From b44cfd252bd06309f7b14b83df94d2a254ae9221 Mon Sep 17 00:00:00 2001
From: Corentin <>
Date: Fri, 20 Jan 2023 15:52:05 +0100
Subject: [PATCH 1/4] starting the merge with streamlit version

---
 myoquant/commands/run_sdh.py |  32 ++++++-----
 myoquant/src/ATP_analysis.py |  19 +++++++
 myoquant/src/HE_analysis.py  |   1 +
 myoquant/src/SDH_analysis.py |  54 ++++++------------
 myoquant/src/common_func.py  |  45 +++++++++++++++
 poetry.lock                  | 106 +++++++++++++++++++++--------------
 setup.py                     |   2 +-
 7 files changed, 163 insertions(+), 96 deletions(-)

diff --git a/myoquant/commands/run_sdh.py b/myoquant/commands/run_sdh.py
index 5f588f4..dd48d17 100644
--- a/myoquant/commands/run_sdh.py
+++ b/myoquant/commands/run_sdh.py
@@ -164,21 +164,23 @@ def sdh_analysis(
         progress.add_task(description="Reading all inputs...", total=None)
         image_ndarray_sdh = imread(image_path)
 
-    if mask_path is not None:
-        mask_ndarray = imread(mask_path)
-        if np.unique(mask_ndarray).shape[0] != 2:
-            console.print(
-                "The mask image should be a binary image with only 2 values (0 and 1).",
-                style="red",
-            )
-            raise ValueError
-        if len(image_ndarray_sdh.shape) > 2:
-            mask_ndarray = np.repeat(
-                mask_ndarray.reshape(mask_ndarray.shape[0], mask_ndarray.shape[1], 1),
-                image_ndarray_sdh.shape[2],
-                axis=2,
-            )
-        image_ndarray_sdh = image_ndarray_sdh * mask_ndarray
+        if mask_path is not None:
+            mask_ndarray = imread(mask_path)
+            if np.unique(mask_ndarray).shape[0] != 2:
+                console.print(
+                    "The mask image should be a binary image with only 2 values (0 and 1).",
+                    style="red",
+                )
+                raise ValueError
+            if len(image_ndarray_sdh.shape) > 2:
+                mask_ndarray = np.repeat(
+                    mask_ndarray.reshape(
+                        mask_ndarray.shape[0], mask_ndarray.shape[1], 1
+                    ),
+                    image_ndarray_sdh.shape[2],
+                    axis=2,
+                )
+            image_ndarray_sdh = image_ndarray_sdh * mask_ndarray
         if cellpose_path is not None:
             mask_cellpose = imread(cellpose_path)
 
diff --git a/myoquant/src/ATP_analysis.py b/myoquant/src/ATP_analysis.py
index c653df0..6ecc8a7 100644
--- a/myoquant/src/ATP_analysis.py
+++ b/myoquant/src/ATP_analysis.py
@@ -44,6 +44,25 @@ def estimate_threshold(intensity_list):
     return threshold
 
 
+def plot_density(all_cell_median_intensity, intensity_threshold):
+    if intensity_threshold == 0:
+        intensity_threshold = estimate_threshold(all_cell_median_intensity)
+    fig, ax = plt.subplots(figsize=(10, 5))
+    density = gaussian_kde(all_cell_median_intensity)
+    density.covariance_factor = lambda: 0.25
+    density._compute_covariance()
+
+    # Create a vector of 256 values going from 0 to 256:
+    xs = np.linspace(0, 255, 256)
+    density_xs_values = density(xs)
+    ax.plot(xs, density_xs_values, label="Estimated Density")
+    ax.axvline(x=intensity_threshold, color="red", label="Threshold")
+    ax.set_xlabel("Pixel Intensity")
+    ax.set_ylabel("Density")
+    ax.legend()
+    return fig
+
+
 def predict_all_cells(histo_img, cellpose_df, intensity_threshold):
     all_cell_median_intensity = get_all_intensity(histo_img, cellpose_df)
     if intensity_threshold is None:
diff --git a/myoquant/src/HE_analysis.py b/myoquant/src/HE_analysis.py
index a9a78b6..be92cf6 100644
--- a/myoquant/src/HE_analysis.py
+++ b/myoquant/src/HE_analysis.py
@@ -45,6 +45,7 @@ def single_cell_analysis(
     y_fiber,
     cell_label,
     internalised_threshold=0.75,
+    draw_and_return=False,
 ):
     n_nuc, n_nuc_intern, n_nuc_periph = 0, 0, 0
     new_row_lst = []
diff --git a/myoquant/src/SDH_analysis.py b/myoquant/src/SDH_analysis.py
index ae59542..a5e284e 100644
--- a/myoquant/src/SDH_analysis.py
+++ b/myoquant/src/SDH_analysis.py
@@ -4,9 +4,7 @@
 
