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{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Create plots for panels used in figure 2"
   ],
   "metadata": {
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    "collapsed": false
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 25,
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
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    "import matplotlib.patches as patches\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
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   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Examples how to plot the control probability cure in figure 2A can be found in `plots_fig1`"
   ],
   "metadata": {
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    "collapsed": false
  {
   "cell_type": "code",
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   "execution_count": 16,
   "outputs": [],
   "source": [
    "# folder to save all panels for figure 2\n",
    "savefolder = r\"plots\\fig2\"\n",
    "\n",
    "# file containing the data for the controls\n",
    "results_ctrl_file = r\"data\\shape_analysis\\histograms_HealthyControl_deformed_undeformed.txt\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
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  {
   "cell_type": "markdown",
   "source": [
    "## Panel A"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 17,
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   "outputs": [],
   "source": [
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    "def asymptotic_exponential_growth(x, lambda_):\n",
    "    \"\"\"(Inverted) exponential growth function with maximum at 1 for x->infinity:\n",
    "    f(x) = 1 - exp(-lambda * x)\"\"\"\n",
    "    return 1 - np.exp(-lambda_ * x)"
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   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 18,
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   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x252 with 1 Axes>",
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      "image/png": 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\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_ctrl = np.loadtxt(results_ctrl_file)\n",
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    "\n",
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    "label_fontsize = 12\n",
    "\n",
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    "v_min = 0.\n",
    "v_max = 3.\n",
    "\n",
    "v_ctrl = results_ctrl[:,0]\n",
    "probs_ctrl = results_ctrl[:,3]\n",
    "probs_ctrl_err = results_ctrl[:,4]\n",
    "\n",
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    "fit_bounds=(0, np.inf)\n",
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    "\n",
    "ind_vmax = v_ctrl <= v_max\n",
    "v_ctrl = v_ctrl[ind_vmax]\n",
    "probs_ctrl = probs_ctrl[ind_vmax]\n",
    "probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
    "\n",
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    "popt_ctrl, pcov_ctrl = curve_fit(asymptotic_exponential_growth, v_ctrl, probs_ctrl,\n",
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    "                                 sigma = probs_ctrl_err, absolute_sigma=False,\n",
    "                                 bounds = fit_bounds)\n",
    "\n",
    "v_plot = np.linspace(v_min, v_max, 100)\n",
    "\n",
    "with sns.axes_style(\"darkgrid\"):\n",
    "    plt.figure(0,(5,3.5))\n",
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    "    plt.errorbar(v_ctrl, probs_ctrl, probs_ctrl_err,\n",
    "                 color='k', marker='.',\n",
    "                 alpha=.5, lw=2.5, zorder=1, label='CTRL')\n",
    "    plt.plot(v_plot, asymptotic_exponential_growth(v_plot, *popt_ctrl),\n",
    "             color='darkcyan', ls='--', lw=2.5, zorder=100, label='CTRL fit')\n",
    "    # plt.axhline(1, c='C0', ls='--', lw=1.5)\n",
    "    # plt.vlines(0.5, -0.1, 0.5, ls='--', color='orange', lw=1.5)\n",
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    "    plt.xlim(0,3)\n",
    "    plt.ylim(0,1.05)\n",
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    "    plt.xlabel(\"Cell velocity [mm/s]\", fontsize=label_fontsize)\n",
    "    plt.ylabel(\"Probability\", fontsize=label_fontsize)\n",
    "    plt.title(\"Probability for deformed cell\", fontsize=label_fontsize+1)\n",
    "    plt.tick_params(labelsize=label_fontsize-1)\n",
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    "    plt.legend(loc='lower right')\n",
    "    plt.tight_layout()\n",
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    "    savename = \"fig2A_explain_FitValues\"\n",
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    "    savepath = os.path.join(savefolder,savename)\n",
    "    plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Panels B-D"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 19,
   "outputs": [],
   "source": [
    "#define a color seed for each patient\n",
    "color_dict = {'VS': 'C0', 'VL': 'C1', 'RS': 'C2',\n",
    "              'KM': 'C3', 'LM': 'C4'}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 20,
   "outputs": [],
   "source": [
    "def deformed_probability_curve(df, v_min=0, v_max=3, binsize=.25):\n",
    "    \"\"\"Compute the values for the shape probability diagram to find a cell\n",
    "    in a deformed state for velocities between v_min and v_max in the DataFrame df\n",
    "\n",
    "    returns: *tuple* (deformed_bins, deformed_hist_normal)\n",
    "        - deformed_bins: *array* limits for the bin ranges of the histogram\n",
    "        - normalized counts for each velocity range\n",
    "    \"\"\"\n",
    "\n",
    "    bins = int(v_max/binsize)   #number of Bins in histogram\n",
    "    #find index of cells in a deformed state. Class definitions are:\n",
    "    #1-parachute, 2-slipper, 3-asym. parachute, 5-multilobe, 7-undefined deformed\n",
    "    #4-discocyte/undeformed, 6-tumbler\n",
    "    deformed_index = ((df['shape'] == 1)\n",
    "                      | (df['shape'] == 2)\n",
    "                      | (df['shape'] == 3)\n",
    "                      | (df['shape'] == 5)\n",
    "                      | (df['shape'] == 7))\n",
    "\n",
    "    #create new column in df that is True for deformed state\n",
    "    df['deformed'] = False\n",
    "    df['deformed'][deformed_index] = True\n",
    "\n",
    "    df_deformed = df[deformed_index]\n",
    "\n",
    "    deformed_hist, deformed_bins = np.histogram(np.array(df_deformed['velocity']),\n",
    "                                                range = (v_min,v_max),\n",
    "                                                bins = bins)\n",
    "    #get the counts for all events to use for normalization\n",
    "    all_hist, all_bins = np.histogram(np.array(df['velocity']),\n",
    "                                      range = (v_min,v_max),\n",
    "                                      bins = bins)\n",
    "\n",
    "    #normalize the deformed histogram\n",
    "    deformed_hist_normal = deformed_hist/all_hist\n",
    "\n",
    "    return deformed_bins, deformed_hist_normal"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 21,
   "outputs": [],
   "source": [
    "#define dict to store fit values\n",
    "dict_fitvalues = {}\n",
    "\n",
    "def dict_fit_values_patient(patient, dict_fitvalues):\n",
    "    result_summary_folder = r\"data\\shape_analysis\\result_summaries\"\n",
    "\n",
    "    v_min = 0.\n",
    "    v_max = 3.\n",
    "    binsize = 0.25\n",
    "\n",
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    "    # bounds of the parameters in the exponential growth function\n",
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    "    fit_bounds=(0, np.inf)\n",
    "\n",
    "    result_file = os.path.join(result_summary_folder, patient + \"_results_MCFM.tsv\")\n",
    "    df_results = pd.read_csv(result_file, sep='\\t')\n",
    "\n",
    "    dates = np.unique(df_results['date'])\n",
    "    dates = np.sort(dates)\n",
    "    day0 = pd.to_datetime(dates[0])\n",
    "\n",
    "    #create dataframes to save fit parameters\n",
    "    df_fit_all = pd.DataFrame()\n",
    "    df_fit_healthy = pd.DataFrame()\n",
    "    df_fit_unhealthy = pd.DataFrame()\n",
    "\n",
    "    for num, date in enumerate(dates):\n",
    "        df_date = df_results[df_results['date']==date]\n",
    "        #create new Dataframe to work with, leave out skipped cells\n",
    "        df = df_date[df_date['shape'] != 0]\n",
    "\n",
    "        healthy_index = df['health'] == 0\n",
    "        df_healthy = df[healthy_index]\n",
    "        unhealthy_index = df['health'] == 1\n",
    "        df_unhealthy = df[unhealthy_index]\n",
    "\n",
    "        #calculate percentage of healthy cells in sample\n",
    "        percentage_healthy = len(df_healthy)/len(df)\n",
    "\n",
    "        bins, deformed_curve = deformed_probability_curve(df, v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "        bins_healthy, deformed_curve_healthy =  deformed_probability_curve(df_healthy,\n",
    "                                                                           v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "        bins_unhealthy, deformed_curve_unhealthy =  deformed_probability_curve(df_unhealthy,\n",
