parameter now defaults to 256 instead of 1024
diff --git a/Dockerfile b/Dockerfile
index db580d2..a8149f3 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -17,7 +17,3 @@ USER root
 
 # Ensure micromamba binaries are in PATH
 ENV PATH="$MAMBA_ROOT_PREFIX/bin:$PATH"
-
-# Copy the rest of the current directory into /app inside the container
-WORKDIR /app
-COPY ./backsub .
\ No newline at end of file
diff --git a/backsub/__init__.py b/backsub/__init__.py
index e69de29..c9194f6 100644
--- a/backsub/__init__.py
+++ b/backsub/__init__.py
@@ -0,0 +1,3 @@
+from ._version import __version__
+from .background_sub import subtract_channels, write_pyramid, extract_img_props
+from .ome_writer import create_ome
diff --git a/backsub/__main__.py b/backsub/__main__.py
new file mode 100644
index 0000000..6fbfb9e
--- /dev/null
+++ b/backsub/__main__.py
@@ -0,0 +1,4 @@
+from backsub.background_sub import main
+
+if __name__ == "__main__":
+    main()
diff --git a/backsub/_version.py b/backsub/_version.py
new file mode 100644
index 0000000..e987ec7
--- /dev/null
+++ b/backsub/_version.py
@@ -0,0 +1,2 @@
+# backsub/_version.py
+__version__ = "0.5.0"
diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 946347f..8788d03 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -16,9 +16,9 @@
 import ome_types
 import zarr
 #local libraries
-import CLI
-import ome_writer
-from metadata2markers_table import meta_from_file,assign_background,make_marker_names_unique
+import backsub.CLI as CLI
+import backsub.ome_writer as ome_writer
+from backsub import __version__
 
 #Decorator function to measure run-time and memory peak of a function
 def memocron(func):
@@ -282,8 +282,8 @@ def write_pyramid(src_img_path,
     return out_file
 
 @memocron
-def main(version):
-    args=CLI.get_args(_version)
+def main():
+    args=CLI.get_args(__version__)
     in_path = args.input
     out_path = args.output
 
@@ -352,12 +352,11 @@ def main(version):
     
     #6) Write metadata in OME format into the pyramidal file
     channel_names=markers_updated["marker_name"].tolist()
-    ome_xml=ome_writer.create_ome(channel_names,src_props,version)
+    ome_xml=ome_writer.create_ome(channel_names,src_props,__version__)
     tifff.tiffcomment(pyramid_abs_path, ome_xml.encode("utf-8"))
 
     logger.info(f'\nSCRIPT FINISHED PROCESSING TASKS ')
     print(f'\nPyramidal image with {levels} levels was successfully written{'' if compression is None else f' with {compression} compression'}.')
 
 if __name__ == '__main__':
-    _version = 'v0.5.0'
-    main(_version)
+    main()
diff --git a/backsub/metadata2markers.py b/backsub/metadata2markers.py
new file mode 100644
index 0000000..2a062e7
--- /dev/null
+++ b/backsub/metadata2markers.py
@@ -0,0 +1,185 @@
+import pathlib
+from ome_types import from_tiff
+import argparse
+import pandas as pd
+import numpy as np
+import warnings
+
+#CLI
+def get_args():
+    parser=argparse.ArgumentParser()
+    parser.add_argument('-i',
+                        '--input_img',
+                        required=True,
+                        type=pathlib.Path,
+                        help='absolute path of the input image stack (.tif)'
+                        )
+    
+    parser.add_argument('-o',
+                        '--output_dir',
+                        required=True,
+                        type=pathlib.Path,
+                        help='absolute path of the directory where the output .csv file will be written'
+                        )
+    
+    parser.add_argument('-fn',
+                        '--output_file_name',
+                        required=False,
+                        type=str,
+                        default="markers.csv",
+                        help='name of the csv file'
+                        )
+    
+    parser.add_argument('-rr',
+                        '--remove_background_channels',
+                        required=False,
+                        action='store_true',
+                        help='If applied, values in the "remove" column will be set to TRUE for any channel used as background. Use with caution as background channel exclusion may affect downstream analysis and autofluroescence measurements can be useful.'
+                        )
+
+    parser.add_argument('-rf',
+                        '--registration_filter',
+                        required=False,
+                        type=str,
+                        default="DAPI",
+                        help='Name of filter used for registration (default: DAPI). Note that all channels where the FluorescenceChannel attribute is this value in the metadata will be considered as reference channels.'
+                        )
+
+    parser.add_argument('-rd',
+                        '--remove_dapi',
+                        required=False,
+                        action='store_true',
+                        help='If applied, values in the "remove" column will be set to TRUE for any additional DAPI reference except the first. Manual correction of the output markers file can specify other DAPI channels if needed.'
+                        )
+
+    parser.add_argument('-krm',
+                        '--keep_registration_markername',
+                        required=False,
+                        type=str,
+                        default="DAPI",
+                        help='Name of registration marker that should be kept if "remove_dapi" is set to TRUE. Default is "DAPI". If provided value is not present in the marker_name column, the first instance with the registration filter will be kept.'
+                        )
+    
+    args=parser.parse_args()
+    return args
+
+def make_marker_names_unique_list(markers):
+    """
+    Ensures all marker names in a list are unique.
+    If a marker appears multiple times, appends an index suffix (_1, _2, ...).
+
+    Example:
+        ["DAPI", "TRITC", "Cy5", "DAPI"] → ["DAPI", "TRITC", "Cy5", "DAPI_1"]
+
+    Args:
+        markers (list[str]): List of marker names.
+
+    Returns:
+        list[str]: List with unique marker names.
+    """
+    seen = {}
+    unique_markers = []
+
+    for name in markers:
+        if name in seen:
+            seen[name] += 1
+            unique_name = f"{name}_{seen[name]}"
+        else:
+            seen[name] = 0
+            unique_name = name
+        unique_markers.append(unique_name)
+
+    return unique_markers
+
+def meta_from_file(src_img_path, registration_filter):
+    #Fetch metadata object
+    ome=from_tiff(src_img_path)
+    #Fetch image attributes from ome
+    ch_names = make_marker_names_unique_list( [ element.name for element in ome.images[0].pixels.channels ] )
+    exp_times = [ element.exposure_time for element in ome.images[0].pixels.planes ]
+
+    filters = [ element.attributes["FluorescenceChannel"] for element in ome.structured_annotations[0].value.any_elements[0].children ]
+    if registration_filter not in filters:
+        raise ValueError(f"Registration filter '{registration_filter}' not found in image metadata filters: {list(set(filters))}")
+
+    background = [None if registration_filter in element
+                else element for element in filters]
+
+    aux_dict = {"channel_number": list(range(1, len(ch_names) + 1)),
+                #"cycle_number":cycles,
+                "marker_name": ch_names,
+                "Filter": filters,
+                "background": background,
+                "exposure": exp_times
+                }
+
+    df = pd.DataFrame(aux_dict)
+    return df
+
+def assign_background(df, rmv_ref=False, rmv_back=False, ref_marker="DAPI", registration_filter="DAPI"):
+    #Create column ["backsub_process"] indicating which rows will be processed with backsub
+    filters_ = df.Filter.unique().tolist()
+    #Strings corresponding to filters/background names are set to False, since the are not processed
+    backsub_process = df["marker_name"].replace(filters_, value=False, regex=True)
+    #Marker_name corresponding to signal will be set to True for processing
+    backsub_process = np.where(backsub_process == False, False, True)
+    df.insert(df.shape[1], "backsub_process", backsub_process)
+
+    #Assign the latest mention of the autofluorescence channel to the correspondent row in the background column
+    rename_background = []
+
+    #List with row indices of background channels to be removed
+    for idx,row in df.iterrows():
+
+        if row.backsub_process:
+            previous_channels=reversed(df.iloc[:idx].marker_name.to_list())
+            for element in previous_channels:
+                #Supposes background name is a subset of the channel/marker name
+                if row.background in element:
+                    rename_background.append(element)
+                    break
+        else: 
+            rename_background.append(None)
+
+    df.drop(columns=["backsub_process"],inplace=True)
+    df.background=rename_background
+
+    if rmv_ref or rmv_back:
+        remove=len(df)*[""]
+        df.insert(df.shape[1],"remove",remove)
+
+        if rmv_back:
+            df.loc[ df["background"].isnull() & (df["Filter"] != registration_filter) , ["remove"] ] = "TRUE"
+
+        if rmv_ref:
+            df.loc[ df["background"].isnull() & (df["Filter"] == registration_filter) , ["remove"] ] = "TRUE"
+            if ref_marker not in df.marker_name.values:
+                first_ref_marker=df[df.Filter == registration_filter].index[0]
+                corr_ref_marker=df.loc[first_ref_marker, "marker_name"]
+                warnings.warn(f"Reference marker '{ref_marker}' not found. Using first instance of registration filter '{registration_filter}' - Channel '{corr_ref_marker}' instead.")
+            else:
+                first_ref_marker=df[df.marker_name == ref_marker].index[0]
+            df.loc[first_ref_marker,"remove"]=""
+
+    return df
+
+def main():
+    args = get_args()
+    img_path = args.input_img
+    out_dir = args.output_dir
+    file_name = args.output_file_name
+    registration_filter = args.registration_filter
+    global_ref_marker = args.keep_registration_markername
+
+    df = meta_from_file(img_path, ref_marker_name=registration_filter)
+    df_updated = assign_background(df, args.remove_background_references, global_ref_marker, registration_filter)
+    df_updated.to_csv(out_dir / file_name, index=False)
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+    
+
diff --git a/backsub/metadata2markers_table.py b/backsub/metadata2markers_table.py
deleted file mode 100644
index 331cacf..0000000
--- a/backsub/metadata2markers_table.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import pathlib
-from ome_types import from_tiff
-import argparse
-import pandas as pd
-import numpy as np
-
-#CLI
-def get_args():
-    parser=argparse.ArgumentParser()
-    parser.add_argument('-i',
-                        '--input_img',
-                        required=True,
-                        type=pathlib.Path,
-                        help='absolute path of the input image stack (.tif)'
-                        )
-    
-    parser.add_argument('-o',
-                        '--output_dir',
-                        required=True,
-                        type=pathlib.Path,
-                        help='absolute path of the directory where the output .csv file will be written'
-                        )
-    
-    parser.add_argument('-fn',
-                        '--output_file_name',
-                        required=False,
-                        type=str,
-                        default="markers.csv",
-                        help='name of the csv file'
-                        )
-    
-    parser.add_argument('-rr',
-                        '--remove_background_references',
-                        required=False,
-                        action='store_true',
-                        help='setup the removal of all reference background channels and all DAPI except the first occurrence.'
-                        )
-
-    parser.add_argument('-rm',
-                        '--registration_marker',
-                        required=False,
-                        type=str,
-                        default="DAPI",
-                        help='Name of marker used for registration (default: DAPI).'
-                        )
-
-    
-    args=parser.parse_args()
-    return args
-
-def meta_from_file(src_img_path,ref_marker_name):
-    #Fetch metadata object
-    ome=from_tiff(src_img_path)
-    #Fetch image attributes from ome
-    ch_names  = [ element.name for element in ome.images[0].pixels.channels ]
-    exp_times = [ element.exposure_time for element in ome.images[0].pixels.planes ]
-
-    filters = [ element.attributes["FluorescenceChannel"] for element in ome.structured_annotations[0].value.any_elements[0].children ]
-    background = [None if ref_marker_name in element
-                else element for element in filters]
-
-    aux_dict = {"channel_number": list(range(1, len(ch_names) + 1)),
-                #"cycle_number":cycles,
-                "marker_name": ch_names,
-                "Filter": filters,
-                "background": background,
-                "exposure": exp_times
-                }
-
-    df = pd.DataFrame(aux_dict)
-    return df
-
-def make_marker_names_unique(df, col='marker_name'):
-    """
-    Ensures all marker names in the dataframe are unique.
-    If a marker appears multiple times, appends an index suffix (_1, _2, ...).
-
-    Example:
-        TRITC, TRITC → TRITC, TRITC_1
-
-    Args:
-        df (pd.DataFrame): The metadata dataframe.
-        col (str): Column name containing the marker names.
-
-    Returns:
-        pd.DataFrame: DataFrame with unique marker names.
-    """
-    mdf = df.copy()
-    # Count duplicates
-    counts = mdf.groupby(col).cumcount()
-    # Append '_' and suffix only for duplicates (count > 0)
-    mdf.loc[counts > 0, col] = mdf.loc[counts > 0, col] + '_' + (counts[counts > 0]).astype(str)
-
-    return mdf
-
-def assign_background(df, rmv_ref=False, ref_marker="DAPI"):
-    #Create column ["backsub_process"] indicating which rows will be processed with backsub
-    filters_ = df.Filter.unique().tolist()
-    #Strings corresponding to filters/background names are set to False, since the are not processed
-    backsub_process = df["marker_name"].replace(filters_, value=False, regex=True)
-    #Marker_name corresponding to signal will be set to True for processing
-    backsub_process = np.where(backsub_process == False, False, True)
-    df.insert(df.shape[1], "backsub_process", backsub_process)
-
-    #Assign the latest mention of the autofluorescence channel to the correspondent row in the background column
-    rename_background = []
-
-    #List with row indices of background channels to be removed
-    for idx,row in df.iterrows():
-
-        if row.backsub_process:
-            previous_channels=reversed(df.iloc[:idx].marker_name.to_list())
-            for element in previous_channels:
-                #Supposes background name is a subset of the channel/marker name
-                if row.background in element:
-                    rename_background.append(element)
-                    break
-        else: 
-            rename_background.append(None)
-
-    df.drop(columns=["backsub_process"],inplace=True)
-    df.background=rename_background
-    if rmv_ref:
-        remove_val=len(df)*[""]
-        df.insert(df.shape[1],"remove",remove_val)
-        df.loc[ df["background"].isnull() , ["remove"] ]="TRUE"
-        first_ref_marker=df[df.marker_name== ref_marker].index[0]
-        df.loc[first_ref_marker,"remove"]=""
-    return df
-
-
-
-def main():
-    args = get_args()
-    img_path = args.input_img
-    out_dir = args.output_dir
-    file_name = args.output_file_name
-    global_ref_marker = args.registration_marker
-
-    df = meta_from_file(img_path, global_ref_marker)
-    df_unique = make_marker_names_unique(df, col='marker_name')
-    df_updated = assign_background(df_unique, args.remove_background_references, global_ref_marker)
-    df_updated.to_csv(out_dir / file_name, index=False)
-
-
-if __name__ == '__main__':
-    main()
-
-
-
-    
-
diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
index a08522f..b655023 100644
--- a/backsub/ome_schema.py
+++ b/backsub/ome_schema.py
@@ -3,7 +3,6 @@
 from ome_types.model import OME,Image,Pixels,TiffData,Channel,Plane
 import platform
 
