{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Markov state model for pentapeptide\n", "=====\n", "\n", "In this notebook we will give a brief overview of some of PyEMMA's capabilities by analyzing MD simulations of a Pentapeptide with Markov state models. We will demonstrate how to compute metastable states and visualize their structures, how to compute the equilibrium probabilities of and transition rates between metastable states, and how to compute transition pathways." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we import pyemma and check what version we are using." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.5.4+48.gdc553f78.dirty'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pyemma\n", "pyemma.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook has been tested for version 2.5. If you are using a different version some adaptations may be required.\n", "\n", "Now we import a few general packages, including basic numerics and algebra routines (numpy) and plotting routines (matplotlib), and makes sure that all plots are shown inside the notebook rather than in a separate window (nicer that way)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "matplotlib.rcParams.update({'font.size': 12})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we import the PyEMMA modules required for the following steps." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pyemma.coordinates as coor\n", "import pyemma.msm as msm\n", "import pyemma.plots as mplt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "def plot_sampled_function(xall, yall, zall, ax=None, nbins=100, nlevels=20, cmap=plt.cm.bwr, cbar=True, cbar_label=None):\n", " # histogram data\n", " xmin = np.min(xall)\n", " xmax = np.max(xall)\n", " dx = (xmax - xmin) / float(nbins)\n", " ymin = np.min(yall)\n", " ymax = np.max(yall)\n", " dy = (ymax - ymin) / float(nbins)\n", " # bin data\n", " eps = x\n", " xbins = np.linspace(xmin - 0.5*dx, xmax + 0.5*dx, num=nbins)\n", " ybins = np.linspace(ymin - 0.5*dy, ymax + 0.5*dy, num=nbins)\n", " xI = np.digitize(xall, xbins)\n", " yI = np.digitize(yall, ybins)\n", " # result\n", " z = np.zeros((nbins, nbins))\n", " N = np.zeros((nbins, nbins))\n", " # average over bins\n", " for t in range(len(xall)):\n", " z[xI[t], yI[t]] += zall[t]\n", " N[xI[t], yI[t]] += 1.0\n", " \n", " with warnings.catch_warnings() as cm:\n", " warnings.simplefilter('ignore')\n", " z /= N\n", " # do a contour plot\n", " extent = [xmin, xmax, ymin, ymax]\n", " if ax is None:\n", " ax = plt.gca()\n", " ax.contourf(z.T, 100, extent=extent, cmap=cmap)\n", " if cbar:\n", " cbar = plt.colorbar()\n", " if cbar_label is not None:\n", " cbar.ax.set_ylabel(cbar_label)\n", " \n", " return ax" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def plot_sampled_density(xall, yall, zall, ax=None, nbins=100, cmap=plt.cm.Blues, cbar=True, cbar_label=None):\n", " return plot_sampled_function(xall, yall, zall, ax=ax, nbins=nbins, cmap=cmap, cbar=cbar, cbar_label=cbar_label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load pentapeptide coordinates and select features\n", "------\n", "\n", "We first have to load the PDB file and the trajectory data, in this case for WW-pentapeptide. They are stored on a FTP server and can easily be downloaded with mdshare. Please use `pip install mdshare` for installation." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from mdshare import fetch" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "topfile = fetch('pentapeptide-impl-solv.pdb')\n", "traj_list = [fetch('pentapeptide-*-500ns-impl-solv.xtc')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can decide here which features we would like to use in the further analysis. In this case backbone torsions. As we want to do TICA on those coordinates, which requires subtracting the mean from each feature, we cannot use angles directly but have to transform them into a space where an arithmetic mean can be computed. We are using the cos/sin transform to do this, specified by the *cossin* option." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "feat = coor.featurizer(topfile)\n", "feat.add_backbone_torsions(cossin=True)\n", "feat.add_sidechain_torsions(which=['chi1'], cossin=True)\n", "#feat.add_distances(feat.pairs(feat.select_Heavy()))\n", "# describe the features\n", "# feat.describe()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feat.dimension()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define the source of input coordinates (we don't load them into memory at this stage - they will be loaded as needed). Compute a few basic data statistics gives:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of trajectories = 1\n", "trajectory length = 125025\n", "trajectory time step = 0.0039992321474276935 ns\n", "number of dimension = 24\n" ] } ], "source": [ "inp = coor.source(traj_list, feat)\n", "print('number of trajectories = ',inp.number_of_trajectories())\n", "print('trajectory length = ',inp.trajectory_length(0))\n", "print('trajectory time step = ', 500.0 / (inp.trajectory_length(0)-1),'ns')\n", "print('number of dimension = ',inp.dimension())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TICA and clustering \n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For TICA we have to choose a *lag* time and we have to define the output dimension. This can be either set by the *dim* keyword, or by specify a percentage the kinetic variance we want to keep. Here we choose 90%, which gives us three dimensions. From the original 16-dimensional space, most of the relevant kinetic information is in a four-dimensional subspace." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TICA dimension 4\n" ] } ], "source": [ "tica_obj = coor.tica(inp, lag=20, var_cutoff=0.9, kinetic_map=True)\n", "print('TICA dimension ', tica_obj.dimension())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can have a look at the cumulative kinetic variance, which is similar to the cumulative variance in PCA. Three dimensions explain 78% of the data, five dimensions 95%." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.28356454, 0.55635369, 0.77896131, 0.91687513, 0.95379078,\n", " 0.97751625, 0.99390137, 0.99867432, 0.99918769, 0.99932942,\n", " 0.9994593 , 0.99958471, 0.99969495, 0.9997838 , 0.99983961,\n", " 0.99988354, 0.99991884, 0.99994675, 0.99996639, 0.99997953,\n", " 0.9999894 , 0.99999821, 0.99999983, 1. ])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tica_obj.cumvar" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# here we do a little trick to ensure that eigenvectors always have the same sign structure. \n", "# That's irrelevant to the analysis and just nicer plots - you can ignore it.\n", "for i in range(2):\n", " if tica_obj.eigenvectors[0, i] > 0: \n", " tica_obj.eigenvectors[:, i] *= -1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we get the TICA output, i.e. the coordinates after being transformed to the three slowest components. You can think of this as a low-dimensional space of good reaction coordinates. \n", "Having a look at the shape of the output reveals that we still have 25 trajectories, each of length 5001, but now only three dimensions." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6ab8d88f8cd84bda93ae4c3c21d53b82", "version_major": 2, "version_minor": 0 }, "text/html": [ "
Failed to display Jupyter Widget of type HBox
.
