diff --git a/General/Projectile-Motion.ipynb b/General/Projectile-Motion.ipynb
index 82e80c9..25bfdb5 100644
--- a/General/Projectile-Motion.ipynb
+++ b/General/Projectile-Motion.ipynb
@@ -22,7 +22,7 @@
"source": [
"# Intro to Jupyter Notebooks\n",
"\n",
- "Currently, we are using a jupyter notebook. This format can support either Julia, Python, or R. The setup is quite similar to that present in the propriety software Mathematica. \n",
+ "Currently, we are using a Jupyter notebook. This format can support either Julia, Python, or R. The setup is quite similar to that present in the propriety software Mathematica. \n",
"\n",
"We have two types of cells:\n",
"* Markdown cells\n",
@@ -113,7 +113,7 @@
"\\frac{d v_x}{dt} = 0 \\;\\;\\;\\; \\frac{d v_y}{dt}= g\n",
"\\end{equation}\n",
"\n",
- "To put this into an equation, we take the derivative and break it into a courser-grained version\n",
+ "To put this into an equation, we take the derivative and break it into a coarser-grained version\n",
"\\begin{equation}\n",
"\\frac{dx}{dt} \\approx \\frac{ \\Delta x}{\\Delta t}.\n",
"\\end{equation}\n",
@@ -124,7 +124,7 @@
"y(t_{n+1})= y(t_n)+ v_y(t_n) \\Delta t\n",
"\\end{equation}\n",
"\n",
- "We can also think of this as finding a small enough interval such that we can treat the y-velocity as if it's constant.\n",
+ "We can also think of this as finding a small enough interval such that we can treat the $y$-velocity as if it's constant.\n",
"\n",
"\n",
"Bonus note: Different types of algorithms, like symplectic, evaluate the velocity at different time points. "
@@ -280,7 +280,7 @@
"\n",
"We use [Plots.jl](http://docs.juliaplots.org/latest/) to display our results here.\n",
"\n",
- "Tips from Expierence: Always include x and y labels, title, legends, and relevant units on the graph. \n",
+ "Tips from Experience: Always include $x$ and $y$ labels, title, legends, and relevant units on the graph. \n",
"\n",
"The graph might seem obvious to you now, but the labeling might not seem obvious to you next week, next month, or next year. And it probably won't seem obvious to someone else looking at your work.\n",
"\n",
@@ -3245,7 +3245,7 @@
}
],
"source": [
- "# Lets choose our step sizes\n",
+ "# Let's choose our step sizes\n",
"dta=[.001,.01,.1,.2]"
]
},
@@ -4156,7 +4156,7 @@
"\n",
"Real objects encounter air resistance proportional to velocity. That effect can't be solved analytically, but our code can handle it easily.\n",
"\n",
- "We include air resistence by adding a force against the direction motion and proportional to the velocity squared in strength. We then have to project it along the x and y directions.\n",
+ "We include air resistence by adding a force against the direction motion and proportional to the velocity squared in strength. We then have to project it along the $x$ and $y$ directions.\n",
"\\begin{equation}\n",
"\\vec{F}=-\\text{sign}(\\vec{v}) \\frac{1}{2}\\rho C_d A v^2 = -\\text{sign}( \\vec{v}) R v^2,\n",
"\\end{equation}\n",
diff --git a/Graduate/1D-Spin-Chain-Prerequisites.ipynb b/Graduate/1D-Spin-Chain-Prerequisites.ipynb
index c1f0d0b..de04c9c 100644
--- a/Graduate/1D-Spin-Chain-Prerequisites.ipynb
+++ b/Graduate/1D-Spin-Chain-Prerequisites.ipynb
@@ -203,7 +203,7 @@
"# psi is an array of all our wavefunctions\n",
"psi=convert.(Int8,collect(0:(nstates-1)) )\n",
"\n",
- "# Lets look at each state both in binary and base 10\n",
+ "# Let's look at each state both in binary and base 10\n",
"println(\"binary form \\t integer\")\n",
"for p in psi\n",
" println(bitstring(p)[end-n:end],\"\\t\\t \",p)\n",
@@ -401,7 +401,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "So now lets test how the first of our three masks behaves:\n",
+ "So now let's test how the first of our three masks behaves:\n",
"We know that if the mask changes a 01 for a 10 (or vice versa) that the overall magnetization will not be changed. So, we test is our mask is successful by comparing the remaining magnetization. The rows offset by two spaces have matching magnetizations."
