homework 1, version 3
+xxxxxxxxxxmd"_homework 1, version 3_"Submission by: Jazzy Doe (jazz@mit.edu)
+xxxxxxxxxxmd"""Homework 1 - convolutions
+18.S191, fall 2020
This notebook contains built-in, live answer checks! In some exercises you will see a coloured box, which runs a test case on your code, and provides feedback based on the result. Simply edit the code, run it, and the check runs again.
+For MIT students: there will also be some additional (secret) test cases that will be run as part of the grading process, and we will look at your notebook and write comments.
+Feel free to ask questions!
+xxxxxxxxxxmd""""Jazzy Doe"
"jazz"
xxxxxxxxxx# edit the code below to set your name and kerberos ID (i.e. email without @mit.edu)student = (name = "Jazzy Doe", kerberos_id = "jazz")# press the ▶ button in the bottom right of this cell to run your edits# or use Shift+Enter# you might need to wait until all other cells in this notebook have completed running. # scroll down the page to see what's upLet's create a package environment:
+xxxxxxxxxxmd"_Let's create a package environment:_"xxxxxxxxxxbegin import Pkg Pkg.activate(mktempdir())endWe set up Images.jl again:
+xxxxxxxxxxmd"_We set up Images.jl again:_"xxxxxxxxxxbegin Pkg.add(["Images", "ImageIO", "ImageMagick"]) using ImagesendxxxxxxxxxxbigbreakExercise 1 - Manipulating vectors (1D images)
+A Vector is a 1D array. We can think of that as a 1D image.
xxxxxxxxxxmd"""0.5
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xxxxxxxxxxexample_vector = [0.5, 0.4, 0.3, 0.2, 0.1, 0.0, 0.7, 0.0, 0.7, 0.9]xxxxxxxxxxcolored_line(example_vector)Exerise 1.1
+👉 Make a random vector random_vect of length 10 using the rand function.
xxxxxxxxxxmd"#### Exerise 1.1missingxxxxxxxxxxrandom_vect = missing # replace this with your code!xxxxxxxxxxcolored_line(random_vect)Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(random_vect)Hint
You can find out more about any function (like rand) by creating a new cell and typing:
?randOnce the Live Docs are open, you can select any code to learn more about it. It might be useful to leave it open all the time, and get documentation while you type code.
+ +xxxxxxxxxxhint(md"You can find out more about any function (like `rand`) by creating a new cell and typing:👉 Make a function mean using a for loop, which computes the mean/average of a vector of numbers.
xxxxxxxxxxmd"👉 Make a function `mean` using a `for` loop, which computes the mean/average of a vector of numbers."mean (generic function with 1 method)xxxxxxxxxxfunction mean(x) return missingendmissingxxxxxxxxxxmean([1, 2, 3])Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(mean)👉 Define m to be the mean of random_vect.
xxxxxxxxxxmd"👉 Define `m` to be the mean of `random_vect`."missingxxxxxxxxxxm = missingHere we go!
Replace missing with your answer.
xxxxxxxxxxif !(m)👉 Write a function demean, which takes a vector x and subtracts the mean from each value in x.
xxxxxxxxxxmd"""👉 Write a function `demean`, which takes a vector `x` and subtracts the mean from each value in `x`."""demean (generic function with 1 method)xxxxxxxxxxfunction demean(x) return missingendLet's check that the mean of the demean(random_vect) is 0:
Due to floating-point round-off error it may not be exactly 0.
+xxxxxxxxxxmd"Let's check that the mean of the `demean(random_vect)` is 0:Info
The following cells error because random_vect is not yet defined. Have you done the first exercise?
xxxxxxxxxxif ismissing(random_vect)UndefVarError: copy_of_random_vect not defined
- top-level scope@Local: 1
- top-level scope@Parse.jl:111
- (::Distributed.var"#103#104"{Distributed.CallMsg{:call}})()@process_messages.jl:290
- run_work_thunk(::Distributed.var"#103#104"{Distributed.CallMsg{:call}}, ::Bool)@process_messages.jl:79
- run_work_thunk(::Distributed.RemoteValue, ::Function)@process_messages.jl:88
- (::Distributed.var"#96#98"{Distributed.RemoteValue,Distributed.var"#103#104"{Distributed.CallMsg{:call}}})()@task.jl:356
xxxxxxxxxxmean(demean(copy_of_random_vect))MethodError: no method matching copy(::Missing)
Closest candidates are:
copy(!Matched::Random.DSFMT.GF2X) at /build/julia/src/julia-1.5.0/usr/share/julia/stdlib/v1.5/Random/src/DSFMT.jl:111
copy(!Matched::DataStructures.IntSet) at /home/shashi/.julia/packages/DataStructures/6txFj/src/int_set.jl:11
copy(!Matched::Markdown.MD) at /build/julia/src/julia-1.5.0/usr/share/julia/stdlib/v1.5/Markdown/src/parse/parse.jl:30
...
- top-level scope@Local: 1
- top-level scope@Parse.jl:111
- (::Distributed.var"#103#104"{Distributed.CallMsg{:call}})()@process_messages.jl:290
- run_work_thunk(::Distributed.var"#103#104"{Distributed.CallMsg{:call}}, ::Bool)@process_messages.jl:79
- run_work_thunk(::Distributed.RemoteValue, ::Function)@process_messages.jl:88
- (::Distributed.var"#96#98"{Distributed.RemoteValue,Distributed.var"#103#104"{Distributed.CallMsg{:call}}})()@task.jl:356
xxxxxxxxxxcopy_of_random_vect = copy(random_vect); # in case demean modifies `x`Exercise 1.2
+👉 Generate a vector of 100 zeros. Change the center 20 elements to 1.
+xxxxxxxxxxmd"""create_bar (generic function with 1 method)xxxxxxxxxxfunction create_bar() return missingendxxxxxxxxxxcolored_line(create_bar())Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(create_bar)Exercise 1.3
+👉 Write a function that turns a Vector of Vectors into a Matrix.
xxxxxxxxxxmd"""vecvec_to_matrix (generic function with 1 method)xxxxxxxxxxfunction vecvec_to_matrix(vecvec) return missingendmissingxxxxxxxxxxvecvec_to_matrix([[1,2], [3,4]])Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(vecvec_to_matrix)👉 Write a function that turns a Matrix into aVector of Vectors .
xxxxxxxxxxmd"""matrix_to_vecvec (generic function with 1 method)xxxxxxxxxxfunction matrix_to_vecvec(matrix) return missingendmissingxxxxxxxxxxmatrix_to_vecvec([6 7; 8 9])Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(matrix_to_vecvec)colored_line (generic function with 2 methods)xxxxxxxxxxbeginxxxxxxxxxxbigbreakExercise 2 - Manipulating images
+In this exercise we will get familiar with matrices (2D arrays) in Julia, by manipulating images. Recall that in Julia images are matrices of RGB color objects.
Let's load a picture of Philip again.
+xxxxxxxxxxmd""""/tmp/jl_4reFYZ"xxxxxxxxxxphilip_file = download("https://i.imgur.com/VGPeJ6s.jpg")xxxxxxxxxxphilip = let original = Images.load(philip_file) decimate(original, 8)endHi there Philip
+xxxxxxxxxxmd"_Hi there Philip_"Exercise 2.1
+👉 Write a function mean_colors that accepts an object called image. It should calculate the mean (average) amounts of red, green and blue in the image and return a tuple (r, g, b) of those means.
xxxxxxxxxxmd"""mean_colors (generic function with 1 method)xxxxxxxxxxfunction mean_colors(image) return missingendmissingxxxxxxxxxxmean_colors(philip)Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(mean_colors)Enter cell code...
xxxxxxxxxxExercise 2.2
+👉 Look up the documentation on the floor function. Use it to write a function quantize(x::Number) that takes in a value
xxxxxxxxxxmd"""quantize (generic function with 3 methods)xxxxxxxxxxbegin function quantize(x::Number) return missing end function quantize(color::AbstractRGB) # you will write me in a later exercise! return missing end function quantize(image::AbstractMatrix) # you will write me in a later exercise! return missing endendmissing
missing
xxxxxxxxxxquantize(0.267), quantize(0.91)Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(quantize)Exercise 2.3
+👉 Write the second method of the function quantize, i.e. a new version of the function with the same name. This method will accept a color object called color, of the type AbstractRGB.
Write the function in the same cell as quantize(x::Number) from the last exercise. 👆
Here, ::AbstractRGB is a type annotation. This ensures that this version of the function will be chosen when passing in an object whose type is a subtype of the AbstractRGB abstract type. For example, both the RGB and RGBX types satisfy this.
