use double-quotes for strings in code blocks

Author: tobyhodges

episodes/03-skimage-images.md

@@ -185,14 +185,14 @@ In this case, the `.tif` extension causes the image to be saved as a TIFF.
 > So, we could load in the chair image in the sample code above
 > using positional parameters like this:
 >
-> `image = skimage.io.imread('data/chair.jpg')`
+> `image = skimage.io.imread("data/chair.jpg")`
 >
 > Since the function expects the first argument to be the file name,
-> there is no confusion about what `'data/chair.jpg'` means.
+> there is no confusion about what `"data/chair.jpg"` means.
 >
 > The style we will use in this workshop is to name each parameters, like this:
 >
-> `image = skimage.io.imsave(fname='data/chair.jpg')`
+> `image = skimage.io.imsave(fname="data/chair.jpg")`
 >
 > This style will make it easier for you to learn how to use the variety of
 > functions we will cover in this workshop.
@@ -253,7 +253,7 @@ In this case, the `.tif` extension causes the image to be saved as a TIFF.
 > > import skimage.transform
 > >
 > > # read in image
-> > image = skimage.io.imread('data/chair.jpg')
+> > image = skimage.io.imread("data/chair.jpg")
 > >
 > > # resize the image
 > > new_shape = (image.shape[0] // 10, image.shape[1] // 10, image.shape[2])
@@ -310,7 +310,7 @@ We will start by reading the image and displaying it.
 import skimage.io
 
 # read input image
-image = skimage.io.imread('data/maize-root-cluster.jpg')
+image = skimage.io.imread("data/maize-root-cluster.jpg")
 
 # display original image
 fig, ax = plt.subplots()
@@ -362,7 +362,7 @@ import skimage.io
 import skimage.color
 
 # read input image
-image = skimage.io.imread('data/chair.jpg')
+image = skimage.io.imread("data/chair.jpg")
 
 # display original image
 fig, ax = plt.subplots()
@@ -371,7 +371,7 @@ plt.imshow(image)
 # convert to grayscale and display
 gray_image = skimage.color.rgb2gray(image)
 fig, ax = plt.subplots()
-plt.imshow(gray_image, cmap='gray')
+plt.imshow(gray_image, cmap="gray")
 ~~~
 {: .language-python}
 
@@ -386,11 +386,11 @@ 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("data/chair.jpg", as_gray=True)
 
 # display grayscale image
 fig, ax = plt.subplots()
-plt.imshow(image, cmap='gray')
+plt.imshow(image, cmap="gray")
 ~~~
 {: .language-python}
 
@@ -421,7 +421,7 @@ plt.imshow(image, cmap='gray')
 > > ~~~
 > > import skimage.io
 > >
-> > image = skimage.io.imread(fname='data/sudoku.png', as_gray=True)
+> > image = skimage.io.imread(fname="data/sudoku.png", as_gray=True)
 > > ~~~
 > > {: .language-python }
 > >
@@ -436,7 +436,7 @@ plt.imshow(image, cmap='gray')
 > >
 > > ~~~
 > > fig, ax = plt.subplots()
-> > plt.imshow(image, cmap='gray')
+> > plt.imshow(image, cmap="gray")
 > > ~~~
 > > {: .language-python}
 > {: .solution}
@@ -540,7 +540,7 @@ as shown in the final image produced by the program:
 >
 > Using the techniques you just learned, write a script that
 > creates, displays, and saves a sub-image containing
-> only the plant and its roots from 'data/maize-root-cluster.jpg'
+> only the plant and its roots from "data/maize-root-cluster.jpg"
 >
 > > ## Solution
 > >

