{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst Weights\n", "\n", "*This notebook generates plots for burst weights section of the paper:*\n", "\n", "
\n", "
\n", "FRETBursts: Open Source Burst Analysis Toolkit for Confocal Single-Molecule FRET
\n", "Ingargiola et al. (2016). biorXiv doi:10.1101/039198\n", "
\n", "\n", "> The main notebook is: [FRETBursts Paper Main Notebook](FRETBursts Paper Main Notebook.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading software\n", "\n", "Load software and define default figure style." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Optimized (cython) burst search loaded.\n", " - Optimized (cython) photon counting loaded.\n", "--------------------------------------------------------------\n", " You are running FRETBursts (version 0.5.5).\n", "\n", " If you use this software please cite the following paper:\n", "\n", " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", " Ingargiola et al. (2016). http://dx.doi.org/10.1101/039198 \n", "\n", "--------------------------------------------------------------\n" ] } ], "source": [ "from fretbursts import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.patches import Polygon\n", "from scipy.stats import binom, expon, gamma, poisson" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns = init_notebook(seaborn_style='ticks')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "figure_size = (5, 4)\n", "default_figure = lambda: plt.subplots(figsize=figure_size)\n", "save_figures = True\n", "\n", "def savefig(filename, **kwargs):\n", " if not save_figures:\n", " return\n", " import os\n", " name, extension = os.path.splitext(os.path.basename(filename))\n", " dir_ = '../figures/%s/' % name\n", " kwargs_ = dict(dpi=150, bbox_inches='tight', \n", " frameon=True, facecolor='white', transparent=False)\n", " kwargs_.update(kwargs)\n", " plt.savefig(dir_ + name + extension, **kwargs_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Estimators Variance\n", "\n", "Following the discussion in section SI-8.6, here we compare empirically \n", "the performances for weighted and unweighted estimators ($\\hat{E}_{uw}$ and $\\hat{E}_{w}$)\n", "of a static FRET population PR $E_p$:\n", "\n", "$$\\hat{E}_{uw} = \\frac{1}{N} \\sum_i E_i$$\n", "\n", "$$\\hat{E}_w = \\frac{1}{N} \\sum_i w_i E_i\n", "= \\frac{1}{N} \\frac{\\sum_i n_{ai}}{\\sum_i n_{ti}} \\quad\\textrm{with}\\quad\n", "w_i = \\frac{n_{ti}}{\\sum_i n_{ti}}$$\n", "\n", "For this purpose, we generated a static FRET population of N=200 bursts \n", "by extracting burst-sizes from an exponential distribution ($\\lambda = 50$)\n", "and acceptor counts from a binomial distribution ($E_p = 0.2$). \n", "\n", "By repeatedly (`ntrials = 2000`) fitting the population parameter $E_p$ \n", "we can build the distribution of the two estimators and verify that,\n", "as predicted by theory, the size-weighted estimator ($\\hat{E}_w$ )has lower\n", "variance than the unweighted one ($\\hat{E}_{uw}$).