]
},
diff --git a/Graduate/Winding-Number.ipynb b/Graduate/Winding-Number.ipynb
index 8f2b8a7..3476cbf 100644
--- a/Graduate/Winding-Number.ipynb
+++ b/Graduate/Winding-Number.ipynb
@@ -6,7 +6,7 @@
"source": [
"# The Winding Number and the SSH model\n",
"\n",
- "The Chern number isn't the only topological invariant. We have multiple invariants, each convenient in their own situations. The Chern number just happened to appear one of the biggest, early examples, the Integer Quantum Hall Effect, but the winding number actually occurs much more often in a wider variety of circumstances.\n",
+ "The Chern number isn't the only topological invariant. We have multiple invariants, each convenient in their own situations. The Chern number just happened to appear in one of the biggest, early examples, the Integer Quantum Hall Effect, but the winding number actually occurs much more often in a wider variety of circumstances.\n",
"\n",
"How many times does the phase wrap as we transverse a closed loop?\n",
"$$\n",
@@ -219,7 +219,7 @@
"$$\n",
"U H U^{-1} = -H \\qquad \\qquad U U^{\\dagger} =\\mathbb{1}.\n",
"$$\n",
- "Finding $U$ if even exists and determining its form if it exists is a problem for another time. Today, multiple places said that $\\sigma_z$ works for the SSH model, and we can confirm that it does. \n",
+ "Finding if $U$ even exists and determining its form if it exists is a problem for another time. Today, multiple places said that $\\sigma_z$ works for the SSH model, and we can confirm that it does. \n",
"\n",
"A little less intellectually satisfying (at least for me), but it works.\n",
"\n",
@@ -284,7 +284,7 @@
"=\\pm \\sqrt{v^2+w^2 \\cos^2 k -2 vw \\cos k + w^2 \\sin^2 k} \n",
"= \\pm \\sqrt{v^2 - 2 vw \\cos k + w^2}\n",
"$$\n",
- "The difference between the upper and lower band will be at it's minimum when $\\cos k$ is greatest,$k=0$.\n",
+ "The difference between the upper and lower band will be at its minimum when $\\cos k$ is greatest, $k=0$.\n",
"$$\n",
"=\\pm \\sqrt{(v-w)^2}\n",
"$$\n",
@@ -1059,7 +1059,7 @@
"$$\n",
"Here we have a 1-1 correspondence between the Hamiltonian and a geometric object, this $\\vec{R}$ vector. When we look at how it depends on $k$, we get insight into how $\\mathcal{H}$ depends on $k$ as well.\n",
"\n",
- "The two different groups, purple and turquoise, will have two different behaviors. $\\vec{R}(k)$ for purple will circle the origin like $S^1$ the unit circle, whereas $\\vec{R}(k)$ for turquoise not circle the origin and will not be like $S^1$."
+ "The two different groups, purple and turquoise, will have two different behaviors. $\\vec{R}(k)$ for purple will circle the origin like $S^1$ the unit circle, whereas $\\vec{R}(k)$ for turquoise will not circle the origin and will not be like $S^1$."