-
 def INPUTS(frame):
     """
     This function creates a dictionary with the metadata of the tiles.
diff --git a/backsub/ome_writer.py b/backsub/ome_writer.py
index 953c244..aaa40b6 100644
--- a/backsub/ome_writer.py
+++ b/backsub/ome_writer.py
@@ -1,4 +1,4 @@
-import ome_schema as schema
+import backsub.ome_schema as schema
 import pandas as pd
 
 def create_ome(conformed_markers, info, software_version):
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..1d17be8
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,40 @@
+[project]
+name = "backsub"
+dynamic = ["version"]
+description = "Channel subtraction scaled by exposure times"
+readme = "README.md"
+requires-python = ">=3.9"
+license = { file = "LICENSE" }
+authors = [
+    { name="Krešimir Bestak", email="kbestak@gmail.com" }
+]
+keywords = ["autofluorescence", "imaging", "immunofluorescence", "subtraction"]
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License"
+]
+dependencies = [
+    "pandas",
+    "tifffile",
+    "ome_types",
+    "numpy",
+    "loguru",
+    "dask",
+    "dask-image",
+    "scikit-image",
+    "zarr>=3"
+]
+[project.urls]
+Homepage = "https://github.com/schapirolabor/background_subtraction"
+Issues = "https://github.com/schapirolabor/background_subtraction/issues"
+
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.dynamic]
+version = {attr = "backsub._version.__version__"}
+
+[project.scripts]
+backsub = "backsub.background_sub:main"

From 33335cca77cb6ff336fa4e0cba04c545fc332623 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Thu, 13 Nov 2025 18:25:45 +0100
Subject: [PATCH 20/21] init cleanup and ran python black

---
 backsub/CLI.py              | 206 ++++++++-------
 backsub/__init__.py         |   4 +-
 backsub/background_sub.py   | 500 +++++++++++++++++++++---------------
 backsub/metadata2markers.py | 232 +++++++++--------
 backsub/ome_schema.py       |  92 +++----
 backsub/ome_writer.py       |  41 +--
 6 files changed, 595 insertions(+), 480 deletions(-)

diff --git a/backsub/CLI.py b/backsub/CLI.py
index 180f7d9..e3588e2 100644
--- a/backsub/CLI.py
+++ b/backsub/CLI.py
@@ -2,10 +2,11 @@
 import pathlib
 from argparse import ArgumentParser as AP
 
-#---CLI-BLOCK---#
+
+# ---CLI-BLOCK---#
 def get_args(_version):
     # Script description
-    description="""
+    description = """
                 Subtracts background from an image (signal) 
                 acquired with fluorescence microscopy.  
                 Subtraction is carried out via the formula (SignalImage-factor*BackgroundImage), 
@@ -13,117 +14,126 @@ def get_args(_version):
                 """
 
     # Add parser
-    parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser = AP(
+        description=description, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
 
