diff --git a/SZ/auswertung/a_1.ipynb b/SZ/auswertung/a_1.ipynb
index f23bbb5..82d46b8 100644
--- a/SZ/auswertung/a_1.ipynb
+++ b/SZ/auswertung/a_1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 57,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 58,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -465,9 +465,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "an_light (-0.02630074615384615, 0.5599925566487172, 0.30999911523151735, 0.156638522040846, 0.1282742291712253, 0.06024558540032538)\n",
-      "fol_light (-3.2946186e-05, 7.129314361652983, 4.02000070175182, 0.002029756267025768, 17.568978096781162, 0.0008119025068103072)\n",
-      "org_light (-0.0040643187499999995, 0.9183601229960922, 0.7400008818112345, 0.00016271274581558095, 0.5744693424183512, 0.025423866533684523)\n"
+      "an_light (0.02630074615384615, 0.5599925566487172, 0.30999911523151735, 0.156638522040846, 0.40904859440059965, 0.06024558540032538)\n",
+      "fol_light (3.2946186e-05, 7.129314361652983, 4.02000070175182, 0.002029756267025768, 0.3456614690624081, 0.0008119025068103072)\n",
+      "org_light (0.0040643187499999995, 0.9183601229960922, 0.7400008818112345, 0.00016271274581558095, 0.6811469583998824, 0.025423866533684523)\n"
      ]
     }
    ],
diff --git a/SZ/auswertung/b.ipynb b/SZ/auswertung/b.ipynb
new file mode 100644
index 0000000..ee71de2
--- /dev/null
+++ b/SZ/auswertung/b.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from importlib import reload\n",
+    "import utility\n",
+    "\n",
+    "reload(utility)\n",
+    "from utility import *\n",
+    "\n",
+    "from scipy.optimize import curve_fit\n",
+    "from SecondaryValue import SecondaryValue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "org_compliance = 0.0098\n",
+    "a_an = 26  # cm^2\n",
+    "a_org = 6.4e-2  # cm^2\n",
+    "a_fol = 25  # cm^2\n",
+    "i_ein = 100e-3  # watt/cm^2\n",
+    "u_ref = 32.2e-3  # volt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "intensity = SecondaryValue('u/u_ref*i0', defaults=dict(i0=i_ein, u_ref=u_ref))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "intensities, d_intensites = intensity(u=([.011, 0.017, .021, .026, .032], 1e-3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ccurves = [parse_ccurve(f'../messungen/191114_OM_VB/2_{point}.dat') \\\n",
+    "           for point in ['a', 'b', 'c', 'd', 'e']]\n",
+    "\n",
+    "ccurve_specs = [(intsy, analyze_ccurve(curve, a_an, intsy)) \\\n",
+    "                for curve, intsy in zip(ccurves, intensities)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt5\n",
+    "fig, ax = plot_ccurve(ccurves[0], label=intensities[0]*1000, area=a_an)\n",
+    "\n",
+    "for ccurve, intsy in zip(ccurves[1:], intensities[1:]):\n",
+    "    plot_ccurve_line(ax, ccurve, label=intsy*1000, area=a_an)\n",
+    "\n",
+    "ax.legend()\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.04347826, 0.05279503, 0.06521739, 0.08074534, 0.09937888])"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.clf()\n",
+    "plt.plot(*np.array([[intsy*1000, params['j_c']] \\\n",
+    "                    for intsy, params in ccurve_specs]).T, marker='*')\n",
+    "plt.xlabel('Intensitaet [$mW/cm^2$]')\n",
+    "plt.ylabel('$j_{SC}$ [$A/cm^2$]')\n",
+    "plt.savefig('./figs/B/j_sc.pdf', dpi=300)\n",
+    "plt.grid()\n",
+    "#plt.xscale('log')\n",
+    "#plt.yscale('log')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.clf()\n",
+    "plt.plot(*np.array([[(intsy*1000), params['u_cc']] \\\n",
+    "                    for intsy, params in ccurve_specs]).T, marker='*')\n",
+    "plt.xlabel('Intensitaet [$mW/cm^2$]')\n",
+    "plt.ylabel('$U_{CC}$ [$V$]')\n",
+    "plt.xscale('log')\n",
+    "plt.grid(which='both')\n",
+    "plt.savefig('./figs/B/u_cc.pdf', dpi=300)\n",
+    "#plt.yscale('log')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0168614765323676"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.011 * (.032/.011)**(2/5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0.034161490683229816,\n",
+       "  {'j_c': 0.0025912392307692305,\n",
+       "   'u_cc': 0.5086008577882963,\n",
+       "   'u_mlp': 0.3977111360504424,\n",
+       "   'p_mlp': 0.022268705563519103,\n",
+       "   'ff': 0.6498857684076272,\n",
+       "   'eta': 0.025071759410675354}),\n",
+       " (0.052795031055900624,\n",
+       "  {'j_c': 0.01341955,\n",
+       "   'u_cc': 0.5600084576663334,\n",
+       "   'u_mlp': 0.37999930322896436,\n",
+       "   'p_mlp': 0.11432399757691399,\n",
+       "   'ff': 0.585101909146971,\n",
+       "   'eta': 0.08328580818951652}),\n",
+       " (0.06521739130434784,\n",
+       "  {'j_c': 0.016769280769230767,\n",
+       "   'u_cc': 0.5613136094311547,\n",
+       "   'u_mlp': 0.36000019842026715,\n",
+       "   'p_mlp': 0.13293315750161686,\n",
+       "   'ff': 0.5431752390708393,\n",
+       "   'eta': 0.07839647750095351}),\n",
+       " (0.08074534161490683,\n",
+       "  {'j_c': 0.02099583076923077,\n",
+       "   'u_cc': 0.5641834857878043,\n",
+       "   'u_mlp': 0.3399999063957519,\n",
