Merge pull request #52 from fab4100/docker

Fixing examples Jupyter notebooks and adding Dockerfile

docker/.gitignore

@@ -0,0 +1,6 @@
+.ipynb*
+.cache
+.config
+.ipython
+.jupyter
+.local

docker/Dockerfile

@@ -0,0 +1,46 @@
+FROM ubuntu:20.04
+
+# update aptitude
+ARG DEBIAN_FRONTEND=noninteractive
+ENV TZ=Europe/Zurich
+RUN apt-get update && apt-get -y --fix-missing upgrade
+
+# install aptitude essentials
+RUN apt-get -y install \
+    build-essential \
+    meson \
+    cmake \
+    git \
+    vim \
+    curl \
+    openmpi-bin \
+    openmpi-common \
+    libhdf5-openmpi-dev \
+    python3-dev \
+    python3-pip \
+    python3-numpy \
+    python3-matplotlib \
+    python3-pandas \
+    python3-scipy \
+    python3-xlrd \
+    python3-ipython \
+    dirmngr apt-transport-https lsb-release ca-certificates
+RUN curl -sL https://deb.nodesource.com/setup_12.x | bash -
+RUN apt-get -y install nodejs
+
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install jupyter jupyterlab ipympl
+RUN python3 -m pip install pylj
+RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager
+RUN jupyter labextension install jupyter-matplotlib
+
+ENTRYPOINT ["jupyter", "lab"]
+CMD ["--ip=0.0.0.0", "--port=8888", "--no-browser"]
+
+# run the container with:
+# $ docker run -it --rm -p 8888:8888 <image name>
+
+# add non-root user
+RUN useradd -m pylj
+WORKDIR /home/pylj
+USER pylj

docker/build.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -e
+docker build -f Dockerfile -t 'pythoninchemistry:latest' .

docker/run.sh

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+set -e
+
+CWD=$(pwd -P)
+docker run \
+    -u $(id -u):$(id -g) -v ${CWD}:${CWD} -w ${CWD} -e HOME=${CWD} \
+    -it --rm -p 8888:8888 pythoninchemistry:latest

examples/ideal_gas_law/ideal_gas_law.ipynb

@@ -6,9 +6,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import warnings\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from pylj import md, comp, util, sample"
+    "from pylj import md, util, sample\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
    ]
   },
   {
@@ -28,7 +31,7 @@
     "\n",
     "One of the most popular models used to simulate interparticle interactions is known as the **Lennard-Jones** function. This models the London dispersion interactions between atoms. Hopefully the London dispersion interactions are familiar as consisting of: \n",
     "\n",
-    "- **van der Waals attraction**: the attraction between atoms that occurs as a result of the formation of instantenous dipole formation,\n",
+    "- **van der Waals attraction**: the attraction between atoms that occurs as a result of the formation of instantaneous dipole formation,\n",
     "- **Pauli's exclusion principle**: the repulsion which stops atoms overlapping as no two electrons can have the same quantum state. \n",
     "\n",
     "This means that the Lennard-Jones function is attractive when particles are close enough to induce dipole formation but **very** repulsive when the particles are too close. The Lennard-Jones function has the following form,\n",
@@ -69,6 +72,8 @@
    "source": [
     "r = np.linspace( ◽◽◽, ◽◽◽, ◽◽◽ )\n",
     "E = ◽◽◽\n",
+    "\n",
+    "%matplotlib widget\n",
     "plt.plot( ◽◽◽, ◽◽◽ )\n",
     "plt.xlabel( ◽◽◽ )\n",
     "plt.ylabel( ◽◽◽ )\n",
@@ -120,6 +125,8 @@
     "\n",
     "r = np.linspace( ◽◽◽, ◽◽◽, ◽◽◽ )\n",
     "F = ◽◽◽\n",
+    "\n",
+    "%matplotlib widget\n",
     "plt.plot( ◽◽◽, ◽◽◽ )\n",
     "plt.xlabel( ◽◽◽ )\n",
     "plt.ylabel( ◽◽◽ )\n",
@@ -150,7 +157,7 @@
    "source": [
     "def md_simulation(number_of_particles, temperature, box_length, number_of_steps, sampling_class):\n",
     "    # Creates the visualisation environment\n",
-    "    %matplotlib notebook\n",
+    "    %matplotlib widget\n",
     "    # Initialise the system\n",
     "    system = md.initialise(number_of_particles, temperature, box_length, 'square')\n",
     "    # This sets the sampling class\n",
@@ -160,16 +167,13 @@
     "    # Begin the molecular dynamics loop\n",
     "    for i in range(0, number_of_steps):\n",
     "        # At each step, calculate the forces on each particle and get acceleration\n",
-    "        system.particles, system.distances, system.forces, system.energies = comp.compute_forces(system.particles, \n",
-    "                                                                                                 system.box_length, \n",
-    "                                                                                                 system.cut_off)\n",
+    "        system.compute_force() \n",
     "        # Run the equations of motion integrator algorithm\n",
-    "        system.particles = md.velocity_verlet(system.particles, system.timestep_length, system.box_length, \n",
-    "                                              system.cut_off)\n",
+    "        system.integrate(md.velocity_verlet)\n",
     "        # Sample the thermodynamic and structural parameters of the system\n",
     "        system = md.sample(system.particles, system.box_length, system.initial_particles, system)\n",
     "        # Allow the system to interact with a heat bath\n",
-    "        system.particles = comp.heat_bath(system.particles, system.temperature_sample, temperature)\n",
+    "        system.heat_bath(temperature)\n",
     "        # Iterate the time\n",
     "        system.time += system.timestep_length\n",
     "        system.step += 1\n",