     # INPUT GROUPS
     inputs = parser.add_argument_group(title="INPUTS")
-    
-    inputs.add_argument("-r", ## deprecation warning can be added later
-                        "--root", ## deprecation warning can be added later
-                        '-in',
-                        '--input',
-                        dest="input", 
-                        action="store",
-                        type=pathlib.Path,
-                        required=True, 
-                        help="File path to input image file.")
-    
+
     inputs.add_argument(
-                        "-m",
-                        "--markers", 
-                        dest="markers", 
-                        action="store",
-                        type=pathlib.Path,
-                        required=True,
-                        help="""File path to the markers.csv file containing the list of marker names
+        "-r",  ## deprecation warning can be added later
+        "--root",  ## deprecation warning can be added later
+        "-in",
+        "--input",
+        dest="input",
+        action="store",
+        type=pathlib.Path,
+        required=True,
+        help="File path to input image file.",
+    )
+
+    inputs.add_argument(
+        "-m",
+        "--markers",
+        dest="markers",
+        action="store",
+        type=pathlib.Path,
+        required=True,
+        help="""File path to the markers.csv file containing the list of marker names
                         and their respective background channels.
-                        """
-                        )
-    
-    inputs.add_argument("-mpp",
-                        "--pixel-size", 
-                        metavar="SIZE", 
-                        dest = "pixel_size",
-                        required=False,
-                        type=float, 
-                        default = None, 
-                        action = "store",
-                        help="pixel size in microns, i.e. microns per pixel(mpp)"
-                        )
-
-    inputs.add_argument("-ts",
-                        "--tile-size", 
-                        dest="tile_size", 
-                        required=False, 
-                        type=int, 
-                        default=256, 
-                        help="""Tile size for image pyramid creation. Has to be a multiple of 16.
-                        """
-                        )
-
-    inputs.add_argument("-dsf",
-                        "--downscale-factor", 
-                        dest="downscale_factor", 
-                        required=False, 
-                        type=int, 
-                        default=2, 
-                        help="""Downscale factor for the image pyramid.
+                        """,
+    )
+
+    inputs.add_argument(
+        "-mpp",
+        "--pixel-size",
+        metavar="SIZE",
+        dest="pixel_size",
+        required=False,
+        type=float,
+        default=None,
+        action="store",
+        help="pixel size in microns, i.e. microns per pixel(mpp)",
+    )
+
+    inputs.add_argument(
+        "-ts",
+        "--tile-size",
+        dest="tile_size",
+        required=False,
+        type=int,
+        default=256,
+        help="""Tile size for image pyramid creation. Has to be a multiple of 16.
+                        """,
+    )
+
+    inputs.add_argument(
+        "-dsf",
+        "--downscale-factor",
+        dest="downscale_factor",
+        required=False,
+        type=int,
+        default=2,
+        help="""Downscale factor for the image pyramid.
                         This value will be only used if the input image is NOT pyramidal.
                         If input image is pyramidal, the number of levels in the output image 
                         will be the same as in the input so the downscale factor is not applied.
-                        """
-                        )
+                        """,
+    )
 
-    inputs.add_argument('-sr',
-                        '--save-ram',
-                        action='store_true',
-                        help="""RAM usage is cut in half when using this flag.  
+    inputs.add_argument(
+        "-sr",
+        "--save-ram",
+        action="store_true",
+        help="""RAM usage is cut in half when using this flag.  
                         Notice that the dimensions of the reduced resolution levels (sub-levels) of 
                         the output pyramidal image will slightly differ whether or not using this argument.                    
-                        """
-                        )
-
-    inputs.add_argument('-comp',
-                        '--compression',
-                        dest='compression',
-                        required=False,
-                        type=str,
-                        default='lzw',
-                        choices=['lzw', 'none', 'deflate', 'zlib'],
-                        help="""If set, the output pyramidal image will be compressed using the specified
+                        """,
+    )
+
+    inputs.add_argument(
+        "-comp",
+        "--compression",
+        dest="compression",
+        required=False,
+        type=str,
+        default="lzw",
+        choices=["lzw", "none", "deflate", "zlib"],
+        help="""If set, the output pyramidal image will be compressed using the specified
                         compression method. Set to "none" for no compression. Default is LZW. An alternative is zlib.
-                        """
-                        )
-
-    #VERSION CONTROL
-    inputs.add_argument("-v",
-                        "--version", 
-                        action="version", 
-                        version=_version
-                        )
-    
-    #OUTPUTS
+                        """,
+    )
+
+    # VERSION CONTROL
+    inputs.add_argument("-v", "--version", action="version", version=_version)
+
+    # OUTPUTS
     outputs = parser.add_argument_group(title="OUTPUTS")
 
-    outputs.add_argument("-o", 
-                         "--output", 
-                         dest="output", 
-                         action="store",
-                         type=pathlib.Path,
-                         required=True, 
-                         help="File path where the output pyramidal OME-TIFF will be saved."
-                         )
-    
-    outputs.add_argument("-mo", 
-                         "--marker-output",
-                         dest="markerout", 
-                         action="store",
-                         type=pathlib.Path,
-                         required=True, 
-                         help="Path to the output .csv marker file matching the channels in the output image."
-                         )
+    outputs.add_argument(
+        "-o",
+        "--output",
+        dest="output",
+        action="store",
+        type=pathlib.Path,
+        required=True,
+        help="File path where the output pyramidal OME-TIFF will be saved.",
+    )
+
+    outputs.add_argument(
+        "-mo",
+        "--marker-output",
+        dest="markerout",
+        action="store",
+        type=pathlib.Path,
+        required=True,
+        help="Path to the output .csv marker file matching the channels in the output image.",
+    )
 
     arg = parser.parse_args()
 
     return arg
-#---END_CLI-BLOCK---#
+
+
+# ---END_CLI-BLOCK---#
diff --git a/backsub/__init__.py b/backsub/__init__.py
index c9194f6..7df9f7a 100644
--- a/backsub/__init__.py
+++ b/backsub/__init__.py
@@ -1,3 +1 @@
-from ._version import __version__
-from .background_sub import subtract_channels, write_pyramid, extract_img_props
-from .ome_writer import create_ome
+from ._version import __version__
\ No newline at end of file
diff --git a/backsub/background_sub.py b/backsub/background_sub.py
index 8788d03..ae452fc 100755
--- a/backsub/background_sub.py
+++ b/backsub/background_sub.py
@@ -1,125 +1,159 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-#standard libraries
+# standard libraries
 import pandas as pd
 import numpy as np
 import tifffile as tifff
 from loguru import logger
-from skimage.transform import resize,rescale
+from skimage.transform import resize, rescale
 import time
 import dask.array as da
-from dask.diagnostics import ProgressBar,ResourceProfiler
+from dask.diagnostics import ProgressBar, ResourceProfiler
 import tracemalloc
 from dask_image.ndfilters import gaussian_filter
 from skimage.util import img_as_float32
 from skimage.exposure import rescale_intensity
 import ome_types
 import zarr
-#local libraries
+
+# local libraries
 import backsub.CLI as CLI
 import backsub.ome_writer as ome_writer
 from backsub import __version__
 
-#Decorator function to measure run-time and memory peak of a function
+
+# Decorator function to measure run-time and memory peak of a function
 def memocron(func):
-    def wrapper(*args,**kwargs):
+    def wrapper(*args, **kwargs):
         tracemalloc.start()
         st = time.time()
-        result=func(*args,**kwargs)
-        print(f'\nRESOURCES USED by {func.__name__} function ')
-        print("Memory peak:",((10**(-9))*tracemalloc.get_traced_memory()[1],"GB"))
+        result = func(*args, **kwargs)
+        print(f"\nRESOURCES USED by {func.__name__} function ")
+        print("Memory peak:", ((10 ** (-9)) * tracemalloc.get_traced_memory()[1], "GB"))
         rt = time.time() - st
         tracemalloc.stop()
-        print("Runtime:",f"{rt // 60:.0f}m {rt % 60:.0f}s")
+        print("Runtime:", f"{rt // 60:.0f}m {rt % 60:.0f}s")
         return result
+
     return wrapper
 
