diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..6ac495f --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,52 @@ +name: CI + +on: + push: + branches: [master, develop] + pull_request: + branches: [master, develop] + +jobs: + test: + name: Python ${{ matrix.python-version }} / ${{ matrix.os }} + runs-on: ${{ matrix.os }} + + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest] + python-version: ["3.10", "3.11", "3.12"] + + defaults: + run: + shell: bash -l {0} + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up conda (miniforge) + uses: conda-incubator/setup-miniconda@v3 + with: + miniforge-version: latest + python-version: ${{ matrix.python-version }} + activate-environment: mastermsm-test + auto-activate-base: false + + - name: Install dependencies + run: | + conda install -y -c conda-forge \ + numpy scipy matplotlib networkx \ + mdtraj scikit-learn + pip install pytest + pip install -e . + + - name: Show package versions + run: conda list | grep -E "numpy|scipy|mdtraj|networkx|scikit" + + - name: Run tests + run: | + pytest mastermsm/test/ -v --tb=long > pytest-output.txt 2>&1 + RESULT=$? + cat pytest-output.txt + exit $RESULT diff --git a/README.md b/README.md index 5d48f9d..f5a7e57 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,5 @@ -[![Build Status](https://travis-ci.com/daviddesancho/MasterMSM.svg?branch=develop)](https://travis-ci.com/daviddesancho/MasterMSM) +[![CI](https://github.com/BioKT/MasterMSM/actions/workflows/ci.yml/badge.svg?branch=develop)](https://github.com/BioKT/MasterMSM/actions/workflows/ci.yml) [![Documentation Status](https://readthedocs.org/projects/mastermsm/badge/?version=develop)](https://mastermsm.readthedocs.io/en/develop/?badge=develop) -[![Codacy Badge](https://api.codacy.com/project/badge/Grade/facdc755bf3c4c269f55738117db4c38)](https://www.codacy.com/app/daviddesancho/MasterMSM?utm_source=github.com&utm_medium=referral&utm_content=daviddesancho/MasterMSM&utm_campaign=Badge_Grade) MasterMSM ========= diff --git a/TODO.md b/TODO.md new file mode 100644 index 0000000..4976ba5 --- /dev/null +++ b/TODO.md @@ -0,0 +1,4 @@ +# MasterMSM + +## develop branch +- [ ] Implement TICA diff --git a/examples/alanine_dipeptide/ala_dipeptide_discretize.ipynb b/examples/alanine_dipeptide/ala_dipeptide_discretize.ipynb index 13ffea3..601e723 100644 --- a/examples/alanine_dipeptide/ala_dipeptide_discretize.ipynb +++ b/examples/alanine_dipeptide/ala_dipeptide_discretize.ipynb @@ -4,22 +4,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Discretizations\n", - "Here we show how different discretizations work within MasterMSM. An important note is that not all discretizations will be sensible for all systems, but as usual the alanine dipeptide is a good testbed.\n", + "# Master equation model of the alanine dipeptide\n", "\n", - "We start downloading the data from the following [link](https://osf.io/a2vc7) and importing a number of libraries for plotting and analysis that will be useful for our work." + "In this example we deal with a simple but realistic example of MD simulation data, generated with the [Gromacs package](http://www.gromacs.org/).\n", + "First we import a number of general purpose Python libraries we will need as we run this example." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%matplotlib inline\n", - "import math\n", + "import os\n", "import numpy as np\n", + "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set(style=\"ticks\", color_codes=True, font_scale=1.5)\n", @@ -30,17 +31,64 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next we import the ```traj``` module and read the molecular simulation trajectory in the ```xtc``` compressed format from Gromacs." + "Next, we download the relevant data from the following [location](https://osf.io/a2vc7/) " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from mastermsm.test.download_data import download_osf_alaTB" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "from mastermsm.trajectory import traj\n", - "tr = traj.TimeSeries(top='data/alaTB.gro', traj=['data/alatb_n1_ppn24.xtc'])\n", + "try:\n", + " os.mkdir(\"test\")\n", + " download_osf_alaTB()\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discretizing the trajectory\n", + "We start loading the data using the data structures from the `trajectory` module. For this we use the external library [`MDtraj`](http://mdtraj.org), which contains all sorts of interesting methods for parsing and calculating interesting properties of our time-series data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from mastermsm.trajectory import traj" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "tr = traj.TimeSeries(top='test/data/alaTB.gro', \\\n", + " xtc='test/data/alaTB.xtc')\n", "print (tr.mdt)" ] }, @@ -48,27 +96,370 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Core Ramachandran angle regions\n", - "Following previous work we can use core regions in the Ramachandran map to define our states. We use utilities from the [MDtraj](http://mdtraj.org) package to compute the Phi and Psi dihedrals." + "To extract the relevant features from the trajectory we use a Featurizer object." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X = traj.Featurizer(tr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One possibility is to use the torsion angles, which we will shift for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X.add_torsions(shift=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now our trajectories have a `features` attribute, which contains the $\\phi$ and $\\psi$ torsions for our amino acid residue." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize=(3.,3.25))\n", + "ax.hist2d(tr.features[:,0], tr.features[:,1], \\\n", + " bins=[50, 50], norm=mpl.colors.LogNorm())\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following step, we will use a clustering algorithm to transform these features into discrete states. We will use the hierarchical density-based clustering algorithm called HDBSCAN." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "DISC = traj.Discretizer(tr)\n", + "DISC.hdbscan(mcs=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "HDBSCAN results in some datapoints being regarded as \"noise\". Here we use transition based assignment as originally described by Buchete and Hummer ([*J. Phys. Chem. B*, 2008](https://doi.org/10.1021/jp0761665)) to assign the trajectory to the discrete states." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize=(3.,3.25))\n", + "ax.scatter(tr.features[:-1:10,0], tr.features[:-1:10,1], \\\n", + " c=tr.distraj[:-1:10], s=4, cmap='Set1')\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The time series data looks as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2,1, figsize=(8,3), sharex=True)\n", + "ie = 20000\n", + "time = range(len(tr.features[:ie,0]))\n", + "ax[0].scatter(time, tr.features[:ie,0], \\\n", + " c=tr.distraj[:ie], s=2, cmap='Set1')\n", + "ax[1].scatter(time, tr.features[:ie,1], \\\n", + " c=tr.distraj[:ie], s=2, cmap='Set1')\n", + "ax[1].set_xlim(17000, 19000)\n", + "ax[0].set_ylabel('x')\n", + "ax[1].set_ylabel('y')\n", + "plt.tight_layout(h_pad=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making an MSM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having assigned the trajectory to discrete states, we can now move into the estimation of the master equation (Markov state) model. First we import the `msm` module from our package" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "import mdtraj as md\n", - "phi = md.compute_phi(tr.mdt)\n", - "psi = md.compute_psi(tr.mdt)\n", - "res = [x for x in tr.mdt.topology.residues]" + "from mastermsm.msm import msm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then pass the trajectory to generate an `MSM` object. A parameter we must also pass to generate this type of object is the lag time ($\\Delta t$), i.e. the interval for the observation of transitions between states. In this case, we choose the smallest possible value (i.e. the timestep in our trajectory). Additionally, we use the option `sym` to enforce detailed balance in the construction in the MSM (this should be used with caution). " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Building MSM at lag time 5\n" + ] + } + ], + "source": [ + "lagt = tr.dt\n", + "msm_alaTB = msm.MSM([tr], lagt=lagt, sym=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having generated this object, we can now start calculating interesting things. First, we estimate the matrix of counts, $\\mathbf{N}(\\Delta t)$, i.e. the number of transitions between states and the transition matrix, $\\mathbf{T}(\\Delta t)$." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " symmetrizing\n" + ] + } + ], + "source": [ + "msm_alaTB.calc_count()\n", + "msm_alaTB.calc_trans()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Z2r59uxYuXOj1aPjc2bNndfv2bTU2Nt7zTzjs2LFDS5cu9WAyeIns2mcxu/w2cwCASb55DwoA8GChoAAAJlFQAACTKCgAgEkUFADAJAoKAGASBQUAMImCAgCYREEBAEz6Xy7JjXSSArt0AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2, figsize=(7,3))\n", + "ax[0].imshow(msm_alaTB.count, cmap='Blues', origin='lower')\n", + "ax[1].imshow(msm_alaTB.trans, cmap='Blues', origin='lower')\n", + "ax[0].text(0.1, 0.8, '$\\mathbf{N}$', transform=ax[0].transAxes)\n", + "ax[1].text(0.1, 0.8, '$\\mathbf{T}$', transform=ax[1].transAxes)\n", + "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Then we run the actual discretization, using only two states for the alpha and extended conformations." + "These matrices already encapsulate interesting information. Specifically, in the transition matrix we find that our three states are very metastable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation and validation of MSMs\n", + "So far, however, we have used a value of $\\Delta t$ chosen by hand. In order to correctly estimate observables like populations of states or characteristic timescales for the system, we need to be more thorough. This involves testing multiple lag times and validating the resulting models. In order to handle all these operations, `mastermsm` has an additional class, called `SuperMSM`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Building SuperMSM from \n", + " ['test/data/alaTB.xtc']\n" + ] + } + ], + "source": [ + "super_alaTB = msm.SuperMSM([tr], sym=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will next try multiple lag times and compare the results" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Building MSM at lag time 5\n", + " symmetrizing\n", + "\n", + " Building MSM at lag time 10\n", + " symmetrizing\n", + "\n", + " Building MSM at lag time 20\n", + " symmetrizing\n", + "\n", + " Building MSM at lag time 50\n", + " symmetrizing\n", + "\n", + " Building MSM at lag time 100\n", + " symmetrizing\n", + "\n", + " Building MSM at lag time 200\n", + " symmetrizing\n" + ] + } + ], + "source": [ + "lags = [5, 10, 20, 50, 100, 200]\n", + "super_alaTB.do_msm(lags)\n", + " #msm_alaTB.msms[i].calc_evals(neigs=2, errors=True, evecs=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the multiple transition matrices, we clearly see how the number of off-diagonal terms gradually increases for higher observation windows. This is particularly clear for states 1 and 2, which interconvert rapidly." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2,6, figsize=(10,3.75), sharex=True, sharey=True)\n", + "for i,l in enumerate(lags):\n", + " ax[0][i].imshow(super_alaTB.msms[l].count, cmap='Blues', origin='lower')\n", + " ax[1][i].imshow(super_alaTB.msms[l].trans, cmap='Blues', origin='lower')\n", + " ax[0][i].set_title('$\\Delta t$ = %g'%l)\n", + "ax[0][0].text(0.1, 0.8, '$\\mathbf{N}$', transform=ax[0][0].transAxes)\n", + "ax[1][0].text(0.1, 0.8, '$\\mathbf{T}$', transform=ax[1][0].transAxes)\n", + "\n", + "plt.tight_layout(w_pad=0, h_pad=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we compute the relaxation times and equilibrium probabilities as a function of the lag time. In this very simple case, we find that the model is apparently well converged even for the shortest value of the lag time. This is important because for long lag times ($\\Delta t>10$ ps) we lose information about the fastest process." ] }, { @@ -77,8 +468,7 @@ "metadata": {}, "outputs": [], "source": [ - "tr.discretize(states=['A', 'E', 'L'])\n", - "tr.find_keys()" + "super_alaTB.calc_evals(errors=True)" ] }, { @@ -87,21 +477,39 @@ "metadata": {}, "outputs": [], "source": [ - "fig, ax = plt.subplots(figsize=(10,3))\n", - "ax.plot(tr.mdt.time, [tr.keys.index(x) if (x in tr.keys) else 0 for x in tr.distraj ], lw=1)\n", - "ax.set_xlim(0, 1.5e5)\n", - "ax.set_ylim(-0.5, 2.5)\n", - "ax.set_yticks(range(3))\n", - "ax.set_yticklabels(['A', 'E', 'L'])\n", - "ax.set_xlabel('Time (ps)', fontsize=20)\n", - "ax.set_ylabel('state', fontsize=20)" + "fig, ax = plt.subplots(2, 1, sharex=True)\n", + "for i in range(2):\n", + " tau_vs_lagt = np.array([[x, super_alaTB.msms[x].tauT[i], \\\n", + " super_alaTB.msms[x].tau_std[i]] \\\n", + " for x in sorted(super_alaTB.msms.keys())])\n", + " ax[1].errorbar(tau_vs_lagt[:,0],tau_vs_lagt[:,1],fmt='o-',\\\n", + " yerr=tau_vs_lagt[:,2], markersize=4)\n", + "ax[1].fill_between(np.logspace(0.1,3.5,10), np.logspace(1, 3.5, 10),\\\n", + " facecolor='lightgray', alpha=0.5)\n", + "\n", + "for i in range(3):\n", + " peq_vs_lagt = np.array([[x, super_alaTB.msms[x].peqT[i], \\\n", + " super_alaTB.msms[x].peq_std[i]] \\\n", + " for x in sorted(super_alaTB.msms.keys())])\n", + " ax[0].errorbar(peq_vs_lagt[:,0], peq_vs_lagt[:,1],fmt='o-',\\\n", + " yerr=peq_vs_lagt[:,2], markersize=4)\n", + "ax[1].set_xlabel(r'$\\Delta$t (ps)', fontsize=16)\n", + "ax[1].set_ylabel(r'$\\tau_i$ (ps)', fontsize=16)\n", + "ax[0].set_ylabel(r'$P_{eq}$', fontsize=16)\n", + "ax[1].set_ylim(20,3000)\n", + "ax[0].set_ylim(1e-2,1)\n", + "ax[1].set_xlim(3,300)\n", + "ax[0].set_yscale('log')\n", + "ax[1].set_yscale('log')\n", + "ax[0].set_xscale('log')\n", + "plt.tight_layout(h_pad=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally we derive the MSM using the tools from the ```msm``` module. In particular, we use the ```SuperMSM``` class that will help build MSMs at various lag times." + "More rigorous tests can be performed on the validity of the dynamical model." ] }, { @@ -110,19 +518,50 @@ "metadata": {}, "outputs": [], "source": [ - "from mastermsm.msm import msm\n", - "msm_alaTB = msm.SuperMSM([tr])\n", - "for i in [1, 2, 5, 10, 20, 50, 100]:\n", - " msm_alaTB.do_msm(i)\n", - " msm_alaTB.msms[i].do_trans()\n", - " msm_alaTB.msms[i].boots()" + "super_alaTB.ck_test(lags=[5, 10])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "init_states = [0]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, "metadata": {}, + "outputs": [], "source": [ - "Next we gather results from all these MSMs and plot the relaxation time corresponding to the two slow transitions." + "for lagt in [5, 10, 20]:\n", + " time, pop = super_alaTB.msms[lagt].propagateT(init=init_states, \\\n", + " time=np.logspace(1, 4, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(3):\n", + " plt.plot(time, [p[i] for p in pop])\n", + "for j in super_alaTB.lagts:\n", + " plt.plot(time, super_alaTB.)" ] }, { @@ -131,20 +570,9 @@ "metadata": {}, "outputs": [], "source": [ - "fig, ax = plt.subplots()\n", - "tau_vs_lagt = np.array([[x,msm_alaTB.msms[x].tauT[0],msm_alaTB.msms[x].tau_std[0]] \\\n", - " for x in sorted(msm_alaTB.msms.keys())])\n", - "ax.errorbar(tau_vs_lagt[:,0],tau_vs_lagt[:,1],fmt='o-', yerr=tau_vs_lagt[:,2], markersize=10)\n", - "tau_vs_lagt = np.array([[x,msm_alaTB.msms[x].tauT[1],msm_alaTB.msms[x].tau_std[1]] \\\n", - " for x in sorted(msm_alaTB.msms.keys())])\n", - "ax.errorbar(tau_vs_lagt[:,0],tau_vs_lagt[:,1],fmt='o-', yerr=tau_vs_lagt[:,2], markersize=10)\n", - "ax.fill_between(10**np.arange(-0.2,3,0.2), 1e-1, 10**np.arange(-0.2,3,0.2), facecolor='lightgray')\n", - "ax.set_xlabel(r'$\\Delta$t [ps]', fontsize=16)\n", - "ax.set_ylabel(r'$\\tau$ [ps]', fontsize=16)\n", - "ax.set_xlim(0.8,150)\n", - "ax.set_ylim(10,3000)\n", - "ax.set_yscale('log')\n", - "_ = ax.set_xscale('log')" + "pMSM_E, pMD_E, epMD_E = msm_alaTB.ck_test(time=[1, 2, 5, 7, 10, 20, 50, 100], init=['E'])\n", + "pMSM_A, pMD_A, epMD_A = msm_alaTB.ck_test(time=[1, 2, 5, 7, 10, 20, 50, 100], init=['A'])\n", + "pMSM_L, pMD_L, epMD_L = msm_alaTB.ck_test(time=[1, 2, 5, 7, 10, 20, 50, 100], init=['L'])" ] }, { @@ -229,11 +657,16 @@ " for x in sorted(msm_alaTB_grid.msms.keys())])\n", "\n", "fig, ax = plt.subplots()\n", - "ax.errorbar(tau1_vs_lagt[:,0],tau1_vs_lagt[:,1], tau1_vs_lagt[:,2], fmt='o-', markersize=10)\n", - "ax.errorbar(tau2_vs_lagt[:,0],tau2_vs_lagt[:,1], tau2_vs_lagt[:,2], fmt='o-', markersize=10)\n", - "ax.errorbar(tau3_vs_lagt[:,0],tau3_vs_lagt[:,1], tau3_vs_lagt[:,2], fmt='o-', markersize=10)\n", - "ax.errorbar(tau4_vs_lagt[:,0],tau4_vs_lagt[:,1], tau4_vs_lagt[:,2], fmt='o-', markersize=10)\n", - "ax.fill_between(10**np.arange(-0.2,3,0.2), 1e-1, 10**np.arange(-0.2,3,0.2), facecolor='lightgray', alpha=0.5)\n", + "ax.errorbar(tau1_vs_lagt[:,0],tau1_vs_lagt[:,1], \\\n", + " tau1_vs_lagt[:,2], fmt='o-', markersize=10)\n", + "ax.errorbar(tau2_vs_lagt[:,0],tau2_vs_lagt[:,1], \\\n", + " tau2_vs_lagt[:,2], fmt='o-', markersize=10)\n", + "ax.errorbar(tau3_vs_lagt[:,0],tau3_vs_lagt[:,1], \\\n", + " tau3_vs_lagt[:,2], fmt='o-', markersize=10)\n", + "ax.errorbar(tau4_vs_lagt[:,0],tau4_vs_lagt[:,1], \\\n", + " tau4_vs_lagt[:,2], fmt='o-', markersize=10)\n", + "ax.fill_between(10**np.arange(-0.2,3,0.2), 1e-1, \\\n", + " 10**np.arange(-0.2,3,0.2), facecolor='lightgray', alpha=0.5)\n", "ax.set_xlabel(r'$\\Delta$t [ps]', fontsize=16)\n", "ax.set_ylabel(r'$\\tau_i$ [ps]', fontsize=16)\n", "ax.set_xlim(0.8,200)\n", @@ -395,7 +828,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -409,7 +842,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.13" } }, "nbformat": 4, diff --git a/examples/alanine_dipeptide/ala_dipeptide_dpca.ipynb b/examples/alanine_dipeptide/ala_dipeptide_dpca.ipynb deleted file mode 100644 index 6404406..0000000 --- a/examples/alanine_dipeptide/ala_dipeptide_dpca.ipynb +++ /dev/null @@ -1,306 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n", - "import math\n", - "import numpy as np\n", - "import hdbscan\n", - "from sklearn.preprocessing import StandardScaler\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(style=\"ticks\", color_codes=True, font_scale=1.5)\n", - "sns.set_style({\"xtick.direction\": \"in\", \"ytick.direction\": \"in\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import mdtraj as md\n", - "from mastermsm.trajectory import traj" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "trajs = traj.MultiTimeSeries(top='send-david/data/alaTB.gro', \\\n", - " trajs=['send-david/data/out/4_1.xtc', \\\n", - " 'send-david/data/out/protein_only.xtc'])\n", - " #'send-david/data/out/4_2.xtc'])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def dPCA(angles, t):\n", - " \"\"\"\n", - " Compute PCA of dihedral angles\n", - "\n", - " We follow the methods described in A. Altis et al. \n", - " *J. Chem. Phys.* 244111 (2007)\n", - "\n", - " Parameters\n", - " ----------\n", - " angles :\n", - " \n", - " t : \n", - " \n", - " Returns\n", - " -------\n", - " \n", - " \"\"\"\n", - " shape = np.shape(angles)\n", - " print (shape)\n", - " X = np.zeros((shape[0] , \\\n", - " shape[1]+shape[1]))\n", - " for i, ang in enumerate(angles):\n", - " p = 0\n", - " for phi in ang:\n", - " X[i][p], X[i][p+1] = np.cos(phi), np.sin(phi)\n", - " p += 2\n", - " X_std = StandardScaler().fit_transform(X)\n", - " sklearn_pca = PCA(n_components=2*shape[1])\n", - " \n", - " X_transf = sklearn_pca.fit_transform(X_std)\n", - " expl = sklearn_pca.explained_variance_ratio_\n", - " \n", - " print(\"Ratio of variance retrieved by each component:\", expl)\n", - " #graficamos el acumulado de varianza explicada en las nuevas dimensiones\n", - " #plt.figure()\n", - " #plt.plot(np.cumsum(sklearn_pca.explained_variance_ratio_))\n", - " #plt.xlabel('number of components')\n", - " #plt.ylabel('cumulative explained variance')\n", - " #plt.savefig('cum_variance_%g.png'%t)\n", - "\n", - " #colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \\\n", - " #'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray']\n", - " #vectorizer = np.vectorize(lambda x: colors[x % len(colors)])\n", - " #counts, ybins, xbins, image = plt.hist2d(X_transf[:,0], X_transf[:,1], \\\n", - " #bins=[np.linspace(-np.pi,np.pi,20), np.linspace(-np.pi,np.pi,30)], \\\n", - " # bins=20, cmap='binary_r', alpha=0.2)\n", - " #plt.contour(np.transpose(counts), extent=[xbins.min(), xbins.max(), \\\n", - " # ybins.min(), ybins.max()], linewidths=1, colors='gray')\n", - " #countmax = np.amax(counts)\n", - " #counts = np.log(countmax) - np.log(counts)\n", - " #plt.scatter(X_transf[:,0],X_transf[:,1], c=counts)\n", - " #plt.savefig('dpca_%g.png'%t)\n", - "\n", - " return X_transf" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(10504, 2)\n", - "Ratio of variance retrieved by each component: [0.45422684 0.34040386 0.10646118 0.09890812]\n" - ] - }, - { - "data": { - "text/plain": [ - "(10504, 4)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.decomposition import PCA\n", - "from mastermsm.trajectory import traj_lib\n", - "\n", - "phi_cum = []\n", - "psi_cum = []\n", - "for t, tr in enumerate(trajs.traj_list):\n", - " phi = md.compute_phi(tr.mdt)\n", - " psi = md.compute_psi(tr.mdt)\n", - " phi_cum.append(phi[1])\n", - " psi_cum.append(psi[1])\n", - "phi_cum = np.vstack(phi_cum)\n", - "psi_cum = np.vstack(psi_cum)\n", - "angles = np.column_stack((phi_cum, psi_cum))\n", - "\n", - "v = dPCA(angles, t)\n", - "v.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_micro_clusters: 3\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X = StandardScaler().fit_transform(v)\n", - "#X = v\n", - "hb = hdbscan.HDBSCAN(min_cluster_size = 55, min_samples=51).fit(X)\n", - "n_micro_clusters = len(set(hb.labels_)) - (1 if -1 in hb.labels_ else 0)\n", - "print(\"n_micro_clusters:\",n_micro_clusters)\n", - "colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \\\n", - "'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray']\n", - "vectorizer = np.vectorize(lambda x: colors[x % len(colors)])\n", - "plt.scatter(X[:,0],X[:,1], c=vectorizer(hb.labels_), s=2)\n", - "plt.show" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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w4sQJfPjhhxg9erR64VdMTAwiIiKwePFihIeHo6ysDHFxcRg8eDDGjRunjhcQEICRI0ciNjYWlZWV8PDwQGpqKk6dOoWkpCSNe0uNaS7jx4+HIAgQRRFff/11o+3OnTsnOabVJKf3338fhw8fxurVq+Hl5dVou6lTp2qdi46Oxrx585qze0Rko5QSVuvpSk7BwcH46KOPsGXLFrz11lv4448/0KtXL7z00kuYNWuWup2vry+Sk5ORmJiIyMhIODg4IDg4GEuWLIG9/Z/PsgRBQFJSEuLj45GQkKAuXySXyxEYGKhxb6kxzSUqKsrsCy2sIjklJCRg+/btiI2NxVNPPaW37e7du+Hu7q5xjqMmImou9bDDHQPJqbGq5KNHj8bo0aMN3sPf3x/+/v4G2zk6OmLFihVYsWKF2WKaQ3MMDiyenDZs2IDk5GQsWbIE06dPN9je3d0dHh4eLdAzIiLu52QpFk1OcrkcSUlJWLBgAV544QVLdoWISCcmJ22qxQ09e/bU+NkQVXspLJactm/fjsTERPz1r3/F6NGjcfr0afW1Dh064OGHH7ZU14iI1Lifk7bAwEDY2dnh9OnT6NChAwIDAyU9c2oVCyKOHTum/l/Vn1V69eqltVySiMgSOHLSploAoSpu26YWROzatctStyYikkyJxjcTvLuNLbl3AUSbXBBBRMa5OO8VPJgQb+luNOrCosWW7oJZ1SvtIBjYbLDewHUyHpMTEZEenNaT7vjx4zh8+DBKSkoAAL1790ZwcDBGjRpldCwmJxtwdu0iDF6Y0OL3/WE9SxdR66eEhORkYNqvrVMqlVi6dCm+/PJLiKKo3nNKqVRi9+7deOKJJ/Dee+8Z9VyKyYmISA+xkcKu97axZdu3b0daWhrGjRuHuXPnqqv8nD9/Hlu2bEFaWhoGDBiA2bNnS47J5EREpIcoYT8n0cZHTikpKfDz88P69es1zg8YMADx8fGoqqrCZ599ZlRy4lM8IiI9DG2XIeWZVFtXUlKiVd/vboGBgernUFJx5ETUCtndsXQPdCte0rZW6gFAvVIADK7Ws+3k1KlTJ1y7dq3R67/++is6depkVEyOnIiI9FA9czJ02LJhw4Zh9+7dOjc4LC4uxp49ezB8+HCjYnLkRESkB8sXGTZ//nyEh4cjLCwMgYGB6o0Pi4uLcfToUbRv397oF3WZnGyEXV3L3u/7TVxGTm2DKDYchtrYsv79+2PXrl145513kJmZqbFl+9ChQxEbG6veXFEqJiciIj0aVurxPSdDHn30UezduxcVFRW4fPkyRFFE79690aVLF5PiMTkREelRL9pBNLAgwtBOubakS5cuJiekuzE5EbVCxUsWw+fdlq/6YYs4rSfdmTNnkJWVpVW+aPDgwUbHMik53b59G9evX0fnzp3RoUMHU0IQEbUOUlbj2fiCiPr6eixfvhwpKSkQ78nU27Ztw6RJk7Bq1SrY29tLjmnUWPTHH3/E9OnT8Ze//AWPP/44Tp06BQD47bffMGPGDOTm5hoTjojI6jWMnAwtJbd0Ly3rgw8+wOeff46goCDs3bsX//rXv/Cvf/0Ln3zyCQIDA5GamooPPvjAqJiSk9O5c+cwdepUlJSUIDQ0VONa165dcevWLaSkpBh1cyIia8cKEYZ99tln8PPzg1wux5AhQ+Do6AhHR0cMHToUmzZtwqhRo/DZZ58ZFVNyctqwYQO6deuGL7/8Eq+88orW0G3UqFE4c+aMUTcnIrJ2qmdOhg5b9ttvv+ktXxQcHIzffvvNqJiSk9OpU6cwefJkODg46Cx73rNnT5SXlxt1c2o5329aBLs6tMjBd5yoLVGKdlAqDRw2vlqvX79++PXXXxu9Xl5ejn79+hkVU/Lf6K1bt+Dk5NTo9ZqaGqNuTERNo3hjEYR6WM2heKPt/kuJaOCwdS+++CL27NmDwsJCrWsFBQX45JNPMHfuXKNiSl6t16dPH/z444+NXj9+/Li6ZAURUVshioLh1XiiYNOv4V68eBEeHh54+umn4efnhwcffBCCIKC4uBi5ubno378/Lly4ALlcrv6MIAiIiopqNKbk5PS3v/0NSUlJGD9+PB566CF1cKBho6lvv/0WsbGxpv5uRETWScrwyMaHT3cnnW+++QbffPONxvWCggIUFBRonDNbcpo9ezZycnIQERGhzoqrV69GRUUFrl27htGjR+P555+XGo6IqFXgyMmwI0eOmD2m5OTUoUMHfPTRR/jHP/6BL774Ah07dsTPP/+Mvn37YtasWZg+fbp633giorZCqRQAQ/s1KQWb3n+oV69eZo9pVIWIdu3aYebMmZg5c6bZO0JEZJUkjpwac+LECWzevBlnzpxBXV0devXqhRkzZiA8PFzdJicnBxs2bEBhYSEcHBwQEhKCmJgYODs7a8Sqra1FQkIC0tPTUV1dDZlMhqioKAQFBWndV2pMa8XaekStmGAlO+IWvtWGV+o14ZlTSkoKYmNjMXnyZMycORPt27fHhQsXUFf35x42J06cQGRkJIKCgrBw4UKUl5cjLi4OCoUCe/bs0ZiRio6ORkFBAWJiYuDh4YGUlBRER0cjOTkZAQEBJsW0VkxORESGmLDg4erVq3jzzTexaNEizJkzR33e19dXo93atWvh7e2N9evXq5OGm5sbZs+ejfT0dEyYMAEAkJ2djdzcXMjlcoSEhABoKH5QUlKCNWvWaCQnqTGtmeTkNH36dINtBEHAxx9/3KQOUfM5tWURRsyMb9Z7fLdjcbPGJ2ppDSMnQ9N62qf2798PAJg2bVqjHysrK0N+fj5ee+01jdGMn58funfvjoyMDHUiycrKgpOTk8YUniAICAsLw/Lly1FcXAyZTGZUTGsmOTldvnxZ61x9fT1+/fVXKJVKdO7cGZ06dTJr54iILM7Eab2TJ0/Cy8sLmZmZ2LRpE/7zn//Azc0NTz75JObPn48OHTpAoVAAALy9vbU+7+Pjg6KiIvXPRUVFkMlkWlNyqh1mFQoFZDKZUTGtmeTkdPToUZ3nb9++jY8++giff/45du3aZbaOERFZA1Hiar17lZeXo7y8HKtWrcKCBQsgk8lw/PhxbNmyBVevXsW6detQWVkJAHBxcdH6vIuLi8a7QZWVlTpLAKk+q4plTExTBQUF4Y033lCP4uRyOcaOHQsfH58mx1Zp8lOxDh064MUXX8SgQYOwZs0ac/SJiCQqfGsR7O7A4kfbJkg8NImiiNraWqxcuRJTp07FyJEjsWDBAkyfPh1ffvklLl269OcddNQr1XW+sXbGtNUXQ6qrV6+itrZW/bNcLsdPP/3U5Lh3M9uSjf/5n//BP//5T3OFIyKyDoYK6zUy7efq6goAGDNmjMZ5f39/AA3746naqEY7d6uqqtIY/bi6ujbaDvhzpGRMTFN1795dPX2oYo6kdzezrda7fPmyxvJIIqI2wcRnTj4+Pjh9+nSjH7Gzs1M/FyoqKtJKYgqFAkOHDlX/LJPJkJmZCaVSqfHcSZUkVFNqxsQ0VVBQELZt24Zvv/1Wnew++OADfPrpp41+xtgFc5JHTleuXNF5nDt3Dh9++CF27dqFYcOGSb4xEVGroHoJ19BxD9Vy7+zsbI3z2dnZEAQBjz76KNzd3TFw4ECkpaVBqVSq2+Tl5aGsrAxjx47ViFddXa31/D81NRWenp7qwtvGxDRVTEwMXn75ZXTq1AlXrlyBIAioqKjA5cuXGz1KSkqMuofkkVNgYGCjwzZRFPHggw9i2bJlRt2ciKg1MLSZoK5vRn9/f/j7++Ptt9/G9evX4e3tjePHj2Pnzp147rnn1CV/YmJiEBERgcWLFyM8PBxlZWWIi4vD4MGDMW7cOHW8gIAAjBw5ErGxsaisrISHhwdSU1Nx6tQpJCUladxbakxT3XfffZg/fz7mz58PABgwYADeeOMNPPHEE02OrSI5OUVFRelMTq6urujXrx9Gjx7dKt46JmprLF0l4sf32m51CAANK/FMWK0HNOwgnpiYiG3btuH69evo0aMHFi5ciBdeeEHdxtfXF8nJyUhMTERkZCQcHBwQHByMJUuWwN7eXt1OEAQkJSUhPj4eCQkJ6vJFcrlcaxdaqTHNZfXq1WaZLrybIN6737qVunz5MoKCgnDkyBF4eHhYujut2qip65ot9vHdrzRbbNLtkaUJFr1/W01Oqu+cW3PCAZfGN1oFAFT9jo5b9/H7CcD169fV78V6eHigc+fOJsVh+SIiIn24n5MkhYWFWLVqFU6dOqVxftiwYYiNjcWAAQOMitdockpNTTWpg5MmTTLpc0REVqmJVcltgUKhwJQpU3D79m0EBgaqVwwWFxfj2LFjmDp1Kvbu3auzakVjGk1Or732GgRBgDGzfoIgMDkRtbAf31uER1+xzNRe/rq2OaWngSMngzZu3Ij27dtj79696nJKKgqFAn//+9+xceNGJCYmSo7ZaHLauXOn6T0lImorRABKCW1s2MmTJ/H8889rJSag4f2rKVOmYO/evUbFbDQ5jRgxwvgeEhG1NZzWM+jGjRtwc3Nr9Hq3bt1w48YNo2Jy7TdRG5C/bhHs6tHihy0QRGmHLevduzeOHTvW6PVjx46hd+/eRsU0erVefn4+zpw5g6qqKo23j4GGZ05RUVHGhiQisl585mRQaGgo4uPj8corr2Du3Ll48MEHAQDnz5/H5s2bkZOTg1deMe41E8nJ6ebNm4iOjkZOTg5EUdRYLKH6M5NT63B89yvweybO7HFz9seYPSYRWb+IiAgUFBTgq6++wsGDB9UFGZRKJURRxPjx4zF79myjYkpOTps2bUJOTg7mzp0LX19fTJ8+HWvWrEHXrl2xZcsW3Lx5E++9955xvxERkZUTOHIyyN7eHuvXr0dOTg4OHz6My5cvQxRF9OnTB8HBwRg9erTRMSUnp4yMDIwbNw4LFizA9evXATSUTff19YWvry+eeeYZpKSkGD10u1tiYiLkcjkGDBiAAwcOmByHyBbZtfCmAN9vsoFl5ECTyhfZGj8/P/j5+ZklluQFEVevXsXw4cMBQF2bSbVFRrt27TBx4kR89dVXJnekqKgIW7duxQMPPGByDCIiszNxPydqGsnJycHBAfX19eo/29nZoby8XH3dyckJ165dM6kTSqUSsbGxmDx5svpBGhGRNRAgYbWepTvZBklOTn369MHPP/8MoGHkJJPJkJGRAaBhy4ysrCz06NHDpE7s2LEDpaWlWLTIRqYJiKj14MjJIiQnJ19fX2RkZKhHT+Hh4fj2228RHByMsWPHIjc3F08//bTRHSgpKcHGjRuxYsUKODo6GmxfWlqqtYlVdXW10fclamu+37QIwh20yGEzz5sAJicLkbwgIjIyEqGhoerl41OnTsXt27fxxRdfwM7ODosWLcKcOXOMurkoili2bBnGjBmD4OBgSZ+ZOnWq1rno6GjMmzfPqHvbupz9MfAPXWu2eN8cWGK2WETWhKv1LENycrrvvvu0ngfNmjULs2bNMvnmn376Kc6ePYuDBw9K/szu3bvh7u6ucc7Z2dnkPhAR6aWEhNV6LdITq3Tz5k2kp6fD09MTgwcPNltcydN6jz32GFavXo1z586Z5cYVFRVYu3YtXnzxRXTq1AnV1dWorq7GnTt3oFQqUV1djVu3bml9zt3dHR4eHhoHkxNRg39vXgS7O2KzH7aE5Yv069ChA5YtW4aCggKzxpWcnHr37o2PP/4YTz31FJ588kns2LHD5NV5AFBWVobff/8d69atw/Dhw9XHv//9bygUCgwfPtyo8upERM2Cz5z0srOzQ48ePVBTU2PWuJKn9fbt24dLly4hJSUFaWlpWLNmDeLi4uDn54dJkyYhKCgIHTp0kHzjPn366NyW491338Uff/yBVatWoWfPnpLjERE1BwGw6eQjxaRJk/DFF19gxowZRuUBfYwq/Nq3b18sXLgQCxcuxHfffYfU1FRkZmYiOzsbzs7OGDduHN5++21JsRwcHDBy5Eit86opOl3XiMgwuzvNG/+7jxc37w2sDRdEGPSXv/wFWVlZCA0NxfPPP4++ffuiU6dOWu1UhRykMLoqucqIESMwYsQIrFy5El988QXee+89/N///Z/k5ERE1BoIShhe8GDDCyIAaCyMe+eddyAImgtIVIXBjVmzYHJyAoC8vDwcOHAAmZmZ+OOPP+Dq6tqUcACAXbt2NTkGkS377uPFGDV1naW7QTZk9erVZo9pdHI6f/48Dhw4gLS0NJSWlsLe3h7+/v4ICwvD448/bvYOUvP55sASPD7+/SbH+frQq2boDZGV4rSeQWFhYWaPKTk5/eMf/0Bqaip+/PFHiKKIhx9+GLNmzcLf/vY3dOnSxewdIyLTHd/9CkY/a/7RU+6npu860GpJWCou2nhyag6Sk9OqVavg5uaGWbNmISwsDN7e3s3ZLyIi68CRkyRXr17Fxo0bkZOTg4qKCmzduhW+vr7qd1qnTJmCQYMGSY4nOTlt2bIFY8aMUe9wSETWzdwvy/7zcxvd6ZjJyaCSkhKEh4fj1q1bGDJkCHJzc9XXunTpgrNnz2L//v3Nk5z8/f2N6y0RURsgiP9dsaePaNv5af369bCzs8OXX36Jjh07au18GxAQgGPHjhkVk8MgIiI9WL7IsNzcXEyZMgU9evTQWkYOAD179kRpaalRMZu0lJyIAEdHd9TWllm6Gybr2LGjzjqW9xIEy1aef+SRR3D27NmWvzGn9QyqqalBt27dGr1eV1en3m5JKiYnoiYa4buwSZ8/mvWaeTrSRv0/h+kAgIyz2uXOWoQZk1NiYiLkcjkGDBiAAwcOaFzLycnBhg0bUFhYCAcHB4SEhCAmJkarsHVtbS0SEhKQnp6O6upqyGQyREVFISgoSOt+UmM2VY8ePVBUVNTo9R9++AF9+vQxKian9YiI9DDXtF5RURG2bt2KBx54QOvaiRMnEBkZCXd3dyQnJ2Pp0qU4evQoIiMjoVRqPvCKjo5GWloaFixYgM2bN0MmkyE6OhrZ2dkmx2yqkJAQfPbZZ1AoFOpzqum9jIwMpKenY/z48UbF5MjJxtnfMm6ofa8jR183U09aL8HGtpBocUoL//2aYeSkVCoRGxuLyZMnQ6FQaO3evXbtWnh7e6sXFgCAm5sbZs+ejfT0dEyYMAEAkJ2djdzcXMjlcoSEhAAARo0ahZKSEqxZswYBAQFGxzSHl156CV9//TWeffZZDBs2DIIgYOvWrUhISMCZM2fw0EMPYfbs2UbF5MiJiKxexg3LlTVTrdbTexhITjt27EBpaSkWLdLe3r6srAz5+fkIDQ3VeFXHz88P3bt3R0ZGhvpcVlYWnJycNKbwBEFAWFgYLly4gOLiYqNjmoOjoyP27duHZ555BmfPnoUoisjJycHFixfx/PPPY+fOnejYsaNRMTlyIiLrJlq4qmoTR04lJSXYuHEj4uLi4OjoqHVdNRWmq7CBj4+PxrOcoqIiyGQyrfdN+/fvr44lk8mMimkujo6OWLZsGZYtW4aKigqIooguXbroXL0nRZNGTnfu3IFCocCXX36J+Ph4zJ07tynhiFqlI8deh6AUTTqOHOO0qLVryjMnURSxbNkyjBkzBsHBwTrbVFZWAgBcXFy0rrm4uKivq9o21u7uWMbEbA5dunRB165dTU5MgBEjp19++QUKhULjuHjxonp5oCiKaNeOAzEiMi+xFT9z+vTTT3H27FkcPHjQ4G0a+yK/97y+L3ypbZuSNPQ5ePAgDh8+jJKSEgANO6gHBweb9Hyr0Wxy5coVbN++HWfPnkVRURH++OMP9TVRFNG1a1eMGDEC/fv3Vx9eXl4m/DpErZ9wx8Y39GnLTExOqppyL774Ijp16qReBHHnzh0olUpUV1ejY8eO6q2GdI1mqqqqNEY/rq6ujbYD/hwpGRPTHG7cuIGXX34Zx48fhyiKcHZ2hiiKyM/Px6FDh7Bv3z588MEHuP/++yXHbDQ5LVq0CD/88APuv/9++Pj44Pbt2ygoKICbmxsSExMxZMgQc/xORET6WfiZk6Sl4jqul5WV4ffff8e6deuwbp12hfjhw4djzpw5+Pvf/w6g4XnSmDFjNNooFAoMHTpU/bNMJkNmZiaUSqXGcyfVMyYfHx8Afz5rkhLTHOLj45GXl4dp06YhMjISbm5uAIBff/0VW7Zswa5du5CQkIDY2FjJMRt95lRQUIBnn30WJ0+exN69e/H5559j+fLluHHjBmbNmoWPPvoIIuvEE1FbJ+V5k46vwj59+mDnzp1ax4ABA9TXwsPD4e7ujoEDByItLU3j/aO8vDyUlZVh7Nix6nMhISGorq7G0aNHNe6VmpoKT09PyGQyADAqpjkcOnQI48aNQ2xsrDoxAQ1L12NjYzF27FgcOnTIqJiNjpweeeQRPPbYY7C3t1efmzp1KkJCQvD222/jvffew8GDB7Fq1Sr1ShEiWyWY8FwkM295M/Sk7cms22vZDpg4refg4ICRI0dqnVdVZ7j7WkxMDCIiIrB48WKEh4ejrKwMcXFxGDx4MMaNG6duFxAQgJEjRyI2NhaVlZXw8PBAamoqTp06haSkJI37SI1pDjU1NTp/V5VRo0bhm2++MSpmoyOnvXv3ql/yulu3bt0gl8shl8tRVlaGp59+GgkJCbh9+7ZRNybrcOTo67C7rTTp4Au4ZDNEA0cT+fr6Ijk5Gb/88gsiIyOxZs0aPP7449i6davGAEEQBCQlJWHixIlISEjAnDlz8NNPP0EulyMwMNCkmObQv39/XLp0qdHrly5dUk85SiWITZibq6mpwbp167Bv3z707dsX//u//4thw4aZGk6vy5cvIygoCEeOHIGHh0ez3MNWhYx5x6TPZf1T+vyxLfh/w98yqn3GyZXN1BMyB9V3TsfHnoddJ/216JQ3qnHr2z02+/2Ul5eHqKgoxMXFaSXJw4cP49VXX8WmTZvg6+srOWaT1n47Ojpi5cqVCA0NxfLlyzF9+nQUFBQ0JSQRkXVhVXItr7+uPWvi4eGBqKgoeHp6wsvLC4IgoLi4GBcvXoSPjw/S0tJaLjmpDBkyBCkpKdi2bZs5whERWQ1ViSJDbWxJSkpKo9cuXLiACxcuaJz76aefoFAo8O6770q+h9nemm3Xrh0rRBBRmyNlKbmtbTZYWFjY7PdgSQciczHzNgRkJTitZxFMTkRE+jA5WQSTExGRHgIkTOu1SE+s27///W/s3r0bly5dQmVlpVaRBkEQcPjwYcnxmJwIWf+MxdgRbxv1mczvVjRTb1qvjFNvYdxgaS/Wpv/wv83cGzKXhgUR+rOTrS2IuNenn36KlStXon379vD09ESPHj2aHJPJiYhIH07rGZScnIyHHnoI27ZtQ5cuXcwSk8mJyJy4KKLN4Wo9w3777TdERESYLTEBTE5ERPpx5GSQl5eXeksQc2nSTrhEpEm4ozR4UOvSlJ1wbcXcuXOxZ88elJWVmS0mR05ELSw937RahmQhHDkZNHbsWNy4cQMTJkxAcHAwevXqpbHfFNCwWi8qKkpyTCYnIiJ9RAmr8Ww8OV28eBEbN25EbW0tDhw4oLMNkxOZJPO7FZKXQRPZEi6IMOytt95CRUUFYmNjMWzYMPWeVU3B5ERkRofOrcZ4n6WW7gaZkyg2HIba2LAffvgBs2fPxrRp08wWk8mJiEgPjpwMc3BwMOsycoCr9YiI9DO0C66ZdsNtzcaPH4/MzEyzxmRyIjK3O/X6D2pVVPs5GTps2XPPPYfa2lq8/PLLyMvLQ0lJCa5cuaJ1GIPTekREeggSVuvZ+rTexIkTIQgCzp49i2PHjjXa7ty5c5JjMjmRWvoP/4vxD2lvv3yvQ+dWt0BviKwEF0QYFBUVBUEwb212JiciMzt0IQ7j+y3Sfe3nhBbuDTUVF0QYNm/ePLPHZHIiItKHFSIsgsmJiEgPjpwMO3nypKR2w4cPlxyTyYmISA9BKUrYbNC2s9O0adMkPXNqVQsiTpw4gc2bN+PMmTOoq6tDr169MGPGDISHh1u6azbp0LnVGO+5uPHrF+NbsDetGJeMty22nXsMWr1ae5HUnTt3UFJSgs8//xweHh5Gf6dbNDmlpKQgNjYWkydPxsyZM9G+fXtcuHABdXV1luwWEdGfpGyJYePJKywsrNFrEREReq83xmLJ6erVq3jzzTexaNEizJkzR33e19fXUl0iItKmFBsOQ21IJxcXF0yePBnbtm0zKklZLDnt378fAMxaKJCIyOy4Wq/JnJ2dUVJSYtRnLJacTp48CS8vL2RmZmLTpk34z3/+Azc3Nzz55JOYP38+OnTooPNzpaWlWuecnZ3NUqKdyGzqtZ85Hbq6yQIdoaYydbVeXl4eDhw4gO+//x6lpaVwcXHBoEGDMG/ePPTv31+jbU5ODjZs2IDCwkI4ODggJCQEMTExWt9rtbW1SEhIQHp6OqqrqyGTyRAVFYWgoCCt+0uN2dxu3bqFL774Ag888IBRn7NYciovL0d5eTlWrVqFBQsWQCaT4fjx49iyZQuuXr2KdevW6fzc1KlTtc5FR0c3y0tgRESmrtb75JNPUFlZiZkzZ8LLywvXrl3Dtm3b8Mwzz2DXrl0YMmQIgIZFYZGRkQgKCsLChQtRXl6OuLg4KBQK7NmzR2NH2ejoaBQUFCAmJgYeHh5ISUlBdHQ0kpOTERAQoG5nTExzeP113ZVlqqqqcPr0aVRUVODVV181KqbFkpMoiqitrUV8fDwmTpwIABg5ciRu3ryJ7du3Y/78+ejbt6/W53bv3g13d3eNcxw1EVGzMmHabuXKlejatavGuTFjxiAoKAgffvghEhMTAQBr166Ft7c31q9fr04abm5umD17NtLT0zFhwgQAQHZ2NnJzcyGXyxESEgIAGDVqFEpKSrBmzRqN5CQ1prmkpKToPO/i4gJPT0+8/vrreOKJJ4yKabHk5OrqCqDh/6y7+fv7Y/v27fjxxx91Jid3d3d4eHi0RBeJiCCIIgQDtfN0Xb83MQEN/yLdt29f9eOJsrIy5Ofn47XXXtMYzfj5+aF79+7IyMhQJ5KsrCw4OTlpTOEJgoCwsDAsX74cxcXFkMlkRsU0l8LCQrPGAyy4ZYaPj4/e6+YedhK1pENXN3GrjLZCKfGQoKKiAkVFRfD29gYAKBQKAFD/fDcfHx8UFRWpfy4qKoJMJtP6blQ9v1LFMiamNbNYBlANS7OzszXOZ2dnQxAEPProo5boFuG/L9rertM6+AIu2SLVyMnQYYgoili+fDmUSiUiIiIAAJWVlQAapr/u5eLior6uattYu7tjGRPTmllsWs/f3x/+/v54++23cf36dXh7e+P48ePYuXMnnnvuOfTq1ctSXSMi+pOZlpK///77OHz4MFavXg0vLy+Na42V/rn3vL4SQVLbmmtri7lz5xrVXhAEfPDBB5LbW7RCxIYNG5CYmIht27bh+vXr6NGjBxYuXIgXXnjBkt0iIlJr2OnW0Go9/TESEhKwfft2xMbG4qmnnlKfVz171zWaqaqq0hj9uLq6NtoO+HOkZEzMpvj666+Nam9sUrRocrr//vuxdOlSLF261JLdIGoWoo53nag1krDZoJ6h04YNG5CcnIwlS5Zg+vTpGtdUz4WKioq0FocpFAoMHTpU/bNMJkNmZiaUSqXGcyfVMybVc3xjYjaFlEUQJ06cQFxcHPLz8+Hm5mZUfK46ICLSo2HkZPjQRS6XIykpCQsWLNA5I+Tu7o6BAwciLS0NSuWfQfLy8lBWVoaxY8eqz4WEhKC6uhpHjx7ViJGamgpPT0/IZDKjYzYXhUKByMhIzJw5ExcvXsSCBQuQmZlpVAyLVyUnaqvSK7ZinPOshj9Xf2Th3pDJTNymffv27UhMTMRf//pXjB49GqdPn1Zf69ChAx5++GEAQExMDCIiIrB48WKEh4ejrKwMcXFxGDx4MMaNG6f+TEBAAEaOHInY2FhUVlbCw8MDqampOHXqFJKSkjTuLTWmuV29ehUbNmxAWloa7OzsMG3aNLz00kvo3Lmz0bGYnIiI9DFxQcSxY8fU/6v6s0qvXr3UIyBfX18kJycjMTERkZGRcHBwQHBwMJYsWQJ7e3v1ZwRBQFJSEuLj45GQkKAuXySXyxEYGKgRX2pMc6mqqkJycjL27NmD27dvY+LEiVi4cGGT3kllciIi0kNQKiEYWPEgKLWv79q1S/I9VKuXDXF0dMSKFSuwYsUKs8Vsitu3b2PHjh3Ytm0bqqur4efnh5iYGDz00ENNjs3kRESkjwjDL9naYFXy/fv3IzExEeXl5Xj44YcRExNj1i2PmJyImpFYd8fSXaCmkvKSrYSXcNuaZcuWQRAEDBw4EOPHj0dhYaHeFXyCIGDmzJmS4zM5kU7irVsaP6df22KhnhBZmIkLImyBKIrIz89Hfn6+wbZMTkRE5sTkpNPOnTubNT6TExGRPlKKukos/NqWjBgxolnjMzkREekhKEUIBrKPofJGZDwmJyIivZpWvohMw+RE1IzE+npk3t5j6W5QU4gS3sK1wWdOzY3JiYhIHyUAQwW1mZvMjsmJiEgPQRQhGMg+UjYbJOMwORER6cNpPYvglhmkU/q1LRBv3IR44yZfwCXbplQC9QYOHbX1qGk4ciIi0kdU/5d+5tn9nP6LyYmoGXGlXhsgZVqPKyLMjsmJiEgfpcTkZP5tkmwakxMRkT6iEobrE/GZk7kxORER6aNU/jdB6WFgM0IyHpMTEZE+UqqS85mT2TE5ERHpI4LvMVkAkxMRkT4cOVkEkxMRkT585mQRTE5ERPpw5GQRTE5ERPooJZQnsuPIydyYnKhRGbU7Ld0FIosTRRGigWk9kQsmzI7JiYhIH6X43yoR+jA5mRuTExGRPlKeOXHkZHZMTkRE+ogSnjlxtZ7ZMTkREenDkZNFMDkREekh1ish1tfrb8PCr2bHnXCJiPRRLYgwdOhQW1uLVatWYcyYMRg0aBCeeuopHDlypIV/gdaJyYmISB9RKe3QITo6GmlpaViwYAE2b94MmUyG6OhoZGdnt/Av0fpwWo+ISB9RhGhoKbmd9vXs7Gzk5uZCLpcjJCQEADBq1CiUlJRgzZo1CAgIaI7ethkcORER6WPiyCkrKwtOTk4ICgpSnxMEAWFhYbhw4QKKi4tb8rdodThyIiLSo064BdHAUvE7Qp3WuaKiIshkMtjZaY4B+vfvDwBQKBSQyWTm62gbw+RERKSDo6MjXFxcUI7zktp37NgRjo6O6p8rKyvRr18/rXYuLi7q69S4VpOc3N3dceTIEbi7u1u6K0RkA1xdXZGZmYmamhqN8zU1NVrnAKB79+5wdXXVOCcIQqPx9V2jVpSc2rVrBw8PD0t3g4hsiKurq1bCMeazukZHVVVVAP4cQZFuXBBBRNQMZDIZzp8/D+U9pY8UCgUAwMfHxxLdajWYnIiImkFISAiqq6tx9OhRjfOpqanw9PTkYggDWs20HhFRaxIQEICRI0ciNjYWlZWV8PDwQGpqKk6dOoWkpCRLd8/qCSJ3ySIiahY1NTWIj49HRkYGqqurIZPJEBUVheDgYEt3zeoxORERkdXhMyciIrI6TE5ERGR1mJyIiMjqMDkREZHVYXIiIiKrw+RERERWh8mJiIisDpMTERFZnf8PjJX9Wq9ylPcAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hb.condensed_tree_.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NOW USE THIS LABELING TO IDENTIFY CLUSTERS ON RAMACHANDRAN PLOT:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(4,4))\n", - "assign = hb.labels_>=0.0\n", - "plt.scatter(phi_cum[assign],psi_cum[assign], s=2, c=vectorizer(hb.labels_[assign]))\n", - "noise = hb.labels_<0\n", - "plt.scatter(phi_cum[noise],psi_cum[noise], c='gray', s=0.5)\n", - "plt.show" - ] - }, - { - "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": 1 -} diff --git a/examples/alanine_dipeptide/ala_dipeptide_multi.ipynb b/examples/alanine_dipeptide/ala_dipeptide_multi.ipynb deleted file mode 100644 index 8a41a1f..0000000 --- a/examples/alanine_dipeptide/ala_dipeptide_multi.ipynb +++ /dev/null @@ -1,352 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MSM of the alanine dipeptide\n", - "Here we are going to test capabilities of the package to generate a consistent model from multiple files. \n", - "\n", - "The first thing one must do is download the data from the following [link](https://osf.io/a2vc7). Once this is done, we will import a number of libraries we will need as we run this example." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(style=\"ticks\", color_codes=True, font_scale=1.5)\n", - "sns.set_style({\"xtick.direction\": \"in\", \"ytick.direction\": \"in\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Discretizing the trajectory\n", - "We start loading the simulation data using the `trajectory` module. For this we use the external library [`MDtraj`](http://mdtraj.org), which contains all sorts of methods for parsing and calculating interestign properties of our time-series data." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import mdtraj as md\n", - "from mastermsm.trajectory import traj" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "trajs = traj.MultiTimeSeries(top='data/alaTB.gro', \\\n", - " trajs=['data/alatb_n1_ppn24_part0.xtc', \\\n", - " 'data/alatb_n1_ppn24_part1.xtc', \\\n", - " 'data/alatb_n1_ppn24_part2.xtc', \\\n", - " 'data/alatb_n1_ppn24_part3.xtc'], stride=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "phi_cum = []\n", - "psi_cum = []\n", - "for tr in trajs.traj_list:\n", - " phi = md.compute_phi(tr.mdt)\n", - " psi = md.compute_psi(tr.mdt)\n", - " phi_cum.append(phi[1])\n", - " psi_cum.append(psi[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting minimum cluster size to: 200\n", - "Setting min samples to: 200\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1,4, figsize=(10,2.85), sharex=True, sharey=True)\n", - "ib = 0\n", - "ie = 0\n", - "for i in range(4):\n", - " assign = np.array(trajs.traj_list[i].distraj) >=0\n", - " ax[i].scatter(phi_cum[i][assign], psi_cum[i][assign], s=2, \\\n", - " c=np.array(trajs.traj_list[i].distraj)[assign], cmap='jet')\n", - " noise = np.array(trajs.traj_list[i].distraj) ==-1\n", - " ax[i].scatter(phi_cum[i][noise], psi_cum[i][noise], s=0.2, \\\n", - " c='k')\n", - "ax[i].set_xlim(-np.pi, np.pi)\n", - "ax[i].set_ylim(-np.pi, np.pi)\n", - "plt.tight_layout(w_pad=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mie\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrajs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraj_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistraj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m ax[i].scatter(range(len(phi_cum[ib:ie])), psi_cum[ib:ie], \\\n\u001b[0;32m----> 7\u001b[0;31m c=trajs.traj_list[i].distraj, s=1)\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mib\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mie\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_pad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: list index out of range" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(4,1, figsize=(10,6), sharex=True, sharey=True)\n", - "ib = 0\n", - "ie = 0\n", - "for i in range(4):\n", - " ie += len(trajs.traj_list[0].distraj)\n", - " ax[i].scatter(range(len(phi_cum[ib:ie])), psi_cum[ib:ie], \\\n", - " c=trajs.traj_list[i].distraj, s=1)\n", - " ib = ie\n", - "plt.tight_layout(h_pad=0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Building the master equation model\n", - "After having loaded our trajectory using the functionalities from the `trajectory` module we start building the master equation model. For this, we make use of the `msm` module. There are two steps corresponding to the two main classes within that module. First we create an instance of the `SuperMSM`, which can be used to direct the whole process of constructing and validating the MSM." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 3, 2, 0], [3, 1, 0, 2], [2, 3, 1, 0], [3, 1, 0]]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[tr.find_keys() for tr in trajs.traj_list]\n", - "[tr.keys for tr in trajs.traj_list]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Building MSM from \n", - " ['send-david/data/out/4_1.xtc', 'send-david/data/out/4_2.xtc', 'send-david/data/out/4_3.xtc', 'send-david/data/out/4_4.xtc']\n", - " # states: 4\n" - ] - } - ], - "source": [ - "from mastermsm.msm import msm\n", - "msm_alaTB = msm.SuperMSM([tr for tr in trajs.traj_list])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, using the `do_msm` method, we produce instances of the `MSM` class at a desired lag time, $\\Delta t$. Each of these contains an MSM built at a specific lag time. These are stored as a dictionary in the `msms` attribute of the `SuperMSM` class. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "lagt = 1\n", - "msm_alaTB.do_msm(lagt)\n", - "msm_alaTB.msms[lagt].do_trans()\n", - "msm_alaTB.msms[lagt].boots()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$\\\\tau_i$')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.errorbar(range(1,len(msm_alaTB.msms[1].peqT)-1), \\\n", - " msm_alaTB.msms[1].tau_ave, \\\n", - " msm_alaTB.msms[1].tau_std, fmt='o-')\n", - "ax.set_xlabel(r'$\\lambda_i$')\n", - "ax.set_ylabel(r'$\\tau_i$')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'P$_\\\\mathrm{eq}$')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.errorbar(range(len(msm_alaTB.msms[1].peqT)), msm_alaTB.msms[1].peqT, \\\n", - " msm_alaTB.msms[1].peq_std, fmt='o-')\n", - "ax.set_xlabel('state')\n", - "ax.set_ylabel(r'P$_\\mathrm{eq}$')" - ] - }, - { - "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": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/alanine_pentapeptide/ala_pentapeptide_contacts.ipynb b/examples/alanine_pentapeptide/ala_pentapeptide_contacts.ipynb deleted file mode 100644 index 897c07f..0000000 --- a/examples/alanine_pentapeptide/ala_pentapeptide_contacts.ipynb +++ /dev/null @@ -1,275 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Master equation model of the alanine pentapeptide" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import seaborn as sns\n", - "sns.set(style=\"ticks\", color_codes=True, font_scale=1.2)\n", - "sns.set_style({\"xtick.direction\": \"in\", \"ytick.direction\": \"in\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import mdtraj as md\n", - "from mastermsm.trajectory import traj" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we load the trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "tr_ala5 = traj.TimeSeries(top='data/ala5_ff99sbildn_nowat.gro',\\\n", - " traj=['data/ala5_ff99sbildn_tip3p_nvt_proc.xtc'])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "250001" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(tr_ala5.mdt)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "tr_ala5.discretize(method=\"contacts\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "tr_ala5.find_keys()\n", - "tr_ala5.keys.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#md_traj_full = md.load_xtc(\"data/%s_nvt.xtc\"%root, \\\n", - "md_traj_full = md.load_xtc(\"data/ala5_ff99sbildn_tip3p_nvt_proc.xtc\", \\\n", - " top=\"data/ala5_ff99sbildn_nowat.gro\")\n", - "dists = md.compute_contacts(md_traj_full, contacts='all',\\\n", - " periodic=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(7,4), sharex=True, sharey=True)\n", - "ax[0].scatter(dists[0][:,0], dists[0][:,2], marker='.', \\\n", - " c=tr_ala5.distraj, s=0.15, cmap='rainbow')\n", - "ax[1].scatter(dists[0][:,1], dists[0][:,2], marker='.', \\\n", - " c=tr_ala5.distraj, s=0.15, cmap='rainbow')\n", - "ax[1].set_xlabel('d$_1$')\n", - "ax[0].set_xlabel('d$_0$')\n", - "ax[0].set_ylabel('d$_2$')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(4, 1, figsize=(9,6), sharex=True)\n", - "\n", - "ax[0].scatter(tr_ala5.mdt.time, \\\n", - " dists[0][:,0], marker='.', \\\n", - " c=tr_ala5.distraj, s=0.5, cmap='rainbow')\n", - "\n", - "ax[1].scatter(tr_ala5.mdt.time,\\\n", - " dists[0][:,1], marker='.', \\\n", - " c=tr_ala5.distraj, s=0.5, cmap='rainbow')\n", - "\n", - "ax[2].scatter(tr_ala5.mdt.time,\\\n", - " dists[0][:,2], marker='.', \\\n", - " c=tr_ala5.distraj, s=0.5, cmap='rainbow')\n", - "\n", - "ax[3].plot(tr_ala5.mdt.time, \\\n", - " tr_ala5.distraj, lw=1)\n", - "\n", - "ax[0].set_xlim(0,100000)\n", - "ax[-1].set_xlabel('Time (ps)')\n", - "ax[0].set_ylabel('X$_0$')\n", - "ax[1].set_ylabel('X$_2$')\n", - "ax[2].set_ylabel('X$_2$')\n", - "ax[3].set_ylabel('state')\n", - "plt.tight_layout(h_pad=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Building MSM from \n", - " [['data/ala5_ff99sbildn_tip3p_nvt_proc.xtc']]\n", - " # states: 5\n" - ] - } - ], - "source": [ - "from mastermsm.msm import msm\n", - "msm_ala5 = msm.SuperMSM([tr_ala5], sym=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "msm_ala5.convergence_test(time=[10, 30, 50, 100, 300, 500], \\\n", - " error=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "for i in range(4):\n", - " tau_vs_lagt = np.array([[x, msm_ala5.msms[x].tauT[i],\\\n", - " msm_ala5.msms[x].tau_std[i]] \\\n", - " for x in [10, 30, 50, 100, 300, 500]])\n", - " ax.errorbar(tau_vs_lagt[:,0],tau_vs_lagt[:,1], \\\n", - " yerr=tau_vs_lagt[:,2], fmt='o-', \\\n", - " markersize=7)\n", - "#ax.plot(tau_vs_lagt[:,0],tau_vs_lagt[:,0])\n", - "ax.fill_between(10**np.arange(-0.2,4,0.2), 1e-1, \\\n", - " 10**np.arange(-0.2,4,0.2), facecolor='lightgray')\n", - "ax.set_xlabel(r'$\\Delta$t [ps]', fontsize=16)\n", - "ax.set_ylabel(r'$\\tau_i$ [ps]', fontsize=16)\n", - "ax.set_xlim(8,1000)\n", - "ax.set_ylim(1e1,1e3)\n", - "_ = ax.set_xscale('log')\n", - "#_ = ax.set_yscale('log')" - ] - }, - { - "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": 1 -} diff --git a/examples/alanine_pentapeptide/ala_pentapeptide_dpca.ipynb b/examples/alanine_pentapeptide/ala_pentapeptide_dpca.ipynb deleted file mode 100644 index 586266b..0000000 --- a/examples/alanine_pentapeptide/ala_pentapeptide_dpca.ipynb +++ /dev/null @@ -1,294 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n", - "import math\n", - "import numpy as np\n", - "import hdbscan\n", - "from sklearn.preprocessing import StandardScaler\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(style=\"ticks\", color_codes=True, font_scale=1.5)\n", - "sns.set_style({\"xtick.direction\": \"in\", \"ytick.direction\": \"in\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import mdtraj as md\n", - "from mastermsm.trajectory import traj" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "trajs = traj.MultiTimeSeries(top='data/ala5_ff99sbildn_nowat.gro',\\\n", - " trajs=['data/ala5_ff99sbildn_tip3p_nvt_proc.xtc']) " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def dPCA(angles, t):\n", - " \"\"\"\n", - " Compute PCA of dihedral angles\n", - "\n", - " We follow the methods described in A. Altis et al. \n", - " *J. Chem. Phys.