The method you write should return a new RGB object, in which each component (
xxxxxxxxxxmd"""Exercise 2.4
+👉 Write a method quantize(image::AbstractMatrix) that quantizes an image by quantizing each pixel in the image. (You may assume that the matrix is a matrix of color objects.)
Write the function in the same cell as quantize(x::Number) from the last exercise. 👆
xxxxxxxxxxmd"""Let's apply your method!
+xxxxxxxxxxmd"Let's apply your method!"missingxxxxxxxxxxquantize(philip)Exercise 2.5
+👉 Write a function invert that inverts a color, i.e. sends
xxxxxxxxxxmd"""invert (generic function with 1 method)xxxxxxxxxxfunction invert(color::AbstractRGB) return missingendLet's invert some colors:
+xxxxxxxxxxmd"Let's invert some colors:"xxxxxxxxxxblack = RGB(0.0, 0.0, 0.0)missingxxxxxxxxxxinvert(black)xxxxxxxxxxred = RGB(0.8, 0.1, 0.1)missingxxxxxxxxxxinvert(red)Can you invert the picture of Philip?
+xxxxxxxxxxmd"Can you invert the picture of Philip?"missingxxxxxxxxxxphilip_inverted = missingExercise 2.6
+👉 Write a function noisify(x::Number, s) to add randomness of intensity clamp function, but you should write your own function myclamp(x).)
xxxxxxxxxxmd"""noisify (generic function with 3 methods)xxxxxxxxxxbegin function noisify(x::Number, s) return missing end function noisify(color::AbstractRGB, s) # you will write me in a later exercise! return missing end function noisify(image::AbstractMatrix, s) # you will write me in a later exercise! return missing endendHint
The rand function generates (uniform) random floating-point numbers between
xxxxxxxxxxhint(md"The `rand` function generates (uniform) random floating-point numbers between $0$ and $1$.")👉 Write the second method noisify(c::AbstractRGB, s) to add random noise of intensity
Write the function in the same cell as noisify(x::Number) from the last exercise. 👆
xxxxxxxxxxmd"""xxxxxxxxxx color_noise Slider(0:0.01:1, show_value=true)missingxxxxxxxxxxnoisify(red, color_noise)Enter cell code...
xxxxxxxxxx👉 Write the third method noisify(image::AbstractMatrix, s) to noisify each pixel of an image.
Write the function in the same cell as noisify(x::Number) from the last exercise. 👆
xxxxxxxxxxmd"""xxxxxxxxxx philip_noise Slider(0:0.01:8, show_value=true)missingxxxxxxxxxxnoisify(philip, philip_noise)Enter cell code...
xxxxxxxxxx👉 For which noise intensity does it become unrecognisable?
+You may need noise intensities larger than 1. Why?
+xxxxxxxxxxmd"""The image is unrecognisable with intensity ...
+xxxxxxxxxxanswer_about_noise_intensity = md"""The image is unrecognisable with intensity ..."""Enter cell code...
xxxxxxxxxxxxxxxxxxxxbegin Pkg.add("PlutoUI") using PlutoUIenddecimate (generic function with 2 methods)xxxxxxxxxxdecimate(image, ratio=5) = image[1:ratio:end, 1:ratio:end]xxxxxxxxxxbigbreakExercise 3 - Convolutions
+As we have seen in the videos, we can produce cool effects using the mathematical technique of convolutions. We input one image
Conceptually we think of Matrix of color objects, and we may need to take that into account. Ideally, however, we should write a generic function that will work for any type of data contained in the matrix.
A convolution works on a small window of an image, i.e. a region centered around a given point
The result of the convolution over a given window, centred at the point
To get started let's restrict ourselves to convolutions in 1D. So a window is just a 1D region from
xxxxxxxxxxmd"""+
Let's create a vector v of random numbers of length n=100.
xxxxxxxxxxmd"""100xxxxxxxxxxn = 1000.64771
0.22933
0.415342
0.703819
0.854471
0.27837
0.446598
0.686857
0.511744
0.191256
0.558569
0.0947994
0.276961
0.521417
0.987073
0.616009
0.332129
0.755946
0.7061
0.560034
0.857643
0.947328
0.161807
0.447422
0.496497
0.222904
0.368214
0.647241
0.625254
0.83159
0.103692
0.62409
0.0142629
0.344406
0.677014
0.926745
0.574309
0.65493
0.614455
0.92009
0.707832
0.470791
0.931427
0.965792
0.656162
0.413477
0.154902
0.76583
0.487643
0.530998
xxxxxxxxxxv = rand(n)Feel free to experiment with different values!