 
-def pyramidal_levels(img_arr,sub_levels,dims_schedule=None):
-    
+def pyramidal_levels(img_arr, sub_levels, dims_schedule=None):
+
     if dims_schedule is None:
-        down_factor=2
-        height,width=img_arr.shape
-        factor_schedule=[ down_factor**i for i in range(1,sub_levels+1) ]
-        dims_schedule= [np.rint([height/f,width/f]) for f in factor_schedule]
+        down_factor = 2
+        height, width = img_arr.shape
+        factor_schedule = [down_factor**i for i in range(1, sub_levels + 1)]
+        dims_schedule = [np.rint([height / f, width / f]) for f in factor_schedule]
     else:
-        down_factor=np.rint( 
-                            np.mean( [ dims_schedule[i][0]/dims_schedule[i+1][0] 
-                                        for i in range(0,len(dims_schedule)-1)] 
-                                    ) 
-                            ).astype("int")
-
-    ref_dtype=img_arr.dtype.name
-    val_range=(np.min(img_arr), np.max(img_arr))
-    std_dev=np.ceil((down_factor - 1)/2)
-    img_aux=img_arr
+        down_factor = np.rint(
+            np.mean(
+                [
+                    dims_schedule[i][0] / dims_schedule[i + 1][0]
+                    for i in range(0, len(dims_schedule) - 1)
+                ]
+            )
+        ).astype("int")
+
+    ref_dtype = img_arr.dtype.name
+    val_range = (np.min(img_arr), np.max(img_arr))
+    std_dev = np.ceil((down_factor - 1) / 2)
+    img_aux = img_arr
     for dims in dims_schedule:
-        img_dask=da.from_array(img_aux,chunks="auto")
-        result=gaussian_filter(img_dask, sigma=std_dev, order=0,truncate=down_factor)
-        img_aux=resize(img_as_float32(result.compute()), dims, order=1, preserve_range=True, anti_aliasing=False)
-        img_aux=np.rint(rescale_intensity(img_aux,out_range=val_range) ).astype(ref_dtype)
+        img_dask = da.from_array(img_aux, chunks="auto")
+        result = gaussian_filter(img_dask, sigma=std_dev, order=0, truncate=down_factor)
+        img_aux = resize(
+            img_as_float32(result.compute()),
+            dims,
+            order=1,
+            preserve_range=True,
+            anti_aliasing=False,
+        )
+        img_aux = np.rint(rescale_intensity(img_aux, out_range=val_range)).astype(
+            ref_dtype
+        )
         yield img_aux
 
 
-def pyramid_save_ram(img_arr,sub_levels):
-    chunksize=(2048,2048)#This value was found to be optimal, in terms of balanced RAM and runtime, for uint16 data
-    down_factor=2
-    #TODO:need to optimize speed and match dimensions of levels with those of the src image
-    ref_dtype=img_arr.dtype.name
-    border_overlap=down_factor-1
-    ref_dtype=img_arr.dtype.name
-    rescale_args={
-            "scale":1/down_factor,
-            "order":1,
-            "preserve_range":True,
-            "anti_aliasing":True
-                    }
-    img_aux=img_arr
+def pyramid_save_ram(img_arr, sub_levels):
+    chunksize = (
+        2048,
+        2048,
+    )  # This value was found to be optimal, in terms of balanced RAM and runtime, for uint16 data
+    down_factor = 2
+    # TODO:need to optimize speed and match dimensions of levels with those of the src image
+    ref_dtype = img_arr.dtype.name
+    border_overlap = down_factor - 1
+    ref_dtype = img_arr.dtype.name
+    rescale_args = {
+        "scale": 1 / down_factor,
+        "order": 1,
+        "preserve_range": True,
+        "anti_aliasing": True,
+    }
+    img_aux = img_arr
     for _ in range(sub_levels):
-        img_chunk=da.from_array(img_aux, chunks=chunksize)
-        float_half=img_chunk.astype("float32")
-        dim_rescale=da.map_overlap(rescale, float_half, depth=border_overlap, boundary="reflect", **rescale_args)
-        img_aux=(da.rint(dim_rescale).astype(ref_dtype)).compute()
+        img_chunk = da.from_array(img_aux, chunks=chunksize)
+        float_half = img_chunk.astype("float32")
+        dim_rescale = da.map_overlap(
+            rescale,
+            float_half,
+            depth=border_overlap,
+            boundary="reflect",
+            **rescale_args,
+        )
+        img_aux = (da.rint(dim_rescale).astype(ref_dtype)).compute()
         yield img_aux
 
 
 def process_markers(markers):
-    markers['ind'] = range(0, len(markers))
-    if 'remove' not in markers:
-        markers['remove'] = ["False" for i in range(len(markers))]
+    markers["ind"] = range(0, len(markers))
+    if "remove" not in markers:
+        markers["remove"] = ["False" for i in range(len(markers))]
     else:
-        markers['remove'] = markers['remove'] == True
+        markers["remove"] = markers["remove"] == True
 
-    markers['keep'] = markers['remove'] == False
+    markers["keep"] = markers["remove"] == False
 
-    markers = markers.drop(columns=['remove'])
+    markers = markers.drop(columns=["remove"])
 
-    markers.insert(markers.shape[1], "processed", ~ markers.background.isnull())
+    markers.insert(markers.shape[1], "processed", ~markers.background.isnull())
 
     missing_bgs = [
-        bg for bg in markers['background'].dropna().unique()
-        if bg not in markers['marker_name'].values
+        bg
+        for bg in markers["background"].dropna().unique()
+        if bg not in markers["marker_name"].values
     ]
     if missing_bgs:
-        raise ValueError(f"Background markers not found in marker_name column: {missing_bgs}")
+        raise ValueError(
+            f"Background markers not found in marker_name column: {missing_bgs}"
+        )
 
-    if markers['background'].dropna().empty:
-        print("\nWarning: No background markers specified in the 'background' column of the markers CSV file. No background subtraction was performed on the output file.\n")
+    if markers["background"].dropna().empty:
+        print(
+            "\nWarning: No background markers specified in the 'background' column of the markers CSV file. No background subtraction was performed on the output file.\n"
+        )
 
-    scaling_factor=np.full(markers.shape[0],np.nan)
-    background_idx=np.full(markers.shape[0],np.nan)
+    scaling_factor = np.full(markers.shape[0], np.nan)
+    background_idx = np.full(markers.shape[0], np.nan)
 
     for channel in range(len(markers)):
 
         if markers.processed[channel]:
-            bg_idx = markers.loc[ markers.marker_name == markers.background[channel],"ind"  ].tolist()
+            bg_idx = markers.loc[
+                markers.marker_name == markers.background[channel], "ind"
+            ].tolist()
 
-            if len(bg_idx)>1:
-                print(f"""Warning: Background channel with name {markers.background[channel]} 
+            if len(bg_idx) > 1:
+                print(
+                    f"""Warning: Background channel with name {markers.background[channel]} 
                       appears several times in the column "marker_name".  
-                      Only the first occurrence will be used for the subtraction.""" )
-            bg_idx=bg_idx[0]
-            scaling_factor[channel] = markers.exposure[channel] / markers.exposure[bg_idx]
+                      Only the first occurrence will be used for the subtraction."""
+                )
+            bg_idx = bg_idx[0]
+            scaling_factor[channel] = (
+                markers.exposure[channel] / markers.exposure[bg_idx]
+            )
             background_idx[channel] = bg_idx
 
     markers.insert(markers.shape[1], "factor", scaling_factor)
@@ -128,162 +162,192 @@ def process_markers(markers):
     return markers
 
 
-def extract_img_props(img_path,downscale_factor,tile_size,pixel_size=None):
+def extract_img_props(img_path, downscale_factor, tile_size, pixel_size=None):
 
-    #Extract data_type, pyramidal specs, height, width
+    # Extract data_type, pyramidal specs, height, width
     with tifff.TiffFile(img_path) as tif:
-        pyr_levels=len(tif.series[0].levels)
-        is_pyramid=pyr_levels > 1
-        data_type=tif.series[0].dtype.name
-        height,width=tif.series[0].shape[-2::]
-        dask_chunksize=da.from_array(tif.pages[0].asarray(), chunks='auto').chunksize
+        pyr_levels = len(tif.series[0].levels)
+        is_pyramid = pyr_levels > 1
+        data_type = tif.series[0].dtype.name
+        height, width = tif.series[0].shape[-2::]
+        dask_chunksize = da.from_array(tif.pages[0].asarray(), chunks="auto").chunksize
         if is_pyramid:
-            #dimensions of the reduced resolution layers(subresolution_dimensions)
-            subres_dims=[tif.series[0].levels[lvl].pages[0].shape
-                             for lvl in range(1,pyr_levels)
-                             ]
+            # dimensions of the reduced resolution layers(subresolution_dimensions)
+            subres_dims = [
+                tif.series[0].levels[lvl].pages[0].shape for lvl in range(1, pyr_levels)
+            ]
         else:
-            subres_dims=None
+            subres_dims = None
 
     if downscale_factor <= 1:
         raise ValueError("downscale_factor must be greater than 1")
-    max_dim = max(height,width)
-    extracted_levels = int(np.floor(np.log(max_dim / tile_size) / np.log(downscale_factor)) + 1)
-        
-    #Try to extract pixel size from ome-xml
+    max_dim = max(height, width)
+    extracted_levels = int(
+        np.floor(np.log(max_dim / tile_size) / np.log(downscale_factor)) + 1
+    )
+
+    # Try to extract pixel size from ome-xml
     if pixel_size is None:
-        print('Pixel size not specified in the arguments (-mpp)')
+        print("Pixel size not specified in the arguments (-mpp)")
         try:
             metadata = ome_types.from_tiff(img_path)
             pixel_size = metadata.images[0].pixels.physical_size_x
             pixel_size_unit = metadata.images[0].pixels.physical_size_x_unit
         except Exception as err:
             print(err)
-            print('Pixel size or pixel size unit detection using ome-types failed')
+            print("Pixel size or pixel size unit detection using ome-types failed")
             pixel_size = 1
-            pixel_size_unit="pixel"
+            pixel_size_unit = "pixel"
     else:
-        pixel_size=pixel_size
-        pixel_size_unit="µm"
-
-    
-    img_props={"pixel_size":pixel_size,
-               "pixel_size_unit":pixel_size_unit,
-               "data_type":data_type,
-               "pyramid":is_pyramid,
-               "levels":pyr_levels,
-               "extracted_levels": max(extracted_levels, 1),
-               "sub_levels_dims":subres_dims,
-               "size_x":width,
-               "size_y":height ,
-               "chunksize":dask_chunksize
-               }
-    
+        pixel_size = pixel_size
+        pixel_size_unit = "µm"
+
+    img_props = {
+        "pixel_size": pixel_size,
+        "pixel_size_unit": pixel_size_unit,
+        "data_type": data_type,
+        "pyramid": is_pyramid,
+        "levels": pyr_levels,
+        "extracted_levels": max(extracted_levels, 1),
+        "sub_levels_dims": subres_dims,
+        "size_x": width,
+        "size_y": height,
+        "chunksize": dask_chunksize,
+    }
+
     return img_props
 