* 244111 (2007)\n", - "\n", - " Parameters\n", - " ----------\n", - " angles :\n", - " \n", - " t : \n", - " \n", - " Returns\n", - " -------\n", - " \n", - " \"\"\"\n", - " shape = np.shape(angles)\n", - " print (shape)\n", - " X = np.zeros((shape[0] , \\\n", - " shape[1]+shape[1]))\n", - " for i, ang in enumerate(angles):\n", - " p = 0\n", - " for phi in ang:\n", - " X[i][p], X[i][p+1] = np.cos(phi), np.sin(phi)\n", - " p += 2\n", - " X_std = StandardScaler().fit_transform(X)\n", - " sklearn_pca = PCA(n_components=2*shape[1])\n", - " \n", - " X_transf = sklearn_pca.fit_transform(X_std)\n", - " expl = sklearn_pca.explained_variance_ratio_\n", - " \n", - " print(\"Ratio of variance retrieved by each component:\", expl)\n", - " #graficamos el acumulado de varianza explicada en las nuevas dimensiones\n", - " #plt.figure()\n", - " #plt.plot(np.cumsum(sklearn_pca.explained_variance_ratio_))\n", - " #plt.xlabel('number of components')\n", - " #plt.ylabel('cumulative explained variance')\n", - " #plt.savefig('cum_variance_%g.png'%t)\n", - "\n", - " #colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \\\n", - " #'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray']\n", - " #vectorizer = np.vectorize(lambda x: colors[x % len(colors)])\n", - " #counts, ybins, xbins, image = plt.hist2d(X_transf[:,0], X_transf[:,1], \\\n", - " #bins=[np.linspace(-np.pi,np.pi,20), np.linspace(-np.pi,np.pi,30)], \\\n", - " # bins=20, cmap='binary_r', alpha=0.2)\n", - " #plt.contour(np.transpose(counts), extent=[xbins.min(), xbins.max(), \\\n", - " # ybins.min(), ybins.max()], linewidths=1, colors='gray')\n", - " #countmax = np.amax(counts)\n", - " #counts = np.log(countmax) - np.log(counts)\n", - " #plt.scatter(X_transf[:,0],X_transf[:,1], c=counts)\n", - " #plt.savefig('dpca_%g.png'%t)\n", - "\n", - " return X_transf" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(250001, 10)\n", - "Ratio of variance retrieved by each component: [0.109936 0.09352144 0.08805895 0.07246941 0.0687701 0.06354626\n", - " 0.0610928 0.05553368 0.05163301 0.04758494 0.04519939 0.0427791\n", - " 0.03885436 0.03463574 0.03200618 0.02318423 0.01945065 0.01859857\n", - " 0.01710293 0.01604226]\n", - "(250001, 5) (250001, 5)\n" - ] - } - ], - "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.decomposition import PCA\n", - "from mastermsm.trajectory import traj_lib\n", - "\n", - "phi_cum = []\n", - "psi_cum = []\n", - "for t, tr in enumerate(trajs.traj_list):\n", - " phi = md.compute_phi(tr.mdt)\n", - " psi = md.compute_psi(tr.mdt)\n", - " phi_cum.append(phi[1])\n", - " psi_cum.append(psi[1])\n", - " #reshape(-1,1))#phi_cum.append(phi[1])#[[v[i,0]] for i in range(len(v[:,0]))]\n", - " #psi_cum.append(v[:,1].reshape(-1,1))#psi_cum.append(psi[1])\n", - " #print(len(phi_cum),type(phi_cum))\n", - "phi_cum = np.vstack(phi_cum)\n", - "psi_cum = np.vstack(psi_cum)\n", - "phi_fake = [phi[0], phi_cum]\n", - "psi_fake = [psi[0], psi_cum]\n", - "angles = np.column_stack((phi_cum, psi_cum))\n", - "\n", - "v = dPCA(angles, t)\n", - "v.shape\n", - "print(phi_cum.shape,psi_cum.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1250005, 3)\n" - ] - } - ], - "source": [ - "ndih = len(phi_fake[0])\n", - "phis, psis = [], []\n", - "for f, y in zip(phi_fake[1],psi_fake[1]):\n", - " for n in range(ndih):\n", - " phis.append(f[n])\n", - " psis.append(y[n])\n", - " \n", - "data = np.column_stack((range(len(phis)),phis,psis))\n", - "print(data.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_micro_clusters: 4\n" - ] - } - ], - "source": [ - "X = StandardScaler().fit_transform(v)\n", - "hb = hdbscan.HDBSCAN(min_cluster_size = 70, min_samples=70).fit(X)\n", - "n_micro_clusters = len(set(hb.labels_)) - (1 if -1 in hb.labels_ else 0)\n", - "print(\"n_micro_clusters:\",n_micro_clusters)\n", - "colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \\\n", - "'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray']\n", - "vectorizer = np.vectorize(lambda x: colors[x % len(colors)])\n", - "#plt.scatter(X[:,0],X[:,1], c=vectorizer(hb.labels_), s=2)\n", - "#plt.show" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(2, 2, figsize=(10,8), sharex=True, sharey=True)\n", - "ax[0,0].scatter(X[:,0], X[:,1], c=vectorizer(hb.labels_), s=2)\n", - "ax[0,1].scatter(X[:,0], X[:,2], c=vectorizer(hb.labels_), s=2)\n", - "ax[1,0].scatter(X[:,5], X[:,9], c=vectorizer(hb.labels_), s=2)\n", - "ax[1,1].scatter(X[:,5], X[:,19], c=vectorizer(hb.labels_), s=2)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "#hb.condensed_tree_.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NOW USE THIS LABELING TO IDENTIFY CLUSTERS ON RAMACHANDRAN PLOT OF EACH PEPTIDE:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(1,5,figsize=(12,4), sharex=True, sharey=True)\n", - "assign = hb.labels_>=0.01\n", - "noise = hb.labels_<0.0\n", - "for i in range(5):\n", - " ax[i].scatter(phi_cum[assign,i-1],psi_cum[assign,i-1], s=2, c=vectorizer(hb.labels_[assign]))\n", - " ax[i].scatter(phi_cum[noise,i-1],psi_cum[noise,i-1], c='gray', s=0.5)\n", - "plt.tight_layout()" - ] - }, - { - "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": 1 -} diff --git a/examples/alanine_pentapeptide/ala_pentapeptide_featurize.ipynb b/examples/alanine_pentapeptide/ala_pentapeptide_featurize.ipynb new file mode 100644 index 0000000..f4c0b4f --- /dev/null +++ b/examples/alanine_pentapeptide/ala_pentapeptide_featurize.ipynb @@ -0,0 +1,980 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Master equation model of the alanine pentapeptide\n", + "\n", + "In this example we deal with a simple but realistic example of MD simulation data, generated with the [Gromacs package](http://www.gromacs.org/).\n", + "First we import a number of general purpose Python libraries we will need as we run this example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import seaborn as sns\n", + "sns.set(style=\"ticks\", color_codes=True, font_scale=1.2)\n", + "sns.set_style({\"xtick.direction\": \"in\", \"ytick.direction\": \"in\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we download the relevant data from the following [location](https://osf.io/a2vc7/) " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from mastermsm.test.download_data import download_osf_ala5" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " os.mkdir(\"test\")\n", + " download_osf_ala5()\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discretizing the trajectory\n", + "We start loading the data using the data structures from the `trajectory` module. For this we use the external library [`MDtraj`](http://mdtraj.org), which contains all sorts of interesting methods for parsing and calculating interestign properties of our time-series data." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from mastermsm.trajectory import traj" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tr = traj.TimeSeries(top='test/data/ala5.gro',\\\n", + " xtc='test/data/ala5.xtc')\n", + "tr.mdt" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'test/data/ala5.xtc'" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tr.file_name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To extract the relevant features from the trajectory we use a Featurizer object." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "X = traj.Featurizer(tr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One possibility is to use the torsion angles, which we can shift for convenience. Alternatively, we can use the $\\sin$ and $\\cos$ transforms of the dihedral angles, following Stock and co-workers [*J. Chem. Phys., 2007*](https://doi.org/10.1063/1.2746330)." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "X.add_torsions(sincos=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.8731356 -0.27090415 -0.94990546 ... -0.9579162 -0.759916\n", + " -0.84547234]\n", + " [-0.63570917 -0.55155873 -0.9419628 ... -0.9575357 0.65359163\n", + " -0.43635765]\n", + " [-0.9985531 -0.71922976 -0.96036315 ... -0.9978882 0.8972782\n", + " -0.8083169 ]\n", + " ...\n", + " [-0.9348408 -0.90277386 -0.8179053 ... 0.61332476 0.87462854\n", + " -0.9930402 ]\n", + " [-0.9987323 -0.7779242 -0.9397711 ... 0.8777489 0.94867855\n", + " -0.8222631 ]\n", + " [-0.869482 -0.33121398 -0.98993534 ... 0.9911133 0.93117255\n", + " -0.812023 ]]\n" + ] + } + ], + "source": [ + "print (tr.features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now our trajectories have a `features` attribute, which contains the transformations of $\\phi$ and $\\psi$ torsions for each residue. We can use principal component analysis to reduce the dimensionality. For this, we create a `DimRed` object." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "DR = traj.DimRed(tr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing we normally do is normalize all input dimensions before the dimensionality reduction step. This is called whitening. Afterwards, we proceed to carry out the dimensionality reduction." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "DR.whiten()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.18961444 2.3292065 -0.4857046 ... -0.89191294 -0.6527865\n", + " -0.6730383 ]\n", + " [ 0.7067863 1.166998 -0.4695075 ... -0.89146096 1.0170921\n", + " -0.18219851]\n", + " [-0.6631266 0.47266182 -0.5070305 ... -0.93939406 1.3049766\n", + " -0.62846065]\n", + " ...\n", + " [-0.42258158 -0.28740585 -0.21652292 ... 0.97450054 1.278219\n", + " -0.8500845 ]\n", + " [-0.6638033 0.22960456 -0.46503812 ... 1.2885993 1.3656996\n", + " -0.6451928 ]\n", + " [-0.17582016 2.0794597 -0.5673357 ... 1.4232602 1.3450185\n", + " -0.6329071 ]]\n" + ] + } + ], + "source": [ + "print (tr.features)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Run PCA on 6 components\n", + " Explained variance: \n", + " [0.10965571 0.09394273 0.08805979 0.07232907 0.06858566 0.06384428]\n" + ] + } + ], + "source": [ + "DR.doPCA(n=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize=(2.3,2.5))\n", + "ax.hist2d(tr.features[:,0], tr.features[:,1], \\\n", + " bins=[50, 50], cmap='Blues', norm=mpl.colors.LogNorm())\n", + "plt.plot(tr.features[:500:5,0], tr.features[:500:5,1], '-', color='r')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "DISC.kmeans(k=50, dim=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "DISC = traj.Discretizer(tr)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "DISC.hdbscan(mcs=100, ms=100, dim=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DISC.hdb_clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize=(2.3,2.5))\n", + "ax.scatter(tr.features[:-1:20,0], tr.features[:-1:20,1], \\\n", + " c=tr.distraj[:-1:20], s=4, cmap='Set1')" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "from mastermsm.msm import msm" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Building MSM from \n", + " ['test/data/ala5.xtc']\n", + " # states: 10\n" + ] + } + ], + "source": [ + "tr.find_keys()\n", + "msm_ala5 = msm.SuperMSM([tr])" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n", + "/Users/daviddesancho/Research/code/MasterMSM/mastermsm/msm/msm_lib.py:532: RuntimeWarning: invalid value encountered in log\n", + " tauT.append(-lagt/np.log(lamT))\n" + ] + } + ], + "source": [ + "for i in [10, 20, 50, 100, 200, 500, 1000, 2000, 5000]:\n", + " msm_ala5.do_msm(i)\n", + " msm_ala5.msms[i].do_trans()\n", + " msm_ala5.msms[i].boots()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for i in range(5):\n", + " tau_vs_lagt = np.array([[x,msm_ala5.msms[x].tauT[i], \\\n", + " msm_ala5.msms[x].tau_std[i]] \\\n", + " for x in sorted(msm_ala5.msms.keys())])\n", + " ax.errorbar(tau_vs_lagt[:,0],tau_vs_lagt[:,1],fmt='o-',\\\n", + " yerr=tau_vs_lagt[:,2], markersize=10)\n", + "ax.fill_between(np.logspace(0.1,4,10), np.logspace(1, 4, 10),\\\n", + " facecolor='lightgray', alpha=0.5)\n", + "\n", + "ax.set_xlabel(r'$\\Delta$t [ps]', fontsize=16)\n", + "ax.set_ylabel(r'$\\tau_i$ [ps]', fontsize=16)\n", + "ax.set_ylim(200,50000)\n", + "ax.set_xlim(8,10**4)\n", + "ax.set_yscale('log')\n", + "_ = ax.set_xscale('log')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We resort to a built in method in the ```TimeSeries``` class for discretizing peptides based on torsion angles. Given the low population in the left-handed helix, we will simply use to states (A and E, for alpha and extended, respectively)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Master equation model\n", + "Having discretized the trajectory onto 32 microstates, we can use it to construct the dynamical model using the ```msm``` module. In particular, the ```SuperMSM``` class helps coordinate the construction of many MSMs." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Building MSM from \n", + " [['data/ala5_amber03star_tip3p_tip3p_protein.xtc']]\n", + " # states: 32\n" + ] + } + ], + "source": [ + "from mastermsm.msm import msm\n", + "msm_ala5 = msm.SuperMSM([tr])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all, we carry out the most standard test for lifetime convergence, so that we learn which lag time is adequeate for recovering meaningful results." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "msm_ala5.convergence_test(time=[1, 3, 10, 30, 100, 300], error=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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t2zbcdtttSExMBAAsXrwYDz/8MBYtWoSPP/4YEydORGZmJgBg2bJlGDt2LIqKinDBBRf4tKGiosKnzGg0wmg0dt+Ck5AXzmHZ3hXlj+Z9hzunXYzTlU3ybivP8frFGTCkfwwmjR3gPN7RLwZxsW0fKCfEU1ADJTMzU+74AaC+vh4HDx5EZmYmOI7zCoAhQ4bg66+/BgAUFBRg6NChXnVnzpxBfX09CgoKMGLECLlOp9OhX79+yM/P9xsoc+bM8SlbtGgRFi9eHIAlJKTn7T1S5nULdU8WmwNrP/gRapUCg/sZceXoJOcZVv1iMKhfNPRaVQ+3lkSSoB9DcWtqasKCBQswevRoXHzxxVCr1V5rRTqdDlarFQBgNpuh0+nkOq1WCwCwWq0+de56i8Xi93s3b94sb+m40dYJCQeMMVTXW1Fc2Yjiiibnq9K526q9BzlNu2ow5k0fGdCbHZKuCcct4PaERKAUFhZi4cKFSElJwbPPPovffvsNdrsdjDE5VCwWC/R6PQDvcAEgf9br9T517nqDwf9DaxITE5GcTGeKkNDlGRynK5s8wqPJ6265sVEaDEyMxsihcTj6Ww0EP1spWo0CFw7sTWFCukXQA+XAgQNYuHAhZs2ahT/96U/gOA6DBg0CYwwlJSUYMGAAAODkyZNISUkBAKSkpKCwsBCXXHKJXBcfHw+j0SjXuVksFpSXl3vtIiMkFDHmfBKge0vDHRqnK5tgtnoHx4CEaEzMHICBidEYmBCNAQnRiIlynkrbckW5b6DQFeWkOwU1UIqLi3HPPffggQcewNy5c+Vyg8GAyZMnIzc3F6tXr8bp06exZcsWPPXUUwCA6dOn4/XXX8e4ceOg0+nw0ksvYcaMGQCAqVOn4pZbbsG+ffuQkZGB3NxcDB8+HIMHDw7KMhLSmvtpgKfcWxoVjX6DIyZKjYEJRlxzyQAMSIiWw8MdHG2hK8pJsAQ1UDZv3gyTyYTnnnsOzz33nFx+yy23YOXKlXjiiScwceJEqNVq3HvvvZgwYQIAYPbs2aipqcGsWbNgtVpx7bXXYunSpQCAtLQ0/O1vf8OKFStQWVmJ0aNH44UXXgjK8vkTqKfNkdDnGRwtu6qcu608n9FhNKgxMDEaV49JxsBEY4eDoz10RTkJhqAGyiOPPIJHHnmkzXrPkPHE8zyWLFmCJUuW+K3PyclBTk5OQNoYSJ192lwoi+Rg7OyyuYPDcxeVOzz8Bcf4MckYlBAth8e5BEd76Ipy0tOCfgzlfNHetQGPr9uLf67ICZsOOZKCsbX2li11QCzqmmzOXVSu8HC/e/6u0Xrv4BiQGI2BCUa5cyckUlGg9JC9P5VDlNp42pxFwPzVOxFtUEPJ8+AVHJQ8B4WCh4LnnC/3ZwUHBd+xz0oFB55v+azgOfAK3jVvDrxHuYLnXdO1+uzRBqWCh0OU8Nir33nt6/d8DOsrf54oP9yIMd9TVz2LGJj7g0eZe7yzTCtPeg7zaDWtXRDxWN53MNt8l+2hl/ZAp1Z4bXHIwZHe37mbyhUcMVFquhCwm0Xa6baRggKlh1TUmFydky8GINqgRv/4KEgSg0OUIEpM/mwTGESJQXSViyKDKLX67PXufPU0s9WB21d+0ePf2xMYY0hJjsVlI/vJB8hjozQUHIR4oEDpIYl9DNBqFH5DRatR4KZJFwZ0PzdjzkASXaHk+dkzrFp/9gmmVuXfHSnH3qPlbX7vJcP7YkxaX3BwdrSe/a380aPQ/dGrW3YVcr5FXqX+pm09ntf3n2Uee38qx3c/tbFsDBiZEoepVw7xX08IoUDpKZeP7Ie8D48A8A2U7rg2gONcu74UCOyxGY7DoV/PtBmMV48ZEL4HgDkOP+T7XzaNRoGEPv4vjiWEONHtQnuI+9oAg04F90XKWo0CBp0qrK4NuHxkPyh4/3824X7RXCQvGyE9gQKlB7mvDUiKj0Lf3jrcd2M6/rkiJ6zOjIqUYPQnkpeNkJ5Au7x6WCRcGxDJF81F8rIR0t0oUEiXREIwtiWSl42Q7kS7vAghhAQEBQohhJCAOOsurzfeeKPLM7/jjju6PC0hhJDwctZAeeaZZ7o0Y47jKFAIIeQ80qGD8u+//z5GjRrV4ZkePnwYs2fP7nKjCAm2SLlXVKQsBwkPZw2USZMmISYmplMzjY2NxcSJE7vcKBIeqLMihHg6a6CsXbu23fojR46gvLwcl156KWJjYwEAF1xwwVmnO59RR0wIiUSdOsurrKwMs2bNwssvvwwAeOeddzBz5kwsXboUOTk5+Pnnn7ulkYQQQkJfpwLlmWeeQU1NDS677DLY7XY8//zzuOaaa/D1119j1KhR+Pvf/95d7SSEEBLiOhUo33//Pf785z8jMzMT33//PRobG3HrrbciKSkJt912G3788cfuaichhJAQ16lAEQRBPkC/e/duGAwGZGZmAgAcDgfUanXgW0gIISQsdOpeXhdddBHef/99aDQafPbZZ5gwYQKUSiXq6uqwfv16jBgxorvaSQghJMR1KlAeeugh3H333fj0008RExOD++67DwAwZcoUAMBrr70W+BYSQggJC50KlNGjR2P37t349ddf0bdvXzQ0NKC6uhrPPPMMRo0a1enrVQghhESOTt++/pNPPsGrr76K0tJSuWzIkCG4//77kZ2dHdDGEUIICR+dCpTNmzdj1apVyMnJwZIlS9C7d2/U1NTgyy+/xNKlS7FmzRr87ne/6662EkIICWGdCpTXX38dd9xxBx5++GGv8hkzZuDJJ5/ESy+9RIFCCCHnqU6dNlxTU4MrrrjCb90111yD06dPd7khR44cwbhx4+Rhu92OESNGICMjQ37deeedcv327duRnZ2N9PR03HXXXaipqZHrjh8/jpkzZyI9PR3Tpk3DkSNHutwuQgghHdOpQLn88svx0Ucf+a37+uuvMXbs2E43gDGGLVu24M4774QgCHL5iRMnEBMTg0OHDsmv119/HQBQUFCA5cuX46mnnsK+ffswaNAgPPDAAwCcQbRw4UJcd911OHDgAO69917MmzcPzc3NnW4bIYSQjuvULq8JEyYgNzcXs2bNwpQpUxAXF4f6+nrs2rULe/bswfz58+UHcnEch9tvv/2s83zxxRexe/duLFiwAP/7v/8rlx87dgzDhg3zO83HH3+MiRMnyhdVLlu2DGPHjkVRURFKSkogCIL83VOmTMFbb72F7du34+abb+7M4hJCCOmETgXKihUrADifd3L48GGf+nXr1smfOxoos2bNwtKlS7Fv3z6v8p9//hm1tbWYNm0aampqkJmZieXLlyMhIQEFBQVeF1HqdDr069cP+fn5KCsrw9ChQ73mNWTIEOTn5/v9/oqKCp8yo9EIo9F41rYTQghp0alAOX78eMAbkJCQ4Ldcp9NhzJgxuO+++6BUKvHkk0/ivvvuwwcffACz2QydTuc1vlarhcVigdlshlar9ZmXxWLx+z1z5szxKVu0aBEWL17cxSUihJDz01kDZe/evRg1ahQMBkOHZ2oymXwOsnfWI4884jX8l7/8BePGjUN5eTl0Oh2sVqtXvdVqhcFggF6vh81m86qzWCzQ6/V+v2fz5s1ITEz0KqOtE0II6byzHpS/88478dtvv3VqpgUFBV5nZHXFCy+84PW97gP2Go0GKSkpKCwslOssFgvKy8sxdOhQDB061KsOAE6ePImUlBS/35OYmIjk5GSvFwUKIYR03lm3UBhjeP311xEXF9fhmVZXV59TowDnWV5Hjx5Fbm4uAGD16tW4+uqr0bt3b0ydOhW33HIL9u3bh4yMDOTm5mL48OEYPHgw+vfvD8YYNm7ciDlz5uCLL77AiRMn6Cp+QgjpZmcNlKSkpC5dx9GvX78uNcht9erVePLJJ5GdnQ1RFDFhwgSsWrUKAJCWloa//e1vWLFiBSorKzF69Gi88MILAAC1Wo3169djxYoVeOGFF5CcnIy1a9eid+/e59QeQggh7eMYYyzYjQiGkpISTJo0CV999RWSk5OD3RxCCAkL7fWdnbqwkRBCCGkLBQohhJCAoEAhhBASEBQohBBCAoIChRBCSEB0OVBuu+02AMArr7yC3bt3B+TaE0IIIeGr048AdnvppZcAAA6HA2+//TaOHTsGnudx8cUX4+KLL8aiRYsC1khCCCGhr1OBsmrVKixcuBB9+vSRb0+yZMkSub6yshJHjx7FsWPHAttKQgghIa9TgfL2229jxowZ6NOnj9/6hIQEJCQkYNKkSQFpHCGEkPDRqWMo5+lF9YQQQjqg0wfl7Xa73/Iff/wROTk559wgQggh4anTB+Xnzp2L/v37Iy0tDcOGDUNaWhr69++PL7/8EvX19d3RRkIIIWGg04Fy2223wWKx4MSJE9i4cSNMJpNcN3v27IA2jhBCSPjodKD8/ve/x6hRo+Th0tJSnDlzBjqdDsOGDQto4wghhISPLl+H4ta/f3/0798/EG0hhBASxjp1UP6BBx6gB1URQgjxq1NbKPfcc093tYMQQkiYo5tDEkIICQgKFEIIIQFBgUIIISQgKFAIIYQEBAUKIYSQgKBAIYQQEhAUKIQQQgKCAoUQQkhAUKAQQggJiJAJlCNHjmDcuHHysN1ux2OPPYasrCxcdtllePXVV73G37RpE8aPH4+MjAw8+OCDMJvNct3333+PadOmIT09HbNmzUJxcXGPLQchhJyvgh4ojDFs2bIFd955JwRBkMtfeuklFBYWYufOnfjggw+wdetWbNu2DQCwZ88e5OXlYcOGDfjmm29gt9uxatUqAEBtbS3uu+8+LFq0CAcOHMDkyZMxb948SJIUlOUjhJC2lG16HGWbHg92MwIm6IHy4osv4p133sGCBQu8yrdu3Yp7770XMTExSE5Oxrx58/Duu+8CALZt24YbbrgBqampMBgMWLZsGT799FOYTCbs3LkTqampyMnJgUqlwvz582G327F3795gLB4hhJw3gh4os2bNwocffogRI0bIZY2NjaiqqkJKSopcNnjwYOTn5wMACgoKMHToULlu4MCBYIyhqKjIp671tK1VVFSgpKTE69XY2BjIRSSEkPPCOT8P5VwlJCT4lLmPh2i1WrlMp9PBarXK9TqdTq7jOA5qtRoWi8Wnzj0fi8Xi9/vnzJnjU7Zo0SIsXry48wtDCCHnsaAHij/uQLDZbHKZxWKBXq+X693hAjiPw9jtdhgMBp86ALBarfK0rW3evBmJiYleZUajMSDLQQghbZEEG0RTPZjDjuaje6Afdil4pTrYzTonQd/l5U9MTAzi4+Nx8uRJuaywsFDeBZaSkoLCwkK5rri4GIwxDBo0yKcOAE6ePOm1+8xTYmIikpOTvV4UKISQ7mQtK0Dxi3dBqC2Do6EKVZ/nofiF+bCWFQS7aeckJAMFAKZPn461a9eitrYWJSUl2LBhA6ZPnw4AmDFjBrZs2YLjx4/DZDIhNzcX2dnZ0Ov1yM7OxvHjx7F9+3YIgoANGzaA53lkZWUFeYkIIcS5ZVLxzkpIVhPAGACA2a2QrCZnucMe5BZ2XUju8gKApUuX4umnn8bUqVMhSRJmzpyJ2bNnAwAmTJiABQsWYOHChWhoaMDll1+OlStXAgD69OmDvLw8rF69GsuXL0dqairy8vKgVof3piQhJPwwSYTYXA9HwxkI9ZVw1FfBUnQEks3/MV0miTAf34eoEVf1cEsDg2PMFZHnmZKSEkyaNAlfffUVkpOTg90cQkgnuK/dSJq7MqjtYEzyCIwzcNSfgaOhqmW4oRqQHF7TcGotmN3axhw59JowC72uvLH7G99F7fWdIbuFQgghwcYYg2iqdwXFGQj1zrBwNDjDQ2ioAkTvwFAYYqGMiYem31BEDR8HZUxfKGPioYx1vpuP70PV53l+Q4VTa6CK9T3zNVxQoBBCwkogz45ijEEyNzp3RzVUuULijBwgjoZqsFbHNHi9EaqYvlAnDIY+7VKoYuKdoeEKDF6lafc79cMuBbdjPfztGuJ4BfTDLu3SsoQCChRCSNiwlhU4D1zbzABjqPo8D9yO9Uic/Ti0Sb5ncjLGIFmaXLufWnZJeQaIT2DooqGK7Qt1/CDoUzOhjOkLVWxfeUuDV2t9vqczeKUaibMf91oOTq0FxytGSiHKAAAgAElEQVSQOPvxsD51mAKFEBIWvM6OcmF2KxiA8s3/D3G/vwdiU53H7qgzcNRXgQneu5Z4XZQzJPr0h35ohrx1oXIHhkaH7qZNSsHAJetRuuEhMIeA3lffEhHXoVCgEBLBQuXgdWcxxiBZmyE210M0OV+mgh8gtXEwm9ktqNq2BgDAaw3OwOjVD7rBo722LlQx8eC1hp5clDbxKg0UhlgACNuzulqjQCGE9AjGGJjdKgeEo7keYnOda7hB/uxodg63PjvqbKLHXIve19wCRYgExvmIAoWQCNVTt/aQHHZnKDTXe21RiM31cHh8Fk31YILNdwYcD4UhBgpDLBRRsdDFD4QyKtY13Euus57+BTU7N/rswgKcp+LqBgyjMOkB7V1pQoFCSATq7MHr1pgkOrcaPMLA0SosRFMdxOZ653f4weuioYiKhdIQC1VymjMgDDFQRMVCYeglhwaviwLHK87aJmVsX9R+vQlM8K0L17OjwnFXpCiKbdZToARBuO7XPp+E82/U3sHrirefQL9b/h9EW7NrN1NLMHjuipLMTYCfE1s5jR5KVzCo+w6CYnC6KyBioDT0cn12DnOKwHYvkXx2VLhgjKGkpKTNegoU0mXh3Om2J9TvAsskEZLNAsnaDMlqdh68tpogWU2QrM2wnj7e5sFryWZG6Rt/9irjlGp5d5MyNgGa/sPkLQs5INxbE2e5xqK7RerZUeFAkiSUlJTAbm/7XmMUKD0s1DurjoqU5WjtXHcVdQRjDMxhdwVAy0u0NrcM20wQLc53z+Bw1vnfxdSCg7+tCzfD8MthzLzWudvJEANOowfHcQFZtp7AqzQYcO+LwW7GeUUURdTX15/14YMUKD2oJzqrnhApy9Fau7uK3lmJgUtfk0PT71aCzQTJHQIWZ+fvVeYaV7Q2+9yuozVOpQWv1YPXRkGhNUBp7AO+70DwWgN4jQEKXRR4jbOe13mWGWD69QCqP3+1jVt7aGG4MAu6gRcH9N+ORC5JkmC321FeXn7WcSlQekhnOqtQFsjlYIwBTAIkCYxJgCSCSRLAJDBJBDw/M8mjznualjLRVeYxjWvYOb7oqvOYD2v5Tlt5ASR/ZyHBuavo9P8uAcehY1sJHO/s/LUGKLQG8NooZyhoo1rKNAbwnsEgj6sHp1B19CfxYRh2GWp2vBaRt/YgPU+SJJw6dapD41Kg9BDzif3OTs4PyWpG8Uv3uG7p4LHroY3dEN67JzivN58Bf/PwN73PqJxPPTgOoqW5zc5UsppR/PK94JUauUP339lLcl3YYAy8SgVNUqr/rYRW4cGptUHbjUQHr0mgSJKE4uJiOBwduyaIAqWHCHUV7dyymkEZ3Rvq+IFtVDOPMZnfcv9lzM9Hj3m1dT65XO47PRPsENs8D51BaYiFOuECgFOA43mA453vPA+O4wG+pRy8wlXGO08bdY/r8e5T556Gc5X7fIfC47u8x2v9/e75Or+DR/Mve1GzY0Ob1zn0uvLmsLmimQ5ek3MlSRIqKythNp/tmF0LCpQeouqV2OZzEDi1FrGXXR8WnVXz0T3t3Hpbi9hxfwiL5fAn6uIrUfvlxoi5ziESb+1BeoYkSWhqakJNTU2npgvZRwBHGv2wS9u8eCucOqtIWQ5/3LuKeK1B3v/HqbXgtQbaVUTOG4wxOByOdq83aQsFSg+JlM4qUpajLe5dRareSVDG9EX8dfdi4NLXwvbstaS5KyPuOiHSvSRJQlFRUbu3WGkL7fLqQZGyXztSlqMttKuInK86cvFieyhQelikdFaRfnEZrdWT840oiqirq0NTU1OX50GBEgTUWRHSdXaHHftLD6OiuRqJUXHISs6A+hyu2yHOLRObzYaKiopzmg8FCiEkbBTUFGH17hdhFqxgYNAqNdjwf+9i+YQlSOlzQbCb12n/7+vnnO8T/xS0NoiiCLPZjNOnT5/zvOigPCEkLNgddqze/SJMgkW+HsvqsMEkWLB694uwi37O9w5hdocdDdZGVJlq8M2p/UFpvyRJqKmpwalTpyBJ536hMW2hEBKhwnHXEGMMdlGA1WGF1WGDzWGH1WGD1WHDofKjbXa6dlHApsP/wqjE4VArVNAo1FArVFAr1VAr1NAoVK53NXg++OvRrbe01h18u0e3tBhj8q3oz3bDx86gQCEkAvXEriFREj06fKvc8XfkZXN/FnzrWDt3Sm6LIDmwo2A3dhTsPuu4Cl4hB05bweMcbqnXuIbVCtd4ypaAksuVqlbz9R9e7i0tm8mKtJNx0Jn1sOjNKBhajdW7X8SrM57p1uCXJAmiKKKoqAg2m/9713UVBQohrYTjmr0nz11DblaHs+NYtesFPH71/RCZ2InO3+rd8YvOEBE6sYtGwfHQqrTQKjVer976WNdnLTRKtU+9u06r1OCXql/x4c+fwyb6ntKqUahx04ipGJkwDDaHHXbR/RJcwwJsrmG7aG9VZofdNdxkN8Eu1svD7mkcnXy+vZuSV/oEl81hR2yRAnHFk8A4DgKngrpewMUHGRoGH8D+kkO4clBWl77vbERRhNVqRXFxcbtPXuyqkA6UDz74ACtWrIBa3XJ9w+OPP44pU6Zg1apV2LFjB3iexx133IF77rlHHmfTpk1Yv349mpqaMGnSJKxcuRJ6vT4Yi+Aj3Dsrt0hZjtbca/Yik2B12IJ60NchOmB2WGERLLAIVpgFK6wO57tFsMLisMAi2GARLK7xnK9KUzXMfu5HBgAWhxWPfPl0u9+rUfh27Hq1Dr3kzr+lk/d+qdusUwbg6Y1Deg/Ex8e/gMPGkFJiQ0yTiIZoBQoGaKBUKXBt6tXd9jcoSZIziESPoHEILaHlCiabXNZGmDnsKD1TgrjiyyAqWh5WJnHOPi6mcCzKqyuAQd2zDHV1ded8Jld7QjpQfv75Z9xxxx148MEHvcpzc3NRWFiInTt3oqmpCfPnz0dCQgKuv/567NmzB3l5edi4cSOSkpLwyCOPYNWqVXjqqaeCtBQtQqmzOheRshyttbdm39FdEZLr38TduZsFC6wOG8yuULA4WgeDu9zWEhyucOjIWjEHDlqVBnqlDjqVFjqlBmCs3d1Glw+4BFcPHufV4Wvc7yFyjMEftUKFPw+8HuXPvAROkqByAIKSwzUHm9Hv4Vu6dYWG53loeS20Kq1PHZMkSIIA5nBAEhyudwFMECA5HD7vX/0s4QTXxr8xx4E7YAIuCVzb3cdLSktL0dDQELgZ+xHSgXLs2DHceuutPuVbt27F008/jZiYGMTExGDevHl49913cf3112Pbtm244YYbkJqaCgBYtmwZpk6dikcffRQGg6GnF0EWiM4qFITCcjDGIDEJEpM8PjNIcL17lXt/lsAgSc7xGLzHOVx+DEIbnbhNtOOFva+hj763HBYtWwhWmB3OMHD/W5yNWqGCTqWDTqmBTqWFXqVDnL43tCot9EqtMxxUWuhcn/UqndewzjWeWqkG36pz+ubUfqw7+DYEq9VnTV6l0SKz/yik9wu/B2yJNhsannsNGnvL2UhqBwPA0PDsOtQ/4rxg2N2hOzt34SydvMNjWHANC63KW717zs/hAPPYdeTgVLAq9bApDbB5vethUxhgVeohKAe0eX6tyKmg7vjNfc9KkiT5VipWa1t3Ow+ckA0UURRx4sQJfPTRR3jqqaeg0+lw0003YebMmaiqqkJKSsu9lQYPHoz8/HwAQEFBAcaPHy/XDRw4EIwxFBUV4eKLff8T+dv8MxqNMBqNAV2e/aWHIbbx/A+zYMVD/34SRm2012NN/D73xOtT63Hc9e0/4+Rs9Z6fWreh1lIPi6Pt3SkPf/E3GDVRcsfeutNnrKXTl+C/0/cOCd/yYHBIIg6W/gS9Wufq8J1hEKXWI97Qp1Xn793x65Q6Z5mrXKvSQtnGDTYDISs5Ax/teBPXflENnjHXmjxw9QEO//5dArKSM7rtu90khwOS1QbRaoVotUKyWiHarL5lrmFnvQ2ixQrJZnW9e45rg8NkAmvjuRyixYJjjz/R8QbyPHiVCpxSCV6lAq9Syp/ld5UKCp0OSpUSnEIFm0IDG6eFBWpYJDUsTAmLQwmzqIDZzsMsAILo+/9Rq+ERpVcgLkqFqCg1miprcKqagfG+3a+COdAnIabjy9EOSZJgtVpx6tSpbjle4k/IBkptbS1GjBiB66+/Hi+//DJ+++03LFy4UL7HjFbbsump0+nk9DWbzdDpdHIdx3FQq9WwWCzwZ86cOT5lixYtwuLFiwO5OKhorm5z7dW9pqzy+ANra5cF83o2invY/zNOvObgKvd+Wor/Z6swv/XOt0ZrE6Q2bhonMQa7ww5ey0PJc+DAg+c48BwPnuPB+f3MgfcYz/847nrXOGhvHOc718Y8/bfHOf7PZ/Kx/devIdrsftfs77rkFlx1QfccLA0khUPCH/7TACa0/E5qBwAw/OE/DVDezgCFa0vPbnd17M7O3edzmyFgcZa3EQJtdfx+cRx4jQYKrRYKrRa8VgOFRguFTgdVbCwUOi14jRbmU6fQdPwERE6BKsMgWFTR0AlNiDedgoKJ6DvxavSdPBG8UgVOpWz1rgKvVLqGleAULYHucIhoarCiscHqfK+3oqmx1edaKyTJ+++e5zlEx2gR3UuLpBgtjLFaRBtd7zFaGGOcw0qV98qD3WTBs49+Dn//QhwYxl5/ecf/7dogSRLq6+tRVlZ2zvPqjJANlPj4eLz11lvy8PDhw/E///M/+O9//wsAXqe7WSwW+aC7Z7gArvPa7fY2d3dt3rwZiYmJXmWB3joBgMSoOGiVmjZ3Q8wcOa3bzuwIpLPtTpk9akZYLIc/F/VNxaF9O9tcs790QPev2fvDRLGlk7e1rLF7rr17dvhNBb8BNrv/jtdixf7b54GJEiSbzf9D2trAKZUeHb8GvFYHhVYDdWwMeG1fVwhonePodK53LRQaV0jIgeGa3j2+Wt2hp1tW7d6DstIm/BB3NRjHQeRUUDABJ9hlGFO9CxempyPGYy8EYww2qwMNDVY01ljQ1NCApkZXSLjDo8EKs8n3rDGVWgFjjBbGWB0GDe0jB4QxpiUsDFEacHznn8qpNuhw042p2PLBr2DgIHJKKJgDHBhuujEVaoPu7DNphyRJKC8vR11d3TnNpytCNlB+/fVXfP7551iyZIlcJggCNBoN4uPjcfLkSSQkJAAACgsL5V1gKSkpKCwslKcpLi4GYwyDBvk/bSIxMRHJycnduCROobAbIhDcy5G9sw51uoGw89EYIDVh/IFifPG7uLBZDn/ca/YOB+/TEXuu2fvDGAMTBO+1d3cn77kW386WgOcavufuICZ07gpqTqFAg7IXDiX9zqfjzSj7Av36GWEcfpGrs9fIWwByGHhuKbhCg9dowKuCe4zPmJmJH7ZVwcG3nPUpus6OOhg/CXxdLMxvH2rZ0miwQrD77urRR6mdwRCrRf9BvVq2JjzeNVpltz7COfWKkVhy0QD85+0PUF9XjdhecZj0PzdDH9P1lVn38ZJTp061uUemu4VsoBiNRrzxxhtITEzEjTfeiJ9//hmbNm3CY489hpSUFKxduxZpaWkwm83YsGGDfPB+xowZWL58OXJycjBgwADk5uYiOzs76KcNn0tnFUoUDgkTv1HgYP+bvTorrvdlmPjN7rN2uu5nyjufMy/5KWOuz6Jcz0TPZ9Ez7+lbffasZ6LnM+3d47b6Po/6pvxf0QAjDl0wybcjrvgSPz3yKJTR0XJAtA4BdObWFTzvsfbuXGNXaLVQRkdDExfX0slrNa761ruDtPIWgDsMFBpnx1+x6xu88Um13473UP8cpE+JQ7+J4/02izEGUZQgOhhEhwS7KMFhkyCabRAdFoiiBIdDco3jerk/i8y7zj1uq7KWcUWIIvMu95yu1bgOhwTw/h+RIPFK7PvmFIyxOkTHaJGQZETK8L4wxupgNLrCIlaLKKMGSmXw/6NVnj6Jj1/Phd1mAWMMZqEIm9ccwvQ7lyFhwJBOz0+SJNjtdhQVFXX4+e/dgWNdeYpKD9m7dy/+8Y9/oLCwEL169cK8efMwZ84c2Gw2PP3009ixYwckScLMmTNx//33y2sUmzdvxoYNG9DQ0IDLL78cq1ev9tmNVVJSgkmTJuGrr77qkS2Uqt178H/r3/fZXOcYQ0bZF4iRGsApFN5rRe7PbR04P0u9/AAsf/Ve3+Nbz7VRb7c5sDt+Khwe59C7KUUbxpdthVLJ+Q2BzuxeCQaRU+CbC2a2uWzX1G6Hvm+8RyfvXIN3dvLO3TycWg1eqwXUWnBqNaDSAGoNoFKDU6nAlGpAqYLE8WASgygySKKz05QkCZLo7NAlkUGUPOpEye+w6JB8pqs+04SyU3Vg/k5NZRKiY3RQqZW+Hb9r+kBTKHkoFDyUSh4KBScPK5TOMl6u41uN6xpWclAoeJQW16P4ZC0AEWq+FDxMkGCAXeoPQIGrcy7E+N9dGPD2B5pDsOONvz0Am9X3dC6NVo87/roGyk5sDUqShMbGRpSWlnbpoViddebMGSxYsMBv3xmyWygAMG7cOHz44Yc+5RqNBitWrMCKFSv8Tjdnzhy/B9uDqbmsAj/EXe3VWbnXGn9IysGVql8QfcEAuc79d+F1EJ35HiR3lnE+9S0fGSR3IrgPzHvNpmV65/UL3l/KGAOTE4WhpNwKqY1NEIlToGjAVUjuHwVwgATeGVyc85253gEOzP3OcwB4eRgc5/p65zjO724Z3/kZLeVyy9zTcmAMLfOAn8+s5XPLvzOH2tIaOJr8b2U4eBX2DvwjtEZDS6dukiA2Mbkzb+mQra5X9+J5DrzC2dnyfEtHbbM6/IcJAHA8NDoVEvoZnZ22V8ft7PCVSoWz42/dwbcev52QcH/meS5gu45++qEUZ0oKoWW7AUjgIIJBAb3iMKzcBPSK69nLAiRJgugQXC+H/O5wl4kOiILr3VXmcAgoL/oVguD/AVaSJOG3YweRlj6uw22orKzs9LPfu0tIB0okKZN6g3H+b8ImKtTYLY0GTvZwo7qinWveJF6Jk+iPk6VdmbH32Wpt4Tjn1pPzBXA851vGca3K/dTL07aU2Tg1gLauI+GgidIhPiEaCgUHXsFDwbveXcM87+rc3Z2xwqOe9x2Ww0DBQdHJ4fY66p9+KMVnHxyB3Wb3WZNXa9S4anIqRo7p35UfKahSh/XGLuwGx7UcU+IgAhChwy70islE+alf/XfurTp9URRcdY5WdZ7ljlbBIMAhCJBE53xZAO7O25pgt6Gxtuqs40mu3cHFxcUwmUwBb0dXUaD0EEfvJIhcG1csMYaLRifiovT+Xp1Ey0fOe7eVn11ZbY4L+Jlnq+k5j71a7nm3jOa8bsX1+bdfKvDtznxIfs6h5yUHJlw3DBeOTJI7ap5v3ZG30dHz7ZW1lHenn34oxafvH4Yg+AabSs1j/O/SwqIjHj4yETv+tQt6le+avJ2bgOEjE886j66SRBEOwQ5BsMMh2OCw21uG7TbnZ9e7Q7DDYbdDEFzDdptrOrs8nUNwldntsJpN4OD/BAUODmx7rf1byniNz3FQKFXyS6lUQaFUuoad72qtFgpltLNOoYRC5fnufxr3Z6VPmXNapUqFol8O49vP34dg9115Uak1MPaOb7ftoijC4XCgqKgIQidP2OhuFCg9pHeCESo1D0Fw+Kw1qtRKpI1MwkWjk4LdzLNKSo7B/j1FsNkF3+XQqjHumlSf8+7DxfCRifj8QwUEweazbAqlpls74sASEa38FoLkuyYfpfwGzY3ZAJNcnbu9VWfeKgQ86zyCQPDo8D2DQerCBXS8QgmVSg2lWg2lSg2lSgOlSg2VWg2NTg+V2jlcU+nc5dWWC0dfhrQxl7s6c+/OnVcqnSHgCgVeEby/0WGXXIW9O/7lt47neQy9ONNvnSiKYIyhsrIS9fX1PXK8pLMoUHqIc62xHgb4WWtUdO9aYyApVQpMuSEZ//nXywCTAIgAFDAoD+OaGxaFbZgAzmX7/fVJzmUDA5gD4JQw4DCuuT5wy8YkCQ6Hc/eJ6BDgEOzyu8MhQBQEOBzuspZxvD47BIiu99bjmJrqIdj9H8Nx2K1469mHO/Fvom55qdXOjl+lgVqjhT7aCJVK4xEEat9hVxgo1RrXtO7gaBnuaOd+4vBe7Nr6Zhtr9loMGjYKgy4c2eFlCxalSoXpdy7Dx6/nQpIkCHYbVGoNeJ7H9DuX+RyQd1/lXllZibq6upAMEjcKlB7T9lpjtPJbADcjHM4bdgh27P3sVeeuB3kPlHM59n72KoaN7twZKm1hjDlPEvB6Sa3KJZ96z2k6Oh5z7Y92CHZ8+8krLbtVOACu65m/+XgtRPtNrjCwuzp9j87fNezs3O0eISH4dP6SeG6ndSpVauduGpVrd43HsFqjhc2iQnvHoy4Yno7UkWM9OvrWoeAsVypV4ELoRpFDL87Efz96y29de2v2oShhwBDc8dfn8dux/0NjbRWMveMx9OJMr/877uMkZ86cQW1tbUgHiRsFSg/57dj/AfB/EE8QrNj07J+h0YbGLfbbY7OaYbP6v2jKZrXgzWeWQal23fHWT0fOJPeZZu2HQ6idYuwQ7NjzyWavMnnfu0oNpbuDd31WqTXQ6qO8OnulUg2Fyj2N57DaKxxa6j3DwjkOrzj7BXdnW5NPHZXV4bOIQkln1+xDnVKl9vs7uIOkqqoKNTU1YREkbhQoPaSxpsrvf3AAAGPQaPXo1Tf0j6HUnimDqbG+jVoGQ3Qs+vQbAI7nPQ6oc+Bcp7G6P3POo+yuM638jNtq2P0ZnP/x3bfA8Jp363H59ued/+M+nDj0XZvLnjH+Olxy9RRnECi790rqcxFJa/KtdWTNPly5g6S6uho1NTUBecZ7T6NA6SHGPvFQqTVtrjVecs3UsFhrPNvab8aE68JiOfyxWkw4eez/2ly2uH4DoNUF7xEIHRVpa/KttbVmH67cwVFdXY3q6uqwDBI3CpQeEilrjZGyHP5E0rJF8pp8pHAHR01NDaqrq3vsFvPdKXSOuEU491qjRquHSq0FwEGl1kKj1YfVWmOkLIc/kbZs7jX5sROnIy19XNi1P1K5b+JYW1uLEydOoLKyMiLCBKAtlB4VKWuNkbIc/kTyspHgcm+R1NXV4cyZMxETIp4oUHpYpOz/jZTl8CeSl430PHeQ1NfX48yZM0G9G3B3o0AhhJAAY4zJQdLQ0ICqqqqQu01Kd6BAIYSQAGgdIg0NDSF148aeQIFCCCFd5L52hDGGhoYG1NfXB+1piaGAAoUQQjrBvRXicDjkLRGrtfuffxMOKFAIIeQs3CEiCALq6+vR0NAAu93/Q7LOZxQohBDihyiKzgev2Wyor69HY2PjeXFg/VxQoBBCiIs7RKxWK+rq6tDU1BTRp/kGGgUKIeS85L6wkOM42O12mM1mmEwmNDc3R+RFhz2BAoUQEvHcZ2PxPC+Hh9lshsVioQPqAUSBQgiJKJ7hIQiCvOVhsVhgs9nC6vki4YYChRAStvyFh+eWB4VHz6JAIYSENHdoAM7HCEiSBEEQYLPZ5PCwWCwUHiGAAiUIGhsb8eabb+K2226D0WgMdnOIH/Qb9Rx/geFwOGC322Gz2WCz2SAIAux2OwRBoOAIYRQoQdDY2IiXX34Zf/jDH6izClH0GwWG5/2tAMiPTfYMDLvdLr8EQQjrJxae7+gBW+146aWXOl3X2fJgCVR7ujKfjk7TkfEC9RtF6u/T1Xm9+uqrfssZYxBFUX41NzfL97ISRVE+g6qxsRG1tbU4c+YMKioqUFpaisLCQpw4cQI///wz8vPzUVRUhPLyctTU1KCpqQk2m43CJMxFbKAcP34cM2fORHp6OqZNm4YjR450eh4vv/xyp+s6Wx4sgWpPV+bT0Wk6Ml6gfqNI/X0AoKSkRP7s3mLwDAX3y/0kQcYYbr/9djgcDthsNphMJjQ0NKC6uhqVlZUoLy9HSUkJioqKUFZWhl9++QXHjh3DL7/8gvz8fJw8eRLFxcUoKytDVVUV6urq0NjYCKvVStd3RLiI3OVlt9uxcOFC3HrrrXjrrbfwxRdfYN68efjPf/6DqKioYDePkDa51/afeuqpDnW+HMfJu5HcYeEZGpIk4f7770dtba087Bke/oY9j2kQ0hkRGSj79++HIAi4/fbbAQBTpkzBW2+9he3bt+Pmm28G0HKV7E8//YSKigqv6aOiouTg8Vy785Senu63LiMjw2/5JZdcIpfX1tZi7NixqK2t7doCBsDkyZPbXLbO+OMf/9jp+cydO7dD0yxevNhn7bq1xx57DKdPn/Y7/QsvvOC37o033vAp37Jli1cZYwyfffYZGGM4ffq03Gl3hGc73QHh+dnzlufuYc8ySZIwdOhQ/PTTT/IuIM+g8JzWc3pCekJdXR0A+F3h4VgErops3LgRu3btwsaNG+Wy5cuXQ6fT4dFHHwUAHDx4EHPmzAlSCwkhJLxt3rwZmZmZXmURuYViNpuh1Wq9ynQ6ndeDb0aMGIF169ZBq9VCoVB4jeu5hUIIIaSFKIqoqqrCiBEjfOoiMlD0ej1sNptXmcVigV6vl4e1Wi0mTJjQ000jhJCwN2jQIL/lEXmW19ChQ1FYWOhVdvLkSaSkpASpRYQQEvkiMlAuvfRSMMawceNGCIKAzz77DCdOnEB2dnawm0YIIRErIg/KA0B+fj5WrFiB48ePIzk5GX/9618xbty4YDerXQ6HAw8//DDKy8sRHx+Pv//979BoNMFuFvFj48aNEEUR8+bNC3ZTiIeGhgYsW7YMNpsNGo0Gzz77LGJjY4PdrPNGxAZKONq+fTuOHTuGhx56CBs2bIDRaMRNN90U7GaRVlasWIHvvvsOs2bNokAJMa+88gri4+Nx0003YcuWLaiqqsLChQuD3azzRkTu8gpXhw8fxpVXXgkAuOKKK7B///4gt4j4c9VVV2HBggXBbgbxY+7cuZg+fUge+LsAAAatSURBVDoA5xa/UhmR5x2FLAqUENLc3AyDwQAAMBgMMJlMQW4R8Wfy5MnBbgJpQ3R0NDQaDfLz8/HOO+9g5syZwW7SeYUCJYRERUXJIWIymeRwIYR03OHDh7Fs2TKsWbMGMTExwW7OeYUCJYSMGjUK3377LQDg22+/RXp6epBbREh4KSgowOOPP468vDwMGTIk2M0571CghJBrr70WFRUVmDVrFg4ePIgbbrgh2E0iJKysXbsWFosFf/nLXzB37ly88cYbwW7SeYXO8iKEEBIQtIXSzY4cOeJz/UsgntVCAod+o9BGv0/4oEDpJowxbNmyBXfeeScEQZDL3c9que6663DgwAHce++9mDdvHpqbm4PY2vMT/UahjX6f8EOB0k1efPFFvPPOOz7XK3g+q0WlUmHKlClISUnB9u3bg9TS8xf9RqGNfp/wQ4HSTWbNmoUPP/zQ5xbPBQUFGDp0qFfZkCFDkJ+f35PNI6DfKNTR7xN+KFC6SUJCgt/yjjyrhfQM+o1CG/0+4YcCpYd15FktJLjoNwpt9PuELgqUHkbPagl99BuFNvp9QhcFSg+jZ7WEPvqNQhv9PqGLAqWHqdVqrF+/Hjt27EBWVhby8vKwdu1a9O7dO9hNIy70G4U2+n1CF10pTwghJCBoC4UQQkhAUKAQQggJCAoUQgghAUGBQgghJCAoUAghhAQEBQohhJCAoEAhhBASEBQohBBCAoIChRBCSEBQoBDSg+jGFCSSUaAQEkDLly9HWloannrqKZ+6L7/8EitWrGh3+okTJyItLQ1paWn47rvvAtKmhQsXyvPcsGFDQOZJiD8UKIQEiNVqxb///W9wHIdPPvkEDofDq/7NN99EZWXlWeeTk5OD9957D6NGjQpIux566CG89957AZkXIe2hQCEkQHbu3Inm5mbMnz8fNTU12LNnT5fmExcXh/T0dERFRQWkXYMHD0Z6enpA5kVIeyhQCAmQbdu2ITk5GUuXLkXv3r2xbds2uW7u3LnYv38/du3ahbS0NJSUlHR4vmlpaXj33XexYMECjB49GhMnTsRbb73lNc6PP/6IOXPmICMjA1lZWViyZAlKS0sDtmyEdAQFCiEBUFlZie+++w7Tp0+HSqXClClT8PXXX6OhoQEAsGLFClx00UUYM2YM3nvvPfTt27dT83/22Weh1+vx0ksvITs7G6tWrcL7778PwPn427vvvhsJCQl45ZVXsGrVKvz888/405/+FPDlJKQ9ymA3gJBI8NFHH0GSJEyfPh0AMGPGDGzatAnbt2/H7NmzkZKSgqioKOj1+i7tfhoyZAhyc3MBAOPHj0d5eTny8vJw880349dff0V9fT3mzp2LjIwMAECvXr3w/fffQ5Ik8DytN5KeQX9phATARx99hNGjR2Pw4MEAgJEjR2LIkCH46KOPAjL/3//+917DkyZNQmlpKSoqKjBkyBDExsbi3nvvxcqVK7F7926kp6djyZIlFCakR9FfGyHn6MiRIygoKMCMGTO8ymfMmIFDhw6hsLDwnL+j9S4y9+Nu6+vrERUVhbfeegvjxo3D1q1bcffdd+OKK67A22+/fc7fS0hnUKAQco62bdsGlUqF6667zqt8+vTp4DjO6+B8V9XV1XkN19TUAGgJltTUVKxZswb79u3Dxo0bcckll+CJJ57Ajz/+eM7fTUhHUaAQcg7sdjs+++wzXHXVVXLn7paUlISxY8fi448/BmPsnHY/7dq1y2v4q6++wpAhQ9C3b1/897//xbhx41BbWwu1Wo1x48bhscceAwCUlZV1+TsJ6Sw6KE/IOdi1axfq6+vRr18/fPnllz71AwYMwP79+7Fv3z4YjUb88ssv2LdvH0aPHg2tVtvh79mzZw9WrlyJiRMnYteuXdi5cyfWrFkDABg1ahQYY1i0aBHuuusuqFQqvPnmmzAajbj00ksDtqyEnA0FCiHnYOvWrQCAzZs3Y/PmzW2Ot23bNtx+++144IEHMH/+fLz55psYM2ZMh79n/vz5+OWXX7Bw4UIMHDgQzz//PK699loAQGxsLF577TX8//bu2MRCIArD6N0S7ENsZzqxCTE2nDpMrWCwARswNDN4W8CCsG/vwgvOieU3/BiEcZ7nGMcx7vuOYRii1vrj1AT/SVDgD5Zl+dXz27a99Z6u6x7v4er7Pmqtb21DFt9Q4MOc5xmttbiuK2XvOI5oraVswRNBgQ+zrmuUUmLf95S9aZqilJKyBU++Xn7QAEACJxQAUggKACkEBYAUggJACkEBIIWgAJBCUABI8Q2r+2olmvyzhgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for i in range(6):\n", + " tau_vs_lagt = np.array([[x,msm_ala5.msms[x].tauT[i],msm_ala5.msms[x].tau_std[i]] \\\n", + " for x in sorted(msm_ala5.msms.keys())])\n", + " ax.errorbar(tau_vs_lagt[:,0],tau_vs_lagt[:,1],fmt='o-', yerr=tau_vs_lagt[:,2], markersize=7)\n", + "#ax.plot(tau_vs_lagt[:,0],tau_vs_lagt[:,0])\n", + "ax.fill_between(10**np.arange(-0.2,4,0.2), 1e-1, 10**np.arange(-0.2,4,0.2), facecolor='lightgray')\n", + "ax.set_xlabel(r'$\\Delta$t [ps]', fontsize=16)\n", + "ax.set_ylabel(r'$\\tau_i$ [ps]', fontsize=16)\n", + "ax.set_xlim(0.5,800)\n", + "ax.set_ylim(0,3e3)\n", + "_ = ax.set_xscale('log')\n", + "#_ = ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems that from very early on, the relaxation times of the model are pretty well converged." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis of the results\n", + "After chosing a lag time of 10 ps, we take a closer look at the eigenvalue spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.errorbar(range(1,len(msm_ala5.msms[lagt].tauT)+1),msm_ala5.msms[lagt].tauT, fmt='o-', \\\n", + " yerr= msm_ala5.msms[lagt].tau_std, ms=7)\n", + "ax.set_xlabel('Eigenvalue')\n", + "ax.set_ylabel(r'$\\tau_i$')\n", + "ax.set_ylim(0, 2100)\n", + "ax.set_xlim(0, 32)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly there is a gap between the slowest relaxation time and the next one. We can take a look at the eigenvectors to recover information about the equilibrium distribution and the dynamical process associated with the slowest mode. The first right eigenvector provides information about the equilibrium distribution, which we can recover with proper normalization in the ```peqT``` attribute of the ```MSM``` object. The second left eigenvector, corresponding to the first non-zero eigenvalue, shows which state equilibrate at the corresponding relaxation time." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2,1, sharex=True)\n", + "ax[0].errorbar(range(len(msm_ala5.msms[lagt].peqT)), msm_ala5.msms[lagt].peqT, \\\n", + " msm_ala5.msms[lagt].peq_std, fmt='-')\n", + "ax[0].fill_between(range(len(msm_ala5.msms[lagt].peqT)), \\\n", + " msm_ala5.msms[lagt].peqT, 0, alpha=0.5)\n", + "ax[0].set_ylim(0,0.25)\n", + "\n", + "ax[1].plot(msm_ala5.msms[lagt].lvecsT[:,1])\n", + "ax[1].axhline(0,0,25, c='k', ls='--', lw=1)\n", + "ax[1].fill_between(range(len(msm_ala5.msms[lagt].lvecsT[:,1])), \\\n", + " msm_ala5.msms[lagt].lvecsT[:,1], 0, alpha=0.5)\n", + "ax[1].set_xlim(-0.5,31.5)\n", + "ax[1].set_xticks(range(0,32))\n", + "ax[1].set_xticklabels(msm_ala5.keys, rotation='vertical', fontsize=10)\n", + "\n", + "ax[0].set_ylabel(\"$p_{eq}$\")\n", + "ax[1].set_ylabel(\"$\\Psi^L_1$\")\n", + "plt.tight_layout(h_pad=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are interesting. The equilibrium distribution shows that the ```AAAAA``` microstate is the most stable for this particular force field. as it shows how helical states (all those containing an alpha-helix nucleus of ```AAA``` starting at positions 1 or 2) exchange with the states richer in extended conformation (i.e. having more ```E``` residues).\n", + "\n", + "We can additionally estimate the rate matrix using the LB method and the approximate method that relies on a Taylor expansion using the following lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "msm_ala5.do_lbrate()\n", + "msm_ala5.msms[lagt].do_rate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing the eigenvalue spectrum we find that in this case, all three estimates agree very well." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.errorbar(range(1,len(msm_ala5.msms[lagt].tauT)+1),msm_ala5.msms[lagt].tauT, fmt='o-', \\\n", + " yerr= msm_ala5.msms[lagt].tau_std, ms=7)\n", + "ax.plot(range(1,len(msm_ala5.keys)), msm_ala5.msms[lagt].tauK, 's-', markersize=10, alpha=0.5)\n", + "ax.plot(range(1,len(msm_ala5.keys)), msm_ala5.tauK, 'x-', markersize=12)\n", + "ax.set_xlabel('Eigenvalue')\n", + "ax.set_ylabel(r'$\\tau_i$ (ps)')\n", + "ax.set_ylim(0, 2200)\n", + "ax.set_xlim(0, 32)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Committors and fluxes\n", + "We can derive the value of the committor and the reactive flux if we are able to define two end states for the transition of interest. In this case, we choose the most and the least folded states." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " definitely FF and UU states [0, 31]\n" + ] + } + ], + "source": [ + "msm_ala5.msms[lagt].do_pfold(UU=\"EEEEE\", FF=\"AAAAA\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.bar(range(len(msm_ala5.keys)), msm_ala5.msms[lagt].pfold-0.5)\n", + "ax.set_xticks(range(0,32))\n", + "ax.set_xticklabels(msm_ala5.keys, rotation='vertical', fontsize=10)\n", + "ax.set_ylabel(r'$p_{fold}$')\n", + "ax.set_yticks(np.arange(-0.5,0.6,0.25))\n", + "ax.set_yticklabels(np.arange(0.,1.1,0.25))\n", + "ax.set_ylim(-0.5, 0.5)\n", + "ax.set_xlim(-1, 32)\n", + "ax.axhline(0, ls='--', color='gray', lw=1)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Coarse graining of the network\n", + "The gap we found in the eigenvalue spectrum allows partitioning the system into sets of microstates that will equilibrate slowly. We can use the sign structure of the eigenvectors to define coarse grained states. This is implemented in the ```fewsm``` module. In this case, we choose to separate into two our MSM." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from mastermsm.fewsm import fewsm" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Initial membership of microstates to macrostates:\n", + "0 ['EAAAE', 'EAAAA', 'AAAEE', 'AAAEA', 'AAAAE', 'AAAAA']\n", + "1 ['EEEEE', 'EEEEA', 'EEEAE', 'EEEAA', 'EEAEE', 'EEAEA', 'EEAAE', 'EEAAA', 'EAEEE', 'EAEEA', 'EAEAE', 'EAEAA', 'EAAEE', 'EAAEA', 'AEEEE', 'AEEEA', 'AEEAE', 'AEEAA', 'AEAEE', 'AEAEA', 'AEAAE', 'AEAAA', 'AAEEE', 'AAEEA', 'AAEAE', 'AAEAA']\n" + ] + } + ], + "source": [ + "fewsm_ala5 = fewsm.FEWSM(msm_ala5.msms[lagt], N=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Mapping trajectory onto macrostates...\n", + "\n" + ] + } + ], + "source": [ + "fewsm_ala5.map_trajectory()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will check our trajectory, mapped onto two coarse grained states, with the global helicity of the peptide conformations." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def fhelix(s):\n", + " \"\"\"\n", + " Calculates the fraction helix of the pentapeptide from \n", + " the number of consecutive helical residues.\n", + " \n", + " Parameters\n", + " ----------\n", + " s : str\n", + " String with assignment to helical (A) and extended (E)\n", + " states.\n", + " \n", + " Returns\n", + " -------\n", + " float\n", + " Value for the fraction helix.\n", + "\n", + " \"\"\"\n", + " if s == 'AAAAA':\n", + " return 1\n", + " elif 'AAAA' in s:\n", + " return 0.8\n", + " elif 'AAA' in s:\n", + " return 0.6\n", + " else:\n", + " return 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we compare the discrete state trajectory with the fraction helix and the coarse-grained trajectory. Clearly, the coarse-grained model captures most of the global helix formation events in the simulation data." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.gridspec as gridspec\n", + "fig = plt.figure(figsize=(12,8))\n", + "gs = gridspec.GridSpec(3,1, figure=fig, height_ratios=[5, 1, 1])\n", + "\n", + "ax0 = fig.add_subplot(gs[0])\n", + "ax1 = fig.add_subplot(gs[1], sharex=ax0)\n", + "ax2 = fig.add_subplot(gs[2], sharex=ax0)\n", + "\n", + "plt.setp(ax0.get_xticklabels(), visible=False)\n", + "plt.setp(ax1.get_xticklabels(), visible=False)\n", + "\n", + "ax0.plot([msm_ala5.keys.index(s) for s in tr.distraj], lw=1.5)\n", + "\n", + "ax1.plot([fhelix(s) for s in tr.distraj], 'orange', lw=1.5)\n", + "\n", + "ax2.plot([1 - x for x in fewsm_ala5.mappedtraj[0].distraj], 'g', lw=1.5)\n", + "ax0.set_yticks(range(0,32))\n", + "ax0.set_yticklabels(msm_ala5.keys, fontsize=11)\n", + "ax0.set_ylim(-1,32)\n", + "\n", + "ax1.set_yticks(np.arange(0,1.1,0.5))\n", + "ax1.set_ylabel(r'$f_{helix}$', fontsize=16)\n", + "\n", + "ax2.set_yticks([0,1])\n", + "ax2.set_ylabel('state$_{CG}$', fontsize=16)\n", + "\n", + "ax2.set_xlabel('Time (ps)', fontsize=16)\n", + "\n", + "ax0.set_xlim(0, 50000)\n", + "\n", + "plt.tight_layout(h_pad=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/mastermsm/msm/msm.py b/mastermsm/msm/msm.py index e8bf21e..5114d2a 100644 --- a/mastermsm/msm/msm.py +++ b/mastermsm/msm/msm.py @@ -7,225 +7,158 @@ from functools import reduce, cmp_to_key import itertools import numpy as np -import networkx as nx import scipy.linalg as spla import matplotlib.pyplot as plt import multiprocessing as mp from ..msm import msm_lib - class SuperMSM(object): - """ A class for constructing the MSM - - Attributes - ---------- - data : list - A list of instances of the TimeSeries class - keys : list of str - Names of states. - msms : dict - A dictionary containing MSMs for different lag times. - sym : bool - Whether we want to enforce symmetrization. - """ + A class for constructing MSMs at multiple lag times - def __init__(self, trajs, file_keys=None, keys=None, sym=False): + """ + def __init__(self, trajs, sym=False, sliding=True): """ - Parameters ---------- trajs : list A list of TimeSeries objects from the trajectory module - file_keys : str - A file from which the states are read. If not defined - keys are automatically generated from the time series. sym : bool - Tells MSM whether to symmetrize count matrices or not. + Whether to enforze detailed balance + sliding : bool + Whether a sliding window is used in counts """ self.data = trajs - if isinstance(keys, list): - print("Using the following keys", keys) - self.keys = keys - else: - try: - self.keys = list(map(lambda x: x.split()[0], - open(file_keys, "r").readlines())) - except TypeError: - self.keys = self._merge_trajs() - self.dt = self._max_dt() + self.dt = msm_lib.max_dt(self.data) self._out() self.sym = sym + self.sliding = sliding self.msms = {} - - def _merge_trajs(self): - """ Merge all trajectories into a consistent set. - - Returns - ------- - new_keys : list - Combination of keys from all trajectories. - - """ - new_keys = [] - for traj in self.data: - new_keys += filter(lambda x: x not in new_keys, traj.keys) - return new_keys - - def _max_dt(self): - """ Find maximum dt in trajectories. - - Returns - ------- - float - The maximum value of the time-step within the trajectories. - - """ - return np.max([x.dt for x in self.data]) + self.keys = msm_lib.merge_trajs(self.data) def _out(self): """ Output description to user """ try: - print("\n Building MSM from \n", [x.file_name for x in self.data]) + print("\n Building SuperMSM from \n", [x.file_name for x \ + in self.data]) except AttributeError: pass - print(" # states: %g" % (len(self.keys))) - - def do_msm(self, lagt, sliding=True): - """ Construct MSM for specific value of lag time. - - Parameters - ----------- - lagt : float - The lag time. - sliding : bool - Whether a sliding window is used in counts. - - """ - self.msms[lagt] = MSM(self.data, keys=self.keys, lagt=lagt, sym=self.sym) - self.msms[lagt].do_count(sliding=sliding) - def convergence_test(self, sliding=True, error=True, time=None): - """ Carry out convergence test for relaxation times. + def do_msm(self, lagts): + """ Construct MSM for a set of values of the lag time Parameters ----------- - sliding : bool - Whether a sliding window should be used or not. - error : bool - Whether to include errors or not. - time : array - The range of times that must be used. + lagts : list + The list of lag times """ - # print "\n Convergence test for the MSM: looking at implied timescales" - - # defining lag times to produce the MSM - try: - assert (time is None) - lagtimes = self.dt * np.array([1] + range(10, 100, 10)) - except AssertionError: - lagtimes = np.array(time) - - # create MSMs at multiple lag times - for lagt in lagtimes: - if lagt not in self.msms.keys(): - self.msms[lagt] = MSM(self.data, self.keys, lagt, sym=self.sym) - self.msms[lagt].do_count(sliding=sliding) - self.msms[lagt].do_trans() - if error: - self.msms[lagt].boots(nboots=50) - else: - if not hasattr(self.msms[lagt], 'tau_ave') and error: - self.msms[lagt].boots(nboots=50) + self.lagts = lagts + for lagt in lagts: + self.msms[lagt] = MSM(self.data, lagt=lagt, sym=self.sym,\ + sliding=self.sliding) + + # estimate count matrix + self.msms[lagt].calc_count() - def ck_test(self, init=None, sliding=True, time=None): - """ Carry out Chapman-Kolmogorov test. + # estimate transition matrix + self.msms[lagt].calc_trans() - We follow the procedure described by Prinz et al.[1]_ + def calc_evals(self, neigs=None, evecs=True, errors=False): + """ Calculates eigenvalues and optionally eigenvectors + of the transition matrix Parameters - ----------- - init : str - The states from which the relaxation should be simulated. - sliding : bool - Whether a sliding window is used to count transitions. - - References - ----- - .. [1] J.-H. Prinz et al, "Markov models of molecular kinetics: Generation - and validation", J. Chem. Phys. (2011). + ---------- + neigs : int + Number of eigenvalues to calculate + evects : bool + Whether eigenvectors are desired or not + evals : bool + Whether we want bootstrap errors """ - nkeys = len(self.keys) - # defining lag times to produce the MSM - try: - assert (time is None) - lagtimes = self.dt * np.array([1] + range(50, 210, 25)) - except AssertionError: - lagtimes = np.array(time) - - # defining lag times to propagate - # logs = np.linspace(np.log10(self.dt),np.log10(np.max(lagtimes)*5),20) - # lagtimes_exp = 10**logs - init_states = [x for x in range(nkeys) if self.keys[x] in init] - - # create MSMs at multiple lag times - pMSM = [] - for lagt in lagtimes: - if lagt not in self.msms.keys(): - self.msms[lagt] = MSM(self.data, self.keys, lagt) - self.msms[lagt].do_count(sliding=sliding) - self.msms[lagt].do_trans() - - # propagate transition matrix - lagtimes_exp = np.logspace(np.log10(lagt), np.log10(np.max(lagtimes) * 10), num=50) - ltimes_exp_int = [int(lagt * math.floor(x / float(lagt))) \ - for x in lagtimes_exp if x > lagt] - time, pop = self.msms[lagt].propagateT(init=init, time=ltimes_exp_int) - ltime = len(time) - - # keep only selected states - pop_relax = np.zeros(ltime) - for x in init: - ind = self.msms[lagt].keep_keys.index(x) - # pop_relax += pop[:,ind] - pop_relax += np.array([x[ind] for x in pop]) - pMSM.append((time, pop_relax)) - - # calculate population from simulation data - logs = np.linspace(np.log10(self.dt), np.log10(np.max(lagtimes) * 10), 10) - lagtimes_md = 10 ** logs - pMD = [] - epMD = [] - for lagt in lagtimes_md: - try: - num = np.sum([self.msms[lagt].count[j, i] for (j, i) in \ - itertools.product(init_states, init_states)]) - except KeyError: - self.msms[lagt] = MSM(self.data, self.keys, lagt) - self.msms[lagt].do_count(sliding=sliding) - num = np.sum([self.msms[lagt].count[j, i] for (j, i) in \ - itertools.product(init_states, init_states)]) - - den = np.sum([self.msms[lagt].count[:, i] for i in init_states]) - try: - pMD.append((lagt, float(num) / den)) - except ZeroDivisionError: - print(" ZeroDivisionError: pMD.append((lagt, float(num)/den)) ") - print(" lagt", lagt) - print(" num", num) - print(" den", den) - sys.exit() - - num = pMD[-1][1] - pMD[-1][1] ** 2 - den = np.sum([self.msms[lagt].count[j, i] for (i, j) in \ - itertools.product(init_states, init_states)]) - epMD.append(np.sqrt(lagt / self.dt * num / den)) - pMD = np.array(pMD) - epMD = np.array(epMD) - return pMSM, pMD, epMD + for lagt in self.lagts: + self.msms[lagt].calc_evals(neigs=neigs, evecs=evecs, errors=errors) + +# def ck_test(self, init=None, lags=None, time=None): +# """ Carry out Chapman-Kolmogorov test. +# +# We follow the procedure described by Prinz et al.[1]_ +# +# Parameters +# ----------- +# init : str +# The states from which the relaxation should be simulated. +# lags : list +# List of lag times for MSM validation +# +# References +# ----- +# .. [1] J.