Episodes modified using Matplotlib Object Oriented Style

episodes/02-image-basics.md

@@ -228,7 +228,8 @@ With that taken care of, let us display the image we have loaded, using
 the `imshow` function from the `matplotlib.pyplot` module.
 
 ```python
-plt.imshow(eight)
+fig, ax = plt.subplots()
+ax.imshow(eight)
 ```
 
 ![](fig/eight.png){alt='Image of 8'}
@@ -289,9 +290,8 @@ zero = iio.imread(uri="data/eight.tif")
 zero[2,1]= 1.0
 
 # The following line of code creates a new figure for imshow to use in displaying our output.
-# Without it, plt.imshow() would overwrite our previous image in the cell above
 fig, ax = plt.subplots()
-plt.imshow(zero)
+ax.imshow(zero)
 print(zero)
 ```
 
@@ -365,7 +365,7 @@ five = iio.imread(uri="data/eight.tif")
 five[1,2]= 1.0
 five[3,0]= 1.0
 fig, ax = plt.subplots()
-plt.imshow(five)
+ax.imshow(five)
 print(five)
 ```
 
@@ -402,7 +402,7 @@ three_colours = three_colours * 128
 # so you end up with the values 0., 128., and 255.
 three_colours[2,:] = 255.
 fig, ax = plt.subplots()
-plt.imshow(three_colours)
+ax.imshow(three_colours)
 print(three_colours)
 ```
 
@@ -436,7 +436,7 @@ a mapped continuum of intensities: greyscale.
 
 ```python
 fig, ax = plt.subplots()
-plt.imshow(three_colours,cmap=plt.cm.gray)
+ax.imshow(three_colours,cmap=plt.cm.gray)
 ```
 
 ![](fig/grayscale.png){alt='Image in greyscale'}
@@ -472,7 +472,7 @@ pseudorandomizer = np.random.RandomState(2021)
 checkerboard = pseudorandomizer.randint(0, 255, size=(4, 4, 3))
 # restore the default map as you show the image
 fig, ax = plt.subplots()
-plt.imshow(checkerboard)
+ax.imshow(checkerboard)
 # display the arrays
 print(checkerboard)
 ```
@@ -535,23 +535,23 @@ a 1d matrix that has a one for the channel we want to keep and zeros for the res
 ```python
 red_channel = checkerboard * [1, 0, 0]
 fig, ax = plt.subplots()
-plt.imshow(red_channel)
+ax.imshow(red_channel)
 ```
 
 ![](fig/checkerboard-red-channel.png){alt='Image of red channel'}
 
 ```python
 green_channel = checkerboard * [0, 1, 0]
 fig, ax = plt.subplots()
-plt.imshow(green_channel)
+ax.imshow(green_channel)
 ```
 
 ![](fig/checkerboard-green-channel.png){alt='Image of green channel'}
 
 ```python
 blue_channel = checkerboard * [0, 0, 1]
 fig, ax = plt.subplots()
-plt.imshow(blue_channel)
+ax.imshow(blue_channel)
 ```
 
 ![](fig/checkerboard-blue-channel.png){alt='Image of blue channel'}

episodes/03-skimage-images.md

@@ -60,13 +60,13 @@ Next, we will do something with the image:
 
 ```python
 fig, ax = plt.subplots()
-plt.imshow(chair)
+ax.imshow(chair)
 ```
 
 Once we have the image in the program,
-we first call `plt.subplots()` so that we will have
-a fresh figure with a set of axis independent from our previous calls.
-Next we call `plt.imshow()` in order to display the image.
+we first call `fig, ax = plt.subplots()` so that we will have
+a fresh figure with a set of axes independent from our previous calls.
+Next we call `ax.imshow()` in order to display the image.
 