 
-def subtract_channels(src_img_path,
-                      signal_index,
-                      background_index,
-                      factor,
-                      ref_chunksize,
-                      ref_dtype,
-                      task_no
-                      ):
-    factor=np.float32(factor) #limiting precision to float32 saves memory
-    signal_as_zarr = zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0,key=int(signal_index) ) )
-    background_as_zarr =zarr.open( tifff.imread( src_img_path, aszarr=True, series=0, level=0, key=int(background_index) ) )
-    signal=da.from_zarr(signal_as_zarr, chunks=ref_chunksize )
-    background=da.from_zarr(background_as_zarr, chunks=ref_chunksize)
+def subtract_channels(
+    src_img_path,
+    signal_index,
+    background_index,
+    factor,
+    ref_chunksize,
+    ref_dtype,
+    task_no,
+):
+    factor = np.float32(factor)  # limiting precision to float32 saves memory
+    signal_as_zarr = zarr.open(
+        tifff.imread(
+            src_img_path, aszarr=True, series=0, level=0, key=int(signal_index)
+        )
+    )
+    background_as_zarr = zarr.open(
+        tifff.imread(
+            src_img_path, aszarr=True, series=0, level=0, key=int(background_index)
+        )
+    )
+    signal = da.from_zarr(signal_as_zarr, chunks=ref_chunksize)
+    background = da.from_zarr(background_as_zarr, chunks=ref_chunksize)
     if np.issubdtype(ref_dtype, np.integer):
         info = np.iinfo(ref_dtype)
-        subtraction=da.clip(signal-factor*background, info.min, info.max).astype(ref_dtype)
-    else: # float32/64, no upper clipping
-        subtraction=da.clip(signal-factor*background, 0, None).astype(ref_dtype)
+        subtraction = da.clip(signal - factor * background, info.min, info.max).astype(
+            ref_dtype
+        )
+    else:  # float32/64, no upper clipping
+        subtraction = da.clip(signal - factor * background, 0, None).astype(ref_dtype)
     with ResourceProfiler(dt=0.25) as resources:
         with ProgressBar():
-            result=subtraction.compute()
+            result = subtraction.compute()
     print(f"Resources used by dask during subtraction {task_no}:")
-    print(resources.results[0],"([sec],[MB],[% CPU usage])")
+    print(resources.results[0], "([sec],[MB],[% CPU usage])")
     return result
 
+
 def extract_sublevels_from_tiff(path, ch, levels):
     with tifff.TiffFile(path) as tif:
         for l in range(1, levels):
             yield tif.series[0].levels[l].pages[ch].asarray()
 
-def write_pyramid(src_img_path,
-                  tasks_table,
-                    out_file,
-                    levels,
-                    sub_lvls_dims,
-                    src_data_type,
-                    pyramid_tile_shape,
-                    compression,
-                    is_src_pyramid=False,
-                    save_ram=False
-                    ):
 
-    sub_levels=levels-1
+def write_pyramid(
+    src_img_path,
+    tasks_table,
+    out_file,
+    levels,
+    sub_lvls_dims,
+    src_data_type,
+    pyramid_tile_shape,
+    compression,
+    is_src_pyramid=False,
+    save_ram=False,
+):
+
+    sub_levels = levels - 1
 
-    total_operations=tasks_table['processed'].values.sum() #Count True values
-    count=1
+    total_operations = tasks_table["processed"].values.sum()  # Count True values
+    count = 1
 
-    if compression == 'none':
-        compression=None
+    if compression == "none":
+        compression = None
 
-    with tifff.TiffWriter(out_file,bigtiff=True) as tif:
-        #write first the original resolution image,i.e. first layer
-        for _,channel in tasks_table.iterrows():
+    with tifff.TiffWriter(out_file, bigtiff=True) as tif:
+        # write first the original resolution image,i.e. first layer
+        for _, channel in tasks_table.iterrows():
             if channel.processed:
-                operation_count=f"({count}/{total_operations})"
-                print(f"\n {operation_count} Calculating subtraction of background {channel.background} from {channel.marker_name} signal:")
-                first_layer=subtract_channels(src_img_path, channel.ind, channel.bg_idx, channel.factor, (4096,4096), src_data_type, operation_count)
-                pyramid_action="calculate"
-                count+=1
+                operation_count = f"({count}/{total_operations})"
+                print(
+                    f"\n {operation_count} Calculating subtraction of background {channel.background} from {channel.marker_name} signal:"
+                )
+                first_layer = subtract_channels(
+                    src_img_path,
+                    channel.ind,
+                    channel.bg_idx,
+                    channel.factor,
+                    (4096, 4096),
+                    src_data_type,
+                    operation_count,
+                )
+                pyramid_action = "calculate"
+                count += 1
             else:
-                first_layer=tifff.imread(src_img_path,series=0,level=0,key=int(channel.ind))
+                first_layer = tifff.imread(
+                    src_img_path, series=0, level=0, key=int(channel.ind)
+                )
 
-                if (save_ram or not is_src_pyramid):
-                    pyramid_action="calculate"
+                if save_ram or not is_src_pyramid:
+                    pyramid_action = "calculate"
                 else:
-                    pyramid_action="extract"
-            
+                    pyramid_action = "extract"
+
             tif.write(
                 first_layer,
                 subifds=sub_levels,
                 tile=pyramid_tile_shape,
-                photometric='minisblack',
-                compression=compression
+                photometric="minisblack",
+                compression=compression,
             )
-            
-            if pyramid_action=="calculate":
+
+            if pyramid_action == "calculate":
                 if save_ram:
-                    pyramid=pyramid_save_ram(first_layer,sub_levels)
+                    pyramid = pyramid_save_ram(first_layer, sub_levels)
                 else:
-                    pyramid=pyramidal_levels(first_layer,sub_levels,sub_lvls_dims)
+                    pyramid = pyramidal_levels(first_layer, sub_levels, sub_lvls_dims)
 
-            elif pyramid_action=="extract":
-                pyramid=extract_sublevels_from_tiff(src_img_path,int(channel.ind),levels)
+            elif pyramid_action == "extract":
+                pyramid = extract_sublevels_from_tiff(
+                    src_img_path, int(channel.ind), levels
+                )
 
-            
             for sub_layer in pyramid:
-                    tif.write(
-                        sub_layer,
-                        subfiletype=1,
-                        tile=pyramid_tile_shape,
-                        photometric='minisblack',
-                        compression=compression
-                            # lzw works better when saving channel-by-channel
-                            # jpeg2000 works better saving the whole stack at once (not done here)
-                            # zlib is an alternative that's slower when writing, but gives better compression ratio
-                    )
-                
+                tif.write(
+                    sub_layer,
+                    subfiletype=1,
+                    tile=pyramid_tile_shape,
+                    photometric="minisblack",
+                    compression=compression,
+                    # lzw works better when saving channel-by-channel
+                    # jpeg2000 works better saving the whole stack at once (not done here)
+                    # zlib is an alternative that's slower when writing, but gives better compression ratio
+                )
+
     return out_file
 
+
 @memocron
 def main():
-    args=CLI.get_args(__version__)
+    args = CLI.get_args(__version__)
     in_path = args.input
     out_path = args.output
 
@@ -292,71 +356,79 @@ def main():
 
     if args.tile_size % 16 != 0:
         raise ValueError("tile_size has to be a multiple of 16")
-    if args.compression not in ['none','lzw','deflate','zlib']:
-        raise ValueError("Compression has to be one of the following: 'none','lzw','deflate','zlib'")
+    if args.compression not in ["none", "lzw", "deflate", "zlib"]:
+        raise ValueError(
+            "Compression has to be one of the following: 'none','lzw','deflate','zlib'"
+        )
 
     tile_shape = (args.tile_size, args.tile_size)
 
