consistently use named arguments

Author: tobyhodges

episodes/03-skimage-images.md

@@ -374,7 +374,7 @@ import skimage.io
 import skimage.color
 
 # read input image
-image = skimage.io.imread("data/chair.jpg")
+image = skimage.io.imread(fname="data/chair.jpg")
 
 # display original image
 fig, ax = plt.subplots()
@@ -398,7 +398,7 @@ import skimage.io
 import skimage.color
 
 # read input image, based on filename parameter
-image = skimage.io.imread("data/chair.jpg", as_gray=True)
+image = skimage.io.imread(fname="data/chair.jpg", as_gray=True)
 
 # display grayscale image
 fig, ax = plt.subplots()

episodes/04-drawing.md

@@ -66,7 +66,7 @@ import matplotlib.pyplot as plt
 %matplotlib widget
 
 # Load and display the original image
-image = skimage.io.imread("data/maize-seedlings.tif")
+image = skimage.io.imread(fname="data/maize-seedlings.tif")
 
 fig, ax = plt.subplots()
 plt.imshow(image)
@@ -201,7 +201,7 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > >
 > > ~~~
 > > # Draw a blue circle with centre (200, 300) in (y, x) coordinates, and radius 100
-> > rr, cc = skimage.draw.disk((200, 300), 100, shape=image.shape[0:2])
+> > rr, cc = skimage.draw.disk(center=(200, 300), radius=100, shape=image.shape[0:2])
 > > image[rr, cc] = (0, 0, 255)
 > > ~~~
 > > {: .language-python}
@@ -210,7 +210,7 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > >
 > > ~~~
 > > # Draw a green line from (400, 200) to (500, 700) in (y, x) coordinates
-> > rr, cc = skimage.draw.line(400, 200, 500, 700)
+> > rr, cc = skimage.draw.line(r0=400, c0=200, r1=500, c1=700)
 > > image[rr, cc] = (0, 255, 0)
 > > ~~~
 > > {: .language-python}
@@ -239,7 +239,7 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > >
 > > # draw a blue circle at a random location 15 times
 > > for i in range(15):
-> >     rr, cc = skimage.draw.disk((
+> >     rr, cc = skimage.draw.disk(center=(
 > >          random.randrange(600),
 > >          random.randrange(800)),
 > >          radius=50,
@@ -270,7 +270,7 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > >     x = random.random()
 > >     if x < 0.33:
 > >         # draw a blue circle at a random location
-> >         rr, cc = skimage.draw.disk((
+> >         rr, cc = skimage.draw.disk(center=(
 > >             random.randrange(600),
 > >             random.randrange(800)),
 > >             radius=50,
@@ -280,10 +280,10 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > >     elif x < 0.66:
 > >         # draw a green line at a random location
 > >         rr, cc = skimage.draw.line(
-> >             random.randrange(600),
-> >             random.randrange(800),
-> >             random.randrange(600),
-> >             random.randrange(800),
+> >             r0=random.randrange(600),
+> >             c0=random.randrange(800),
+> >             r1=random.randrange(600),
+> >             c1=random.randrange(800),
 > >         )
 > >         color = (0, 255, 0)
 > >     else:
@@ -393,7 +393,7 @@ The resulting masked image should look like this:
 > >
 > > ~~~
 > > # Load the image
-> > image = skimage.io.imread("data/remote-control.jpg")
+> > image = skimage.io.imread(fname="data/remote-control.jpg")
 > >
 > > # Create the basic mask
 > > mask = np.ones(shape=image.shape[0:2], dtype="bool")
@@ -420,7 +420,7 @@ The resulting masked image should look like this:
 >
 > ~~~
 > # Load the image
-> image = skimage.io.imread("data/wellplate-01.jpg")
+> image = skimage.io.imread(fname="data/wellplate-01.jpg")
 >
 > # Display the image
 > fig, ax = plt.subplots()
@@ -449,7 +449,7 @@ The resulting masked image should look like this:
 > >
 > > ~~~
 > > # read in original image
-> > image = skimage.io.imread("data/wellplate-01.jpg")
+> > image = skimage.io.imread(fname="data/wellplate-01.jpg")
 > >
 > > # create the mask image
 > > mask = np.ones(shape=image.shape[0:2], dtype="bool")
@@ -463,7 +463,7 @@ The resulting masked image should look like this:
 > >         y = int(coordinates[1])
 > >
 > >         # ... and drawing a circle on the mask
-> >         rr, cc = skimage.draw.disk((y, x), radius=16, shape=image.shape[0:2])
+> >         rr, cc = skimage.draw.disk(center=(y, x), radius=16, shape=image.shape[0:2])
 > >         mask[rr, cc] = False
 > >
 > > # apply the mask
@@ -506,7 +506,7 @@ The resulting masked image should look like this:
 > >
 > > ~~~
 > > # read in original image
-> > image = skimage.io.imread("data/wellplate-01.jpg")
+> > image = skimage.io.imread(fname="data/wellplate-01.jpg")
 > >
 > > # create the mask image
 > > mask = np.ones(shape=image.shape[0:2], dtype="bool")
@@ -529,7 +529,7 @@ The resulting masked image should look like this:
 > >     for col in range(8):
 > >
 > >         # ... and drawing a circle on the mask
-> >         rr, cc = skimage.draw.disk((y, x), radius=16, shape=image.shape[0:2])
+> >         rr, cc = skimage.draw.disk(center=(y, x), radius=16, shape=image.shape[0:2])
 > >         mask[rr, cc] = False
 > >         x += deltaX
 > >     # after one complete row, move to next row