episodes/04-drawing.md

@@ -117,7 +117,7 @@ mask[rr, cc] = False
 
 # Display mask image
 fig, ax = plt.subplots()
-plt.imshow(mask, cmap='gray')
+plt.imshow(mask, cmap="gray")
 plt.show()
 ~~~
 {: .language-python}

episodes/05-creating-histograms.md

@@ -52,11 +52,11 @@ import matplotlib.pyplot as plt
 %matplotlib widget
 
 # read the image of a plant seedling as grayscale from the outset
-image = skimage.io.imread(fname='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()
-plt.imshow(image, cmap='gray')
+plt.imshow(image, cmap="gray")
 plt.show()
 ~~~
 {: .language-python}
@@ -197,11 +197,11 @@ 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("data/plant-seedling.jpg", as_gray=True)
 > >
 > > # display the image
 > > fig, ax = plt.subplots()
-> > plt.imshow(image, cmap='gray')
+> > plt.imshow(image, cmap="gray")
 > > plt.show()
 > >
 > > # create mask here, using np.zeros() and skimage.draw.rectangle()
@@ -211,7 +211,7 @@ it produces this histogram:
 > >
 > > # display the mask
 > > fig, ax = plt.subplots()
-> > plt.imshow(mask, cmap='gray')
+> > plt.imshow(mask, cmap="gray")
 > > plt.show()
 > >
 > > # mask the image and create the new histogram
@@ -247,7 +247,7 @@ A program to create color histograms starts in a familiar way:
 
 ~~~
 # read original image, in full color
-image = skimage.io.imread('data/plant-seedling.jpg')
+image = skimage.io.imread("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("data/wellplate-02.tif")
 >
 > # display the image
 > fig, ax = plt.subplots()

episodes/07-thresholding.md

@@ -94,7 +94,7 @@ gray_image = skimage.color.rgb2gray(image)
 blurred_image = skimage.filters.gaussian(gray_image, sigma=1.0)
 
 fig, ax = plt.subplots()
-plt.imshow(blurred_image, cmap='gray')
+plt.imshow(blurred_image, cmap="gray")
 plt.show()
 ~~~
 {: .language-python}
@@ -159,7 +159,7 @@ t = 0.8
 binary_mask = blurred_image < t
 
 fig, ax = plt.subplots()
-plt.imshow(binary_mask, cmap='gray')
+plt.imshow(binary_mask, cmap="gray")
 plt.show()
 ~~~
 {: .language-python}
@@ -265,7 +265,7 @@ plt.show()
 > > binary_mask = gray_image > t
 > >
 > > fig, ax = plt.subplots()
-> > plt.imshow(binary_mask, cmap='gray')
+> > plt.imshow(binary_mask, cmap="gray")
 > > plt.show()
 > > ~~~
 > > {: .language-python}
@@ -382,7 +382,7 @@ those below the threshold will be turned off.
 binary_mask = blurred_image > t
 
 fig, ax = plt.subplots()
-plt.imshow(binary_mask, cmap='gray')
+plt.imshow(binary_mask, cmap="gray")
 plt.show()
 ~~~
 {: .language-python}
@@ -720,7 +720,7 @@ data/trial-293.jpg,0.13607895611702128
 > > binary_mask = blurred_image < t
 > >
 > > fig, ax = plt.subplots()
-> > plt.imshow(binary_mask, cmap='gray')
+> > plt.imshow(binary_mask, cmap="gray")
 > > plt.show()
 > > ~~~
 > > {: .language-python}

episodes/08-connected-components.md

@@ -296,7 +296,7 @@ labeled_image, count = connected_components("data/shapes-01.jpg", sigma=2.0, t=0
 
 fig, ax = plt.subplots()
 plt.imshow(labeled_image)
-plt.axis('off')
+plt.axis("off")
 plt.show()
 ~~~
 {: .language-python}
@@ -356,7 +356,7 @@ colored_label_image = skimage.color.label2rgb(labeled_image, bg_label=0)
 
 fig, ax = plt.subplots()
 plt.imshow(colored_label_image)
-plt.axis('off')
+plt.axis("off")
 plt.show()
 ~~~
 {: .language-python}
@@ -689,7 +689,7 @@ This will produce the output
 > >
 > > fig, ax = plt.subplots()
 > > plt.imshow(colored_label_image)
-> > plt.axis('off')
+> > plt.axis("off")
 > > plt.show()
 > >
 > > print("Found", count, "objects in the image.")
@@ -734,8 +734,8 @@ This will produce the output
 > > fig, ax = plt.subplots()
 > > im = plt.imshow(colored_area_image)
 > > cbar = fig.colorbar(im, ax=ax, shrink=0.85)
-> > cbar.ax.set_title('Area')
-> > plt.axis('off')
+> > cbar.ax.set_title("Area")
+> > plt.axis("off")
 > > plt.show()
 > > ~~~
 > > {: .language-python}

episodes/09-challenges.md

@@ -83,7 +83,7 @@ and `data/colonies-03.tif`.
 > >
 > > # display the gray image
 > > fig, ax = plt.subplots()
-> > plt.imshow(gray_bacteria, cmap='gray')
+> > plt.imshow(gray_bacteria, cmap="gray")
 > > plt.show()
 > > ~~~
 > > {: .language-python}
@@ -116,7 +116,7 @@ and `data/colonies-03.tif`.
 > > ~~~
 > > mask = blurred_image < 0.2
 > > fig, ax = plt.subplots()
-> > plt.imshow(mask, cmap='gray')
+> > plt.imshow(mask, cmap="gray")
 > > plt.show()
 > > ~~~
 > > {: .language-python}