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    "                                                                               v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "\n",
    "        bins_plot = bins[:-1]+binsize/2\n",
    "\n",
    "        #exclude nan values before fitting\n",
    "        ind_nonnan_all = ~np.isnan(deformed_curve)\n",
    "        ind_nonnan_healthy = ~np.isnan(deformed_curve_healthy)\n",
    "        ind_nonnan_unhealthy = ~np.isnan(deformed_curve_unhealthy)\n",
    "\n",
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    "        x_all = bins_plot[ind_nonnan_all]\n",
    "        y_all = deformed_curve[ind_nonnan_all]\n",
    "        x_healthy = bins_plot[ind_nonnan_healthy]\n",
    "        y_healthy = deformed_curve_healthy[ind_nonnan_healthy]\n",
    "        x_unhealthy = bins_plot[ind_nonnan_unhealthy]\n",
    "        y_unhealthy = deformed_curve_unhealthy[ind_nonnan_unhealthy]\n",
    "\n",
    "        popt_all_exp, pcov_all_exp = curve_fit(asymptotic_exponential_growth,\n",
    "                                               x_all, y_all,\n",
    "                                               bounds=fit_bounds\n",
    "                                               )\n",
    "        popt_healthy_exp, pcov_healthy_exp = curve_fit(asymptotic_exponential_growth,\n",
    "                                                       x_healthy, y_healthy,\n",
    "                                                       bounds=fit_bounds\n",
    "                                                       )\n",
    "        popt_unhealthy_exp, pcov_unhealthy_exp = curve_fit(asymptotic_exponential_growth,\n",
    "                                                           x_unhealthy, y_unhealthy,\n",
    "                                                           bounds=fit_bounds\n",
    "                                                           )\n",
    "        #days since treatment start\n",
    "        treatment_days = (pd.to_datetime(date) - day0).days\n",
    "\n",
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    "        df_fit_all = df_fit_all.append({'lambda': popt_all_exp[0], 'lambda_err': np.sqrt(pcov_all_exp[0,0]),\n",
    "                                        'days': treatment_days,\n",
    "                                        'percent healthy': percentage_healthy\n",
    "                                        },\n",
    "                                       ignore_index=True)\n",
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    "        df_fit_healthy = df_fit_healthy.append({'lambda': popt_healthy_exp[0], 'lambda_err': np.sqrt(pcov_healthy_exp[0,0]),\n",
    "                                                'days': treatment_days\n",
    "                                                },\n",
    "                                               ignore_index=True)\n",
    "        df_fit_unhealthy = df_fit_unhealthy.append({'lambda': popt_unhealthy_exp[0], 'lambda_err': np.sqrt(pcov_unhealthy_exp[0,0]),\n",
    "                                                    'days': treatment_days\n",
    "                                                    },\n",
    "                                                   ignore_index=True)\n",
    "\n",
    "    dict_fitvalues[patient] = {'all': df_fit_all, 'healthy': df_fit_healthy, 'unhealthy': df_fit_unhealthy}\n",
    "\n",
    "    return dict_fitvalues"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Fill dictionary with patient data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 22,
   "outputs": [],
   "source": [
    "patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
    "labels = [\"P1\", \"P2\", \"P3\", \"P4\", \"P5\"]\n",
    "\n",
    "for patient in patients:\n",
    "    dict_fitvalues = dict_fit_values_patient(patient, dict_fitvalues)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plot data for P1-P3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 792x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patients = ['VS', 'VL', 'RS']\n",
    "labels = [\"P1\", \"P2\", \"P3\"]\n",
    "\n",
    "# plot variables\n",
    "fontsize = 18\n",
    "linewidth = 5\n",
    "markersize = 12\n",
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    "errbar_width = 5\n",
    "xlabel = 'Day since treatment start'\n",
    "\n",
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    "# color for the control interval\n",
    "ctrl_clr = 'darkslategray'\n",
    "\n",
    "# compute control fit values\n",
    "results_ctrl = np.loadtxt(results_ctrl_file)\n",
    "\n",