\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='getting output of TICA'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=25), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "number of trajectories = 25\n", "number of frames = 5001\n", "number of dimensions = 4\n" ] } ], "source": [ "Y = tica_obj.get_output() # get tica coordinates\n", "print('number of trajectories = ', np.shape(Y)[0])\n", "print('number of frames = ', np.shape(Y)[1])\n", "print('number of dimensions = ',np.shape(Y)[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that at this point we loaded the compressed coordinates into memory. We don't have to do this, but it will significantly speed up any further analysis. It is also easy because it's low-dimensional. In general, after the TICA-transformation we can often keep the data in memory even if we are working with massive data of a large protein. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we look at the distribution on the two dominant TICA coordinates (three are hard to visualize). For that, we build a histogram of the first two TICA dimensions and then compute a free energy by taking\n", "$F_i = -\\ln z_i$, where $z_i$ is the number of bin counts." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "def plot_labels(ax=None):\n", " if ax is None:\n", " ax = plt.gca()\n", " ax.text(-2, -4.7, '1', fontsize=20, color='black')\n", " ax.text(-1.2, -5, '2', fontsize=20, color='black')\n", " ax.text(-4.2, 1.5, '3', fontsize=20, color='black')\n", " ax.text(-0.1, 0, '4', fontsize=20, color='white') " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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B6f3aqQng4nD8QBwx0+uFPrhlysQWeWUyjqiBuC7ORH2PVr9FQNXKBBvGufB2VLjPiojsAv4I+HHn3Pw8/gtgUUReRG4AbgDo37hxMS6hvQwPQ6FAmlks5NnKAQwNkUYt7hNe4Ct79kCxyMOFAttGY7fMGVTI+xJNttmzh2qxSLlQiCNpQpXLcN46wvWl8MXSbEF15RC6kM030W0lMx93TROIyI8CP8DsUTVh3GuBO4FrnHP/0LormJtFCaF0zt3unNvpnNu5vq9vMS6h7cz77pmIyglx7bUbhG/D9zgadjnpPzuGhtCH/Rk3jxD7PttNZXAQBgamR2ha/PvKIIS5FgoW6tpe3gF80TmXbNGAiGzxsfNb/KHfANYBf5mIq//K+bhAc9e0A994u+z93TNoVPclm4W9eynnckRo9MtFwFP5fOyCGRgAP2f4h0vmV5V9qYJZnyBmudYZ12Esb/yTJAMDGu2Vz0MuF/+fWM2lpltsyTvnds9y/AkScXHOufMSLtmIVodQRiKSQoNCOkQkdb6yupYcvj9qQ4aH4/Z+9YyMMJHJ8BTqTw+CXQ2NtAcGapZ+PZFvNlL7vNlmIMHSN4Ff/gwNqcDDtMS8CK1wmkZrFzE0ZE9tq4RWC/CvAx9I7A8AtwA3t/g8i8tCFyjnsqL27tU+sIlfwqhUojo8rIu5mQzVbJZywu9fAbXcAibaS5dWLnDXu2JKJf1/UCgQ5fNU8Q3kMxkqQLpUihvKz3Ydrby++cw1NLR6nzDaSKtDKG9mpQl6PcHX2e5H3rrFsmryETx8ls3qzSCMN1Ff+oTqn3MtcBeL6mZBS1/MGOs/T2YsV2H6/49keKw/VvHfmTUvYniYVKGgRsNsPYibJfyfbeZ3JZdjM/BULkd1oec9Gx1MTy5cBaxOV8pCaDalv1GM/EIIIt7Aj28sI/bubc669U9xKVSYq8mnNN8HuApM1LkEo0KBarFIVCqx3h87iQ+P9fOF3ImJ4NYJ5/PrOeVmRDn5BBF+lvoonmKxVvG0MjQ0+89cLNYS9FJApd1Cv8owkT8X5vgFjXK52oJryGRtiZVtlvrKYJ7/jim03hBML5cxw8L3T5mpUokN4XtouG2oVRS+30lcxyjyY2tuv7MJfOIJIlDxN5xqodDQAIrQdYDqWYyeil8kZniYdKmkQt+OcN4LMEveWDjVPXs05jyI+3zilJPuF8PAu2MyGSLvT+9Bw2fLoQVjwFvEyV/qUPyu0+/3EWc+48eG/sKgIbo1F2D9DQRIJXIwNifOU8vdyOe1UTwq7MknhxAsEOophWuaxBfN808ggSg8wYTkQeOckObr6bSHyy+91H324x9f1Gs4Z3xf15b4w70VFqytlvhEjeWL92dHJMpfeH95sNKPEkfOJAnHkuKdQusZBUKFUhJjAk+R8PF7wjmSCXbpfJ6LgN4dTKuEGubd2I2W4wgd0saA2+JxXd2Jzw7AE5N6A5hIWPXTRB/IwANNlhpoyM7t4u7/3ebGypsXdq6lglnyCyEIez5/bi30AmGBCqbHMhurF2841JLcwmvCj96Dim4Q6T7i3gGhEF1N3G8kLlcNdB2AjQfqSlR7tvl5gqsnzAV6Y6n4TNpuoPcaakLd5cdsxJ+rX8tlVxL5jmt36bkBeFXcN6HjRtgyBkd2J9YKvBuopdjCqzFv9u6dJtLzFvq6xBVg+i+2sXppEFEDseCGRjBBpyvAlm4vsruIk36yTK9kClr9fFdcyA6IewoDG+9G6yRNJizua33hvAMFEOBL2uCmWvcoEPYnumE8BeWEyqQvgu2vgOgMVL2/JjoD9EN6A2y8E/quh8f971M5UZajCmpQGfPCRL4VBHfNbAlOZ6NYtHoxRtOERdKJBp+dARXq9/nXZDcxL95T3SrC0QbtWVC7EaTjzyp9kN4BHddCVxHIal+Dcj/wH4Gb9CtJEX8ocapSYjse+ksmbwRr4apOTaDpT3yW7ob+FKz5Elx6nXZUs4XXhWMi3yrONZzRQiCNedLVDX3efA+LmcE1UxPtMmcVs0of0FfXXJ7YCi/3Q9QHqR06ttITW+TlCL6WgruAe88QlzqtzLKR+NzPf28f5FJoo3p/bHcKfvUiPbTmLi/0uZxa82YEnTMm8oaxXBgcpFws8oSPWgHfJIZEA5lgwSfbRibEvpry1noIeemB1MmZYh/GHt423d0yntKU9nuPEjvuw2NFBThddwzAPU9fo61+4oMgz8R3pl6gH/b1AxtU6DdVoOOTsP56nydgQQjnjIn8uWB1143FYu9ejvoom2mRM43GhvKifhF0xseRbukofjKodsbHx1PqhgmuF4B7T6I3kAm/nSC22N3zVMhTV0NvwkSvXWWI3Cf+0plx+O4wnHgGKrCvD34qrW6bjrt9P4VWCvwF6pZaTZjIz5dWpn0bxrkwOKgSGUIsgYlJ6B+FrhCSGJq+gy68jgP9scWeAuiJfepJV8w3IxX1u0j41MM27l9PoCLvnqeivnYrdGylFolfSdxVqmndAlE5sW2FdVvh5DA88QD0wQcuga+iTXOWw++YiFyHPuBsQf+GBp1zX28w5hb0dnsK+ArwHufciXZfn4n8fPE9XM2SNxaNYrFWE6bWKL5U4iTw0tuA5DJPaBOZWFgNpE7qR8kImG9GCV97sNaDwE+g7pgJYNKL+5orqQl79UIV98ne6f748FqruN4LPb1xZtb6R2DtIBw7COPPcG+/rgO0w+B2F8yMBloIIvIfgY8CbwX+Aa0Q3oh7gaucc0+LSBrYh/bD/sXWXU1jTOTPBRN4Y7FoVKjOUxke5olCgS23UWvwDsAB4FroqIuJr6ZU6PtRoR9PJVwz9SKdFPgzr4T01bHlnhT3cFN4CDjsv384vK8L97kS3S7fDn1lSA/Ckd+BCZhYD+uWh1vlFuCDzrn7/P6/NhrknDtUd2iKlla2nx0TecNYTgRhb5SP4f30R0a1LWSgKzSE3wUdWVTVNyQiabwKpJPps3Wu82kWfPpq6HgJ0KfiXumHkxfo5w8B95EQ9Xjr53Ttmsbpgvv6dfuNCyDaDumJ2tNDeYNeby2jfHG4UETuT+zf7py7PeyISAewE/gzERlDn0v+F3CTc25GmpmIvAr4C3Sp+XvANe28+ICJvGEsNxqJXi7HBuIEqWlZr5PAqG69Pvu1A4h8THwyeqaWZ51cI8W/Dy6ajq3U/O5Jgf9rvNUewntU2K+mwlaqrGOqNt23WcNdYeehfujvhXTKR+I8o9e0C6LdhWmlkheKk3m5a56eo6zB89G/5muBH0NTFb6E9tV4/4xzO/cNYJ2I/ADw88DBRpOKyIVoc/AfB16Gtg08Dnwb9eV/xjl3tNkfYlF6vBqG0WIymVrma7I15FG/BS8Kwaovw5pjuhCbnqt2QLDmo63eB+/979X0dIG/Dzg8DoxxJUe4jhMMUualPMsanqWc+PNSnuU6TgDj+r0JdM5oK1T8jadfA4OiQuHcEg3bT/gr/5hz7inn3NPA7wJvPNuXnHP/ijrR7qr/TEQ+AnwLuBS4A00/u8y/3gFcAvyjiPxWsxdplnw7CNUBzXdvnC98N7EQWlkhLt9b8YW+0mioZC3csgypCbgQSPfAWNOZoKk4YqaCPjYk3DP9nGYrVV7GKapUqVChSpUnid3SL+ISXsYpDvI97jvMjH6WDwGvS6uZHBHXw18orVx4dc4dE5HDwLlUeYyAixscfxLY7pw71eCzbwF/4lusvrPZE5kl3y6sNKpxvvEFzToT+80YG1HFR9qU1WTMwMzSlI04c4GK87SF1TJXU5km8E/wOP/A17mPe7iPezjMQaqzlR6begD64U3eld/HkrdEPw28R0Q2ish6YAj4cv0gEbleRLaI8kLgN9FI0Wk45z4G/OezndA5V3HO3dbsBbb0709Evh99pPhPwNPArzrn/qSV51gWWC1443wQSl0HgrWOj1YMSXu+Q9MGoOsaGsZ0RBXV9e0RXJeGUiccD/lLTVu+0xdXq/7P2ThIBJv8RUeHYQvs3gz9j6Ju/aXPh9CHoUfQW97ngd8UkS3APwOXO+eeAC5HQy3Xo+0A/hL41Vnm3Ad8rlUX2Oqb5B+g6/DPR1Mw/kJEvu2ce7DF5zGM1U3oAuV3g5RWUB/8NEolevCLriG0MlHuIKpoeOWaMlR3wBUpyERwb33C6hnOicOJ9cVNbGUTW4mIOE6HRtlcCfQ8B1Pfga3wnoquF3BAf5YytKzGk5O4+mVL5nPuDPBuvyV5gkRBCefc+2mwGDsL0pqrU1om8iKyFngLkHHOlYFviMifAW9D6+IZhtEqvDswwoug7wFbReu9R6WSxtMXi3FVg2TzDoBXAaEapV+QXZeG7Z1wXR+UeuD4BLElXz0IU+PQ0afZqp3PQSrp8U0zTpngYI/8n01s5TAHawL/AjaTIsVf6bMDvBzoK8KJ34E+X5lyDE7s9z1sV18Rvw4ReQ1nEXvn3N80O1krLfkXA1POuUcSx74NvLqF5zAMI0EFtMFMnYuw6pOmUqUS3WjwysYDiQE+E7YD9C5xwB97FUTbYt98zZpPoUXFqgc1hDKUJaBX3S2bgMNquB4kYitV1lIlIuIFbOYFbOZJDrGFbUREfJs13MdGuDING54DDkI/XLUB0o8C7/R1a1os8M/J9JDRJcoa1O09m8g74EXNTtbKHzeNxnImOU7c4rGGiNwA3ADQv3FjCy/BMFYPoVXkDK93sUjko2zCL/hR4Oio9mXtDW0Ab0Ot+yzT6spPI5S5DBb9mYMqQaAi3+dLGNREPs1BTnOQiJf5uPgUKapU2cI2UqR4ljXcRa+e9E2oFf/sXfAS2FeBNX+kteQbFMZcLTzrnGtaxOeildE1/rY+jV4SFSsCzrnbnXM7nXM71/f11X9sGEYTJJtk1xgeJpXPsx5d4auiq3yVPXuo7NnDIeBEIhv29K3A3ajI79J2fdN81qm6rXoQpg4ClYTLBvWrbwLoZ5zv56C31oPLJkWKNGkeZC3DpPWEQ8RWfM8D8BJ4yaNw5NbpLihjYbTSkn8EiETkEufco/7YywBbdDWMVjM4SHlwkHIy7d8XLovQNdKQ/VqLMy8W42CZMXjMW8ub90PvdtSix9ezSUEmpVW19CANXDYpFfpUr95trgTuS8PhNPdR5mAt0kZN/4NE3Mf3Af1wbVrTevqKcPy34IdhpBM6Rv3CcZsahUwtD3dNSxdeW2bJO+eeBb4IfFBE1orIVcCbgc+26hyGYdRRH67rK1NOoNZ7dc8ebchUKNBXKrEZbTDy2KQuagbrntuo1ZyPKrDpJPxU/bm6iK35kAYbleMbQPDN00+w6O+il3tIcQ8p9cGzHa7s18XW9Y8AB+GiZ+AlkJ0A7vZumlWcSOic6wEQkf/S6HMRuWU+87X6nvZu4FPAEeAZ4F0WPmkY55FSqdYHtuIzryPU0O4Hurw/vmdUyxNXh4fVsg+umlScEdpfgY+l4D3BJx+oj7LpPkHNUxvcNvf1+wSpcR/u7mM2r0UFfvOTEI3BiZsgCx/rhL5DxJE/beI5loUlH/gtETnpnPtKOODLHuxC69c3RUt/XOfcvwM/2co5DWNVMDQUN4RvxFylMoaHa4utF+FdMqUSlEoq7GGBFSANG8uw8W7gQAnuhFNXzEz3T1fhiipc1aM9WWuLr5MkXDZPQ5SGzrSGU27wX74SnwnrHw82+WObgfUnIPUdOH6zVmjZqhmuHUdrlzfdDbV6eSPwVyLyNufc34rI7wL/AXjtfCZZPvc0w1itDA2RKpWozFJHnqGhWm2aDUDvNaioq9dkRrOQGlcAH42t92onRGdm9nudEUrZ/QxM3qPFxDq2QuqgD6fcrkKf8hdyGbow0EMcpdPzpBf498EL1U0z0gkXatFKGPPRQlYWBOfcd0TkGuBLInIv2nnqtfPtJmUibxhLgUbx4Ims1h7UCJ4oFDhTKEwb1oNv5u3LCLM9dr1ALOKHZ4TizCQdQboz7vEaOkWpYz/5+gxU7olLD0eoCV7pj616iLs/dZ+A1Li6aI7fXBP43Ru0Zk7kWwsemYRKGxOglnqcvIg0stLvAHYDvwDsFJFFS4YyDKNV+GSmNBoKubEbuLEuRjn4r0NkTBqmtkG5rmn3RLdWmPz5q0lLAAAgAElEQVRT4obcgQy6wBrKDZcjfR/6vtbGJ8MoT+Nj5x/won91QujLcYXKalp7+KXGvaV/UCNp1j8D/bBuM/xMFS48Fj89rPZFV1TQG1Ehbuy4aMlQhmG0Ch/ueBGJ5CXf3alWd+ZV/rh3yVT64On103u2gor7vjPM7LmKumH2peCqTrgO9bCUIy31W2sFCLG7JdnUOwVU6oSelCp2FFr9+cFT34HysAr8Rr3mDwMvOTbdPdTC9qsNmUR/tqWKc25bq+c0kTeMpcjgIFE+T283MAank3Xg+5nWmLuagoqvBx/cKyXgeBD2RlvAW+f39ul2VWfcHare6q8JPZAoNhkLfbTV++n7qfl2psbVrTP1gD6SbNE51m3QRd0g8B2T+nMutyxXEbkHXVIOicf/6py79Czju4B/AtLOuU3tv0ITecNYmmSzlEdGeDCXI4XWiO8bhd4DwGDsc6/0qTtmPAWf9gJ//AxxK6j6ZtyhX2s4nkJ9QH7svb4wWSBDHanEd5JUHtCImyD04EMtH9AbQy/62q+v1+ELkeErTo4Bt51zocvF5kbn3CebHHsTGmLesEWLiHwT+G3gS8650w0+70IjGN/rnPvhZk5oIm8YS5jKyAgVHz0DXuR3qbEcImLKkQr8vrNZ7hOotARxd89LnOSZaWOPB1dMyteVb6S8YRF22sU+A1PPqLCH1eJwQ+iLt3U9caJVVEGjaor6tDJXJ8KFMkmDJ5TzhIhsAwaAXwb+aJZh7wA+CHxCRP4ReJg4RunFwCuAvwEGmz2vibxhLBPO4N02voJk1K0hj+W0F656UQ/vfStVOl7pO3hvja1tgGPDKtBwdqd4UtjP5lcJETXBvxSsfy/w16GLu9N6y27XTNxa19TlxUd8z9WHgfc75+6ZZdzHgF/jLD+lc+6fgWtFpB/t67oDbUpyDPhj4G3OuSPzuTgTecNYbowB49CRTjTijpgu8EHkTwDHnqeinro64S9PxV9IXa1x76efaXi6dZ2+vOxcDvPgs28g7kH4ryOO5onCE0IoeL8dolHayjwt+QtF5P7E/u3OudvrxvwK2gHqNPrj/bmIZJ1zjyUH+Xj3yDm3X0SunuvEzrlxWlQSxkTeMJYBEYnerdDYo5tiugV/BJh8nop4SFya1s/PC33Sqicess4vwk4TxfrF26Sgh/3kKeqKzJbQOdORupqqKX0i6UgDu6CzzSI/T552zu082wDn3P9J7H5GRH4azVT9WDjoGyr9tj9+3jGRN4yljq9HU0t48mGUp9arSJYjn5UKc/RkrTQYkDDPEwIdBD4svN5b7wIK50q+ktgPFnzigYGK+vhLUEue6o90Abajub+J5YBjZhXJS4CtwNdFBPRvep2IjANXOucOtvOCTOQNYxkQio4B2sXpWliTVpGvuWuSYluLa38mjnqZgVfeyj1aqsB/56rZeqDWh182SxD6CW0OXvLzX+YTrqopWDMGT9wKR9tUYjhQoXULryLSB/ww8L/RNeO3orVlhuqGltCE5cCPonU/X0GDlrytxkTeMJY4wVXTtQONmR/1XpJrYS3Q36O132cIcBdxZmp1q/fHw7TV06mDehMItRF8eCPE8fb3JqN2koQHg/r9MG6ibj+8908J6apa8Wu/Do/thomRkfn8tSwFOvE5XcAU8B3gJ51zD4vIjwFfcc6lnXNV8MU4ARH5d+A573efFRHpcM5NLfQiTeQNY4lT3bOHk/n8tI5OXT6UkrI2+bgi7atFhgHJrNQU2rYv2ur9IonU1WDF+zDHqzo1SSm4gKZZvanE3EkqDd7X3wDCaT2XoSKfOgnc7TtBnQem3CwhoeeAc+4oWuat0WdfZ5ZYeB9900wi1FMi8jngs865++ccPQutbP9nGEY7yGapjIzwKNPLuofaNVFFi3zdAuwOheN90tEMpsZ1lqmD2lc1JCsF/7knXZ0lESq8zib4gdNoZM8JZkT9lIhLC0QV2l5DfhnzBvQJ4c9F5CER+TUR2TLfSUzkDWO5sGcPx6gTem8CR2fU9ZHB+9SDcAeXzdQDarVXD8Kp+7SOzNQDejPoRQuGbdAbRX+lLoYdpkfLhBvCbAu800oeMEPsj59RS37TSVjzNXhsFKrLz1XTdpxzDzjnfhn4AeCXgMuBURH5moj8rI/amRMTecNYRlTRWO/TIZ0m4RBIV1U8MzA9wqULFfPOB+DUF2IXTT9aLGwrXLUZ/qqqTwR9kyr0VzSy5mF65Ey90AeBny0ap0+7QO08CutuhQfPty/eJa5prm2J4Jx7DvX3fwddqP0B4HrgkIi8ba7vm8gbxnIhm4VMpqafjAE+ISqqxOI8w5oPtWN89UcuekYLhW2hJvC/4xdB+yYhNaG+8mDNr+tkprAn564X+qRATibKJ/jCZG8qw7q/hsdu1bINRmNEZL2I7BaRbwAPoOL+dufci51zrwNeD/z+XPPYwqthLCf27qU6PMyxQoGNB4BBTSRaO6lVKTcBb6rCZWl4S4+vQwOxjyeIshfoq3piF03fZOOuUBnQOJJOoCdejD2ebAk4TuMF2BCa6W8wX0Drx3N3++vUNOQ5lpSVPgeHga+hQv4l59yp5IfOuW+KyJfmmqQlIi8iN6IFc3YAn3PODbZiXsMwGjA4SLlQ4MgkbByn5rLpSPu3/Zpk9OGUb8KdJBQJ64xLDAQXTXgiCARL/jrUDQTTa7Hf1QmlDRr7XhP6aQsGHi/w63wXqLWPAgeWk9YuGi9yzv3b2QY0o7WtsuSfRONFX4/2gjEMo50MDHC0UKBvN3TtY0awXroKr6nAx1Jw1wbfiJvpzUH6K7qlTqqLBqb3gg1zjDdYYA3WfAbUuu/xYh+EPmTPQi3a5wvApsfh9OtgFMBcNXPxUhF5aYPjp4DDzrnvNjNJS0TeOfdFABHZSXPxn4ZhzIehIW3gncgGrQCPA9t2Q9dNaNx8HVdUNbP0oc54P0TPBHHvmETFuQwd2+NyCRAnLNU6TfnXUIMmk3hfCjeTIPTBwu+H3X6xteN6KLaxh+ucLC93zR3AC/z7Z4CwwHEE6BeRfwKuc849erZJFsUnLyI3ADcA9G/cuBiXYBjLi7lS/Q8AWegow7pJYBu1Z+pyFCcf9VfinqprjqGCPO6/D7AL1vTrNtXt06b64tr1/b4UwWsSp74i0o5UAHRCabP31/saOF8ArjwEa38cHhwF9gy24C9kVXAHsA74H865SRHpRpdQjqP9Xn8H+DhaknhWFkXkfbnO2wEuv/RStxjXYBjLisHBGYdSaEe9rtAD9m60qfd2WFcGdsD4+ti3vt1nx659Co2vHweKwAEtlQC+Vv0unSMUDVvr3S2n1sdeoaRbp5qCdGgQQuzCyaBPDj/8j8A7VeAX3YgOIZTLg/8OXOTLIuCF/v3Ak8653xSR96KLs2dlTpH3PQxfPcvH9zrnXjXLZ4ZhtAvf6HtjcgWsLnN0XRq2d2qiVFSpE/cyWiLLNyAB1b6uMX8sOdd2IAtrtseHosR5K33Q36kW/RXETwzRGUiPA+8kLsmQaRh5bzTmWfSv9O8Tx14JfM+/f66ZSeYUeefc1fO9MsMw2k8VODGZaLcafPJBoPuhrxs6jhKLexDwA3BkUoW9f1SfBrqgJvhh3OnJxJNCmjiSpxy/j1K6hTDMdY8nzne3zpPCi01psZrvLUv+B/D/icifAYfwEbLAe/znr0P/hs9Kq0IoIz9XB9AhIimgGh4zDMNoPRU0/TE1Gleo5MB0Ye7w/VNJWOhHJrWXXOBxYNsodF3jDyQEvgIaCjMKXbcBnyT22XihX1NW/366/oZygNoNp4KPix8YaGsp4TlZRguvzrk/9o29r0UXYB8BfsS3CMQ592Xgy3PN0yqf/K8DH0jsD6ALBDe3aH7DMALFIlGpRBUNYqkCF42q2IdKAn2j0Bv86160T0xC8NjUl59/HLhoP/ReQ82a7xqj1o30caA6CZuvh947UaEvM618ZAdouGRge3zeQ0ClzbXiFxMRuQS9Hd7tnBto8PnNwPvR8MfADzrn/mWW+TqArwKvd859aCHX1qoQypsxQTeM1lEsEuXzgJYaronj8DBRoaBW8Z49AJTzeY6iJeFPEmeSViYhtT8uxTIJlDMZGByk6r9Xc6OgN4CJ/bClG7gRvTnsV4Evewv8UD7PJdf72Hz/5DANv/AL1BZ0nwIqi23BB9pnyf8B8M05xvzPRjeARjjnpkRkGy0oPWNlDQxjKVIsAvoLWh0ejuPKvUCnCwXK/ibQB2xIfDWUcj9GrGeheVS6VKKcz9d+8SuZDFGpVLPqJ4HHJmHDrbpfa1uUuB4AboMjo9RuLluC395/dmJySdb6agsich36EPV3xLe4VnAL8AkR+QAaRVOLRPRFy5rCCpQZxhKlihfIegt4cDC4w9kBXNwNvX7b6GPbT6LWd3VkRMv4jozoE4H/XgRUfVJSdWBgmhBXUev7KeKnglSpRFQoEPlremwUDmUyVEZGODowwGOjqL9/Pzzs3ULHSDxZLAUrfv5cKCL3J7Yb6geISC/wQeC9Tcz3JhH5dxF5UETe1cT4TwJvB/4Fre95Bv3rnFfbE7PkDWMpksxubSSQAwNUCwV9X2c79o7BU5MzvxKs8YvwvvtSSY/544HyWcIcJ0olytS5kAYHmRgc5MFcLr5ZJOdYrOzWRswvTv5p59zOOcZ8CLjDOXfIN+mejc+juUH/hvaF/YKITDjnPneW72xr+krPgom8YSxV5rB+Q1mD9aO+bLx3mZxuJPDel99NrHFpYr98D+qqqcCcojxbyNxqKxssIlkgB7x8rrEhIsbzdyLye2jUzKwiH2rTiMgFwPOdc0+dy3WayBvGMueY3yKfcFRh7kiWFPrMn0azZlOoyFdXerJSaxderwa2Ak94Kz6NhpBf7px7xRzfdcBZTX8R6UPLFlyL/nOtFZGfAH7IOffrzV6kibxhLHPKMD3+vJG4Dw5SzWaZzOentX4NeneUeWjf8LCeY3n62VvJ7cBdif08Kvoz/O0i8mbgb1FP2RXALwK/Nsf8f4jev18IhCeBv0dr1pjIG8aKxteyKRcK6v9uUNumEWFRdX3iWDLsck7hHhoiXSpRLRQ0LLLJ865EnHPfIy4xgIiUgYpz7qiI/BjwFedcSB27DvgUsAaNlPmoc+4zc5zidcALnHNnRMT5cx4VkXlVdTSRN4zlSliczee1FHE9oYRAcMH4BKpKJkO5VKovQd8cpRIRunj7VKFAuVhcWgurc+GY3mi8lVNrvlB4/3USVf6dcz99DlMeBy5E78sAiMiW5H4zWAilYSxnstlaUlRUKtW2lBfxPjQ2PuXFuTowoKI8MkLZh06GrZrJNOWCCeUUQtx9wxuM0Qo+iUbhvAa4QER+BPgM6sZpGrPkDWO5k83Wer8GqlALjewrlejGu2WS4ZLeT18LoZzL9TI0RAp9Eqj577PZ5eWyWUa1a4CPolf7B2iH3U8B+4Dfm88kJvKGsVKYRWzLuVwtgqZSXwWy2QXUXE6/v8r98OcT55xDm4MsyB9mIm8YK5lisVaMbF5pkvWMjFApFi2i5jwjIpcCL6Oui69z7lPNzmEibxgrleFhSJQiqICGWp4rK0Hgl1FnKBH5NbSm/LdJRPGgP4WJvGGsarzA4yNpyqALtCtBqFcPQ2ji0z8tZBITecNYSRSLKvClkol6I5bXwusk8J2FTmIhlIaxkgiibgK/EvgN4GMicpGIXJDc5jOJibxhLDcSoZIN2bvXBH5lMAz8PJohewYrNWwYq4BQGtgiXc6RDnB9cw8D4Jm2XkkTLI1SwyKyBq2UlgO+H20K9mvOua8sdG7DMOrwiU/GymcplRqO0D69rwaeAN4IfF5EdjjnDrZgfsMwjNYgayDa2uTgxbXkW1VqeME+eefcs865m51zB51zzznnvoz2MnjlQuc2DMNYyohIQUSeEpETIvKIiLzzLGNfJCJfFpGTIvK0iPz2HNP/IVqk7IXEZdX+HnjrfK6x5T55EXk+8GLgwVbPbRiGscT4CPBzzrlTIvIS4B4R+ZZz7oHkIBHpAv4arUPzVmAK1cmzsfRKDYtIJ3An8Bnn3Kzxnb4h7g0A/Rvndb2GYRjnjnTNw13zwJwjnHNJY9b57eIGXx4EnnTO/W7i2FxJTuen1LCI3CMibpbtG4lxFwCfRR8rbjzbnM65251zO51zO9f3NbvSbRiGsfQQkY+LyPfQxKWngL9sMOxK4KCIfMW7au4RkR1zTH1+Sg07566ea4xog8M7gOcDb3TOLagWkmEYRnuYz8IrF4rI/Yn9251zt9cPcs69W0TeA/wI2vf1VIO5NgGvAX4C+Crw34EvichLnHOztTFZUqWGPwFcBuScc416xRuGYSw3nnbO7WxmoHNuCviGiAygPV5/v27IJPCNEFouInvQPq2XoQXIGs3ZklLDC46uEZEXAruBLDAuImW/Xb/QuQ3DMJYZEeqTr+efUH/9eWfBlrwP2JcWXIthGEZ7kU7o6G/NVBrl8lrgy6ilngN+GvivDYYXgPeKSA74GvCLwNPAQy25mLNgtWsMwzDODYe6Zg4Dx4A9wJBz7ksissV7NLYAOOceBgbQRdNjwJuBnziLP75lWO0awzBWEYL2yVo4zrmjaKZ/o8+eYGY3py8CX2zJyeeBWfKGYRhLEFF+XkT+RkT+yR/7DyLyX+Yzj4m8YRjG0uSDwM8BtwNb/LHDwK/MZxJz1xiGsYq4gFa5a84Dg8DLnXNPi8gn/LHHgRfNZ5KlKfKhXjbA4OCiXophGMYi0QHanpc4/DKdONYUS0/kh4eJCoXabrVYtPrZhrFQcjl9HRlZ3OtYdJaVJf+XwO+KyC9BrbLAh4A/n88kS8snPzREqlAghd59qpmMNiTO5WLL3jCMuQlPw8PDRLmcWnN79iz2VRnz45eBF6CFytahFvwLWZY++WKRKJ+v3V8reIHfu7f2Gfk81YEBc98YxtkIwl4q1X65q0B11Vvwyw/n3AngJ33S1QuBQ8658fnOszQs+WyWKiruZVAxT7hoqugDVlQozN3E2DBWM4m+rykSjgl7EvaEOPlmtsVHRJ4HvB54jXNuXEReICKb5jPH0hB5gJERqnv26CNlvbWeyag1Ata82DDmYu9eqgMDajChK3WpfN4MpGWGiLwaeBi4HvgNf/gStCBk0ywNd02gkYBnszA4SGV4WP3zhmE0RTXxPgKz5gFcB1TTc49bGuwF3uqc+6qIHPPH/g/wQ/OZZOlY8mcjdKjPZNQ/bxaJYSiNfhdyOTYUClwCrAd6gHKdC9RYFmx1zn3Vvw8hlKeZp3G+PEQ+sHcv1UyGlPnmjdVOsUgqlyNdKMDQUO1wKpfjld2wZR/03qTHDlnAwnLln0Xk9XXHcsDofCZZWu6aZti7l0qxaL55Y3VR/3++WKSK/gKnSiUqQ0NQKtHjPz69W1MjyxZVMx13wXJy17wX+LKI/AXQLSL7gDehFSybZnlZ8gETeGM1USxCPj/NYofY5x6EPo3GhJye9AI/MHB+r3OVISJrROQOEfmuiJwUkW+JyBtmGfuHiYZKZRE5JSInzza/c+4+4AeBB9HWf48DP+Sc++Z8rnP5WfKGsdoYHiYComCxZ7PgkwYDkd9SQFc32sLCmElrLfkIOISWG34CeCPweRHZ4Zw7OO20zv0C8AthX0SGgedmm1hEOtBesK93zv32Qi5yeVryhrGaSMa+l0pEhQJpYANwEQ0ste1mvZ0PnHPPOududs4ddM4955z7Mmptv/Js3xORtcBbgM+cZe4pYBst0OiWiLyIFETkKRE5ISKPiMg7WzGvYRjA4CDVgQEqmQyVTIZqJkMFCM/6m4FuNIqmdwewHToX61pXFheKyP2J7YazDRaR5wMvRt0rZ+MtwFHgb+cYdwvwCRF5oYh0iMgFYWv6J6B1N/yPAD/nnDslIi8B7hGRbznnHmjR/IaxuqmLjqkODzNRKFBFrfkt3cCNQBa4u8H3QzTaqo+ymVec/NPOuZ3NDBSRTuBO4DPOue/MMfwdwB875+Zq7P1J//q25KnQcMqOZq4LWiTyzrnkncv57WLARN4w2kTyl/f0JHSNAWNwer9Pyk8UKaNUsgJlbcJb1p9FY9hvnGPsZtSH//NNTL1t4VfXQtediHwcLXLfDXwLLZNpGEYb6favFYD98fsU0FcqUc7nAbRkiEWlqfl5pnVLkb787x3A84E3OufOzPGVtwN/55z7l7PM2e+cG3fOfbcV19iyn9Y5927ULfhjaLPaU7ONFZEbgp/r2MREqy7BMFYPg4NUMxkmgaf89rjfwn6o91QdGTGBbx+fAC4D3uScayam6e3A8BxjHknuiMiCmn/PacmLyD3M0pEcuNc596qw41eEvyEiA8C7gN9v9CXn3O1o30Iuv/TSufxShmE0Yu9eynNlfq96H3z7EJEXArtRg3ZcjXrwx74O/DNwuXPuCT/+R4BNwJ/ONXXd/tULuc45Rd45dy4niFCfvGEY7cREfH48h/dtLRzvTqkX5CTTVnidc38PrG1m6oVcVz0L9sn7gvavBb6MpmDkgJ8G/utC5zYMowHDw40rSloBspVCJCKvIb6B1O/jnPubpidrwQU51DXzh6iP/7vAkHPuSy2Y2zCMJL4HcvIXN2S7TuRy1sN1LlpoybeRI2gZg8AzdfsOeFGzky1Y5J1zR5ndZ28YS5flVuiuWISEwFcymemfmyW/InDObW3lfJb9bKxOhodJFQpUllMZ3mxWu6SVSnEPZMOYAxN5Y3UyOKglq5eLwAf27qU6PLz8rnup4FgO7pqWYgXKjNVLs5ZwsTijzO95YXi4cXMcE3hjHpglbxjNcD5898mImeFhUqVS3MDehL01TBFXdlslmMgbxlxks+0X+VyOFDN/IauNxhrGPDB3jWEsJsUiUS7HBrRk8Da/dbPqXMdGmzBL3jBaSXC51Fv+uZy+1oc9+rZ9fUBvN7AdToxqVmF1YACyWaJ8nmqhYDHwreAMcHixL+L8YiJvGK0i9GIFSIRmRrkcF6GW+aT3swcitKpfCmB7fLwK024UEVAdGrKwSWPemMgbRjM0KiVQKk3bTfmtBzhZKFD1iUsXh4Yevt77kdHprpg+oOsaaiLfOwapSagkImuqyymefylzGrPkDcOoY2iIqFRqWEog2Wav5nK5ETaO+YPb0W5NaWCXHto4BgzBCV+YtmsHcG1iol2wYT9UQnSNCfySRURuRPto7AA+55wbnGWcAB8Cfgb93/At4L/VNVxqCybyhjEXPgGp5mZJWPRRqcR6VOBrLpcxYtFOx9up9VBNQXUHrMtC791+/HaY2gAdk0BZv9s7BhtGNdqv7Nv8mdAvSZ4EPgy8nriHSyN+CvhZ4FVofa8Po92kXtHuCzSRN4xmmEVgq8PDnCwUaq4aggVfRsW9H6a6vbj7baIbqi+G6CY/h38cSJ2EqAJr0sCHYeMwbDwARybhWKFAuVhs7JMPLf6yWbsRzEWL3TXOuS8CiMhOtFb8bGwDvhE6QolIAfil1l3J7JjIG8ZCSAp8YBfQz6wCP56CcgQP+eGXAekq9Ef6CrAGaj76CnrPmHXRNYj7ciq2tvq4C3iriLwYbeD1DuDA+TixibxhnAvFIlE+X3PVAHR5f/zU22JRh8Rrpwr8pyP9jQc4fgbWdUImguvScEUVXnIG1hwDxuCJSTgKc4dPmsA3x/xCKC8UkfsT+7f7rnbnwlNot6iH0bzbQ2gfjrZjIm8Y50I2SzWT4UwywmYXcC1MbJ45vBzFAr/vJHF4TQWOA/em4N4+uKoT9vXAOv9xauZUxvnjaefczhbN9QHgCjTnbRwYAP5GRF7qnPtei87REMt4NYxzZe9eJjIZJvBifK26Z8pRvI2nYCyt23uTAl8BJuq2CpTQ75xaD+yiYakDY1nyMuB/OucOO+eqzrlhYD1webtPbP9/DGOBnMGHQaZj10w5il+De+b4SWpiXtuSTKhVT4/fH9MhVr+mlTyHX+FoCSISomk7gA4RSQFV51z9P9s3gZ8SkbtQD9z1aATuGG3GRN4w5kOIZPGkSiXV5DFq2hEEPiyiZmqtnOrmSvljqelbuup98retuoKJy5FfR10xgQHgFhH5FPDPwOXOuSeAjwIbgSLazHsMeItzbqLdF9hykReRS4BR4G7n3ECr5zeMRWV4mCjhh68Cx4DKJPRdD7074KUfhmdfrlZ9uhOuiOC6CPb1oZZ8ktT09+s6YfsEMA6nJ70VX1/vxlgAU7TSknfO3QzcPMvH6cS4CvDf/HZeaYcl/wfoo4lhrDxCZ6YEZaBcLHK0VCIahdSb4dJrYOoWqPRpaOTPpIBOuGuDd9skFl4BFfge+AIaL99CHTJWOS0VeRG5DrVV/o5p5ZYMYwVxloSjKqrPj+VyXDwGaz+pCU7pDfCeFGRScFcPlHo0fDK4a9Z1qsBnJ2DtU8DdGoJRmeN8hjEXLRN5EekFPgi8Dvi5Vs1rGMuOoSE2oCWDe++GNa9SoU/1wZvWwxUp+GYEpU7dMmjOe3YC+g4BRTixv8n4eGOetHbhdTnQSkv+Q8AdzrlDWotndkTkBuAGgP6NG1t4CYaxyORy9JFYY71V69B0XAtr+yHaDH190N8Nr0ks0PY/BWseQn3xuzUl0gTeaAVNibyI3AO8epaP70ULqeaAlzczn88aux3g8ksvdc18xzCWBXv2UM7na7ZiBLz0AJooVYY1h7xV7zNiUxPQ8Vk4cSv8GxqOOZHJWN34tmGWfEOcc1ef7XMRGQK2Ak94Kz6Nxoxe7pxre5U1w1gyZLNUExZ4tVjksXyei28D3geUoaOsQdVrgBNvVqu9ala70SZa5a65nbgcB0AeFf13tWh+w1ieZLNMjIzwYC7Hhut9g5BuDY+cQMMvIyzhyWgfLSlr4Jz7nnNuPGzo81DFOXe0FfMbxgyGh2FoaOnNNQuVkREODQzwEPDgpJ2UAv8AAAyWSURBVCaSHNqzh/KePeq/Hxqa2XnKaDkRz9HP6aa2lUJbMl59goBhtJe69nvnhO/6BFDN5dq72Dk4SDWbpVosTg+LHBlp+03GWL1YWQNjeTI4uPD4cZ+9Wh3widmFgoptO2uzZ7ON57aF1vPCGmBrk86x8fZeynnDqlAaq5OhIaLQVi8IbyajVn0+b64TY8VglryxsigWG1vK3h0SlUozarRX8nmqaEhYNzAJlPP56TVjWmndz3aNhtEGTOSNlcPwsLpcknHmvmpkyov7BjSqZTLxtTRa8zU0455AfzEqCZ9/JZ+HPXsWLs5DQ7qW0Iq5jHnThWvaXXNfm6/lfGEib6wMgnh6l0sql6OK/gfvRsV7YzewF3oBboPTo75lH8SVlrZrPdiNB+AxXwWyAq0T5b17zZJfQYjI9wN3AP8JeBr4VefcnzQY1wf8HvAGf+jj5ytAxUTeWN6E+u4J67jqBf8SfDOPwI1AVrs3dWShKwT7glb5ZvrYi4GHb4VqJtNaUTaBXzTWzMOSb5I/AE4DzweywF+IyLedcw/Wjft/ge9D84c2Al8Vke865z7dyotphIm8sbzJZmf6y7NZKqUSE+hvU81K79e2euUNfv/FEJ3R0gLRDug4iop+GqY26P62WzWm3TDqEZG1wFuAjHOuDHxDRP4MeBua35zkTcAbfD/XgyJyB/CzgIm8YcxJvWWczVItFDgKVEahb1SbeQSSrfnKabTTJpC+BPoT3ZtCduo5MzQU34SSx0olKz62PLhQRO5P7N/u624FXgxMOeceSRz7NrPX+ZK69+elG4yJvLHiqaCVIBmHaJtWfRxP6fYQ2jz7LoAIrktp2d/tZXjeUTgxeZaJ5yKb1TDNkPyUz8eRPbkclYGBxq4bc+e0jQ4c65hqdvjTzrmdZ/k8jW/Lm+A4cZfeJAeA94nIO1DXzs+i7pu2YyJvrGjOoIuuJyZ9yd/tWtc9HanYXxapyCdNqv6Klv4NjbTPWXQHB3Xxt1CAfF4jdrywR/k86UJBo4GYXrumAmbpLw/K+HX8BL00bs37i8DHgEeBZ4DPAT/d1qvzmMgbK49sFvbsqcW/V/D/0X1t97WDsDYNmzZoe76dKZjoVvfNeAo2ndSSwAxpAbGFEqGm3bFwbdksVTTqp5+Z/b0ngGO5nFWmbAMOR7V1C6+PAJGIXOKce9QfexlQv+iKc+7fgevDvoj8P8A/tOpCzoaJvLEyyWZhZIRqsai/0sUihwoFNuzX8Ei2q1W/dheQhnX9cGozbOqDdY8DB+DIZIPqkCETtt66z+Wm/TJF6BNEDxq6eWJSzbvkfKH7n7E8cc49KyJfBD4oIu9Eo2veDPxo/VgRuRi9f0+g4ZY3MLvvvqWYyBsrmyDG2SyVbJan8nlOTgKj0DkKqf0qxilgzQ5Ys11b71VIiHJS0IeHp9efKRaJ8nnW+zkCG7vRkM3twBj03gbRJE3FyB8Dqnv2LPQnN84P7wY+BRxB3TDvcs49KCI/BnzFOZf2414J7EXX8x8Brm8QZtkWTOSN1YNv6DFRX/ExZLaOQjQaW+FVGljy9YXEikVSqNulawcq6rv0dcqHanZk9dilY3B6d4FKoaDRPrsS89ym1v4hrIFIO3HQSndNcMP8ZIPjX0cXZsP+54HPt+zE88BE3lh9nKXiYxD2SiiRMFsETB1d3WhktHf7lDfEoZrpqsbjR5cAT8CaCjzrzf7UBHRMog/610Mlc16i6oxVhIm8YTQiJFjNJfDZLJVCgccm4eK7gfclkq2IF3PTVaDHx+b7yJ50Ffp7IHUS1gK910B6f4mylT1oGy1eeF0WWKlhw5iNJoU2RPCc3g+MqWgH0l5PyhF8LQWfjuADwDejOFa/0qOZuFwLl+6AdD6vvn/DaAEm8obRAmqPxEVY+1R8PFju5QguQ+Pxg0PmocTn1eDY3wUXAalCwWraGy3B3DWGsUBCpUsAxoAi9HVDeZta6qAWfRD6y+q+X46gL4Uu021PROmYy6YNrD53TctEXkTuAa4kDkj4V+fcpa2a3zCWJD5i52guxySwfj/07YeufbAJSG+uq5WT+I3rr8SLsulxYAxO7NaUSMt4NVpFqy35G51zn2zxnIax9BkZoTw8TKVQ4BiwbbcK/fMmIb1eM2vTnbHQ91fUdx9VYM0xVOCvh0eTDU+MlnOa0zzJocW+jPOKuWsMo1X4WjXlQoGHgPRu2IyWG1znY+if5zNsKaIlq8bg9KQ2jT46MLDw5uSGUUerRf4jIvJbwMPA+51z9zQaJCI3oGm99G/c2OJLMIxFxIdeVoGJ/7+9uwuRsorjOP79pdJCi1RYQQZGYpoVWdlVGV0YlhAFBWkvdNEbSYHURTdFpYUYVBe9mWBpKUEXWVmBRNGFQpZRUfayvWlZRq2QOauuYr8uzjM2bbvN7DQ759nx/4HnYp49M/xYZ/+eOc8z//Pxx/wO6QKqgbWf0b02/dFVKDYjmcy/2xGH0EKtLPJ3A5+TdkmZB6yTNMP2twMHFj2ZlwNMnzrVLcwQQn41rRQGqqxcmYp+LMlkcYD9bGdr7hht1VCRLy6qDtVMZ6PtC2xvqjm3StJ8YC6pvWYIAWLGHtquoSJv+6ImXtv8cyeUEELIaj/9h91MviVfhpJ0tKQ5krokjZV0LXAhsL4Vrx9CCGUk6VhJayX1Sdom6ZohxknSUkk7i+NhSW2ZBLdqTX4c8CAwDTgIfAlcYfurFr1+CCGU0ZOk65AnkNrMvSHpk0HaCN9C6lZ5FmmV4y3gO2DZSAdsSZG3/RtwXiteK4QQRsr+Fl54lXQUcCVwhu0KsEHSa8D1pJ6ktW4AHrG9vXjuI8DNtKHIR++aEEJozqnAQds9Nec+AU4fZOzpxc/qjWu57F+G+qKnp3fm7Nnb6gybAPS2I08TIltzIltzDvdsk/7Pk82f6/fSN6H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"text/plain": [ "Failed to display Jupyter Widget of type HBox
