Simplify 3-D distance measurement

_includes/datatypes/inspect_12bit_saturation.md

@@ -1,4 +1,4 @@
-<h4 id="saturation_12bit"><a href="#saturation_12bit">Find saturated pixels in an 12-bit image</a></h4> 
+<h4 id="saturation_12bit"><a href="#saturation_12bit">Inspect a 12-bit image</a></h4> 
 
 Saturation, i.e. pixel value at the upper end of the datatype, is a typical problem in fluorescence microscopy images. 
 

_includes/datatypes/inspect_8bit_saturation.md

@@ -1,4 +1,4 @@
-<h4 id="saturation_8bit"><a href="#saturation_8bit">Find saturated pixels in an 8-bit image</a></h4> 
+<h4 id="saturation_8bit"><a href="#saturation_8bit">Inspect an 8-bit image</a></h4> 
 
 Saturation, i.e. pixel value at the upper end of the datatype, is a typical problem in fluorescence microscopy images.
 

_includes/pixels/pixels_act1_skimage_napari.py

@@ -60,10 +60,11 @@
 print(image.min(), image.max())
 
 # %%
-# Compute the image histogram
+# Compute and show the image histogram
 import matplotlib.pyplot as plt
 import numpy as np
-plt.hist(image.flatten(), bins=np.arange(image.min(), image.max() + 1));
+plt.hist(image.flatten(), bins=np.arange(image.min(), image.max() + 1))
+plt.show() # instead, we could end the above line with ";"
 
 # %%
 # Napari:

_includes/spatial_calibration/spatial_calibration_act1_skimage_napari.py

@@ -68,15 +68,8 @@
 print("Calibrated points:\n", points_cal)
 
 # %%
-# Compute distance between points in voxel indices
-# - Pythagoras: sqrt( (z1-z0)^2 + (y1-y0)^2 + (x1-x0)^2 )
-diff_vector = points_cal[1] - points_cal[0]
-print("diff_vector:", diff_vector)
-
-# %%
-sqr_diff_vector = diff_vector**2
-print("sqr_diff_vector:", sqr_diff_vector)
-
-# %%
-distance = np.sqrt(sqr_diff_vector.sum())
-print("distance:", distance)
+# Compute the distance between the first and second point
+print("First point:" , points_cal[0])
+print("Second point:" , points_cal[1])
+from scipy.spatial import distance
+distance.euclidean(points_cal[0], points_cal[1])