From df64f92aa8b37450be4c29c8f6b36c00543fb3cf Mon Sep 17 00:00:00 2001 From: daviddesancho Date: Wed, 18 Nov 2020 13:54:52 +0100 Subject: [PATCH] Revert "Merge pull request #3 from BioKT/develop" This reverts commit 87d88e8587cf08ed0bfe5477f6c36d28f6280205, reversing changes made to 95c83b89c740fd5eea5d65fcc51a18fea3027a4e. --- .../ala_dipeptide_multi.ipynb | 307 ------------------ .../__pycache__/__init__.cpython-37.pyc | Bin 0 -> 162 bytes .../__pycache__/traj.cpython-37.pyc | Bin 0 -> 5261 bytes .../__pycache__/traj_lib.cpython-37.pyc | Bin 0 -> 5844 bytes mastermsm/trajectory/traj.py | 55 +--- mastermsm/trajectory/traj_lib.py | 51 ++- 6 files changed, 21 insertions(+), 392 deletions(-) delete mode 100644 examples/alanine_dipeptide/ala_dipeptide_multi.ipynb create mode 100644 mastermsm/trajectory/__pycache__/__init__.cpython-37.pyc create mode 100644 mastermsm/trajectory/__pycache__/traj.cpython-37.pyc create mode 100644 mastermsm/trajectory/__pycache__/traj_lib.cpython-37.pyc diff --git a/examples/alanine_dipeptide/ala_dipeptide_multi.ipynb b/examples/alanine_dipeptide/ala_dipeptide_multi.ipynb deleted file mode 100644 index 8f91135..0000000 --- a/examples/alanine_dipeptide/ala_dipeptide_multi.ipynb +++ /dev/null @@ -1,307 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MSM of the alanine dipeptide\n", - "Here we run through most of the things that can be done with this package using a simple two-state model. There are more sophisticated examples that enable for further possibilities.\n", - "\n", - "The first thing one must do is download the data from the following [link](https://drive.google.com/drive/folders/1hkx7G1qPutEX-4s-AHHtdvpHqsTwgSNn?usp=sharing). Once this is done, we will import a number of 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", - "%matplotlib inline\n", - "import math\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": 27, - "metadata": {}, - "outputs": [], - "source": [ - "trajs = traj.MultiTimeSeries(top='data/alaTB.gro', \\\n", - " trajs=['send-david/data/out/4_1.xtc', \\\n", - " 'send-david/data/out/4_2.xtc', \\\n", - " 'send-david/data/out/4_3.xtc', \\\n", - " 'send-david/data/out/4_4.xtc'])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "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])\n", - "\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]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "trajs.joint_discretize()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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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", - " ie += len(trajs.traj_list[0].distraj)\n", - " ax[i].scatter(phi_cum[ib:ie], psi_cum[ib:ie], \\\n", - " c=trajs.traj_list[i].distraj, s=1)\n", - " ib = ie\n", - "plt.tight_layout(w_pad=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "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=(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": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 3, 2, 0], [3, 1, 0, 2], [2, 3, 1, 0], [3, 1, 0]]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[tr.keys for tr in trajs.traj_list]" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "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": 64, - "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": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$\\\\tau_i$')" - ] - }, - "execution_count": 85, - "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": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'P$_\\\\mathrm{eq}$')" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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a/mastermsm/trajectory/traj.py +++ b/mastermsm/trajectory/traj.py @@ -3,7 +3,6 @@ """ import os -import numpy as np import mdtraj as md from ..trajectory import traj_lib @@ -25,58 +24,6 @@ def _load_mdtraj(top=None, traj=None): """ return md.load(traj, top=top) -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): - """ - 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) - self.traj_list.append(tr) - - def joint_discretize(self, mcs=None, ms=None): - """ - Analyze jointly torsion angles from all trajectories. - - Produces a fake trajectory comprising a concatenated set - to recover the labels from HDBSCAN. - - """ - 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) - phi_fake = [phi[0], phi_cum] - psi_fake = [psi[0], psi_cum] - - labels = traj_lib.discrete_hdbscan(phi_fake, psi_fake, mcs=mcs, ms=ms) - - i = 0 - for tr in self.traj_list: - ltraj = tr.mdt.n_frames - tr.distraj = list(labels[i:i+ltraj]) #labels[i:i+ltraj] - i +=ltraj - class TimeSeries(object): """ A class to read and discretize simulation trajectories. When simulation trajectories are provided, frames are read @@ -162,6 +109,8 @@ 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=185, ms=185): """ Discretize the simulation data. diff --git a/mastermsm/trajectory/traj_lib.py b/mastermsm/trajectory/traj_lib.py index a1e382d..b8ad3e6 100644 --- a/mastermsm/trajectory/traj_lib.py +++ b/mastermsm/trajectory/traj_lib.py @@ -212,7 +212,7 @@ def _stategrid(phi, psi, nbins): ibin = int(0.5*nbins*(phi/math.pi + 1.)) + int(0.5*nbins*(psi/math.pi + 1))*nbins return ibin -def discrete_hdbscan(phi, psi, mcs=None, ms=None): +def discrete_hdbscan(phi, psi, mcs, ms): """ Assign a set of phi, psi angles to coarse states by using the HDBSCAN algorithm @@ -228,49 +228,36 @@ def discrete_hdbscan(phi, psi, mcs=None, ms=None): min_samples for HDBSCAN """ - if len(phi[0]) != len(psi[0]): - sys.exit() - + if len(phi[0]) != len(psi[0]): sys.exit() ndih = len(phi[0]) phis, psis = [], [] - for f, y in zip(phi[1],psi[1]): + for f,y in zip(phi[1],psi[1]): for n in range(ndih): - phis.append(f[n]) - psis.append(y[n]) + phis.append(f[n]*180/math.pi) + psis.append(y[n]*180/math.pi) data = np.column_stack((range(len(phis)),phis,psis)) X = StandardScaler().fit_transform(data[:,[1,2]]) - if not mcs: - mcs = 10 - ms = mcs - - while True: - hb = hdbscan.HDBSCAN(min_cluster_size = mcs, min_samples = ms).fit(X)#fit_predict(X) - labels = hb.labels_ - n_micro_clusters = len(set(labels)) - (1 if -1 in labels else 0) - if n_micro_clusters > 0: - print("HDBSCAN mcs value set to %g"%mcs) - break - elif mcs < 200: - mcs += 10 - else: - sys.exit("Cannot found any valid HDBSCAN mcs value") - #n_noise = list(labels).count(-1) + hb = hdbscan.HDBSCAN(min_cluster_size = mcs, min_samples = ms).fit(X)#fit_predict(X) + + labels = hb.labels_ + #n_micro_clusters = len(set(labels)) - (1 if -1 in labels else 0) + #n_noise = list(labels).count(-1) # remove from clusters points with small (<0.1) probability for i, x_i in enumerate(labels): if hb.probabilities_[i] < 0.1: labels[i] = -1 - ## plot clusters and corresponding tree - #import matplotlib.pyplot as plt - #colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \ - #'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray'] - #vectorizer = np.vectorize(lambda x: colors[x % len(colors)]) - #plt.scatter(X[:,0],X[:,1], c=vectorizer(labels)) - #plt.savefig('alaTB_hdbscan.png') - #hb.condensed_tree_.plot() - #plt.savefig('tree.png') + # plot clusters and corresponding tree + import matplotlib.pyplot as plt + colors = ['royalblue', 'maroon', 'forestgreen', 'mediumorchid', \ + 'tan', 'deeppink', 'olive', 'goldenrod', 'lightcyan', 'lightgray'] + vectorizer = np.vectorize(lambda x: colors[x % len(colors)]) + plt.scatter(X[:,0],X[:,1], c=vectorizer(labels)) + plt.savefig('alaTB_hdbscan.png') + hb.condensed_tree_.plot() + plt.savefig('tree.png') # remove noise from microstate trajectory and apply TBA (Buchete JPCB 2008) i = 0