 import pandas as pd
 import tensorflow as tf
-from rich.progress import track
-from skimage.measure import regionprops_table
-
+from .common_func import extract_single_image, df_from_cellpose_mask
 from .gradcam import make_gradcam_heatmap, save_and_display_gradcam
 import numpy as np
 
@@ -32,14 +30,7 @@ def resize_batch_cells(histo_img, cellpose_df):
     img_array_full = np.empty((len(cellpose_df), 256, 256, 3))
     # for index in track(range(len(cellpose_df)), description="Resizing cells"):
     for index in range(len(cellpose_df)):
-        single_cell_img = histo_img[
-            cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
-            cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
-        ].copy()
-
-        single_cell_mask = cellpose_df.iloc[index, 9].copy()
-        single_cell_img[~single_cell_mask] = 0
-
+        single_cell_img = extract_single_image(histo_img, cellpose_df, index)
         img_array_full[index] = tf.image.resize(single_cell_img, (256, 256))
     return img_array_full
 
@@ -54,7 +45,9 @@ def predict_all_cells(histo_img, cellpose_df, _model_SDH):
         predicted_class_array[index_counter] = prediction_result.argmax()
         predicted_proba_array[index_counter] = np.amax(prediction_result)
         index_counter += 1
-    return predicted_class_array, predicted_proba_array
+    cellpose_df["class_predicted"] = predicted_class_array
+    cellpose_df["proba_predicted"] = predicted_proba_array
+    return cellpose_df
 
 
 def paint_full_image(image_sdh, df_cellpose, class_predicted_all):
@@ -78,43 +71,28 @@ def paint_full_image(image_sdh, df_cellpose, class_predicted_all):
 
 
 def run_sdh_analysis(image_array, model_SDH, mask_cellpose):
-    props_cellpose = regionprops_table(
-        mask_cellpose,
-        properties=[
-            "label",
-            "area",
-            "centroid",
-            "eccentricity",
-            "bbox",
-            "image",
-            "perimeter",
-        ],
-    )
-    df_cellpose = pd.DataFrame(props_cellpose)
-    class_predicted_all, proba_predicted_all = predict_all_cells(
-        image_array, df_cellpose, model_SDH
-    )
-    df_cellpose["class_predicted"] = class_predicted_all
-    df_cellpose["proba_predicted"] = proba_predicted_all
-    count_per_label = np.unique(class_predicted_all, return_counts=True)
-    class_and_proba_df = pd.DataFrame(
-        list(zip(class_predicted_all, proba_predicted_all)),
-        columns=["class", "proba"],
-    )
+    df_cellpose = df_from_cellpose_mask(mask_cellpose)
+
+    df_cellpose = predict_all_cells(image_array, df_cellpose, model_SDH)
+    count_per_label = np.unique(df_cellpose["class_predicted"], return_counts=True)
 
