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infer_subc/organelles/peroxisome

fixed_infer_perox(in_img)

Procedure to infer peroxisome from linearly unmixed input with fixed parameters.

Parameters

in_img: np.ndarray a 3d image containing all the channels

Returns

peroxi_object mask defined extent of peroxisome object

Source code in infer_subc/organelles/peroxisome.py 
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def fixed_infer_perox(in_img: np.ndarray) -> np.ndarray:
    """
      Procedure to infer peroxisome from linearly unmixed input with fixed parameters.

    Parameters
     ------------
     in_img: np.ndarray
         a 3d image containing all the channels

     Returns
     -------------
     peroxi_object
         mask defined extent of peroxisome object
    """
    median_sz = 0
    gauss_sig = 3.0
    dot_scale = 1.0
    dot_cut = 0.01
    small_obj_w = 2

    return infer_perox(
        in_img,
        median_sz,
        gauss_sig,
        dot_scale,
        dot_cut,
        small_obj_w,
    )

get_perox(in_img, meta_dict, out_data_path)

load peroxisome if it exists, otherwise calculate and write to ome.tif file

Parameters

in_img

a 3d np.ndarray image of the inferred organelle (labels or boolean)

meta_dict

dictionary of meta-data (ome)

out_data_path

Path object where tiffs are written to

Returns

exported file name

Source code in infer_subc/organelles/peroxisome.py 
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def get_perox(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    load peroxisome if it exists, otherwise calculate and write to ome.tif file

    Parameters
    ------------
    in_img:
        a 3d  np.ndarray image of the inferred organelle (labels or boolean)
    meta_dict:
        dictionary of meta-data (ome)
    out_data_path:
        Path object where tiffs are written to

    Returns
    -------------
    exported file name

    """
    try:
        start = time.time()
        print("starting segmentation...")
        peroxisome = import_inferred_organelle("perox", meta_dict, out_data_path)
        end = time.time()
        print(f"loaded peroxisome in ({(end - start):0.2f}) sec")
    except:
        start = time.time()
        print("starting segmentation...")
        peroxisome = infer_and_export_perox(in_img, meta_dict, out_data_path)
        end = time.time()
        print(f"inferred (and exported) peroxisome in ({(end - start):0.2f}) sec")

    return peroxisome

infer_and_export_perox(in_img, meta_dict, out_data_path)

infer peroxisome and write inferred peroxisome to ome.tif file

Parameters

in_img

a 3d np.ndarray image of the inferred organelle (labels or boolean)

meta_dict

dictionary of meta-data (ome)

out_data_path

Path object where tiffs are written to

Returns

exported file name

Source code in infer_subc/organelles/peroxisome.py 
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def infer_and_export_perox(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    infer peroxisome and write inferred peroxisome to ome.tif file

    Parameters
    ------------
    in_img:
        a 3d  np.ndarray image of the inferred organelle (labels or boolean)
    meta_dict:
        dictionary of meta-data (ome)
    out_data_path:
        Path object where tiffs are written to

    Returns
    -------------
    exported file name

    """
    peroxisome = fixed_infer_perox(in_img)
    out_file_n = export_inferred_organelle(peroxisome, "perox", meta_dict, out_data_path)
    print(f"inferred peroxisome. wrote {out_file_n}")
    return peroxisome

infer_perox(in_img, median_sz, gauss_sig, dot_scale, dot_cut, small_obj_w)

Procedure to infer peroxisome from linearly unmixed input.

Parameters

in_img: a 3d image containing all the channels

median_sz

width of median filter for signal

gauss_sig: sigma for gaussian smoothing of signal dot_scale: scales (log_sigma) for dot filter (1,2, and 3) dot_cut: threshold for dot filter thresholds (1,2,and 3) small_obj_w: minimu object size cutoff for nuclei post-processing

Returns

peroxi_object mask defined extent of peroxisome object

Source code in infer_subc/organelles/peroxisome.py 
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def infer_perox(
    in_img: np.ndarray,
    median_sz: int,
    gauss_sig: float,
    dot_scale: float,
    dot_cut: float,
    small_obj_w: int,
) -> np.ndarray:
    """
    Procedure to infer peroxisome from linearly unmixed input.

    Parameters
     ------------
     in_img:
         a 3d image containing all the channels
    median_sz:
        width of median filter for signal
     gauss_sig:
         sigma for gaussian smoothing of  signal
     dot_scale:
         scales (log_sigma) for dot filter (1,2, and 3)
     dot_cut:
         threshold for dot filter thresholds (1,2,and 3)
     small_obj_w:
         minimu object size cutoff for nuclei post-processing

     Returns
     -------------
     peroxi_object
         mask defined extent of peroxisome object
    """
    peroxi_ch = PEROX_CH
    ###################
    # EXTRACT
    ###################
    peroxi = select_channel_from_raw(in_img, peroxi_ch)

    ###################
    # PRE_PROCESSING
    ###################
    peroxi = scale_and_smooth(peroxi, median_sz=median_sz, gauss_sig=gauss_sig)  # skips for median_sz < 2

    ###################
    # CORE_PROCESSING
    ###################
    s3_param = [[dot_scale, dot_cut]]
    bw = dot_2d_slice_by_slice_wrapper(peroxi, s3_param)

    ###################
    # POST_PROCESSING
    ###################
    # struct_obj = size_filter_linear_size(bw, min_size=small_obj_w, connectivity=1)
    struct_obj = fill_and_filter_linear_size(bw, hole_min=0, hole_max=0, min_size=small_obj_w)

    return label_uint16(struct_obj)