 Now, we will save the image in another format:
 
@@ -181,7 +181,7 @@ If we don't convert it before saving,
 
 Next, write the resized image out to a new file named `resized.jpg`
 in your data directory.
-Finally, use `plt.imshow()` with each of your image variables to display
+Finally, use `ax.imshow()` with each of your image variables to display
 both images in your notebook.
 Don't forget to use `fig, ax = plt.subplots()` so you don't overwrite
 the first image with the second.
@@ -214,9 +214,9 @@ iio.imwrite(uri="data/resized_chair.jpg", image=resized_chair)
 
 # display images
 fig, ax = plt.subplots()
-plt.imshow(chair)
+ax.imshow(chair)
 fig, ax = plt.subplots()
-plt.imshow(resized_chair)
+ax.imshow(resized_chair)
 ```
 
 The script resizes the `data/chair.jpg` image by a factor of 10 in both dimensions,
@@ -271,7 +271,7 @@ maize_roots = np.array(maize_roots)
 
 # display original image
 fig, ax = plt.subplots()
-plt.imshow(maize_roots)
+ax.imshow(maize_roots)
 ```
 
 Now we can threshold the image and display the result.
@@ -282,7 +282,7 @@ maize_roots[maize_roots < 128] = 0
 
 # display modified image
 fig, ax = plt.subplots()
-plt.imshow(maize_roots)
+ax.imshow(maize_roots)
 ```
 
 The NumPy command to ignore all low-intensity pixels is `roots[roots < 128] = 0`.
@@ -331,12 +331,12 @@ chair = iio.imread(uri="data/chair.jpg")
 
 # display original image
 fig, ax = plt.subplots()
-plt.imshow(chair)
+ax.imshow(chair)
 
 # convert to grayscale and display
 gray_chair = ski.color.rgb2gray(chair)
 fig, ax = plt.subplots()
-plt.imshow(gray_chair, cmap="gray")
+ax.imshow(gray_chair, cmap="gray")
 ```
 
 We can also load colour images as grayscale directly by
@@ -350,7 +350,7 @@ gray_chair = iio.imread(uri="data/chair.jpg", mode="L")
 
 # display grayscale image
 fig, ax = plt.subplots()
-plt.imshow(gray_chair, cmap="gray")
+ax.imshow(gray_chair, cmap="gray")
 ```
 
 The first argument to `iio.imread()` is the filename of the image.
@@ -415,7 +415,6 @@ sudoku_gray_background[sudoku_gray_background > 192] = 192
 Finally, display the original and modified images side by side. Note that we have to specify `vmin=0` and `vmax=255` as the range of the colorscale because it would otherwise automatically adjust to the new range 0-192.
 
 ```python
-fig, ax = plt.subplots()
 fig, ax = plt.subplots(ncols=2)
 ax[0].imshow(sudoku, cmap="gray", vmin=0, vmax=255)
 ax[1].imshow(sudoku_gray_background, cmap="gray", vmin=0, vmax=255)
@@ -430,11 +429,11 @@ ax[1].imshow(sudoku_gray_background, cmap="gray", vmin=0, vmax=255)
 ## Plotting single channel images (cmap, vmin, vmax)
 
 Compared to a colour image, a grayscale image contains only a single
-intensity value per pixel. When we plot such an image with `plt.imshow`,
+intensity value per pixel. When we plot such an image with `ax.imshow`,
 Matplotlib uses a colour map, to assign each intensity value a colour.
 The default colour map is called "viridis" and maps low values to purple
 and high values to yellow. We can instruct Matplotlib to map low values
-to black and high values to white instead, by calling `plt.imshow` with
+to black and high values to white instead, by calling `ax.imshow` with
 `cmap="gray"`.
 [The documentation contains an overview of pre-defined colour maps](https://matplotlib.org/stable/gallery/color/colormap_reference.html).
 
