diff --git a/figure_plots/20220218_new_shape_analysis_test.ipynb b/figure_plots/20220218_new_shape_analysis_test.ipynb
index 31f68f6f2d4cd91beda330443d98115514d81bde..22eba3c95c4b768dff64a3b093032cc39bd757c4 100644
--- a/figure_plots/20220218_new_shape_analysis_test.ipynb
+++ b/figure_plots/20220218_new_shape_analysis_test.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {
     "collapsed": true
    },
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "outputs": [],
    "source": [
     "# folder to save all panels for figure 2\n",
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 48,
    "outputs": [],
    "source": [
     "def logistic_growth(x, A, x_0, k):\n",
@@ -51,6 +51,9 @@
     "    '''A simplified logistic growth fct'''\n",
     "    return 1/(1+np.exp(-k*(x-x_0)))\n",
     "\n",
+    "def sigmoid_with_offest(x, x_0, k=20):\n",
+    "    return 1/(1+np.exp(-k*(x-x_0)))\n",
+    "\n",
     "def hyperbola(x, a):\n",
     "    '''Hyperbolic function with growth rate a and limit 1 with\n",
     "    root at f(0)=0: f(x) = a/(x-a) + 1\n",
@@ -69,12 +72,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 49,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 360x252 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -97,9 +100,10 @@
     "probs_ctrl = probs_ctrl[ind_vmax]\n",
     "probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
     "\n",
-    "popt_ctrl, pcov_ctrl = curve_fit(logistic_growth_simple, v_ctrl, probs_ctrl,\n",
+    "popt_ctrl, pcov_ctrl = curve_fit(sigmoid_with_offest, v_ctrl, probs_ctrl,\n",
     "                                 sigma = probs_ctrl_err, absolute_sigma=False,\n",
-    "                                 # bounds = fit_bounds\n",
+    "                                 p0=1,\n",
+    "                                 bounds = [0,3]\n",
     "                                 )\n",
     "popt_ctrl_exp, pcov_ctrl_exp = curve_fit(exponential_inverted, v_ctrl, probs_ctrl,\n",
     "                                            sigma = probs_ctrl_err, absolute_sigma=False,\n",
@@ -114,7 +118,7 @@
     "    plt.errorbar(v_ctrl, probs_ctrl, probs_ctrl_err,\n",
     "                 color='k', marker='.', alpha=.5, lw=2.5, zorder=1,\n",
     "                 label='CTRL')\n",
-    "    # plt.plot(v_plot, logistic_growth_simple(v_plot, *popt_ctrl), 'r-', alpha=1, lw=2, zorder=100)\n",
+    "    plt.plot(v_plot, sigmoid_with_offest(v_plot, *popt_ctrl), 'r-', alpha=1, lw=2, zorder=100)\n",
     "    plt.plot(v_plot, exponential_inverted(v_plot, *popt_ctrl_exp),\n",
     "             c='darkcyan', ls='--', alpha=1, lw=2.5, zorder=100,\n",
     "             label='exponential growth fit')\n",
@@ -153,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 50,
    "outputs": [],
    "source": [
     "#define a color seed for each patient\n",
@@ -169,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 51,
    "outputs": [],
    "source": [
     "def deformed_probability_curve(df, v_min=0, v_max=3, binsize=.25):\n",
@@ -219,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 52,
    "outputs": [],
    "source": [
     "#define dict to store fit values\n",
@@ -301,12 +305,22 @@
     "        popt_unhealthy_exp, pcov_unhealthy_exp = curve_fit(exponential_inverted,\n",
     "                                                           x_unhealthy, y_unhealthy\n",
     "                                                           )\n",
+    "        popt_all_sig, pcov_all_sig = curve_fit(sigmoid_with_offest,\n",
+    "                                               x_all, y_all\n",
+    "                                               )\n",
+    "        popt_healthy_sig, pcov_healthy_sig = curve_fit(sigmoid_with_offest,\n",
+    "                                                       x_healthy, y_healthy\n",
+    "                                                       )\n",
+    "        popt_unhealthy_sig, pcov_unhealthy_sig = curve_fit(sigmoid_with_offest,\n",
+    "                                                           x_unhealthy, y_unhealthy\n",
+    "                                                           )\n",
     "        #days since treatment start\n",
     "        treatment_days = (pd.to_datetime(date) - day0).days\n",
     "\n",
     "        df_fit_all = df_fit_all.append({'x0': popt_all_log[0], 'x0_err': np.sqrt(pcov_all_log[0,0]),\n",
     "                                        'k': popt_all_log[1], 'k_err': np.sqrt(pcov_all_log[1,1]),\n",
     "                                        'a': popt_all_exp[0], 'a_err': np.sqrt(pcov_all_exp[0,0]),\n",
+    "                                        'v_0': popt_all_sig[0], 'v_0_err': np.sqrt(pcov_all_sig[0,0]),\n",
     "                                        'days': treatment_days,\n",
     "                                        'percent healthy': percentage_healthy\n",
     "                                        },\n",
@@ -314,12 +328,14 @@
     "        df_fit_healthy = df_fit_healthy.append({'x0': popt_healthy_log[0], 'x0_err': np.sqrt(pcov_healthy_log[0,0]),\n",
     "                                                'k': popt_healthy_log[1], 'k_err': np.sqrt(pcov_healthy_log[1,1]),\n",
     "                                                'a': popt_healthy_exp[0], 'a_err': np.sqrt(pcov_healthy_exp[0,0]),\n",
+    "                                                'v_0': popt_healthy_sig[0], 'v_0_err': np.sqrt(pcov_healthy_sig[0,0]),\n",
     "                                                'days': treatment_days\n",
     "                                                },\n",
     "                                               ignore_index=True)\n",
     "        df_fit_unhealthy = df_fit_unhealthy.append({'x0': popt_unhealthy_log[0], 'x0_err': np.sqrt(pcov_unhealthy_log[0,0]),\n",
