{"cells": [{"cell_type": "raw", "id": "3ef6e4ee", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Controlling Plotting Functions\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": "bec4fd47", "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 34 iterations"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["The model converged after 122 iterations"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["The model converged after 395 iterations"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["2"]}, "execution_count": 1, "metadata": {}, "output_type": "execute_result"}], "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()"]}, {"cell_type": "raw", "id": "710bb05b", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. _custstateplot:\n", "\n", "\n", "Global Plot Customizations\n", "**************************\n", "\n", "\n", "\n", "The central plotting functions of burstH2MM are all highly customizable.\n", "\n", "The first and simplest form of customization is using the ``ax`` keyword argument, which is universal to all plotting functions. \n", "This lets you make a `matplotlib.axes `_ (usually made with `plt.subplots() `_ or related functions), and then plot all elements within that axes. \n", "This also lets overlapping different plots into one axes.\n", "\n", "So here\u2019s an example, and we\u2019ll set the title of the axes afterward:"]}, {"cell_type": "code", "execution_count": 2, "id": "830fecbb", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["# make the axes\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "bhm.dwell_ES_scatter(bdata.models[2], ax=ax)\n", "ax.set_title(\"Dwell Based Results\");"]}, {"cell_type": "raw", "id": "4afbbfd8", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["For E and S based parameters, there is the option to use the raw values (calculated from the photons alone), or the values corrected for the values set for leakage, direct excitation, *gamma* and *beta* that have been set in the ``fretbursts.Data`` object used to create the BurstData object that you are working on. \n", "For dwell based parameters, corrections for background are also applied.\n", "\n", ".. note::\n", "\n", " See the :ref:`On demand data creation section ` to understand how, and most importantly when parameters are calculated.\n", " Make sure that your ``leakage`` , ``dir_ex`` , ``gamma`` and ``beta`` values are set before you try to plot or otherwise access any dwell value that involves correcting for these factors.\n", " If you want to recalculate, use the :meth:`H2MM_result.trim_data() ` method on your :class:`H2MM_result ` object to clear the value:\n", "\n", "This is done quite simply using the ``add_corrections`` keyword argument:"]}, {"cell_type": "code", "execution_count": 3, "id": "10aea48f", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": [" - Applying background correction.\n", " - Applying leakage correction.\n", " - Applying background correction.\n", " - Applying leakage correction.\n", " - Applying direct excitation correction.\n"]}, {"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["# trim data, NOT necessary if corrected values have not been accessed yet\n", "bdata.models[2].trim_data()\n", "\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "# add correction factors (determine for your own setup)\n", "\n", "bdata.data.leakage = 0.0603\n", "bdata.data.dir_ex = 0.0471\n", "bdata.data.gamma = 1.8\n", "bdata.data.beta = 0.69\n", "# note the addition of add_corrections=True\n", "bhm.dwell_ES_scatter(bdata.models[2], add_corrections=True, ax=ax)\n", "# set limits on the values, since with corrections, some dwells with\n", "# few photons in a stream will have extreme values\n", "ax.set_xlim([-0.2, 1.2])\n", "ax.set_ylim([-0.2, 1.2]);"]}, {"cell_type": "raw", "id": "8334b13f", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. _by_state:\n", "\n", "\n", "Customizing plots by state\n", "**************************\n", "\n", "\n", "\n", "Additional customizations focus on how states are individually plotted. \n", "This is done by passing lists of keyword argument dictionaries to specific keyword arguments.\n", "\n", "The first of these keyword arguments we will explore is ``state_kwargs`` . \n", "This is universal to all plotting functions beginning with ``dwell_`` . \n", "For it you pass a list of dictionaries of keyword arguments for the underlying matploplib function, one dictionary for each state.\n", "\n", "Here\u2019s a simple example, where we assign a color to each state in the :func:`dwell_ES_scatter() ` plot:"]}, {"cell_type": "code", "execution_count": 4, "id": "aa4400e3", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["# set up list, same length as number of states in the model\n", "state_color = [{'color':'m'}, {'color':'yellow'}, {'color':'c'}]\n", "bhm.dwell_ES_scatter(bdata.models[2], state_kwargs=state_color);"]}, {"cell_type": "raw", "id": "0458fbb2", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["So what happened here? Since ``models[2]`` has 3 states, the input state_kwargs keyword argument needs to be a list or tuple of length 3. \n", "The model stores states in arrays, which gives the states an arbitrary order. \n", "Each element of the list is passed per state to the `ax.scatter() `_ function as keyword arguments, according to the order established in the model. \n", "So the first state gets the keyword argument ``color='m'`` , the second state ``color='yellow'`` and the third ``color='m'`` .\n", "\n", ".. note::\n", "\n", " The different plotting functions use different matplotlib and seaborn functions.\n", " So plotting functions that create histograms use `ax.hist() `_ , while scatter functions use `ax.scatter() `_ , and kde plot functions use `sns.kdeplot() `_\n", "\n", "\n", ".. _statedisp:\n", "\n", "\n", "Displaying subsets of states\n", "============================\n", "\n", "\n", "\n", "What if you want to only look at a few states? \n", "You can select, and control the order of the plotting of different states with the ``states`` keyword argument.\n", "\n", "Let\u2019s say we want to only look at the FRET states (which are the 0th and 1st states in sample data set, but might be different when you are using other datasets). \n", "To do this, we make an array of just the indices of those states, and then pass that array to the states keyword argument:"]}, {"cell_type": "code", "execution_count": 5, "id": "4657f861", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["# make the axes\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "# specify the states we want\n", "states = np.array([0, 1])\n", "\n", "# now we plot\n", "bhm.dwell_ES_scatter(bdata.models[2], ax=ax, states=states)\n", "ax.set_title(\"FRET states\");"]}, {"cell_type": "raw", "id": "35b6d01e", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Selecting states and controlling their plotting\n", "===============================================\n", "\n", "\n", "\n", "So how do we combine the states and ``state_kwargs`` ? \n", "It\u2019s pretty simple, states serves like a \u201cmaster\u201d, and so each state specified in states is matched with an element of ``state_kwargs`` , assuming they come in the same order. \n", "So, you specify ``state_kwargs`` dictionaries in the same order as the states you specify in states, and obviously, they need to be the same length, otherwise you will get an error.