     # Result table dict
     headers = ["Feature", "Raw Count", "Proportion (%)"]
     data = []
-    data.append(["Muscle Fibers", len(class_predicted_all), 100])
+    data.append(["Muscle Fibers", len(df_cellpose["class_predicted"]), 100])
     for elem in count_per_label[0]:
         data.append(
             [
                 labels_predict[int(elem)],
                 count_per_label[1][int(elem)],
-                100 * count_per_label[1][int(elem)] / len(class_predicted_all),
+                100
+                * count_per_label[1][int(elem)]
+                / len(df_cellpose["class_predicted"]),
             ]
         )
     result_df = pd.DataFrame(columns=headers, data=data)
     # Paint The Full Image
-    full_label_map = paint_full_image(image_array, df_cellpose, class_predicted_all)
+    full_label_map = paint_full_image(
+        image_array, df_cellpose, df_cellpose["class_predicted"]
+    )
     return result_df, full_label_map, df_cellpose
diff --git a/myoquant/src/common_func.py b/myoquant/src/common_func.py
index cb1c643..92e35ed 100644
--- a/myoquant/src/common_func.py
+++ b/myoquant/src/common_func.py
@@ -130,6 +130,51 @@ def run_stardist(image, model_stardist, nms_thresh=0.4, prob_thresh=0.5):
     return mask_stardist
 
 
+def df_from_cellpose_mask(mask):
+    props_cellpose = regionprops_table(
+        mask,
+        properties=[
+            "label",
+            "area",
+            "centroid",
+            "eccentricity",
+            "bbox",
+            "image",
+            "perimeter",
+            "feret_diameter_max",
+        ],
+    )
+    df_cellpose = pd.DataFrame(props_cellpose)
+    return df_cellpose
+
+
+def df_from_stardist_mask(mask):
+    props_stardist = regionprops_table(
+        mask,
+        properties=[
+            "label",
+            "area",
+            "centroid",
+            "eccentricity",
+            "bbox",
+            "image",
+            "perimeter",
+        ],
+    )
+    df_stardist = pd.DataFrame(props_stardist)
+    return df_stardist
+
+
+def extract_single_image(raw_image, df_props, index):
+    single_entity_img = raw_image[
+        df_props.iloc[index, 5] : df_props.iloc[index, 7],
+        df_props.iloc[index, 6] : df_props.iloc[index, 8],
+    ].copy()
+    single_entity_mask = df_props.iloc[index, 9]
+    single_entity_img[~single_entity_mask] = 0
+    return single_entity_img
+
+
 def label2rgb(img_ndarray, label_map):
     """Convert a label map to RGB image
 
diff --git a/poetry.lock b/poetry.lock
index af4de0c..f6f211b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -414,21 +414,6 @@ traitlets = ">=5.3"
 [package.extras]
 test = ["pytest"]
 
-[[package]]
-name = "commonmark"
-version = "0.9.1"
-description = "Python parser for the CommonMark Markdown spec"
-category = "main"
-optional = false
-python-versions = "*"
-files = [
-    {file = "commonmark-0.9.1-py2.py3-none-any.whl", hash = "sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9"},
-    {file = "commonmark-0.9.1.tar.gz", hash = "sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60"},
-]
-
-[package.extras]
-test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
-
 [[package]]
 name = "contourpy"
 version = "1.0.7"
@@ -1428,6 +1413,31 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 [package.extras]
 testing = ["coverage", "pyyaml"]
 
+[[package]]
+name = "markdown-it-py"
+version = "2.1.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "markdown-it-py-2.1.0.tar.gz", hash = "sha256:cf7e59fed14b5ae17c0006eff14a2d9a00ed5f3a846148153899a0224e2c07da"},
+    {file = "markdown_it_py-2.1.0-py3-none-any.whl", hash = "sha256:93de681e5c021a432c63147656fe21790bc01231e0cd2da73626f1aa3ac0fe27"},
+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark (>=3.2,<4.0)"]
+code-style = ["pre-commit (==2.6)"]
+compare = ["commonmark (>=0.9.1,<0.10.0)", "markdown (>=3.3.6,<3.4.0)", "mistletoe (>=0.8.1,<0.9.0)", "mistune (>=2.0.2,<2.1.0)", "panflute (>=2.1.3,<2.2.0)"]
+linkify = ["linkify-it-py (>=1.0,<2.0)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["attrs", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.2"
@@ -1565,6 +1575,18 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
+    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
+]
+
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -1655,14 +1677,14 @@ files = [
 
