new_interpolation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install MDAnalysis\n",
    "# !pip install MDAnalysisTests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ce6d2ca-5f5f-4234-baee-5a4703e334e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bpy\n",
    "import numpy as np\n",
    "import MolecularNodes as mn\n",
    "import MDAnalysis as mda\n",
    "\n",
    "# !pip install MDAnalysisTests\n",
    "from MDAnalysis.tests.datafiles import PSF, DCD\n",
    "from MDAnalysis.analysis import distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "404a2610-f445-4efe-a173-12d2e004c234",
   "metadata": {},
   "outputs": [],
   "source": [
    "bpy.context.scene.render.engine = 'CYCLES'\n",
    "bpy.context.scene.cycles.device = \"GPU\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = mda.Universe(PSF, DCD)\n",
    "\n",
    "custom_selections = {'name_ca': 'name CA',\n",
    "                    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use log 6 for frame_mapping\n",
    "frame_mapping = np.logspace(0, np.log(len(u.trajectory)) / np.log(100), 150, base=100, dtype=int) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(frame_mapping, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f569b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mda_session = mn.mda.MDAnalysisSession()\n",
    "mda_session.show(u,\n",
    "                selection='protein',\n",
    "                name='protein',\n",
    "                custom_selections=custom_selections,\n",
    "                frame_mapping=frame_mapping,\n",
    "                subframe=3\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51496469-d7d7-4072-908f-63bee7f3a264",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add transformation\n",
    "from MDAnalysis.transformations import PositionAverager\n",
    "\n",
    "transformation = PositionAverager(3, check_reset=True)\n",
    "u.trajectory.add_transformations(transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d3fe7ef-acf9-428a-bad4-df111afffc7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"/Users/ugmhackathon/blender_and_mda.blend\"\n",
    "\n",
    "bpy.ops.wm.save_as_mainfile(filepath=file_path)\n",
    "\n",
    "# the .blend file can be opened by\n",
    "# file_path = \"blender_and_mda.blend\"\n",
    "# bpy.ops.wm.open_mainfile(filepath=file_path)\n",
    "# mda_session = bpy.context.scene.mda_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa099c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the .blend file can be opened by\n",
    "import bpy\n",
    "file_path = \"/Users/ugmhackathon/blender_and_mda.blend\"\n",
    "bpy.ops.wm.open_mainfile(filepath=file_path)\n",
    "mda_session = bpy.context.scene.mda_session"
   ]
  }
 ],
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