diff --git a/Plots_Droplet_FRAP.ipynb b/Plots_Droplet_FRAP.ipynb
index c10bb9ceab73e2af7e804664ec171daea83da780..3bb41a4d9788b0ac2fc2c74dca818a34316051fd 100644
--- a/Plots_Droplet_FRAP.ipynb
+++ b/Plots_Droplet_FRAP.ipynb
@@ -84,6 +84,13 @@
     "eps = np.linspace(0, 0.23, 100)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Multi Drop"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -218,6 +225,13 @@
     "save_nice_fig(fol+'Fig3/spat_recov_neighbours.pdf')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Coverslip"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -240,9 +254,10 @@
     "                                       phi_tot_ext, G_in, G_out, range(len(me))):\n",
     "    if i in [0, 1, 2, 3, 4, 5]:\n",
     "        f_cs.append(frap_solver(p, 'Meshes/single_drop_'+m,\n",
-    "                    name='FRAP_'+m[:-4]+str(G_i), T=240, phi_tot_int=p_i, dt=0.02,\n",
-    "                    phi_tot_ext=p_e, G_in=G_i, G_out=G_o))\n",
-    "        f_cs[-1].solve_frap()"
+    "                    name='FRAP_coverslip/FRAP_'+m[:-4]+str(G_i), T=240,\n",
+    "                    phi_tot_int=p_i, dt=0.02, phi_tot_ext=p_e, G_in=G_i,\n",
+    "                    G_out=G_o))\n",
+    "#         f_cs[-1].solve_frap()"
    ]
   },
   {
@@ -252,18 +267,38 @@
    "outputs": [],
    "source": [
     "z = [0.25, 1.5, 4, 0.25, 1.5, 4]\n",
+    "\n",
+    "# eps = np.linspace(0, 3.5, 100) # for full profile\n",
+    "eps = np.r_[np.linspace(0, 0.4, 100), np.linspace(0.41, 2, 20)]\n",
     "profs_cs = []\n",
     "for i, z_i in enumerate(z):\n",
     "    profs_cs.append([])\n",
     "    for j in range(240):\n",
     "        values=[]\n",
-    "        fs = fem_utils.load_time_point('FRAP_coverslip/' + f_cs[i].name+\n",
+    "        fs = fem_utils.load_time_point(f_cs[i].name+\n",
     "                                       't_p_'+str(j)+'.h5', f_cs[i].mesh)\n",
     "        for n in ns:\n",
     "            values.append([fs([4, 4, z_i]+e*n) for e in eps])\n",
     "        profs_cs[i].append(np.mean(np.transpose(values), 1))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write out profiles to .csv\n",
+    "for i in [0, 1, 2, 3, 4, 5]:\n",
+    "    z = str(f_cs[i].cent_poin[-1])\n",
+    "    P = str(f_cs[i].phi_tot_int/f_cs[i].phi_tot_ext)\n",
+    "    np.savetxt('eps_'+str(i)+'.csv', eps, delimiter=',')\n",
+    "    np.savetxt('t_p_long_coverslip_' + P + '_' + z +'.csv',\n",
+    "               profs_cs[i][1:], delimiter=',')\n",
+    "    meta_data = np.r_[f_cs[i].dt, f_cs[i].T-1, eps, 0.25] # last param: droplet radius\n",
+    "    np.savetxt('meta_data_long_coverslip_' + P + '_' + z + '.csv', meta_data, delimiter=',')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,