 [[package]]
 name = "mkdocstrings"
-version = "0.19.1"
+version = "0.20.0"
 description = "Automatic documentation from sources, for MkDocs."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "mkdocstrings-0.19.1-py3-none-any.whl", hash = "sha256:32a38d88f67f65b264184ea71290f9332db750d189dea4200cbbe408d304c261"},
-    {file = "mkdocstrings-0.19.1.tar.gz", hash = "sha256:d1037cacb4b522c1e8c164ed5d00d724a82e49dcee0af80db8fb67b384faeef9"},
+    {file = "mkdocstrings-0.20.0-py3-none-any.whl", hash = "sha256:f17fc2c4f760ec302b069075ef9e31045aa6372ca91d2f35ded3adba8e25a472"},
+    {file = "mkdocstrings-0.20.0.tar.gz", hash = "sha256:c757f4f646d4f939491d6bc9256bfe33e36c5f8026392f49eaa351d241c838e5"},
 ]
 
 [package.dependencies]
@@ -2769,18 +2791,18 @@ rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
 [[package]]
 name = "rich"
-version = "13.1.0"
+version = "13.2.0"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 category = "main"
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "rich-13.1.0-py3-none-any.whl", hash = "sha256:f846bff22a43e8508aebf3f0f2410ce1c6f4cde429098bd58d91fde038c57299"},
-    {file = "rich-13.1.0.tar.gz", hash = "sha256:81c73a30b144bbcdedc13f4ea0b6ffd7fdc3b0d3cc259a9402309c8e4aee1964"},
+    {file = "rich-13.2.0-py3-none-any.whl", hash = "sha256:7c963f0d03819221e9ac561e1bc866e3f95a02248c1234daa48954e6d381c003"},
+    {file = "rich-13.2.0.tar.gz", hash = "sha256:f1a00cdd3eebf999a15d85ec498bfe0b1a77efe9b34f645768a54132ef444ac5"},
 ]
 
 [package.dependencies]
-commonmark = ">=0.9.0,<0.10.0"
+markdown-it-py = ">=2.1.0,<3.0.0"
 pygments = ">=2.6.0,<3.0.0"
 typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""}
 
@@ -2804,28 +2826,28 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.0.226"
+version = "0.0.227"
 description = "An extremely fast Python linter, written in Rust."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.0.227-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:79f9fe7d512e4fef9b448509cbdfe9ee67db17a0abd3f84c1e5d6efe8f544327"},
+    {file = "ruff-0.0.227-py3-none-musllinux_1_2_i686.whl", hash = "sha256:956b9a806da5f8c1e5b54c933d6a1782a892849e401cfaa901d0e1f0a8542683"},
+    {file = "ruff-0.0.227-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:f08ec3a4d9ecf70baee3922900d19098a56bf707410703411ea40e0e0db25fff"},
+    {file = "ruff-0.0.227-py3-none-win32.whl", hash = "sha256:fbb2bb2a16041b192f10fee6ddab36f0ca847f0d33ca6a49128004ac1ba6d1ca"},
+    {file = "ruff-0.0.227-py3-none-win_amd64.whl", hash = "sha256:59ee7ab9e610d43220ee12bc5f8845dddff0f857a48b08f630c87d337abcd5f0"},
+    {file = "ruff-0.0.227.tar.gz", hash = "sha256:1da5eca99f4b35b8329391f0d5802b379c2672525a2526a2e0b64083e52adc03"},
 ]
 
 [[package]]
@@ -2986,14 +3008,14 @@ stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
 
