diff --git a/tutorial-3/notebooks/tutorial-3-task-2-solution.ipynb b/tutorial-3/notebooks/tutorial-3-task-2-solution.ipynb
index 29c0d502a3b24d304f5c822b1ee75eba233e5ee0..c5fa6de5b84bf131ec242479e05b5e7b00f39472 100644
--- a/tutorial-3/notebooks/tutorial-3-task-2-solution.ipynb
+++ b/tutorial-3/notebooks/tutorial-3-task-2-solution.ipynb
@@ -234,7 +234,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78c4d2b9b0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee975a630>"
       ]
      },
      "execution_count": 10,
@@ -440,7 +440,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 3.998594045639038\n",
+      "  Time: 3.898280382156372\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -638,7 +638,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78c04b8b38>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee4f55c18>"
       ]
      },
      "execution_count": 19,
@@ -774,7 +774,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 8.340548753738403\n",
+      "  Time: 7.731746435165405\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -939,7 +939,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78c16755c0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee6148748>"
       ]
      },
      "execution_count": 26,
@@ -1002,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1079,7 +1079,7 @@
        "2012-01-01 04:00:00    0.0  0.641201"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1101,10 +1101,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78c1675860>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee623d2e8>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1127,7 +1127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1151,7 +1151,7 @@
        "              dtype='datetime64[ns]', name='name', length=744, freq=None)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1162,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1175,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1193,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1222,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1279,7 +1279,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 8.114485740661621\n",
+      "  Time: 8.02032470703125\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -1294,7 +1294,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1316,7 +1316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1329,7 +1329,7 @@
        "525.895790860553"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1341,7 +1341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1356,7 +1356,7 @@
        "Name: p_nom_opt, dtype: float64"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1369,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1391,7 +1391,7 @@
        "Name: p_nom_opt, dtype: float64"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1421,7 +1421,7 @@
        "Name: e_nom_opt, dtype: float64"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1434,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1444,10 +1444,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78bfd405f8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee4855e48>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1470,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1480,10 +1480,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78bfdfab00>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee49060f0>"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1520,7 +1520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1539,7 +1539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1562,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1591,7 +1591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1601,10 +1601,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78bff63358>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee4a5e240>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1628,7 +1628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1638,10 +1638,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78bffd6860>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee4af0198>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1665,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1675,10 +1675,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78c0dee4e0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee4b2fa90>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     },
diff --git a/tutorial-5/notebooks/tutorial-5-task-2-solution.ipynb b/tutorial-5/notebooks/tutorial-5-task-2-solution.ipynb
index bad4051cb00cbff8fb68ee2f32852989c362c6a6..cbbbb7f2e4a0c559348495b80aa960990377aca0 100644
--- a/tutorial-5/notebooks/tutorial-5-task-2-solution.ipynb
+++ b/tutorial-5/notebooks/tutorial-5-task-2-solution.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -133,10 +133,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba94945160>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f09f5771fd0>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -289,7 +289,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 0.18898773193359375\n",
+      "  Time: 0.1875760555267334\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -304,7 +304,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -339,7 +339,7 @@
        "14706.193806194"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -377,7 +377,7 @@
        "Name: p_nom_opt, dtype: float64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -422,10 +422,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba91c55860>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f09f2a4c0b8>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -470,10 +470,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba91c85e10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f09f2a33d30>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -507,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -518,10 +518,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba92018128>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f09f2aa7550>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -583,7 +583,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -624,7 +624,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -654,7 +654,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/tutorial-5/notebooks/tutorial-5-task-3-solution.ipynb b/tutorial-5/notebooks/tutorial-5-task-3-solution.ipynb
index 5c4416a6783b2dc4320772bad715cd53590857fc..db8e5d7354aff630986dbe32543b7aa12e1a3aee 100644
--- a/tutorial-5/notebooks/tutorial-5-task-3-solution.ipynb
+++ b/tutorial-5/notebooks/tutorial-5-task-3-solution.ipynb
@@ -75,8 +75,6 @@
     "\n",
     "* The marginal costs are illustrative, not accurate.\n",
     "\n",
-    "* Only the first day of 2011 is in the github dataset, which is not representative. The full year of 2011 can be downloaded [here](http://www.pypsa.org/examples/scigrid-with-load-gen-trafos-2011.zip).\n",
-    "\n",
     "* The ENTSO-E total load for Germany may not be scaled correctly; it is scaled up uniformly by factor 1.12 (a simplification of the methodology in Schumacher, Hirth (2015), which suggests monthly factors).\n",
     "\n",
     "* Biomass from the EEG Stammdaten are not read in at the moment.\n",
@@ -98,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -138,12 +136,12 @@
     }
    },
    "source": [
-    "Some general settings (You may have to adjust this path to where you downloaded the network data!):"
+    "Some general settings:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -170,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -190,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -257,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -267,6 +265,7 @@
    "source": [
     "network.lines[\"s_nom_original\"] = network.lines.s_nom\n",
     "\n",
+    "# extendable, but no reduction of line capacities\n",
     "network.lines.s_nom_extendable = True\n",
     "network.lines.s_nom_min = network.lines.s_nom\n",
     "\n",
@@ -286,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -306,7 +305,7 @@
    },
    "source": [
     "***\n",
-    "## (a) Describe the nework as well as its regional and temporal characteristics."