\n", "\n", "So here\u2019s an example where we re-plot the FRET states, but in reverse order, and see how the ``state_kwargs`` are also reordered:"]}, {"cell_type": "code", "execution_count": 6, "id": "595adf62", "metadata": {"scrolled": false}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["# make the axes\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "# specify the states we want, now with 1 before 0\n", "states = np.array([1, 0])\n", "# make the state_kwargs, we'll add labels this time\n", "state_kwargs = [{'color':'yellow', 'label':'FRET state 1'}, {'color':'m', 'label':'FRET state 2'}]\n", "\n", "# now we plot\n", "bhm.dwell_ES_scatter(bdata.models[2], ax=ax, states=states, state_kwargs=state_kwargs)\n", "\n", "# add title, and legend to the plot\n", "ax.set_title(\"FRET states\")\n", "ax.legend();"]}, {"cell_type": "raw", "id": "69397ee8", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["State Overlays\n", "**************\n", "\n", "\n", "\n", "Especially for E-S plots, burstH2MM provides a number of overlays for adding markers for where the model value of each state.\n", "\n", "The :func:`scatter_ES() ` function\n", "=================================================================\n", "\n", "\n", "\n", "The first, already introduced in the tutorial is the :func:`scatter_ES() ` :\n", "\n", ".. note::\n", "\n", " Since states often do not span the entire (0, 1) range of E and S, we manually set these in the plot bellow.\n", " When combined with dwell-based plots however, those plots usually automatically set the range to something reasonable.\n", " Therefore it is more often than not unnecessary to set the x and y axes limits.\n", "\n", ""]}, {"cell_type": "code", "execution_count": 7, "id": "5552491d", "metadata": {}, "outputs": [{"data": {"image/png": 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0MCP21fK60uc3PlcBJ4GNfPDG7u8vWPNXfPjNz58O+w3blvd3NZ07eD437Hnb3tuC9U8xWhdVmvzurgX+rdZ+BHgR+INhz97i/p4A/r4+/xRwCrhy2LMvYY+/w4UvqiyrK319hpgr/La/hvv7GvAJ4PF6JnU2R+DG+oZ7G1lN9peZL0fED4EXgPeAJzNzJP6Fpoa/v0eApyLiOJ1wPJiZI/Gv4ETEd4FbgSsjYhb4OrAaLq4r3qkiScU7VSSpGERJKgZRkopBlKRiECWpGERJKgZRkopBlKTyfyySb9xlyRKRAAAAAElFTkSuQmCC\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "6d42e168", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Specific states can be specified in the same way as with the other dwell-based plotting functions, through the ``states`` keyword argument, and ``add_corrections`` is also present.\n", "Additional keyword arguments are forwarded to `ax.scatter() `_ "]}, {"cell_type": "code", "execution_count": 8, "id": "0a8fd02a", "metadata": {}, "outputs": [{"data": {"image/png": 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tWEP2V1XfT/JN4Cngl8B9VbUj/oWmgb+/zwAPJHmalXDcVVU74l/BSfIV4AbgqiTLwKeB18CldcU7VSSpeaeKJDWDKEnNIEpSM4iS1AyiJDWDKEnNIEpSM4iS1P4fjxk9fFiT+8UAAAAASUVORK5CYII=\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax, states=np.array([0,1]), c='k', add_corrections=True)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "3f7868ca", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["The :func:`trans_arrow_ES() ` function\n", "=========================================================================\n", "\n", "\n", "\n", "Another useful function, is :func:`trans_arrow_ES() ` \n", "This allows the incorporation of arrows indicating the transition rates directly into the plots.\n", "\n", "We also have the standard ``add_corrections`` keyword argument that works the same as in all other places, whether or not to plot corrected or raw parameter values.\n", "\n", "Under to hood, :func:`trans_arrow_ES() ` calls `ax.annotate() `_ , so additional keyword arguments are passed along to it, like in other :mod:`Plotting ` functions."]}, {"cell_type": "code", "execution_count": 9, "id": "538225c9", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax, add_corrections=True)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, add_corrections=True, fontsize=11)\n", "\n", "ax.set_xlim([-0.1,1.1])\n", "ax.set_ylim([-0.1,1.1]);"]}, {"cell_type": "raw", "id": "bd38863d", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["The :func:`trans_arrow_ES() ` Minimum Rate Filter\n", "====================================================================================\n", "\n", "\n", "\n", "If you are using the same data that this documentation was produced with, you will notice that in the above plot, one arrow seems to be missing.\n", "Between the donor-only and high-FRET states, there is only the high-FRET to donor-only transition, but the reverse rate does not appear.\n", "It is note because it does not exist, but rather, this rate is so slow that :func:`trans_arrow_ES() ` chooses not the plot it.\n", "\n", "The excat threshold bellow which a transition is considered irrelevant through the ``min_rate`` keyword arguemnt.\n", "The default is ``min_rate=1e1`` , so we know that this transition rate is slower that 10 s\\ :sup:`-1`\\ .\n", "\n", "For the data we are using, it turns out that the transition rate is just barely under this threshold, so let's set ``min_rate=1.0`` so we will lower the threshold by an order of magnitude, and so see all the transition rates."]}, {"cell_type": "code", "execution_count": 10, "id": "a18496b8", "metadata": {}, "outputs": [{"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", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, min_rate=1.0)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "cf603762", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Of course, this default is actually rather slow, since H :sup:`2`\\ MM is far more sensitive to transition rates that are on the scale of burst durations (milliseconds), let's raise the threshold so we don't see any of the transitions between the donor-only and high-FRET states:"]}, {"cell_type": "code", "execution_count": 11, "id": "1081a815", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, min_rate=1e2)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "1ba1de43", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Basic :func:`trans_arrow_ES() ` customizations\n", "=================================================================================\n", "\n", "\n", "\n", "Some other customizations for the display of :func:`trans_arrow_ES() ` are:\n", "- ``positions`` : set where the number is displayed, either as a single number, or as an array of the same shape as the transition probability matrix\n", "- ``sep`` the separation between forward and backward transition rate arrows\n", "- ``fstring`` the number format for the transition rate, same as used in python f-strings\n", "- ``unit`` whether to display the units of transition rate\n", "- ``rotate`` whether to dispaly the transition rate rotated with the arrow, or always horizontally\n", "\n", "So lets see some examples:\n", "\n", "First we will set the ``positions=0.4`` so instead of the numbers being placed directly in the middle, they will be a little closer to ``from_state`` , and we will set ``fstring='1.1e`` so that now units will be given in scientific notation, and we'll set ``unit=True`` so we can see the units:"]}, {"cell_type": "code", "execution_count": 12, "id": "9e983281", "metadata": {}, "outputs": [{"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", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, positions=0.4, fstring='1.1e', unit=True)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "4bfd0d87", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Now let's see the numbers horizontal and increase the separation between arrows: "]}, {"cell_type": "code", "execution_count": 