bug fix atp

Author: Corentin

.gitignore

@@ -170,4 +170,5 @@ debug_data/
 !cytoplasm.tif
 !binary_mask_sdh.tif
 data/*
-*intensity_plot.png
+*intensity_plot.png
+*.idea

myoquant/commands/run_atp.py

@@ -166,7 +166,7 @@ def atp_analysis(
             if channel_first:
                 # Put the channel as third dimension instead of first
                 image_ndarray = np.moveaxis(image_ndarray, 0, -1)
-        image_ndarray = image_ndarray[:, :, channel]
+            image_ndarray = image_ndarray[:, :, channel]
         if rescale_exposure:
             image_ndarray = rescale_intensity(
                 image_ndarray,

myoquant/src/ATP_analysis.py

@@ -33,7 +33,7 @@ def get_all_intensity(
 
 def estimate_threshold(intensity_list, n_classes=2):
     density = gaussian_kde(intensity_list)
-    density.covariance_factor = lambda: 0.25
+    density.covariance_factor = lambda: 0.05
     density._compute_covariance()
 
     # Create a vector of 256 values going from 0 to 256:
@@ -64,13 +64,16 @@ def plot_density(all_cell_median_intensity, intensity_threshold, n_classes=2):
         intensity_threshold = estimate_threshold(all_cell_median_intensity, n_classes)
     fig, ax = plt.subplots(figsize=(10, 5))
     density = gaussian_kde(all_cell_median_intensity)
-    density.covariance_factor = lambda: 0.25
+    density.covariance_factor = lambda: 0.1
     density._compute_covariance()
 
-    # Create a vector of 256 values going from 0 to 256:
+    # Create a vector of 256 values going from 0 to 25
     xs = np.linspace(0, 255, 256)
     density_xs_values = density(xs)
-    ax.plot(xs, density_xs_values, label="Estimated Density")
+    ax.hist(
+        all_cell_median_intensity, bins=255, density=True, alpha=0.5, label="Histogram"
+    )
+    ax.plot(xs, density_xs_values, label="Estimated Density", linewidth=3)
     for values in intensity_threshold:
         ax.axvline(x=values, color="red", label="Threshold")
     ax.set_xlabel("Pixel Intensity")

notebooks/atp_exploration.ipynb

@@ -18,12 +18,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ATP_IMAGE_DF = df[df[\"Staining method\"] == \"ATP 9.4\"]\n",
-    "ATP_IMAGE_NAME = ATP_IMAGE_DF[\"Number\"].tolist()\n",
     "IMAGE_INDEX = 189\n",
     "# show the first image in the list using the image name\n",
     "from IPython.display import Image\n",
-    "Image(filename='../data/muscle_atlas/images/' + ATP_IMAGE_NAME[IMAGE_INDEX])"
+    "Image(filename='../sample_img/sample_atp.jpg')"
    ]
   },
   {
@@ -39,7 +37,7 @@
     "except ImportError:\n",
     "    from imageio import imread\n",
     "\n",
-    "image_array = imread('../data/muscle_atlas/images/' + ATP_IMAGE_NAME[IMAGE_INDEX])\n",
+    "image_array = imread('../sample_img/sample_atp.jpg')\n",
     "model_cellpose = load_cellpose()\n",
     "mask_cellpose = run_cellpose(image_array, model_cellpose)\n",
     "plt.imshow(mask_cellpose)"
@@ -92,6 +90,19 @@
     "    all_cell_median_intensity.append(single_cell_median_intensity)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Histogram plot of the median pixel intensity of all cells\n",
+    "plt.hist(all_cell_median_intensity, bins=255, density=True, alpha=0.5)\n",
+    "plt.plot(xs,density(xs))\n",
+    "plt.xlim(50,220)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -103,7 +114,7 @@
     "# Build a \"density\" function based on the dataset\n",
     "# When you give a value from the X axis to this function, it returns the according value on the Y axis\n",
     "density = gaussian_kde(all_cell_median_intensity)\n",
-    "density.covariance_factor = lambda : .25\n",
+    "density.covariance_factor = lambda : .05\n",
     "density._compute_covariance()\n",
     "\n",
     "# Create a vector of 256 values going from 0 to 256:\n",
@@ -115,10 +126,19 @@
     "# Make the chart\n",
     "# We're actually building a line chart where x values are set all along the axis and y value are\n",
     "# the corresponding values from the density function\n",
+    "\n",
     "plt.plot(xs,density(xs))\n",
+    "plt.xlim(50,220)\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -179,7 +199,9 @@
     "threshold = sorted_peaks[0]+xs[min_index]\n",
     "print(threshold)\n",
     "# Plot the data\n",
-    "plt.plot(xs, density(xs), label='Density')\n",
+    "plt.hist(all_cell_median_intensity, bins=255, density=True, alpha=0.5, label='Histogram')\n",
+    "plt.plot(xs,density(xs), label='Density', linewidth=3)\n",
+    "plt.xlim(50,220)\n",
     "plt.axvline(threshold, color='r', label='Threshold')\n",
     "plt.legend()\n",
     "plt.show()"