+    "## (a) Describe the network as well as its regional and temporal characteristics."
    ]
   },
   {
@@ -322,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -332,10 +331,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff028fd2d30>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fca759d7128>"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -354,7 +353,7 @@
    ],
    "source": [
     "# TASK\n",
-    "network.loads_t.p_set.loc[network.snapshots[:]].T.sum().plot()"
+    "network.loads_t.p_set.sum(axis=1).plot()"
    ]
   },
   {
@@ -370,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -615,7 +614,7 @@
        "[5 rows x 27 columns]"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -639,7 +638,7 @@
        "<BarContainer object of 14 artists>"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -657,11 +656,10 @@
     }
    ],
    "source": [
-    "fig,ax = plt.subplots(1,1)\n",
-    "fig.set_size_inches(16,6)\n",
+    "fig, ax = plt.subplots(figsize=(16,6))\n",
     "\n",
     "# TASK\n",
-    "groups = network.generators.groupby(\"carrier\")[\"p_nom\"].sum()\n",
+    "groups = network.generators.groupby(\"carrier\").p_nom.sum()\n",
     "plt.bar(groups.index,groups)"
    ]
   },
@@ -684,12 +682,13 @@
     }
    },
    "source": [
-    "### (a)-(iii) | Plot the regional distribution of the loads for different snapshots. What are the major load centres?"
+    "### (a)-(iii) | Plot the regional distribution of the loads for different snapshots. What are the major load centres?\n",
+    "> **Hint:** Use the pandas function `groupby`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -711,11 +710,10 @@
    ],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                        subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
-    "for i,timestep in enumerate(timesteps):\n",
+    "for i, timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
     "    \n",
@@ -744,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -774,16 +772,14 @@
     "    n_rows_g = n_graphs_g // n_cols_g\n",
     "else:\n",
     "    n_rows_g = n_graphs_g // n_cols_g + 1\n",
-    "\n",
     "    \n",
     "fig, axes = plt.subplots(nrows=n_rows_g, ncols=n_cols_g,\n",