13, "id": "ee14f01b", "metadata": {}, "outputs": [{"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", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, sep=4e-2, rotate=False)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "7a4b53fe", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Also, if you want, you can specify the ``positions`` as an array (organized ``[from_state, to_state]`` , diagonal elements ignored):"]}, {"cell_type": "code", "execution_count": 14, "id": "4b6facff", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "pos = np.array([[0, 0.4, 0.4],[0.6, 0.0, 0.5],[0.6, 0.5, 0.0]])\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, sep=4e-2, positions=pos)\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "44805da5", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Manually selecting states for :func:`trans_arrow_ES() `\n", "==========================================================================================\n", "\n", "\n", "\n", ":func:`trans_arrow_ES() ` has ``states`` and ``state_kwargs`` keyword arguments, but they behave a little differently from those same arguments in other :mod:`Plotting ` functions, becuase it is concerned with transitions, as opposed to states.\n", "\n", "Now ``states`` is given as a list/tuple of 2 element arrays\n", "\n", ".. note::\n", "\n", " Even if you are only plotting a single transition rate, the input to ``states`` must be \"2-dimensional\".\n", " Meaning that it cannot be just ``[from_state, to_state]`` but rather ``[[from_state, to_state], ]``\n", "\n", ""]}, {"cell_type": "code", "execution_count": 15, "id": "bf4e0016", "metadata": {}, "outputs": [{"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", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax, states=(0,1))\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, states=((0,1), (1,0)))\n", "\n", "ax.set_xlim([0,1])\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "21cc04cd", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Customizing the arrows\n", "======================\n", "\n", "\n", "\n", "``state_kwargs`` is a list of dictionaries.\n", "The arrows in :func:`trans_arrow_ES() ` , as mentioned before are created using `ax.annotate() `_ but it is a little more compliacted, as for every transition, 2 annotations are actually created.\n", "These are the **from arrow** and **to arrow** annotations, which link the **from state** and **to state** positions to the central transition rate number.\n", "\n", "Basically the **from arrow** starts at the rate and points to the **from state** (and by default is just a line, and thus has no arrow head).\n", "While the **to arrow** starts at the rate and points to the **to state**.\n", "\n", "In order to change the size, style etc. of the arrows, there are the ``from_arrow`` , ``to_arrow`` keyword arguments to set the overall arrow style, and the ``from_props`` and ``to_props`` keyword arguments set additional properties of these arguments.\n", "\n", ".. see also::\n", "\n", " It is **highly** recomended to review the matplotlib documentation regarding `ax.annotate() `_ and\n", " `matplotlib.patches.FancyArrowPatch `_ as the ``from_arrow`` , ``to_arrow`` , ``from_props`` and ``to_props`` are all just repackaged into\n", " parts of the keyword arguments handed to these functions.\n", " \n", " See `https://matplotlib.org/stable/tutorials/text/annotations.html `_ for matplotlib's tutorial regarding annotations and how to change the arrow properties\n", " These functions are complicated, and thus :func:`trans_arrow_ES() ` is also complicated,\n", " and unfortunately the only way to really use it is through practice/experimentation and detailed reading.\n", "\n", "Since there are two types of arrows, it is necessary to allow separate specification of certain `ax.annotate() `_ keyword arguments.\n", "\n", "Specifically, this means modifying the ``arrowprops`` keyword argument of `ax.annotate() `_ , which is itself necessarily a dictionary.\n", "``from_arrow`` and ``to_arrow`` arguments are packaged into the ``arrowstyle`` keyword argument for `matplotlib.patches.FancyArrowPatch `_ , which is handed there via the ``arrowprops`` keyword argument of `ax.annotate() `_ , by *updating* the default dictionary with the user provided arguments, so defaults are not lost.\n", "\n", "``from_props`` and ``to_props`` are packaged into the ``arrowprops`` keyword argument, and thus serve as other keyword arguments to `matplotlib.patches.FancyArrowPatch `_ , again, by *updating* the default dictionaries so defaults are not lost.\n", "\n", "Finally, there is the ``arrowprops`` keyword argument, which is basically a way of passing the same argument to both ``from_props`` and ``to_props`` , and again it works by *updating* the dictionaries.\n", "\n", "So let's see examples of how these work:\n", "\n", "The thickness and color of the arrows can be set in the ``arrowprops`` via the key ``linewidth`` :"]}, {"cell_type": "code", "execution_count": 16, "id": "d99c9472", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, arrowprops=dict(linewidth=4.0))\n", "\n", "ax.set_xlim([-0.1,1.1])\n", "ax.set_ylim([-0.1,1.1]);"]}, {"cell_type": "raw", "id": "1b92e459", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["We can give individual colors or different linewidths to the **from arrow** and **to arrow** with the ``from_arrow`` and ``to_arrow`` keyword arguments:"]}, {"cell_type": "code", "execution_count": 17, "id": "f0cf3dd9", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, from_props=dict(linewidth=2.0, color='m'),\n", " to_props=dict(linewidth=4.0, color='b'))\n", "\n", "ax.set_xlim([-0.1,1.1])\n", "ax.set_ylim([-0.1,1.1]);"]}, {"cell_type": "raw", "id": "a0daeef2", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["As you will note near the top of the matplotlib documentation pages, passing some strings to ``from_arrow`` and ``to_arrow`` can make the arrows take on different shapes at their ends:"]}, {"cell_type": "code", "execution_count": 18, "id": "7d7c64a2", "metadata": {}, "outputs": [{"data": {"image/png": 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Dg/fff586deowZcoU2+zHZ8+eZceOHWzbto3IyEi6dOmCj4975lIYO3YsYWFhjB49GmMMEyZMoHfv3m6pRSl308BzwJQpU5g9ezb33XcfaWlphIeH07NnT/7617/SsmVLLl++TPfu3QE4ffo0jz76KKtXryY8PJwLFy5w4sQJHn/8ccaPH++W3tWIESMIDw9n7969PPnkky4/vlLlRmEP6JX1V0V48LigCxcuXLdu9+7dMmrUKBHJHYpWuXJladq0qTRo0EBq1KghkyZNknPnzklISIiMHz/e1SUrZTkU8eCxPpbigIyMDD788EMOHz5MTk4OaWlp7Nu3j5ycHCIjI5k+fTopKSm88cYb3HnnnezatYsDBw7QsGFDVq5cSaVKlYiMjOTbb79194+ilKVp4DnAx8eHl156ibNnz+Lh4UFAQACPPPII//u//wvkTgn/6aef4u/vz8GDBwFYs2YNf/zxh+0FQL169aJatWpu+xmUUhp4DvP09GT+/Pm25V27djF+/Hgg9+Xdx48f54UXXuDYsWPUqFGDv/zlL3h6evL6668DMG7cuAo3JbtOKqpuNhp4DmrVqhULFizg/PnzfPjhh4SEhJCdnY0xhpMnT5KQkEDXrl1ZuXIlo0aNolOnTnz99dfXjL+tSO+VzcnJwcsr955W37596dy5s5srUqr0dKSFg7Zt28ajjz5KcnIyYWFhdO7cmalTp3LmzBmioqLcXV6ZuXrqnpWVRVhYGAcOHHB3SUoVSUdaOEFMTAxLlizhzJkznDt3jpiYGACqV69u67ldfQ7vZjFlyhQ+//xzPvvsM/r370+TJk2oU6cOBw8etPX+lKpI9P/aG1CpUqXrRlvAzRd0VwUGBuLl5cXEiRPp378/e/fuZcWKFRp2qsK6Of9SVYlc7almZGQAMGzYMFJTU9m1a5ftvR1xcXEV6lqkUvlp4CkbDw8Pdu3aRevWrbl48SKQO+7W39/fNr+eh4eHrUd78uRJdu3a5bZ6lbpRGnhlKDs7m61bt7Js2TJ3l+KwqKgo0tPTadKkiW3+v8DAQNuNmZycHJ566ilGjx5N06ZNadasGf/4xz/cXLVSjtGLMWWkd+/e/PDDD3h7e5Oens7FixcrzDTrBw4cIDY2ljZt2nDLLbfQokULevbsScOGDalbty4HDx7kxRdfJDs7m6ioKHr16uXukpVyiPbwysju3bu5fPkyFy5cwN/fn927d7u7pBuydu1aFi5cyL59+/jqq69YvHgxBw8e5MEHH+TXX3+1vRf3zTffpF27du4uVymHaOCVkX79+uHp6QnkTtCwfft2N1d04zp27IiHhwd+fn4MHTqUp556ihEjRhAZGcm6desYP348d955p7vLVMphGnglNG7cOGrUqGF7Q1hBffr0ITAwEIBLly6xZcsWF1bnXFlZWWRlZTFp0iQ6derE3r