+       "   'p_mlp': 0.1534276805047185,\n",
+       "   'ff': 0.4981692745272282,\n",
+       "   'eta': 0.07308241586171502}),\n",
+       " (0.09937888198757765,\n",
+       "  {'j_c': 0.025274496153846155,\n",
+       "   'u_cc': 0.5675239554252917,\n",
+       "   'u_mlp': 0.32765580118957827,\n",
+       "   'p_mlp': 0.17174272136274898,\n",
+       "   'ff': 0.4605091752711381,\n",
+       "   'eta': 0.06646773591202546})]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ccurve_specs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f0eb414d940>,\n",
+       " <matplotlib.lines.Line2D at 0x7f0eb414dc50>]"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.clf()\n",
+    "points = np.arange(0,6)\n",
+    "ints = .011 * (.032/.011)**(points/5)\n",
+    "plt.plot(np.arange(1,6), intensities, ints)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.03416149, 0.05279503, 0.06521739, 0.08074534, 0.09937888])"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "autoscroll": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.011     , 0.01361897, 0.01686148, 0.02087599, 0.02584631,\n",
+       "       0.032     ])"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ints"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.0"
+  },
+  "name": "b.ipynb"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SZ/auswertung/c.ipynb b/SZ/auswertung/c.ipynb
new file mode 100644
index 0000000..edbf9e4
--- /dev/null
+++ b/SZ/auswertung/c.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from importlib import reload\n",
+    "import utility\n",
+    "\n",
+    "reload(utility)\n",
+    "from utility import *\n",
+    "\n",
+    "from scipy.optimize import curve_fit\n",
+    "from SecondaryValue import SecondaryValue\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "area = 26  # cm^2\n",
+    "int_ein = 100e-3 # w/cm^2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ccurves = pd.DataFrame(columns=['desc', 'curve', 'area', 'j_c', 'u_cc', 'ff', 'eta', 'p_mlp', 'u_mlp'])\n",
+    "for point, desc, a in [\n",
+    "        ('a', '3x3_hell', 6),\n",
+    "        ('b_1', '3x3_schaltung_1', 6),\n",
+    "        ('b_2', '3x3_schaltung_2', 6),\n",
+    "        ('b_31', '3x3_schaltung_3', 6),\n",
+    "        ('b_41', '3x3_schaltung_4', 6),\n",
+    "        ('c_1', '3x3_verschattung_1', 6),\n",
+    "        ('c_2', '3x3_verschattung_2', 6),\n",
+    "        ('c_3', '3x3_verschattung_3', 6),\n",
+    "        ('d_1', 'huge_hell', 13),\n",
+    "        ('d_2', 'huge_verbraucher', 13)]:\n",
+    "        row = pd.Series({'desc': desc, 'area': a*area, 'curve': parse_ccurve(f'../messungen/191114_OM_VB/3_{point}.dat')})\n",
+    "        \n",
+    "        ccurves.loc[desc] = pd.concat((row, pd.Series(analyze_ccurve(row['curve'], row['area'], int_ein))))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "# Plot all ccurves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for _, curve in ccurves.iterrows():\n",
+    "    plot_ccurve(curve['curve'], area=area * curve['area'], save=f'C/{curve[\"desc\"]}.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "desc                                             huge_hell\ncurve    [[-1.0, -0.1947314], [-0.9775, -0.1942969], [-...\narea                                                   338\nj_c                                            0.000567871\nu_cc                                               7.02243\nff                                                0.653348\neta                                              0.0260545\np_mlp                                             0.880641\nu_mlp                                               5.3225\nName: huge_hell, dtype: object"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ccurves.loc['huge_hell']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "argv": [
+    "/usr/bin/python3",
+    "-m",
+    "ipykernel_launcher",
+    "-f",
+    "{connection_file}"
+   ],
+   "display_name": "Python 3",
+   "env": null,
+   "interrupt_mode": "signal",
+   "language": "python",
+   "metadata": null,
+   "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": "3.8.0"
+  },
+  "name": "c.ipynb"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SZ/auswertung/figs/B/j_sc.pdf b/SZ/auswertung/figs/B/j_sc.pdf
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diff --git a/SZ/auswertung/figs/C/3x3_verschattung_1.pdf b/SZ/auswertung/figs/C/3x3_verschattung_1.pdf
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diff --git a/SZ/auswertung/figs/C/huge_verbraucher.pdf b/SZ/auswertung/figs/C/huge_verbraucher.pdf
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diff --git a/SZ/auswertung/utility.py b/SZ/auswertung/utility.py
index c95c164..2d98c8f 100644
--- a/SZ/auswertung/utility.py
+++ b/SZ/auswertung/utility.py
@@ -1,7 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
-import seaborn
 from matplotlib import rc
 from scipy import interpolate
 from scipy import optimize
@@ -37,7 +36,7 @@ def plot_ccurve(ccurve, log=False, area=None, compliance=.99, median=False,
     if median:
         compliance = np.median(ccurve[:, 0])
 