 [[package]]
 name = "setuptools"
-version = "66.0.0"
+version = "66.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "setuptools-66.0.0-py3-none-any.whl", hash = "sha256:a78d01d1e2c175c474884671dde039962c9d74c7223db7369771fcf6e29ceeab"},
-    {file = "setuptools-66.0.0.tar.gz", hash = "sha256:bd6eb2d6722568de6d14b87c44a96fac54b2a45ff5e940e639979a3d1792adb6"},
+    {file = "setuptools-66.1.0-py3-none-any.whl", hash = "sha256:fc19f9f62120a763300fd78e234b3cbd3417be098f08c156eaaf36420627e57b"},
+    {file = "setuptools-66.1.0.tar.gz", hash = "sha256:78a02bdea8a5cb66dec1c507598c443bcc75562817d2556c1a17f7a344615bb4"},
 ]
 
 [package.extras]
diff --git a/setup.py b/setup.py
index eaa2e4e..9b0742c 100644
--- a/setup.py
+++ b/setup.py
@@ -2,7 +2,7 @@
 
 setup(
     name="myoquant",
-    version="0.1.0",
+    version="0.3.0",
     packages=find_packages(),
     install_requires=[],
 )

From 30cbc35b41b7e381dcc853ef1c6d95182d90efbf Mon Sep 17 00:00:00 2001
From: Corentin <>
Date: Mon, 23 Jan 2023 17:07:37 +0100
Subject: [PATCH 2/4] reworking towards st demo

---
 myoquant/src/ATP_analysis.py | 23 ++--------
 myoquant/src/HE_analysis.py  | 88 +++++++++++++++++++-----------------
 myoquant/src/common_func.py  |  5 +-
 pyproject.toml               |  2 +-
 4 files changed, 54 insertions(+), 64 deletions(-)

diff --git a/myoquant/src/ATP_analysis.py b/myoquant/src/ATP_analysis.py
index 6ecc8a7..98a439e 100644
--- a/myoquant/src/ATP_analysis.py
+++ b/myoquant/src/ATP_analysis.py
@@ -2,7 +2,7 @@
 from skimage.measure import regionprops_table
 from scipy.stats import gaussian_kde
 from sklearn.mixture import GaussianMixture
-
+from .common_func import extract_single_image, df_from_cellpose_mask
 import numpy as np
 
 labels_predict = {1: "fiber type 1", 2: "fiber type 2"}
@@ -12,13 +12,8 @@
 def get_all_intensity(image_array, df_cellpose):
     all_cell_median_intensity = []
     for index in range(len(df_cellpose)):
-        single_cell_img = image_array[
-            df_cellpose.iloc[index, 5] : df_cellpose.iloc[index, 7],
-            df_cellpose.iloc[index, 6] : df_cellpose.iloc[index, 8],
-        ].copy()
+        single_cell_img = extract_single_image(image_array, df_cellpose, index)
 
-        single_cell_mask = df_cellpose.iloc[index, 9].copy()
-        single_cell_img[~single_cell_mask] = 0
         # Calculate median pixel intensity of the cell but ignore 0 values
         single_cell_median_intensity = np.median(single_cell_img[single_cell_img > 0])
         all_cell_median_intensity.append(single_cell_median_intensity)
@@ -95,19 +90,7 @@ def paint_full_image(image_atp, df_cellpose, class_predicted_all):
 