     # 1) Extract image properties
-    src_props = extract_img_props(in_path,
-                                  args.downscale_factor,
-                                  args.tile_size,
-                                  args.pixel_size)
+    src_props = extract_img_props(
+        in_path, args.downscale_factor, args.tile_size, args.pixel_size
+    )
 
     # 2) Modify pyramid_levels if required
     if src_props["pyramid"]:
-        levels=src_props["levels"]
+        levels = src_props["levels"]
     else:
-        levels=src_props["extracted_levels"]
+        levels = src_props["extracted_levels"]
 
     # 3) Read/Create markers table and update it to include the information of the processing tasks
     markers = process_markers(pd.read_csv(args.markers))
 
-    markers_updated=markers.loc[ markers.keep ]
+    markers_updated = markers.loc[markers.keep]
 
-    #4) Write updated markers.csv without appended columns. This file contains the markers information of the final image stack
-    markers_preview = markers_updated.drop(columns=['keep','ind','processed','factor','bg_idx'])
-    markers_preview["channel_number"] = np.arange(1, len(markers_preview)+1)
+    # 4) Write updated markers.csv without appended columns. This file contains the markers information of the final image stack
+    markers_preview = markers_updated.drop(
+        columns=["keep", "ind", "processed", "factor", "bg_idx"]
+    )
+    markers_preview["channel_number"] = np.arange(1, len(markers_preview) + 1)
     markers_preview.to_csv(args.markerout, index=False)
 
-    logger.info("\nTASKS PREVIEW:\n{}",markers_updated)
-    tasks=1
-    for _,channel in markers_updated.iterrows():
-        if channel.processed: 
-            print(f"\n(Task_{tasks}): background subtraction, Channel {channel.marker_name} (Background {channel.background})")
-            tasks+=1
+    logger.info("\nTASKS PREVIEW:\n{}", markers_updated)
+    tasks = 1
+    for _, channel in markers_updated.iterrows():
+        if channel.processed:
+            print(
+                f"\n(Task_{tasks}): background subtraction, Channel {channel.marker_name} (Background {channel.background})"
+            )
+            tasks += 1
 
-    #5) Calculate subtractions and write output file
-    out_file=args.output
+    # 5) Calculate subtractions and write output file
+    out_file = args.output
 
-    logger.info(f"\nTASKS PROGRESS" )
+    logger.info(f"\nTASKS PROGRESS")
 
     print(f"\nCommencing writing of pyramidal ome.tif file into {out_path}\n")
     print(f"\nCommencing subtraction tasks\n")
-    if args.compression=='none':
-        compression=None
+    if args.compression == "none":
+        compression = None
     else:
-        compression=args.compression
-    pyramid_abs_path=write_pyramid(
-                    in_path,
-                    markers_updated,
-                    out_file,
-                    levels,
-                    src_props["sub_levels_dims"],
-                    src_props["data_type"],
-                    tile_shape,
-                    compression,
-                    is_src_pyramid=src_props["pyramid"],
-                    save_ram=args.save_ram
-                    )
-    
-    #6) Write metadata in OME format into the pyramidal file
-    channel_names=markers_updated["marker_name"].tolist()
-    ome_xml=ome_writer.create_ome(channel_names,src_props,__version__)
+        compression = args.compression
+    pyramid_abs_path = write_pyramid(
+        in_path,
+        markers_updated,
+        out_file,
+        levels,
+        src_props["sub_levels_dims"],
+        src_props["data_type"],
+        tile_shape,
+        compression,
+        is_src_pyramid=src_props["pyramid"],
+        save_ram=args.save_ram,
+    )
+
+    # 6) Write metadata in OME format into the pyramidal file
+    channel_names = markers_updated["marker_name"].tolist()
+    ome_xml = ome_writer.create_ome(channel_names, src_props, __version__)
     tifff.tiffcomment(pyramid_abs_path, ome_xml.encode("utf-8"))
 
-    logger.info(f'\nSCRIPT FINISHED PROCESSING TASKS ')
-    print(f'\nPyramidal image with {levels} levels was successfully written{'' if compression is None else f' with {compression} compression'}.')
+    logger.info(f"\nSCRIPT FINISHED PROCESSING TASKS ")
+    print(
+        f"\nPyramidal image with {levels} levels was successfully written{'' if compression is None else f' with {compression} compression'}."
+    )
+
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/backsub/metadata2markers.py b/backsub/metadata2markers.py
index 2a062e7..b112ee3 100644
--- a/backsub/metadata2markers.py
+++ b/backsub/metadata2markers.py
@@ -5,64 +5,73 @@
 import numpy as np
 import warnings
 
-#CLI
+
+# CLI
 def get_args():
-    parser=argparse.ArgumentParser()
-    parser.add_argument('-i',
-                        '--input_img',
-                        required=True,
-                        type=pathlib.Path,
-                        help='absolute path of the input image stack (.tif)'
-                        )
-    
-    parser.add_argument('-o',
-                        '--output_dir',
-                        required=True,
-                        type=pathlib.Path,
-                        help='absolute path of the directory where the output .csv file will be written'
-                        )
-    
-    parser.add_argument('-fn',
-                        '--output_file_name',
-                        required=False,
-                        type=str,
-                        default="markers.csv",
-                        help='name of the csv file'
-                        )
-    
-    parser.add_argument('-rr',
-                        '--remove_background_channels',
-                        required=False,
-                        action='store_true',
-                        help='If applied, values in the "remove" column will be set to TRUE for any channel used as background. Use with caution as background channel exclusion may affect downstream analysis and autofluroescence measurements can be useful.'
-                        )
-
-    parser.add_argument('-rf',
-                        '--registration_filter',
-                        required=False,
-                        type=str,
-                        default="DAPI",
-                        help='Name of filter used for registration (default: DAPI). Note that all channels where the FluorescenceChannel attribute is this value in the metadata will be considered as reference channels.'
-                        )
-
-    parser.add_argument('-rd',
-                        '--remove_dapi',
-                        required=False,
-                        action='store_true',
-                        help='If applied, values in the "remove" column will be set to TRUE for any additional DAPI reference except the first. Manual correction of the output markers file can specify other DAPI channels if needed.'
-                        )
-
-    parser.add_argument('-krm',
-                        '--keep_registration_markername',
-                        required=False,
-                        type=str,
-                        default="DAPI",
-                        help='Name of registration marker that should be kept if "remove_dapi" is set to TRUE. Default is "DAPI". If provided value is not present in the marker_name column, the first instance with the registration filter will be kept.'
-                        )
-    
-    args=parser.parse_args()
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-i",
+        "--input_img",
+        required=True,
+        type=pathlib.Path,
+        help="absolute path of the input image stack (.tif)",
+    )
+
+    parser.add_argument(
+        "-o",
+        "--output_dir",
+        required=True,
+        type=pathlib.Path,
+        help="absolute path of the directory where the output .csv file will be written",
+    )
+
+    parser.add_argument(
+        "-fn",
+        "--output_file_name",
+        required=False,
+        type=str,
+        default="markers.csv",
+        help="name of the csv file",
+    )
+
+    parser.add_argument(
+        "-rr",
+        "--remove_background_channels",
+        required=False,
+        action="store_true",
+        help='If applied, values in the "remove" column will be set to TRUE for any channel used as background. Use with caution as background channel exclusion may affect downstream analysis and autofluroescence measurements can be useful.',
+    )
+
+    parser.add_argument(
+        "-rf",
+        "--registration_filter",
+        required=False,
+        type=str,
+        default="DAPI",
+        help="Name of filter used for registration (default: DAPI). Note that all channels where the FluorescenceChannel attribute is this value in the metadata will be considered as reference channels.",
+    )
+
+    parser.add_argument(
+        "-rd",
+        "--remove_dapi",
+        required=False,
+        action="store_true",
+        help='If applied, values in the "remove" column will be set to TRUE for any additional DAPI reference except the first. Manual correction of the output markers file can specify other DAPI channels if needed.',
+    )
+
+    parser.add_argument(
+        "-krm",
+        "--keep_registration_markername",
+        required=False,
+        type=str,
+        default="DAPI",
+        help='Name of registration marker that should be kept if "remove_dapi" is set to TRUE. Default is "DAPI". If provided value is not present in the marker_name column, the first instance with the registration filter will be kept.',
+    )
+
+    args = parser.parse_args()
     return args
 
+
 def make_marker_names_unique_list(markers):
     """
     Ensures all marker names in a list are unique.
@@ -91,78 +100,100 @@ def make_marker_names_unique_list(markers):
 
     return unique_markers
 
-def meta_from_file(src_img_path, registration_filter):
-    #Fetch metadata object
-    ome=from_tiff(src_img_path)
-    #Fetch image attributes from ome
-    ch_names = make_marker_names_unique_list( [ element.name for element in ome.images[0].pixels.channels ] )
-    exp_times = [ element.exposure_time for element in ome.images[0].pixels.planes ]
 