     "                                                    'k': popt_unhealthy_log[1], 'k_err': np.sqrt(pcov_unhealthy_log[1,1]),\n",
     "                                                    'a': popt_unhealthy_exp[0], 'a_err': np.sqrt(pcov_unhealthy_exp[0,0]),\n",
+    "                                                    'v_0': popt_unhealthy_sig[0], 'v_0_err': np.sqrt(pcov_unhealthy_sig[0,0]),\n",
     "                                                    'days': treatment_days\n",
     "                                                    },\n",
     "                                                   ignore_index=True)\n",
@@ -346,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 53,
    "outputs": [],
    "source": [
     "patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
@@ -373,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 54,
    "outputs": [
     {
      "data": {
@@ -550,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 55,
    "outputs": [
     {
      "data": {
@@ -710,21 +726,169 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### sigmoid fit"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 56,
    "outputs": [
     {
      "data": {
-      "text/plain": "410.29999999999995"
+      "text/plain": "<Figure size 720x432 with 0 Axes>"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x432 with 0 Axes>"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x432 with 2 Axes>",
+      "image/png": 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\n"
      },
-     "execution_count": 77,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "float(np.diff(axis_limits))"
+    "patients = ['VS', 'VL', 'RS']\n",
+    "labels = [\"P1\", \"P2\", \"P3\"]\n",
+    "\n",
+    "# plot variables\n",
+    "fontsize = 18\n",
+    "linewidth = 5\n",
+    "markersize = 12\n",
+    "errbar_width = 5\n",
+    "xlabel = 'Day since treatment start'\n",
+    "\n",
+    "# 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",
+    "popt_ctrl, pcov_ctrl = curve_fit(sigmoid_with_offest, v_ctrl, probs_ctrl,\n",
+    "                                 sigma = probs_ctrl_err, absolute_sigma=False\n",
+    "                                 )\n",
+    "perr_ctrl = np.sqrt(np.diag(pcov_ctrl))\n",
+    "\n",
+    "# limits of the 95% confidence interval\n",
+    "ci_lower = popt_ctrl - perr_ctrl\n",
+    "ci_upper = popt_ctrl + perr_ctrl\n",
+    "\n",
+    "with sns.axes_style('darkgrid'):\n",
+    "\n",
+    "    # plot control values in every plot\n",
+    "    for ii in range(3):\n",
+    "        if ii==1:\n",
+    "            plt.figure(ii,(10,6))\n",
+    "        else:\n",
+    "            plt.figure(ii,(10,6))\n",
+    "\n",
+    "    fig = plt.figure(1,(11,6))\n",
+    "\n",
+    "    params = ['v_0']\n",
+    "    plot_titles = ['Normocytes', 'Acanthocytes']\n",
+    "    ylims = [(0, 2.3)]\n",
+    "\n",
+    "    for jj, patient in enumerate(patients):\n",
+    "        data = dict_fitvalues[patient]\n",
+    "        color = color_dict[patient]\n",
+    "\n",
+    "        for ii in range(len(params)):\n",
+    "            para = params[ii]\n",
+    "            fig = plt.figure(ii)\n",
+    "\n",
+    "            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, #label=labels[jj],\n",
+    "                             ls='--', lw=linewidth, marker='X', markersize=markersize,\n",
+    "                             ecolor='gray', elinewidth=errbar_width)\n",
+    "\n",
+    "                plt.ylim(ylims[ii])\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, 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[ii], ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
+    "                        ax.axhline(ci_upper[ii], 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[ii]),\n",
+    "                                                       np.diff(axis_limits), ci_upper[ii]-ci_lower[ii],\n",
+    "                                                       color=ctrl_clr, alpha=0.15, zorder=0,\n",
+    "                                                       label = 'CTRL'\n",
+    "                                                       )\n",
+    "                                     )\n",
+    "                        ax.get_yaxis().set_ticklabels([])\n",
+    "                        # plt.axhline(popt_ctrl[ii], color='darkgray', ls='--',\n",
+    "                        #             lw=1.5*linewidth, alpha=.75, label='CTRL')\n",
+    "                    else:\n",
+    "                        # plt.axhline(popt_ctrl[ii], color='darkgray', ls='--',\n",
+    "                        #             lw=1.5*linewidth, alpha=.75)\n",
+    "                        ax.axhline(ci_lower[ii], ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
+    "                        ax.axhline(ci_upper[ii], 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[ii]),\n",
+    "                                                       np.diff(axis_limits), ci_upper[ii]-ci_lower[ii],\n",
+    "                                                       color=ctrl_clr, alpha=0.1, zorder=0,\n",
+    "                                                       # label = 'CTRL'\n",
+    "                                                       )\n",
+    "                                     )\n",
+    "                # set alpha of errorbars\n",
+    "                for collection in ax.collections:\n",
+    "                    collection.set_alpha(.4)\n",
+    "\n",
+    "    fig=plt.figure(0)\n",
+    "    fig.supylabel(\"Sigmoid 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",
+    "    # savename = \"fig2C_transition_velocity_dasatinib\"\n",
+    "    # savepath = os.path.join(savefolder,savename)\n",
+    "    # plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
    ],
    "metadata": {
     "collapsed": false,