17CQsLY+jQofqIiqpQNPBKaN26dfz2228MHz4ce6NVOnXqZHu8Q0RISkpydYlO4+XlhZeXF5MnTyY5OZn+/ftTo0YNzp496+7SlLoh+s9zCW3cuBGAOXPm0LZtW55++ulrtvv4+NChQwd+/PFHgJvi8Y1+/fpx/Phx26QJSlU02sMrgSNHjtieU0tPT+fZZ5/l559/vq7dwIEDba9LvHDhQoXvEdWuXVvDTlVoGnglsHbtWjIzM/Hw8KBy5co8/PDDhIeHX9euZ8+ettNdf39/duzY4epSlVL5aOCVQPfu3Vm2bBkLFy4kMjKS999/n0aNGl3XrkGDBlSuXBmAzMzMCnmnVqmbiQZeCVSpUoW4uDhuu+02duzYYRt2Zc+f/vQnjDGkp6ezfv16F1aplCpIA68UAgICaN68OUXN6/fnP/+Z4OBg4P9udCil3EMDr5RiY2NZu3Ztodvj4uK4fPkyAPv27bP7CItSyjU08EqpuMALCgqyTRYKcOzYMVeUVW60atWKkJAQnnrqKZYsWUJaWpq7S1IWpoFXSlcDr6ie28CBA/H19cXHx8dyNy5GjBjB+fPnmTJlCvfeey8hISHMmDHD3WUpi9LAK6XatWsTEBDA/v37C23Tq1cvvLy8SEtLY9u2bS6szv0efvhh27OI58+fJyAgQMffKrfRwHOC4k5rW7Rogbe3N5mZmRV6iFlJ+Pn58eCDD+Lh4YGXlxfVqlXDz8/P3WUpi9LAc4LY2FjWrFlT6HZjDD179gRg69atriqr3HjyySfx8PBg7ty5PPjgg8TGxt4UQ+1UxaNjaZ0gNja22OtS/fr147vvviMlJYWsrCxLzTISFRXFqVOnqFSpEnfddRe1atUiLi6O2bNn6/tulUs51MMzxvQ0xuwxxuw3xrxQSJs4Y8wWY8wvxpifnFtm+RYTE8ORI0eKHCvbrVs3MjIy8Pb2Zt++fa4rrpzI/7a3Bx98kE8//ZR+/foxe/ZsN1alrKbYwDPGeAJTgV5AM2CwMaZZgTYhwDTgzyLSHBjo/FLLL29vb9q2bVvk9bnQ0FAaNWrE5cuXLXen1p4777yTJUuWMGrUKCZPnuzucpRFONLDaw/sF5GDIpIBfAncXaDN/cAcETkCICJ/OLfM8q+4GxcA/fv3Jycnp0JPBupMrVu3Zs2aNUydOpXnn39eX/+oypwjgVcLOJpvOSVvXX5NgFuMMT8aYzYaYx5wVoEVhSOBd9dddwFY7k5tUerVq8eaNWtYs2YNQ4cOLXQGaaWcwZHAM3bWFXzK1gtoA9wF9ABeNsY0uW5Hxow0xmwwxmxITU294WLLsw4dOpCcnExWVlahbdq2bUtgYCC//PKLCysr/6pUqcKyZctIT0+nV69enDt3zt0lqZuUI4GXAkTkW64NHLfTZrGIXBKRk8BK4NaCOxKR90SkrYi0tTd/XEVWpUoVatWqVeScd56ensTFxfHHH3/YJhBVufz9/fn2229p1qwZXbp0sdwQPOUajgTeeqCxMaa+McYHuA+YX6DNPKCLMcbLGBMA/BdguQetHDmtHTBgAAA7d+50RUkViqenJ5MnT2bIkCHExsZqT1g5XbGBJyJZwJPAEnJD7GsR+cUY85gx5rG8NruAxcA2IBn4QEQsN72vI4F3dViV3qm1zxjDmDFjeP3117njjjv46SdLPeGkyphDz+GJSKKINBGRhiLyet66GSIyI1+bf4lIMxFpISJvl1G95ZojgVezZk3q1aunc+MVY+jQoXz++ecMHDiQr7/+2t3lqJuEDi1zosjISM6dO8eJEyeKbNevX78iJw1Vubp168bSpUt55plnmDhxorvLUTcBDTwn8vDwoGPHjsX28s6dO8cvv/xCcnIymZmZLqquYmrZsiVr1qzh/fff55lnntFn9VSpaOA5mSOntffccw8ZGRkMGzaM0NBQunXrxt///ndWrFihE2TaUbduXVavXs2GDRsYPHiwPqunSkwDz8nsBd7czcfo9M8V1H9hIZ3+uYLMmi1JSkpi+/btHDlyhKeffpq0tDReeuklwsPD6dChAwkJCcyfP5/Tp0+76ScpX0JDQ1m6dCk5OTn06NGDM2fOuLskVQEZd71joW3btnIzXse6dOkS1apV49y5c3h6ejJ38zHGztlOema2rY2/tyfj74mmb6uCA1YgLS2N5ORkVq1axapVq0hKSqJOnTrcdtttdOnShS5dulC7dm1X/kjlSnZ2Ns888wzLly9n0aJFREREFP8hZSnGmI0i0tbuNg0859u5cyfNmuXOr9Dpnys4dvb61zjWCvFnzQt3FLuvrKwsNm/ebAvA1atXExQUZAu/Ll26EBkZiTH2BsTcnESEt956i7fffpvExESio6PdXZIqRzTw3Kj+CwuvG4cHueP1Dv3zrhven4iwe/duWwCuWrWKtLQ0OnfubOsF3nrrrZaYb++LL75g9OjRfPXVV8THx7u7HFVOFBV4N/9fhZvVDPG328OrGeJfov0ZY4iKiiIqKoqRI0cCcOTIEVv4vf/++6SkpNChQwdbD7B9+/b4+xd+vIsXLxIUFFSietxp8ODBVK9enUGDBvHOO+8wePBgd5ekyjm9aVHGEnpE4u/tec06f29PEnpEOu0YderUYciQIcyYMYNffvmFAwcO8MQTT3D27FkSEhIIDw+nc+fOjB07lsTExGsG5x8+fJjQ0FCmTZvm0LFSUlJ49tlnWbRokdPqL434+HiWL1/OmDFjePPNN/W9v6pIekrrAnM3H+NfS/Zw/Gw6NUP8SegRafeGRVm5ePEiSUlJtl5gcnIyjRs3pkuXLuTk5PDhhx/i4eHBf//3fzNlypQiT4e/+uorEhISbHeP//3vf/Poo4+66kcp1NGjR+nVqxddu3blrbfewtPTs/gPqZtSUae0iIhbvtq0aSNWcvnyZenQoYMMHDhQ3nrrLVm5cqVcuHDBLbVcuXJF1q5dKxMmTJCaNWsKudN9iaenpzRp0kRSU1ML/eypU6ds399///3SoEED2/KXX34pEREREhsbKwsXLizTn8GeM2fOyO233y79+/eX9PR0lx9flQ/ABikkd/SU1kV8fX2ZMWMGMTExzJo1i/j4eCpVqkRERAT9+vXjnXfeYfXq1S6ZNsrHx4eOHTvy/PPP5/5P4OFhu4Z3/Phx1q1bV+hnQ0NDOX/+PADp6em2a4N/+9vfGDduHK1bt+b06dO8/PLLts988sknvPjiixw+fLgMfyoICQlhyZIleHl50b17d32GUV2vsCQs6y+r9fAKOnfunMydO1eGDRsmVatWtfWyjDFSu3Ztueeee+Sdd96R1atXy8WLF8usjvvuu0/GjBkjiYmJcubMmWLbX+057dy5U/z8/OTpp58WEZHQ0FAZNmyYiIgcPXpU6tatKx9//LGIiERERIgxRoKCgqRKlSoybdq0svlh8mRnZ8uzzz4rUVFR8uuvv5bpsVT5QxE9PA28cmLfvn0yZcoUiYuLEx8fH1sAAuLh4SEREREyYMAAmTRpkqxdu1YuXbrktlrHjRsnfn5+ct9999nWGWPk6NGjtmUfHx9ZunSpiIjccsstsnXrVhERWbBggezcudMldU6cOFFq1aolmzdvdsnxVPmggVfBXLlyRX744Qd59tlnpWHDhuLl5SVeXl4CiLe3t1SqVEm8vLykbt26MnDgQJk8ebL8/PPPkpaWVqZ1rV+/Xnr06CERERHy5ptv2tZ/9NFH4uXlZVvesWOHGGNERCQxMVGAMu/VFebrr7+W8PBw+f77791yfOV6GngV3G+//Saffvqp9OvXT4KDgyUoKOiaXqC/v78tBOvVqyeDBg2SqVOnSlJSktNCMDU1VSpVqiTGGElISLhm2wMPPCD169e3LT/++ONSuXJlERFZvHixNGrUSKpUqSI1a9aU/fv3O6WeG/HTTz9J1apV5dNPP3X5sZXraeDdRLKzs2XTpk3y+uuvS6tWrcTHx8cWRPlPg/OHYP369eW+++6TadOmybp160p0B/PKlSsybtw4iY2NlSpVqoi3t7ftmt3EiROlWbNmIiLy+++/S7169WTAgAHX7SMmJkZiY2NtP4cr7dixQ+rUqSPjx4+XnJwclx5buZYG3k3s/PnzMn/+fBk+fLhUr15d/P39xd/f/5rwsxeCDRo0kPvvv1+mT58uycnJcvny5WKP9de//lUWL14sIrmPp2zfvt22rXbt2rabEj179pS9e/fKlStXrvl8mzZtpGvXrsUeZ+nSpdK8eXMZM2aMrFy5UjIyMm7wt2JfSkqKxMTEyBNPPCFZWVlO2acqfzTwLOTAgQMybdo0ueOOO8TPz08qVaoknp6edgMQkICAAKlUqZJ4e3tLw4YNZfbs2Xb3O3fuXAGkUqVK4ufnJ40bN76uzQcffCCjRo2yLb/44ovSrFkzefrpp+Xbb78VT09PmThxYrE/w8MPPyyAeHl5SXBwsPj5+UnXrl1l+vTpcvDgwRL/bkREzp49K127dpW+ffuW+TVP5R4aeBaVkZEhP/30kyQkJEjjxo3F19dXgoODCw0/X19fmTRpkt19DRgwQFq0aCEiuY+kfPDBByIisnHjRmncuLGEhoZKfHy8zJs3z3a6unPnTunXr5/UrVtXqlSpIs8995xDddetW7fQcPbz85MaNWrIiBEjZMGCBSV6ePvKlSsyZMgQ6dixo5w8efKGP6/Kt6ICT4eWWUhqairff/89c+bM4fvvvycnJ4fMzEzbDMJBQUGsWrWKli1bXvfZmjVr4uPjQ3JyMlWrVgVyp67y8PDg0KFDLFq0iBkzZuDt7c3mzZs5ffo0H374IZcuXWLYsGHUrVvXoRqzs7MJCAjAz8+PtLS0Ql9sbowhODiYy5cvEx0dzYABA+jZsye33nqrQ1Nl5eTk8OKLLzJ37lwWLVpE/fr1HapPlX86tExdJycnR7Zs2SLjx4+XNm3aiI+PjwQGBhZ6bWvQoEESEREh/v7+MmjQILttVq9eLbVq1ZKZM2fKzp07pW/fvlKlShXx8fGR+Ph4h2s7deqULF26VF5//XW58847JSwszHZz5urjOQW/fH19JSgoSCpVqiT9+/eXzz77TH7//fdijzV58mSpWbOmbNy40eH6VPmGntKq4ly8eFEOHDhQbLvNmzdLQECAJCcn29b17NlTqlevLnXq1JH/+q//svtgceXKlW0jL0oiNTVVFi9eLK+99pp069bNFqSFhWBwcLD4+PhIo0aNJCEhQX788cfrbqJcNXv2bAkPD7fdkFEVmwaeKrGCj4+sWrVKmjVrJv/zP/9jW3f48GEZNGiQhIWFXderuhoyxhhJSkpyam1//PGHLFq0SP7xj3/IHXfcIaGhoeLr63tdCHp5edlutsTHx8vUqVOvex5w9erVUq1aNfnPf/7j1BqV62ngqRLLzMyU+Ph4iY+Pl6VLl0q3bt2kfv36Mnfu3OvaxsfH2053rwbd4cOHZcCAARIcHOySen///XdJTEyUV155ReLj4+WWW24RX19fqVy5su1utY+Pj/j7+0u1atVk+PDhMm/ePDl//rzs2rVL6tWrJ6+99po+q1eBaeCpUpkwYYK0atVKgoKCpGnTpvLVV1+JiMi0adNsY2TPnTsnERER0rdvX9vnZs2aJY0aNZJatWrJ+vXrRcT1DxyLiJw4cUIWLlwo48aNk7i4OAkJCRFfX99reoEeHh7SqlUrGTNmjDRp0kRGjhwpmZmZLq9VlV5Rgad3adUNycjIwMfHB4AOHTqwZcsWAgMD8fX1pXr16kyePJlOnToxdOhQfvzxR7p3786ECROoWrUqOTk5eHg4PiPZpUuXWLduHU2aNHH6m9p+++03Nm7cSHJyMj/99BNbtmy5ZiZoT09PwsPDefXVV/nTn/5EtWrVnHp8VXb0Lq0qM9nZ2fLxxx/Lc889Z+u99enTRwCJjY219QAd9cEHH8igQYOkcePG4u3t7fDDys5w/PhxWbBggbz00kvSuXNn8fb2tvUAGzVqJM8++6ysWLGi0JsfqnxAT2mVK1wNvOTkZBk8eLA0adJE/Pz8JDQ01KG59kRE2rVrd93jJv/5z3+cNrzsRuTk5MioUaOkRo0a8sQTT8jtt98uYWFhEhgYKO+++67L61GO0cBTbrVjxw6H2+bk5MjYsWPFz8/PNu18ixYtJCgoSLp27SqvvPKKLF++3KXzAU6fPl1q1KghycnJkpOTIykpKTpCoxwrKvD0Gp4ql2bNmsVDDz1Ey5YtSU5O5syZM6xZs8b2IqKtW7cSHR1texdvp06dCA0NLbN65s+fz/Dhw/n444/p3bt3mR1HlZ6+iFtVSBs3buTy5ct06tTpum1paWkkJyfbAjApKYm6deva3sXbpUsXp9/oSEpKom/fvgx4NIHN/q3c9hY6VTQNPHXTy8rKYvPmzbYAXL16NUFBQbbwu+2222jSpIlD42yLMnXuKl567Q0qd3vMts7f25Px90Rr6JUTGnjKckSE3bt32wJw5cqVXL58mc6dO9tC8NZbby3yHbz2dPrnCo6dTb9ufa0Qf9a8cIezylelUFTg3dh/baUqCGMMUVFRREVFMXLkSACOHDliC8D333+flJQUOnbsaAvA9u3b4+fnV+R+j9sJu6LWq/JFA09ZRp06dRgyZAhDhgwB4OTJk6xevZpVq1bx3HPPsXPnTlq2bGkLwE6dOlG5cuVr9lEzxN9uD69miL9LfgZVOnpKq1SeixcvkpSUZDsFXr9+PY0bN77mRkjSiWzGztlOema27XN6Da98KfU1PGNMT+AdwBP4QET+WUi7dkASMEhEvi1qnxp4qrzLyMhg48aNttPgtWvX8s0333D+lkj+tWSP3qUtp0oVeMYYT2Av0B1IAdYDg0Vkp5123wOXgZkaeOpmIyKlvsuryl5RgefISO72wH4ROSgiGcCXwN122j0FzAb+KHGlSpVjGnYVnyOBVws4mm85JW+djTGmFtAPmOG80pRSyrkcCTx7/6wVPA9+GxgjItl22v7fjowZaYzZYIzZkJqa6mCJSinlHI48lpICRORbrg0cL9CmLfBlXpc/DOhtjMkSkbn5G4nIe8B7kHsNr4Q1K6VUiTgSeOuBxsaY+sAx4D7g/vwNRMT2jjtjzEfAdwXDTiml3K3YwBORLGPMk8ASch9LmSkivxhjHsvbrtftlFIVgkMjLUQkEUgssM5u0InIQ6UvSymlnM/xFwwopVQFp4GnlLIMDTyllGVo4CmlLEMDTyllGRp4SinL0MBTSlmGBp5SyjI08JRSlqGBp5SyDA08pZRlaOAppSxDA08pZRkaeEopy9DAU0pZhgaeUsoyNPCUUpahgaeUsgwNPKWUZWjgKaUsQwNPKWUZGnhKKcvQwFNKWYYGnlLKMjTwlFKWoYGnlLIMDTyllGVo4CmlLEMDTyllGRp4SinL0MBTSlmGBp5SyjI08JRSlqGBp5SyDA08pZRlaOAppSzDocAzxvQ0xuwxxuw3xrxgZ/sQY8y2vK+1xphbnV+qUkqVTrGBZ4zxBKYCvYBmwGBjTLMCzQ4Bt4tIDPAq8J6zC1VKqdJypIfXHtgvIgdFJAP4Erg7fwMRWSsiZ/IWk4Dazi1TKaVKz5HAqwUczbeckreuMMOBRaUpSimlyoKXA22MnXVit6Ex8eQGXudCto8ERgLUqVPHwRKVUso5HOnhpQAR+ZZrA8cLNjLGxAAfAHeLyCl7OxKR90SkrYi0DQ8PL0m9SilVYo4E3nqgsTGmvjHGB7gPmJ+/gTGmDjAH+G8R2ev8MpVSqvSKPaUVkSxjzJPAEsATmCkivxhjHsvbPgP4f0AVYJoxBiBLRNqWXdlKKXXjjIjdy3Flrm3btrJhwwa3HFspdfMyxmwsrMOlIy2UUpahgaeUsgwNPKWUZWjgKaUsQwNPKWUZGnhKKcvQwFNKWYYGnlLKMjTwlFKWoYGnlLIMDTyllGVo4CmlLEMDTyllGRp4SinL0MBTSlmGBp5SyjI08JRSlqGBp5SyDA08pZRlaOAppSxDA08pZRkaeEopy9DAU0pZhgaeUsoyNPCUUpahgaeUsgwNPKWUZWjgKaUsQwNPKWUZGnhKKcvQwFNKWYYGnlLKMjTwlFKWoYGnlLIMDTyllGVo4CmlLMOhwDPG9DTG7DHG7DfGvGBnuzHGTMrbvs0Y09r5pSqlVOkUG3jGGE9gKtALaAYMNsY0K9CsF9A472skMN3JdSqlVKk50sNrD+wXkYMikgF8CdxdoM3dwCeSKwkIMcbUcHKtSilVKo4EXi3gaL7llLx1N9pGKaXcypHAM3bWSQnaYIwZaYzZYIzZkJqa6kh9SinlNI4EXgoQkW+5NnC8BG0QkfdEpK2ItA0PD7/RWpVSqlQcCbz1QGNjTH1jjA9wHzC/QJv5wAN5d2s7AOdE5ISTa1VKqVLxKq6BiGQZY54ElgCewEwR+cUY81je9hlAItAb2A+kAcPKrmSllCqZYgMPQEQSyQ21/Otm5PtegL84tzSllHIuHWmhlLIMDTyllGVo4CmlLEMDTyllGRp4SinL0MBTSlmGBp5SyjI08JRSlqGBp5SyDA08pZRlaOAppSxDA08pZRkaeEopy9DAU0pZhgaeUsoyNPCUUpahgaeUsgwNPKWUZZjc2dndcGBjUoHDZbT7MOBkGe27rFXU2itq3VBxa6+odUPZ1l5XROy+FtFtgVeWjDEbRKStu+soiYpae0WtGypu7RW1bnBf7XpKq5SyDA08pZRl3KyB9567CyiFilp7Ra0bKm7tFbVucFPtN+U1PKWUsudm7eEppdR1KnTgGWN6GmP2GGP2G2NesLPdGGMm5W3fZoxp7Y467XGg9iF5NW8zxqw1xtzqjjoLKq7ufO3aGWOyjTEDXFlfURyp3RgTZ4zZYoz5xRjzk6trtMeB/1cqG2MWGGO25tU9zB11FmSMmWmM+cMYs6OQ7a7/+xSRCvkFeAIHgAaAD7AVaFagTW9gEWCADsA6d9d9A7XHArfkfd+rPNTuSN352q0AEoEB7q77Bn7nIcBOoE7ectUKUveLwIS878OB04BPOaj9NqA1sKOQ7S7/+6zIPbz2wH4ROSgiGcCXwN0F2twNfCK5koAQY0wNVxdqR7G1i8haETmTt5gE1HZxjfY48jsHeAqYDfzhyuKK4Ujt9wNzROQIgIiUh/odqVuAYGOMAYLIDbws15Z5PRFZmVdLYVz+91mRA68WcDTfckreuhtt4w43Wtdwcv8ldLdi6zbG1AL6ATNcWJcjHPmdNwFuMcb8aIzZaIx5wGXVFc6RuqcAUcBxYDswWkRyXFNeqbj879OrLHdexoyddQVvOTvSxh0crssYE09u4HUu04oc40jdbwNjRCQ7t8NRbjhSuxfQBugK+AM/G2OSRGRvWRdXBEfq7gFsAe4AGgLfG2NWicj5Mq6ttFz+91mRAy8FiMi3XJvcf+FutI07OFSXMSYG+ADoJSKnXFRbURypuy3wZV7YhQG9jTFZIjLXJRUWztH/X06KyCXgkjFmJXAr4M7Ac6TuYcA/JffC2H5jzCGgKZDsmhJLzPV/n+6+sFmKC6JewEGgPv93Mbd5gTZ3ce1F0WR3130DtdcB9gOx7q73Ruou0P4jys9NC0d+51HA8ry2AcAOoEUFqHs68Ere99WAY0CYu3/nefXUo/CbFi7/+6ywPTwRyTLGPAksIfdO1kwR+cUY81je9hnk3iXsTW5wpJH7L6HbOVj7/wOqANPyektZ4uaB4g7WXS45UruI7DLGLAa2ATnAByJi95EKV3Hwd/4q8JExZju54TFGRNw+i4ox5gsgDggzxqQA4wBvcN/fp460UEpZRkW+S6uUUjdEA08pZRkaeEopy9DAU0pZhgaeUsoyNPCUUpahgaeUsgwNPKWUZfx/RaSYrjBzVmAAAAAASUVORK5CYII=\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "bhm.scatter_ES(bdata.models[2], ax=ax)\n", "bhm.trans_arrow_ES(bdata.models[2], ax=ax, to_arrow='wedge', from_arrow='<|-')\n", "\n", "ax.set_xlim([-0.1,1.1])\n", "ax.set_ylim([-0.1,1.1]);"]}, {"cell_type": "raw", "id": "aef67aa6", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["\\ :func:`axline_E() ` and\n", "======================================================\n", "\n", ":func:`axline_S() `\n", "\n", "For times when you want to plot histograms, there also are a set of functions that plot bars for states.\n", "We have :func:`axline_E() ` , and :func:`axline_S() ` , which have the same fundamental function signature.\n", "\n", "The ``add_corrections`` , ``states`` and ``state_kwargs`` keyword arguments all work the same as in :func:`scatter_ES() ` .\n", "So ``add_corrections`` sets whether or not to plot the corrected values, as opposed to the raw values. \n", "``states`` is an array of the states that are to be plotted, and ``state_kwargs`` is a list or tuple of dictionaries that are the keyword arguments for either `ax.axvline() `_ or `ax.axhline() `_ .\n", "\n", "There is a keyword argument ``horizontal`` that sets whether to plot horizontal bars ( ``horizontal=True`` ) or the default of vertical bars ( ``horizontal=False`` )."]}, {"cell_type": "code", "execution_count": 19, "id": "ebbe8b24", "metadata": {}, "outputs": [{"data": {"text/plain": ["[,\n", " ]"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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"text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "\n", "bhm.axline_E(bdata.models[2], states=np.array([0,2]), state_kwargs=({'c':'r'}, {'c':'b'}))"]}, {"cell_type": "code", "execution_count": 20, "id": "4113fc24", "metadata": {}, "outputs": [{"data": {"text/plain": ["[,\n", " ,\n", " ]"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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"text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "\n", "bhm.axline_S(bdata.models[2], add_corrections=True, horizontal=True)"]}, {"cell_type": "raw", "id": "42876400", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Selecting photon streams\n", "************************\n", "\n", "\n", "\n", "But what about the :attr:`H2MM_result.dwell_nano_mean ` parameter? \n", "It has not only information per state, but also information per stream. \n", "Some other dwell parameters are similar. \n", "To select and/or specify a stream, we have the streams keyword argument, and the ``stream_kwargs`` keyword argument to customize those plotting for those functions as well. \n", "For this we will use the :func:`dwell_tau_hist() ` function.\n", "\n", ".. warning::\n", "\n", " Remember to set the ``irf_thresh`` (see the :ref:`divisor approach ` section of the tutorial for more explanation.\n", " This should be done once, before a paremeter based on nanotimes is accessed\n", "\n", ""]}, {"cell_type": "code", "execution_count": 21, "id": "d6d7648c", "metadata": {}, "outputs": [], "source": ["bdata.irf_thresh = np.array([2355, 2305, 220])"]}, {"cell_type": "raw", "id": "703db830", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Now we can see the default appearance of :func:`dwell_tau_hist() ` :"]}, {"cell_type": "code", "execution_count": 22, "id": "50e31a40", "metadata": {}, "outputs": [{"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, 3))\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax);"]}, {"cell_type": "raw", "id": "c2b9dd73", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["By default, :func:`dwell_tau_hist() ` only shows the mean nanotimes for the D\\ :sub:`ex`\\ D\\ :sub:`em`\\ photon stream. \n", "But what if we wanted to look at a different stream? \n", "To do this we use the ``streams`` keyword argument. \n", "It functions like the ``states`` keyword argument before (introduced in :ref:`Displaying subsets of states ` ).\n", "\n", "So, let\u2019s look at the D\\ :sub:`ex`\\ D\\ :sub:`em`\\ and D\\ :sub:`ex`\\ A\\ :sub:`em`\\ streams:"]}, {"cell_type": "code", "execution_count": 23, "id": "e3d9a215", "metadata": {}, "outputs": [{"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, 3))\n", "streams = [frb.Ph_sel(Dex=\"Dem\"), frb.Ph_sel(Dex=\"Aem\")]\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax, streams=streams);"]}, {"cell_type": "raw", "id": "923b5efa", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Or just the D :sub:`ex`\\ A\\ :sub:`em`\\ stream:"]}, {"cell_type": "code", "execution_count": 24, "id": "72469cbd", "metadata": {}, "outputs": [{"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, 3))\n", "streams = [frb.Ph_sel(Dex=\"Aem\")]\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax, streams=streams);"]}, {"cell_type": "raw", "id": "da4da87d", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Customizing plotting of photon streams\n", "======================================\n", "\n", "\n", "\n", "For plots where there are specific selections per stream in addition to per state, the ``stream_kwargs`` keyword argument exists. \n", "It functions much like the ``state_kwargs`` argument, matching the order of streams and needing to be the same length.