]
},
{
diff --git a/Numerics_Prog/Jacobi-Transformation.ipynb b/Numerics_Prog/Jacobi-Transformation.ipynb
index 841f0a0..b66e12d 100644
--- a/Numerics_Prog/Jacobi-Transformation.ipynb
+++ b/Numerics_Prog/Jacobi-Transformation.ipynb
@@ -41,7 +41,7 @@
"\\begin{equation}\n",
"A^{\\prime}= P^{T}_{pq} \\cdot A \\cdot P_{pq}\n",
"\\end{equation}\n",
- "where each iteration brings A closer to diagonal form. Thus in our implementing our algorithm, we need to determine two things\n",
+ "where each iteration brings A closer to diagonal form. Thus in implementing our algorithm, we need to determine two things\n",
"
\n",
"- The values of c and s\n",
"
- The pattern of sweeping p and q\n",
diff --git a/Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb b/Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb
index 22a7a17..a2de59c 100644
--- a/Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb
+++ b/Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb
@@ -13,10 +13,10 @@
"### Intro\n",
"\n",
"If you didn't check it out already, take a look at the post that introduces using random numbers in calculations. Any such simulation is a Monte Carlo simulation. The most used kind of Monte Carlo simulation is a Markov Chain, also known as a random walk, or drunkard's walk. A Markov Chain is a series of steps where\n",
- "* each new state is chosen probabilitically\n",
+ "* each new state is chosen probabilistically\n",
"* the probabilities only depend on the current state (no memory)\n",
"\n",
- "Imagine a drunkard trying to walk. At any one point, they could progress either left or right rather randomly. Also, just because they had been traveling in a straight line so far does not guaruntee they will continue to do. They've just had extremely good luck. \n",
+ "Imagine a drunkard trying to walk. At any one point, they could progress either left or right rather randomly. Also, just because they had been traveling in a straight line so far does not guarantee they will continue to do. They've just had extremely good luck. \n",
"\n",
"We use Markov Chains to approximate probability distributions. \n",
"\n",
@@ -40,7 +40,7 @@
"\\pi_i p_{i j} = \\pi_j p_{j i}.\n",
"\\end{equation}\n",
"\n",
- "Detailed Balance further constricts the transition probabilities we can assign and makes it easier to design an algorithm. Almost all MCMC algorithms out there use detailed balance, and only lately have certain applied mathematicians begun looking and breaking detailed balance to increase efficiency in certain classes of problems. \n",
+ "Detailed Balance further constricts the transition probabilities we can assign and makes it easier to design an algorithm. Almost all MCMC algorithms out there use detailed balance, and only lately have certain applied mathematicians begun looking at breaking detailed balance to increase efficiency in certain classes of problems. \n",
"\n",
"### Today's Test Problem\n",
"\n",
@@ -2938,7 +2938,7 @@
"\n",
"Monte Carlo simulations are as much of an art as a science. You need to live them, love them, and breathe them till you find out exactly why they are behaving like little kittens that can finally jump on top of your countertops, or open your bedroom door at 1am. \n",
"\n",
- "For all their mishaving, you love the kittens anyway.\n"
+ "For all their misbehaving, you love the kittens anyway.\n"
]
},
{
diff --git a/Numerics_Prog/Monte-Carlo-Pi.ipynb b/Numerics_Prog/Monte-Carlo-Pi.ipynb
index 95deea3..4ec88ec 100644
--- a/Numerics_Prog/Monte-Carlo-Pi.ipynb
+++ b/Numerics_Prog/Monte-Carlo-Pi.ipynb
@@ -35,9 +35,9 @@
"## Buffon's Needle: Calculation of π\n",
"Even back in the 18th century, Georges-Louis Leclerc, Comte de Buffon posed a problem in geometric probability. Nowdays, we use a slightly different version of that problem to calculate π and illustrate Monte Carlo simulations. \n",
"\n",
- "Suppose we have a square dartboard, and someone with really bad, completely random aim, even though he/she always at least hits inside the dartboard. We then inscribe a circle inside that dartboard. After an infinity number of hits, what is the ratio between hits in the circle, and hits in the square?\n",
+ "Suppose we have a square dartboard, and someone with really bad, completely random aim, even though he/she always at least hits inside the dartboard. We then inscribe a circle inside that dartboard. After an infinite number of hits, what is the ratio between hits in the circle, and hits in the square?\n",
"\n",
- "\n",
+ "\n",
"\n",
"\\begin{equation}\n",
" f= \\frac{N_{circle}}{N_{square}} =\\frac{\\text{Area of circle}}{\\text{Area of square}} =\\frac{\\pi r^2}{4 r^2}= \\frac{\\pi}{4}\n",
@@ -77,7 +77,7 @@
"source": [
"We will generate our random numbers on the unit interval. Thus the radius in our circumstance is $.5$.\n",