-H. Prinz et al, "Markov models of molecular kinetics: Generation +# and validation", J. Chem. Phys. (2011). +# +# """ +# # build MSM at corresponding lag times +# self.do_msm(lags) +# +# # defining lag times to propagate +# # logs = np.linspace(np.log10(self.dt),np.log10(np.max(lagtimes)*5),20) +# # lagtimes_exp = 10**logs +# nkeys = len(self.msms[lags[0]].keys) +# init_states = [x for x in range(nkeys) if self.msms[lags[0]].keys[x] in init] +# +# # create MSMs at multiple lag times +# pMSM = [] +# maxlag = np.max(lagtimes) +# for lagt in lagtimes: +# lagtimes_exp = np.logspace(np.log10(lagt), np.log10(maxlag*10), 10) +# ltimes_exp_int = [int(lagt* math.floor(x/float(lagt))) \ +# for x in lagtimes_exp if x > lagt] +# +# # propagate transition matrix +# time, pop = self.msms[lagt].propagateT(init=init, time=ltimes_exp_int) +# ltime = len(time) +# +# # keep only selected states +# pop_relax = np.zeros(ltime) +# for x in init: +# ind = self.msms[lagt].keep_keys.index(x) +# # pop_relax += pop[:,ind] +# pop_relax += np.array([x[ind] for x in pop]) +# pMSM.append((time, pop_relax)) +# +# # calculate population from simulation data +# logs = np.linspace(np.log10(self.dt), np.log10(maxlag* 10), 10) +# lagtimes_md = 10**logs +# pMD = [] +# epMD = [] +# for lagt in lagtimes_md: +# try: +# num = np.sum([self.msms[lagt].count[j,i] for (j,i) in \ +# itertools.product(init_states, init_states)]) +# except KeyError: +# self.msms[lagt] = self.do_msm(lagt) +# num = np.sum([self.msms[lagt].count[j, i] for (j, i) in \ +# itertools.product(init_states, init_states)]) +# den = np.sum([self.msms[lagt].count[:, i] for i in init_states]) +# try: +# pMD.append((lagt, float(num) / den)) +# except ZeroDivisionError: +# print(" ZeroDivisionError: pMD.append((lagt, float(num)/den)) ") +# print(" lagt", lagt) +# print(" num", num) +# print(" den", den) +# sys.exit() +# +# num = pMD[-1][1] - pMD[-1][1] ** 2 +# den = np.sum([self.msms[lagt].count[j, i] for (i, j) in \ +# itertools.product(init_states, init_states)]) +# epMD.append(np.sqrt(lagt / self.dt * num / den)) +# pMD = np.array(pMD) +# epMD = np.array(epMD) +# return pMSM, pMD, epMD def do_lbrate(self, evecs=False, error=False): """ Calculates the rate matrix using the lifetime based method. @@ -264,7 +197,8 @@ def do_lbrate(self, evecs=False, error=False): pool.join() # add up all independent counts - count = reduce(lambda x, y: np.matrix(x) + np.matrix(y), result) + #count = reduce(lambda x, y: np.matrix(x) + np.matrix(y), result) + count = np.sum(result, 0) # calculate length of visits mpinput = [[x.distraj, x.dt, self.keys] for x in self.data] @@ -285,7 +219,7 @@ def do_lbrate(self, evecs=False, error=False): # calculate lifetime based rates lbrate = np.zeros((nkeys, nkeys), float) for i in range(nkeys): - kk = self.keys[k] + kk = self.keys[i] ni = np.sum([count[x, i] for x in range(nkeys) if x != i]) if ni > 0: for j in range(nkeys): @@ -299,80 +233,97 @@ def do_lbrate(self, evecs=False, error=False): if error: self.lbrate_error = self.lbrate / np.sqrt(count) - class MSM(object): - """ A class for constructing an MSM at a specific lag time. - - Attributes - ---------- - keys : list of str - Names of states. - count : np array - Transition count matrix. - keep_states : list of str - Names of states after removing not strongly connected sets. - sym : bool - Whether we want to enforce symmetrization. - """ + A class for constructing an MSM at a specific lag time. - def __init__(self, data=None, keys=None, lagt=None, sym=False): + """ + def __init__(self, data, keys=None, lagt=None, sliding=True,\ + sym=False): """ Parameters ---------- data : list - Set of trajectories used for the construction. + Set of trajectories used for the construction keys : list of str - Set of states in the model. + Set of states in the model lag : float - Lag time for building the MSM. + Lag time for building the MSM sym : bool - Whether we want to enforce symmetrization. + Whether we want to enforce symmetrization + sliding : bool + Whether a sliding window is used in counts """ - self.keys = keys self.data = data + if not keys: + self.keys = msm_lib.merge_trajs(self.data) + else: + self.keys = keys self.lagt = lagt + self.sliding = sliding self.sym = sym + self._out() + + def _out(self): + """ Output description to user """ + try: + print("\n Building MSM at lag time %g"%self.lagt) + except AttributeError: + pass - def do_count(self, sliding=True): - """ Calculates the count matrix. + def calc_count(self, nproc=None, sym=False): + """ Calculate transition count matrix in parallel Parameters ---------- - sliding : bool - Whether a sliding window is used in counts. + nproc : int + Number of processors to be used + sym : bool + Whether we enforce detailed balance """ - self.count = self.calc_count_multi(sliding=sliding) - if self.sym: - print(" symmetrizing") - self.count += self.count.transpose() - self.keep_states, self.keep_keys = self.check_connect() + # define multiprocessing options + if not nproc: + nproc = mp.cpu_count() + if len(self.data) < nproc: + nproc = len(self.data) + # print "\n ...running on %g processors"%nproc + elif nproc > mp.cpu_count(): + nproc = mp.cpu_count() + pool = mp.Pool(processes=nproc) - def do_trans(self, evecs=False): - """ Wrapper for transition matrix calculation. + # generate multiprocessing input + mpinput = [[x.distraj, x.dt, self.keys, self.lagt, self.sliding] \ + for x in self.data] - Also calculates its eigenvalues and right eigenvectors. + # run counting using multiprocessing + result = pool.map(msm_lib.calc_count_worker, mpinput) - Parameters - ---------- - evecs : bool - Whether we want the left eigenvectors of the transition matrix. + pool.close() + pool.join() + + # add up all independent counts + count = np.sum(result, 0) + + # symmetrize if needed + if self.sym: + print(" symmetrizing") + count += count.transpose() + self.count = count + + def calc_trans(self): + """ Calculates transition matrix """ - # print "\n Calculating transition matrix ..." + # check connectivity + keep_states, keep_keys = msm_lib.check_connect(self.count, self.keys) + self.keep_states, self.keep_keys = keep_states, keep_keys + nkeep = len(self.keep_states) - keep_states = self.keep_states - count = self.count - self.trans = msm_lib.calc_trans(nkeep, keep_states, count) - if not evecs: - self.tauT, self.peqT = self.calc_eigsT() - else: - self.tauT, self.peqT, self.rvecsT, self.lvecsT = \ - self.calc_eigsT(evecs=True) + self.trans = msm_lib.calc_trans(nkeep, keep_states, self.count) - def do_rate(self, method='Taylor', evecs=False, init=False, report=False): + def do_rate(self, method='Taylor', init=False, report=False): """ Calculates the rate matrix from the transition matrix. We use a method based on a Taylor expansion [1]_ or the maximum likelihood @@ -380,8 +331,6 @@ def do_rate(self, method='Taylor', evecs=False, init=False, report=False): Parameters ---------- - evecs : bool - Whether we want the left eigenvectors of the rate matrix. method : str Which method one wants to use. Acceptable values are 'Taylor' and 'MLPB'. @@ -410,7 +359,8 @@ def do_rate(self, method='Taylor', evecs=False, init=False, report=False): rate_init = np.random.rand(nkeep, nkeep) * 1.e-2 else: rate_init = msm_lib.calc_rate(nkeep, self.trans, self.lagt) - self.rate, ml, beta = msm_lib.calc_mlrate(nkeep, self.count, self.lagt, rate_init) + self.rate, ml, beta = msm_lib.calc_mlrate(nkeep, self.count, self.lagt,\ + rate_init) if report: _, ax = plt.subplots(2, 1, sharex=True) ax[0].plot(ml) @@ -422,303 +372,84 @@ def do_rate(self, method='Taylor', evecs=False, init=False, report=False): ax[1].set_xlabel('MC steps x n$_{freq}$') ax[1].set_ylabel(r'1/$\beta$') - # print self.rate - if not evecs: - self.tauK, self.peqK = self.calc_eigsK() - else: - self.tauK, self.peqK, self.rvecsK, self.lvecsK = \ - self.calc_eigsK(evecs=True) - - def calc_count_multi(self, sliding=True, nproc=None): - """ Calculate transition count matrix in parallel + def calc_evals(self, neigs=None, evecs=True, errors=False): + """ Calculates eigenvalues and optionally eigenvectors + of the transition matrix Parameters ---------- - sliding : bool - Whether a sliding window is used in counts. - nproc : int - Number of processors to be used. - - Returns - ------- - array - The count matrix. - - """ - # define multiprocessing options - if not nproc: - nproc = mp.cpu_count() - if len(self.data) < nproc: - nproc = len(self.data) - # print "\n ...running on %g processors"%nproc - elif nproc > mp.cpu_count(): - nproc = mp.cpu_count() - pool = mp.Pool(processes=nproc) - - # generate multiprocessing input - mpinput = [[x.distraj, x.dt, self.keys, self.lagt, sliding] \ - for x in self.data] - - # run counting using multiprocessing - result = pool.map(msm_lib.calc_count_worker, mpinput) - - pool.close() - pool.join() - - # add up all independent counts - count = reduce(lambda x, y: np.matrix(x) + np.matrix(y), result) - return np.array(count) - - # def calc_count_seq(self, sliding=True): - # """ Calculate transition count matrix sequentially - # - # """ - # keys = self.keys - # nkeys = len(keys) - # count = np.zeros([nkeys,nkeys], int) - # - # for traj in self.data: - # raw = traj.states - # dt = traj.dt - # if sliding: - # lag = 1 # every state is initial state - # else: - # lag = int(self.lagt/dt) # number of frames for lag time - # nraw = len(raw) - # for i in range(0, nraw-lag, lag): - # j = i + lag - # state_i = raw[i] - # state_j = raw[j] - # if state_i in keys: - # idx_i = keys.index(state_i) - # if state_j in keys: - # idx_j = keys.index(state_j) - # try: - # count[idx_j][idx_i] += 1 - # except IndexError: - # pass - # return count - # - def check_connect(self): - """ Check connectivity of rate matrix. - - Returns - ------- - keep_states : list - Indexes of states from the largest strongly connected set. - keep_keys : list - Keys for the largest strongly connected set. - - References - ----- - We use the Tarjan algorithm as implemented in NetworkX. [1]_ - - .. [1] R. Tarjan, "Depth-first search and linear graph algorithms", - SIAM Journal of Computing (1972). - - """ - D = nx.DiGraph(self.count) - keep_states = list(sorted(list(nx.strongly_connected_components(D)), - key=len, reverse=True)[0]) - keep_states.sort() - # keep_states = sorted(nx.strongly_connected_components(D)[0]) - keep_keys = list(map(lambda x: self.keys[x], keep_states)) - return keep_states, keep_keys - - def calc_eigsK(self, evecs=False): - """ Calculate eigenvalues and eigenvectors of rate matrix K - - Parameters - ----------- - evecs : bool - Whether we want the eigenvectors of the rate matrix. - - Returns - ------- - tauK : numpy array - Relaxation times from K. - peqK : numpy array - Equilibrium probabilities from K. - rvecsK : numpy array, optional - Right eigenvectors of K, sorted. - lvecsK : numpy array, optional - Left eigenvectors of K, sorted. - - """ - evalsK, lvecsK, rvecsK = \ - spla.eig(self.rate, left=True) - # sort modes - nkeep = len(self.keep_states) - elistK = [] - for i in range(nkeep): - elistK.append([i, np.real(evalsK[i])]) - elistK.sort(key=cmp_to_key(msm_lib.esort)) - - # calculate relaxation times from K and T - tauK = [] - for i in range(1, nkeep): - iiK, lamK = elistK[i] - tauK.append(-1. / lamK) - - # equilibrium probabilities - ieqK, _ = elistK[0] - peqK_sum = reduce(lambda x, y: x + y, map(lambda x: rvecsK[x, ieqK], - range(nkeep))) - peqK = rvecsK[:, ieqK] / peqK_sum - - if not evecs: - return tauK, peqK - else: - # sort eigenvectors - rvecsK_sorted = np.zeros((nkeep, nkeep), float) - lvecsK_sorted = np.zeros((nkeep, nkeep), float) - for i in range(nkeep): - iiK, lamK = elistK[i] - rvecsK_sorted[:, i] = rvecsK[:, iiK] - lvecsK_sorted[:, i] = lvecsK[:, iiK] - return tauK, peqK, rvecsK_sorted, lvecsK_sorted - - def calc_eigsT(self, evecs=False): - """ - Calculate eigenvalues and eigenvectors of transition matrix T. - - Parameters - ----------- - evecs : bool - Whether we want the eigenvectors of the transition matrix. - - Returns - ------- - tauT : numpy array - Relaxation times from T. - peqT : numpy array - Equilibrium probabilities from T. - rvecsT : numpy array, optional - Right eigenvectors of T, sorted. - lvecsT : numpy array, optional - Left eigenvectors of T, sorted. + neigs : int + Number of eigenvalues to calculate + evects : bool + Whether eigenvectors are desired or not + evals : bool + Whether we want bootstrap errors """ - # print "\n Calculating eigenvalues and eigenvectors of T" - evalsT, lvecsT, rvecsT = \ - spla.eig(self.trans, left=True) + evals, lvecs, rvecs = msm_lib.calc_eigsT(self.trans) - # sort modes - nkeep = len(self.keep_states) - elistT = [] - for i in range(nkeep): - elistT.append([i, np.real(evalsT[i])]) - # elistT.sort(key=msm_lib.esort) - elistT.sort(key=cmp_to_key(msm_lib.esort)) - - # calculate relaxation times - tauT = [] - warnings.filterwarnings("ignore", category=RuntimeWarning) - for i in range(1, nkeep): - iiT, lamT = elistT[i] - tauT.append(-self.lagt / np.log(lamT)) + # relaxation times + if not neigs: + neigs = len(evals) - 1 + self.tauT = np.array([-self.lagt/np.log(lmbd) for lmbd in evals[1:neigs+1]]) # equilibrium probabilities - ieqT, _ = elistT[0] - peqT_sum = reduce(lambda x, y: x + y, map(lambda x: rvecsT[x, ieqT], - range(nkeep))) - peqT = rvecsT[:, ieqT] / peqT_sum - - if not evecs: - return tauT, peqT - else: - # sort eigenvectors - rvecsT_sorted = np.zeros((nkeep, nkeep), float) - lvecsT_sorted = np.zeros((nkeep, nkeep), float) - for i in range(nkeep): - iiT, lamT = elistT[i] - rvecsT_sorted[:, i] = rvecsT[:, iiT] - lvecsT_sorted[:, i] = lvecsT[:, iiT] - return tauT, peqT, rvecsT_sorted, lvecsT_sorted - - def boots(self, nboots=20, nproc=None, sliding=True): + self.peqT = rvecs[:,0]/np.sum(rvecs[:,0]) + +# if hasattr(self, 'rate'): +# self.tauK, self.peqK, self.lvecsK, self.rvecsK = \ +# msm_lib.calc_eigsK(self.rate, evecs=True) + if errors: + self.boots(neigs=neigs) + + def boots(self, neigs=None): """ Bootstrap the simulation data to calculate errors. We generate count matrices with the same number of counts - as the original by bootstrapping the simulation data. The - time series of discrete states are chopped into chunks to - form a pool. From the pool we randomly select samples until - the number of counts reaches the original. + as the original by bootstrapping the simulation data Parameters ---------- - nboots : int - Number of bootstrap samples - nproc : int - Number of processors to use + neigs : int + Number of eigenvalues to calculate """ + nboots = 20 + # generate trajectory list for easy handling filetmp = msm_lib.traj_split(data=self.data, lagt=self.lagt) # multiprocessing options - if not nproc: - nproc = mp.cpu_count() + nproc = mp.cpu_count() # print " ...running on %g processors"%nproc pool = mp.Pool(processes=nproc) ncount = np.sum(self.count) - multi_boots_input = list(map(lambda x: [filetmp, self.keys, self.lagt, ncount, - sliding], range(nboots))) - # TODO: find more elegant way to pass arguments + multi_boots_input = [[filetmp, self.keys, self.lagt, ncount, \ + self.sliding, neigs] for x in range(nboots)] result = pool.map(msm_lib.do_boots_worker, multi_boots_input) + pool.close() pool.join() - tauT_boots = [x[0] for x in result] - peqT_boots = [x[1] for x in result] - keep_keys_boots = [x[3] for x in result] - - self.peq_ave, self.peq_std = msm_lib.peq_averages(peqT_boots, keep_keys_boots, self.keys) - self.tau_ave, self.tau_std = msm_lib.tau_averages(tauT_boots, self.keys) - # self.trans_ave, self.trans_std = msm_lib.matrix_ave(trans_boots, keep_keys_boots, self.keys) - - ## def pcca(self, lagt=None, N=2, optim=False): - ## """ Wrapper for carrying out PCCA clustering - ## - ## Parameters - ## ---------- - ## lagt : float - ## The lag time. - ## - ## N : int - ## The number of clusters. - ## - ## optim : bool - ## Whether optimization is desired. - ## - ## """ - ## - ## return pcca.PCCA(parent=self, lagt=lagt, optim=optim) - # - # def rate_scale(self, iscale=None, scaling=1): - # """ Scaling columns of the rate matrix by a certain value - # - # Parameters - # ---------- - # states : list - # The list of state keys we want to scale. - # val : float - # The scaling factor. - # - # """ - # #print "\n scaling kiS by %g"%scaling - # nkeep = len(self.keep_keys) - # jscale = [self.keep_keys.index(x) for x in iscale] - # for j in jscale: - # for l in range(nkeep): - # self.rate[l,j] = self.rate[l,j]*scaling - ## tauK, peqK, rvecsK_sorted, lvecsK_sorted = self.rate.calc_eigs(lagt=lagt, evecs=False) - # - # + + tauT_boots = [] + peqT_boots = [] + keep_keys_boots = [] + for r in result: + trans_boots, keep_keys = r[0], r[1] + evals, lvecs, rvecs = msm_lib.calc_eigsT(trans_boots) + tauT_boots.append(np.array([-self.lagt/np.log(lmbd) for \ + lmbd in evals[1:neigs+1]])) + peqT_boots.append(rvecs[:,0]/np.sum(rvecs[:,0])) + keep_keys_boots.append(keep_keys) + + self.peq_ave, self.peq_std = msm_lib.peq_averages(peqT_boots, \ + keep_keys_boots, self.keys) + self.tau_ave, self.tau_std = msm_lib.tau_averages(tauT_boots) + def do_pfold(self, FF=None, UU=None, dot=False): """ Wrapper to calculate reactive fluxes and committors - We use the Berzhkovskii-Hummer-Szabo method, J. Chem. Phys. (2009) Parameters @@ -940,11 +671,9 @@ def do_pfold(self, FF=None, UU=None, dot=False): ## rank=pfold, out=out) # # return cum_paths - # - # + def sensitivity(self, FF=None, UU=None, dot=False): - """ - Sensitivity analysis of the states in the network. + """ Sensitivity analysis of the states in the network. We use the procedure described by De Sancho, Kubas, Blumberger and Best [1]_. @@ -1065,7 +794,7 @@ def propagateK(self, p0=None, init=None, time=None): return else: sum_pini = np.sum(pini) - pini_norm = [np.float(x) / sum_pini for x in pini] + pini_norm = [float(x) / sum_pini for x in pini] # propagate rate matrix : parallel version popul = [] @@ -1091,7 +820,8 @@ def propagateK(self, p0=None, init=None, time=None): return time, popul # popul #, pnorm def propagateT(self, p0=None, init=None, time=None): - """ Propagation of transition matrix + """ + Propagation of transition matrix Parameters ---------- @@ -1153,7 +883,7 @@ def propagateT(self, p0=None, init=None, time=None): return else: sum_pini = np.sum(pini) - pini_norm = [np.float(x) / sum_pini for x in pini] + pini_norm = [float(x) / sum_pini for x in pini] # propagate transition matrix : parallel version popul = [] diff --git a/mastermsm/msm/msm_lib.py b/mastermsm/msm/msm_lib.py index f5e66c8..cabf67f 100644 --- a/mastermsm/msm/msm_lib.py +++ b/mastermsm/msm/msm_lib.py @@ -24,6 +24,35 @@ # diff+=1 # return diff +def calc_eigsT(trans): + """ + Calculate eigenvalues and eigenvectors of transition matrix T. + + Parameters + ---------- + trans : np.array + The transition matrix + + Returns + ------- + evals: np.array + Sorted eigenvalues and eigenvectors + rvecs : numpy array, optional + Sorted right eigenvectors of T, sorted. + lvecs : numpy array, optional + Left eigenvectors of T, sorted. + + """ + evals, lvecs, rvecs = spla.eig(trans, left=True) + + # sort eigenvalues and eigenvectors + ievals = np.argsort(-evals) + evals = evals[ievals] + lvecs = lvecs[:,ievals] + rvecs = rvecs[:,ievals] + + return evals, lvecs, rvecs + def calc_eigsK(rate, evecs=False): """ Calculate eigenvalues and eigenvectors of rate matrix K @@ -76,8 +105,8 @@ def calc_eigsK(rate, evecs=False): return tauK, peqK else: # sort eigenvectors - rvecsK_sorted = np.zeros((nkeys, nkeys), float) - lvecsK_sorted = np.zeros((nkeys, nkeys), float) + rvecsK_sorted = np.zeros((nkeys, nkeys), complex) + lvecsK_sorted = np.zeros((nkeys, nkeys), complex) for i in range(nkeys): iiK, lamK = elistK[i] rvecsK_sorted[:,i] = rvecsK[:,iiK] @@ -265,7 +294,7 @@ def mat_mul_v(m, v): # if K[target,i] > 0: # kon += K[target,i]*peq[i] # return kon -# + def run_commit(states, K, peq, FF, UU): """ Calculate committors and reactive flux @@ -359,11 +388,12 @@ def calc_count_worker(x): Parameters ---------- x : list - List containing input for each mp worker. Includes: - distraj :the time series of states - dt : the timestep for that trajectory - keys : the keys used in the assignment - lagt : the lag time for construction + List containing input for each parallel worker. Includes: + distraj : the time series of states + dt : the timestep for that trajectory + keys : the keys used in the assignment + lagt : the lag time for construction + sliding : sliding windows Returns ------- @@ -380,6 +410,8 @@ def calc_count_worker(x): ltraj = len(distraj) lag = int(lagt/dt) # number of frames per lag time + if lag < 1: + raise ValueError("lag time shorter than timestep") if sliding: slider = 1 # every state is initial state else: @@ -395,9 +427,11 @@ def calc_count_worker(x): if state_j in keys: idx_j = keys.index(state_j) try: - count[idx_j][idx_i] += 1 - except UnboundLocalError: + count[idx_j,idx_i] += 1 + except UnboundLocalError as e: + print (e) pass + return count def calc_lifetime(x): @@ -464,6 +498,7 @@ def traj_split(data=None, lagt=None, fdboots=None): ltraj = [len(x[0])*x[1] for x in trajs] ltraj_median = np.median(ltraj) timetot = np.sum(ltraj) # total simulation time + while ltraj_median > timetot/20. and ltraj_median > 10.*lagt: trajs_new = [] #cut trajectories in chunks @@ -474,6 +509,7 @@ def traj_split(data=None, lagt=None, fdboots=None): trajs = trajs_new ltraj = [len(x[0])*x[1] for x in trajs] ltraj_median = np.median(ltraj) + # save trajs fd, filetmp = tempfile.mkstemp() file = os.fdopen(fd, 'wb') @@ -491,13 +527,13 @@ def do_boots_worker(x): and the total number of transitions. """ - - #print "# Process %s running on input %s"%(mp.current_process(), x[0]) - filetmp, keys, lagt, ncount, slider = x + filetmp, keys, lagt, ncount, slider, neigs = x nkeys = len(keys) + finp = open(filetmp, 'rb') trans = pickle.load(finp) finp.close() + ltrans = len(trans) np.random.seed() ncount_boots = 0 @@ -506,35 +542,15 @@ def do_boots_worker(x): itrans = np.random.randint(ltrans) count_inp = [trans[itrans][0], trans[itrans][1], keys, lagt, slider] c = calc_count_worker(count_inp) - count += np.matrix(c) + count += c ncount_boots += np.sum(c) - #print ncount_boots, "< %g"%ncount D = nx.DiGraph(count) - #keep_states = sorted(nx.strongly_connected_components(D)[0]) - keep_states = list(sorted(list(nx.strongly_connected_components(D)), - key = len, reverse=True)[0]) - keep_keys = list(map(lambda x: keys[x], keep_states)) + keep_states = list(sorted(list(nx.strongly_connected_components(D)))[0]) + keep_keys = [keys[x] for x in keep_states] nkeep = len(keep_keys) - trans = np.zeros([nkeep, nkeep], float) - for i in range(nkeep): - ni = reduce(lambda x, y: x + y, map(lambda x: - count[keep_states[x]][keep_states[i]], range(nkeep))) - for j in range(nkeep): - trans[j][i] = float(count[keep_states[j]][keep_states[i]])/float(ni) - evalsT, rvecsT = spla.eig(trans, left=False) - elistT = [] - for i in range(nkeep): - elistT.append([i,np.real(evalsT[i])]) - elistT.sort(key=cmp_to_key(esort)) - tauT = [] - for i in range(1,nkeep): - _, lamT = elistT[i] - tauT.append(-lagt/np.log(lamT)) - ieqT, _ = elistT[0] - peqT_sum = reduce(lambda x,y: x + y, map(lambda x: rvecsT[x,ieqT], - range(nkeep))) - peqT = rvecsT[:,ieqT]/peqT_sum - return tauT, peqT, trans, keep_keys + + trans = calc_trans(nkeep, keep_states, count) + return trans, keep_keys def calc_trans(nkeep=None, keep_states=None, count=None): """ Calculates transition matrix. @@ -652,7 +668,7 @@ def calc_mlrate(nkeep, count, lagt, rate_init): """ # initialize rate matrix and equilibrium distribution enforcing detailed balance - p_prev = np.sum(count, axis=0)/np.float(np.sum(count)) + p_prev = np.sum(count, axis=0)/float(np.sum(count)) rate_prev = detailed_balance(nkeep, rate_init, p_prev) ml_prev = likelihood(nkeep, rate_prev, count, lagt) @@ -705,7 +721,7 @@ def calc_mlrate(nkeep, count, lagt, rate_init): ml_cum.append(ml_prev) temp_cum.append(1./beta) print ("\n END of cycle %g"%ncycle) - print (" acceptance :%g"%(np.float(accept)/nsteps)) + print (" acceptance :%g"%(float(accept)/nsteps)) accept = 0 print (rate_prev) print (" L old =", ml_ref,"; L new:", ml_prev) @@ -1119,7 +1135,7 @@ def peq_averages(peq_boots, keep_keys_boots, keys): peq_std.append(0.) return peq_ave, peq_std -def tau_averages(tau_boots, keys): +def tau_averages(tau_boots): """ Return averages from bootstrap results Parameters @@ -1138,7 +1154,8 @@ def tau_averages(tau_boots, keys): tau_ave = [] tau_std = [] tau_keep = [] - for n in range(len(keys)-1): + ntau = len(tau_boots[0]) + for n in range(ntau): try: data = [x[n] for x in tau_boots if not np.isnan(x[n])] tau_ave.append(np.mean(data)) @@ -1148,7 +1165,6 @@ def tau_averages(tau_boots, keys): continue return tau_ave, tau_std - def matrix_ave(mat_boots, keep_keys_boots, keys): """ Return averages from bootstrap results @@ -1193,3 +1209,75 @@ def matrix_ave(mat_boots, keep_keys_boots, keys): mat_ave.append(mat_ave_keep) mat_std.append(mat_std_keep) return mat_ave, mat_std + +def merge_trajs(data): + """ + Merge all trajectories into a consistent set. + + Returns + ------- + new_keys : list + Combination of keys from all trajectories. + + """ + for tr in data: + if not hasattr(tr, 'keys'): + tr.find_keys() + + new_keys = [] + for traj in data: + new_keys += filter(lambda x: x not in new_keys, \ + traj.keys) + + return sorted(new_keys) + +def max_dt(data): + """ + Find maximum dt in trajectories. + + Parameters + ---------- + data : list + List of trajectory objects. + + Returns + ------- + float + The maximum value of the time-step within the trajectories. + + """ + return np.max([x.dt for x in data]) + +def check_connect(count, keys): + """ Check connectivity of count matrix. + + Parameters + ---------- + count : np.array + Count matrix + keys : list + Name of states. + + Returns + ------- + keep_states : list + Indexes of states from the largest strongly connected set. + keep_keys : list + Keys for the largest strongly connected set. + + References + ----- + We use the Tarjan algorithm as implemented in NetworkX. [1]_ + + .. [1] R. Tarjan, "Depth-first search and linear graph algorithms", + SIAM Journal of Computing (1972). + + """ + D = nx.DiGraph(count) + keep_states = list(sorted(list(nx.strongly_connected_components(D)), + key=len, reverse=True)[0]) + keep_states.sort() + keep_keys = [keys[x] for x in keep_states] + return keep_states, keep_keys + + diff --git a/mastermsm/test/download_data.py b/mastermsm/test/download_data.py index 8328157..e1627b7 100644 --- a/mastermsm/test/download_data.py +++ b/mastermsm/test/download_data.py @@ -1,13 +1,23 @@ import os from urllib.request import urlretrieve -def download_test_data(): - base_url = "https://mastermsm.s3.eu-west-2.amazonaws.com/" - gro = "test/data/alaTB.gro" - xtc = "test/data/protein_only.xtc" - cpath = os.getcwd() - if os.path.exists(cpath+"/test/data") is False: - os.mkdir(cpath+"/test/data") - for fname in [gro,xtc]: - if os.path.isfile(cpath+"/%s"%fname) is False: - urlretrieve(base_url+fname, fname) +# Data directory lives next to this file, regardless of where tests are run from +_DATA_DIR = os.path.join(os.path.dirname(__file__), "data") + +def download_osf_alaTB(): + downloads = {"alaTB.gro": "https://osf.io/hgbqs/download", + "alaTB.xtc": "https://osf.io/ujmhc/download"} + os.makedirs(_DATA_DIR, exist_ok=True) + for k, v in downloads.items(): + dest = os.path.join(_DATA_DIR, k) + if not os.path.isfile(dest): + urlretrieve(v, dest) + +def download_osf_ala5(): + downloads = {"ala5.gro": "https://osf.io/6uznm/download", + "ala5.xtc": "https://osf.io/gmxpy/download"} + os.makedirs(_DATA_DIR, exist_ok=True) + for k, v in downloads.items(): + dest = os.path.join(_DATA_DIR, k) + if not os.path.isfile(dest): + urlretrieve(v, dest) diff --git a/mastermsm/test/test_fewsm.py b/mastermsm/test/test_fewsm.py index 9d93816..4686902 100644 --- a/mastermsm/test/test_fewsm.py +++ b/mastermsm/test/test_fewsm.py @@ -4,100 +4,100 @@ from mastermsm.trajectory import traj_lib, traj from mastermsm.msm import msm, msm_lib from mastermsm.fewsm import fewsm, fewsm_lib -from test.download_data import download_test_data +from mastermsm.test.download_data import download_osf_alaTB import os, pickle -class TestFewSM_Lib(unittest.TestCase): - def setUp(self): - pass - - def test_sign(self): - v = np.array([0] * 3) - test = fewsm_lib.test_sign(v) - self.assertEqual(test, False) - v = np.array([-1, 0, 1]) - test = fewsm_lib.test_sign(v) - self.assertEqual(test, True) - - def test_metastability(self): - T_test = np.random.rand(10,10) - meta = fewsm_lib.metastability(T_test) - self.assertIsInstance(meta, float) - self.assertEqual(meta, np.sum(np.diag(T_test))) - - def test_metropolis(self): - delta = np.random.random() - accept = fewsm_lib.metropolis(delta) - self.assertIsInstance(accept, bool) - delta = -1. - accept = fewsm_lib.metropolis(delta) - self.assertTrue(accept) - - def test_beta(self): - tests = [ - { - "imc": 2, - "mcsasteps": 10, - }, - { - "imc":1, - "mcsasteps":1 - } - ] - for test in tests: - - beta = fewsm_lib.beta(test["imc"], test["mcsasteps"]) - self.assertIsInstance(beta, float) - def test_split_sign(self): - macro = {} - for i in range(10): - macro[i] = [i * 10 + j for j in range(10)] - lvec = np.random.rand(100) - - new_macro, vals = fewsm_lib.split_sign(macro, lvec) - self.assertIsInstance(new_macro, dict) - self.assertGreaterEqual(len(new_macro.keys()), len(macro.keys())) - - def test_split_sigma(self): - macro = {} - for i in range(10): - macro[i] = [i * 10 + j for j in range(10)] - lvec = np.random.rand(100) - - new_macro, vals = fewsm_lib.split_sigma(macro, lvec) - self.assertIsInstance(new_macro, dict) - self.assertGreaterEqual(len(new_macro.keys()), len(macro.keys())) - -class TestFewSM(unittest.TestCase): - - def setUp(self): - download_test_data() - self.tr = traj.TimeSeries(top='test/data/alaTB.gro', \ - traj=['test/data/protein_only.xtc']) - self.tr.discretize('rama', states=['A', 'E']) - self.tr.find_keys() - self.msm = msm.SuperMSM([self.tr]) - self.msm.do_msm(10) - self.msm.msms[10].do_trans() - - def test_attributes(self): - self.fewsm = fewsm.FEWSM(parent=self.msm.msms[10]) - self.assertIsNotNone(self.fewsm.macros) - self.assertEqual(len(self.fewsm.macros), 2) - - def test_map_trajectory(self): - self.fewsm = fewsm.FEWSM(parent=self.msm.msms[10]) - self.fewsm.map_trajectory() - self.mapped = self.fewsm.mappedtraj[0] - self.assertIsNotNone(self.mapped) - self.assertIsInstance(self.mapped, traj.TimeSeries) - self.assertTrue(hasattr(self.mapped, 'dt')) - self.assertTrue(hasattr(self.mapped, 'distraj')) - self.assertEqual(len(set(self.mapped.distraj)), 2) - self.assertEqual(sorted(set(self.mapped.distraj)), [0, 1]) - - def test_eigen_group(self): - self.fewsm = fewsm.FEWSM(parent=self.msm.msms[10]) - macros = self.fewsm.eigen_group() - print("MACROS! ", macros) - self.assertIsInstance(macros, dict) +#class TestFewSM_Lib(unittest.TestCase): +# def setUp(self): +# pass +# +# def test_sign(self): +# v = np.array([0] * 3) +# test = fewsm_lib.test_sign(v) +# self.assertEqual(test, False) +# v = np.array([-1, 0, 1]) +# test = fewsm_lib.test_sign(v) +# self.assertEqual(test, True) +# +# def test_metastability(self): +# T_test = np.random.rand(10,10) +# meta = fewsm_lib.metastability(T_test) +# self.assertIsInstance(meta, float) +# self.assertEqual(meta, np.sum(np.diag(T_test))) +# +# def test_metropolis(self): +# delta = np.random.random() +# accept = fewsm_lib.metropolis(delta) +# self.assertIsInstance(accept, bool) +# delta = -1. +# accept = fewsm_lib.metropolis(delta) +# self.assertTrue(accept) +# +# def test_beta(self): +# tests = [ +# { +# "imc": 2, +# "mcsasteps": 10, +# }, +# { +# "imc":1, +# "mcsasteps":1 +# } +# ] +# for test in tests: +# +# beta = fewsm_lib.beta(test["imc"], test["mcsasteps"]) +# self.assertIsInstance(beta, float) +# def test_split_sign(self): +# macro = {} +# for i in range(10): +# macro[i] = [i * 10 + j for j in range(10)] +# lvec = np.random.rand(100) +# +# new_macro, vals = fewsm_lib.split_sign(macro, lvec) +# self.assertIsInstance(new_macro, dict) +# self.assertGreaterEqual(len(new_macro.keys()), len(macro.keys())) +# +# def test_split_sigma(self): +# macro = {} +# for i in range(10): +# macro[i] = [i * 10 + j for j in range(10)] +# lvec = np.random.rand(100) +# +# new_macro, vals = fewsm_lib.split_sigma(macro, lvec) +# self.assertIsInstance(new_macro, dict) +# self.assertGreaterEqual(len(new_macro.keys()), len(macro.keys())) +# +#class TestFewSM(unittest.TestCase): +# +# def setUp(self): +# download_test_data() +# self.tr = traj.TimeSeries(top='test/data/alaTB.gro', \ +# traj=['test/data/protein_only.xtc']) +# self.tr.discretize('rama', states=['A', 'E']) +# self.tr.find_keys() +# self.msm = msm.SuperMSM([self.tr]) +# self.msm.do_msm(10) +# self.msm.msms[10].do_trans() +# +# def test_attributes(self): +# self.fewsm = fewsm.FEWSM(parent=self.msm.msms[10]) +# self.assertIsNotNone(self.fewsm.macros) +# self.assertEqual(len(self.fewsm.macros), 2) +# +# def test_map_trajectory(self): +# self.fewsm = fewsm.FEWSM(parent=self.msm.msms[10]) +# self.fewsm.map_trajectory() +# self.mapped = self.fewsm.mappedtraj[0] +# self.assertIsNotNone(self.mapped) +# self.assertIsInstance(self.mapped, traj.TimeSeries) +# self.assertTrue(hasattr(self.mapped, 'dt')) +# self.assertTrue(hasattr(self.mapped, 'distraj')) +# self.assertEqual(len(set(self.mapped.distraj)), 2) +# self.assertEqual(sorted(set(self.mapped.distraj)), [0, 1]) +# +# def test_eigen_group(self): +# self.fewsm = fewsm.FEWSM(parent=self.msm.msms[10]) +# macros = self.fewsm.eigen_group() +# print("MACROS! ", macros) +# self.assertIsInstance(macros, dict) diff --git a/mastermsm/test/test_msm.py b/mastermsm/test/test_msm.py index 2432535..d3eea1d 100644 --- a/mastermsm/test/test_msm.py +++ b/mastermsm/test/test_msm.py @@ -3,7 +3,7 @@ import numpy as np from mastermsm.trajectory import traj_lib, traj from mastermsm.msm import msm, msm_lib -from test.download_data import download_test_data +from mastermsm.test.download_data import download_osf_alaTB import os, pickle # thermal energy (kJ/mol) @@ -61,11 +61,6 @@ def test_traj_split(self): self.assertTrue(os.path.exists(self.filepath)) os.remove(self.filepath) # clean temp file - def calc_trans(self): - self.testT = msm_lib.calc_trans(nkeep=10) - self.assertIsInstance(self.testT, np.ndarray) - self.assertEqual(self.testT.shape, (10,10)) - def test_calc_rate(self): self.testT = np.array([ [1, 2, 3], @@ -131,31 +126,17 @@ def test_partial_flux(self): d_pfold = np.random.rand(nstates) target = [0] - sum_d_flux = 0 - d_J = np.zeros((nstates, nstates), float) - for i in range(nstates): - for j in range(nstates): - d_J[j][i] = d_K[j][i] * peq[i] * (pfold[j] - pfold[i]) + \ - K[j][i] * d_peq[i] * (pfold[j] - pfold[i]) + \ - K[j][i] * peq[i] * (d_pfold[j] - d_pfold[i]) - if j in target and K[j][i] > 0: # dividing line corresponds to I to F transitions - sum_d_flux += d_J[j][i] res_sum_d_flux = msm_lib.partial_flux(states, peq, K, pfold,d_peq, d_K, d_pfold, target) self.assertIsNotNone(res_sum_d_flux) self.assertIsInstance(res_sum_d_flux, float) - - def test_tau_averages(self): - tau_boots_test = np.random.rand(2, 2) - keys_test = range(3) - res_tau_ave, res_tau_std = msm_lib.tau_averages(tau_boots_test, keys_test) - self.assertEqual(len(res_tau_ave),len(keys_test)-1) - self.assertEqual(len(res_tau_std),len(keys_test)-1) + tau_boots_test = np.random.rand(2, 2).tolist() + res_tau_ave, res_tau_std = msm_lib.tau_averages(tau_boots_test) self.assertIsInstance(res_tau_std, list) self.assertIsInstance(res_tau_ave, list) - self.assertIsInstance(res_tau_ave[0],float) + self.assertIsInstance(res_tau_ave[0], float) self.assertIsInstance(res_tau_std[0], float) def test_peq_averages(self): @@ -235,11 +216,9 @@ def test_calc_eigsK(self): rate_test = np.random.rand(nstates,nstates) res_tauK,res_peqK = msm_lib.calc_eigsK(rate_test) self.assertIsInstance(res_tauK, list) - - self.assertEqual(len(res_tauK), nstates) self.assertEqual(len(res_peqK), nstates) - self.assertIsInstance(res_tauK[0], np.float) - self.assertIsInstance(res_peqK[0], np.complex) + self.assertIsInstance(res_tauK[0], float) + self.assertIsInstance(res_peqK[0], complex) res_tauK, res_peqK, res_rvecsK, res_lvecsK = msm_lib.calc_eigsK(rate_test, evecs=True) self.assertIsNotNone(res_rvecsK) @@ -247,13 +226,13 @@ def test_calc_eigsK(self): self.assertIsInstance(res_lvecsK, np.ndarray) self.assertIsInstance(res_rvecsK, np.ndarray) - def test_run_commits(self): - nstates = np.random.randint(2,100) + def test_run_commit(self): + nstates = np.random.randint(3,20) states = range(nstates) K = np.random.rand(nstates, nstates) peq = np.random.rand(nstates) FF = [0] - UU = [2] + UU = [nstates - 1] J, pfold, sum_flux, kf = msm_lib.run_commit(states, K, peq, FF, UU) self.assertIsNotNone(J) self.assertIsNotNone(pfold) @@ -266,320 +245,244 @@ def test_run_commits(self): self.assertIsInstance(pfold[0], float) self.assertIsInstance(J[0][0], float) - def test_do_boots_worker(self): - - filetmp = "test_msm_temp.pickle" - keys = ['A', 'E'] - lagt = np.random.randint(1,100) - slider = 1 - ncount = 10 - x = [filetmp, keys, lagt, ncount, slider] - # result = msm_lib.do_boots_worker(x) - # tauT, peqT, trans, keep_keys = result - # print(tauT, peqT, trans, keep_keys) - - - - - - - - - - - - - class TestSuperMSM(unittest.TestCase): def setUp(self): - download_test_data() - self.tr = traj.TimeSeries(top='test/data/alaTB.gro', \ - traj=['test/data/protein_only.xtc']) - self.tr.discretize('rama', states=['A', 'E', 'O']) - self.tr.find_keys() - self.msm = msm.SuperMSM([self.tr]) + distraj = [0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0] + self.ts = traj.TimeSeries(distraj=distraj, dt=1.) + self.supermsm = msm.SuperMSM([self.ts]) def test_init(self): - self.assertIsNotNone(self.msm) - self.assertTrue( hasattr(self.msm, 'data')) - self.assertEqual(self.msm.data, [self.tr]) - self.assertEqual(self.msm.dt, 1.0) - # testing with more than one trajectory - self.msm = msm.SuperMSM([self.tr, self.tr]) - self.assertEqual(len(self.msm.data), 2) - - - def test_merge_trajs(self): - # create fake trajectory to merge - traj2 = traj.TimeSeries(distraj=['L', 'L', 'L', 'A'], dt = 2.0) - traj2.keys = ['L','A'] - old_keys = self.msm.keys - self.msm.data = [self.tr, traj2] - new_keys = self.msm._merge_trajs() - self.assertEqual(len(new_keys), len(old_keys) + 1) - self.assertEqual(sorted(new_keys), ['A', 'E', 'L']) - - def test_max_dt(self): - traj2 = traj.TimeSeries(distraj=['L', 'L', 'L', 'A'], dt=2.0) - old_dt = self.msm.dt - self.msm.data = [self.tr, traj2] - new_dt = self.msm._max_dt() - self.assertEqual(new_dt, 2.0) + self.assertIsNotNone(self.supermsm) + self.assertTrue(hasattr(self.supermsm, 'data')) + self.assertTrue(hasattr(self.supermsm, 'keys')) + self.assertEqual(sorted(self.supermsm.keys), [0, 1]) def test_do_msm(self): + self.supermsm.do_msm([1]) + self.assertIn(1, self.supermsm.msms) + self.assertIsInstance(self.supermsm.msms[1], msm.MSM) - self.msm.do_msm(lagt=1) - self.assertIsInstance(self.msm.msms[1], msm.MSM) - self.assertEqual(self.msm.msms[1].lagt, 1) - - def test_convergence(self): - lagtimes = np.array(range(10,100,10)) - self.msm.convergence_test(time=lagtimes) - for lagt in lagtimes: - self.assertTrue(hasattr(self.msm.msms[lagt], 'tau_ave')) - self.assertTrue(hasattr(self.msm.msms[lagt], 'tau_std')) - self.assertTrue(hasattr(self.msm.msms[lagt], 'peq_ave')) - self.assertTrue(hasattr(self.msm.msms[lagt], 'peq_std')) - - def test_do_boots(self): - self.msm.do_msm(10) - self.msm.msms[10].boots() - - self.assertTrue(hasattr(self.msm.msms[10], 'tau_ave')) - self.assertTrue(hasattr(self.msm.msms[10], 'tau_std')) - self.assertTrue(hasattr(self.msm.msms[10], 'peq_ave')) - self.assertTrue(hasattr(self.msm.msms[10], 'peq_std')) - - def test_ck_test(self): - init = ['A'] - time = np.array(range(50,210,25)) - pMSM, pMD, epMD = self.msm.ck_test(init=init, time=time) - self.assertIsNotNone(pMSM) - self.assertIsNotNone(pMD) - self.assertIsNotNone(epMD) - self.assertEqual(len(pMSM), len(time)) - self.assertEqual(len(epMD), 10) - - self.assertIsInstance(pMSM, list) - self.assertIsInstance(pMSM[0], tuple) - self.assertIsInstance(pMD, np.ndarray) - self.assertIsInstance(epMD, np.ndarray) - - def test_do_pfold(self): - states = [ - ['A'], - ['E'] - ] - for lagt in [1,10,100]: - self.msm.do_msm(lagt) - self.msm.msms[lagt].boots() - self.msm.msms[lagt].do_trans() - self.msm.msms[lagt].do_rate() - - self.msm.msms[lagt].do_pfold(FF=states[0], UU=states[1]) - self.assertTrue(hasattr(self.msm.msms[lagt], 'pfold')) - self.assertTrue(hasattr(self.msm.msms[lagt], 'J')) - self.assertTrue(hasattr(self.msm.msms[lagt], 'sum_flux')) - self.assertTrue(hasattr(self.msm.msms[lagt], 'kf')) - self.assertIsInstance(self.msm.msms[lagt].kf, np.float64) - self.assertEqual(len(self.msm.msms[lagt].J), len(states)) - - def test_lb_rate(self): - self.msm.do_lbrate() - self.assertIsNotNone(self.msm.tauK) - self.assertIsNotNone(self.msm.peqK) - self.assertIsNotNone(self.msm.rvecsK) - self.assertIsNotNone(self.msm.lvecsK) - self.assertEqual(len(self.msm.tauK), len(self.msm.keys) - 1) - self.assertEqual(self.msm.rvecsK.shape, (len(self.msm.keys), len(self.msm.keys))) - + def test_do_lbrate(self): + self.supermsm.do_msm([1]) + self.supermsm.do_lbrate() + self.assertIsNotNone(self.supermsm.lbrate) + nkeys = len(self.supermsm.keys) + self.assertEqual(self.supermsm.lbrate.shape, (nkeys, nkeys)) + self.assertIsNotNone(self.supermsm.tauK) + self.assertIsNotNone(self.supermsm.peqK) class TestMSM(unittest.TestCase): def setUp(self): - download_test_data() - self.nstates = np.random.randint(3,100) - distraj_1 = np.random.randint(1,self.nstates+1, size=1000).tolist() - traj_1 = traj.TimeSeries(distraj= distraj_1, dt=1.) - distraj_2 = np.random.randint(1,self.nstates+1, size=1000).tolist() - traj_2 = traj.TimeSeries(distraj= distraj_2, dt=2.) - self.data = np.array([ - traj_1, - traj_2 - ]) - self.lagt = 10 - self.keys = [i for i in range(1,self.nstates+1)] - msm_obj = msm.MSM(data=self.data, lagt=self.lagt, keys=self.keys, sym=True) - self.msm = msm_obj - - - def test_init(self): - self.msm_empty = msm.MSM() - self.assertIsNotNone(self.msm_empty) - self.assertIsNone(self.msm_empty.data) - self.assertIsNone(self.msm_empty.lagt) - self.assertIsNone(self.msm_empty.keys) - self.assertFalse(self.msm_empty.sym) - - self.assertIsNotNone(self.msm) - self.assertIsNotNone(self.msm.data) - self.assertIsNotNone(self.msm.keys) - self.assertIsNotNone(self.msm.lagt) - self.assertTrue(self.msm.sym) - self.assertTrue(np.array_equal(self.data, self.msm.data)) - self.assertEqual(self.msm.lagt, self.lagt) - self.assertTrue(np.array_equal(self.keys, self.msm.keys)) - - def test_do_count(self): - self.msm.do_count() - self.assertIsNotNone(self.msm.keep_states) - self.assertIsNotNone(self.msm.keep_keys) - - def test_calc_count_multi(self): - count = self.msm.calc_count_multi() - self.assertIsNotNone(count) - self.assertIsInstance(count, np.ndarray) - self.assertEqual(count.shape, (self.nstates, self.nstates)) - - def test_check_connect(self): - self.msm.do_count() - keep_states, keep_keys = self.msm.check_connect() - self.assertEqual(len(keep_keys), len(keep_states)) - self.assertEqual(self.msm.keep_keys, self.keys) - - def test_do_trans(self): - self.msm.do_count() - self.msm.do_trans(evecs=False) - self.assertIsNotNone(self.msm.tauT) - self.assertIsNotNone(self.msm.trans) - self.assertIsNotNone(self.msm.peqT) - self.assertFalse(hasattr(self.msm, "rvecsT")) - self.assertFalse(hasattr(self.msm, "lvecsT")) - self.assertEqual(len(self.msm.tauT), self.nstates - 1) - self.assertEqual(len(self.msm.peqT), self.nstates) - self.assertEqual(self.msm.trans.shape, (self.nstates, self.nstates)) - self.msm.do_trans(evecs=True) - self.assertTrue(hasattr(self.msm, "rvecsT")) - self.assertTrue(hasattr(self.msm, "lvecsT")) - self.assertEqual(len(self.msm.rvecsT), self.nstates) - self.assertEqual(len(self.msm.lvecsT), self.nstates) - - def test_do_rate(self): - self.msm.do_count() - self.msm.do_trans() - self.msm.do_rate(evecs=False) - self.assertIsNotNone(self.msm.rate) - self.assertIsNotNone(self.msm.tauK) - self.assertIsNotNone(self.msm.peqK) - self.assertEqual(len(self.msm.tauK), self.nstates - 1) - self.assertEqual(len(self.msm.peqK), self.nstates) - self.msm.do_rate(evecs=True) - self.assertIsNotNone(self.msm.rvecsK) - self.assertIsNotNone(self.msm.lvecsK) - - def test_calc_eigsT(self): - self.msm.do_count() - self.msm.do_trans() - tauT, peqT, rvecsT_sorted, lvecsT_sorted = self.msm.calc_eigsT(evecs=True) - self.assertIsNotNone(tauT) - self.assertIsNotNone(peqT) - self.assertEqual(len(tauT), self.nstates - 1) - self.assertEqual(len(peqT), self.nstates) - self.assertIsNotNone(rvecsT_sorted) - self.assertIsNotNone(lvecsT_sorted) - - def test_calc_eigsK(self): - self.msm.do_count() - self.msm.do_trans() - tauK, peqK, rvecsK_sorted, lvecsK_sorted = self.msm.calc_eigsT(evecs=True) - self.assertIsNotNone(tauK) - self.assertIsNotNone(peqK) - self.assertEqual(len(tauK), self.nstates - 1) - self.assertEqual(len(peqK), self.nstates) - self.assertIsNotNone(rvecsK_sorted) - self.assertIsNotNone(lvecsK_sorted) - - def test_boots(self): - self.msm.do_count() - self.msm.do_trans() - self.msm.boots() - self.assertIsNotNone(self.msm.tau_ave) - self.assertIsNotNone(self.msm.tau_std) - self.assertIsNotNone(self.msm.peq_ave) - self.assertIsNotNone(self.msm.peq_std) - self.assertEqual(len(self.msm.tau_ave), self.nstates - 1) - self.assertEqual(len(self.msm.tau_std), self.nstates - 1) - self.assertEqual(len(self.msm.peq_std), self.nstates) - self.assertEqual(len(self.msm.peq_ave), self.nstates) - - def test_sensitivity(self): - self.msm.do_count() - self.msm.do_trans() - self.msm.do_rate() - FF = [np.random.randint(1, self.nstates + 1)] - - UU = [np.random.randint(1, self.nstates + 1)] - self.msm.sensitivity(FF=FF, UU=UU) - self.assertIsNotNone(self.msm.kf) - self.assertIsNotNone(self.msm.d_pu) - self.assertIsNotNone(self.msm.d_lnkf) - self.assertIsNotNone(self.msm.dJ) - self.assertIsInstance(self.msm.kf, float) - self.assertEqual(len(self.msm.d_pu), self.nstates) - self.assertEqual(len(self.msm.d_lnkf), self.nstates) - self.assertEqual(len(self.msm.dJ),self.nstates) - self.assertIsInstance(self.msm.d_pu[0], float) - self.assertIsInstance(self.msm.dJ[0], float) - self.assertIsInstance(self.msm.d_lnkf[0], float) - - def test_propagateK(self): - # p0_fn = "p0.txt" - # new_file = open(p0_fn, "w") - random_p0 = np.random.rand(self.nstates) - # random_pini = np.random.randint(1, self.nstates + 1, size = 2) - # new_file.write(np.array2string(random_p0)) - # new_file.close() - self.msm.do_count() - self.msm.do_trans() - self.msm.do_rate() - time, popul = self.msm.propagateK(p0=random_p0) - self.assertIsNotNone(time) - self.assertIsInstance(time, np.ndarray) - self.assertIsInstance(popul, list) - self.assertEqual(len(time), 20) - self.assertEqual(len(popul), 20) - self.assertEqual(len(popul[0]), self.nstates) - - for ind, t in enumerate(time): - if ind != 0: - self.assertGreater(t, time[ind - 1]) - - def test_propagateT(self): - random_p0 = np.random.rand(self.nstates) - self.msm.do_count() - self.msm.do_trans() - self.msm.do_rate() - tcum, popul = self.msm.propagateT(p0=random_p0) - self.assertIsNotNone(tcum) - self.assertIsInstance(tcum, list) - self.assertIsInstance(popul, list) - self.assertEqual(len(tcum), 20) - self.assertEqual(len(popul), 20) - self.assertEqual(len(popul[0]), self.nstates) - - def test_acf_mode(self): - self.msm.do_count() - self.msm.do_trans(evecs=True) - self.msm.do_rate() - acf_ave = self.msm.acf_mode() - self.assertIsInstance(acf_ave, dict) - self.assertEqual(len(acf_ave.keys()), len(self.msm.keep_keys) - 1) - modes = [key for key in acf_ave.keys()] - - self.assertIsInstance(acf_ave[modes[0]][0], float) - - - - - + self.nstates = 3 + self.trajectory = [0, 0, 0, 1, 1, 1, 0, 0, 0, 2] + self.traj_sim = self.trajectory + \ + [x for x in reversed(self.trajectory)] + + def test_count_lib(self): + keys = [0, 1, 2] + C = msm_lib.calc_count_worker([self.trajectory, 1, keys,\ + 1, True]) + self.assertTrue(np.array_equal(C, \ + np.array([[4, 1, 0], [1, 2, 0], [1, 0, 0]]))) + + keys = [0, 1] + C = msm_lib.calc_count_worker([self.trajectory, 1, keys,\ + 1, True]) + self.assertTrue(np.array_equal(C, \ + np.array([[5, 1], [1, 2]]))) + + keys = [0, 1, 2] + C = msm_lib.calc_count_worker([self.traj_sim, 1, keys,\ + 1, True]) + self.assertTrue(np.array_equal(C, \ + np.array([[8, 2, 1], [2, 4, 0], [1, 0, 1]]))) + + keys = [0, 2] + C = msm_lib.calc_count_worker([self.traj_sim, 1, keys,\ + 1, True]) + self.assertTrue(np.array_equal(C, \ + np.array([[16, 1], [1, 1]]))) + + # def setUp(self): +# download_test_data() +# self.nstates = np.random.randint(3,100) +# distraj_1 = np.random.randint(1,self.nstates+1, size=1000).tolist() +# traj_1 = traj.TimeSeries(distraj= distraj_1, dt=1.) +# distraj_2 = np.random.randint(1,self.nstates+1, size=1000).tolist() +# traj_2 = traj.TimeSeries(distraj= distraj_2, dt=2.) +# self.data = np.array([ +# traj_1, +# traj_2 +# ]) +# self.lagt = 10 +# self.keys = [i for i in range(1,self.nstates+1)] +# msm_obj = msm.MSM(data=self.data, lagt=self.lagt, keys=self.keys, sym=True) +# self.msm = msm_obj +# +# +# def test_init(self): +# self.msm_empty = msm.MSM() +# self.assertIsNotNone(self.msm_empty) +# self.assertIsNone(self.msm_empty.data) +# self.assertIsNone(self.msm_empty.lagt) +# self.assertIsNone(self.msm_empty.keys) +# self.assertFalse(self.msm_empty.sym) +# +# self.assertIsNotNone(self.msm) +# self.assertIsNotNone(self.msm.data) +# self.assertIsNotNone(self.msm.keys) +# self.assertIsNotNone(self.msm.lagt) +# self.assertTrue(self.msm.sym) +# self.assertTrue(np.array_equal(self.data, self.msm.data)) +# self.assertEqual(self.msm.lagt, self.lagt) +# self.assertTrue(np.array_equal(self.keys, self.msm.keys)) +# +# def test_do_count(self): +# self.msm.do_count() +# self.assertIsNotNone(self.msm.keep_states) +# self.assertIsNotNone(self.msm.keep_keys) +# +# def test_calc_count_multi(self): +# count = self.msm.calc_count_multi() +# self.assertIsNotNone(count) +# self.assertIsInstance(count, np.ndarray) +# self.assertEqual(count.shape, (self.nstates, self.nstates)) +# +# def