{ "metadata": { "name": "", "signature": "sha256:8d60e2987b8a19f58d02a19f85af04564c69a16cc4b2bc2448008740dba3761f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple API level usage of the XFEL Data Exploration Toolbox\n", "*n.b.* you will need `cctbx.xfel` installed to run this, and will need to run it with the command `libtbx.ipython`. \n", "\n", "API documentation can be found at http://cci.lbl.gov/cctbx_docs/xfel/xfel.clustering.html#cluster-cluster \n", "Note that this is in active development, so may be updated freequently/contain new methods or classes that are not yet finished.\n", "\n", "This tutorial will demonstrate the API level usage of the XFEL data exploration toolkit, using a test data set with only 49 images, for simplicity. On my local machine, this is at: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "TESTDATA = ['/users/oli/Dropbox/Stanford_Postdoc/CODING/cctbx_testing/PolG_test_data']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "import logging\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import logging\n", "import brewer2mpl\n", "# Set up logging\n", "reload(logging) # work-around for IPython\n", "FORMAT = '%(message)s'\n", "logging.basicConfig(level=logging.INFO, format=FORMAT)\n", "# pretty colors\n", "cols = brewer2mpl.get_map('BrBG', 'Diverging', 3).mpl_colors" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by creating a cluster object. We will use the `from_directories` class method to create a cluster object." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from xfel.clustering.cluster import Cluster\n", "t_clus = Cluster.from_directories(TESTDATA)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll do some hierarchical clustering on this to get a sense of the unit cells and point groups that make up our data. sub_clusters will be a list of clusters, and we're ignoring the other return value (the axes object we just plotted onto)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sub_clusters, _ = t_clus.ab_cluster(labels=False, write_file_lists=False)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Hierarchical clustering of unit cells\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Using Andrews-Bernstein distance from Andrews & Bernstein J Appl Cryst 47:346 (2014)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Distances have been calculated\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So let's look at the composition `sub_clusters` list we got. Since this is acting on a *group of clusters*, we will import a tool from the `cluster_groups` module." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from xfel.clustering.cluster_groups import unit_cell_info\n", "pretty_str = unit_cell_info(sub_clusters)\n", "print pretty_str" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "12 clusters.\n", "C_id Num in cluster Med_a Med_b Med_c Med_alpha Med_beta Med_gamma \n", "cluster_10 2 230.7(1.1 ) 290.2(3.7 ) 784.3(1.4 ) 90.00 (0.00) 90.00 (0.00) 90.00 (0.00)\n", "2 in P222.\n", "cluster_11 14 224.6(4.6 ) 286.5(1.8 ) 400.0(7.0 ) 90.00 (0.00) 90.00 (0.00) 90.00 (0.00)\n", "10 in C222, 4 in P222.\n", "cluster_12 24 227.4(1.6 ) 227.4(1.6 ) 286.8(2.4 ) 90.00 (0.00) 90.00 (0.00) 120.00(0.00)\n", "24 in P3.\n", "Standard deviations are in brackets.\n", "9 singletons:\n", "\n", " Point group a b c alpha beta gamma \n", "C2 225.7 270.5 1187.4 90.0 90.0 94.0 \n", "C222 231.2 286.7 1131.1 90.0 90.0 90.0 \n", "P2 225.9 229.4 287.2 90.0 90.0 119.4 \n", "C2 228.4 299.2 392.5 93.1 90.0 90.0 \n", "C2 23.7 330.1 358.3 97.9 90.0 90.0 \n", "C2 226.3 290.9 399.7 91.3 90.0 90.0 \n", "P222 226.8 396.7 572.0 90.0 90.0 90.0 \n", "C2 223.1 486.0 682.3 92.7 90.0 90.0 \n", "P1 484.1 600.6 689.2 99.9 91.1 104.5 \n", "\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see the two biggest clusters that were in red and green in the plot above. Lets call these `clu_a` and `clu_b`. These are just the last two elements of the list, since it is sorted." ] }, { "cell_type": "code", "collapsed": false, "input": [ "clu_b, clu_a = sub_clusters[-2:]\n", "print \"clu_a size: {}\\nclu_b size: {}\".format(len(clu_a.members), len(clu_b.members))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "clu_a size: 24\n", "clu_b size: 14\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now pretend we are interested only in the smaller cluster, `clu_b`. Let us examine the intensity distribution for this, again ignoring the returned axes object: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "_ = clu_b.all_frames_intensity_stats()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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HirouJTiLwtdxSspsnML4KYaTxFkLFpMxJYsvthp3L32LjJUk7t45pdZXZVn/Jdx1kpiO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\ngg==\n", "text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's not looking great: a very high B value, and some rising in the intensities at higher resoltion. These are in fact pretty poor quality data, so that's consitent. Now let's look at the point group composition and see if either of these look better separately:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "clu_b.pg_composition" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "{'C222': 10, 'P222': 4}" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "clu_bC = clu_b.point_group_filter('C222')\n", "clu_bP = clu_b.point_group_filter('P222') " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "_ = clu_bC.all_frames_intensity_stats()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Dl+TeER7DfTH7/dIvifPJD1DTPIIToCEKPS9/R/IYnGtJ9nEghO6nB8j9optDrsskxFxg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\nncAOntI5AfgTsBk4yOP1XAQ86vb/HfBWT+l82+27DrgbmOIpncDXsD4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"text": [ "" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Still looking a bit woking at high res.." ] }, { "cell_type": "code", "collapsed": false, "input": [ "_ = clu_bP.all_frames_intensity_stats()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mbWW/dtZR1LDcsO5OTWdfZD1lBss2EzTarDDpZjeZEI0i0SHGFEU80BtxYfhv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\n3nY1Qtb5aGSfLNJGuvjbJAmepMDbuKB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"text": [ "" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks a little more sensible. Now, let's look at the info string:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print clu_bP.info" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Made from files in ['/users/oli/Dropbox/Stanford_Postdoc/CODING/cctbx_testing/PolG_test_data']\n", "############################## Next filter ##############################\n", "Made using ab_cluster with t=10000, distance method, and single linkage\n", "14 of 49 images passedon to this cluster\n", "############################## Next filter ##############################\n", "Cluster filtered by for point group P222.\n", "4 of 14 images passedon to this cluster\n" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how this keeps track of the cluster's provenance, so we know where it came from. Let's now write these images of interest out (so that we can, for example look at them individually using `cctbx.image_viewer`, " ] }, { "cell_type": "code", "collapsed": false, "input": [ "clu_bP.dump_file_list(out_file_name='temp.lst')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "cat temp.lst" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/users/oli/Dropbox/Stanford_Postdoc/CODING/cctbx_testing/PolG_test_data/int-s00-2013-11-10T17:53Z14.813_00000.pickle\r\n", "/users/oli/Dropbox/Stanford_Postdoc/CODING/cctbx_testing/PolG_test_data/int-s03-2013-11-10T17:51Z07.396_00000.pickle\r\n", "/users/oli/Dropbox/Stanford_Postdoc/CODING/cctbx_testing/PolG_test_data/int-s03-2013-11-10T17:54Z36.359_00000.pickle\r\n", "/users/oli/Dropbox/Stanford_Postdoc/CODING/cctbx_testing/PolG_test_data/int-s04-2013-11-10T18:28Z45.434_00000.pickle\r\n" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Marvelous!" ] } ], "metadata": {} } ] }