Merge pull request #67 from ubermag/minor_corrections

minor corrections and two changes in the examples

Author: lang-m

examples/10-tutorial-standard-problem-fmr.ipynb

@@ -14,13 +14,13 @@
     "\n",
     "## Problem specification\n",
     "\n",
-    "We choose a cuboidal thin film permalloy sample measuring $120 \\times 120 \\times 10 \\,\\text{nm}^{3}$. The choice of a cuboid is important as it ensures that the finite difference method employed by OOMMF does not introduce errors due to irregular boundaries that cannot be discretized well. We choose the thin film geometry to be thin enough so that the variation of magnetization dynamics along the out-of-film direction can be neglected. Material parameters based on permalloy are:\n",
+    "We choose a cuboidal thin film permalloy sample measuring $120 \\times 120 \\times 10 \\,\\text{nm}^{3}$. The choice of a cuboid is important as it ensures that the finite difference method employed by OOMMF does not introduce errors due to irregular boundaries that cannot be discretised well. We choose the thin film geometry to be thin enough so that the variation of magnetisation dynamics along the out-of-film direction can be neglected. Material parameters based on permalloy are:\n",
     "\n",
     "- exchange energy constant $A = 1.3 \\times 10^{-11} \\,\\text{J/m}$,\n",
     "- magnetisation saturation $M_\\text{s} = 8 \\times 10^{5} \\,\\text{A/m}$,\n",
     "- Gilbert damping $\\alpha = 0.008$.\n",
     "\n",
-    "An external magnetic bias field with magnitude $80 \\,\\text{kA/m}$ is applied along the direction $e = (1, 0.715, 0)$. We choose the external magnetic field direction slightly off the sample diagonal in order to break the system’s symmetry and thus avoid degenerate eigenmodes. First, we initialize the system with a uniform out-of-plane magnetization $m_{0} = (0, 0, 1)$. The system is allowed to relax for $5 \\,\\text{ns}$, which was found to be sufficient time to obtain a well-converged equilibrium magnetization configuration. We refer to this stage of simulation as the relaxation stage, and its final relaxed magnetization configuration is saved to serve as the initial configuration for the next dynamic stage. Because we want to use a well defined method that is supported by all simulation tools, we minimize the system’s energy by integrating the LLG equation with a large, quasistatic Gilbert damping $\\alpha = 1$ for $5 \\,\\text{ns}$. In the next step (dynamic stage), a simulation is started using the equilibrium magnetisation configuration from the relaxation stage as the initial configuration. Now, the direction of an external magnetic field is altered to $e = (1, 0.7, 0)$. This simulation stage runs for $T = 20 \\,\\text{ns}$ while the (average and spatially resolved) magnetization $M(t)$ is recorded every $\\Delta t = 5 \\,\\text{ps}$. The Gilbert damping in this dynamic simulation stage is $\\alpha = 0.008$.\n",
+    "An external magnetic bias field with magnitude $80 \\,\\text{kA/m}$ is applied along the direction $e = (1, 0.715, 0)$. We choose the external magnetic field direction slightly off the sample diagonal in order to break the system’s symmetry and thus avoid degenerate eigenmodes. First, we initialize the system with a uniform out-of-plane magnetisation $m_{0} = (0, 0, 1)$. The system is allowed to relax for $5 \\,\\text{ns}$, which was found to be sufficient time to obtain a well-converged equilibrium magnetisation configuration. We refer to this stage of simulation as the relaxation stage, and its final relaxed magnetisation configuration is saved to serve as the initial configuration for the next dynamic stage. Because we want to use a well defined method that is supported by all simulation tools, we minimize the system’s energy by integrating the LLG equation with a large, quasistatic Gilbert damping $\\alpha = 1$ for $5 \\,\\text{ns}$. In the next step (dynamic stage), a simulation is started using the equilibrium magnetisation configuration from the relaxation stage as the initial configuration. Now, the direction of an external magnetic field is altered to $e = (1, 0.7, 0)$. This simulation stage runs for $T = 20 \\,\\text{ns}$ while the (average and spatially resolved) magnetisation $M(t)$ is recorded every $\\Delta t = 5 \\,\\text{ps}$. The Gilbert damping in this dynamic simulation stage is $\\alpha = 0.008$.\n",
     "\n",
     "Details of this standard problem specification can be found in Ref. 1.\n",
     "\n",

