Merge pull request #132 from pitmonticone/master

Author: fonsp

src/homework/hw0.jl

@@ -66,7 +66,7 @@ That’s it, but if you like you can do the _OPTIONAL_ exercises that follow."
 
 # ╔═╡ 430a260e-6cbb-11eb-34af-31366543c9dc
 md"""# Installation
-Before being able to run this notebook succesfully locally, you will need to [set up Julia and Pluto.](/Spring21/installation/)
+Before being able to run this notebook successfully locally, you will need to [set up Julia and Pluto.](/Spring21/installation/)
 
 One you have Julia and Pluto installed, you can click the button at the top right of this page and follow the instructions to edit this notebook locally and submit.
 """

src/homework/hw1.jl

@@ -58,7 +58,7 @@ student = (name = "Jazzy Doe", kerberos_id = "jazz")
 
 # ╔═╡ 5f95e01a-ee0a-11ea-030c-9dba276aba92
 md"""
-#### Intializing packages
+#### Initializing packages
 
 _When running this notebook for the first time, this could take up to 15 minutes. Hang in there!_
 """
@@ -91,7 +91,7 @@ colored_line(example_vector)
 # ╔═╡ ad6a33b0-eded-11ea-324c-cfabfd658b56
 md"""
 $(html"<br>")
-#### Exerise 1.1
+#### Exercise 1.1
 👉 Make a random vector `random_vect` of length 10 using the `rand` function.
 """
 
@@ -149,7 +149,7 @@ begin
 end
 
 # ╔═╡ 77adb065-bfd4-4680-9c2a-ad4d92689dbf
-md"#### Exerise 1.2
+md"#### Exercise 1.2
 👉 Make a function `my_sum` using a `for` loop, which computes the total of a vector of numbers."
 
 # ╔═╡ bd907ee1-5253-4cae-b5a5-267dac24362a
@@ -185,7 +185,7 @@ hint(md"""Check out this page for a refresher on basic Julia syntax:
 	[Basic Julia Syntax](https://computationalthinking.mit.edu/Spring21/basic_syntax/)""")
 
 # ╔═╡ cf738088-eded-11ea-2915-61735c2aa990
-md"#### Exerise 1.3
+md"#### Exercise 1.3
 👉 Use your `my_sum` function to write a function `mean`, which computes the mean/average of a vector of numbers."
 
 # ╔═╡ 0ffa8354-edee-11ea-2883-9d5bfea4a236
@@ -235,7 +235,7 @@ else
 end
 
 # ╔═╡ e2863d4c-edef-11ea-1d67-332ddca03cc4
-md"""#### Exerise 1.4
+md"""#### Exercise 1.4
 👉 Write a function `demean`, which takes a vector `xs` and subtracts the mean from each value in `xs`. Use your `mean` function!"""
 
 # ╔═╡ ea8d92f8-159c-4161-8c54-bab7bc00f290
@@ -426,7 +426,7 @@ RGB(0.1, 0.4, 0.7)
 
 # ╔═╡ f52e4914-2926-4a42-9e45-9caaace9a7db
 md"""
-#### Exerise 2.1
+#### Exercise 2.1
 👉 Write a function **`get_red`** that takes a single pixel, and returns the value of its red channel.
 """
 
@@ -455,7 +455,7 @@ end
 
 # ╔═╡ d8cf9bd5-dbf7-4841-acf9-eef7e7cabab3
 md"""
-#### Exerise 2.2
+#### Exercise 2.2
 👉 Write a function **`get_reds`** (note the extra `s`) that accepts a 2D color array called `image`, and returns a 2D array with the red channel value of each pixel. (The result should be a 2D array of _numbers_.) Use your function `get_red` from the previous exercise.
 """
 
@@ -507,7 +507,7 @@ md"""
 
 Great! By extracting the red channel value of each pixel, we get a 2D array of numbers. We went from an image (2D array of RGB colors) to a matrix (2D array of numbers).
 
-#### Exerise 2.3
+#### Exercise 2.3
 Let's try to visualize this matrix. Right now, it is displayed in text form, but because the image is quite large, most rows and columns don't fit on the screen. Instead, a better way to visualize it is to **view a number matrix as an image**.
 