\n", "\n", "We start by definig the function to generate bursts:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gen_burst_counts_exp(E_p, num_bursts, mean):\n", " min_burst_size = 3\n", " nt = (min_burst_size + expon.rvs(scale=mean, size=num_bursts)).astype(int)\n", " na = binom.rvs(nt, E_p)\n", " nd = nt - na\n", " return nd, na" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_p = 0.2\n", "N = 100\n", "ntrials = 5000\n", "mean_burst_size = 10\n", "\n", "np.random.seed(54321)\n", "nd, na = gen_burst_counts_exp(E_p, num_bursts=N*ntrials, mean=mean_burst_size)\n", "nd = nd.reshape(ntrials, N)\n", "na = na.reshape(ntrials, N)\n", "nt = na + nd\n", "EE = na / nt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5000, 100)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "EE.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Empirical mean burst size: 12.511758\n" ] }, { "data": { "image/png": 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FroiIiAkUuCIiIiZQ4IqIiJhAgSsiImICBa6IiIgJFLgiIiImUOCKiIiYQIErIiJiAgWu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\nKHBFRERMoMAVERExwf8DNPAeLL+5HwsAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Empirical mean burst size: ', (nd + na).ravel().mean())\n", "plt.hist((nd + na).ravel(), bins=40, histtype='step', lw=1.5, );" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Y7XaysrIAiIuLIyEhAbvdTl1dHVarldjYWACys7Oprq4GoLa2NuAaERGR4cJnoDqdThIT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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bins = np.arange(-0.075, 1, 0.025) - 0.025/2\n", "plt.hist(EE.ravel(), bins, histtype='step', color='k', lw=1.3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unweighted Estimator" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unweighted E estimator\n", "Mean = 0.19991 Std.Dev. = 1.4644 %\n" ] } ], "source": [ "Eh = np.nanmean(EE, axis=1)\n", "Eh_mean, Eh_std = np.nanmean(Eh), np.nanstd(Eh)*100\n", "print('Unweighted E estimator')\n", "print('Mean = %.5f Std.Dev. = %.4f %%' % (Eh_mean, Eh_std))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weighted Estimator" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weighted E estimator\n", "Mean = 0.20002 Std.Dev. = 1.1383 %\n" ] } ], "source": [ "Ehw = np.nansum(EE * nt, axis=1)/ np.nansum(nt, axis=1)\n", "Ehw_mean, Ehw_std = np.nanmean(Ehw), np.nanstd(Ehw)*100\n", "print('Weighted E estimator')\n", "print('Mean = %.5f Std.Dev. = %.4f %%' % (Ehw_mean, Ehw_std))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Percentage variance increase for the unweighted estimator: 28.65 %\n" ] } ], "source": [ "delta = 100 * (Eh_std - Ehw_std) / Ehw_std\n", "print('Percentage variance increase for the unweighted estimator: %.2f %%' % delta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimator distribution plots" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_style('ticks')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = default_figure()\n", "bins = np.arange(0, 1, 0.025) + 0.025*0.5\n", "kws = dict(bins=bins, histtype='step', lw=1.3, normed=True)\n", "_, _, (p0,) = ax.hist(EE.ravel(), color='k', **kws);\n", "ax.set_xlabel('E')\n", "\n", "ax2 = plt.twinx()\n", "ax2.grid(False)\n", "ebins = np.arange(0, 0.4, 0.005)\n", "kws.update(bins=ebins, lw=1.5)\n", "_, _, (p1,) = ax2.hist(Eh[np.isfinite(Eh)], **kws);\n", "_, _, (p2,) = ax2.hist(Ehw[np.isfinite(Ehw)], **kws);\n", "ax.set_xlim(0, 0.5);\n", "fig.legend((p0, p1, p2), \n", " (r'$\\{E_i\\}$', r'$\\{\\hat{E}_{\\rm uw}\\}$', r'$\\{\\hat{E}_{\\rm w}\\}$'),\n", " fontsize=16, loc='upper right');\n", "ax.yaxis.set_visible(False)\n", "ax2.yaxis.set_visible(False)\n", "sns.despine(fig, left=True, offset=0);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ODgagqKiIuLg4nE4n+fn57vlFRUWEh4fXWkR2djYrV650v75YEIuIiNTH0KFD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\noDq/nzSdRg/KEydOkJ6ezvr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ebins = np.arange(0, 0.4, 0.005)\n", "plt.hist(Eh[np.isfinite(Eh)], ebins, histtype='step', lw=1.5, label='Unweighted');\n", "plt.hist(Ehw[np.isfinite(Ehw)], ebins, histtype='step', lw=1.5, label='Weighted');\n", "plt.xlim(0.2 - 0.08, 0.2 + 0.08);\n", "plt.legend();\n", "plt.xlabel('E');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gamma-distributed burst sizes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how to call a scipy's gamma distribution:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.arange(0,300, 0.1)\n", "mean_ = 50\n", "a = 2\n", "y = gamma.pdf(x, a=a, scale=mean_/a)\n", "y2 = gamma.pdf(x, a=1, scale=mean_) # this is an exponential" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JMJvNSEhIQO/evevd6ejRo5GZmYmEhARYrVbEx8cjMTGxwY12BvXdd0N5U0eUf/Ip/JKf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\nQ/wUCPJVIECngL9OhL9ORED1uwL+Qc2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y)\n", "plot(x, y2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's perform the same simulation but with a gamma-distributed burst sizes:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gen_burst_counts_gamma(E_p, num_bursts, mean, a=2):\n", " nt = gamma.rvs(scale=mean/a, a=a, size=num_bursts).astype(int)\n", " na = binom.rvs(nt, E_p)\n", " nd = nt - na\n", " return nd, na" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_p = 0.2\n", "N = 100\n", "ntrials = 5000\n", "mean_burst_size = 10\n", "\n", "np.random.seed(54321)\n", "nd, na = gen_burst_counts_gamma(E_p, num_bursts=N*ntrials, mean=mean_burst_size)\n", "nd = nd.reshape(ntrials, N)\n", "na = na.reshape(ntrials, N)\n", "nt = na + nd\n", "EE = na / nt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unweighted Estimator" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unweighted E estimator (Gamma)\n", "Mean = 0.20031 Std.Dev. = 1.7917 %\n" ] } ], "source": [ "Eh = np.nanmean(EE, axis=1)\n", "Eh.shape, np.nanmean(Eh), np.nanstd(Eh)*100\n", "print('Unweighted E estimator (Gamma)')\n", "print('Mean = %.5f Std.Dev. = %.4f %%' % (np.nanmean(Eh), np.nanstd(Eh)*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weighted Estimator" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weighted E estimator (Gamma)\n", "Mean = 0.20014 Std.Dev. = 1.2984 %\n" ] } ], "source": [ "Ehw = np.nansum(EE * nt, axis=1)/ np.nansum(nt, axis=1)\n", "print('Weighted E estimator (Gamma)')\n", "print('Mean = %.5f Std.Dev. = %.4f %%' % (np.nanmean(Ehw), np.nanstd(Ehw)*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimator distribution plots" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = default_figure()\n", "bins = np.arange(0, 1, 0.025) + 0.025*0.5\n", "kws = dict(bins=bins, histtype='step', lw=1.3, normed=True)\n", "_, _, (p0,) = ax.hist(EE.ravel(), color='k', **kws);\n", "ax.set_xlabel('E')\n", "\n", "ax2 = plt.twinx()\n", "ax2.grid(False)\n", "ebins = np.arange(0, 0.4, 0.005)\n", "kws.update(bins=ebins, lw=1.5)\n", "_, _, (p1,) = ax2.hist(Eh[np.isfinite(Eh)], **kws);\n", "_, _, (p2,) = ax2.hist(Ehw[np.isfinite(Ehw)], **kws);\n", "ax.set_xlim(0, 0.5);\n", "fig.legend((p0, p1, p2), \n", " (r'$\\{E_i\\}$', r'$\\{\\hat{E}_{\\rm uw}\\}$', r'$\\{\\hat{E}_{\\rm w}\\}$'),\n", " fontsize=16, loc='upper right');\n", "ax.yaxis.set_visible(False)\n", "ax2.yaxis.set_visible(False)\n", "sns.despine(fig, left=True, offset=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. FRET Histograms\n", "\n", "## Getting the data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#url = 'http://files.figshare.com/4917046/smFRET_44f3da_P_20_s0_20_s20_D_6.0e11_6.0e11_E_75_30_EmTot_200k_200k_BgD1500_BgA800_t_max_600s.hdf5'\n", "#download_file(url, save_dir='./data')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "URL: http://files.figshare.com/4917049/smFRET_44f3da_P_20_s0_20_s20_D_6.0e11_6.0e11_E_75_30_EmTot_160k_160k_BgD1500_BgA800_t_max_600s.hdf5\n", "File: smFRET_44f3da_P_20_s0_20_s20_D_6.0e11_6.0e11_E_75_30_EmTot_160k_160k_BgD1500_BgA800_t_max_600s.hdf5\n", " \n", "File already on disk: /Users/anto/src/fretbursts_paper/notebooks/data/smFRET_44f3da_P_20_s0_20_s20_D_6.0e11_6.0e11_E_75_30_EmTot_160k_160k_BgD1500_BgA800_t_max_600s.hdf5 \n", "Delete it to re-download.\n" ] } ], "source": [ "url = 'http://files.figshare.com/4917049/smFRET_44f3da_P_20_s0_20_s20_D_6.0e11_6.0e11_E_75_30_EmTot_160k_160k_BgD1500_BgA800_t_max_600s.hdf5'\n", "download_file(url, save_dir='./data')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'./data/smFRET_44f3da_P_20_s0_20_s20_D_6.0e11_6.0e11_E_75_30_EmTot_160k_160k_BgD1500_BgA800_t_max_600s.hdf5'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = './data/' + url.split('/')[-1]\n", "filename" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "URL: http://files.figshare.com/4916914/008_dsDNA_22d_500pM_green100u_red40u.hdf5\n", "File: 008_dsDNA_22d_500pM_green100u_red40u.hdf5\n", " \n", "File already on disk: /Users/anto/src/fretbursts_paper/notebooks/data/008_dsDNA_22d_500pM_green100u_red40u.hdf5 \n", "Delete it to re-download.\n" ] } ], "source": [ "url2 = 'http://files.figshare.com/4916914/008_dsDNA_22d_500pM_green100u_red40u.hdf5'\n", "download_file(url2, save_dir='./data')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'./data/008_dsDNA_22d_500pM_green100u_red40u.hdf5'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename2 = './data/' + url2.split('/')[-1]\n", "filename2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the Data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = loader.photon_hdf5(filename)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#donor: 699249 #acceptor: 1428691 \n", "\n" ] } ], "source": [ "d2 = loader.photon_hdf5(filename2)\n", "loader.alex_apply_period(d2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background Estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Background estimation automatic threshold and a background period of 20s:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Calculating BG rates ... [DONE]\n", " - Calculating BG rates ... [DONE]\n" ] } ], "source": [ "d.calc_bg(bg.exp_fit, time_s=40, tail_min_us='auto', F_bg=1.7)\n", "d2.calc_bg(bg.exp_fit, time_s=40, tail_min_us='auto', F_bg=1.7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> For more info see [Background estimation](http://nbviewer.jupyter.org/github/tritemio/FRETBursts_notebooks/blob/master/notebooks/FRETBursts%20-%20us-ALEX%20smFRET%20burst%20analysis.ipynb#Background-estimation) in the us-ALEX tutorial." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LF893Ou+99x4TJkxAo9Hg4uLC9OnTCQ8P5+7du6xYsYLly5ejUqnw9vZm5MiR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\n8KmnnlJBQYF+/OMfB4Z84uLitHLlSs2dO5f2OkVBQYFeeeUVuVwuXXH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(d, timetrace_bg);" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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27Ik33njDJc/u3buRmpqKTZs2YePGjdiyZQuOHDkSiOIQQgghxE8BCQwMBgPm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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(d2, timetrace_bg);" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ...[DONE]\n", " - Calculating burst periods ...[DONE]\n", " - Counting D and A ph and calculating FRET ... \n", " - Applying background correction.\n", " - Applying leakage correction.\n", " [DONE Counting D/A]\n" ] } ], "source": [ "d.burst_search(F=4)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = d.select_bursts(select_bursts.size, th1=10)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns = init_notebook(fs=13, savefig_dpi=75, seaborn_style='ticks')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Overwriting the old E_fitter object with the new weights.\n" ] }, { "data": { "image/png": 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OHdvlxRG1RZIk9Nm2xSwgaiqqcSMu2klVdR+SJGHqsNeg1tfjyvpw+Bk1cQit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\nYkATESnU/wPcQVyk6gCdqAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = default_figure()\n", "kws = dict(hist_style='step', hist_plot_style=dict(alpha=1, drawstyle='steps-mid', marker=None))\n", "dplot(ds, hist_fret, **kws, ax=ax)\n", "dplot(ds, hist_fret, weights='size', ax=ax, **kws)\n", "ax.set_title('');\n", "sns.despine()\n", "ax.grid(False)\n", "ax.legend(['Unweighted', 'Weighted'],\n", " bbox_to_anchor=(0.6, 1.05), loc=2, borderaxespad=0.)\n", "\n", "savefig('weight_fret_hist_sim_mixture.png')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ...[DONE]\n", " - Calculating burst periods ...[DONE]\n", " - Counting D and A ph and calculating FRET ... \n", " - Applying background correction.\n", " - Applying leakage correction.\n", " - Applying direct excitation correction.\n", " [DONE Counting D/A]\n" ] } ], "source": [ "d2.burst_search(F=4)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Deep copy executed.\n", "Deep copy executed.\n", "Deep copy executed.\n", " - Performing burst search (verbose=False) ... - Recomputing background limits for Dex ... [DONE]\n", " - Recomputing background limits for all ... [DONE]\n", " - Fixing burst data to refer to ph_times_m ... [DONE]\n", "[DONE]\n", " - Calculating burst periods ...[DONE]\n", " - Performing burst search (verbose=False) ... - Recomputing background limits for AexAem ... [DONE]\n", " - Recomputing background limits for all ... [DONE]\n", " - Fixing burst data to refer to ph_times_m ... [DONE]\n", "[DONE]\n", " - Calculating burst periods ...[DONE]\n", " - Calculating burst periods ...[DONE]\n", " - Counting D and A ph and calculating FRET ... \n", " - Applying background correction.\n", " - Applying leakage correction.\n", " - Applying direct excitation correction.\n", " [DONE Counting D/A]\n" ] } ], "source": [ "dd2 = bext.burst_search_and_gate(d2, F=4)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d2t = dd2.select_bursts(select_bursts.time, time_s2=600)\n", "ds2 = d2t.select_bursts(select_bursts.size, th1=10)\n", "ds2a = d2t.select_bursts(select_bursts.naa, th1=10)\n", "#ds2h = d2t.select_bursts(select_bursts.size, th1=20)\n", "#ds2ha = d2t.select_bursts(select_bursts.naa, th1=20)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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duUaTh7etPZ55eeRkppGVkUpSXCy5Odlo8nO5npFOXlISedk3yMpIJT8vF40m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8lsP9t6ItjMMuUBO6t3RwgOeZnfotKZHKTaVkZGVdgTO8TcoIehiEZ/LE6z30NPcoR9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\nJOpt+nO9M01ujB0ZK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sTjaTIZlbKyQmGeEt5aSEQj/OQnP+Gpp55ClmU+9alP8ZnPfAaH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alex_jointplot(ds2)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Overwriting the old E_fitter object with the new weights.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EeIaEQDliOGQqlavDIiJye0yEiMqBKIoQdToAhUlQ2gsjkZ90SSrXbNqEWlu3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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 600s, DCBS, F=4, size th1 = 10\n", "kws = dict(hist_style='step', hist_plot_style=dict(alpha=1, drawstyle='steps-mid'))\n", "ax = dplot(ds2, hist_fret, **kws)\n", "dplot(ds2, hist_fret, weights='size', ax=ax, **kws)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Overwriting the old E_fitter object with the new weights.\n", " - Overwriting the old E_fitter object with the new weights.\n" ] }, { "data": { "image/png": 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/HtNdMHv2bBw9etTikZGR4eqyyMXq55m1pyYry2qKRCKluLQl68zVXZVKBZVK\nZbHN19djGuIkk8bmmQUs55olchWXphS/vlFrsDXPLACr/lkiV3BZyN51110oKChw1emJiBThMX2y\nRESeiCFLRCQjhiwRkYwYskREMuI9UJ5GCGhM1XWjm0w/r0pLRO6JIetBhBDYkf8S4suP43oUuBIt\nkQdgd4EnqaxEfPnxRl9uqxPBELkztmQ9VIfM7xEQFGixra1MBEPkSRiyHqotTwTTHA1n6BJVJmiM\nVaj0Ubu0JmobGLJuimt5ta7b5zA4BeBQ+3shxG7XFERtBkPWDTW2lpfGWIVTLqrJE9XP0FWTlWXz\n9UHlx4HKSkATaPN1otbAkHVDdtfyukWjZt9rUxqboUt/tQI3BsfaPMbWN4iGtGqJ/d7kFIasm7NY\ny8tgwPWouu38oDvG1gxdUtXP3S76apP5uYDAA0vO4MiZqkbfj+uCkbMYsm6ufi0vADCZfHhvbCvr\nMecUKlWOL09fvy5YgD9DlhzDkKU2R6OW7P6yGtjdv661Ctvrgtm6AMluBGoMQ5banIZheCktwqo7\noanA5PLi5AyO+KI2Tev/89Lq9Q9bQcnlxam52JIlcoAkSfhmWXeb9y5zeXGyhyHrQo3dLsRBB+5J\nkiRe8CKnMWRdpLEBB0TkXRiyLtLkgAMhkNjLB/7GSpgMda0nzhvb+hrOaVCPE+1Qa2LIuoGGAw6A\nulZuxZSJMG79Hj9tdWFhbcDtcxoAdVNGdt6RzqClVsG7C9xA/YCD+odWVMOY/X2j+3Pe2Japn9Og\nMTVZWVZDcYmaiy1ZN9c1N8d6WCi/zrZIY3MaCIPB3LK11Y0A8N+enMeQdXOcN1YetuY0aBiqtroR\nAHYlkPPYXUB0S1PdCEBdV4KptBQmgwEmgwHCYIDGWAUIAX21Cfoqy4cQHKDQ1rElS3RLY90IgGVX\nQmMTgIfOeBO4rYXL4bbEkHUlG8t7A7xVy5VsdSMAgHBgAnCNqdpqRi/O2kUMWQXYGtlVUWXk8t4e\nxJGLZQ0nm2lq1i6AM3e1FQxZmdldSsbO8t4Ab9VyN01dLNP6+8DH3/oyR2NzG7AroW1gyMrMkaVk\nQo9kwycgwGo7bxfyXPWzdu0/0XjXD7sS2gaGbCtqaoXZxpaS8QkI4G1aXqaxWbsAztzV1jBkW4kj\nE75wKRnvZmsAg5bfRto8hmwraapbYFiEFlquMOvVmjMPApey8X4MWSc5MgesrQlfNKYqiMpK1B/J\n27S8g9QPBxptAAALX0lEQVTErV31gxcaXjATVSZojFWo9FHb7DawtcZYUxjM7kvxkL169SoWLlyI\nzMxMdOvWDQsXLkRCQoLSZTSLo3PANuwWEELgyuMTcf3wYSVKJIU5cmuXrRZu/QCGCVHWAxiOnKlC\n+ycLnaqDdyq4L8VDdv78+VCr1fjkk0+wb98+zJs3D3v27EFISIjSpdjV2EWs5swBW2MnYHmbluez\ndWtXU4MXgLoBDOXvdjcfKyDwwJIzOHKmyukaeKeC+1I0ZM+cOYODBw/i22+/RVBQEHr16oX9+/dj\n+/btmDVrlpKl2GW3xXprlFaxrrdFlwCEQMUTk2DcerzROWA5o1bb4egQXY2pClKD0X6HX+uKSicW\nZNRXm9BjzilU+qjZv+umFA3ZvLw83HPPPQgKCjJvi46OxpEjR5Qsw0KjLdZCAzSmaovtEgS257+M\n+/SnURvn3Cgtv/h4+AQH8we+DWlsiK4js3054xSAowG90GP6aojb+nEjenbENyk9nOrfbQwDu3kU\nDdnS0lKrboHOnTvj0qVLsp7XZDLBUGZ9oUlAYOTys8g7ax2me26FqbPa9euHzjvSrfrZ2GKlek1d\nLGuO+/SncfK7X1ptP5rfC6FF1uHbHP3D1fjn4nDHA9vJn3khBNDIZOnaO7Tw8fHMSQMVDdnKykr4\n+flZbGvXrh1u3rzZovc1XDeg4mqFzdeEMCFn+GPode0Hm6+nN+N8jQUpwDClptnrSnCaELj8y4mo\nPXbM5suNhW+zfAfc+Ivjux8N6IUJUY4FfMNvibZkd/ovRO/7DJLk+qB1NvAVDVm1Wm0VqDdv3oTG\ngQs/RqMRJpNln1PlrR/Sk+MfQbmdeTvbAfhPM4LPt/d/IUi3zipMq9RqVJSUOP1+RHIQ698Bqqtv\n2yhwdc5c1J6y3bhQQgdDEb7MnODUMY19TtuVncbRAfe1RlktVhzcB4O3v2vVmLr77rutGpGAwiEb\nGhqKK1euWGy7cuUKunTp0uSxqamp0Ol0Nl9L1vrb3N5iFy8AE5z7ISFyKwG8c6XVVZ0Dxo612vz5\n55+jZ8+eVtsVDdkBAwbg7NmzKCsrwx133AEAyMnJweDBg5s8dvbs2VZ3IBgMBuTk5KBbt25o166d\nLDW3RG1tLcaNG4eMjAz4+rrvuA/W2bpYZ+vylDrvvvtum9sVrTgsLAyDBw/GggUL8PLLL+Obb75B\nbm4uVq5c2eSxKpUKKpXKYlvHjh3x0EMPyVRty9V3jfTo0cMtfwnUY52ti3W2Lk+pszGK9yKvWrUK\nJpMJkydPxvbt26HT6RzqLiAi8kSKt707d+6MtLQ0pU9LROQSrr8fgojIi6mWLl261NVFeDNJkjBo\n0CC3v5GadbYu1tm6PKVOWyTBheGJiGTjeb8WiIg8CEOWiEhGDFkiIhkxZImIZMSQJSKSEUOWiEhG\nDNkWunr1KmbNmoXo6GiMHTsW3377rc39hBBYsWIFBg8ejISEBGzatMkt6ywvL0dycjKGDBmCBx54\nAG+88UaL5/uVo86GVqxYgWnTpilQ3c+cqXPz5s0YPnw4Bg0ahOTkZFTfPi2hG9RZVVWFJUuW4P77\n78eDDz6INWvWKFZjQ2VlZRg6dChKS0ttvu7qz1GzCGqR6dOni+eee0788MMPYtOmTSI6OlqUlJRY\n7bdhwwYxatQokZ+fL/bu3Svi4uLEl19+6XZ1vvTSS+J//ud/RGFhocjJyRGjR48Wb731ltvVWe/Y\nsWPi3nvvFdOmTVOsRiEcr/Ozzz4TsbGx4uuvvxYFBQXikUceEatWrXK7OletWiUmTJggCgsLRVZW\nlkhISBDbtm1TrE4hhLhx44b41a9+JSIjI8WVK1ds7uPqz1FzMGRboLi4WPTt21eUlpaat02fPl2s\nX7/eat+EhASxZ88e8/N3331XPPPMM25VZ3V1tYiKihL5+fnmbbt27RIjR450qzrrmUwmMWnSJDF1\n6lRFQ9aZOh9//HGxefNm8/MvvvjC7f6/CyHEuHHjxN/+9jfz85UrV