+    "                         figsize=(width_factor*n_cols_g,height_factor*n_rows_g),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
     "width_factor = 4\n",
     "height_factor = 4\n",
     "\n",
-    "fig.set_size_inches(width_factor*n_cols_g,height_factor*n_rows_g)\n",
-    "\n",
     "for i,tech in enumerate(techs):\n",
     "    i_row = i // n_cols_g\n",
     "    i_col = i % n_cols_g\n",
@@ -792,7 +788,7 @@
     "    \n",
     "    # TASK\n",
     "    gens = network.generators[network.generators.carrier == tech]\n",
-    "    gen_distribution = gens.groupby(\"bus\").sum()[\"p_nom\"].reindex(network.buses.index,fill_value=0.)\n",
+    "    gen_distribution = gens.groupby(\"bus\").p_nom.sum().reindex(network.buses.index, fill_value=0.)\n",
     "    \n",
     "    network.plot(ax=ax,\n",
     "                 bus_sizes=0.2*gen_distribution,\n",
@@ -820,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -830,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -874,7 +870,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 2.014338731765747\n",
+      "  Time: 2.007324695587158\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -924,7 +920,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 3.3788039684295654\n",
+      "  Time: 3.4703311920166016\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -974,7 +970,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 3.858164072036743\n",
+      "  Time: 4.207515001296997\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -1024,7 +1020,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 4.040640115737915\n",
+      "  Time: 3.9404618740081787\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -1074,7 +1070,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 3.4348020553588867\n",
+      "  Time: 3.4019927978515625\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -1124,7 +1120,7 @@
       "      Number of bounded subproblems: 0\n",
       "      Number of created subproblems: 0\n",
       "  Error rc: 0\n",
-      "  Time: 2.649223804473877\n",
+      "  Time: 2.475904703140259\n",
       "# ----------------------------------------------------------\n",
       "#   Solution Information\n",
       "# ----------------------------------------------------------\n",