episodes/05-creating-histograms.md

@@ -197,7 +197,7 @@ it produces this histogram:
 > > import skimage.draw
 > >
 > > # read the image as grayscale from the outset
-> > image = skimage.io.imread("data/plant-seedling.jpg", as_gray=True)
+> > image = skimage.io.imread(fname="data/plant-seedling.jpg", as_gray=True)
 > >
 > > # display the image
 > > fig, ax = plt.subplots()
@@ -247,7 +247,7 @@ A program to create colour histograms starts in a familiar way:
 
 ~~~
 # read original image, in full color
-image = skimage.io.imread("data/plant-seedling.jpg")
+image = skimage.io.imread(fname="data/plant-seedling.jpg")
 
 # display the image
 fig, ax = plt.subplots()
@@ -376,7 +376,7 @@ Finally we label our axes and display the histogram, shown here:
 >
 > ~~~
 > # read the image
-> image = skimage.io.imread("data/wellplate-02.tif")
+> image = skimage.io.imread(fname="data/wellplate-02.tif")
 >
 > # display the image
 > fig, ax = plt.subplots()
@@ -409,7 +409,7 @@ Finally we label our axes and display the histogram, shown here:
 > > ~~~
 > > # create a circular mask to select the 7th well in the first row
 > > mask = np.zeros(shape=image.shape[0:2], dtype="bool")
-> > circle = skimage.draw.disk((240, 1053), radius=49, shape=image.shape[0:2])
+> > circle = skimage.draw.disk(center=(240, 1053), radius=49, shape=image.shape[0:2])
 > > mask[circle] = 1
 > >
 > > # just for display:

episodes/06-blurring.md

@@ -235,7 +235,7 @@ import matplotlib.pyplot as plt
 import skimage.filters
 %matplotlib widget
 
-image = skimage.io.imread("data/gaussian-original.png")
+image = skimage.io.imread(fname="data/gaussian-original.png")
 
 # display the image
 fig, ax = plt.subplots()

episodes/07-thresholding.md

@@ -311,7 +311,7 @@ we have seen before in
 [the _Image Representation in skimage_ episode]({{ page.root }}{% link _episodes/03-skimage-images.md %}).
 