-    plot_ccurve_line(ax, ccurve, compliance=compliance, **pyplot_args)
+    plot_ccurve_line(ax, ccurve, area=area, compliance=compliance, **pyplot_args)
     if log:
         ax.set_yscale('log')
 
@@ -51,11 +50,11 @@ def plot_ccurve_line(ax, ccurve, area=None, marker='.', compliance=.99, **pyplot
     if area:
         c /= area
 
-    ax.errorbar(v, c, linestyle='None', marker=marker, markersize=1.5, alpha=1,
+    ax.errorbar(v, c, linestyle='None', marker=marker, markersize=2, alpha=1,
                 **pyplot_args)
     ax.set_xlabel("Spannung V [V]")
     ax.set_ylabel("Stromstaerke I [A]" \
-                  if area else r"Stromdichte j [$\frac{A}{cm^2}$]")
+                  if not area else r"Stromdichte j [$\frac{A}{cm^2}$]")
     ax.grid(True, which='both')
     ax.set_xlim(v[0], v[-1])
 
@@ -84,7 +83,8 @@ def analyze_ccurve(ccurve, area, int_ein):
                                      bracket=(0, u_cc), bounds=(0, u_cc),
                                      method='bounded').x
     p_mlp = -interpolated(u_mlp)*u_mlp
-    ff = -p_mlp / i_c * u_cc
+    ff = -p_mlp / (i_c * u_cc)
 
     eta = p_mlp / (int_ein * area)
-    return -j_c, u_cc, u_mlp, p_mlp, ff, eta
+    return {'j_c': -j_c, 'u_cc': u_cc, 'u_mlp': u_mlp, 'p_mlp': p_mlp,
+       'ff': ff, 'eta': eta}