@@ -1162,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1181,6 +1177,11 @@
    "source": [
     "# TASK\n",
     "p_by_carrier = network.generators_t.p.groupby(network.generators.carrier, axis=1).sum()\n",
+    "#reorder\n",
+    "cols = ['Nuclear', 'Run of River', 'Brown Coal', 'Hard Coal', 'Gas',\n",
+    "        'Storage Hydro', 'Waste', 'Wind Offshore', 'Wind Onshore', 'Solar']\n",
+    "p_by_carrier = p_by_carrier[cols]\n",
+    "p_by_carrier_gw = p_by_carrier / 1e3 # convert MW to GW\n",
     "\n",
     "colors = {\"Brown Coal\" : \"sienna\",\n",
     "          \"Hard Coal\" : \"dimgrey\",\n",
@@ -1193,23 +1194,20 @@
     "          \"Waste\" : \"forestgreen\",\n",
     "          \"Storage Hydro\" : \"darkmagenta\"\n",
     "          }\n",
-    "#reorder\n",
-    "cols = ['Nuclear', 'Run of River', 'Brown Coal', 'Hard Coal', 'Gas',\n",
-    "        'Storage Hydro', 'Waste', 'Wind Offshore', 'Wind Onshore', 'Solar']\n",
-    "p_by_carrier = p_by_carrier[cols]\n",
-    "\n",
-    "fig,ax = plt.subplots(1,1)\n",
     "\n",
-    "fig.set_size_inches(16,8)\n",
+    "fig, ax = plt.subplots(figsize=(16,8))\n",
     "\n",
-    "(p_by_carrier/1e3).plot(kind=\"area\",ax=ax,linewidth=0,color=[colors[col] for col in p_by_carrier.columns])\n",
+    "p_by_carrier_gw.plot(\n",
+    "    kind=\"area\",\n",
+    "    ax=ax,\n",
+    "    linewidth=0,\n",
+    "    color=[colors[col] for col in p_by_carrier.columns]\n",
+    ")\n",
     "\n",
-    "ax.legend(ncol=5,loc=\"upper left\")\n",
+    "ax.legend(ncol=5, loc=\"upper left\")\n",
     "\n",
     "ax.set_ylabel(\"GW\")\n",
     "\n",
-    "ax.set_xlabel(\"\")\n",
-    "\n",
     "ax.set_ylim(0,100);"
    ]
   },
@@ -1222,7 +1220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 67,
    "metadata": {
     "scrolled": false
    },
@@ -1230,16 +1228,16 @@
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 0, '')"
+       "Text(0, 0.5, 'MWh')"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuIAAAFtCAYAAABC7Q+vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdeXhU5fXA8e+dLZksk52EsO+bgCKI7CKI2rpbKVJXrCtaXFqxaq21rq1Lrbhr1dYFtcqvVq07iriCyCJLgAAJSciezCSTyazv74/JDAFCSMIMM5M5n+fhIcy9c+8ZAsnJO+c9R1NKIYQQQgghhDiydJEOQAghhBBCiHgkibgQQgghhBARIIm4EEIIIYQQESCJuBBCCCGEEBEgibgQQgghhBARIIm4EEIIIYQQEWCIdACR8vnnn6uEhIRIhyGEEEIIIbqxpqam6lmzZuW0dSxuE3G9Xs/w4cMjHYYQQgghhOjG1qxZU3SwY3FbmuL1eiMdghBCCCGEiGNxm4gbjcZIhyCEEEIIIeJY3Cbibrc70iEIIYQQQog4Frc14pqmRToEIYQQQhwhSikaGxtRSkU6FNFNaZpGSkpKp3LMuE3E9Xp9pEMQQgghxBHS2NhIQkICJpMp0qGIbsrlctHY2EhqamqHnxO3pSkejyfSIQghhBDiCFFKSRIuwspkMnX6HZe4TcRlRVwIIYQQQkRS3CbiUiMmhBBCiCMpOzub6dOnM3nyZC655BKampoiHRLFxcVMnjz5gMdXrlzJvHnzunzdV199lZtvvvlwQtvH6aefzo8//gjA3LlzsVqtIbv2ypUr+e6779o952B/T4crbhNxn88X6RCEEEIIEUfMZjMrVqzg66+/xmQy8cILL0Q6pCMuFKXBb7zxBmlpaSGIxm/lypV8//33IbteZ8RtIi59xIUQQggRKZMmTWLnzp0HrLQ+9thj3H///YB/FfjWW2/l5z//ORMnTmTNmjVcdNFFjB8/nnvuuQfwr9ROnDiRa665hqlTp3LxxRcHV9rXrl3LaaedxsyZMzn33HMpLy8PPj5t2jTmzJnDc889d9AY7XY7F198MRMnTuSKK65AKcUXX3zBhRdeGDxn+fLlXHTRRQC88sorTJgwgdNOO22fFeaFCxdy2223ccYZZ3DnnXdSV1fHBRdcwNSpUznppJPYuHHjAfd2OBxcdtllTJ06lQULFuBwOILHxo4dS01NDXa7nV/+8pdMmzaNyZMn8/bbbweP33nnncyePZvZs2ezY8cOAD744ANmz57NjBkzOPvss6msrKS4uJgXX3yRp556iunTp/PNN99QWVnJhRdeyLRp05g2bVrwtXi9XhYtWsSkSZM455xz9ompq+K2a4r0ERdCCCHi05znfgzLdT/69TEdOs/j8fDJJ58wa9asQ55rMpl47733eOqpp7jgggv47LPPyMjIYNy4cVx99dUAbNu2jUcffZTjjz+ea6+9lueff56rrrqKxYsX88orr5Cdnc3bb7/N3XffzZIlS7j22mt54IEHmDJlCnfcccdB771+/Xq+/vprevbsySmnnMJ3333H9OnTufnmm6muriY7O5tXX32V+fPnU15ezv3338/y5cuxWCycccYZjBkzJnitwsJCli1bhl6vZ/HixYwePZqXX36ZFStWcPXVV7NixYp97v2Pf/wDs9nMypUr2bhxIyeccMIB8X366afk5eXx+uuvA2Cz2YLHUlNT+eSTT1i6dCm33norS5cu5fjjj+fjjz9G0zT++c9/8ve//527776bSy65hOTkZK677joAFixYwOTJk/nXv/6F1+vFbrdTX1/Pjh07eO6553j00Ue59NJL+e9//8vcuXMP+TlsT9wm4jpd3L4ZIIQQ3Y7P5caxew9NO0uw7yqhaWcJHmvjEY8jeXBfBi66WGZViDY5HA6mT58O+FfEL7jgguAq9cGccsopAIwcOZJhw4aRl5cHQP/+/SktLSUtLY1evXpx/PHHA/766WeeeYZZs2axefNmzjnnHMC/mpubm4vNZsNqtTJlyhQAfvnLX/LJJ5+0ee9x48bRq1cvAEaPHk1xcTHHH388c+fO5Y033uBXv/oVq1at4sknn+TDDz9k6tSpZGdnA3D22WdTWFgYvNaZZ54ZbJTx7bff8tJLLwEwffp0amtrsdlsWCyW4PnffPMNV1xxBQCjRo1i1KhRB8Q3cuRI7rjjDu68805OPvlkJk2aFDx27rnnBn+/7bbbACgrK2PBggVUVFTgdrvp27dvm6/7yy+/5MknnwT8zT0sFgv19fX069eP0aNHA3D00UdTXFzc5vM7QxJxIYQQMcHb7MRRVEbTrhLsO/3JdtOuEpp2luIoKYco2fuTPfN40sYOj3QYoh0dXbkOtUCNeGt6vX6ffWtOp3Of4wkJCYA/bwl8HPhzoN56/x/8An8ePnw4H3300T7HrFZrh39QbH0/vV4fvN/8+fOZP38+iYmJnHnmmRgMhjbjaC0pKSn4cUcbZhwqzsGDB7N8+XI+/vhj7rrrLmbOnBncINr6uYGPFy9ezDXXXMOpp57KypUreeCBBzoUR0Dr9pet//4PR9wm4tJHXAghoo/H7sBRXOZPsgOr2zt207SrlOaySjjYN3BNI7F3HskDepM0oDdJ/XtjykqHI7gyXfLau9R98yO