 
 def run_atp_analysis(image_array, mask_cellpose, intensity_threshold=None):
-    props_cellpose = regionprops_table(
-        mask_cellpose,
-        properties=[
-            "label",
-            "area",
-            "centroid",
-            "eccentricity",
-            "bbox",
-            "image",
-            "perimeter",
-        ],
-    )
-    df_cellpose = pd.DataFrame(props_cellpose)
+    df_cellpose = df_from_cellpose_mask(mask_cellpose)
     class_predicted_all, intensity_all = predict_all_cells(
         image_array, df_cellpose, intensity_threshold
     )
diff --git a/myoquant/src/HE_analysis.py b/myoquant/src/HE_analysis.py
index be92cf6..4ba50b7 100644
--- a/myoquant/src/HE_analysis.py
+++ b/myoquant/src/HE_analysis.py
@@ -1,39 +1,27 @@
 import numpy as np
 import pandas as pd
 from skimage.draw import line
-from skimage.measure import regionprops_table
-
-from .draw_line import *
+from .common_func import (
+    extract_single_image,
+    df_from_cellpose_mask,
+    df_from_stardist_mask,
+)
+from .draw_line import (
+    line_equation,
+    calculate_intersection,
+    calculate_closest_point,
+    calculate_distance,
+)
+import matplotlib.pyplot as plt
 
 
 def extract_ROIs(histo_img, index, cellpose_df, mask_stardist):
-    single_cell_img = histo_img[
-        cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
-        cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
-    ].copy()
-    nucleus_single_cell_img = mask_stardist[
-        cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
-        cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
-    ].copy()
+    single_cell_img = extract_single_image(histo_img, cellpose_df, index)
+    nucleus_single_cell_img = extract_single_image(mask_stardist, cellpose_df, index)
     single_cell_mask = cellpose_df.iloc[index, 9]
-    single_cell_img[~single_cell_mask] = 0
-    nucleus_single_cell_img[~single_cell_mask] = 0
-
-    props_nuc_single = regionprops_table(
-        nucleus_single_cell_img,
-        intensity_image=single_cell_img,
-        properties=[
-            "label",
-            "area",
-            "centroid",
-            "eccentricity",
-            "bbox",
-            "image",
-            "perimeter",
-            "feret_diameter_max",
-        ],
+    df_nuc_single = df_from_stardist_mask(
+        nucleus_single_cell_img, intensity_image=single_cell_img
     )
-    df_nuc_single = pd.DataFrame(props_nuc_single)
     return single_cell_img, nucleus_single_cell_img, single_cell_mask, df_nuc_single
 
 
@@ -47,6 +35,11 @@ def single_cell_analysis(
     internalised_threshold=0.75,
     draw_and_return=False,
 ):
+    if draw_and_return:
+        fig_size = (5, 5)
+        f, ax = plt.subplots(figsize=fig_size)
+        ax.scatter(x_fiber, y_fiber, color="white")
+
     n_nuc, n_nuc_intern, n_nuc_periph = 0, 0, 0
     new_row_lst = []
     new_col_names = df_nuc_single.columns.tolist()
@@ -54,6 +47,8 @@ def single_cell_analysis(
     new_col_names.append("score_eccentricity")
     new_col_names.append("cell label")
     for _, value in df_nuc_single.iterrows():
+        if draw_and_return:
+            ax.scatter(value[3], value[2], color="red")
         n_nuc += 1
         value = value.tolist()
         # Extend line and find closest point
@@ -69,6 +64,21 @@ def single_cell_analysis(
             m, b, (single_cell_img.shape[0], single_cell_img.shape[1])
         )
         border_point = calculate_closest_point(value[3], value[2], intersections_lst)
+        if draw_and_return:
+            ax.plot(
+                (x_fiber, border_point[0]),
+                (y_fiber, border_point[1]),
+                "ro--",
+                linewidth=1,
+                markersize=1,
+            )
+            ax.plot(
+                (x_fiber, value[3]),
+                (y_fiber, value[2]),
+                "go--",
+                linewidth=1,
+                markersize=1,
+            )
         rr, cc = line(
             int(y_fiber),
             int(x_fiber),
@@ -96,6 +106,8 @@ def single_cell_analysis(
                         value.append(ratio_dist)
                         value.append(cell_label)
                     new_row_lst.append(value)
+                    if draw_and_return:
+                        ax.scatter(coords[1], coords[0], color="red", s=10)
                     break
             except IndexError:
                 coords = list(zip(rr, cc))[index3 - 1]
@@ -115,8 +127,13 @@ def single_cell_analysis(
                     value.append(ratio_dist)
                     value.append(cell_label)
                 new_row_lst.append(value)
+                if draw_and_return:
+                    ax.scatter(coords[1], coords[0], color="red", s=10)
+                    ax.axis("off")
                 break
     df_nuc_single_stats = pd.DataFrame(new_row_lst, columns=new_col_names)
+    if draw_and_return:
+        return n_nuc, n_nuc_intern, n_nuc_periph, df_nuc_single_stats, ax
     return n_nuc, n_nuc_intern, n_nuc_periph, df_nuc_single_stats
 