"\n",
- " Write a function `incircle(r2)` such that if `r2` is in the circle, it returns true, else, it returns false. We will use this with the julia function `filter`. Assume `r2` is the radius squared, and already centered around the middle of the unit circle"
+ "Write a function `incircle(r2)` such that if `r2` is in the circle, it returns true, else, it returns false. We will use this with the Julia function `filter`. Assume `r2` is the radius squared, and already centered around the middle of the unit circle"
]
},
{
diff --git a/Numerics_Prog/Roots_1D.ipynb b/Numerics_Prog/Roots_1D.ipynb
index 0d31d73..57eb49b 100644
--- a/Numerics_Prog/Roots_1D.ipynb
+++ b/Numerics_Prog/Roots_1D.ipynb
@@ -1287,7 +1287,7 @@
"source": [
"## Interchangeability \n",
"\n",
- "The first time I implement an algorithm, I usually don't wrap it up into a function at all. Once I do wrap it into a function, the function, I take just the inputs required for what I wrapped up in some random order and send back some large chunk of data. There is nothing wrong with just slapping things together till they work, but like in this post here when I have functions that achieve the same thing, a little editing can make things better. \n",
+ "The first time I implement an algorithm, I usually don't wrap it up into a function at all. Once I do wrap it into a function, I take just the inputs required for what I wrapped up in some random order and send back some large chunk of data. There is nothing wrong with just slapping things together till they work, but like in this post here when I have functions that achieve the same thing, a little editing can make things better. \n",
"\n",
"Some of the methods here require brackets, some points, some derivative functions, or just some other combination of all these. With this variability, how do we standardize the inputs? Fiddling till I find something that works. I wrapped inputs up into values that get interated and values that don't.\n",
"\n",
@@ -1368,7 +1368,7 @@
" print(\"midpoint is zero\")\n",
" return [c,c]\n",
" else \n",
- " println(\"Mid point doesn't bracket a zero... somethign weird...\")\n",
+ " println(\"Mid point doesn't bracket a zero... something weird...\")\n",
" print(c,\"\\t\",f(c))\n",
" end\n",
"end"
@@ -1696,7 +1696,7 @@
"source": [
"## Newton-Raphson Method\n",
"\n",
- "The Newton-Raphson Method uses the derivative at a point to extrapolate to where a zero would occur if the function was sufficiently well approximated by the derivative. Even curvature and higher expansion terms do largely influence the function, we at least get going in the right direction, so the next iteration will be better. We'll see that with $x^2$ near $0$.\n",
+ "The Newton-Raphson Method uses the derivative at a point to extrapolate to where a zero would occur if the function was sufficiently well approximated by the derivative. Even when curvature and higher expansion terms do largely influence the function, we at least get going in the right direction, so the next iteration will be better. We'll see that with $x^2$ near $0$.\n",
"\n",
"When the function is well approximated by its derivative, this method works extremely well. \n",
"\n",
@@ -1733,7 +1733,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can analytically calculating derivatives using 'ForwardDiff.jl'. The syntax is\n",
+ "We can analytically calculate derivatives using 'ForwardDiff.jl'. The syntax is\n",
"\n",
" f_prep(x::Vector)=f(x[1])\n",
" f_prep2=(x->ForwardDiff.gradient(f_prep,x))\n",
@@ -2102,7 +2102,7 @@
"\n",
"Even more problematically, these cycles tend to be stable, as the evaluation at the second starting point shows us.\n",
"\n",
- "More sophisticated methods than the one's I'm showing here and check to see if NR is returning less than linear convergence, and default to the Midpoint method in those circumstances. This way, you can achieve the NR speedup, but keep the Midpoint method's robustness.\n",
+ "More sophisticated methods than the ones I'm showing here check to see if NR is returning less than linear convergence, and default to the Midpoint method in those circumstances. This way, you can achieve the NR speedup, but keep the Midpoint method's robustness.\n",
"\n",
"These are somehow related to [Newton Fractals](https://en.wikipedia.org/wiki/Newton_fractal) and some other mathematics I don't yet understand. Feel free to look them up if you feel so inclined."
]
@@ -2681,7 +2681,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Here we can see in black the stationary cycle. Colored by iteration, we can see the perturbed attempt.While is does move over a little to the right once, the path moves right back over to it's attractive stationary region. No amount of iterations will get us any closer to a zero."