examples/molecular_dynamics/intro_to_molecular_dynamics.ipynb

@@ -6,9 +6,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import warnings\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from pylj import md, comp, util, sample, pairwise"
+    "from pylj import md, util, sample, forcefields\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
    ]
   },
   {
@@ -94,10 +97,12 @@
    "outputs": [],
    "source": [
     "r = np.linspace( 3e-10, 8e-10, 100 )\n",
-    "E = pairwise.lennard_jones_energy(1.36e-134, 9.27e-78, r)\n",
+    "E = forcefields.lennard_jones(r, [1.36e-134, 9.27e-78])\n",
+    "\n",
+    "%matplotlib widget\n",
     "plt.plot( r, E )\n",
-    "plt.xlabel( 'r/m' )\n",
-    "plt.ylabel( 'E/J' )\n",
+    "plt.xlabel( '$r/m$' )\n",
+    "plt.ylabel( '$E/J$' )\n",
     "plt.show()"
    ]
   },
@@ -142,7 +147,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib notebook\n",
+    "%matplotlib widget\n",
     "simulation = md.initialise(16, 300, 50, 'square')\n",
     "sample_system = sample.JustCell(simulation)"
    ]
@@ -194,7 +199,7 @@
     "\n",
     "$$ \\mathbf{f} = -\\frac{\\partial E}{\\partial r}. $$\n",
     "\n",
-    "The force of a given interaction can be found using the `pairwise.lennard_jones_force` function available in pylj, below plot the force for the interaction between two argon atoms as was completed for the energy above. "
+    "The force of a given interaction can be found using the `forcefields.lennard(dr, constants, force=True)` function available in pylj, below plot the force for the interaction between two argon atoms as was completed for the energy above. "
    ]
   },
   {
@@ -203,9 +208,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "r = ◽◽◽\n",
     "f = ◽◽◽\n",
+    "\n",
+    "%matplotlib widget\n",
     "plt.plot( ◽◽◽, ◽◽◽ )\n",
     "plt.xlabel( ◽◽◽ )\n",
     "plt.ylabel( ◽◽◽ )\n",
@@ -281,7 +287,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib notebook\n",
+    "%matplotlib widget\n",
     "simulation = md.initialise(16, 300, 50, 'square')\n",
     "sample_system = sample.JustCell(simulation)\n",
     "simulation.compute_force()\n",

examples/monte_carlo/intro_to_monte_carlo.ipynb

@@ -6,9 +6,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import warnings\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from pylj import mc, comp, util, sample, pairwise"
+    "from pylj import mc, util, sample, forcefields\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
    ]
   },
   {
@@ -67,9 +70,11 @@
    "source": [
     "r = np.linspace( ◽◽◽, ◽◽◽, ◽◽◽ )\n",
     "E = ◽◽◽\n",
-    "plt.plot( ◽◽◽, ◽◽◽ )\n",
-    "plt.xlabel( ◽◽◽ )\n",
-    "plt.ylabel( ◽◽◽ )\n",
+    "\n",
+    "%matplotlib widget\n",
+    "plt.plot(r, E)\n",
+    "plt.xlabel(\"$r/m$\")\n",
+    "plt.ylabel(\"$E/J$\")\n",
     "plt.show()"
    ]
   },
@@ -84,7 +89,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are going to use the software pylj. This is a Python library, which means it is a collection of functions to enable the simulation by Monte Carlo. An example of a function in pylj is the `pairwise.lennard_jones_energy`, which can be used similar to the `lennard_jones` function that you have defined above."
+    "We are going to use the software pylj. This is a Python library, which means it is a collection of functions to enable the simulation by Monte Carlo. An example of a function in pylj is the `forcefields.lennard_jones`, which can be used similar to the `lennard_jones` function that you have defined above."
    ]
   },
   {
@@ -94,7 +99,9 @@
    "outputs": [],
    "source": [
     "r = np.linspace( 3e-10, 8e-10, 100 )\n",
-    "E = pairwise.lennard_jones_energy(1.36e-134, 9.27e-78, r)\n",
+    "E = forcefields.lennard_jones(r, [1.36e-134, 9.27e-78])\n",
+    "\n",
+    "%matplotlib widget\n",
     "plt.plot( r, E )\n",
     "plt.xlabel( 'r/m' )\n",
     "plt.ylabel( 'E/J' )\n",
@@ -142,7 +149,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib notebook\n",
+    "%matplotlib widget\n",
     "simulation = mc.initialise(16, 300, 20, 'square')\n",
     "sample_system = sample.JustCell(simulation)"
    ]
@@ -176,7 +183,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "pylj has a function for the determination of the energy of each interaction, `pairwise.compute_energy`. This can be run as follows. "
+    "pylj has a function for the determination of the energy of each interaction, `System.compute_energy`. This can be run as follows. "
    ]
   },
   {
@@ -207,8 +214,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "random_particle = simulation.select_random_particle()\n",
-    "simulation.new_random_position(random_particle)"
+    "simulation.select_random_particle()\n",
+    "simulation.new_random_position()"
    ]
   },
   {

examples/simple_examples/molecular_dynamics.ipynb

@@ -22,7 +22,7 @@
    "source": [
     "def md_simulation(number_of_particles, temperature, box_length, number_of_steps, sample_frequency):\n",
     "    # Creates the visualisation environment\n",
-    "    %matplotlib notebook\n",
+    "    %matplotlib widget\n",
     "    # Initialise the system\n",
     "    system = md.initialise(number_of_particles, temperature, box_length, 'square')\n",
     "    # This sets the sampling class\n",

examples/simple_examples/monte_carlo.ipynb

@@ -22,7 +22,7 @@
    "source": [
     "def mc_simulation(number_of_particles, temperature, box_length, number_of_steps, sample_frequency):\n",
     "    # Creates the visualisation environment\n",
-    "    %matplotlib notebook\n",
+    "    %matplotlib widget\n",
     "    # Initialise the system placing the particles on a square lattice\n",
     "    system = mc.initialise(number_of_particles, temperature, box_length, 'square')\n",
     "    # This sets the sampling class as Energy, which shows the energy of the system\n",