\n", "\n", "Also, ``state_kwargs`` and ``stream_kwargs`` merge dictionaries, so you can specify both, and not have a problem.\n", "\n", "So let\u2019s see an example:"]}, {"cell_type": "code", "execution_count": 25, "id": "be4044ba", "metadata": {}, "outputs": [{"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, 3))\n", "streams = [frb.Ph_sel(Dex=\"Dem\"), frb.Ph_sel(Dex=\"Aem\")]\n", "stream_kw = [{'color':'b'}, {'color':'r'}]\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax, streams=streams, stream_kwargs=stream_kw);"]}, {"cell_type": "raw", "id": "51f66a16", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["But now, the problem is we have no idea which state goes with what, so let\u2019s use the ``states`` keyword argument to specify only the 0th state:"]}, {"cell_type": "code", "execution_count": 26, "id": "9060776c", "metadata": {}, "outputs": [{"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, 3))\n", "streams = [frb.Ph_sel(Dex=\"Dem\"), frb.Ph_sel(Dex=\"Aem\")]\n", "stream_kw = [{'color':'b'}, {'color':'r'}]\n", "state = np.array([0])\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax, streams=streams, stream_kwargs=stream_kw, states=state);"]}, {"cell_type": "raw", "id": "4d804777", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Finally, ``stream_kwargs`` and ``state_kwargs`` work together: the two dictionaries for a particular stream and state combination are merged:\n", "\n", ".. note::\n", "\n", " In the dictionary merging process, if the same key is present in both dictionaries, then the value in ``stream_kwargs`` will be used, and the values in ``state_kwargs`` over-written.\n", "\n", ""]}, {"cell_type": "code", "execution_count": 27, "id": "45ff1314", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5, 3))\n", "streams = [frb.Ph_sel(Dex=\"Dem\"), frb.Ph_sel(Dex=\"Aem\")]\n", "stream_kw = [{'color':'b'}, {'color':'r'}]\n", "state_kw = [{'edgecolor':'darkblue'}, {'edgecolor':'darkorange'}, {'edgecolor':'olive'}]\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax, streams=streams, stream_kwargs=stream_kw, state_kwargs=state_kw);"]}, {"cell_type": "raw", "id": "63ee02ae", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Control of Plotting State and Stream in Unified Argument Array\n", "==============================================================\n", "\n", "\n", "\n", "Now, sometimes you need even more control, because the two keyword argument arrays clash. For this there is the kwarg_arr keyword argument. In kwarg_arr, you provide an array of dictionaries that will be the keyword arguments for `ax.scatter() `_ , the outer dimension indicates which state, and the inner dimension indicates which the stream.\n", "\n", ".. note::\n", "\n", " ``kwarg_arr`` is meant to take the place of the combination of ``state_kwargs`` and ``stream_kwargs`` .\n", " As such, ``kwarg_arr`` and ``state_kwargs`` cannot be specified at the same time. If ``stream_kwargs`` is specified at the same time as ``kwarg_arr`` , then burstH2MM will make a check.\n", " If ``kwarg_arr`` is formatted like ``state_kwargs`` , then it will be treated like ``state_kwargs`` .\n", " On the other hand, if it is formatted as demonstrated below, ``stream_kwargs`` will be ignored, and a warning will be presented.\n", "\n", ""]}, {"cell_type": "code", "execution_count": 28, "id": "c019b9d0", "metadata": {}, "outputs": [{"data": {"image/png": 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lMXHiRA4fPsyUKVNYtGgReXl5jBs3jrlz5wZPZ1u0aBGDBw9m6NChwfBRSUkJY8eO5dlnn6WgoIDs7Gx+8pOfBPts2rQpq1evZtSoUQwcOJDHH3+cTZs2MW/ePPbu3Vtvn62MzimISNKcbqWzo8sCLV++nGXLljF16lQAjh49yq5du8jIyOCJJ54gKyuL6dOn06lTJwDy8/Np3bo1l112GSkpKWRnZ7Nv3z4++eQTNm3axM033wzAiRMnaNeuXbCfspIb3bp1IzMzM1iWnp7Oxx9/TMuWLevt80Gck4KZhYDPgRPAcXfvbWaXAIuAVCAEfMfd90XaPwiMjLQf5+6vxDM+EUm+06l0dll567J9LVmypNInuG3cuJGWLVueVC57wYIFvPfee8EQ1sGDB1myZAnXXnstmZmZvPnmm5XuM96lsstLxPBRX3fvEVV8aRLwmrt3Bl6LvMfMLgeGAJnAAOC/zKxRAuITkSRpCKWz16xZw/e+970aYw2FQvzwhz9k7NixAPTv358ZM2YEvYd169YB8NFHH/Gb3/yGdevW8fLLL/P2229TWlrKM888w4YNGwiFQoRCIZYuXcqCBQvo2rUrxcXFQVIoKSlh8+bNNR+8OEnG8NFAoE9kej6wEvhxZP5Cdz8G7DSz7cA1QOXpU0TqXaIr3yaydPZjjz3Gr3/9a/7+97/TvXt3br31Vp588kl27drFeeedV2l8O3bsoGfPnsElqWPHjuXuu+8G4Gc/+xnjx4+ne/fuuDupqam88MILjBw5kqlTp3LppZcyZ84cRowYwaOPPkr79u1p3759sO2srCy2bNkSPGdh3LhxHDhwgOPHjzN+/HgyMzPjddirFdfS2Wa2E9gHODDL3Web2X53bxHVZp+7X2xmM4G33P1PkflzgJfd/dly28wBcgA6dux41UcffRS3+E9LKp0ttaDS2TBx4kSGDRtW4fzEmaKhlc6+wd0/NbM2wAoze6+atlbJvAoZy91nA7Mh/DyF+glTRM5Wjz76aLJDaFDiek7B3T+NvBYBzxEeDtpjZu0AIq9Fkea7gQ5Rq6cA1T/UVERE6lXckoKZfcXMmpdNA7cAm4BlQNl94sOBpZHpZcAQMzvXzNKAzsCaeMUnIiIVxXP4qC3wnJmV7edpd/+LmeUDi81sJLALuAPA3Teb2WJgC3AcuN/dT8QxPhERKSduScHdPwQqPGna3fcC/apYZzIwOV4xiYhI9VTmQkREAipzISKB+r7KOJbtJap0drSJEyfywgsv0LRpUzp16sTcuXNp0aJFtes0atSIbt26UVJSQuPGjRk+fDjjx48P6hrVxrx585g4cSIpKSkcOnSI9PR0HnroIa6//vpab6u+qacgIkmTyNLZ0W6++WY2bdrEhg0b6NKlC7/85S9rXOe8885j/fr1bN68mRUrVvDSSy/x8MMP12q/0QYPHsy6devYtm0bkyZNYtCgQWzdurXO26svSgoikjSJLJ0d7ZZbbqFx4/BAyXXXXcfu3btrFXebNm2YPXs2M2fOxN05ceIEEydO5Oqrr6Z79+7MmjULgGnTppGdnQ2E6yFdccUVlSawvn37kpOTE9zNvWPHDgYMGMBVV13FjTfeGFRmHTFiBPfddx99+/YlPT2dVatWkZ2dTUZGRqVFBOtCSUFEkqYhlM7+wx/+wDe/+c1ax56enk5paSlFRUXMmTOHiy66iPz8fPLz83niiSfYuXMn48ePZ/v27Tz33HPcfffdzJo1q8phruiy3Dk5OcyYMYOCggKmTp3K6NGjg3b79u3jr3/9K9OnT+e2225jwoQJbN68mY0bN7J+/fpaf47ydE5BRJIm2aWzJ0+eTOPGjbnzzjvrFH9ZmaDly5ezYcMGnn02XJXnwIEDbNu2jbS0NObNm0f37t259957T6rZVNW2Dh06xN/+9jfuuOOOYFlZHSeA2267DTOjW7dutG3blm7dugGQmZlJKBSiR48edfosZZQURCSpklU6e/78+bz44ou89tprRO6nqpUPP/yQRo0a0aZNG9ydGTNm0L9//wrttm3bxgUXXHBSGe3KlJXlLi0tpUWLFlX+1R/vUtoaPhKRpElW6ey//OUv/OpXv2LZsmUnDed88skn9OtX6W1UJykuLmbUqFGMGTMGM6N///78/ve/p6SkBIAPPviAw4cPc+DAAR544AFWr14dVEOtzKpVq5g9ezb33HMPF154IWlpaTzzzDNAONG9++67NcZUX9RTEJFAogvfJrJ0drQxY8Zw7Nix4Gln1113Hf/93/9NYWFhcAK6vC+++IIePXoEl6QOGzaMH/zgBwB8//vfJxQK0atXL9yd1q1b8/zzzzNhwgRGjx5Nly5dmDNnDn379iUrKwsIP5rz9ddf58iRI6SlpbFkyZKgmulTTz3FfffdxyOPPEJJSQlDhgzhyisr3AscF3EtnR1vvXv39rVr1yY7jIZFpbOlFlQ6+2QzZ86kY8eOwSMwzwQNrXS2iMhpY8yYMckOIel0TkFERAJKCiIiElBSEBGRgJKCiIgElBRERCSgq49EJJCb+0Y9b6/qsg5lElU6+84772Tt2rU0adKEa665hlmzZtGkSZMq24dCITIyMvjqV7/K0aNHad68Offffz/Dhw+vcp1YXHnllVx++eUx3XWdDOopiEjSJLJ09p133sl7773Hxo0b+eKLL3jyySdrXKdTp06sW7eOrVu3snDhQqZPn87cuXNr/mBV2Lp1K6WlpaxevZrDhw/XeTvxpKQgIkmTyNLZt956K2aGmXHNNdfUulx2eno606ZN47HHHgPg8OHDZGdnc/XVV9OzZ0+WLl0KhCu8/uIXvwDglVdeISsri9LSUgCefvpphg0bxi233MKyZcuCbRcUFPCNb3yDq666iv79+1NYWAhAnz59mDBhAllZWWRkZJCfn8+gQYPo3LkzP/3pT2t/wGOgpCAiSZOM0tklJSX88Y9/ZMCAAbWON7q89eTJk7npppvIz88nLy+PiRMncvjwYaZMmcKiRYvIy8tj3LhxzJ07N3g626JFixg8eDBDhw4Nho9KSkoYO3Yszz77LAUFBWRnZ/OTn/wk2GfTpk1ZvXo1o0aNYuDAgTz++ONs2rSJefPmsXfv3tof9BronIKIJE0ySmePHj2arKwsbrzxxlrHG10WaPny5SxbtoypU6cCcPToUXbt2kVGRgZPPPEEWVlZTJ8+nU6dOgGQn59P69atueyyy0hJSSE7O5t9+/bxySefsGnTpqAO04kTJ2jXrl2wn7KSG926dSMzMzNYlp6ezscff0zLli1r/Tmqo6QgIkmVyNLZDz/8MMXFxcGT0WqrrLx12b6WLFlC165dK7TbuHEjLVu2PKlc9oIFC3jvvfdITU0FwpVflyxZwrXXXktmZiZvvvlmpfuMd6ns8uI+fGRmjcxsnZm9GHl/iZmtMLNtkdeLo9o+aGbbzex9M6tYmFxEziiJLJ395JNP8sorr7BgwYJgOAdgzZo1fO9736sx1lAoxA9/+EPGjh0LQP/+/ZkxY0bQe1i3bh0AH330Eb/5zW9Yt24dL7/8Mm+//TalpaU888wzbNiwgVAoRCgUYunSpSxYsICuXbtSXFwcJIWSkhI2b95ch6NZPxLRU3gA2ApcGHk/CXjN3aeY2aTI+x+b2eXAECATuBR41cy6uPuJBMQoIsR2CWl9SmTp7FGjRnHZZZcFw0mDBg3i5z//Obt27eK8886rNL4dO3bQs2fP4JLUsWPHcvfddwPws5/9jPHjx9O9e3fcndTUVF544QVGjhzJ1KlTufTSS5kzZw4jRozg0UcfpX379rRv3z7YdlZWFlu2bAmeszBu3DgOHDjA8ePHGT9+PJmZmXE55jWJa+lsM0sB5gOTgR+4+7+b2ftAH3cvNLN2wEp372pmDwK4+y8j674C5Lp75X0qVDq7UiqdLbWg0tkwceJEhg0bVuH8xJmioZXO/i3wI6B51Ly27l4IEEkMbSLz2wNvRbXbHZl3EjPLAXIAOnbsGIeQReRs8uijjyY7hAYlbucUzOzfgSJ3L4h1lUrmVejGuPtsd+/t7r1bt259SjGKiMjJYkoKZvaAmV1oYXPM7B0zu6WG1W4AbjezELAQuMnM/gTsiQwbEXktirTfDXSIWj8FqP5J1yJyyk7npy9K9erybxtrTyHb3Q8CtwCtgbuBKTUE86C7p7h7KuETyH9197uAZUBZ8ZDhwNLI9DJgiJmda2ZpQGdgTW0+jIjUTrNmzdi7d68SwxnI3dm7dy/NmjWr1XqxnlMoG9q5FZjr7u+aWWXDPbGYAiw2s5HALuAOAHffbGaLgS3AceB+XXkkEl8pKSns3r2b4uLiZIcicdCsWTNSUlJqtU6sSaHAzJYDacCDZtYcKI11J+6+ElgZmd4L9Kui3WTCVyqJSAI0adKEtLS0ZIchDUisSWEk0AP40N2PmFlLwkNIIiJyBon1nMIKd3/H3fdD8Nf+9LhFJSIiSVFtT8HMmgHnA60i5SjKziNcSPiuYxEROYPUNHx0LzCecAIo4J9J4SDwePzCEhGRZKg2Kbj774DfmdlYd5+RoJhERCRJYjrR7O4zzOx6IDV6HXf/nzjFJSIiSRBTUjCzPwKdgPVA2b0DDigpiIicQWK9JLU3cLnrtkcRkTNarElhE/AvQGEcY5F6kLuyD7tCqdW2GZk6LyGxiMjpJ9ak0ArYYmZrgGNlM9399rhEJSIiSRFrUsiNZxAiItIwxHr10ap4ByIiIskX69VHn/PPB940BZoAh939wqrXEhGR002sPYXox2liZt8GrolHQCIikjx1ehynuz8P3FS/oYiISLLFOnw0KOrtOYTvW9A9C6epOaERNbbRZasiZ6dYrz66LWr6OBACBtZ7NCIiklSxnlPQA3VERM4CMZ1TMLMUM3vOzIrMbI+ZLTGz2j34U0REGrxYTzTPBZYRfq5Ce+CFyDwRETmDxJoUWrv7XHc/HvmZB7SOY1wiIpIEsSaFz8zsLjNrFPm5C9gbz8BERCTxYk0K2cB3gL8TrpT6H0C1J5/NrJmZrTGzd81ss5k9HJl/iZmtMLNtkdeLo9Z50My2m9n7Zta/bh9JRETqKtak8P+A4e7e2t3bEE4SuTWscwy4yd2vBHoAA8zsOmAS8Jq7dwZei7zHzC4HhgCZwADgv8ysUe0+joiInIpY71Po7u77yt64+z/MrGd1K0QeyHMo8rZJ5McJ39/QJzJ/PrAS+HFk/kJ3PwbsNLPthEtpvBljjA1Xbm4Cd9YngfsSkTNNrD2Fc8oN81xCDAklcv5hPVAErHD3t4G27l4IEHltE2neHvg4avXdkXnlt5ljZmvNbG1xcXGM4YuISCxi7Sn8BvibmT1L+K/97wCTa1rJ3U8APcysBfCcmV1RTXOrbBOVbHM2MBugd+/eKrUhIlKPYr2j+X/MbC3hIngGDHL3LbHuxN33m9lKwucK9phZO3cvNLN2hHsREO4ZdIhaLQX4NNZ9iIjIqYu5Sqq7b3H3me4+I5aEYGatIz0EzOw84N+A9wjfBDc80mw4sDQyvQwYYmbnmlka0BlYE/MnERGRUxbr8FFdtAPmR64gOgdY7O4vmtmbwGIzGwnsAu4AcPfNZrYY2EK46N79keEnERFJkLglBXffAFS4Qsnd9wL9qlhnMjGcqxARkfio00N2RETkzKSkICIiASUFEREJKCmIiEhASUFERAJKCiIiElBSEBGRgJKCiIgElBRERCSgpCAiIgElBRERCSgpiIhIQElBREQCSgoiIhJQUhARkYCSgoiIBJQUREQkoKQgIiIBJQUREQkoKYiISEBJQUREAkoKIiISiFtSMLMOZpZnZlvNbLOZPRCZf4mZrTCzbZHXi6PWedDMtpvZ+2bWP16xiYhI5eLZUzgO/F93zwCuA+43s8uBScBr7t4ZeC3ynsiyIUAmMAD4LzNrFMf4RESknLglBXcvdPd3ItOfA1uB9sBAYH6k2Xzg25HpgcBCdz/m7juB7cA18YpPREQqSsg5BTNLBXoCbwNt3b0QwokDaBNp1h74OGq13ZF55beVY2ZrzWxtcXFxXOMWETnbxD0pmNkFwBJgvLsfrK5pJfO8wgz32e7e2917t27dur7CFBER4pwUzKwJ4YTwlLv/OTJ7j5m1iyxvBxRF5u8GOkStngJ8Gs/4RETkZPG8+siAOcBWd58WtWgZMDwyPRxYGjV/iJmda2ZpQGdgTbziExGRihrHcds3AMOAjWa2PjLvP4EpwGIzGwnsAu4AcPfNZrYY2EL4yqX73f1EHOMTEZFy4pYU3P11Kj9PANCvinUmA5PjFZOIiFRPdzSLiEhASUFERAJKCiIiEojniWaJs5Xvr6wwL/RZKgeP7K/zNluc36LO64rI6U9JIQlyVzatl+2EPutSYd76osMcO1z5RVsZX1EpKRGpnpLCWWRrFckiWrMvDwMwJ1Qx4ZxkZVNW5L4R875zc2+Iua2IJI+SgpzkaMlRAPbXMAR14LMQH74f2zb7dP3GKUYlIomipBBnuStz6RNaedK80P4a/gqP0f6j+2nRrEW9bEtEBHT1kYiIRFFPIUE+WR8Kpg/+vU3VDWvhyy+PcrDx/pPmHTt8tF62LSJnJ/UUREQkoKQgIiKBs3r4KLcWl1TW1cpQU0Lru9TbkJGISDyppyAiIgElBRERCZzVw0enm62Hrj3p/fETJ2h8TvJKVzTaGIqp3a5CeCN3xSnt64bc3FNaX0Rio56CiIgE1FOQSm09fG21y48WNSO9zbsJikZEEkU9BRERCSgpiIhIQMNHkhBzTvEZErUp0x1NJbtFakc9BRERCcQtKZjZH8ysyMw2Rc27xMxWmNm2yOvFUcseNLPtZva+mfWPV1wiIlK1ePYU5gEDys2bBLzm7p2B1yLvMbPLgSFAZmSd/zIzPTtSRCTB4pYU3H018I9yswcC8yPT84FvR81f6O7H3H0nsB24Jl6xiYhI5RJ9TqGtuxcCRF7LqsS1Bz6Oarc7Mq8CM8sxs7Vmtra4uDiuwYqInG0aytVHVsk8r6yhu88GZgP07t270ja1sTK06lQ3ISJyxkh0T2GPmbUDiLwWRebvBjpEtUsBPk1wbCIiZ71EJ4VlwPDI9HBgadT8IWZ2rpmlAZ2BNQmOTUTkrBe34SMzWwD0AVqZ2W7gIWAKsNjMRgK7gDsA3H2zmS0GtgDHgfvd/US8YhMRkcrFLSm4+9AqFvWrov1kYHK84hERkZrpjmYREQkoKYiISEBJQUREAkoKIiISaCg3r0kdHS+N70VayXwGtIgknnoKIiISUFIQEZGAho+kTpodPspFRftja3woVPcdpabWfV0gt45PbKsPeuqbnI7UUxARkYCSgoiIBM7u4aOVq0jdH0p2FCIiDYZ6CiIiEji7ewrAwb/vT3YIIiINhnoKIiISOOt7ChJ/+4/sr/O6Bz4LAfDh+xWX9en6jTpvNxGSdTmsLoWVU6GegoiIBNRTSJCjXx495W0cP3H2Poyu0cZQhXm7Cut/Px37NOzeh0i8KSlItaoruHf0+KknumaNm53yNkSk/igpSJ1t++LrNbbpfN7rCYhEROqLkoI0aNXWVzqVmkrlnWKNpYZE9Z7kVCgpNBCx/NUt8bdr5aqE7UvnL6QhUlKQpDqV8xL7j+6vsU2LZi3qvH2Rs1GDSwpmNgD4HdAIeNLdpyRy//VxlZCchkKhxOznDBqmakiSOWSWLPEaqmtQScHMGgGPAzcDu4F8M1vm7luSG9mp0dBQ8sTSmzgV6omc7Gz85XymaVBJAbgG2O7uHwKY2UJgIHBaJ4WzWVwT4hcnv03GlU77S/fX2KbF+S3++SaqR7JrXqh804rbj3NSi1aXXvKeS2q+pLjFBS3qEE1FF1VSpywuSblcb+5sO/dj7p7sGAJm9h/AAHf/fuT9MOBadx8T1SYHyIm87QpUUgAhJq2Az04h3EQ5HeI8HWKE0yPO0yFGOD3iVIxVu8zdW1e2oKH1FKySeSdlLXefDcw+5R2ZrXX33qe6nXg7HeI8HWKE0yPO0yFGOD3iVIx109BqH+0GOkS9TwE+TVIsIiJnnYaWFPKBzmaWZmZNgSHAsiTHJCJy1mhQw0fuftzMxgCvEL4k9Q/uvjlOuzvlIagEOR3iPB1ihNMjztMhRjg94lSMddCgTjSLiEhyNbThIxERSSIlBRERCZzxScHMBpjZ+2a23cwmVbLczOyxyPINZtYrCTF2MLM8M9tqZpvN7IFK2vQxswNmtj7y8/MkxBkys42R/a+tZHlSj6WZdY06PuvN7KCZjS/XJinH0cz+YGZFZrYpat4lZrbCzLZFXi+uYt1qv8MJiPNRM3sv8m/6nJm1qGLdar8fcY4x18w+ifp3vbWKdRNyLKuIcVFUfCEzW1/Fugk5jlVy9zP2h/DJ6h1AOtAUeBe4vFybW4GXCd8jcR3wdhLibAf0ikw3Bz6oJM4+wItJPp4hoFU1y5N+LMv92/+d8E06ST+OQBbQC9gUNe/XwKTI9CTgV1V8jmq/wwmI8xagcWT6V5XFGcv3I84x5gI/jOE7kZBjWVmM5Zb/Bvh5Mo9jVT9nek8hKJvh7l8CZWUzog0E/sfD3gJamFm7RAbp7oXu/k5k+nNgK9A+kTHUk6Qfyyj9gB3u/lGS9n8Sd18N/KPc7IHA/Mj0fODblaway3c4rnG6+3J3Px55+xbh+4eSpopjGYuEHcvqYjQzA74DLIjHvk/VmZ4U2gMfR73fTcVftrG0SRgzSwV6Am9XsvhrZvaumb1sZpmJjQwI312+3MwKIuVGymtIx3IIVf+nS/ZxLNPW3Qsh/IcB0KaSNg3pmAJkE+4NVqam70e8jYkMcf2hiqG4hnIsbwT2uPu2KpYn9Tie6UmhxrIZMbZJCDO7AFgCjHf3g+UWv0N4KORKYAbwfILDA7jB3XsB3wTuN7OscssbxLGM3Ph4O/BMJYsbwnGsjQZxTAHM7CfAceCpKprU9P2Ip98DnYAeQCHh4ZnyGsqxHEr1vYRkHsczPinEUjajQZTWMLMmhBPCU+7+5/LL3f2gux+KTL8ENDGzVomM0d0/jbwWAc8R7o5HaxDHkvB/pnfcfU/5BQ3hOEbZUza8FnktqqRNgzimZjYc+HfgTo8MfJcXw/cjbtx9j7ufcPdS4Ikq9p30Y2lmjYFBwKKq2iTzOMKZnxRiKZuxDPhe5MqZ64ADZV36RImMMc4Btrr7tCra/EukHWZ2DeF/u70JjPErZta8bJrwycdN5Zol/VhGVPmXWLKPYznLgOGR6eHA0kraJL30i4UffPVj4HZ3P1JFm1i+H/GMMfrc1f+pYt9JP5bAvwHvufvuyhYm+zgCZ/bVR5E/aG4lfDXPDuAnkXmjgFGRaSP8YJ8dwEagdxJi/DrhbuwGYH3k59ZycY4BNhO+YuIt4PoEx5ge2fe7kTga6rE8n/Av+Yui5iX9OBJOUoVACeG/WEcCLYHXgG2R10sibS8FXqruO5zgOLcTHosv+27+d/k4q/p+JDDGP0a+cxsI/6Jvl8xjWVmMkfnzyr6LUW2Tchyr+lGZCxERCZzpw0ciIlILSgoiIhJQUhARkYCSgoiIBJQUREQkoKQgIiIBJQUREQkoKYjUAzNLtfDzMJ6w8DMxlpvZeWY2zsy2RAq1LUx2nCI10c1rIvUgUt12O+G7uNeb2WLCd9b+Gkhz92Nm1sLd9ycxTJEaqacgUn92uvv6yHQBkEq47MJTZnYX4QqjIg2akoJI/TkWNX0CaAx8i3A9qKuAgkiVTJEGS0lBJH7OATq4ex7wI6AFcEFSIxKpgf5qEYmfRsCfzOwiwhVkp+ucgjR0OtEsIiIBDR+JiEhASUFERAJKCiIiElBSEBGRgJKCiIgElBRERCSgpCAiIoH/D3ck4S0e7mxWAAAAAElFTkSuQmCC\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(6, 4))\n", "kwarr = [[{'color':'g', 'label':'State 0, DexDem'},\n", " {'color':'darkgreen', 'label':'State 0, DexAem'}],\n", " [{'color':'r', 'label':'State 1, DexDem'},\n", " {'color':'darkred', 'label':'State1, DexAem'}],\n", " [{'color':'b', 'label':'State 2, DexDem'},\n", " {'color':'darkblue', 'label':'State2, DexAem'}]]\n", "bhm.dwell_tau_hist(bdata.models[2], ax=ax, kwarg_arr=kwarr, streams=[frb.Ph_sel(Dex=\"Dem\"), frb.Ph_sel(Aex=\"Aem\")])\n", "ax.legend();"]}, {"cell_type": "raw", "id": "eb2f6a81", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["So ``kwarg_arr`` allows the most customization, but is also the longest to define.\n", "\n", "Plotting by dwell position and masking\n", "**************************************\n", "\n", "\n", "\n", "Dwell based plotting functions also include the ``dwell_pos`` keyword arguments. \n", "This arguments allows the user to filter which dwells are plotted, not by state, but by the position (middle of the burst, start, stop or whole), and in its most advanced usage, by any user defined criterion. \n", "There are several possible types of inputs to ``dwell_pos`` , but the most easily understood is by using one of the functions in the :mod:`Masking ` module (see :ref:`Using masking functions to make masks ` ).\n", "\n", "So let\u2019s see ``dwell_pos`` in action:"]}, {"cell_type": "code", "execution_count": 29, "id": "627b29e5", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(5,5))\n", "# plot only dwells in the middle of a burst\n", "bhm.dwell_ES_scatter(bdata.models[2], dwell_pos=bhm.mid_dwell, ax=ax);"]}, {"cell_type": "raw", "id": "d298f392", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. note::\n", "\n", " Functions handed to the ``dwell_pos`` keyword argument must accept a :class:`H2MM_result ` objects as input, and return a mask of dwells\n", "\n", "You will note many fewer points, as there are many beginning, ending and whole burst dwells removed.