 
@@ -173,20 +190,7 @@ def paint_histo_img(histo_img, cellpose_df, prediction_df):
 
 
 def run_he_analysis(image_ndarray, mask_cellpose, mask_stardist, eccentricity_thresh):
-    props_cellpose = regionprops_table(
-        mask_cellpose,
-        properties=[
-            "label",
-            "area",
-            "centroid",
-            "eccentricity",
-            "bbox",
-            "image",
-            "perimeter",
-            "feret_diameter_max",
-        ],
-    )
-    df_cellpose = pd.DataFrame(props_cellpose)
+    df_cellpose = df_from_cellpose_mask(mask_cellpose)
     df_nuc_analysis, all_nuc_df_stats = predict_all_cells(
         image_ndarray, df_cellpose, mask_stardist, eccentricity_thresh
     )
diff --git a/myoquant/src/common_func.py b/myoquant/src/common_func.py
index 92e35ed..5252f5c 100644
--- a/myoquant/src/common_func.py
+++ b/myoquant/src/common_func.py
@@ -13,6 +13,8 @@
 from tensorflow import keras
 import numpy as np
 from PIL import Image
+from skimage.measure import regionprops_table
+import pandas as pd
 
 # from .gradcam import make_gradcam_heatmap, save_and_display_gradcam
 from .random_brightness import RandomBrightness
@@ -148,9 +150,10 @@ def df_from_cellpose_mask(mask):
     return df_cellpose
 
 
-def df_from_stardist_mask(mask):
+def df_from_stardist_mask(mask, intensity_image=None):
     props_stardist = regionprops_table(
         mask,
+        intensity_image=intensity_image,
         properties=[
             "label",
             "area",
diff --git a/pyproject.toml b/pyproject.toml
index 1a9ac5b..17c5ed4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "myoquant"
-version = "0.3.0"
+version = "0.3.1"
 description = "MyoQuant🔬: a tool to automatically quantify pathological features in muscle fiber histology images."
 authors = ["Corentin Meyer <corentin.meyer@etu.unistra.fr>"]
 maintainers = ["Corentin Meyer <corentin.meyer@etu.unistra.fr>"]

From fad29d9c4dc20ddfd666e6aa79d97e92f6b399a9 Mon Sep 17 00:00:00 2001
From: Corentin <>
Date: Tue, 24 Jan 2023 14:57:55 +0100
Subject: [PATCH 3/4] fixing missing density plot import

---
 myoquant/src/ATP_analysis.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/myoquant/src/ATP_analysis.py b/myoquant/src/ATP_analysis.py
index 98a439e..452e239 100644
--- a/myoquant/src/ATP_analysis.py
+++ b/myoquant/src/ATP_analysis.py
@@ -1,9 +1,9 @@
 import pandas as pd
-from skimage.measure import regionprops_table
 from scipy.stats import gaussian_kde
 from sklearn.mixture import GaussianMixture
 from .common_func import extract_single_image, df_from_cellpose_mask
 import numpy as np
+import matplotlib.pyplot as plt
 
 labels_predict = {1: "fiber type 1", 2: "fiber type 2"}
 np.random.seed(42)

From 6df17c48634de9950f6726bf042cf6acf907ce3d Mon Sep 17 00:00:00 2001
From: Corentin <>
Date: Tue, 24 Jan 2023 15:11:37 +0100
Subject: [PATCH 4/4] update poetry