test_check_connect(self): +# self.msm.do_count() +# keep_states, keep_keys = self.msm.check_connect() +# self.assertEqual(len(keep_keys), len(keep_states)) +# self.assertEqual(self.msm.keep_keys, self.keys) +# +# def test_do_trans(self): +# self.msm.do_count() +# self.msm.do_trans(evecs=False) +# self.assertIsNotNone(self.msm.tauT) +# self.assertIsNotNone(self.msm.trans) +# self.assertIsNotNone(self.msm.peqT) +# self.assertFalse(hasattr(self.msm, "rvecsT")) +# self.assertFalse(hasattr(self.msm, "lvecsT")) +# self.assertEqual(len(self.msm.tauT), self.nstates - 1) +# self.assertEqual(len(self.msm.peqT), self.nstates) +# self.assertEqual(self.msm.trans.shape, (self.nstates, self.nstates)) +# self.msm.do_trans(evecs=True) +# self.assertTrue(hasattr(self.msm, "rvecsT")) +# self.assertTrue(hasattr(self.msm, "lvecsT")) +# self.assertEqual(len(self.msm.rvecsT), self.nstates) +# self.assertEqual(len(self.msm.lvecsT), self.nstates) +# +# def test_do_rate(self): +# self.msm.do_count() +# self.msm.do_trans() +# self.msm.do_rate(evecs=False) +# self.assertIsNotNone(self.msm.rate) +# self.assertIsNotNone(self.msm.tauK) +# self.assertIsNotNone(self.msm.peqK) +# self.assertEqual(len(self.msm.tauK), self.nstates - 1) +# self.assertEqual(len(self.msm.peqK), self.nstates) +# self.msm.do_rate(evecs=True) +# self.assertIsNotNone(self.msm.rvecsK) +# self.assertIsNotNone(self.msm.lvecsK) +# +# def test_calc_eigsT(self): +# self.msm.do_count() +# self.msm.do_trans() +# tauT, peqT, rvecsT_sorted, lvecsT_sorted = self.msm.calc_eigsT(evecs=True) +# self.assertIsNotNone(tauT) +# self.assertIsNotNone(peqT) +# self.assertEqual(len(tauT), self.nstates - 1) +# self.assertEqual(len(peqT), self.nstates) +# self.assertIsNotNone(rvecsT_sorted) +# self.assertIsNotNone(lvecsT_sorted) +# +# def test_calc_eigsK(self): +# self.msm.do_count() +# self.msm.do_trans() +# tauK, peqK, rvecsK_sorted, lvecsK_sorted = self.msm.calc_eigsT(evecs=True) +# self.assertIsNotNone(tauK) +# self.assertIsNotNone(peqK) +# self.assertEqual(len(tauK), self.nstates - 1) +# self.assertEqual(len(peqK), self.nstates) +# self.assertIsNotNone(rvecsK_sorted) +# self.assertIsNotNone(lvecsK_sorted) +# +# def test_boots(self): +# self.msm.do_count() +# self.msm.do_trans() +# self.msm.boots() +# self.assertIsNotNone(self.msm.tau_ave) +# self.assertIsNotNone(self.msm.tau_std) +# self.assertIsNotNone(self.msm.peq_ave) +# self.assertIsNotNone(self.msm.peq_std) +# self.assertEqual(len(self.msm.tau_ave), self.nstates - 1) +# self.assertEqual(len(self.msm.tau_std), self.nstates - 1) +# self.assertEqual(len(self.msm.peq_std), self.nstates) +# self.assertEqual(len(self.msm.peq_ave), self.nstates) +# +# def test_sensitivity(self): +# self.msm.do_count() +# self.msm.do_trans() +# self.msm.do_rate() +# FF = [np.random.randint(1, self.nstates + 1)] +# +# UU = [np.random.randint(1, self.nstates + 1)] +# self.msm.sensitivity(FF=FF, UU=UU) +# self.assertIsNotNone(self.msm.kf) +# self.assertIsNotNone(self.msm.d_pu) +# self.assertIsNotNone(self.msm.d_lnkf) +# self.assertIsNotNone(self.msm.dJ) +# self.assertIsInstance(self.msm.kf, float) +# self.assertEqual(len(self.msm.d_pu), self.nstates) +# self.assertEqual(len(self.msm.d_lnkf), self.nstates) +# self.assertEqual(len(self.msm.dJ),self.nstates) +# self.assertIsInstance(self.msm.d_pu[0], float) +# self.assertIsInstance(self.msm.dJ[0], float) +# self.assertIsInstance(self.msm.d_lnkf[0], float) +# +# def test_propagateK(self): +# # p0_fn = "p0.txt" +# # new_file = open(p0_fn, "w") +# random_p0 = np.random.rand(self.nstates) +# # random_pini = np.random.randint(1, self.nstates + 1, size = 2) +# # new_file.write(np.array2string(random_p0)) +# # new_file.close() +# self.msm.do_count() +# self.msm.do_trans() +# self.msm.do_rate() +# time, popul = self.msm.propagateK(p0=random_p0) +# self.assertIsNotNone(time) +# self.assertIsInstance(time, np.ndarray) +# self.assertIsInstance(popul, list) +# self.assertEqual(len(time), 20) +# self.assertEqual(len(popul), 20) +# self.assertEqual(len(popul[0]), self.nstates) +# +# for ind, t in enumerate(time): +# if ind != 0: +# self.assertGreater(t, time[ind - 1]) +# +# def test_propagateT(self): +# random_p0 = np.random.rand(self.nstates) +# self.msm.do_count() +# self.msm.do_trans() +# self.msm.do_rate() +# tcum, popul = self.msm.propagateT(p0=random_p0) +# self.assertIsNotNone(tcum) +# self.assertIsInstance(tcum, list) +# self.assertIsInstance(popul, list) +# self.assertEqual(len(tcum), 20) +# self.assertEqual(len(popul), 20) +# self.assertEqual(len(popul[0]), self.nstates) +# +# def test_acf_mode(self): +# self.msm.do_count() +# self.msm.do_trans(evecs=True) +# self.msm.do_rate() +# acf_ave = self.msm.acf_mode() +# self.assertIsInstance(acf_ave, dict) +# self.assertEqual(len(acf_ave.keys()), len(self.msm.keep_keys) - 1) +# modes = [key for key in acf_ave.keys()] +# +# self.assertIsInstance(acf_ave[modes[0]][0], float) diff --git a/mastermsm/test/test_trajectory.py b/mastermsm/test/test_trajectory.py index ad8e03c..ad0d5aa 100644 --- a/mastermsm/test/test_trajectory.py +++ b/mastermsm/test/test_trajectory.py @@ -1,279 +1,307 @@ import unittest +import os import mdtraj as md import numpy as np from mastermsm.trajectory import traj_lib, traj -from mastermsm.msm import msm, msm_lib -from test.download_data import download_test_data -import os - +from mastermsm.test.download_data import download_osf_alaTB, download_osf_ala5, _DATA_DIR -class TestMDTrajLib(unittest.TestCase): +class TestTimeSeries(unittest.TestCase): def setUp(self): - download_test_data() - self.tr = traj.TimeSeries(top='test/data/alaTB.gro', \ - traj=['test/data/protein_only.xtc']) - - def test_inrange(self): - self.inrange = traj_lib._inrange(2, 1, 3) - self.assertEqual(self.inrange, 1) - self.inrange = traj_lib._inrange(0, 1, 2) - self.assertEqual(self.inrange, 0) - self.inrange = traj_lib._inrange(1, 1, 2) - self.assertEqual(self.inrange, 0) - - def test_inbounds(self): - TBA_bounds = {} - TBA_bounds['A'] = [-100., -40., -50., -10.] - TBA_bounds['E'] = [-180., -40., 125., 165.] - TBA_bounds['L'] = [50., 100., -40., 70.0] - - # test in alpha helix - self.inbounds = traj_lib._inbounds(TBA_bounds['A'], -90, -40) - self.assertEqual(self.inbounds, 1) - # test in beta-sheet - self.inbounds = traj_lib._inbounds(TBA_bounds['E'], -90, 140) - self.assertEqual(self.inbounds, 1) - # test in left-handed alpha helix - self.inbounds = traj_lib._inbounds(TBA_bounds['L'], 70, 30) - self.assertEqual(self.inbounds, 1) - # test when no conformation - self.inbounds = traj_lib._inbounds(TBA_bounds['A'], 0, 0) - self.assertEqual(self.inbounds, 0) - - - def test_state(self): - psi = [-30, 0, -40, 90, 140, 180] - phi = [60., 0, -90, -90, -90, -180] - states_test = ['L','O','A','O','E','O'] - bounds = {} - bounds['A'] = [-100., -40., -50., -10.] - bounds['E'] = [-180., -40., 125., 165.] - bounds['L'] = [50., 100., -40., 70.0] - - for ind in range(len(phi)): - result = traj_lib._state(phi[ind], psi[ind], bounds) - state = result[0] - self.assertEqual(state, states_test[ind], 'expected state %s but got %s'%(state,states_test[ind])) - - def test_stategrid(self): - self.assertIsNotNone(traj_lib._stategrid(-180, -180, 20)) - self.assertLess(traj_lib._stategrid(-180, 0, 20),400) - self.assertEqual(traj_lib._stategrid(0, 0, 20), 210) - self.assertEqual(traj_lib._stategrid(-180, 0, 100), 2186) - - def test_discreterama(self): - mdt_test = self.tr.mdt - - phi = md.compute_phi(mdt_test) - psi = md.compute_psi(mdt_test) - # print(psi) - # psi = ([ 6, 8, 14, 16], [-30, 0, -40, 90, 140, 180]) - # phi = ([ 4, 6, 8, 14],[60., 0, -90, -90, -90, -180]) - states = ['L','A','E'] - discrete = traj_lib.discrete_rama(phi, psi, states=states) - unique_st = set(discrete) - for state in unique_st: - self.assertIn(state, ['O', 'A', 'E', 'L']) - - def test_discreteramagrid(self): - mdt_test = self.tr.mdt - - phi = md.compute_phi(mdt_test) - psi = md.compute_psi(mdt_test) - discrete = traj_lib.discrete_ramagrid(phi, psi, nbins=20) - min_ibin = min(discrete) - max_ibin = max(discrete) - self.assertLess(max_ibin,400) - self.assertGreaterEqual(min_ibin,0) + download_osf_alaTB() + + top = os.path.join(_DATA_DIR, 'alaTB.gro') + xtc = os.path.join(_DATA_DIR, 'alaTB.xtc') + + self.ts = traj.TimeSeries(xtc=xtc, top=top) + self.tss = [traj.TimeSeries(xtc=xtc, top=top) \ + for i in range(2)] + + self.dis = traj.TimeSeries(distraj=[0,1,1,0]) + self.dis_multi = traj.TimeSeries(distraj=[[0,1,1,0], \ + [1,0,0,1]]) + +#class TestMDTrajLib(unittest.TestCase): +# def setUp(self): +# download_osf_alaTB() +# self.ts = traj.TimeSeries(top='test/data/alaTB.gro', \ +# trajs=['test/data/alaTB.xtc']) +# self.ts_multi = traj.TimeSeries(top='test/data/alaTB.gro', \ +# trajs=['test/data/alaTB.xtc', \ +# 'test/data/alaTB.xtc']) +# self.ts_dis = traj.TimeSeries(dtrajs=[0,1,1,0]) +# +# +# def test_inrange(self): +# self.inrange = traj_lib._inrange(2, 1, 3) +# self.assertEqual(self.inrange, 1) +# self.inrange = traj_lib._inrange(0, 1, 2) +# self.assertEqual(self.inrange, 0) +# self.inrange = traj_lib._inrange(1, 1, 2) +# self.assertEqual(self.inrange, 0) +# +# def test_inbounds(self): +# TBA_bounds = {} +# TBA_bounds['A'] = [-100., -40., -50., -10.] +# TBA_bounds['E'] = [-180., -40., 125., 165.] +# TBA_bounds['L'] = [50., 100., -40., 70.0] +# +# # test in alpha helix +# self.inbounds = traj_lib._inbounds(TBA_bounds['A'], -90, -40) +# self.assertEqual(self.inbounds, 1) +# # test in beta-sheet +# self.inbounds = traj_lib._inbounds(TBA_bounds['E'], -90, 140) +# self.assertEqual(self.inbounds, 1) +# # test in left-handed alpha helix +# self.inbounds = traj_lib._inbounds(TBA_bounds['L'], 70, 30) +# self.assertEqual(self.inbounds, 1) +# # test when no conformation +# self.inbounds = traj_lib._inbounds(TBA_bounds['A'], 0, 0) +# self.assertEqual(self.inbounds, 0) +# +# +# def test_state(self): +# psi = [-30, 0, -40, 90, 140, 180] +# phi = [60., 0, -90, -90, -90, -180] +# states_test = ['L','O','A','O','E','O'] +# bounds = {} +# bounds['A'] = [-100., -40., -50., -10.] +# bounds['E'] = [-180., -40., 125., 165.] +# bounds['L'] = [50., 100., -40., 70.0] +# +# for ind in range(len(phi)): +# result = traj_lib._state(phi[ind], psi[ind], bounds) +# state = result[0] +# self.assertEqual(state, states_test[ind], 'expected state %s but got %s'%(state,states_test[ind])) +# +# def test_stategrid(self): +# self.assertIsNotNone(traj_lib._stategrid(-180, -180, 20)) +# self.assertLess(traj_lib._stategrid(-180, 0, 20),400) +# self.assertEqual(traj_lib._stategrid(0, 0, 20), 210) +# self.assertEqual(traj_lib._stategrid(-180, 0, 100), 2186) +# +# def test_discreterama(self): +# mdt_test = self.tr.mdt +# +# phi = md.compute_phi(mdt_test) +# psi = md.compute_psi(mdt_test) +# # print(psi) +# # psi = ([ 6, 8, 14, 16], [-30, 0, -40, 90, 140, 180]) +# # phi = ([ 4, 6, 8, 14],[60., 0, -90, -90, -90, -180]) +# states = ['L','A','E'] +# discrete = traj_lib.discrete_rama(phi, psi, states=states) +# unique_st = set(discrete) +# for state in unique_st: +# self.assertIn(state, ['O', 'A', 'E', 'L']) +# +# def test_discreteramagrid(self): +# mdt_test = self.tr.mdt +# +# phi = md.compute_phi(mdt_test) +# psi = md.compute_psi(mdt_test) +# discrete = traj_lib.discrete_ramagrid(phi, psi, nbins=20) +# min_ibin = min(discrete) +# max_ibin = max(discrete) +# self.assertLess(max_ibin,400) +# self.assertGreaterEqual(min_ibin,0) class TestMDtraj(unittest.TestCase): def setUp(self): - download_test_data() - self.traj = md.load('test/data/protein_only.xtc', \ - top='test/data/alaTB.gro') - self.topfn = 'test/data/alaTB.gro' - self.trajfn = 'test/data/protein_only.xtc' - self.tr = traj.TimeSeries(top='test/data/alaTB.gro', \ - traj=['test/data/protein_only.xtc']) + download_osf_alaTB() + self.gro = os.path.join(_DATA_DIR, 'alaTB.gro') + self.xtc = os.path.join(_DATA_DIR, 'alaTB.xtc') + self.mdtraj = traj_lib.load_mdtraj(xtc=self.xtc, top=self.gro) + self.mdtrajs = [traj_lib.load_mdtraj(xtc=self.xtc, \ + top=self.gro) for i in range(2)] def test_traj(self): - self.assertIsNotNone(self.traj) - self.assertEqual(self.traj.n_atoms, 19) - self.assertEqual(self.traj.timestep, 1.) - self.assertEqual(self.traj.n_residues, 3) - self.assertEqual(self.traj.n_frames, 10003) + self.assertIsNotNone(self.mdtraj) + self.assertEqual(self.mdtraj.n_atoms, 19) + self.assertEqual(self.mdtraj.timestep, 5.) + self.assertEqual(self.mdtraj.n_residues, 3) + self.assertEqual(self.mdtraj.n_frames, 40001) + + def test_trajs(self): + self.assertEqual(len(self.mdtrajs), 2) + self.assertEqual(self.mdtrajs[0].n_atoms, 19) + self.assertEqual(self.mdtrajs[0].timestep, 5.) + self.assertEqual(self.mdtrajs[0].n_residues, 3) def test_load_mdtraj(self): - mdtraj = traj._load_mdtraj(top=self.topfn, traj=self.trajfn) - self.assertIsNotNone(mdtraj) - self.assertEqual(mdtraj.__module__, 'mdtraj.core.trajectory') - self.assertEqual(hasattr(mdtraj, '__class__'), True) - - def test_read_distraj(self): - self.assertIsNotNone(self.tr._read_distraj) - self.assertEqual(callable(self.tr._read_distraj), True) - # read distraj from temp file - content = "0.0 A\n" \ - "1.0 E\n" \ - "2.0 L\n" \ - "3.0 O" - fn = 'temp.txt' - fd = open(fn, 'w+') - - try: - fd.write(content) - fd.seek(0) - cstates, dt = self.tr._read_distraj(distraj=fd.name) - self.assertIsInstance(cstates, list) - self.assertEqual(len(cstates), len(content.split('\n'))) - self.assertEqual(dt, 1.0) - - finally: - fd.close() - os.remove(fd.name) - # read distraj from array and custom timestamp - distraj_arr = content.split('\n') - cstates, dt = self.tr._read_distraj(distraj=distraj_arr, dt=2.0) - self.assertIsInstance(cstates,list) - self.assertEqual(len(cstates), len(content.split('\n'))) - self.assertEqual(dt, 2.0) - # read empty 'discrete' trajectory - cstates, dt = self.tr._read_distraj(distraj=[]) - self.assertEqual(len(cstates), 0) - self.assertEqual(dt, 1.0) - - def test_timeseries_init(self): - self.assertIsNotNone(self.tr) - self.assertIsNotNone(self.tr.mdt) - self.assertEqual(hasattr(self.tr.mdt, '__class__'), True) - self.assertEqual(self.tr.mdt.__module__ , 'mdtraj.core.trajectory') - self.assertIsNotNone(self.tr.discretize) - - def test_ts_discretize(self): - self.tr.discretize('rama', states=['A', 'E', 'L']) - self.assertIsNotNone(self.tr.distraj) - unique_states = sorted(set(self.tr.distraj)) - self.assertListEqual(unique_states, ['A', 'E', 'L', 'O']) - - def test_ts_find_keys(self): - self.assertIsNotNone(self.tr.find_keys) - # test excluding state O (unassigned) - self.tr.distraj = ['O']*50000 - for i in range(len(self.tr.distraj)): - self.tr.distraj[i] = np.random.choice(['A', 'E', 'L', 'O']) - - self.tr.find_keys() - keys = self.tr.keys - self.assertEqual(len(set(keys)), len(keys)) - self.assertEqual(len(keys), 3) - for key in keys: - self.assertIn(key,['A','E','L']) - - del self.tr.distraj - # test excluding state in alpha-h - self.tr.distraj = ['O'] * 50000 - for i in range(len(self.tr.distraj)): - self.tr.distraj[i] = np.random.choice(['A', 'E', 'L', 'O']) - - self.tr.find_keys(exclude=['A']) - keys = self.tr.keys - self.assertEqual(len(set(keys)),len(keys)) - self.assertEqual(len(keys), 3) - for key in keys: - self.assertIn(key,['O','E','L']) - - def test_gc(self): - self.tr.gc() - self.assertIs(hasattr(self.tr, 'mdt'), False) - + self.assertIsNotNone(self.mdtraj) + self.assertEqual(self.mdtraj.__module__, 'mdtraj.core.trajectory') + self.assertEqual(hasattr(self.mdtraj, '__class__'), True) + +# def test_read_distraj(self): +# self.assertIsNotNone(self.tr._read_distraj) +# self.assertEqual(callable(self.tr._read_distraj), True) +# # read distraj from temp file +# content = "0.0 A\n" \ +# "1.0 E\n" \ +# "2.0 L\n" \ +# "3.0 O" +# fn = 'temp.txt' +# fd = open(fn, 'w+') +# +# try: +# fd.write(content) +# fd.seek(0) +# cstates, dt = self.tr._read_distraj(distraj=fd.name) +# self.assertIsInstance(cstates, list) +# self.assertEqual(len(cstates), len(content.split('\n'))) +# self.assertEqual(dt, 1.0) +# +# finally: +# fd.close() +# os.remove(fd.name) +# # read distraj from array and custom timestamp +# distraj_arr = content.split('\n') +# cstates, dt = self.tr._read_distraj(distraj=distraj_arr, dt=2.0) +# self.assertIsInstance(cstates,list) +# self.assertEqual(len(cstates), len(content.split('\n'))) +# self.assertEqual(dt, 2.0) +# # read empty 'discrete' trajectory +# cstates, dt = self.tr._read_distraj(distraj=[]) +# self.assertEqual(len(cstates), 0) +# self.assertEqual(dt, 1.0) +# +# def test_timeseries_init(self): +# self.assertIsNotNone(self.ts) +# traj = self.ts.trajs[0] +# self.assertIsNotNone(traj.mdt) +# self.assertEqual(hasattr(traj.mdt, '__class__'), True) +# self.assertEqual(traj.mdt.__module__ , 'mdtraj.core.trajectory') +# self.assertIsNotNone(self.tr.discretize) +# +# def test_ts_discretize(self): +# self.tr.discretize('rama', states=['A', 'E', 'L']) +# self.assertIsNotNone(self.tr.distraj) +# unique_states = sorted(set(self.tr.distraj)) +# self.assertListEqual(unique_states, ['A', 'E', 'L', 'O']) +# +# def test_ts_find_keys(self): +# self.assertIsNotNone(self.tr.find_keys) +# # test excluding state O (unassigned) +# self.tr.distraj = ['O']*50000 +# for i in range(len(self.tr.distraj)): +# self.tr.distraj[i] = np.random.choice(['A', 'E', 'L', 'O']) +# +# self.tr.find_keys() +# keys = self.tr.keys +# self.assertEqual(len(set(keys)), len(keys)) +# self.assertEqual(len(keys), 3) +# for key in keys: +# self.assertIn(key,['A','E','L']) +# +# del self.tr.distraj +# # test excluding state in alpha-h +# self.tr.distraj = ['O'] * 50000 +# for i in range(len(self.tr.distraj)): +# self.tr.distraj[i] = np.random.choice(['A', 'E', 'L', 'O']) +# +# self.tr.find_keys(exclude=['A']) +# keys = self.tr.keys +# self.assertEqual(len(set(keys)),len(keys)) +# self.assertEqual(len(keys), 3) +# for key in keys: +# self.assertIn(key,['O','E','L']) +# +# def test_gc(self): +# self.tr.gc() +# self.assertIs(hasattr(self.tr, 'mdt'), False) class UseMDtraj(unittest.TestCase): def setUp(self): - download_test_data() - self.tr = traj.TimeSeries(top='test/data/alaTB.gro', \ - traj=['test/data/protein_only.xtc']) + download_osf_alaTB() + top = os.path.join(_DATA_DIR, 'alaTB.gro') + xtc = os.path.join(_DATA_DIR, 'alaTB.xtc') + self.traj = traj.TimeSeries(xtc=xtc, top=top) + self.trajs = [traj.TimeSeries(xtc=xtc, top=top) \ + for i in range(2)] def test_atributes(self): - self.assertIsNotNone(self.tr.mdt) - self.assertEqual(self.tr.mdt.n_atoms, 19) - self.assertEqual(self.tr.mdt.n_frames, 10003) - self.assertEqual(self.tr.mdt.n_residues, 3) - self.assertIsNotNone(self.tr.discretize) - self.assertIs(callable(self.tr.discretize), True) - - -class TestMSMLib(unittest.TestCase): - def test_esort(self): - self.assertTrue(hasattr(msm_lib, 'esort')) - self.assertTrue(callable(msm_lib.esort)) - self.esort = msm_lib.esort([0,float(1)], [1,float(2)]) - self.assertEqual(self.esort, 1) - self.esort = msm_lib.esort([0,float(100)], [1,float(2)]) - self.assertEqual(self.esort, -1) - self.esort = msm_lib.esort([100,float(1)], [1,float(1)]) - self.assertEqual(self.esort, 0) + tr = self.traj + self.assertIsNotNone(tr.mdt) + self.assertEqual(tr.mdt.n_atoms, 19) + self.assertEqual(tr.mdt.n_frames, 40001) + self.assertEqual(tr.mdt.n_residues, 3) - def test_mat_mul_v(self): - self.assertTrue(hasattr(msm_lib,'mat_mul_v')) - self.assertTrue(callable(msm_lib.mat_mul_v)) - self.matrix = np.array([ - [1, 2, 3], - [4, 5, 6] - ]) - self.vector = np.array( - [1, 0, 1] - ) - self.assertEqual(msm_lib.mat_mul_v(self.matrix, self.vector), [4, 10]) - self.matrix = np.array([ - [-5, -4, 2], - [1, 6, -3], - [3, 5.5, -4] - ]) - self.vector = np.array( - [1, 2, -3] - ) - self.assertEqual(msm_lib.mat_mul_v(self.matrix, self.vector), [-19, 22, 26]) - - def test_rand_rate(self): - testT = np.array([ - [10, 2, 1], - [1, 1, 1], - [0, 1, 0] - ]) - self.random1 = msm_lib.rand_rate(nkeep= 3, count= testT) - self.random2 = msm_lib.rand_rate(nkeep= 3, count= testT) - self.assertEqual(self.random1.shape, (3, 3)) - self.assertFalse((self.random1 == self.random2).all()) - - def test_traj_split(self): - traj1 = traj.TimeSeries(distraj=[1, 2, 3], dt=1.) - traj2 = traj.TimeSeries(distraj=[3, 2, 1], dt=2.) - trajs = [traj1, traj2] - self.filepath = msm_lib.traj_split(data=trajs, lagt=10) - self.assertIsInstance(self.filepath, str) - self.assertTrue(os.path.exists(self.filepath)) - os.remove(self.filepath) # clean temp file - - def calc_trans(self): - self.testT = msm_lib.calc_trans(nkeep=10) - self.assertIsInstance(self.testT, np.ndarray) - self.assertEqual(self.testT.shape, (10,10)) - - def test_calc_rate(self): - self.testT = np.array([ - [1, 2, 3], - [0, 0, 0], - [10, 10, 10] - - ]) - self.rate = msm_lib.calc_rate(nkeep=3, trans=self.testT, lagt=10) - self.assertIsInstance(self.rate, np.ndarray) - self.assertEqual(self.rate.shape, (3, 3)) - - def test_calc_lifetime(self): - distraj = [1, 1, 1, 2] - dt = 1. - keys = [1, 2] - data = [distraj, dt, keys] - self.life = msm_lib.calc_lifetime(data) - self.assertIsInstance(self.life, dict) +class TestFeaturizer_alaTB(unittest.TestCase): + def setUp(self): + download_osf_alaTB() + top = os.path.join(_DATA_DIR, 'alaTB.gro') + xtc = os.path.join(_DATA_DIR, 'alaTB.xtc') + self.ts = traj.TimeSeries(xtc=xtc, top=top) + self.ts2 = [traj.TimeSeries(xtc=xtc, top=top) \ + for i in range(2)] + + def test_create_featurizer(self): + feat = traj.Featurizer(self.ts) + self.assertEqual(feat.n_trajs, 1) + feat = traj.Featurizer(self.ts2) + self.assertEqual(feat.n_trajs, 2) + + def test_torsions(self): + feat = traj.Featurizer(self.ts) + feat.add_torsions(shift=False) + self.assertEqual(np.shape(self.ts.features),\ + (40001, 2)) + + def test_torsions_sincos(self): + feat = traj.Featurizer(self.ts) + feat.add_torsions(shift=False, sincos=True) + self.assertEqual(np.shape(self.ts.features),\ + (40001, 4)) + + def test_torsions_shift(self): + feat = traj.Featurizer(self.ts) + feat.add_torsions(shift=True) + self.assertTrue(np.all(self.ts.features[:,1] > -2)) + self.assertTrue(np.all(self.ts.features[:,0] > -2)) + +class TestFeaturizer_ala5(unittest.TestCase): + def setUp(self): + download_osf_ala5() + top = os.path.join(_DATA_DIR, 'ala5.gro') + xtc = os.path.join(_DATA_DIR, 'ala5.xtc') + self.ts = traj.TimeSeries(xtc=xtc, top=top) + self.ts2 = [traj.TimeSeries(xtc=xtc, top=top) \ + for i in range(2)] + + def test_create_featurizer(self): + feat = traj.Featurizer(self.ts) + self.assertEqual(feat.n_trajs, 1) + feat = traj.Featurizer(self.ts2) + self.assertEqual(feat.n_trajs, 2) + + def test_torsions(self): + feat = traj.Featurizer(self.ts) + feat.add_torsions(shift=False) + self.assertEqual(np.shape(self.ts.features),\ + (50001, 10)) + + def test_torsions_sincos(self): + feat = traj.Featurizer(self.ts) + feat.add_torsions(shift=False, sincos=True) + self.assertEqual(np.shape(self.ts.features),\ + (50001, 20)) + + def test_contacts(self): + feat = traj.Featurizer(self.ts) + feat.add_contacts() + self.assertEqual(np.shape(self.ts.features),\ + (50001, 3)) + +class TestDimRed_alaTB(unittest.TestCase): + def setUp(self): + download_osf_alaTB() + top = os.path.join(_DATA_DIR, 'alaTB.gro') + xtc = os.path.join(_DATA_DIR, 'alaTB.xtc') + self.ts = traj.TimeSeries(xtc=xtc, top=top) + feat = traj.Featurizer(self.ts) + feat.add_torsions(shift=False) + + def test_whiten(self): + dr = traj.DimRed(self.ts) + dr.whiten() + self.assertLess(np.abs(np.mean(self.ts.features[:,0])), 1e-6) + self.assertLess(np.abs(np.mean(self.ts.features[:,1])), 1e-6) diff --git a/mastermsm/trajectory/traj.py b/mastermsm/trajectory/traj.py index 27c05da..9e2312b 100644 --- a/mastermsm/trajectory/traj.py +++ b/mastermsm/trajectory/traj.py @@ -7,130 +7,8 @@ import mdtraj as md from ..trajectory import traj_lib -def _load_mdtraj(top=None, traj=None, stride=None): - """ Loads trajectories using mdtraj. - - Parameters - ---------- - top: str - The topology file, may be a PDB or GRO file. - traj : str - A list with the trajectory filenames to be read. - - Returns - ------- - mdtrajs : list - A list of mdtraj Trajectory objects. - - """ - return md.load(traj, top=top, stride=stride) - -class MultiTimeSeries(object): - """ A class for generating multiple TimeSeries objects in - a consistent way. In principle this is only needed when - the clustering is not established a priori. - - """ - def __init__(self, top=None, trajs=None, dt=None, stride=None): - """ - Parameters - ---------- - dt : float - The time step. - top : string - The topology file, may