\n", "\n", "It is also possible to specify dwells by specifying ``dwell_pos`` as an integer corresponding to the dwell position code used in the similarly named :attr:`H2MM_result.dwell_pos ` parameter.\n", "\n", "So to select the mid dwells, we give it 0:"]}, {"cell_type": "code", "execution_count": 30, "id": "adc19a8f", "metadata": {}, "outputs": [{"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", "# plot only dwells in the middle of a burst\n", "bhm.dwell_ES_scatter(bdata.models[2], dwell_pos=1, ax=ax);"]}, {"cell_type": "raw", "id": "c90d5f3e", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["And to select the beginning of bursts: "]}, {"cell_type": "code", "execution_count": 31, "id": "1803639a", "metadata": {}, "outputs": [{"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", "# plot only dwells in the middle of a burst\n", "bhm.dwell_ES_scatter(bdata.models[2], dwell_pos=2, ax=ax);"]}, {"cell_type": "raw", "id": "f15da5c0", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["It is also possible to select multiple types of dwells by using an array of all interested codes: "]}, {"cell_type": "code", "execution_count": 32, "id": "a48adff6", "metadata": {}, "outputs": [{"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", "# make array of code selections (beginning and whole burst dwells)\n", "pos_sel = np.array([2,3])\n", "# plot the selection\n", "bhm.dwell_ES_scatter(bdata.models[2], dwell_pos=pos_sel, ax=ax);"]}, {"cell_type": "raw", "id": "92f523b5", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Another method is to provide a mask of all the dwells, for example, all dwells with a stoichiometry greater than some threshold: "]}, {"cell_type": "code", "execution_count": 33, "id": "5612ae03", "metadata": {}, "outputs": [{"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", "# make mask of dwells with stoichiometry greater than 0.5\n", "dwell_mask = bdata.models[2].dwell_S > 0.5\n", "# plot with a mask\n", "bhm.dwell_ES_scatter(bdata.models[2], dwell_pos=dwell_mask, ax=ax)\n", "# ensure full S range is plotted\n", "ax.set_ylim([0,1]);"]}, {"cell_type": "raw", "id": "98fab652", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Now the previous example plots a selection that is not very useful, however, what if we want to exclude dwells with fewer than a certain number of photons? \n", "Well, you could use :attr:`H2MM_result.dwell_ph_counts ` to make a mask, but there is one :mod:`Masking ` function that is different from the others, and will not work directly as an input to ``dwell_pos`` : this is :func:`dwell_size() ` which needs at least a minimum number of photons as input. \n", "So here, we will employ a Python ``lambda`` function:"]}, {"cell_type": "code", "execution_count": 34, "id": "d2d802a1", "metadata": {}, "outputs": [{"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", "# plot with lambda function, sets ph_min at 10\n", "bhm.dwell_ES_scatter(bdata.models[2], dwell_pos= lambda m: bhm.dwell_size(m, 10), ax=ax);"]}, {"cell_type": "raw", "id": "fd7af653", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Thus you can hand functions that take :class:`H2MM_result ` object as input, and returns a mask as output to select dwells based on whatever parameters you want.\n", "\n", ".. _burstbasedplot:\n", "\n", "\n", "Burst Based Plotting\n", "********************\n", "\n", "\n", "\n", "What if you want to look not at individual dwells, but at how bursts differ based on the bursts within them? \n", "For that there are the burst-based plots. \n", "There is currently 1 burst-based plotting function, but more are likely to come in future versions. \n", "This is :func:`burst_ES_scatter() `\n", "\n", "Now, instead of segmenting the data into dwells, we consider entire bursts, based on what states are present within them. \n", "Under the hood, this is achieved using the :attr:`H2MM_result.burst_type ` attribute. \n", "This means the plots will now have points at the same positions as FRETBursts plotting functions, but will gain additional formatting depending on what states are present within them.\n", "\n", ".. see also::\n", "\n", " :ref:`Burst Based Arrays ` to understand how to directly access the numbers being plotted in the followig section.\n", "\n", "So let\u2019s look at the basic plot produced from :func:`burst_ES_scatter() ` "]}, {"cell_type": "code", "execution_count": 35, "id": "4a277c35", "metadata": {}, "outputs": [{"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.burst_ES_scatter(bdata.models[2], ax=ax);"]}, {"cell_type": "raw", "id": "134f6e75", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Now this plot has a lot of colors in it, and they aren\u2019t labeled. \n", "The number of colors scales with the square of the number of states, so you can imagine these plots get busy quickly. \n", "So there is a keyword argument: ``flatten_dynamics`` . \n", "If ``flatten_dynamics=True`` and then bursts will only be distinguished by whether any sort of transition occurs or if they only contain a single dwell/state:"]}, {"cell_type": "code", "execution_count": 36, "id": "7d638cb9", "metadata": {}, "outputs": [{"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.burst_ES_scatter(bdata.models[2], flatten_dynamics=True, ax=ax);"]}, {"cell_type": "raw", "id": "a7ccf678", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["Finally, if you want to control the plotting by burst type, much like the state_kwargs keyword argument, there is the ``type_kwargs`` keyword argument. \n", "So, what is the order that the list needs to be given? \n", "Well that depends on if flatten_dynamics is ``True`` or ``False`` .\n", "\n", "If it is ``False`` , then the order is based on the binary order, so it will go State0 only, then **State1** only, then **State1** and **State0**, then **State2** etc:"]}, {"cell_type": "code", "execution_count": 37, "id": "fdf12840", "metadata": {}, "outputs": [{"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", "type_kwargs = [\n", " {'label':'State 0'}, {'label':'State 1'},\n", " {'label':'State 0+1'}, {'label':'State 2'},\n", " {'label':'State 2+0'}, {'label':'State 2+1'},\n", " {'label':'State 2+1+0'}\n", "]\n", "bhm.burst_ES_scatter(bdata.models[2],type_kwargs=type_kwargs, ax=ax)\n", "ax.legend();"]}, {"cell_type": "raw", "id": "9e2c652d", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["If ``True`` then the order is simply **State0**, **State1** \u2026 then finally, the last element will be \u201cdynamic\u201d bursts, i.e. a burst with any sort of dynamics:"]}, {"cell_type": "code", "execution_count": 38, "id": "9bc9589d", "metadata": {}, "outputs": [{"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", "type_kwargs = [\n", " {'label':'State 0'}, {'label':'State 1'},\n", " {'label':'State 2'}, {'label':'Dynamics'}\n", "]\n", "bhm.burst_ES_scatter(bdata.models[2],flatten_dynamics=True, type_kwargs=type_kwargs, ax=ax)\n", "ax.legend();"]}, {"cell_type": "raw", "id": "057d0c0f", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": ["That's the end of this How-To, thank you for using burstH2MM. ", "\n", "\n", "Download this documentation as a jupyter notebook here: :download:`PlottingFunctions.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}