 This is easier than you might think! We just want to map each number to an `RGB` object, and the result will be a 2D array of `RGB` objects, which Julia will display as an image.
@@ -537,7 +537,7 @@ Use the ➕ button at the bottom left of this cell to add more cells.
 # ╔═╡ f7825c18-ff28-4e23-bf26-cc64f2f5049a
 md"""
 
-#### Exerise 2.4
+#### Exercise 2.4
 👉 Write four more functions, `get_green`, `get_greens`, `get_blue` and `get_blues`, to be the equivalents of `get_red` and `get_reds`. Use the ➕ button at the bottom left of this cell to add new cells.
 """
 
@@ -886,7 +886,7 @@ end
 # ╔═╡ d896b7fd-20db-4aa9-bbcf-81b1cd44ec46
 md"""
 
-#### Exerise 3.6
+#### Exercise 3.6
 Move the slider below to set the amount of noise applied to the image of Philip.
 """
 

src/homework/hw10.jl

@@ -367,7 +367,7 @@ In the lecture notebook we introduced a _mutable struct_ `EBM` (_energy balance
 - a function `CO2`, which maps a time `t` to the concentrations at that year. For example, we use the function `t -> 280` to simulate a model with concentrations fixed at 280 ppm.
 
 `EBM` also contains the simulation results, in two arrays:
-- `T` is the array of tempartures (°C, `Float64`).
+- `T` is the array of temperatures (°C, `Float64`).
 - `t` is the array of timestamps (years, `Float64`), of the same size as `T`.
 """
 
@@ -497,7 +497,7 @@ t = 1850:2100
 
 # ╔═╡ 06c5139e-252d-11eb-2645-8b324b24c405
 md"""
-We are interested in how the **uncertainty in our input** $B$ (the climate feedback paramter) *propagates* through our model to determine the **uncertainty in our output** $T(t)$, for a given emissions scenario. The goal of this exercise is to answer the following by using *Monte Carlo Simulation* for *uncertainty propagation*:
+We are interested in how the **uncertainty in our input** $B$ (the climate feedback parameter) *propagates* through our model to determine the **uncertainty in our output** $T(t)$, for a given emissions scenario. The goal of this exercise is to answer the following by using *Monte Carlo Simulation* for *uncertainty propagation*:
 
 > 👉 What is the probability that we see more than 2°C of warming by 2100 under the low-emissions scenario RCP2.6? What about under the high-emissions scenario RCP8.5?
 
@@ -531,7 +531,7 @@ In the [lecture notebook](https://github.com/hdrake/simplEarth/blob/master/2_ebm
 
 # ╔═╡ d6d1b312-2543-11eb-1cb2-e5b801686ffb
 md"""
-Below we have an empty diagram, which is already set up with a CO₂ vs $T$ diagram, with a logarthmic horizontal axis. Now it's your turn! We have written some pointers below to help you, but feel free to do it your own way.
+Below we have an empty diagram, which is already set up with a CO₂ vs $T$ diagram, with a logarithmic horizontal axis. Now it's your turn! We have written some pointers below to help you, but feel free to do it your own way.
 """
 
 # ╔═╡ 378aed18-252b-11eb-0b37-a3b511af2cb5
@@ -697,7 +697,7 @@ If you like, make the visualization more informative! Like in the lecture notebo
 md"""
 #### Exercise 2.2
 
-👉 Find the **lowest CO₂ concentration** necessary to melt the Snowball, programatically (i.e., using code).
+👉 Find the **lowest CO₂ concentration** necessary to melt the Snowball, programmatically (i.e., using code).
 """
 
 # ╔═╡ 9eb07a6e-2687-11eb-0de3-7bc6aa0eefb0

src/homework/hw2.jl

@@ -557,7 +557,7 @@ G_y = \begin{bmatrix}
 \end{bmatrix} 
 ```
 
-We can think of these filterrs as derivatives in the $x$ and $y$ directions, as we discussed in lectures.
+We can think of these filters as derivatives in the $x$ and $y$ directions, as we discussed in lectures.
 
 Then we combine them by finding the magnitude of the **gradient** (in the sense of multivariate calculus) by defining
 

src/homework/hw3.jl

@@ -56,7 +56,7 @@ student = (name = "Jazzy Doe", kerberos_id = "jazz")
 
 # ╔═╡ 938185ec-f384-11ea-21dc-b56b7469f798
 md"""
-#### Intializing packages
+#### Initializing packages
 _When running this notebook for the first time, this could take up to 15 minutes. Hang in there!_
 """
 

src/homework/hw4.jl

@@ -166,7 +166,7 @@ Feel free to ask questions!
 
 # ╔═╡ 938185ec-f384-11ea-21dc-b56b7469f798
 md"""
-#### Intializing packages
+#### Initializing packages
 _When running this notebook for the first time, this could take up to 15 minutes. Hang in there!_
 """
 

src/homework/hw5.jl

@@ -46,7 +46,7 @@ student = (name = "Jazzy Doe", kerberos_id = "jazz")
 
 # ╔═╡ aaa41509-a62d-417b-bca7-a120e3a5e5b2
 md"""
-#### Intializing packages
+#### Initializing packages
 _When running this notebook for the first time, this could take up to 15 minutes. Hang in there!_
 """
 
@@ -135,7 +135,7 @@ To begin, we will make a type to represent a rank-1 matrix. A *rank-1 matrix* is
 # ╔═╡ 8ce8c4bc-8505-11eb-2357-c50a70b8745c
 md"""
 #### Exercise 2.1
-Let's make a `FirstRankOneMatrix` type that contains two vectors of floats, `v` and `w`. Here `v` represents a column and `w` the multpliers for each column.
+Let's make a `FirstRankOneMatrix` type that contains two vectors of floats, `v` and `w`. Here `v` represents a column and `w` the multipliers for each column.
 
 We include (in the same cell, due to requirements of Pluto) a constructor that takes a single vector `v` and duplicates it.
 """
@@ -494,7 +494,7 @@ md"""
 
 Why do we need a special type to represent special types of structured matrices? One reason is that not only do they give a more efficient representation in space (requiring less memory to store), they can also be more efficient in time, i.e. *faster*.
 
-   For example, let's look at **matrix--vector mutiplication**. This is a *fundamental* part of many, *many* algorithms in scientific computing, and because of this, we usually want it to be as fast as possible.
+   For example, let's look at **matrix--vector multiplication**. This is a *fundamental* part of many, *many* algorithms in scientific computing, and because of this, we usually want it to be as fast as possible.
 
    For a rank-one matrix given by $M = v w^T$, the matrix--vector product $M \cdot x$ is given by $(w \cdot x) v$. Note that $w \cdot x$ is a number (scalar) which is multiplying the vector element by element. This computation is much faster than the usual matrix-vector multiplication: we are taking advantage of structure!
    

src/homework/hw6.jl

@@ -56,7 +56,7 @@ student = (name = "Jazzy Doe", kerberos_id = "jazz")
 
 # ╔═╡ aaa41509-a62d-417b-bca7-a120e3a5e5b2
 md"""
-#### Intializing packages
+#### Initializing packages
 _When running this notebook for the first time, this could take up to 15 minutes. Hang in there!_
 """
 
@@ -84,7 +84,7 @@ md"""
 #### Exercise 1.1
 👉 Write a function `counts` that accepts a vector `data` and calculates the number of times each value in `data` occurs.
 
-The input will be an array of integers, **with duplicates**, and the result will be a dictionary that maps each occured value to its count in the data.
+The input will be an array of integers, **with duplicates**, and the result will be a dictionary that maps each occurred value to its count in the data.
 
 For example,
 ```julia

src/homework/hw7.jl

@@ -235,7 +235,7 @@ end
 
 # ╔═╡ 8692bf42-0403-11eb-191f-b7d08895274f
 md"""
-#### Exericse 1.4
+#### Exercise 1.4
 👉 Write a function `generate_agents(N)` that returns a vector of `N` freshly created `Agent`s. They should all be initially susceptible, except one, chosen at random (i.e. uniformly), who is infectious.
 
 """

src/homework/hw9.jl

@@ -514,7 +514,7 @@ md"""
 
 Write a function `interact!` that takes two `Agent`s and a `CollisionInfectionRecovery`, and:
 
-- If the agents are at the same spot, causes a susceptible agent to communicate the desease from an infectious one with the correct probability.
+- If the agents are at the same spot, causes a susceptible agent to communicate the disease from an infectious one with the correct probability.
 - if the first agent is infectious, it recovers with some probability
 """