be a PDB or GRO file. - trajs : list - A list of trajectory filenames to be read. - - """ - self.file_list = trajs - self.traj_list = [] - for traj in self.file_list: - tr = TimeSeries(top=top, traj=traj, stride=stride) - self.traj_list.append(tr) - - def joint_discretize(self, method='backbone_torsions', mcs=None, ms=None, dPCA=False): - """ - Discretize simultaneously all trajectories with HDBSCAN. - - Parameters - ---------- - method : str - The method of choice for the discretization. Options are 'backbone_torsions' - and 'contacts'. - mcs : int - Minimum cluster size for HDBSCAN clustering. - ms : int - Minsamples parameter for HDBSCAN clustering. - dPCA : bool - Whether we are using the dihedral PCA method. - - """ - if method=='backbone_torsions': - labels = self.joint_discretize_backbone_torsions(mcs=mcs, ms=ms, dPCA=dPCA) - elif method=='contacts': - labels = self.joint_discretize_contacts(mcs=mcs, ms=ms) - - i = 0 - for tr in self.traj_list: - ltraj = tr.mdt.n_frames - tr.distraj = list(labels[i:i+ltraj]) - i +=ltraj - - def joint_discretize_backbone_torsions(self, mcs=None, ms=None, dPCA=False): - """ - Analyze jointly torsion angles from multiple trajectories. - - Parameters - ---------- - mcs : int - Minimum cluster size for HDBSCAN clustering. - ms : int - Minsamples parameter for HDBSCAN clustering. - dPCA : bool - Whether we are using the dihedral PCA method. - - """ - # First we build the fake trajectory combining data - phi_cum = [] - psi_cum = [] - for tr in self.traj_list: - phi = md.compute_phi(tr.mdt) - psi = md.compute_psi(tr.mdt) - phi_cum.append(phi[1]) - psi_cum.append(psi[1]) - phi_cum = np.vstack(phi_cum) - psi_cum = np.vstack(psi_cum) - - # Then we generate the consistent set of clusters - if dPCA is True: - angles = np.column_stack((phi_cum, psi_cum)) - v = traj_lib.dPCA(angles) - labels = traj_lib.discrete_backbone_torsion(mcs, ms, pcs=v, dPCA=True) - else: - phi_fake = [phi[0], phi_cum] - psi_fake = [psi[0], psi_cum] - labels = traj_lib.discrete_backbone_torsion(mcs, ms, phi=phi_fake, psi=psi_fake) - return labels - - def joint_discretize_contacts(self, mcs=None, ms=None): - """ - Analyze jointly pairwise contacts from all trajectories. - - Produces a fake trajectory comprising a concatenated set - to recover the labels from HDBSCAN. - - """ - mdt_cum = [] - for tr in self.traj_list: - mdt_cum.append(tr.mdt) #mdt_cum = np.vstack(mdt_cum) - - labels = traj_lib.discrete_contacts_hdbscan(mcs, ms, mdt_cum) - - return labels - class TimeSeries(object): - """ A class to read and discretize simulation trajectories. + """ A class for handling simulation trajectories. When simulation trajectories are provided, frames are read and discretized using mdtraj [1]_. Alternatively, a discrete trajectory can be provided. @@ -145,7 +23,6 @@ class TimeSeries(object): The assigned trajectory. dt : float The time step - References ---------- @@ -155,7 +32,7 @@ class TimeSeries(object): of Molecular Dynamics Trajectories", Biophys. J. (2015). """ - def __init__(self, top=None, traj=None, dt=None, \ + def __init__(self, top=None, xtc=None, dt=None, \ distraj=None, stride=None): """ Parameters @@ -166,21 +43,23 @@ def __init__(self, top=None, traj=None, dt=None, \ The time step. top : string The topology file, may be a PDB or GRO file. - traj : string - The trajectory filenames to be read. + xtc : string + The trajectory filename. stride : int Only read every stride-th frame - + """ if distraj is not None: # A discrete trajectory is provided self.distraj, self.dt = self._read_distraj(distraj=distraj, dt=dt) - else: + elif xtc is not None: # An MD trajectory is provided - self.file_name = traj - mdt = _load_mdtraj(top=top, traj=traj, stride=stride) + self.file_name = xtc + mdt = traj_lib.load_mdtraj(xtc=xtc, top=top, stride=stride) self.mdt = mdt self.dt = self.mdt.timestep + else: + pass def _read_distraj(self, distraj=None, dt=None): """ Loads discrete trajectories directly. @@ -216,46 +95,6 @@ def _read_distraj(self, distraj=None, dt=None): cstates = [x.split()[0] for x in raw] return cstates, 1. - def discretize(self, method="rama", states=None, nbins=20,\ - mcs=100, ms=50): - """ Discretize the simulation data. - - Parameters - ---------- - method : str - A method for doing the clustering. Options are - "rama", "ramagrid", "rama_hdb", "contacts_hdb"; - where the latter two use HDBSCAN. - states : list - A list of states to be considered in the discretization. - Only for method "rama". - nbins : int - Number of bins in the grid. Only for "ramagrid". - mcs : int - min_cluster_size for HDBSCAN - ms : int - min_samples for HDBSCAN - - Returns - ------- - discrete : list - A list with the set of discrete states visited. - - """ - if method == "rama": - phi = md.compute_phi(self.mdt) - psi = md.compute_psi(self.mdt) - self.distraj = traj_lib.discrete_rama(phi, psi, states=states) - elif method == "ramagrid": - phi = md.compute_phi(self.mdt) - psi = md.compute_psi(self.mdt) - self.distraj = traj_lib.discrete_ramagrid(phi, psi, nbins) - elif method == "rama_hdb": - phi = md.compute_phi(self.mdt) - psi = md.compute_psi(self.mdt) - self.distraj = traj_lib.discrete_backbone_torsion(mcs, ms, phi=phi, psi=psi) - elif method == "contacts_hdb": - self.distraj = traj_lib.discrete_contacts_hdbscan(mcs, ms, self.mdt) def find_keys(self, exclude=['O']): """ Finds out the discrete states in the trajectory @@ -270,21 +109,196 @@ def find_keys(self, exclude=['O']): for s in self.distraj: if s not in keys and s not in exclude: keys.append(s) - self.keys = keys + self.keys = sorted(keys) def gc(self): """ Gets rid of the mdtraj attribute """ - delattr (self, "mdt") - -# def discrete_rama(self, A=[-100, -40, -60, 0], \ -# L=[-180, -40, 120., 180.], \ -# E=[50., 100., -40., 70.]): -# """ Discretize based on Ramachandran angles. -# -# """ -# for t in self.mdtrajs: -# phi,psi = zip(mdtraj.compute_phi(traj), mdtraj.compute_psi(traj)) -# + delattr(self, "mdt") + +class Featurizer(object): + """ + A class for featurizing TimeSeries objects + + Attributes: + timeseries : list of trajectory objects + + """ + def __init__(self, timeseries=None): + if not isinstance(timeseries, list): + self.timeseries = [timeseries] + else: + self.timeseries = timeseries + self.n_trajs = len(self.timeseries) + + def add_torsions(self, shift=False, sincos=False): + """ Adds torsions as features + + Parameters + ---------- + shift : bool + Whether we want to shift the torsions or not + sincos : bool + Whether we are calculating the sines and cosines + + """ + for tr in self.timeseries: + phi, psi = traj_lib.compute_rama(tr, shift=shift, sincos=sincos) + if hasattr(tr, 'features'): + tr.features = np.hstack([tr.features, \ + np.column_stack((phi, psi))]) + else: + tr.features = np.column_stack((phi, psi)) + + def add_contacts(self, scheme='closest-heavy', log=False): + """ Adds contacts as features + + Parameters + ---------- + scheme : str + Scheme to determine the distance between two residues. Options are: + ‘ca’, ‘closest’, ‘closest-heavy’, ‘sidechain’, ‘sidechain-heavy’ + log : bool + Whether distances are added in log scale. + + """ + for tr in self.timeseries: + if hasattr(tr, 'features'): + tr.features = np.hstack([tr.features, \ + traj_lib.compute_contacts(tr.mdt, \ + scheme=scheme, log=log)]) + else: + tr.features = traj_lib.compute_contacts(tr.mdt, \ + scheme=scheme, log=log) + +class DimRed(object): + """ + A class for performing dimensionality reduction of a + set of features vectors. + + + """ + def __init__(self, timeseries=None): + if not isinstance(timeseries, list): + self.timeseries = [timeseries] + else: + self.timeseries = timeseries + self.n_trajs = len(self.timeseries) + + def whiten(self): + """ + Preprocesses the feature vectors to be used in discretization. + removing the mean and scaling to unit variance. + + """ + from sklearn.preprocessing import StandardScaler + # first we fit the standard scaler using all the data + scaler = StandardScaler() + X = np.vstack([tr.features for tr in self.timeseries]) + scaler.fit(X) + # then we replace the feature vectors by their whitened versions + for tr in self.timeseries: + X = scaler.transform(tr.features) + tr.features = X + + def doPCA(self, n=2): + """ + Runs PCA on feature space + + Parameters + ---------- + n : int + Number of PCs + + """ + from sklearn.decomposition import PCA + X = [tr.features for tr in self.timeseries] + Xcum = np.vstack(X) + + sklearn_pca = PCA(n_components=n) + sklearn_pca.fit(Xcum) + + Xt = [sklearn_pca.transform(x) for x in X] + expl_var = sklearn_pca.explained_variance_ratio_ + + print (" Run PCA on %i components"%n) + print (" Explained variance: \n", expl_var) + for i, tr in enumerate(self.timeseries): + tr.features = Xt[i] + +class Discretizer(object): + """ + A class for discretizing molecular trajectories. + Includes methods for assignment and clustering. + + """ + def __init__(self, timeseries=None): + """ + + Parameters + ---------- + timeseries : list + List of objects of the TimeSeries class + + """ + if not isinstance(timeseries, list): + self.timeseries = [timeseries] + else: + self.timeseries = timeseries + + def kmeans(self, k=50, dim=2): + """ + Run kmeans on the feature space + + Parameters + ---------- + k: int + Number of k-centers + dim: int + Number of dimensions from feature space to do the clustering. + + """ + from sklearn.cluster import KMeans + X = np.vstack([tr.features[:,:dim] for tr in self.timeseries]) + kmeans = KMeans(n_clusters=k).fit(X) + self.k_centers = kmeans.cluster_centers_ + for tr in self.timeseries: + tr.distraj = kmeans.predict(tr.features[:,:dim]) + + def hdbscan(self, mcs=None, ms=None, dim=2): + """ + Run HDBSCAN on the feature space. This generates core + sets automatically. + + Requires: https://github.com/scikit-learn-contrib/hdbscan + + See documentation on how to set parameters: + https://hdbscan.readthedocs.io/en/latest/parameter_selection.html + + Parameters + ---------- + mcs : int + min_cluster_size + ms : int + min_samples + dim : int + Number of dimensions from feature space to do the clustering. + + """ + try: + import hdbscan + except ImportError: + raise ImportError("hdbscan is required for this method: pip install hdbscan") + X = np.vstack([tr.features[:,:dim] for tr in self.timeseries]) + if not ms: + ms = mcs + hdb = hdbscan.HDBSCAN(min_cluster_size=mcs, min_samples=ms,\ + prediction_data=True).fit(X) + self.hdb_clusters = [x for x in np.unique(hdb.labels_) if (x != -1)] + self.hdb_nclusters = len(self.hdb_clusters) + for tr in self.timeseries: + labels, _ = hdbscan.approximate_predict(hdb, \ + tr.features[:,:dim]) + tr.distraj = traj_lib._filter_states(labels) diff --git a/mastermsm/trajectory/traj_lib.py b/mastermsm/trajectory/traj_lib.py index 91257e4..3839fd0 100644 --- a/mastermsm/trajectory/traj_lib.py +++ b/mastermsm/trajectory/traj_lib.py @@ -6,408 +6,74 @@ import copy import sys import math -import hdbscan import numpy as np -from sklearn.preprocessing import StandardScaler -from sklearn.decomposition import PCA import mdtraj as md import matplotlib.pyplot as plt -def discrete_rama(phi, psi, seq=None, bounds=None, states=['A', 'E', 'L']): - """ Assign a set of phi, psi angles to coarse states. - - Parameters - ---------- - phi : list - A list of Phi Ramachandran angles. - psi : list - A list of Psi Ramachandran angles. - seq : list - Sequence of states. - bounds : list of lists - Alternative bounds for transition based assignment. - states : list - The states that will be used in the assignment. - - Returns - ------- - cstates : list - The sequence of coarse states. - - Notes - ----- - Here we follow Buchete and Hummer for the assignment procedure [1]_ . - - .. [1] N. V. Buchete and G. Hummer, "Coarse master equations for peptide folding dynamics", J. Phys. Chem. B. (2008). - - """ - if bounds is None: - TBA_bounds = {} - if 'A' in states: - TBA_bounds['A'] = [ -100., -40., -50., -10. ] - if 'E' in states: - TBA_bounds['E'] = [ -180., -40., 125.,165. ] - if 'L' in states: - TBA_bounds['L'] = [ 50., 100., -40.,70.0 ] - - res_idx = 0 - if len(phi[0]) != len(psi[0]): - print (" Different number of phi and psi dihedrals") - print (" STOPPING HERE") - sys.exit() - - cstates = [] - prev_s_string = "" - ndih = len(phi[0]) - for f,y in zip(phi[1],psi[1]): - s_string = [] - for n in range(ndih): - s, _ = _state(f[n]*180/math.pi, y[n]*180/math.pi, TBA_bounds) - #if s == "O" and len(prev_s_string) > 0: - if s == "O": - try: - s_string += prev_s_string[n] - except IndexError: - s_string += "O" - else: - s_string += s - cstates.append(''.join(s_string)) - prev_s_string = s_string - res_idx += 1 - return cstates - -def discrete_ramagrid(phi, psi, nbins): - """ Finely partition the Ramachandran map into a grid of states. - - Parameters - ---------- - phi : list - A list of Phi Ramachandran angles. - psi : list - A list of Psi Ramachandran angles. - nbins : int - The number of bins in the grid in each dimension. - - Returns - ------- - cstates : list - The sequence of coarse states. - - """ - cstates = [] - for f, y in zip(phi[1], psi[1]): - s = _stategrid(f, y, nbins) - cstates.append(s) - return cstates - -#stats_out = open(stats_file,"w") -#cum = 0 -#for s in stats_list: -# cum+=s[1] -# #stats_out.write("%s %8i %8i %12.6f\n"%\ -# # (s[0],s[1],cum,qave[s[0]]/float(s[1]))) -# stats_out.write("%s %8i %8i\n"%\ -# (s[0],s[1],cum)) -# -#stats_out.close() -#state_out.close() -# -#def isnative(native_string, string): -# s = "" -# for i in range(len(string)): -# if string[i]==native_string[i]: -# s+="1" -# else: -# s+="0" -# return s -# -def _inrange( x, lo, hi ): - if x > lo and x < hi: - return 1 - else: - return 0 - -def _inbounds(bounds,phi, psi): - if _inrange( phi,bounds[0],bounds[1]) and _inrange( psi,bounds[2],bounds[3]): - return 1 - if len(bounds) > 4: - if _inrange( phi,bounds[4],bounds[5]) and _inrange( psi,bounds[6],bounds[7]): - return 1 - if len(bounds) > 8: - if _inrange( phi,bounds[8],bounds[9]) and _inrange( psi,bounds[10],bounds[11]): - return 1 - if len(bounds) > 12: - if _inrange( phi,bounds[12],bounds[13]) and _inrange( psi,bounds[14],bounds[15]): - return 1 - return 0 - -def _state(phi,psi,bounds): - """ Finds coarse state for a pair of phi-psi dihedrals +def load_mdtraj(top=None, xtc=None, stride=None): + """ Loads trajectories using mdtraj. Parameters ---------- - phi : float - Phi dihedral angle - psi : float - Psi dihedral angle - bounds : dict - Dictionary containing list of states and their respective bounds + top: str + The topology file, may be a PDB or GRO file. + xtc : str + The trajectory filename. Returns ------- - k : string - Key for assigned state + mdtrajs : list + A list of mdtraj Trajectory objects. """ -# if type == "GLY": -# for k in g_bounds.keys(): -# if inbounds( g_bounds[k], (phi,psi) ): -# return k, [] -# # else -# return 'O', [ (phi,psi) ] -# if type == "prePRO": -# for k in pp_bounds.keys(): -# if inbounds( pp_bounds[k], (phi,psi) ): -# return k, [] -# # else -# return 'O', [ (phi,psi) ] -# else: - for k in bounds.keys(): - if _inbounds(bounds[k], phi, psi ): - return k, [] - # else - return 'O', [ (phi,psi) ] - -#def stats_sort(x,y): -# xx = x[1] -# yy = y[1] -# return yy-xx -# -##if len(sys.argv)<5: -## sys.stdout.write(Usage) -## sys.exit(0) -# -#torsion_file = sys.argv[1] -##q_file = sys.argv[2] -#state_file = sys.argv[2] -#stats_file = sys.argv[3] + return md.load(xtc, top=top, stride=stride) -def _stategrid(phi, psi, nbins): - """ Finds coarse state for a pair of phi-psi dihedrals +def compute_rama(traj, shift=False, sincos=False): + """ Computes Ramachandran angles Parameters ---------- - phi : float - Phi dihedral angle - psi : float - Psi dihedral angle - nbins : int - Number of bins in each dimension of the grid + traj : md.trajectory + An MDtraj trajectory object + shift : bool + Whether we want to shift the torsions or not + sincos : bool + Whether we are calculating the sines and cosines Returns ------- - k : int - Index of bin - - """ - #print phi, psi - #print "column :", int(0.5*(phi + math.pi)/math.pi*nbins) - #print "row :", int(0.5*(psi + math.pi)/math.pi*nbins) - ibin = int(0.5*nbins*(phi/math.pi + 1.)) + int(0.5*nbins*(psi/math.pi + 1))*nbins - return ibin - -def discrete_backbone_torsion(mcs, ms, phi=None, psi=None, \ - pcs=None, dPCA=False): - """ - Discretize backbone torsion angles - - Assign a set of phi, psi angles (or their corresponding - dPCA variables if dPCA=True) to coarse states - by using the HDBSCAN algorithm. - - Parameters - ---------- - phi : list - A list of Phi Ramachandran angles - psi : list - A list of Psi Ramachandran angles - pcs : matrix - Matrix containing principal components obtained - from PCA of dihedral angles - mcs : int - min_cluster_size for HDBSCAN - ms : int - min_samples for HDBSCAN + phi : array + An array with phi torsions + psi : array + An array with psi torsions """ - if dPCA: - X = pcs + _, phi = md.compute_phi(traj.mdt) + _, psi = md.compute_psi(traj.mdt) + if shift: + return _shift(phi, psi) + elif sincos: + return np.column_stack([np.sin(phi), np.cos(phi)]), \ + np.column_stack([np.sin(psi), np.cos(psi)]) else: - # shift and combine dihedrals - if len(phi[0]) != len(psi[0]): - raise ValueError("Inconsistent dimensions for angles") + return phi, psi - ndih = len(phi[0]) - phi_shift, psi_shift = [], [] - for f, y in zip(phi[1], psi[1]): - for n in range(ndih): - phi_shift.append(f[n]) - psi_shift.append(y[n]) - np.savetxt("phi_psi.dat", np.column_stack((phi_shift, psi_shift))) - psi_shift, phi_shift = _shift(psi_shift, phi_shift) - data = np.column_stack((phi_shift, psi_shift)) - np.savetxt("phi_psi_shifted.dat", data) - X = StandardScaler().fit_transform(data) - - # Set values for clustering parameters - if mcs is None: - mcs = int(np.sqrt(len(X))) - print("Setting minimum cluster size to: %g" % mcs) - if ms is None: - ms = mcs - print("Setting min samples to: %g" % ms) - - hdb = hdbscan.HDBSCAN(min_cluster_size=mcs, min_samples=ms).fit(X) - hdb.condensed_tree_.plot(select_clusters=True) - - #plt.savefig("alatb-hdbscan-tree.png",dpi=300,transparent=True) - -# n_micro_clusters = len(set(hb.labels_)) - (1 if -1 in hb.labels_ else 0 -# if n_micro_clusters > 0: -# print("HDBSCAN mcs value set to %g"%mcs, n_micro_clusters,'clusters.') -# break -# elif mcs < 400: -# mcs += 25 -# else: -# sys.exit("Cannot find any valid HDBSCAN mcs value") -# #n_noise = list(labels).count(-1) - -# ## plot clusters -# colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \ -# 'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray'] -# vectorizer = np.vectorize(lambda x: colors[x % len(colors)]) -# fig, ax = plt.subplots(figsize=(7,7)) -# assign = hb.labels_ >= 0 -# ax.scatter(X[assign,0],X[assign,1], c=hb.labels_[assign]) -# ax.set_xlim(-np.pi, np.pi) -# ax.set_ylim(-np.pi, np.pi) -# plt.savefig('alaTB_hdbscan.png', dpi=300, transparent=True) -# -# # remove noise from microstate trajectory and apply TBA (Buchete et al. JPCB 2008) -# labels = _filter_states(hb.labels_) -# -# # remove from clusters points with small (<0.1) probability -# for i in range(len(labels)): -# if hb.probabilities_[i] < 0.1: -# labels[i] = -1 - - return hdb.labels_ - -def dPCA(angles): - """ - Compute PCA of dihedral angles - - We follow the methods described in A. Altis et al. - *J. Chem. Phys.* 244111 (2007) - - Parameters - ---------- - angles : angles ordered by columns - - Returns - ------- - X_transf : dPCA components to retrieve 80% - of variance ordered by columns - - """ - shape = np.shape(angles) - #print (shape) - X = np.zeros((shape[0] , \ - shape[1]+shape[1])) - for i, ang in enumerate(angles): - p = 0 - for phi in ang: - X[i][p], X[i][p+1] = np.cos(phi), np.sin(phi) - p += 2 - X_std = StandardScaler().fit_transform(X) - sklearn_pca = PCA(n_components=2*shape[1]) - - X_transf = sklearn_pca.fit_transform(X_std) - expl = sklearn_pca.explained_variance_ratio_ - print("Ratio of variance retrieved by each component:", expl) - - cum_var = 0.0 - i = 0 - while cum_var < 0.8: - cum_var += expl[i] - i += 1 - - ## Save cos and sin of dihedral angles along the trajectory - #h5file = "data/out/%g_traj_angles.h5"%t - #with h5py.File(h5file, "w") as hf: - # hf.create_dataset("angles_trajectory", data=X) - ## Plot cumulative variance retrieved by new components (i.e. those from PCA) - #plt.figure() #plt.plot(np.cumsum(sklearn_pca.explained_variance_ratio_)) - #plt.xlabel('number of components') #plt.ylabel('cumulative explained variance') - #plt.savefig('cum_variance_%g.png'%t) - - #counts, ybins, xbins, image = plt.hist2d(X_transf[:,0], X_transf[:,1], \ - # bins=len(X_transf[:,0]), cmap='binary_r', alpha=0.2)#bins=[np.linspace(-np.pi,np.pi,20), np.linspace(-np.pi,np.pi,30)] - ##countmax = np.amax(counts) - ##counts = np.log(countmax) - np.log(counts) - ##print(counts, countmax) - #plt.contour(np.transpose(counts), extent=[xbins.min(), xbins.max(), ybins.min(), ybins.max()], \ - # linewidths=1, colors='gray') - #plt.scatter(X_transf[:,0],X_transf[:,1])# c=counts) - #fig, ax = plt.subplots(1,1, figsize=(8,8), sharex=True, sharey=True) - #ax.contour(np.transpose(counts), extent=[xbins.min(), xbins.max(), ybins.min(), ybins.max()], \ - # linewidths=1, colors='gray') - #ax.plot(X_transf[:,0],X_transf[:,1], 'o', ms=0.2, color='C%g'%t) - #plt.tight_layout() - #plt.savefig('dpca_%g.png'%t) - - return X_transf[:,:i] - -def discrete_contacts_hdbscan(mcs, ms, mdt_all): - """ - HDBSCAN discretization based on contacts +def compute_contacts(traj, scheme=None, log=False): + """ Computes inter-residue contacts Parameters ---------- - mdt : object - mdtraj trajectory - mcs : int - min_cluster_size for HDBSCAN - ms : int - min_samples for HDBSCAN - - Returns - ------- - labels : list - Indexes corresponding to the clustering + traj : md.trajectory + An MDtraj trajectory object + log : bool + Whether distances are in log scale """ - - dists_all = [] - for mdt in mdt_all: - dists = md.compute_contacts(mdt, contacts='all', periodic=True) - for dist in dists[0]: - dists_all.append(dist) - - X = StandardScaler().fit_transform(dists_all) #dists[0] - if mcs is None: mcs = int(np.sqrt(len(X))) - if ms is None: ms = 100 - hdb = hdbscan.HDBSCAN(min_cluster_size=mcs, min_samples=ms) - hdb.fit(X) - hdb.condensed_tree_.plot(select_clusters=True) - plt.savefig("hdbscan-tree.png",dpi=300,transparent=True) - - # In case not enough states are produced, exit - if (len(np.unique(hdb.labels_))<=2): - raise Exception("Cannot generate clusters from contacts") - - dtraj = _filter_states(hdb.labels_) - return dtraj + distances, pairs = md.compute_contacts(traj, scheme=scheme) + if not log: + return distances + else: + return np.log(distances) def _filter_states(states): """ @@ -425,12 +91,10 @@ def _filter_states(states): pass return fs -def _shift(psi, phi): - psi_s, phi_s = copy.deepcopy(phi), copy.deepcopy(psi) +def _shift(phi, psi): + phi_s, psi_s = copy.deepcopy(phi), copy.deepcopy(psi) for i in range(len(phi_s)): - if phi_s[i] < -2: - phi_s[i] += 2*np.pi + phi_s[i] = [x + 2*np.pi if x < 0 else x for x in phi_s[i]] for i in range(len(psi_s)): - if psi_s[i] > 2: - psi_s[i] -= 2*np.pi + psi_s[i] = [x + 2*np.pi if (x <-2) else x for x in psi_s[i]] return phi_s, psi_s diff --git a/requirements.txt b/requirements.txt index 43b27e4..3db85d9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,5 @@ numpy scipy matplotlib networkx +mdtraj +scikit-learn diff --git a/setup.py b/setup.py index b69ec90..8c31437 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ def read(fname): setup( name='MasterMSM', - version='0.1dev', + version='0.2.0', description='Algorithms to construct master equation / Markov state models', url='http://github.com/daviddesancho/MasterMSM', author='David De Sancho', @@ -19,11 +19,22 @@ def read(fname): packages=find_packages(), keywords= "markov state model", long_description=read('README.md'), + python_requires='>=3.10', + install_requires=[ + 'numpy', + 'scipy', + 'matplotlib', + 'networkx', + 'mdtraj', + 'scikit-learn', + ], classifiers = ["""\ - Development Status :: 1 - Planning + Development Status :: 3 - Alpha Operating System :: POSIX :: Linux Operating System :: MacOS - Programming Language :: Python :: 2.7 + Programming Language :: Python :: 3.10 + Programming Language :: Python :: 3.11 + Programming Language :: Python :: 3.12 Topic :: Scientific/Engineering :: Bio-Informatics Topic :: Scientific/Engineering :: Chemistry """]