29VskERcd1qNHD6qrq6mtrSU5OZkPP/ywQyUrrZWUlPD9999z3HHH8dZbbzFx4kQGDx5MTU1N8HG328327dsZMWIEFouFb7/9luOPP54333yz0zH37NmTvLw8HnroId566y0Ajj32WH7/+99TW1tLamoq//nPfzjqqKPafP7kyZN58803+d3vfsfKlSvJysraZzUc/O8YvPnmm0ybNo1Nmza1WUe+Z88eMjIymDt3LsnJybz22mvBY8uWLeP6669n2bJlTJgwAfCXrvTs2RNgn3NTUlJoaGgI/nn69On84x//4Oqrrw6WpoRL3CbigZ/ehBBCHFmeBntwJTtQRhJY3XaWVx/0eZpej7lPXjDR3vt7L5L65qNLiOywFnedlbpvfsS6voA+EY1ExBKj0cjvfvc7TjrpJPr27cvQoUM7fY2hQ4eydOlSbrzxRgYOHMiCBQswmUy8+OKL3HLLLdhsNjweD1dddRUjRoxgyZIlXHfddZjNZk488cQuxf2LX/yC6upqhg/3/9CZl5fH4sWLOfnkk8nNzWXMmDEH7VC3ePFirr32WqZOnYrZbOaJJ5444JwFCxYEzxk9ejTjxo074JxNmzbxxz/+EZ1Oh9Fo5MEHHwweczqdzJ49G6UUzz77bPC+l156KT179mT8+PHB0pJTTjmFSy65hP/973888MAD3Hfffdxwww28/PLL6PV6HnzwwWBJUKhp8dpPe8WKFSpQ5yOEECJ8yt7+iOrl37Uk3yW4qusOeq5mNJDUL3+/RNv/y9w7D50xehdRar/5ke/PXohlzHAmf/SPSIcj9rN/DXJ3UVxczLx58/j666+P6H1vvvlmRo8evU8HlWgxduxYPvvsM7Kyso74vdv6d7ZmzZofZs2aNb6t86P3K1qYSR9xIYQIP2dlDesX/mmfkhJdoomkfr0OSLST+vfG3KsHWoxOPrYc5V/JbNhSiM/lRmeSNrmie5o5cyZJSUn8+c9/jnQoMS9uE3HpIy6EEOFnXbsZlMIyeijD7vwNyQN6k5CXjdYNN8wbUpNJGtSXpsJiGrbsIG3MsEiHJOJA3759j/hq+PLly4/o/Tpr3bp1kQ6hw7rfV8IOkj7iQggRftYfNwOQNX0CWVPGkZjfo1sm4QGB5Nu2fssRuV+Ty0txXfMRuZcQIvS671fDQ5D2hUIIEX7Wdf6ENO3oERGO5MiwBBPxgiNyv79/tZvL39pMYU3TEbmfECK04jYblURcCCHCSymFbZ1/RdwyNl4ScX8HiSORiCulWF1iQwEFVZKICxGL4jYblT7iQggRXs0l5bhq6jFmpmHuE57WX9HGMrplw+bmQnzu8H6fKW90YXN6ASizOQ9xthAiGsVtIi59xIUQIryCZSljh8fNyHejJYWkgX3wOV00FuwI670KKveugksiHhseeughJk2axNSpU5k+fTqrV68G4Mknn6Sp6dDvanT0vI7YunUr06dPZ8aMGezcufOQ599///089thjIbm32CtuE3FpXyiEEOEVb/XhAYFV8XCXp2ytlkQ8lnz//fd8+OGHfP7556xcuZJly5bRq1cvAJ566ikcDschr9HR8zri/fff59RTT+WLL75gwIABIblme7xeb9jvEYskERdCCBEWtpZE3BJn497TWurEAz+IhMuWqr1jt0ttLuJ1QF+sqKioICsri4SEBACysrLo2bMnTz/9NOXl5ZxxxhmcccYZANx0002ceOKJTJo0ifvuuw+gzfM+++wz5syZwwknnMAll1xCY2PjAffdsGEDJ510ElOnTuXCCy+kvr6ejz/+mKeeeoqXX345eK3WPvnkE0444QSmTZvGWWedFXy8oKCA008/nWOOOYann346+PgFF1zAzJkzmTRpEi+++GLw8T59+nDvvfcye/ZsVq1axccff8zEiRM59dRTueWWW5g3bx4Adruda6+9llmzZjFjxgzef//9w/zbjh1xW58hfcSFECJ8lFKtSlPibEV8bPg7p3h9im3V/pXRBL2G0+Oj1uEhK0m+t3XEB3mTw3LdU8oP3s975syZ/PWvf2XChAnMmDGDs88+mylTpnDllVfyxBNP8M477wQnQd5+++1kZGTg9Xo566yz2Lhx4wHn1dTU8NBDD7Fs2TKSk5N59NFHeeKJJ7j55pv3ue/VV1/NAw88wJQpU7j33nuDI9wvueQSkpOTue666/Y5v7q6muuvv5733nuPfv36UVe3dxLutm3beOedd2hsbOS4445jwYIFGI1GHnvsMTIyMnA4