@@ -499,7 +498,7 @@ A script to create the subimage would start by loading the image:
 board = iio.imread(uri="data/board.jpg")
 board = np.array(board)
 fig, ax = plt.subplots()
-plt.imshow(board)
+ax.imshow(board)
 ```
 
 Then we use array slicing to
@@ -509,7 +508,7 @@ create a new image with our selected area and then display the new image.
 # extract, display, and save sub-image
 clipped_board = board[60:151, 135:481, :]
 fig, ax = plt.subplots()
-plt.imshow(clipped_board)
+ax.imshow(clipped_board)
 iio.imwrite(uri="data/clipped_board.tif", image=clipped_board)
 ```
 
@@ -520,7 +519,7 @@ We can also change the values in an image, as shown next.
 color = board[330, 90]
 board[60:151, 135:481] = color
 fig, ax = plt.subplots()
-plt.imshow(board)
+ax.imshow(board)
 ```
 
 First, we sample a single pixel's colour at a particular location of the
@@ -559,12 +558,12 @@ in the image.
 # load and display original image
 maize_roots = iio.imread(uri="data/maize-root-cluster.jpg")
 fig, ax = plt.subplots()
-plt.imshow(maize_roots)
+ax.imshow(maize_roots)
 
 # extract and display sub-image
 clipped_maize = maize_roots[0:400, 275:550, :]
 fig, ax = plt.subplots()
-plt.imshow(clipped_maize)
+ax.imshow(clipped_maize)
 
 
 # save sub-image

episodes/04-drawing.md

@@ -77,7 +77,7 @@ image:
 maize_seedlings = iio.imread(uri="data/maize-seedlings.tif")
 
 fig, ax = plt.subplots()
-plt.imshow(maize_seedlings)
+ax.imshow(maize_seedlings)
 ```
 
 We load and display the initial image in the same way we have done before.
@@ -117,7 +117,7 @@ mask[rr, cc] = False
 
 # Display mask image
 fig, ax = plt.subplots()
-plt.imshow(mask, cmap="gray")
+ax.imshow(mask, cmap="gray")
 ```
 
 Here is what our constructed mask looks like:
@@ -229,7 +229,7 @@ canvas[rr, cc] = (0, 255, 0)
 ```python
 # Display the image
 fig, ax = plt.subplots()
-plt.imshow(canvas)
+ax.imshow(canvas)
 ```
 
 We could expand this solution, if we wanted,
@@ -258,7 +258,7 @@ for i in range(15):
 
 # display the results
 fig, ax = plt.subplots()
-plt.imshow(canvas)
+ax.imshow(canvas)
 ```
 
 We could expand this even further to also
@@ -306,7 +306,7 @@ for i in range(15):
 
 # display the results
 fig, ax = plt.subplots()
-plt.imshow(canvas)
+ax.imshow(canvas)
 ```
 
 :::::::::::::::::::::::::
@@ -374,7 +374,7 @@ Then, we display the masked image.
 
 ```python
 fig, ax = plt.subplots()
-plt.imshow(maize_seedlings)
+ax.imshow(maize_seedlings)
 ```
 
 The resulting masked image should look like this:
@@ -423,7 +423,7 @@ remote[mask] = 0
 
 # Display the result
 fig, ax = plt.subplots()
-plt.imshow(remote)
+ax.imshow(remote)
 ```
 
 :::::::::::::::::::::::::
@@ -443,7 +443,7 @@ wellplate = np.array(wellplate)
 
 # Display the image
 fig, ax = plt.subplots()
-plt.imshow(wellplate)
+ax.imshow(wellplate)
 ```
 
 ![](data/wellplate-01.jpg){alt='96-well plate'}
@@ -491,7 +491,7 @@ wellplate[mask] = 0
 
 # display the result
 fig, ax = plt.subplots()
-plt.imshow(wellplate)
+ax.imshow(wellplate)
 ```
 
 :::::::::::::::::::::::::
@@ -564,7 +564,7 @@ wellplate[mask] = 0
 
 # display the result
 fig, ax = plt.subplots()
-plt.imshow(wellplate)
+ax.imshow(wellplate)
 ```
 
 :::::::::::::::::::::::::