+ "Here we can see in black the stationary cycle. Colored by iteration, we can see the perturbed attempt. While is does move over a little to the right once, the path moves right back over to its attractive stationary region. No amount of iterations will get us any closer to a zero."
]
},
{
diff --git a/Numerics_Prog/Runge-Kutta-Methods.ipynb b/Numerics_Prog/Runge-Kutta-Methods.ipynb
index 0f3d4d1..59b38b8 100644
--- a/Numerics_Prog/Runge-Kutta-Methods.ipynb
+++ b/Numerics_Prog/Runge-Kutta-Methods.ipynb
@@ -183,7 +183,7 @@
"$$\n",
"In case you haven't seen it before, the notation $\\mathcal{O}(x)$ stands for \"errors of the order x\".\n",
"Summing over the entire interval, we accumuluate error according to \n",
- "$$N\\mathcal{O}(h^2)= \\frac{x_f-x_0}{h}\\mathcal{O}(h^2)=h $$,\n",
+ "$$N\\mathcal{O}(h^2)= \\frac{x_f-x_0}{h}\\mathcal{O}(h^2)=h, $$\n",
"making this a first order method. Generally, if a technique is $n$th order in the Taylor expansion for one step, its $(n-1)$th order over the interval. "
]
},
diff --git a/Prerequisites/.ipynb_checkpoints/QHO-checkpoint.ipynb b/Prerequisites/.ipynb_checkpoints/QHO-checkpoint.ipynb
index 05e4ea3..e7982c1 100644
--- a/Prerequisites/.ipynb_checkpoints/QHO-checkpoint.ipynb
+++ b/Prerequisites/.ipynb_checkpoints/QHO-checkpoint.ipynb
@@ -54,7 +54,7 @@
"* demonstrate completeness. This means we can describe every function by a linear combination of Hermite polynomials, provided it is suitably well behaved.\n",
"\n",
"\n",
- "Though a formula exists or calculating a function at n directly, the most efficient method at low n for calculating polynomials relies on recurrence relationships. These recurrence relationships will also be quite handy if you ever need to show orthogonality, or expectation values. \n",
+ "Though a formula exists for calculating a function at $n$ directly, the most efficient method at low $n$ for calculating polynomials relies on recurrence relationships. These recurrence relationships will also be quite handy if you ever need to show orthogonality, or expectation values. \n",
"##### Recurrence Relations\n",
"\\begin{equation}\n",
"H_{n+1}(x) = 2xH_n(x) - H^{\\prime}_n(x)\n",
@@ -154,7 +154,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "So lets generate some Hermite polynomials and look at them. \n",
+ "So let's generate some Hermite polynomials and look at them. \n",
" Make sure you don't call a Hermite you haven't generated yet!"
]
},
@@ -608,7 +608,7 @@
}
],
"source": [
- "# Lets make our life easy and set all units to 1\n",
+ "# Let's make our life easy and set all units to 1\n",
"m=1\n",
"ω=1\n",
"ħ=1\n",
@@ -622,7 +622,7 @@
"metadata": {},
"source": [
"### Finding Zeros\n",
- "The eigenvalue maps to the number of zeros in the wavefunction. Below, I use Julia's roots package to indentify roots on the interval from -3 to 3. "
+ "The eigenvalue maps to the number of zeros in the wavefunction. Below, I use Julia's roots package to identify roots on the interval from -3 to 3. "
]
},
{
diff --git a/Prerequisites/Atomic-Orbitals.ipynb b/Prerequisites/Atomic-Orbitals.ipynb
index 45eedf0..2a0cc99 100644
--- a/Prerequisites/Atomic-Orbitals.ipynb
+++ b/Prerequisites/Atomic-Orbitals.ipynb
@@ -104,7 +104,7 @@
"metadata": {},
"source": [
"#### Cell to Evaluate\n",
- "What's below is a bunch of definitions that makes our calculations easier later on. Here I utalize the Gnu scientific library, GSL imported above, to calculate the special functions. \n",
+ "What's below is a bunch of definitions that makes our calculations easier later on. Here I utilize the Gnu scientific library, GSL imported above, to calculate the special functions. \n",
"\n",
"#### Programming Tip!\n",
"Even though its not necessary, specifying the type of inputs to a function through `m::Int` helps prevent improper inputs and allows the compiler to perform additional optimizations. Julia also implements Abstract Types, so we don't have to specify the exact type of Int. Real allows and numerical, non-complex type. \n",
@@ -209,7 +209,7 @@
}
],
"source": [
- "#We will calculate is spherical coordinates, but plot in cartesian, so we need this array conversion\n",
+ "#We will calculate in spherical coordinates, but plot in cartesian, so we need this array conversion\n",
"function SphtoCart(r::Array,θ::Array,ϕ::Array)\n",
" x=r.*sin.(θ).*cos.(ϕ);\n",
" y=r.*sin.(θ).*sin.(ϕ);\n",
