AP_plots.ipynb

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   "source": [
    "## Sensor responses to APs"
   ]
  },
  {
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   "execution_count": null,
   "id": "strange-exemption",
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   "source": [
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "plot_subset_for_paper = False # False: plot entire dataset. True: plot subset for manuscript\n",
    "data_path = r'data/plotly_AP_traces_WEBSITE.mat'\n",
    "\n",
    "def add_shaded_trace(t, y_mean, y_sterr, stim_subplot_idx, color, construct_label):\n",
    "    '''\n",
    "    plot AP mean trace +/- s.e.m\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    t : 1d numpy array\n",
    "        time array (s).\n",
    "    y_mean : 1d numpy array\n",
    "        mean response.\n",
    "    y_sterr : 1d numpy array\n",
    "        st.err around mean of response.\n",
    "    stim_subplot_idx : int\n",
    "        index of subplot (e.g. 1 for 1AP, 4 for 160AP).\n",
    "    color : str\n",
    "        color of trace (e.g. '#FEAF16').\n",
    "    variant_label : str\n",
    "        construct name for legend.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    None.\n",
    "\n",
    "    '''\n",
    "    y_upper = y_mean + y_sterr\n",
    "    y_lower = y_mean- y_sterr\n",
    "    y_lower = np.flip(y_lower)\n",
    "    t_rev = np.flip(t)\n",
    "    \n",
    "    # draw shaded area\n",
    "    fig.add_trace(go.Scatter(\n",
    "    x=np.append(t, t_rev),\n",
    "    y=np.append(y_upper, y_lower),\n",
    "    fill='toself',\n",
    "    fillcolor= color, # 'rgba(0,100,80,0.2)',\n",
    "    line_color='rgba(255,255,255,0)',\n",
    "    showlegend=False,\n",
    "    legendgroup=construct_label,\n",
    "    name=construct_label,\n",
    "    opacity= 0.5\n",
    "    ), row=(2 if stim_subplot_idx > 2 else 1),\n",
    "    col=(1 if stim_subplot_idx%2 == 1 else 2))\n",
    "    \n",
    "    \n",
    "    # draw mean line\n",
    "    fig.add_trace(go.Scatter(\n",
    "    x=t, y=y_mean,\n",
    "    line_color=color, # 'rgb(0,100,80)',\n",
    "    name=construct_label,\n",
    "    legendgroup=construct_label,\n",
    "    showlegend=(stim_subplot_idx == 1),\n",
    "    ), row=(2 if stim_subplot_idx > 2 else 1),\n",
    "    col=(1 if stim_subplot_idx%2 == 1 else 2),)\n",
    "\n",
    "t_to_ignore_s = 0.5 # initial seconds to remove to get rid of bleaching artifacts\n",
    "pio.templates.default = \"plotly_white\"\n",
    "plot_mat = loadmat(data_path)\n",
    "\n",
    "control_med_med_dff = plot_mat['plot_out'][0,0]['control_med_med_dff'] # [time x nStims]\n",
    "hits_med_med_dff = plot_mat['plot_out'][0,0]['hits_med_med_dff'] # [time x nStims x nHits]\n",
    "time = plot_mat['plot_out'][0,0]['time'][0] # time vector\n",
    "hits = plot_mat['plot_out'][0,0]['hits'] # array of hit name strings\n",
    "hits = [h[0] for h in hits[0]]\n",
    "\n",
    "\n",
    "control = plot_mat['plot_out'][0,0]['control'][0] # control name string\n",
    "hits_med_med_dff_sterr =  plot_mat['plot_out'][0,0]['hits_med_med_dff_sterr']\n",
    "control_med_med_dff_sterr = plot_mat['plot_out'][0,0]['control_med_med_dff_sterr']\n",
    "\n",
    "fs = 1/(time[1]-time[0])\n",
    "t_to_ignore_samples = int(t_to_ignore_s * fs)\n",
    "\n",
    "# ignore samples in beginning and end (due to alignment)\n",
    "time = time[t_to_ignore_samples:-1*t_to_ignore_samples]\n",
    "control_med_med_dff = control_med_med_dff[t_to_ignore_samples:-1*t_to_ignore_samples,]\n",
    "hits_med_med_dff = hits_med_med_dff[t_to_ignore_samples:-1*t_to_ignore_samples,]\n",
    "control_med_med_dff_sterr = control_med_med_dff_sterr[t_to_ignore_samples:-1*t_to_ignore_samples,]\n",
    "hits_med_med_dff_sterr = hits_med_med_dff_sterr[t_to_ignore_samples:-1*t_to_ignore_samples,]\n",
    "\n",
    "# mapping between construct IDs and names\n",
    "mapping = {'GCaMP6s': '10.641' , 'jGCaMP8f': '500.456', 'jGCaMP8m': '500.686', 'jGCaMP8s': '500.688','jGCaMP8.712': '500.712', 'GCaMP6f': '10.693', 'jGCaMP7f': '10.921', 'jGCaMP7s': '10.1473', 'jGCaMP7c': '10.1513', 'jGCaMP7b': '10.1561', 'XCaMP-Gf': '538.1', 'XCaMP-G': '538.2', 'XCaMP-Gf0': '538.3'} # [h[0] for h in hits[0]]\n",
    "\n",
    "control_label= 'GCaMP6s'\n",
    "# hits_label = ['GCaMP6f', 'jGCaMP7f', 'jGCaMP8f', 'jGCaMP8m', 'jGCaMP8s', 'jGCaMP8.712', \n",
    "#              'jGCaMP8.543', 'jGCaMP8.707', 'jGCaMP8.455', 'jGCaMP7s', 'jGCaMP7c', 'jGCaMP7b', 'XCaMP-Gf', 'XCaMP-G', 'XCaMP-Gf0']# [h[0] for h in hits[0]]\n",
    "\n",
    "# labels and order of legend\n",
    "if plot_subset_for_paper:\n",
    "    hits_label = ['jGCaMP8f', 'jGCaMP8m', 'jGCaMP8s', 'GCaMP6s', 'jGCaMP7f', 'jGCaMP7s', 'XCaMP-Gf']# [h[0] for h in hits[0]]\n",
    "else:\n",
    "    hits_label = ['jGCaMP8f', 'jGCaMP8m', 'jGCaMP8s', 'GCaMP6s', 'jGCaMP7f', 'jGCaMP7s', 'XCaMP-Gf', \n",
    "                'GCaMP6f', 'jGCaMP8.712', 'jGCaMP7c', 'jGCaMP7b','XCaMP-G', 'XCaMP-Gf0']\n",
    "n_stims = control_med_med_dff.shape[1]\n",
    "n_hits = hits_med_med_dff.shape[2]\n",
    "\n",
    "stim_names = ('1 AP', '3 AP', '1 AP zoomed', '3 AP zoomed')\n",
    "\n",
    "if plot_subset_for_paper:\n",
    "    colorscheme = ['#336699', '#CC3333', '#666666', '#FFCC99', '#66CC33', '#CC99CC', '#3399CC']\n",
    "else:\n",
    "    # colorscheme = px.colors.qualitative.Alphabet # 0th: control\n",
    "    colorscheme = ['#336699', '#CC3333', '#666666', '#FFCC99', '#66CC33', '#CC99CC', \n",
    "                '#3399CC', '#AA0DFE', '#3283FE', '#85660D', '#782AB6', '#565656', '#1C8356']\n",
    "\n",
    "stim_iter = [0, 1, 0, 1] # [1AP, 3AP, 1AP, 3AP]\n",
    "\n",
    "fig = make_subplots(rows=2, cols=2, subplot_titles=stim_names, x_title='Time (s)',   y_title='dF/F') # go.Figure()\n",
    "\n",
    "# cycle through stim number\n",
    "for plot_i, i in enumerate(stim_iter):\n",
    "\n",
    "    # plot hits\n",
    "    for j,hit in enumerate(hits_label): # for j in range(n_hits):\n",
    "        \n",
    "        if hit == control_label:\n",
    "            # plot control\n",
    "            add_shaded_trace(time, control_med_med_dff[:,i], control_med_med_dff_sterr[:,i], plot_i+1, colorscheme[j], control_label)\n",
    "        else:\n",
    "            hit_id = mapping.get(hit)\n",
    "            hit_idx = hits.index(hit_id)\n",
    "            add_shaded_trace(time, hits_med_med_dff[:,i,hit_idx], hits_med_med_dff_sterr[:,i,hit_idx], plot_i+1, colorscheme[j], hit)\n",
    "\n",
    "fig.update_traces(mode='lines')\n",
    "fig.update_layout(hovermode=\"closest\", #, width=800, height=400,\n",
    "    title=\"<b>AP responses in cultured neuron screen</b>\",\n",
    "    title_font_size=14,\n",
    "    #xaxis_title=\"time (s)\",\n",
    "    legend_title=\"variants\",\n",
    "    font=dict(\n",
    "        family=\"Arial\",\n",
    "        size=14,\n",
    "    ),\n",
    "    height=500,\n",
    "    width=600\n",
    ")\n",
    "\n",
    "\n",
    "fig.update_yaxes(showline=True, linewidth=1, linecolor='black', ticks='inside', showgrid=False)\n",
    "\n",
    "# for 1AP, 3AP case, zoom in\n",
    "fig.update_xaxes(range=[0.95, 1.6], row=1, col=1, ticks='inside', showline=True, linewidth=1, linecolor='black', nticks=6, showgrid=False)\n",
    "fig.update_xaxes(range=[0.95, 1.6], row=1, col=2, ticks='inside', showline=True, linewidth=1, linecolor='black', nticks=6, showgrid=False)\n",
    "\n",
    "# for bottom row, zoom in more\n",
    "fig.update_xaxes(range=[0.99, 1.05], row=2, col=1, ticks='inside', showline=True, linewidth=1, linecolor='black', nticks=6, showgrid=False)\n",
    "fig.update_xaxes(range=[0.99, 1.05], row=2, col=2, ticks='inside', showline=True, linewidth=1, linecolor='black', nticks=6, showgrid=False)\n",
    "\n",
    "fig.show()\n"
   ]
  }
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