+xxxxxxxxxxmd"_Feel free to experiment with different values!_"Exercise 3.1
+You've seen some colored lines in this notebook to visualize arrays. Can you make another one?
+👉 Try plotting our vector v using colored_line(v).
xxxxxxxxxxmd"""Enter cell code...
xxxxxxxxxxTry changing n and v around. Notice that you can run the cell v = rand(n) again to regenerate new random values.
xxxxxxxxxxmd"Try changing `n` and `v` around. Notice that you can run the cell `v = rand(n)` again to regenerate new random values."Exercise 3.2
+We need to decide how to handle the boundary conditions, i.e. what happens if we try to access a position in the vector v beyond 1:n. The simplest solution is to assume that
A better solution is to use the closest value that is inside the vector. Effectively we are extending the vector and copying the extreme values into the extended positions. (Indeed, this is one way we could implement this; these extra positions are called ghost cells.)
+👉 Write a function extend(v, i) that checks whether the position 1:n. If so, return the v; otherwise, return the nearest end value.
xxxxxxxxxxmd"""extend (generic function with 1 method)xxxxxxxxxxfunction extend(v, i) return missingendSome test cases:
+xxxxxxxxxxmd"_Some test cases:_"missingxxxxxxxxxxextend(v, 1)missingxxxxxxxxxxextend(v, -8)missingxxxxxxxxxxextend(v, n + 10)Extended with 0:
+xxxxxxxxxxmd"Extended with 0:"xxxxxxxxxxcolored_line([0, 0, example_vector..., 0, 0])Extended with your extend:
xxxxxxxxxxmd"Extended with your `extend`:"missingxxxxxxxxxxif extend(v,1) === missingHere we go!
Replace missing with your answer.
xxxxxxxxxxif !(extend)Exercise 3.3
+👉 Write a function blur_1D(v, l) that blurs a vector v with a window of length l by averaging the elements within a window from
xxxxxxxxxxmd"""blur_1D (generic function with 1 method)xxxxxxxxxxfunction blur_1D(v, l) return missingendxxxxxxxxxxletExercise 3.4
+👉 Apply the box blur to your vector v. Show the original and the new vector by creating two cells that call colored_line. Make the parameter l_box instead of just l to avoid a variable naming conflict.
xxxxxxxxxxmd"""Enter cell code...
xxxxxxxxxxHint
Have a look at Exercise 2 to see an example of adding interactivity with a slider. You can read the Interactivity and the PlutoUI sample notebooks (right click -> Open in new tab) to learn more.
+ +xxxxxxxxxxhint(md"Have a look at Exercise 2 to see an example of adding interactivity with a slider. You can read the [Interactivity](./sample/Interactivity.jl) and the [PlutoUI](./sample/PlutoUI.jl) sample notebooks _(right click -> Open in new tab)_ to learn more.")Exercise 3.5
+The box blur is a simple example of a convolution, i.e. a linear function of a window around each point, given by
+where
Again, we need to take care about what happens if
👉 Write a function convolve_vector(v, k) that performs this convolution. You need to think of the vector
xxxxxxxxxxmd"""convolve_vector (generic function with 1 method)xxxxxxxxxxfunction convolve_vector(v, k) return missingendHint
l = (length(k) - 1) ÷ 2
xxxxxxxxxxhint(md"`l = (length(k) - 1) ÷ 2`")xxxxxxxxxxcolored_line(test_convolution)missingxxxxxxxxxxtest_convolution = let v = [1, 10, 100, 1000, 10000] k = [0, 1, 0] convolve_vector(v, k)endEdit the cell above, or create a new cell with your own test cases!
+xxxxxxxxxxmd"_Edit the cell above, or create a new cell with your own test cases!_"Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(convolve_vector)Exercise 3.6
+👉 Write a function gaussian_kernel.
The definition of a Gaussian in 1D is
+We need to sample (i.e. evaluate) this at each pixel in a region of size
For simplicity you can take
xxxxxxxxxxmd"""gaussian_kernel (generic function with 1 method)xxxxxxxxxxfunction gaussian_kernel(n) return missingendLet's test your kernel function!