@@ -238,7 +238,7 @@
"Remember that \n",
"\\begin{equation}\n",
"0} S^z_i S_j^z\n",
+ " {\\cal H} = -J \\sum_{} S^z_i S_j^z,\n",
"\\end{equation}\n",
- ", which is basically just the energy. This Ising Model has nearest neighbors interacting, and each spin variablesolely points in the $\\pm z$ direction. \n",
+ "which is basically just the energy. This Ising Model has nearest neighbors interacting, and each spin variable solely points in the $\\pm z$ direction. \n",
"\n",
"At a given temperature $T$ (inverse temperature $\\beta=1/T$, $k_b=1$), the occupancy of a given configuration $c_i$ follows the Maxwell-Boltzmann Probability Distribution,\n",
"\\begin{equation}\n",
diff --git a/Prerequisites/Phase-Transitions.ipynb b/Prerequisites/Phase-Transitions.ipynb
index d266bd7..c09c1b5 100644
--- a/Prerequisites/Phase-Transitions.ipynb
+++ b/Prerequisites/Phase-Transitions.ipynb
@@ -70,7 +70,7 @@
"C_h \\propto \\left( T- T_c \\right)^{-\\alpha_{\\pm}}.\n",
"\\end{equation}\n",
"\n",
- "The divergent scaling of most properties around a critical temperature depends on the divergence of the correlation length $\\xi$, the size of fluctuations in the system. At the critical temperature, fluctuations exist at every length scale. Once we get close enough to the transition temperature for the fluctuations to become greater than our finite simulation lattice, our computationed numbers no longer agree with infinite-lattice-assumed theory. The divergence gets modified by some function of the ratio between the correlation length and our system size,\n",
+ "The divergent scaling of most properties around a critical temperature depends on the divergence of the correlation length $\\xi$, the size of fluctuations in the system. At the critical temperature, fluctuations exist at every length scale. Once we get close enough to the transition temperature for the fluctuations to become greater than our finite simulation lattice, our computed numbers no longer agree with infinite-lattice-assumed theory. The divergence gets modified by some function of the ratio between the correlation length and our system size,\n",
"\\begin{equation}\n",
"C_h \\propto \\left( T - T_c \\right)^{-\\alpha_{\\pm}} g\\left( \\frac{L}{\\xi (T)} \\right).\n",
"\\end{equation}\n",
@@ -212,7 +212,7 @@
"source": [
"Here we set up our lattice.\n",
"\n",
- "If looking for a mind bender, look at an `\"Checkerboard\"`, with an anti-ferromagnetic coupling constant, like `J=-1`. Otherwise, just stick with a square. "
+ "If looking for a mind bender, look at a `\"Checkerboard\"`, with an anti-ferromagnetic coupling constant, like `J=-1`. Otherwise, just stick with a square. "
]
},
{
@@ -1177,7 +1177,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "I presented two different equivalent formulas for the specific heat. Let's check to make sure their equivalent. \n",
+ "I presented two different equivalent formulas for the specific heat. Let's check to make sure they're equivalent. \n",
"\n",
"For the formal defination, we'll take a numeric derivative with respect to $\\beta$,\n",
"\\begin{equation}\n",
diff --git a/Prerequisites/QHO.ipynb b/Prerequisites/QHO.ipynb
index 05e4ea3..e7982c1 100644
--- a/Prerequisites/QHO.ipynb
+++ b/Prerequisites/QHO.ipynb
@@ -54,7 +54,7 @@
"* demonstrate completeness. This means we can describe every function by a linear combination of Hermite polynomials, provided it is suitably well behaved.\n",
"\n",
"\n",
- "Though a formula exists or calculating a function at n directly, the most efficient method at low n for calculating polynomials relies on recurrence relationships. These recurrence relationships will also be quite handy if you ever need to show orthogonality, or expectation values. \n",
+ "Though a formula exists for calculating a function at $n$ directly, the most efficient method at low $n$ for calculating polynomials relies on recurrence relationships. These recurrence relationships will also be quite handy if you ever need to show orthogonality, or expectation values. \n",
"##### Recurrence Relations\n",
"\\begin{equation}\n",
"H_{n+1}(x) = 2xH_n(x) - H^{\\prime}_n(x)\n",
@@ -154,7 +154,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "So lets generate some Hermite polynomials and look at them. \n",
+ "So let's generate some Hermite polynomials and look at them. \n",
" Make sure you don't call a Hermite you haven't generated yet!"
