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Source  : 007126d
Branch  : main
Author  : Kimberly Meechan <[email protected]>
Time    : 2024-11-18 09:29:18 +0000
Message : Merge pull request datacarpentry#331 from datacarpentry/update/workflows

Update Workflows to Version 0.16.6

02-image-basics.md

@@ -32,6 +32,12 @@ as numeric abstractions, approximations of what we see with our eyes in the real
 Before we begin to learn how to process images with Python programs,
 we need to spend some time understanding how these abstractions work.
 
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+Feel free to make use of the [available cheat-sheet](./files/cheatsheet.html) as a guide for the rest of the course material. View it online, share it, or print the [PDF](./files/cheatsheet.pdf)!
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
 ## Pixels
 
 It is important to realise that images are stored as rectangular arrays
@@ -287,7 +293,7 @@ Using array slicing, we can then address and assign a new value to that position
 
 ```python
 zero = iio.imread(uri="data/eight.tif")
-zero[2,1]= 1.0
+zero[2, 1]= 1.0
 
 # The following line of code creates a new figure for imshow to use in displaying our output.
 fig, ax = plt.subplots()
@@ -362,8 +368,8 @@ There are many possible solutions, but one method would be . . .
 
 ```python
 five = iio.imread(uri="data/eight.tif")
-five[1,2]= 1.0
-five[3,0]= 1.0
+five[1, 2] = 1.0
+five[3, 0] = 1.0
 fig, ax = plt.subplots()
 ax.imshow(five)
 print(five)
@@ -400,7 +406,7 @@ three_colours = three_colours * 128
 
 # set the middle row (index 2) to the value of 255.,
 # so you end up with the values 0., 128., and 255.
-three_colours[2,:] = 255.
+three_colours[2, :] = 255.
 fig, ax = plt.subplots()
 ax.imshow(three_colours)
 print(three_colours)
@@ -436,12 +442,12 @@ a mapped continuum of intensities: greyscale.
 
 ```python
 fig, ax = plt.subplots()
-ax.imshow(three_colours,cmap=plt.cm.gray)
+ax.imshow(three_colours, cmap="gray")
 ```
 
 ![](fig/grayscale.png){alt='Image in greyscale'}
 
-Above we have exactly the same underying data matrix, but in greyscale.
+Above we have exactly the same underlying data matrix, but in greyscale.
 Zero maps to black, 255 maps to white, and 128 maps to medium grey.
 Here we only have a single channel in the data and utilize a grayscale color map
 to represent the luminance, or intensity of the data and correspondingly

03-skimage-images.md

@@ -31,6 +31,8 @@ import ipympl
 import matplotlib.pyplot as plt
 import numpy as np
 import skimage as ski
+
+%matplotlib widget
 ```
 
 ## Reading, displaying, and saving images

04-drawing.md

@@ -462,29 +462,33 @@ Your program should produce output that looks like this:
 
 ![](fig/wellplate-01-masked.jpg){alt='Masked 96-well plate'}
 
+*Hint: You can load `data/centers.txt` using*:
+
+```Python
+# load the well coordinates as a NumPy array
+centers = np.loadtxt("data/centers.txt", delimiter=" ")
+```
+
 :::::::::::::::  solution
 
 ## Solution
 
 ```python
+# load the well coordinates as a NumPy array
+centers = np.loadtxt("data/centers.txt", delimiter=" ")
+
 # read in original image
 wellplate = iio.imread(uri="data/wellplate-01.jpg")
 wellplate = np.array(wellplate)
 
 # create the mask image
 mask = np.ones(shape=wellplate.shape[0:2], dtype="bool")
 
-# open and iterate through the centers file...
-with open("data/centers.txt", "r") as center_file:
-    for line in center_file:
-        # ... getting the coordinates of each well...
-        coordinates = line.split()
-        cx = int(coordinates[0])
-        ry = int(coordinates[1])
-
-        # ... and drawing a circle on the mask
-        rr, cc = ski.draw.disk(center=(ry, cx), radius=16, shape=wellplate.shape[0:2])
-        mask[rr, cc] = False
+# iterate through the well coordinates
+for cx, ry in centers:
+    # draw a circle on the mask at the well center
+    rr, cc = ski.draw.disk(center=(ry, cx), radius=16, shape=wellplate.shape[:2])
+    mask[rr, cc] = False
 
 # apply the mask
 wellplate[mask] = 0

05-creating-histograms.md

@@ -450,7 +450,7 @@ ax.set_ylabel("pixel count")
 - In many cases, we can load images in grayscale by passing the `mode="L"` argument to the `iio.imread()` function.
 - We can create histograms of images with the `np.histogram` function.
 - We can display histograms using `ax.plot()` with the `bin_edges` and `histogram` values returned by `np.histogram()`.
-- The plot can be customised using `set_xlabel()`, `set_ylabel()`, `set_xlim()`, `set_ylim()`, and `set_title()`.
+- The plot can be customised using `ax.set_xlabel()`, `ax.set_ylabel()`, `ax.set_xlim()`, `ax.set_ylim()`, and `ax.set_title()`.
 - We can separate the colour channels of an RGB image using slicing operations and create histograms for each colour channel separately.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::

06-blurring.md

@@ -51,7 +51,7 @@ A high-pass filter will retain the smaller details in an image,
 filtering out the larger ones.
 A low-pass filter retains the larger features,
 analogous to what's left behind by a physical filter mesh.
-*High-* and \*low-\*pass, here,
+*High-* and *low*-pass, here,
 refer to high and low *spatial frequencies* in the image.
 Details associated with high spatial frequencies are small,
 a lot of these features would fit across an image.

08-connected-components.md

@@ -610,7 +610,7 @@ The labels of the objects are also returned by `ski.measure.regionprops`.
 We have already seen that we can create boolean arrays using comparison operators.
 Here we can use `object_areas > min_area`
 to produce an array that has the same dimension as `object_labels`.
-It can then used to select the labels of objects whose area is
+It can then be used to select the labels of objects whose area is
 greater than `min_area` by indexing:
 
 ```python

instructor-notes.md

@@ -36,6 +36,12 @@ to share your experience with the lesson Maintainers.
 - Be aware that learners might get surprising results in the *Keeping only low intensity pixels* exercise, if `plt.imshow` is called without the `vmax` parameter.
   A detailed explanation is given in the *Plotting single channel images (cmap, vmin, vmax)* callout box.
 
+## Additional resources
+
+- A cheat-sheet with graphics illustrating some concepts in this lesson is available:
+    - [Cheat-sheet HTML for viewing in browser](../episodes/files/cheatsheet.html). 
+    - [PDF version for printing](../episodes/files/cheatsheet.pdf).
+
 
 ## Questions from Learners
 

md5sum.txt

@@ -4,15 +4,15 @@
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