+xxxxxxxxxxmd"Let's test your kernel function!"3xxxxxxxxxxgaussian_kernel_size_1D = 3 # change this value, or turn me into a slider!xxxxxxxxxxcolored_line(test_gauss_1D_a)xxxxxxxxxxtest_gauss_1D_a = let v = random_vect k = gaussian_kernel(gaussian_kernel_size_1D) if k !== missing convolve_vector(v, k) endendxxxxxxxxxxcolored_line(test_gauss_1D_b)xxxxxxxxxxtest_gauss_1D_b = let v = create_bar() k = gaussian_kernel(gaussian_kernel_size_1D) if k !== missing convolve_vector(v, k) endendxxxxxxxxxxbigbreakExercise 4 - Convolutions of images
+Now let's move to 2D images. The convolution is then given by a kernel matrix
where the sum is over the possible values of
A common notation for this operation is
xxxxxxxxxxmd""" ## **Exercise 4** - _Convolutions of images_ Now let's move to 2D images. The convolution is then given by a **kernel** matrix $K$: $$M'_{i, j} = \sum_{k, l} \, M_{i- k, j - l} \, K_{k, l},$$ where the sum is over the possible values of $k$ and $l$ in the window. Again we think of the window as being *centered* at $(i, j)$.A common notation for this operation is $*$:$$M' = M * K.$$"""Exercise 4.1
+👉 Write a function extend_mat that takes a matrix M and indices i and j, and returns the closest element of the matrix.
xxxxxxxxxxmd"""extend_mat (generic function with 1 method)xxxxxxxxxxfunction extend_mat(M::AbstractMatrix, i, j) return missingendHint
num_rows, num_columns = size(M)
xxxxxxxxxxhint(md"`num_rows, num_columns = size(M)`")Let's test it!
+xxxxxxxxxxmd"_Let's test it!_"xxxxxxxxxxsmall_image = Gray.(rand(5,5))Extended with 0:
xxxxxxxxxxmd"Extended with `0`:"xxxxxxxxxx[get(small_image, (i, j), Gray(0)) for (i,j) in Iterators.product(-1:7,-1:7)]Extended with your extend:
xxxxxxxxxxmd"Extended with your `extend`:"9×9 Array{Missing,2}:
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missingxxxxxxxxxx[extend_mat(small_image, i, j) for (i,j) in Iterators.product(-1:7,-1:7)]Here we go!
Replace missing with your answer.
xxxxxxxxxxif !(extend_mat)283×223 Array{Missing,2}:
+ missing missing missing missing missing … missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing … missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ ⋮ ⋱ ⋮
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing … missing missing missing missing
+ missing missing missing missing missing missing missing missing missing
+ missing missing missing missing missing missing missing missing missingxxxxxxxxxxletExercise 4.2
+👉 Implement a function convolve_image(M, K).
xxxxxxxxxxmd"""convolve_image (generic function with 1 method)xxxxxxxxxxfunction convolve_image(M::AbstractMatrix, K::AbstractMatrix) return missingendHint
num_rows, num_columns = size(K)
xxxxxxxxxxhint(md"`num_rows, num_columns = size(K)`")Let's test it out! 🎃
+xxxxxxxxxxmd"_Let's test it out! 🎃_"xxxxxxxxxxtest_image_with_border = [get(small_image, (i, j), Gray(0)) for (i,j) in Iterators.product(-1:7,-1:7)]3×3 Array{Float64,2}:
+ 0.0 0.0 0.0
+ 0.5 0.0 0.5
+ 0.0 0.0 0.0xxxxxxxxxxK_test = [ 0 0 0 1/2 0 1/2 0 0 0]missingxxxxxxxxxxconvolve_image(test_image_with_border, K_test)Edit K_test to create your own test case!
xxxxxxxxxxmd"_Edit_ `K_test` _to create your own test case!_"missingxxxxxxxxxxconvolve_image(philip, K_test)+
You can create all sorts of effects by choosing the kernel in a smart way. Today, we will implement two special kernels, to produce a Gaussian blur and a Sobel edge detect filter.
+Make sure that you have watched the lecture about convolutions!
+xxxxxxxxxxmd"""Exercise 4.3
+👉 Apply a Gaussian blur to an image.