---
 poetry.lock | 264 ++++++++++++++++++++++++++--------------------------
 1 file changed, 132 insertions(+), 132 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index f6f211b..ff85c1c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -129,14 +129,14 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "cachetools"
-version = "5.2.1"
+version = "5.3.0"
 description = "Extensible memoizing collections and decorators"
 category = "main"
 optional = false
 python-versions = "~=3.7"
 files = [
-    {file = "cachetools-5.2.1-py3-none-any.whl", hash = "sha256:8462eebf3a6c15d25430a8c27c56ac61340b2ecf60c9ce57afc2b97e450e47da"},
-    {file = "cachetools-5.2.1.tar.gz", hash = "sha256:5991bc0e08a1319bb618d3195ca5b6bc76646a49c21d55962977197b301cc1fe"},
+    {file = "cachetools-5.3.0-py3-none-any.whl", hash = "sha256:429e1a1e845c008ea6c85aa35d4b98b65d6a9763eeef3e37e92728a12d1de9d4"},
+    {file = "cachetools-5.3.0.tar.gz", hash = "sha256:13dfddc7b8df938c21a940dfa6557ce6e94a2f1cdfa58eb90c805721d58f2c14"},
 ]
 
 [[package]]
@@ -533,30 +533,30 @@ files = [
 
 [[package]]
 name = "debugpy"
-version = "1.6.5"
+version = "1.6.6"
 description = "An implementation of the Debug Adapter Protocol for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "debugpy-1.6.5-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:696165f021a6a17da08163eaae84f3faf5d8be68fb78cd78488dd347e625279c"},
-    {file = "debugpy-1.6.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:17039e392d6f38388a68bd02c5f823b32a92142a851e96ba3ec52aeb1ce9d900"},
-    {file = "debugpy-1.6.5-cp310-cp310-win32.whl", hash = "sha256:62a06eb78378292ba6c427d861246574dc8b84471904973797b29dd33c7c2495"},
-    {file = "debugpy-1.6.5-cp310-cp310-win_amd64.whl", hash = "sha256:9984fc00ab372c97f63786c400107f54224663ea293daab7b365a5b821d26309"},
-    {file = "debugpy-1.6.5-cp37-cp37m-macosx_10_15_x86_64.whl", hash = "sha256:048368f121c08b00bbded161e8583817af5055982d2722450a69efe2051621c2"},
-    {file = "debugpy-1.6.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:74e4eca42055759032e3f1909d1374ba1d729143e0c2729bb8cb5e8b5807c458"},
-    {file = "debugpy-1.6.5-cp37-cp37m-win32.whl", hash = "sha256:0f9afcc8cad6424695f3356dc9a7406d5b18e37ee2e73f34792881a44b02cc50"},
-    {file = "debugpy-1.6.5-cp37-cp37m-win_amd64.whl", hash = "sha256:b5a74ecebe5253344501d9b23f74459c46428b30437fa9254cfb8cb129943242"},
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 [[package]]
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 [package.dependencies]
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 [package.dependencies]
@@ -1019,14 +1019,14 @@ all = ["matplotlib", "numcodecs", "tifffile"]
 
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@@ -1231,14 +1231,14 @@ test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-com
 
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 [[package]]
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 ]
 
 [package.extras]
@@ -3270,21 +3270,21 @@ files = [
 
 [[package]]
 name = "tifffile"
-version = "2022.10.10"
+version = "2023.1.23.1"
 description = "Read and write TIFF files"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-numpy = ">=1.19.2"
+numpy = "*"
 
 [package.extras]
-all = ["fsspec", "imagecodecs (>=2022.2.22)", "lxml", "matplotlib (>=3.3)", "zarr"]
+all = ["defusedxml", "fsspec", "imagecodecs (>=2023.1.23)", "lxml", "matplotlib", "zarr"]
 
 [[package]]
 name = "tokenize-rt"