-    filters = [ element.attributes["FluorescenceChannel"] for element in ome.structured_annotations[0].value.any_elements[0].children ]
+def meta_from_file(src_img_path, registration_filter):
+    # Fetch metadata object
+    ome = from_tiff(src_img_path)
+    # Fetch image attributes from ome
+    ch_names = make_marker_names_unique_list(
+        [element.name for element in ome.images[0].pixels.channels]
+    )
+    exp_times = [element.exposure_time for element in ome.images[0].pixels.planes]
+
+    filters = [
+        element.attributes["FluorescenceChannel"]
+        for element in ome.structured_annotations[0].value.any_elements[0].children
+    ]
     if registration_filter not in filters:
-        raise ValueError(f"Registration filter '{registration_filter}' not found in image metadata filters: {list(set(filters))}")
-
-    background = [None if registration_filter in element
-                else element for element in filters]
-
-    aux_dict = {"channel_number": list(range(1, len(ch_names) + 1)),
-                #"cycle_number":cycles,
-                "marker_name": ch_names,
-                "Filter": filters,
-                "background": background,
-                "exposure": exp_times
-                }
+        raise ValueError(
+            f"Registration filter '{registration_filter}' not found in image metadata filters: {list(set(filters))}"
+        )
+
+    background = [
+        None if registration_filter in element else element for element in filters
+    ]
+
+    aux_dict = {
+        "channel_number": list(range(1, len(ch_names) + 1)),
+        # "cycle_number":cycles,
+        "marker_name": ch_names,
+        "Filter": filters,
+        "background": background,
+        "exposure": exp_times,
+    }
 
     df = pd.DataFrame(aux_dict)
     return df
 
-def assign_background(df, rmv_ref=False, rmv_back=False, ref_marker="DAPI", registration_filter="DAPI"):
-    #Create column ["backsub_process"] indicating which rows will be processed with backsub
+
+def assign_background(
+    df, rmv_ref=False, rmv_back=False, ref_marker="DAPI", registration_filter="DAPI"
+):
+    # Create column ["backsub_process"] indicating which rows will be processed with backsub
     filters_ = df.Filter.unique().tolist()
-    #Strings corresponding to filters/background names are set to False, since the are not processed
+    # Strings corresponding to filters/background names are set to False, since the are not processed
     backsub_process = df["marker_name"].replace(filters_, value=False, regex=True)
-    #Marker_name corresponding to signal will be set to True for processing
+    # Marker_name corresponding to signal will be set to True for processing
     backsub_process = np.where(backsub_process == False, False, True)
     df.insert(df.shape[1], "backsub_process", backsub_process)
 
-    #Assign the latest mention of the autofluorescence channel to the correspondent row in the background column
+    # Assign the latest mention of the autofluorescence channel to the correspondent row in the background column
     rename_background = []
 
-    #List with row indices of background channels to be removed
-    for idx,row in df.iterrows():
+    # List with row indices of background channels to be removed
+    for idx, row in df.iterrows():
 
         if row.backsub_process:
-            previous_channels=reversed(df.iloc[:idx].marker_name.to_list())
+            previous_channels = reversed(df.iloc[:idx].marker_name.to_list())
             for element in previous_channels:
-                #Supposes background name is a subset of the channel/marker name
+                # Supposes background name is a subset of the channel/marker name
                 if row.background in element:
                     rename_background.append(element)
                     break
-        else: 
+        else:
             rename_background.append(None)
 
-    df.drop(columns=["backsub_process"],inplace=True)
-    df.background=rename_background
+    df.drop(columns=["backsub_process"], inplace=True)
+    df.background = rename_background
 
     if rmv_ref or rmv_back:
-        remove=len(df)*[""]
-        df.insert(df.shape[1],"remove",remove)
+        remove = len(df) * [""]
+        df.insert(df.shape[1], "remove", remove)
 
         if rmv_back:
-            df.loc[ df["background"].isnull() & (df["Filter"] != registration_filter) , ["remove"] ] = "TRUE"
+            df.loc[
+                df["background"].isnull() & (df["Filter"] != registration_filter),
+                ["remove"],
+            ] = "TRUE"
 
         if rmv_ref:
-            df.loc[ df["background"].isnull() & (df["Filter"] == registration_filter) , ["remove"] ] = "TRUE"
+            df.loc[
+                df["background"].isnull() & (df["Filter"] == registration_filter),
+                ["remove"],
+            ] = "TRUE"
             if ref_marker not in df.marker_name.values:
-                first_ref_marker=df[df.Filter == registration_filter].index[0]
-                corr_ref_marker=df.loc[first_ref_marker, "marker_name"]
-                warnings.warn(f"Reference marker '{ref_marker}' not found. Using first instance of registration filter '{registration_filter}' - Channel '{corr_ref_marker}' instead.")
+                first_ref_marker = df[df.Filter == registration_filter].index[0]
+                corr_ref_marker = df.loc[first_ref_marker, "marker_name"]
+                warnings.warn(
+                    f"Reference marker '{ref_marker}' not found. Using first instance of registration filter '{registration_filter}' - Channel '{corr_ref_marker}' instead."
+                )
             else:
-                first_ref_marker=df[df.marker_name == ref_marker].index[0]
-            df.loc[first_ref_marker,"remove"]=""
+                first_ref_marker = df[df.marker_name == ref_marker].index[0]
+            df.loc[first_ref_marker, "remove"] = ""
 
     return df
 
+
 def main():
     args = get_args()
     img_path = args.input_img
@@ -172,14 +203,11 @@ def main():
     global_ref_marker = args.keep_registration_markername
 
     df = meta_from_file(img_path, ref_marker_name=registration_filter)
-    df_updated = assign_background(df, args.remove_background_references, global_ref_marker, registration_filter)
+    df_updated = assign_background(
+        df, args.remove_background_references, global_ref_marker, registration_filter
+    )
     df_updated.to_csv(out_dir / file_name, index=False)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
-
-
-
-    
-
diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
index b655023..1b02784 100644
--- a/backsub/ome_schema.py
+++ b/backsub/ome_schema.py
@@ -1,8 +1,9 @@
 #!/usr/bin/python
 import ome_types
-from ome_types.model import OME,Image,Pixels,TiffData,Channel,Plane
+from ome_types.model import OME, Image, Pixels, TiffData, Channel, Plane
 import platform
 
+
 def INPUTS(frame):
     """
     This function creates a dictionary with the metadata of the tiles.
@@ -12,12 +13,12 @@ def INPUTS(frame):
     Returns:
         dict: dictionary with the metadata of the tiles.
     """
-    inputs=frame.to_dict('list')
+    inputs = frame.to_dict("list")
 
     return inputs
 
 
-def TIFF_array(no_of_channels, inputs={'offset':0}):
+def TIFF_array(no_of_channels, inputs={"offset": 0}):
     """
     This function creates a list of TIFFData objects.
     Args:
@@ -27,13 +28,9 @@ def TIFF_array(no_of_channels, inputs={'offset':0}):
         list: list of TIFFData objects.
     """
     TIFF = [
-        TiffData(
-            first_c=ch,
-            ifd=n,
-            plane_count=1
-            )
-        for n,ch in enumerate(range(0,no_of_channels), start=inputs['offset'])
-        ]
+        TiffData(first_c=ch, ifd=n, plane_count=1)
+        for n, ch in enumerate(range(0, no_of_channels), start=inputs["offset"])
+    ]
 
     return TIFF
 
@@ -53,14 +50,22 @@ def PLANE_array(no_of_channels, inputs):
             the_c=ch,
             the_t=0,
             the_z=0,
-            position_x= inputs['position_x'][ch] if 'position_x' in inputs.keys() else 0,
-            position_y= inputs['position_y'][ch] if 'position_y' in inputs.keys() else 0,
+            position_x=inputs["position_x"][ch] if "position_x" in inputs.keys() else 0,
+            position_y=inputs["position_y"][ch] if "position_y" in inputs.keys() else 0,
             position_z=0,
-            position_x_unit= inputs['position_x_unit'][ch] if 'position_x_unit'in inputs.keys() else "pixel" ,
-            position_y_unit= inputs['position_y_unit'][ch] if 'position_y_unit'in inputs.keys() else "pixel" 
-            )
-        for ch in range(0,no_of_channels)
-        ]
+            position_x_unit=(
+                inputs["position_x_unit"][ch]
+                if "position_x_unit" in inputs.keys()
+                else "pixel"
+            ),
+            position_y_unit=(
+                inputs["position_y_unit"][ch]
+                if "position_y_unit" in inputs.keys()
+                else "pixel"
+            ),
+        )
+        for ch in range(0, no_of_channels)
+    ]
 
     return PLANE
 
@@ -77,12 +82,12 @@ def CHANN_array(no_of_channels, inputs):
 