]
},
@@ -608,7 +608,7 @@
}
],
"source": [
- "# Lets make our life easy and set all units to 1\n",
+ "# Let's make our life easy and set all units to 1\n",
"m=1\n",
"ω=1\n",
"ħ=1\n",
@@ -622,7 +622,7 @@
"metadata": {},
"source": [
"### Finding Zeros\n",
- "The eigenvalue maps to the number of zeros in the wavefunction. Below, I use Julia's roots package to indentify roots on the interval from -3 to 3. "
+ "The eigenvalue maps to the number of zeros in the wavefunction. Below, I use Julia's roots package to identify roots on the interval from -3 to 3. "
]
},
{
diff --git a/Prerequisites/Restricted-3-body.ipynb b/Prerequisites/Restricted-3-body.ipynb
index cdf3e01..4f1ed78 100644
--- a/Prerequisites/Restricted-3-body.ipynb
+++ b/Prerequisites/Restricted-3-body.ipynb
@@ -112,7 +112,7 @@
"using Plots\n",
"using LinearAlgebra\n",
"using DifferentialEquations\n",
- "gr() #plotlyjs doesn't seem to be working write now :("
+ "gr() #plotlyjs doesn't seem to be working right now :("
]
},
{
@@ -202,7 +202,7 @@
"\n",
"If we assume a stationary test mass, we can neglect the velocity-dependent Coriolis force and construct a potential. \n",
"\n",
- "The the case of one mass much larger than the other, the potential due to the larger mass will wash out all other details if the potential is examined normally. Thus, I apply a cutoff and plot asinh of values to pick out smaller details.\n",
+ "In the case where one mass is much larger than the other, the potential due to the larger mass will wash out all other details if the potential is examined normally. Thus, I apply a cutoff and plot asinh of values to pick out smaller details.\n",
"\n",
"The Hill Radius\n",
"$$\n",
@@ -234,7 +234,7 @@
"\n",
"Four and five are stable. \n",
"\n",
- "Objects slightly disturbed from the Lagrangian point will just circle around the general vicinity. These orbits were dubbed tadpole orbits by someone with a great deal of creativity, alcohol, or both. Objects with greater perturbations can occupy horseshoe, moving from one stable Lagrangian point to the other, and back again. We will examine examples of both these types.\n",
+ "Objects slightly disturbed from the Lagrangian point will just circle around the general vicinity. These orbits were dubbed tadpole orbits by someone with a great deal of creativity, alcohol, or both. Objects with greater perturbations can occupy horseshoe orbits, moving from one stable Lagrangian point to the other, and back again. We will examine examples of both these types.\n",
"\n"
]
},
@@ -11248,7 +11248,7 @@
"\n",
"Here I list out some initial conditions for the orbits we will evolve.\n",
"\n",
- "I have two initial conditions around each unstable point to show that they diverge and multiple around the stable points. "
+ "I have two initial conditions around each unstable point to show that they diverge, and multiple initial conditions around the stable points. "
]
},
{
@@ -14363,11 +14363,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Lagrangian points are more than just analytical and numerical curiosities, but appear over and over again in our solar system. Wikipedia contains a full [list](https://en.wikipedia.org/wiki/List_of_objects_at_Lagrangian_points) of objects, both natural and humam-made, at Lagrangian points.\n",
+ "The Lagrangian points are more than just analytical and numerical curiosities, but appear over and over again in our solar system. Wikipedia contains a full [list](https://en.wikipedia.org/wiki/List_of_objects_at_Lagrangian_points) of objects, both natural and human-made, at Lagrangian points.\n",
"\n",
