diff --git a/Chapter01/Links.md b/Chapter01/Links.md index 2fbef6a225c89d8f4aa53a7d1219a7fe7e151780..a747ab9cf8fae14d768996e37385ccc7e4dc607f 100644 --- a/Chapter01/Links.md +++ b/Chapter01/Links.md @@ -1,21 +1,21 @@ # Exercise 1.1.1 Image convolution -- Read the image using `skimage.io.imread` -- For convolution use `scipy.ndimage.convolve` -- After completing the exercise test agains `scipy.ndimage.gaussian_filter` +- Read the image using [`skimage.io.imread`](https://scikit-image.org/docs/stable/api/skimage.io.html#skimage.io.imread) +- For convolution use [`scipy.ndimage.convolve`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.convolve.html) +- After completing the exercise test agains [`scipy.ndimage.gaussian_filter`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html) # Exercise 1.1.3 Curve smoothing -- Make the matrix using `scipy.linalg.circulant` -- For matrix-vector multiplication you can use a dedicated function from `numpy` or `@` operator +- Make the matrix using [`scipy.linalg.circulant`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.circulant.html) +- For matrix-vector multiplication you can use [`numpy.matmul`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) or the shorthand operator `@` (explained at the bottom of page dedicated to `matmul`). # Exericse 1.1.6 Working with volumetric image -- To get hold of all image in a folder, use `sorted(os.listdir(\<FOLDER NAME\>))` +- To get hold of all image in a certain folder, use `sorted(os.listdir(\<FOLDER NAME\>))` # Exercise 1.1.7 PCA of multispectral image -- For eigen decomposition use ` np.linalg.eig` -- You can compare your PCA agains the existing implementation `sklearn.decomposition.PCA` +- For eigendecomposition use `numpy.linalg.eig` +- You can compare your PCA against the existing implementation `sklearn.decomposition.PCA` # Exercise 1.1.8 Bacterial growth from movie frames - To read the movie as a list of images, use following code block