examples/11-tutorial-deriving-fields.ipynb

@@ -12,7 +12,7 @@
    "source": [
     "# Deriving energy values\n",
     "\n",
-    "In this tutorial, we show how derived fields and values can be computed afetr the micromagnetic system is defined.\n",
+    "In this tutorial, we show how derived fields and values can be computed after the micromagnetic system is defined.\n",
     "\n",
     "## Simulation\n",
     "\n",
@@ -126,7 +126,7 @@
    "source": [
     "## Effective field\n",
     "\n",
-    "All computations are performed using `mc.compute`, where `mc` is the micromagnetic calculator we choose at import. `compute` function takes two arguments:\n",
+    "All computations are performed using `oc.compute`, where `oc` is the micromagnetic calculator we choose at import. `compute` function takes two arguments:\n",
     "\n",
     "1. Property we want to compute\n",
     "2. System object\n",
@@ -385,7 +385,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also chack the sum of all individual energy terms and check if it the same as the total energy."
+    "We can also check the sum of all individual energy terms and check if it the same as the total energy."
    ]
   },
   {

examples/12-tutorial-stray-field.ipynb

@@ -17,7 +17,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we compute and relax a skyrmion in a interfacial-DMI material in a confined disk like geometry."
+    "In this tutorial, we compute and relax a skyrmion in an interfacial-DMI material in a confined disk like geometry."
    ]
   },
   {
@@ -105,7 +105,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Disk geometry is set up be defining the saturation magnetisation (norm of the magnetisation field). For that, we define a function:"
+    "Disk geometry is set up by defining the saturation magnetisation (norm of the magnetisation field). For that, we define a function:"
    ]
   },
   {

examples/13-tutorial-skyrmion.ipynb

@@ -12,7 +12,7 @@
    "source": [
     "# Choosing runner\n",
     "\n",
-    "In this tutorial, we show how to choose different \"runners\" to run your simulations. This is helpful if you want to change OOMMF installation you want to use. This is in particular helpful if you want to run OOMMF inside Docker, which allows us to run simulations on a \"small linux machine\", which is automatically pulled from the cloud, simulations are run inside, and in the end it is destroyed automatically. This all happens in the background and requires no special assistance from the user. In order to use Docker, we need to have it installed on our machine - you can download it here: https://www.docker.com/products/docker-desktop.\n",
+    "In this tutorial, we show how to choose different “runners” to run your simulations. This is helpful if you want to change OOMMF installation you want to use. This is in particular helpful if you want to run OOMMF inside Docker, which allows us to run simulations on a “small linux machine”, which is automatically pulled from the cloud, simulations are run inside, and in the end it is destroyed automatically. This all happens in the background and requires no special assistance from the user. In order to use Docker, we need to have it installed on our machine - you can download it here: https://www.docker.com/products/docker-desktop.\n",
     "\n",
     "For that example, we simulate a skyrmion in a sample with periodic boundary conditions."
    ]
@@ -199,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.8.17"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

examples/choosing-runner.ipynb

@@ -12,7 +12,7 @@
    "source": [
     "# Fixed subregions\n",
     "\n",
-    "It is somethomes necessary to \"fix\" specific regions of the mesh to ensure they do not change during driving. In order to do that, the first step is to create a mesh and specify subregions we want to keep fixed.\n",
+    "It is sometimes necessary to \"fix\" specific regions of the mesh to ensure they do not change during driving. In order to do that, the first step is to create a mesh and specify subregions we want to keep fixed.\n",
     "\n",
     "As an example, let us simulate a simple one-dimensional sample and define subregions in such a way that the first and the last discretisation cell remain fixed."
    ]

examples/fixed-subregions.ipynb

@@ -47,7 +47,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we have `system` object we can simulate hysteresis using `HysteresisDriver`. Like all other drivers, `HysteresisDriver` has `drive` method. As input arhuments it takes `system` object (as usual) and:\n",
+    "Now that we have `system` object we can simulate hysteresis using `HysteresisDriver`. Like all other drivers, `HysteresisDriver` has `drive` method. As input arguments it takes `system` object (as usual) and:\n",
     "\n",
     "- `Hmin` - the starting value of magnetic field\n",
     "- `Hmin` - the end value of magnetic field\n",

examples/hysteresis.ipynb

@@ -180,6 +180,7 @@
     "### Spatially varying H\n",
     "\n",
     "There are two different ways how a parameter can be made spatially varying, by using:\n",
+    "\n",
     "1. Dictionary\n",
     "2. `discretisedfield.Field`\n",
     "\n",