    "v_ctrl = results_ctrl[:,0]\n",
    "probs_ctrl = results_ctrl[:,3]\n",
    "probs_ctrl_err = results_ctrl[:,4]\n",
    "\n",
    "v_min = 0.\n",
    "v_max = 3.\n",
    "binsize = 0.25\n",
    "bins = int(v_max / binsize)\n",
    "\n",
    "ind_vmax = v_ctrl <= v_max\n",
    "v_ctrl = v_ctrl[ind_vmax]\n",
    "probs_ctrl = probs_ctrl[ind_vmax]\n",
    "probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
    "\n",
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    "fit_bounds = [0, np.inf]\n",
    "popt_ctrl, pcov_ctrl = curve_fit(asymptotic_exponential_growth, v_ctrl, probs_ctrl,\n",
    "                                 sigma = probs_ctrl_err, absolute_sigma=False,\n",
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    "                                 bounds=fit_bounds\n",
    "                                 )\n",
    "perr_ctrl = np.sqrt(np.diag(pcov_ctrl))\n",
    "\n",
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    "# limits of the 95% confidence interval\n",
    "ci_lower = float(popt_ctrl - perr_ctrl)\n",
    "ci_upper = float(popt_ctrl + perr_ctrl)\n",
    "\n",
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    "with sns.axes_style('darkgrid'):\n",
    "\n",
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    "    fig = plt.figure(0,(11,6))\n",
    "\n",
    "    plot_titles = ['Normocytes', 'Acanthocytes']\n",
    "    para = 'lambda'\n",
    "    para_label = r'$\\lambda$'\n",
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    "    ylim = [0, 2.3]\n",
    "\n",
    "    for jj, patient in enumerate(patients):\n",
    "        data = dict_fitvalues[patient]\n",
    "        color = color_dict[patient]\n",
    "\n",
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    "        for n, health in enumerate(['healthy', 'unhealthy']):\n",
    "            ax=plt.subplot(1,2,n+1)\n",
    "\n",
    "            df_plot = data[health]\n",
    "            xdata = df_plot['days']\n",
    "            ydata = df_plot[para]\n",
    "            yerr = df_plot[para + \"_err\"]\n",
    "\n",
    "            # plot data on treatment\n",
    "            plt.errorbar(xdata[:-1], ydata[:-1], yerr=yerr[:-1],\n",
    "                         c=color, label=labels[jj],\n",
    "                         ls='-', lw=linewidth, marker='X', markersize=markersize,\n",
    "                         ecolor='gray', elinewidth=errbar_width)\n",
    "\n",
    "            # plot data off treatment\n",
    "            plt.errorbar(xdata[-2:], ydata[-2:], yerr=yerr[-2:],\n",
    "                         c=color, ls='--', lw=linewidth, marker='X', markersize=markersize,\n",
    "                         ecolor='gray', elinewidth=errbar_width)\n",
    "\n",
    "            plt.ylim(ylim)\n",
    "            plt.xlabel(xlabel, fontsize=fontsize)\n",
    "            plt.tick_params(axis='both', which='both', labelsize=fontsize-2)\n",
    "            plt.xticks([0,100,200,300])\n",
    "            plt.title(r'{} - {}'.format(para_label, plot_titles[n]), fontsize=fontsize+2)\n",
    "\n",
    "            # plot control region at end only\n",
    "            if patient==patients[-1]:\n",
    "                if health=='unhealthy':\n",
    "                    ax.axhline(ci_lower, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                    ax.axhline(ci_upper, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                    axis_limits = ax.get_xlim()\n",
    "                    ax.add_patch(patches.Rectangle((axis_limits[0], ci_lower),\n",
    "                                                   np.diff(axis_limits), ci_upper-ci_lower,\n",
    "                                                   color=ctrl_clr, alpha=0.15, zorder=0,\n",
    "                                                   label = 'CTRL'\n",
    "                                                   )\n",
    "                                 )\n",
    "                    ax.get_yaxis().set_ticklabels([])\n",
    "\n",
    "                else:\n",
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    "                    ax.axhline(ci_lower, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                    ax.axhline(ci_upper, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                    axis_limits = ax.get_xlim()\n",
    "                    ax.add_patch(patches.Rectangle((axis_limits[0], ci_lower),\n",
    "                                                   np.diff(axis_limits), ci_upper-ci_lower,\n",
    "                                                   color=ctrl_clr, alpha=0.1, zorder=0,\n",
    "                                                   )\n",
