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update n_jobs
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‎applications/pentapeptide_msm/pentapeptide_msm.ipynb

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@@ -778,7 +778,7 @@
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}
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],
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"source": [
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"its = msm.timescales_msm(dtrajs, lags=100, nits=10, errors='bayes', n_jobs=-1)"
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"its = msm.timescales_msm(dtrajs, lags=100, nits=10, errors='bayes', n_jobs=1)"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.8.6"
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}
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"nbformat": 4,

‎applications/trypsin_benzamidine_hmm/trypsin_benzamidine_hmm.ipynb

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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"indir = './data'\n",
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we decide which coordinates to be used in the further analysis. In this example we will use nearest-neighbor heavy-atom contacts between the Benzamidine residue and each other residue. If the nearest-neighbor distance between Benzamidine and a given other residue is smaller than 0.5 nm, the contact is set 1, otherwise 0. This results in a contact vector with 233 elements, i.e. each trajectory frame is now represented by a 233-dimensional binary feature vector."
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"metadata": {},
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"outputs": [],
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"source": [
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"feat = coor.featurizer(topfile)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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"data": {
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"name": "stdout",
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{
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"cell_type": "code",
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"execution_count": null,
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"tica_lag = 10 # tica lagtime\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"name": "stdout",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"name": "stderr",
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"Dall = np.concatenate(dtrajs)\n",
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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"source": [
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"nstates = 5\n",
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"lags=[1,2,3,4,5,7,10]\n",
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"its = msm.timescales_hmsm(dtrajs, nstates, lags=lags, errors='bayes', nsamples=250, n_jobs=-1)"
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"its = msm.timescales_hmsm(dtrajs, nstates, lags=lags, errors='bayes', nsamples=250, n_jobs=1)"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"dG_stats = np.array([binding_free_energy(M) for M in its.models])\n",
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"kon_stats = np.array([binding_rate(M) for M in its.models])\n",
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{
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"cell_type": "code",
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"metadata": {},
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{
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "Sets A and B have to be disjoint",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
@@ -959,8 +912,7 @@
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"\u001b[1;32m/home/marscher/workspace/msmtools/msmtools/analysis/api.pyc\u001b[0m in \u001b[0;36mcommittor\u001b[1;34m(T, A, B, forward, mu)\u001b[0m\n\u001b[0;32m 877\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 878\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdense\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommittor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward_committor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 880\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 881\u001b[0m \u001b[1;34m\"\"\" if P is time reversible backward commitor is equal 1 - q+\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m/home/marscher/workspace/msmtools/msmtools/analysis/dense/committor.pyc\u001b[0m in \u001b[0;36mforward_committor\u001b[1;34m(T, A, B)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mnotAB\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mB\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAB\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Sets A and B have to be disjoint\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 75\u001b[0m \u001b[0mL\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mT\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Generator matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: Sets A and B have to be disjoint"
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],
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"output_type": "error"
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]
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}
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],
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"mplt.plot_flux(tpt, pos=pos, state_sizes=state_sizes, state_colors=state_colors, show_committor=False)\n",
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{
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"cell_type": "code",
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"metadata": {},
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"meta_samples = bhmm.sample_by_observation_probabilities(100)"
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"outfiles = ['./figs/hmm1_100samples.xtc', \n",
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"metadata": {},
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"coor.save_trajs(inp, meta_samples, outfiles=outfiles)"
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import Image\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"display_name": "Python 3",
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"language": "python",
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"language_info": {
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.11"
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}

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