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\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
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\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='initialize kmeans++ centers'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=250), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "05b1ecec790d4c88b55cb9ba48a92026", "version_major": 2, "version_minor": 0 }, "text/html": [ "Failed to display Jupyter Widget of type HBox
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\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='kmeans iterations'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=10), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "15-02-18 14:33:44 pyemma.coordinates.clustering.kmeans.KmeansClustering[2] INFO Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n", "\r" ] } ], "source": [ "clustering = coor.cluster_kmeans(Y,k=n_clusters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The trajectories are now assigned to the cluster centers." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ba1696f6b9c450eaa2511a294c6a8cf", "version_major": 2, "version_minor": 0 }, "text/html": [ "Failed to display Jupyter Widget of type HBox
.
\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='getting output of KmeansClustering'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=25), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r" ] } ], "source": [ "dtrajs = clustering.dtrajs" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Failed to display Jupyter Widget of type HBox
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\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='estimating MaximumLikelihoodMSM'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=12), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r" ] } ], "source": [ "its = msm.timescales_msm(dtrajs, lags=200, nits=10)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Failed to display Jupyter Widget of type HBox
.
\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='estimating BayesianMSM'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=11), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r" ] } ], "source": [ "its = msm.timescales_msm(dtrajs, lags=100, nits=10, errors='bayes', n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Failed to display Jupyter Widget of type HBox
.
\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='Sampling MSMs'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r" ] } ], "source": [ "# test MSM\n", "M = msm.bayesian_markov_model(dtrajs, msm_lag)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b3f53633230445ed9de2d0f9c999debe", "version_major": 2, "version_minor": 0 }, "text/html": [ "Failed to display Jupyter Widget of type HBox
.
\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "
\n", "\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "
\n" ], "text/plain": [ "HBox(children=(HBox(children=(Label(value='estimating BayesianMSM'),), layout=Layout(max_width='35%', min_width='35%')), HBox(children=(IntProgress(value=0, max=10), HTML(value='')), layout=Layout(padding='0 0 0 20px'))), layout=Layout(display='flex', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r" ] } ], "source": [ "ck = M.cktest(4, mlags=11, err_est=False)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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3gGtUdU9ndfqYNi2b5uZNFpjAMOLAhAlPsWfPYYCJpdE76axnuRznKVfq/a24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