 ~~~
-image = skimage.io.imread("data/maize-root-cluster.jpg")
+image = skimage.io.imread(fname="data/maize-root-cluster.jpg")
 
 fig, ax = plt.subplots()
 plt.imshow(image)
@@ -503,7 +503,7 @@ For example, for the file `data/trial-016.jpg` and a sigma value of 1.5,
 we would call the function like this:
 
 ~~~
-measure_root_mass("data/trial-016.jpg", sigma=1.5)
+measure_root_mass(filename="data/trial-016.jpg", sigma=1.5)
 ~~~
 {: .language-python}
 
@@ -524,7 +524,7 @@ end with the **.jpg** suffix.
 ~~~
 all_files = glob.glob("data/trial-*.jpg")
 for filename in all_files:
-    density = measure_root_mass(filename, sigma=1.5)
+    density = measure_root_mass(filename=filename, sigma=1.5)
     # output in format suitable for .csv
     print(filename, density, sep=",")
 ~~~
@@ -644,7 +644,7 @@ data/trial-293.jpg,0.13607895611702128
 > >
 > > all_files = glob.glob("data/trial-*.jpg")
 > > for filename in all_files:
-> >     density = enhanced_root_mass(filename, sigma=1.5)
+> >     density = enhanced_root_mass(filename=filename, sigma=1.5)
 > >     # output in format suitable for .csv
 > >     print(filename, density, sep=",")
 > > ~~~
@@ -690,7 +690,7 @@ data/trial-293.jpg,0.13607895611702128
 > > ## Solution
 > > Here is the code to create the grayscale histogram:
 > > ~~~
-> > image = skimage.io.imread("data/colonies-01.tif")
+> > image = skimage.io.imread(fname="data/colonies-01.tif")
 > > gray_image = skimage.color.rgb2gray(image)
 > > blurred_image = skimage.filters.gaussian(gray_image, sigma=1.0)
 > > histogram, bin_edges = np.histogram(blurred_image, bins=256, range=(0.0, 1.0))

episodes/08-connected-components.md

@@ -292,7 +292,7 @@ We can call the above function `connected_components` and
 display the labeled image like so:
 
 ~~~
-labeled_image, count = connected_components("data/shapes-01.jpg", sigma=2.0, t=0.9, connectivity=2)
+labeled_image, count = connected_components(filename="data/shapes-01.jpg", sigma=2.0, t=0.9, connectivity=2)
 
 fig, ax = plt.subplots()
 plt.imshow(labeled_image)
@@ -683,7 +683,7 @@ This will produce the output
 > > display the resulting labeled image:
 > >
 > > ~~~
-> > labeled_image, count = enhanced_connected_components("data/shapes-01.jpg", sigma=2.0, t=0.9,
+> > labeled_image, count = enhanced_connected_components(filename="data/shapes-01.jpg", sigma=2.0, t=0.9,
 > >                                                      connectivity=2, min_area=min_area)
 > > colored_label_image = skimage.color.label2rgb(labeled_image, bg_label=0)
 > >

episodes/09-challenges.md

@@ -63,7 +63,7 @@ and `data/colonies-03.tif`.
 > > import matplotlib.pyplot as plt
 > > %matplotlib widget
 > >
-> > bacteria_image = skimage.io.imread("data/colonies-01.tif")
+> > bacteria_image = skimage.io.imread(fname="data/colonies-01.tif")
 > >
 > > # display the image
 > > fig, ax = plt.subplots()
@@ -177,7 +177,7 @@ and `data/colonies-03.tif`.
 > >
 > > ~~~
 > > for image_filename in ["data/colonies-01.tif", "data/colonies-02.tif", "data/colonies-03.tif"]:
-> >     count_colonies(image_filename)
+> >     count_colonies(image_filename=image_filename)
 > > ~~~
 > > {: .language-python}
 > >