+Here, the 2D Gaussian kernel will be defined as
+xxxxxxxxxxmd"""#### Exercise 4.3👉 Apply a **Gaussian blur** to an image.Here, the 2D Gaussian kernel will be defined as$$G(x,y)=\frac{1}{2\pi \sigma^2}e^{\frac{-(x^2+y^2)}{2\sigma^2}}$$"""with_gaussian_blur (generic function with 1 method)xxxxxxxxxxfunction with_gaussian_blur(image) return missingendLet's make it interactive. 💫
+xxxxxxxxxxmd"_Let's make it interactive. 💫_"xxxxxxxxxx gauss_raw_camera_data camera_input(;max_size=100)missingxxxxxxxxxxwith_gaussian_blur(gauss_camera_image)xxxxxxxxxxExercise 4.4
+👉 Create a Sobel edge detection filter.
+Here, we will need to create two separate filters that separately detect edges in the horizontal and vertical directions:
+Here
Then we combine them by finding the magnitude of the gradient (in the sense of multivariate calculus) by defining
+For simplicity you can choose one of the "channels" (colours) in the image to apply this to.
+xxxxxxxxxxmd"""with_sobel_edge_detect (generic function with 1 method)xxxxxxxxxxfunction with_sobel_edge_detect(image) return missingendxxxxxxxxxx sobel_raw_camera_data camera_input(;max_size=100)missingxxxxxxxxxxwith_sobel_edge_detect(sobel_camera_image)xxxxxxxxxxxxxxxxxxxxbigbreakExercise 5 - Lecture transcript
+(MIT students only)
+Please see the Canvas post for transcript document for week 1 here.
+We need each of you to correct about 100 lines (see instructions in the beginning of the document.)
+👉 Please mention the name of the video and the line ranges you edited:
+x
md"""## **Exercise 5** - _Lecture transcript__(MIT students only)_Please see the Canvas post for transcript document for week 1 [here](https://canvas.mit.edu/courses/5637/discussion_topics/27880).We need each of you to correct about 100 lines (see instructions in the beginning of the document.)👉 Please mention the name of the video and the line ranges you edited:"""Convolution, lines 100-0 (for example)
+x
lines_i_edited = md"""Convolution, lines 100-0 (_for example_)"""xxxxxxxxxxbigbreakOops!
Before you submit, remember to fill in your name and kerberos ID at the top of this notebook!
+xxxxxxxxxxif student.kerberos_id === "jazz"xxxxxxxxxxbigbreakhint (generic function with 1 method)xxxxxxxxxxhint(text) = Markdown.MD(Markdown.Admonition("hint", "Hint", [text]))almost (generic function with 1 method)xxxxxxxxxxalmost(text) = Markdown.MD(Markdown.Admonition("warning", "Almost there!", [text]))still_missing (generic function with 2 methods)xxxxxxxxxxstill_missing(text=md"Replace `missing` with your answer.") = Markdown.MD(Markdown.Admonition("warning", "Here we go!", [text]))keep_working (generic function with 2 methods)xxxxxxxxxxkeep_working(text=md"The answer is not quite right.") = Markdown.MD(Markdown.Admonition("danger", "Keep working on it!", [text]))Great!
+Yay ❤
+Great! 🎉
+Well done!
+Keep it up!
+Good job!
+Awesome!
+You got the right answer!
+Let's move on to the next section.
+xxxxxxxxxxyays = [md"Great!", md"Yay ❤", md"Great! 🎉", md"Well done!", md"Keep it up!", md"Good job!", md"Awesome!", md"You got the right answer!", md"Let's move on to the next section."]correct (generic function with 2 methods)xxxxxxxxxxcorrect(text=rand(yays)) = Markdown.MD(Markdown.Admonition("correct", "Got it!", [text]))not_defined (generic function with 1 method)xxxxxxxxxxnot_defined(variable_name) = Markdown.MD(Markdown.Admonition("danger", "Oopsie!", [md"Make sure that you define a variable called **$(Markdown.Code(string(variable_name)))**"]))xxxxxxxxxxbigbreak = html"<br><br><br><br><br>";camera_input (generic function with 1 method)xxxxxxxxxxfunction camera_input(;max_size=200, default_url="https://i.imgur.com/SUmi94P.png")process_raw_camera_data (generic function with 1 method)xxxxxxxxxx
button on the left of a cell will show or hide the code.