Skip to content

Instantly share code, notes, and snippets.

@orbeckst

orbeckst/otf-blog.ipynb

Last active March 7, 2020 11:55
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save orbeckst/ddbe912c665340791752c6622575cc6a to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save orbeckst/ddbe912c665340791752c6622575cc6a to your computer and use it in GitHub Desktop.
Download ZIP
On-the-fly transformations blog post
Display the source blob
Display the rendered blob
Raw
otf-blog.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OTF blog post "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5c66d7395164a90a1e8b8b673f48756",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"_ColormakerRegistry()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore') # some attributes are missing \n",
"import MDAnalysis as mda\n",
"from MDAnalysis import transformations\n",
"import nglview as nv\n",
"\n",
"import MDAnalysisData\n",
"\n",
"import numpy as np\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"peptide = MDAnalysisData.datasets.fetch_membrane_peptide()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/oliver/MDAnalysis_data/membrane_peptide/memb_pept.tpr\n",
"/Users/oliver/MDAnalysis_data/membrane_peptide/memb_pept.xtc\n"
]
}
],
"source": [
"print(peptide.topology)\n",
"print(peptide.trajectory)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "62379b9de53747bebcc7f5b68254f4a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u = mda.Universe(peptide.topology, peptide.trajectory)\n",
"u.transfer_to_memory(step=10)\n",
"view = nv.show_mdanalysis(u)\n",
"view.add_unitcell()\n",
"view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/6, 'rzyz').tolist())\n",
"view.control.zoom(-0.3)\n",
"view"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f214def7db04db3bf3c6138108424d9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nglview.contrib.movie import MovieMaker\n",
"movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_raw.gif')\n",
"movie.make()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unwrap "
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"from MDAnalysis import transformations\n",
"\n",
"# a custom atom group can be passed as an argument. In this case we will use all the atoms\n",
"# in the Universe u\n",
"u = mda.Universe(peptide.topology, peptide.trajectory)\n",
"u.transfer_to_memory(step=10)\n",
"\n",
"ag = u.atoms\n",
"# we define the transformation\n",
"workflow = transformations.unwrap(ag)\n",
"\n",
"u.trajectory.add_transformations(workflow)\n"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "08ccd0c8a54444039c4fcde541a96fc7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view = nv.show_mdanalysis(u)\n",
"view.add_unitcell()\n",
"view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/6, 'rzyz').tolist())\n",
"#view.control.zoom(-0.3)\n",
"view"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "93486664ac9444cb8347bbb2e5cdef9b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_wrapped.gif')\n",
"movie.make()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Center "
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"u = mda.Universe(peptide.topology, peptide.trajectory)\n",
"u.transfer_to_memory(step=10)\n",
"\n",
"prot = u.select_atoms(\"protein\")\n",
"ag = u.atoms\n",
"# we will use mass as weights for the center calculation\n",
"workflow = (transformations.unwrap(ag),\n",
" transformations.center_in_box(prot, center='mass'),\n",
" transformations.wrap(ag, compound='fragments'))\n",
"u.trajectory.add_transformations(*workflow)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "25297a79008b4c01b2f77b029db4e1d6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view = nv.show_mdanalysis(u)\n",
"view.add_unitcell()\n",
"view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/3, 'rzyz').tolist())\n",
"view"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fbcf1a66f8e54b18a177aae631230e9c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_centered.gif')\n",
"movie.make()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## fit "
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"u = mda.Universe(peptide.topology, peptide.trajectory)\n",
"u.transfer_to_memory(step=10)\n",
"\n",
"prot = u.select_atoms(\"protein\")\n",
"# we load another universe to define the reference\n",
"# it uses the same input files, but this doesn't have to be always the case\n",
"ref_u = u.copy()\n",
"reference = ref_u.select_atoms(\"protein\")\n",
"ag = u.atoms\n",
"workflow = (transformations.unwrap(ag),\n",
" transformations.center_in_box(prot, center='mass'),\n",
" transformations.wrap(ag, compound='fragments'),\n",
" transformations.fit_rot_trans(prot, reference))\n",
"u.trajectory.add_transformations(*workflow)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "486c7353c589485f95a881caddcd7f65",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view = nv.show_mdanalysis(u)\n",
"view.add_unitcell()\n",
"view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/3, 'rzyz').tolist())\n",
"view"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6586427d3944173bc8664fdc70f7781",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"movie = MovieMaker(view, fps=24, in_memory=True, output='peptide_fitted.gif')\n",
"movie.make()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a1562e6dc1d7439abf5ce687052c5454",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = nv.show_mdanalysis(prot)\n",
"w.add_line()\n",
"w.control.zoom(0.5)\n",
"w\n"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "670ba0b0afe241bbbac9ac1c73044be3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"movie = MovieMaker(w, fps=24, in_memory=True, output='peptideonly_fitted.gif')\n",
"movie.make()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### xy fit "
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"u = mda.Universe(peptide.topology, peptide.trajectory)\n",
"u.transfer_to_memory(step=10)\n",
"\n",
"prot = u.select_atoms(\"protein\")\n",
"ref_u = u.copy()\n",
"reference = ref_u.select_atoms(\"protein\")\n",
"ag = u.atoms\n",
"workflow = (transformations.unwrap(ag),\n",
" transformations.center_in_box(prot),\n",
" transformations.wrap(ag, compound='fragments'),\n",
" transformations.fit_rot_trans(prot, reference, plane='xy', weights=\"mass\"))\n",
"u.trajectory.add_transformations(*workflow)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c4dd8aa4e23403c829ee7d16af3fc49",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"protein_P = u.select_atoms(\"protein or name P\")\n",
"view = nv.show_mdanalysis(protein_P)\n",
"view.add_line()\n",
"view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, 0, 'rzyz').tolist())\n",
"view\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ad471e554ad8429587f4dbacfca25ee8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"movie = MovieMaker(view, fps=24, output='peptide_P_fitted_xy.gif')\n",
"movie.make()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## custom 2 "
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"def protein_up_by_2(ag):\n",
" def wrapped(ts):\n",
" # here's where the magic happens \n",
" # we create a numpy float32 array to avoid reduce floating\n",
" # point errors\n",
" ag.positions += np.asarray([0,0,20])\n",
" return ts\n",
" return wrapped"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"u = mda.Universe(peptide.topology, peptide.trajectory)\n",
"u.transfer_to_memory(step=10)\n",
"\n",
"ag = u.atoms\n",
"prot = u.select_atoms(\"protein\")\n",
"workflow = (transformations.unwrap(ag),\n",
" protein_up_by_2(prot),\n",
" transformations.wrap(ag, compound='fragments'))\n",
"u.trajectory.add_transformations(*workflow)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d30a42df976148fba00107514ac54b48",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NGLWidget(max_frame=100)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view = nv.show_mdanalysis(u)\n",
"view.add_unitcell()\n",
"view.control.rotate(mda.lib.transformations.quaternion_from_euler(-np.pi/2, np.pi/3, np.pi/6, 'rzyz').tolist())\n",
"view.control.zoom(-0.3)\n",
"view"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7ea1babf7204431aa6b3c420e15f58ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntProgress(value=0, description='Rendering ...')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"movie = MovieMaker(view, fps=24, output='peptide_up2.gif')\n",
"movie.make()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment