{"cells": [{"cell_type": "raw", "id": "7a848550", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Simulations\n", "###########\n", "\n", ".. contents::\n", "\n", "\n", "\n", ".. note::\n", "\n", " Download the example file here: `HP3_TE300_SPC630.hdf5 `_\n", "\n", "First, let's run the following code to generate a basic analysis for us to begin working with.\n", "This code is essential the same as that found in the :doc:`tutorial ` ."]}, {"cell_type": "code", "execution_count": 1, "id": "0898aca4", "metadata": {}, "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 0.7.1).\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/10.1371/journal.pone.0160716 \n", "\n", "--------------------------------------------------------------\n", "# Total photons (after ALEX selection): 11,414,157\n", "# D photons in D+A excitation periods: 5,208,392\n", "# A photons in D+A excitation periods: 6,205,765\n", "# D+A photons in D excitation period: 6,611,308\n", "# D+A photons in A excitation period: 4,802,849\n", "\n", " - Calculating BG rates ... get bg th arrays\n", "Channel 0\n", "[DONE]\n", " - 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", " [DONE Counting D/A]\n"]}, {"data": {"text/plain": ["The model converged after 1 iterations"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["The model converged after 36 iterations"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["The model converged after 128 iterations"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["The model converged after 410 iterations"]}, "metadata": {}, "output_type": "display_data"}], "source": ["import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "import fretbursts as frb\n", "import burstH2MM as bhm\n", "\n", "filename = 'HP3_TE300_SPC630.hdf5'\n", "# load the data into the data object frbdata\n", "frbdata = frb.loader.photon_hdf5(filename)\n", "# if the alternation period is correct, apply data\n", "# plot the alternation histogram\n", "# frb.bpl.plot_alternation_hist(frbdata) # commented so not displayed in notebook\n", "frb.loader.alex_apply_period(frbdata)\n", "# calcualte the background rate\n", "frbdata.calc_bg(frb.bg.exp_fit, F_bg=1.7)\n", "# plot bg parameters, to verify quality\n", "# frb.dplot(frbdata, frb.hist_bg) # commented so not displayed in notebook\n", "# now perform burst search\n", "frbdata.burst_search(m=10, F=6)\n", "# make sure to set the appropriate thresholds of ALL size\n", "# parameters to the particulars of your experiment\n", "frbdata_sel = frbdata.select_bursts(frb.select_bursts.size, th1=50)\n", "# make BurstData object to get data into bursth2MM\n", "bdata = bhm.BurstData(frbdata_sel)\n", "# calculate models\n", "bdata.models.calc_models()\n", "# set irf_thresh since later in tutorial we will discuss nanotimes\n", "bdata.irf_thresh = np.array([2355, 2305, 220])"]}, {"cell_type": "raw", "id": "7b514847", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["After an optimization has been conducted, several models are generated and compared. \n", "While statistical discriminators like the **ICL** and **BIC** are perhaps the most useful, another method to \u201ccheck\u201d the reasonableness of a model is to run a Monte-Carlo simulations. \n", "Using the probabilities derived from the model, and a set of arrival times, the :func:`sim.simulate() ` generates a set of photon indices (streams)."]}, {"cell_type": "code", "execution_count": 2, "id": "131b6713", "metadata": {}, "outputs": [], "source": ["sdata = bhm.sim.simulate(bdata.models[2])"]}, {"cell_type": "raw", "id": "10493846", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["The result is a special :class:`sim.Sim_Result ` object, which mimics a :class:`H2MM_result ` object, but uses the simulated times and stores the actual simulated path instead of the Viterbi path in the path variable.\n", "\n", "Thus, you can interogate its parameters just like :class:`H2MM_result ` for instance:"]}, {"cell_type": "code", "execution_count": 3, "id": "c6f37e2c", "metadata": {}, "outputs": [{"data": {"text/plain": ["array([0.08108108, 0.5 , 0.65625 , ..., 0.25 , 0.13793103,\n", " 1. ])"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["sdata.dwell_E"]}, {"cell_type": "raw", "id": "4b5f240e", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["And it even functions in the plotting functions: "]}, {"cell_type": "code", "execution_count": 4, "id": "f9639162", "metadata": {}, "outputs": [{"data": {"text/plain": ["[,\n", " ,\n", " ]"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": "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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "bhm.dwell_ES_scatter(sdata, ax=ax)"]}, {"cell_type": "raw", "id": "4818bf13", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. note::\n", "\n", " The simulation does not simulate the photon nanotimes.\n", " While divisors are simulated, the individual nanotimes are not, and thus trying to access any nanotime-derived parameter in a :class:`sim.Sim_Result ` object will result in an error.\n", "\n", "", "\n", "\n", "Download this documentation as a jupyter notebook here: :download:`Simulations.ipynb `"]}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12"}}, "nbformat": 4, "nbformat_minor": 5}