     CHANN = [
         Channel(
-            id=f"Channel:{str(ch)}", # 'Channel:{y}:{x}:{marker_name}'.format(x=ch,y=100+int( inputs['tile'][ch] ) ,marker_name=inputs['marker'][ch] )
+            id=f"Channel:{str(ch)}",  # 'Channel:{y}:{x}:{marker_name}'.format(x=ch,y=100+int( inputs['tile'][ch] ) ,marker_name=inputs['marker'][ch] )
             name=inputs["name"][ch],
-            samples_per_pixel=1
-            )
-        for ch in range(0,no_of_channels)
-        ]
+            samples_per_pixel=1,
+        )
+        for ch in range(0, no_of_channels)
+    ]
 
     return CHANN
 
@@ -100,24 +105,24 @@ def PIXELS_array(chann_block, plane_block, tiff_block, inputs):
     """
     PIXELS = Pixels(
         id=f"Pixels:{inputs['tile'][0]}",
-        dimension_order='XYCZT',
+        dimension_order="XYCZT",
         size_c=len(chann_block),
         size_t=1,
-        size_x=inputs['size_x'][0],
-        size_y=inputs['size_y'][0],
+        size_x=inputs["size_x"][0],
+        size_y=inputs["size_y"][0],
         size_z=1,
-        type=inputs['type'][0],#bit_depth
+        type=inputs["type"][0],  # bit_depth
         big_endian=False,
         channels=chann_block,
         interleaved=False,
-        physical_size_x=inputs['physical_size_x'][0],
-        physical_size_x_unit=inputs['physical_size_x_unit'][0],
-        physical_size_y=inputs['physical_size_y'][0],
-        physical_size_y_unit=inputs['physical_size_y_unit'][0],
+        physical_size_x=inputs["physical_size_x"][0],
+        physical_size_x_unit=inputs["physical_size_x_unit"][0],
+        physical_size_y=inputs["physical_size_y"][0],
+        physical_size_y_unit=inputs["physical_size_y_unit"][0],
         physical_size_z=1.0,
         planes=plane_block,
-        significant_bits=inputs['significant_bits'][0],
-        tiff_data_blocks=tiff_block
+        significant_bits=inputs["significant_bits"][0],
+        tiff_data_blocks=tiff_block,
     )
 
     return PIXELS
@@ -132,11 +137,8 @@ def IMAGE_array(pixels_block, imageID):
     Returns:
         Image: Image object.
     """
-    
-    IMAGE = Image(
-            id =f'Image:{imageID}',
-            pixels=pixels_block
-            )
+
+    IMAGE = Image(id=f"Image:{imageID}", pixels=pixels_block)
 
     return IMAGE
 
@@ -150,13 +152,15 @@ def OME_metadata(image_block, software):
         OME: OME object.
     """
     ome = OME()
-    ome.creator = " ".join([
-        software,
-        ome_types.__name__,
-        ome_types.__version__,
-        '/ python version-',
-        platform.python_version()
-    ])
+    ome.creator = " ".join(
+        [
+            software,
+            ome_types.__name__,
+            ome_types.__version__,
+            "/ python version-",
+            platform.python_version(),
+        ]
+    )
 
     ome.images = image_block
     ome_xml = ome_types.to_xml(ome)
diff --git a/backsub/ome_writer.py b/backsub/ome_writer.py
index aaa40b6..82a81a2 100644
--- a/backsub/ome_writer.py
+++ b/backsub/ome_writer.py
@@ -1,6 +1,7 @@
 import backsub.ome_schema as schema
 import pandas as pd
 
+
 def create_ome(conformed_markers, info, software_version):
     """
     This function creates an OME-XML file from a pandas dataframe containing the metadata of the tiles.
@@ -10,34 +11,36 @@ def create_ome(conformed_markers, info, software_version):
     Returns:
         str: OME-XML file.
     """
-    software=f'backsub {software_version}'
+    software = f"backsub {software_version}"
     no_of_channels = len(conformed_markers)
-    tile_info_dict={
-        "tile": no_of_channels *[1],
-        "name":conformed_markers ,
-        "type": no_of_channels  *[ info["data_type"] ],
-        "size_x":no_of_channels *[ info["size_x"] ] ,
-        "size_y":no_of_channels *[info["size_y"]],
-        "physical_size_x": no_of_channels *[info["pixel_size"]],
-        "physical_size_x_unit": no_of_channels *[info["pixel_size_unit"]],
-        "physical_size_y": no_of_channels *[info["pixel_size"]],
-        "physical_size_y_unit": no_of_channels *[info["pixel_size_unit"]],
-        "significant_bits": no_of_channels *["16"]
+    tile_info_dict = {
+        "tile": no_of_channels * [1],
+        "name": conformed_markers,
+        "type": no_of_channels * [info["data_type"]],
+        "size_x": no_of_channels * [info["size_x"]],
+        "size_y": no_of_channels * [info["size_y"]],
+        "physical_size_x": no_of_channels * [info["pixel_size"]],
+        "physical_size_x_unit": no_of_channels * [info["pixel_size_unit"]],
+        "physical_size_y": no_of_channels * [info["pixel_size"]],
+        "physical_size_y_unit": no_of_channels * [info["pixel_size_unit"]],
+        "significant_bits": no_of_channels * ["16"],
     }
-    tile_info=pd.DataFrame(tile_info_dict)
-    
-    grouped_tiles = tile_info.groupby(['tile'])
-    
+    tile_info = pd.DataFrame(tile_info_dict)
+
+    grouped_tiles = tile_info.groupby(["tile"])
+
     tiles_counter = 0
     image = []
     for tileID, frame in grouped_tiles:
         metadata = schema.INPUTS(frame)
-        tiff = schema.TIFF_array(no_of_channels, inputs={'offset': no_of_channels * tiles_counter})
+        tiff = schema.TIFF_array(
+            no_of_channels, inputs={"offset": no_of_channels * tiles_counter}
+        )
         plane = schema.PLANE_array(no_of_channels, metadata)
         channel = schema.CHANN_array(no_of_channels, metadata)
         pixels = schema.PIXELS_array(channel, plane, tiff, metadata)
-        image.append(schema.IMAGE_array (pixels, tiles_counter))
+        image.append(schema.IMAGE_array(pixels, tiles_counter))
         tiles_counter += 1
-    ome, ome_xml = schema.OME_metadata(image,software)
+    ome, ome_xml = schema.OME_metadata(image, software)
 
     return ome_xml

From 3d0e51ed73c2dbdc760c740704602e50b3b80d48 Mon Sep 17 00:00:00 2001
From: Kresimir Bestak <kbestak@gmail.com>
Date: Thu, 13 Nov 2025 19:01:10 +0100
Subject: [PATCH 21/21] cleanup

---
 .gitignore            |  2 +-
 Dockerfile            |  6 ++++++
 backsub/__init__.py   |  2 +-
 backsub/ome_schema.py | 20 +++++++++++---------
 environment.yml       |  3 ++-
 5 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/.gitignore b/.gitignore
index ecfe1dc..85f8d50 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,4 +4,4 @@ __pycache__
 .DS_Store
 backsub/testing.ipynb
 build/
-backsub.egg-info/
\ No newline at end of file
+backsub.egg-info/
diff --git a/Dockerfile b/Dockerfile
index a8149f3..f1dbacd 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -17,3 +17,9 @@ USER root
 
 # Ensure micromamba binaries are in PATH
 ENV PATH="$MAMBA_ROOT_PREFIX/bin:$PATH"
+
+# Copy the rest of the current directory into /app inside the container
+WORKDIR /app
+COPY . .
+
+RUN micromamba run -n base pip install .
diff --git a/backsub/__init__.py b/backsub/__init__.py
index 7df9f7a..8dee4bf 100644
--- a/backsub/__init__.py
+++ b/backsub/__init__.py
@@ -1 +1 @@
-from ._version import __version__
\ No newline at end of file
+from ._version import __version__
diff --git a/backsub/ome_schema.py b/backsub/ome_schema.py
index 1b02784..a5fca63 100644
--- a/backsub/ome_schema.py
+++ b/backsub/ome_schema.py
@@ -152,15 +152,17 @@ def OME_metadata(image_block, software):
         OME: OME object.
     """
     ome = OME()
-    ome.creator = " ".join(
-        [
-            software,
-            ome_types.__name__,
-            ome_types.__version__,
-            "/ python version-",
-            platform.python_version(),
-        ]
-    )
+    ome.creator = software
+    # detailed versions provided in environment yml
+    # ome.creator = " ".join( 
+    #     [
+    #         software,
+    #         ome_types.__name__,
+    #         ome_types.__version__,
+    #         "/ python version-",
+    #         platform.python_version(),
+    #     ]
+    # )
 
     ome.images = image_block
     ome_xml = ome_types.to_xml(ome)
diff --git a/environment.yml b/environment.yml
index 8402f61..64b8199 100644
--- a/environment.yml
+++ b/environment.yml
@@ -12,4 +12,5 @@ dependencies:
   - numpy=2.3.2
   - loguru=0.7.3
   - dask-image=2024.5.3
-  - zarr=3.1.1
\ No newline at end of file
+  - zarr=3.1.1
+  - pip
\ No newline at end of file