    "                                 )\n",
    "            # set alpha of errorbars\n",
    "            for collection in ax.collections:\n",
    "                collection.set_alpha(.4)\n",
    "\n",
    "    fig.supylabel(\"Exponential growth rate [(mm/s)$^{-1}$]\", fontsize=fontsize)\n",
    "    plt.legend(loc='lower right', ncol=2, fontsize=fontsize-4, title_fontsize=fontsize)\n",
    "    plt.tight_layout()\n",
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    "    savename = \"fig2C_growth_rate_dasatinib\"\n",
    "    savepath = os.path.join(savefolder,savename)\n",
    "    plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
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   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
    "labels = [\"P1\", \"P2\", \"P3\", \"P4\", \"P5\"]\n",
    "\n",
    "fontsize = 24\n",
    "lw = 5\n",
    "xlabel = 'Day since treatment start'\n",
    "\n",
    "with sns.axes_style('darkgrid'):\n",
    "    plt.figure(0,(8,6))\n",
    "\n",
    "    for jj, patient in enumerate(patients):\n",
    "        data = dict_fitvalues[patient]\n",
    "        color = color_dict[patient]\n",
    "\n",
    "        # plot percentage of acanthocytes\n",
    "        df_all = data['all']\n",
    "\n",
    "        if patient=='LM':\n",
    "            plt.plot(df_all['days'][:-2], (1-df_all['percent healthy'][:-2])*100, c=color, lw=lw, label=labels[jj])\n",
    "            plt.plot(df_all['days'][-3:], (1-df_all['percent healthy'][-3:])*100, '--', lw=lw, c=color)\n",
    "        else:\n",
    "            plt.plot(df_all['days'][:-1], (1-df_all['percent healthy'][:-1])*100, c=color, lw=lw, label=labels[jj])\n",
    "            plt.plot(df_all['days'][-2:], (1-df_all['percent healthy'][-2:])*100, '--', lw=lw, c=color)\n",
    "\n",
    "    plt.ylim(0,100)\n",
    "    plt.xlabel(xlabel, fontsize=fontsize+2)\n",
    "    plt.ylabel(\"Acanthocytes %\", fontsize=fontsize+2)\n",
    "    plt.tick_params(axis='both', which='both', labelsize=fontsize)\n",
    "\n",
    "    plt.figure(0)\n",
    "    plt.autoscale()\n",
    "    plt.legend(fontsize=fontsize-2, ncol=1)\n",
    "    savename = \"fig2E_treatment_acantho_count\"\n",
    "    savepath = os.path.join(savefolder,savename)\n",
    "    plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Panel F"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 648x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in vitro study data\n",
    "path_in_vitro = r\"data\\shape_analysis\\data_in_vitro_dasa_treatment.xlsx\"\n",
    "df_in_vitro = pd.read_excel(path_in_vitro)\n",
    "\n",
    "# plot parameters\n",
    "linewidth = 4\n",
    "markersize = 15\n",
    "markerwidth = 1.5\n",
    "fontsize = 20\n",
    "\n",
    "markers = 'treatment'\n",
    "with sns.axes_style('darkgrid'):\n",
    "    fig = plt.figure(0,(9,6))\n",
    "    ax = plt.subplot(111)\n",
    "\n",
    "    sns.lineplot(x='mean velocity [mm/s]', y='fraction', data=df_in_vitro,\n",
    "                 hue='treatment', style='shape', markers=True,\n",
    "                 ax=ax, palette='viridis',\n",
    "                 linewidth=linewidth, markersize=markersize, markeredgewidth=markerwidth,\n",
    "                 alpha=.85\n",
    "                 )\n",
    "\n",
    "    ax.xaxis.label.set_size(fontsize+2)\n",
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    "    ax.set_xlabel(r\"Mean velocity [mm/s]\", fontsize=fontsize+2)\n",
    "    ax.set_ylabel(r\"Fraction $\\phi$\", fontsize=fontsize+2)\n",
    "    ax.tick_params(axis='both', labelsize=fontsize)\n",
    "\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    label = r\"$\\bf{Treatment}$\"\n",
    "    labels[0] = label\n",
    "    labels[3] = r\"$\\bf{Shape}$\"\n",
    "\n",
    "    handles_new = []\n",
    "    for handle in handles:\n",
    "        if handle.get_color() == 'w':\n",
    "            handle.set_linewidth(0)\n",
    "        else:\n",
    "            handle.set_linewidth(linewidth)\n",
    "        handle.set(markersize=markersize,\n",
    "                   markeredgewidth=markerwidth,\n",
    "                   markeredgecolor='w')\n",
    "        handles_new.append(handle)\n",
    "\n",
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    "    leg = plt.legend(handles_new, labels, ncol=1, fontsize=fontsize-3)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    savename = \"fig2F_in_vitro_dasa_treatment\"\n",
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    "    savepath = os.path.join(savefolder,savename)\n",
    "    plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}