- "In our system, Jupiter's Lagrangian points are occupied by two groups of asteriods known as trojans. The $L_4$ group is the Greek camp, and the $L_5$ group is the Trojan camp. Two sets of Saturn's moons follow the same orbit with 60deg offsets. Telesto and Calypso are ahead and behind of Tethys at it's Lagrangian points, and Helene and Polydeuces are ahead and behind of Dione. \n",
+ "In our system, Jupiter's Lagrangian points are occupied by two groups of asteriods known as trojans. The $L_4$ group is the Greek camp, and the $L_5$ group is the Trojan camp. Two sets of Saturn's moons follow the same orbit with 60deg offsets. Telesto and Calypso are ahead and behind of Tethys at its Lagrangian points, and Helene and Polydeuces are ahead and behind of Dione. \n",
"\n",
- "By placing an orbiter at either the Earth-Sun $L_1$ or $L_2$, we can have satelittes that maintain a consistent location relative to Earth yet are free from Earth's magnetic field, heat, and other forms of radiation and interference. While these points are unstable and a natural evolution would wander far, far away over time, a powered orbiter can use thrusters to actively remain at the stationary point. Some famous orbiters include SOHO (Solar and Heliospheric Observatory), WMAP (Wilkinson Microwave Anisotropy Probe), Plank, and the Herschel Space Observatory.\n",
+ "By placing an orbiter at either the Earth-Sun $L_1$ or $L_2$, we can have satellites that maintain a consistent location relative to Earth yet are free from Earth's magnetic field, heat, and other forms of radiation and interference. While these points are unstable and a natural evolution would wander far, far away over time, a powered orbiter can use thrusters to actively remain at the stationary point. Some famous orbiters include SOHO (Solar and Heliospheric Observatory), WMAP (Wilkinson Microwave Anisotropy Probe), Plank, and the Herschel Space Observatory.\n",
"\n",
"## Future Posts\n",
"\n",
diff --git a/Prerequisites/Time-Evolution.ipynb b/Prerequisites/Time-Evolution.ipynb
index 7868672..8b1f449 100644
--- a/Prerequisites/Time-Evolution.ipynb
+++ b/Prerequisites/Time-Evolution.ipynb
@@ -26,7 +26,7 @@
"y(\\delta t)= e^{L_1(y,0) \\delta t+L_2(y,0) \\delta t}y(0)\n",
"\\end{equation}\n",
"\n",
- "For Strang splitting, instead of applying both operators together, we break them up into two. I'll discuss non-commutivity later.\n",
+ "For Strang splitting, instead of applying both operators together, we break them up into two. I'll discuss non-commutativity later.\n",
"\\begin{equation}\n",
"y(\\delta t)= e^{L_1 (y,0) \\delta t} e^{L_2(y,0) \\delta t} y(0) = U_1 U_2 y(0)\n",
"\\end{equation}\n",
@@ -55,7 +55,7 @@
"U_x (\\delta t)= e^{-\\frac{i}{\\hbar}\\mathcal{H}_x \\delta t}\n",
"\\end{equation}\n",
"\n",
- "I mentioned earlier that I would discuss non-communitvity. We need to do that now. We can't simply seperate the evolution operator for the full Hamiltonian into two parts, because we would introduce terms proportional to the commutator. \n",
+ "I mentioned earlier that I would discuss non-commutativity. We need to do that now. We can't simply separate the evolution operator for the full Hamiltonian into two parts, because we would introduce terms proportional to the commutator. \n",
"\\begin{equation}\n",
"e^{A+B}=e^{A}e^{B}e^{[A,B]}\n",
"\\end{equation}\n",
@@ -562,7 +562,7 @@
},
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Julia 1.1.0",
+ "display_name": "Julia 1.1.1",
"language": "julia",
"name": "julia-1.1"
},
@@ -570,7 +570,7 @@
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.1.0"
+ "version": "1.1.1"
}
},
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