4p58+YpUqcQQuTn54tRo0aJ\n8ePH2w1ZV36OmovdBS3g6MKQJSUluHLlCmJiYiz2y83Ndas6gbrJ0SMiIszPJUlS7Outswttbtu2\nDV26dMHDDz+sSH31HK2zoqICBQUFSExMNG8bNWoU3n//fbeqEwA6deqEPXv2QK/X4/Lly/j222/R\nr18/ReoEgO+++w7jxo3D22+/XbfWlw2u/hw1F0O2BRxdGLJ+NYiGUzoGBwfDYDBAr9e7TZ1+fn5I\nSEiwmLPzz3/+s8UPtTvUCQCXLl3Chg0bsHjx4kY/lHJxtM7z589DkiRcvHgRkyZNwvDhw5GSkoKa\nmhq3qhMA5s+fjx9++AHx8fF48MEHERISgqSkJEXqBIAZM2Zgzpw58PX1bXSNPFd/jpqLIdsCji4M\nWVlZaTXZcP1zJT5wzV3Act26dcjPz8ecOXPkLM/MmTpXrFiBp556CnfeeacitTXkaJ31H/zXX38d\nL774IlavXo2DBw9i1apVblUnAJw9exY9evTAhx9+iA0bNuD06dN49913FanTUa7+HDUXQ7YFHF0Y\nUq1Wo7a21mo/ANBqtfIWieYtYLlx40Zs2LABq1atQq9eveQuEYDjde7btw8nT57E9OnTFanrdo7W\n6evrCyEE5s2bh2HDhiE+Ph4LFy7EJ5984lZ1VlRUYPHixVi0aBFiYmIwfPhwLFy4EO+9957V4qWu\n5OrPUXO574I5HsDRhSFDQ0MhhMDVq1fN/WOlpaVo37491Gq129RZb/369Vi3bh1WrFiB0aNHy15f\nPUfr3LNnD86dO4e4uDgAdWtAGY1GxMTEIDs7223qrH/eo0cP87YePXqgpqbG4mfB1XUWFRWhurra\nYhHAvn37Qq/Xo6ysTPY6HeXqz1FzsSXbAg0XhqyXk5ODAQMGWOwXEhKCrl27WgTA999/j/79+7tV\nnQDw6aefYt26dVi2bBkef/xxReqr52id8+fPx+7du7Fz507s3LkT06ZNQ1RUFD777DO3qvPOO+9E\nly5dUFhYaN52+vRpaLVa80Ki7lBnSEgIhBD44Yeflw8vKiqCRqNxm4AFXP85ajZX3trgDaZPny5m\nzpwpTp48KTZt2iRiY2PFpUuXRE1Njbh8+bIwmUxCCCHWr18vRowYIXJycsz39+3du9et6qyoqBDx\n8fFi4cKF4vLly+ZHY7fTuKJOo9Fodcy7777rkvtkHfn/vnHjRvHQQw+JzMxMkZeXJx599FGxfPly\nt6vz2WefFVOnThUFBQXi8OHDYvTo0WL16tWK1Vnv/PnzIiIiwvwz526fo+ZgyLbQ5cuXxYwZM8SA\nAQPEI488Ig4ePCiEECIzM1NERkaKCxcuCCGEqK2tFSkpKSI2NlYkJCSILVu2uF2de/fuFZGRkRaP\niIgI0b9/f7eq83auCFln6ly3bp0YMmSIiIuLE0uXLhXV1dVuV+f169fFggULxJAhQ8Tw4cPF6tWr\nxc2bNxWrs9758+ct7pN1t89Rc3BlBCIiGbFPlohIRgxZIiIZMWSJiGTEkCUikhFDlohIRgxZIiIZ\nMWSJiGTEuQuoTUpMTMTFixe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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 600s, DCBS, F=4, size th1 = 10\n", "fig, ax = default_figure()\n", "kws = dict(ax=ax, hist_style='step', \n", " hist_plot_style=dict(alpha=1, linestyle='-', marker=None, drawstyle='steps-mid'))\n", "dplot(ds2, hist_fret, **kws)\n", "dplot(ds2, hist_fret, weights='size', **kws)\n", "sns.despine()\n", "ax.set_title('')\n", "ax.grid(False)\n", "ax.legend(['Unweighted', 'Weighted']);\n", "\n", "savefig('weight_fret_hist_measurement.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Note on \"alternative\" Weights\n", "\n", "It may be tempting to use weights that put even more \"weight\" on\n", "large-size bursts (such as size^2, size^3, etc.).\n", "While the use of weight proportional to burst size is grounded\n", "on statistical basis, other these \"alternative\" weights are arbitrary.\n", "\n", "While these weights may results in FRET histograms with sharper peaks, \n", "they also place a disproportionate amount of \"weight\" in the highest bursts.\n", "In this case, a few high bursts may dominate the FRET histogram,\n", "resulting in more statistical noise (especially in the bins close to the peak)\n", "and a decrease in accuracy in the peak position. \n", "\n", "Empirically, FRET histograms built with these \"faster increasing\" weights \n", "resemble unweighed histograms obtained with high burst-size thresholds." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }