Updated README

a46a058a · cmaffeo2 · 5d719373 · a46a058a · a46a058a
Commit a46a058a authored 5 years ago by cmaffeo2
--- a/1-basics.ipynb
+++ b/1-basics.ipynb
@@ -117,7 +117,7 @@
   "source": [
    "### Step 5: Visualize the results\n",
    "\n",
-    "We recommend using VMD for visualizing the simulations trajectories with:\n",
+    "We recommend using [VMD](https://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=VMD) for visualizing the simulations trajectories with:\n",
    "```bash\n",
    "vmd sims/1-argon/1-lj.psf sims/1-argon/output/1-lj.dcd\n",
    "```\n",

 %% Cell type:markdown id: tags:

 # Basic usage of the `arbdmodel` package

 %% Cell type:markdown id: tags:

 ### Step 1: Create particle types

 %% Cell type:code id: tags:

 ``` python
 import numpy as np
 from arbdmodel import ParticleType, PointParticle, ArbdModel

 system_size = 620
 num_particles = 6400

 ## diffusion of liquid argon: https://www.sciencedirect.com/science/article/pii/0375960171901290
 ## units "60 * 3.41 AA * sqrt(119.5 k K / 40 dalton)" "AA**2/ns"
 argon = ParticleType(name="Ar",
                     mass=39.95, # dalton
                     damping_coefficient=5000, # per ns
                     custom_property="my_value",
                     epsilon=0.177,  # kcal_mol
                     radius=3.345/2) # Angstroms

 print(argon)
 ```

 %% Cell type:markdown id: tags:

 ### Step 2: Build system

 %% Cell type:code id: tags:

 ``` python
 ## Generate Nx3 array of random cartesian coordinates
 positions = system_size*(np.random.random([num_particles,3])-0.5)

 ## Create a list of point particles located at those positions
 points = [PointParticle(type_=argon, name="Ar", position=positions[i])
                        for i in range(num_particles)]

 model = ArbdModel(points,
                  dimensions=[system_size for i in range(3)], # Ångstroms
                  timestep = 20e-6, # ns; can be specified below with engine instead
                  )

 print(model)
 print(model.children[:2])
 ```

 %% Cell type:markdown id: tags:

 ### Step 3: Describe the interactions between the particles

 %% Cell type:code id: tags:

 ``` python
 from arbdmodel.interactions import LennardJones

 lj = LennardJones()
 model.add_nonbonded_interaction(lj)
 ```

 %% Cell type:markdown id: tags:

 ### Step 4: Run the simulation

 %% Cell type:code id: tags:

 ``` python
 from arbdmodel import ArbdEngine

 engine = ArbdEngine(output_period = 1e2, num_steps = 1e5)
 engine.simulate(model, output_name="1-lj", directory='sims/1-argon',
                num_steps=1e3, gpu=1
 )                               # simulation parameters here override those in constructor
 ```

 %% Cell type:markdown id: tags:

 ### Step 5: Visualize the results

-We recommend using VMD for visualizing the simulations trajectories with:
+We recommend using [VMD](https://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=VMD) for visualizing the simulations trajectories with:
 ```bash
 vmd sims/1-argon/1-lj.psf sims/1-argon/output/1-lj.dcd
 ```
 However, for convenience we have provided commands to use the nglview module, which provides browser-based visualization.

 note: you must enable certain jupyter extensions to use nglview as follows
 ```bash
 jupyter-nbextension enable --py --sys-prefix widgetsnbextension
 jupyter-nbextension enable nglview --py --sys-prefix
 ```

 %% Cell type:code id: tags:

 ``` python
 import MDAnalysis as mda
 import nglview as nv

 u = mda.Universe("sims/1-argon/1-lj.pdb", "sims/1-argon/output/1-lj.dcd")
 w = nv.show_mdanalysis(u)
 w.clear_representations()
 w.add_ball_and_stick("all",radius=10)
 w
 ```

 %% Cell type:markdown id: tags:

 ### Step 6: Customize the interactions

 %% Cell type:code id: tags:

 ``` python
 from arbdmodel.interactions import NonbondedPotential

 ## Clear nonbonded interactions
 model.nonbonded_interactions = []

 class BuckinghamPotential(NonbondedPotential):
    def potential(self, r, types):
        ## https://royalsocietypublishing.org/doi/10.1098/rspa.1938.0173
        ## optionally could pull information from typeA, typeB
        typeA, typeB = types
        return 143932.65 * ( 1.69 * np.exp( -r/0.273 ) - 102e-4 / r**6 )

 pot = BuckinghamPotential()
 model.add_nonbonded_interaction( pot )

 engine.simulate(model, output_name="2-buck", directory='sims/1-argon',
                num_steps = 1e3, gpu=1
 )
 ```

 %% Cell type:code id: tags:

 ``` python
 import MDAnalysis as mda
 import nglview as nv

 ## note: you must enable

 u = mda.Universe("sims/1-argon/2-buck.pdb", "sims/1-argon/output/2-buck.dcd")
 w = nv.show_mdanalysis(u)
 w.clear_representations()
 w.add_ball_and_stick("all",radius=10)
 w
 ```

 %% Cell type:markdown id: tags:

 ### Step 6: Add bonds

 %% Cell type:code id: tags:

 ``` python
 from arbdmodel.interactions import HarmonicBond
 bond = HarmonicBond(k=1,           # kcal/mol AA**2
                    r0 = 3         # AA
 )

 ## Go through every other index
 for i in range(0,len(model.children),2):
    j = i+1
    model.add_bond( model.children[i],
                    model.children[j],
                    bond )

 print("numbonds:", len(model.bonds))

 engine.simulate(model, output_name="3-bonds", directory='sims/1-argon',
    num_steps = 1e3, gpu=1
 )
 ```

 %% Cell type:code id: tags:

 ``` python
 import MDAnalysis as mda
 import nglview as nv

 ## note: you must enable

 u = mda.Universe("sims/1-argon/3-bonds.psf","sims/1-argon/3-bonds.pdb", "sims/1-argon/output/3-bonds.dcd")
 w = nv.show_mdanalysis(u)
 w.clear_representations()
 w.add_ball_and_stick("index < 10",radius=10)
 w
 ```

 %% Cell type:markdown id: tags:

 ### More to come shortly...
 First, we'll introduce some classes helpful for working with polymers, and we'll show the polymer models that ship with the class.

 We'll show how beads can experience grid-based potentials.

 Then we'll show how to construct simple rigid body models.

 %% Cell type:code id: tags:

 ``` python
 ```

--- a/README.md
+++ b/README.md
@@ -16,12 +16,7 @@ Currently, a CUDA-enabled GPU is required for running simulations using the ARBD
 Make sure you have the `arbdmodel` package properly installed.
 You will also need the `jupyter` package installed to your Python environment. 
 Finally, you will need to install
-[VMD](https://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=VMD), 
-[NAMD](https://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=NAMD)
+[VMD](https://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=VMD)
 and [ARBD](http://bionano.physics.illinois.edu/arbd).

-<!-- ## Instructions -->
-
-<!-- Follow the pdf for steps 1-2, which cover the usage of a command line utility that makes fast multi-resolution simulations of DNA objects very easy to perform. -->
-<!-- Then launch the Jupyter notebook "step3.ipynb" for an introduction to scripting with the `mrdna` framework. -->
-<!-- If you have any questions or concerns, please contact Chris Maffeo at [cmaffeo2@illinois.edu](mailto:cmaffeo2@illinois.edu). -->
+If you have any questions or concerns, please contact Chris Maffeo at [cmaffeo2@illinois.edu](mailto:cmaffeo2@illinois.edu).