HMyaNYszzjiDzMxM7HY7I0aM4NZbb6W5uZnx48cHr/vrX/86eN2HH36Y6dOns2TJEqxWK7Nnz2bGjBkkJyeH4tMS1eJ2RVz6iAshRPg07SrFY20gITebxJ45kQ7niLKM9ifiDZu2h23DZnF9M06Pj7xUEwMyzYCUp0S7lJQUli9fziOPPEJWVhaXXXYZr776apvn/t///R8nnHACM2bMYMuWLWzZcuC7K6tXr6agoIBTTz2V6dOn89prr7F79+59zrHZbFitVqZMmQLA+eefzzfffNNunKtXr2by5Mn069cPgIyMjOCxOXPmkJCQQFZWFtnZ2VRWVgL+1fpp06YxZ84cSktLKSwsBECv1wdX3Ldt20b//v2D1z333HOD112+fDl/+9vfmD59OqeffjrNzc2UlJS0G2d3Ebcr4tK+UAghwmdv28L4KksBMKalktS/F027SmncuhPLqCEhv8eWlnaFw3KS0GsaW6qaKLM5GZ2XEvJ7dUftrVyHk16vZ+rUqUydOpWRI0eydOlS5s+fv885RUVFLFmyhE8//ZT09HQWLlyI03ngD1lKKU444QSee+65kMaolDro5mqTybTPa/F6vaxcuZIvvviCDz/8kKSkJE4//fRgvImJiej1+uB127vnSy+9xJAhof+/Eu3iNhuNlx38QggRCYGJmmlxmIhD+PuJb22pDx+WnUS+xV9zXGqVFfFotm3btuBKMcBPP/1Enz59AP9qeaC+u6GhgaSkJCwWC5WVlXzyySfB57Q+b/z48Xz33Xfs2OHvztPU1MT27dv3uafFYiE9PT24Cv76668zeXL7ZTkTJkzgq6++oqioCGCf0pS22Gw20tPTSUpKYuvWrcFOMPsbMmQIu3btori4GIBly5YFj5144ok8++yzwWR9/fr17d6zO4nbFXHZvSuEEOETrx1TAiyjh1L+zqf+RPz800J+/cAAn2E9kqlocAFSmhLt7HY7ixcvxmq1YjAYGDhwII888ggAF198MXPnziU3N5d33nmHMWPGMGnSJPr378/EiROD19j/vMcff5zLL788uAJ92223MXjw4H3u+8QTT3DjjTficDjo378/S5YsaTfO7OxsHnnkES666CJ8Ph/Z2dn7JM37mzVrFi+88AJTp05l8ODBjB8/vs3zzGYzDz74IOeddx6ZmZmMGzcueOy3v/0tt956K1OnTkUpRd++fVm6dGn7f6HdhBavu6xXrlypRo0aFekwhBCi21FeL58MPRmvvYmZG94lIScz0iEdcdUrVrF67iLSjh3FpPeeDem1nR4fZ720DgUsu2gMu+qaWfTOVgZnmXni7Ph8B6IjbDYbFosl0mHEtcbGRlJSUlBK8bvf/Y6BAwdyzTXXRDqskGrr39maNWt+mDVrVps/ocRtaYqsiAshRHjYtxfjtTeR2Cs3LpNw2G/DZognORfWOPAq6JeeiNmoD5amlNmc0sJQRLV//vOfTJ8+nUmTJmGz2bjkkksiHVLExW1pinyxEkKI8Ij3shQAU4YFc998HMVl2LcVkTpiUMiuXdBSHz40JwkAS4KeZJMeu8tLfbOHDLO0MBTR6Zprrul2K+CHK+pXxDVNG6Zp2tpWv2yapl2vadqdmqaVtnr8Z62e83tN07ZrmlagadrJbV1X+ogLIUR4WFs6pqQdHd9lEpYx/lXxUA/2CdaH5/h7LGuaRr7F381C6sSFiC1Rn4grpQqUUkcrpY4GjgWagMCugUcCx5RS7wNomjYSmAeMAk4BntA0Tb//daWPuBBChId1baB1YfyuiAOkhWmwT2C0/bCWFXGAXq3KU4QQsSPqE/H9zAIKlVJF7ZxzJrBUKeVUSu0EtgPH7X9SoK+lEEKI0PG5PTRs3AZAWsuKcLwKtjDcELpEvNHpocTqxKjXgoN8gFZ14q6Q3au70TQNl0v+fkT4uFyuTrfHjrUa8XnAa63+fK2maRcBq4GblFJ1QC/g21bnlLQ8to+amhqmTJmCwWDA6/VyzjnnsHDhQsrLy0lOTkav12Oz2cjJyaG2thalFDk5OVRUVJCS4h+Y0NjYSG5uLlVVVWiaRmZmJlVVVVgsFrxeL3a7nby8PMrLyzEajaSlpVFdXU1aWhoulwuHwxE8bjKZSE1NpaamJjgmtrm5OXg8MTERs9lMXV0dWVlZNDQ04HK5gsfNZjMmkwmr1Up2djZWqxW32x08Lq9JXpO8JnlNR+I11fy4CV+zC2PvPKqaGkkz6mP+NXX185TSPx8A64atNDc1UVFVddivaYfdv4jUJ0WHq9lBeeA1tdSF76yyUVNjist/e4d6TU1NTaSnp1NdXY1Op8NoNOJyuTCZTHi9XrxeL4mJiTQ3N6PX69Hr9cHjbrcbpdQ+x3U6HW63m4SEBFwu1wHHNU3D4/G0edxg8KdfHo8n+JimaZhMJpxOJwaDAaXUPjG1Pm40GvH5fAccl9cU2dfU2NiITqdDp9Pt82+vPTHTvlDTNBNQBoxSSlVompYLVAMK+DPQUym1QNO0x4FvlFIvtzzveeB9pdRbra/3+eefq7Fjxx7ZFyGEEN3c7lfeYeNN95N31myOfuquSIcTcZ+PP4fmknKmfP4yqcMHHvb1Xltbzgur93DWqByumdQ7+PjG8kZueHcbQ7OTWHJWfL8TIUS06S7tC08F1iilKgCUUhVKKa9Sygc8y97ykxKgT6vn9cafwO9DNmsKIUTo2aRjyj4Ck0VDVSceGG0/NDtpn8eD0zWlhaEQMSWWEvHzaVWWomlaz1bHzgZ+avn4HWCepmkJmqYNAIYA3+9/MU+I+7oKIYQA69qWRDxOR9vvL9A5xbY+NJ1TtrYk4sN77JuIp5sNmI067C4vDU6ZkyFErIiJGnFN05KAk4ArWz38F03TjsZfmrIrcEwptVHTtDeATYAHWKiUkq9KQggRZj6ni4bN20HTsIweGulwokKwhWEIVsSr7S5qmtwkm/YO8QnwtzBMoLDGQanNiSUxJr69CxH3YuJ/qlKqCcja77EL2zn/HuCe9q4ZKMAXQggRGg2btqPcHlKGDsCQkhzpcKJCWkvnlIYNW1FeL9phdOwqaFWWomujM0MgES+zORnRQ/7+hYgFsVSaElLSR1wIIUIrMLjGImUpQaasdBJ75eJ1NGMv3H1Y19o7yCepzeP50ktciJgTt4m49BEXQojQCo62l0R8H6GqEz9UIi5DfYSIPXGbiAshhAitwETNtGOkY0praSGoE/cpFZyoOTyn7bITWREXIvbEbSLu9cr+TSGECBVvUzONBTvR9HpSRw6JdDhRJThh8zBWxMtsTuwuL1lJRrKS226/20umawoRc+I2EZc+4kIIETq2jdvA5yNl+ED05oRDPyGOBEtTNmxD+XxdusaWyvbLUgAykwwk6DWszR4andKiV4hYELeJuPQRF0KI0LGu3QRIfXhbEnIySczvgdfehL2wuEvXCJSltJeIB1oYgqyKCxEr4jYRF0IIETqBiZoWmajZpr0bNrtWJ15QZQfaT8Rh3wmbQojoF7eJuPQRF0KI0JGOKe3bWyfe+UTc41Nsr3EAB462359s2BQitsRtIi59xIUQIjQ8DXbs24vRTEZSRwyKdDhRKTBptCudU3bWOnB7Fb3TEkhJaH8RKT9NEnEhYkncJuLSR1wIIULDur4AlMIycjA6k2yEb0vgnQLbhoJOb9hsPVHzUGRFXIjYEreJuBBCiNCwyUTNQ0rokUVCXjbexiaadpZ06rkdrQ8HGeojRKyJ20Rc+ogLIURoBAf5jJWNmu0J1IlbO9lPfO9EzbYH+bSWnWzEqNeoc3hocsn3OSGiXdwm4tJHXAghQsO6TiZqdkRgwqZtXcfrxB1uL8X1zeg1GJRlPuT5Ok0jP9W/Kr6nQVbFhYh2cZuIy2ZNIYQ4fK46G46iMnTmBJKH9It0OFGtK51TtlU78CkYkGkmwdCxb9nSwlCI2BG3ibimaZEOQQghYp6tZTXcctRQdNIWtl2WsYEJmx3fsLm1E/XhAfkWEwClVknEhYh2cZuIS9cUIYQ4fMH+4TLI55ASc7NJ6JGFp8FOU1FZh54T7JjSgfrwAOmcIkTsiNtEXEbcCyHE4bPJIJ9OCU7YXNexDZsFLaPth3dqRVzG3AsRK+I2EZcVcSGEOHyBjinSurBjOlMnXu9wU97gIsGgo296YofvIUN9hIgdcZuIK6UiHYIQQsQ0Z2UNzWWV6FOSSB7UN9LhxIS0ljrxjrQw3NqyGj4k24xe1/F9TT2STRh0GjVNbhxuaWEoRDSL20Tc18nJZkIIIfYVrA8fMxxNF7ffTjoluCK+YeshF4QC9eHDO1EfDqDXaeSl+jdsljdIeYoQ0Sxuv3JKH3EhhDg8MlGz8xLysjHlZOKxNuAoKm333K2dGG2/v17SwlCImBC3ibj0ERdCiMMTnKgpHVM6TNO0vYN91m896HlKKbYEJmr26HwiLp1ThIgNcZuI6+RtVCGE6DKlVKvWhbIi3hmBzint1YlXNrqxNnuwJOjJSzF1+h6SiAsRG+I2G5VEXAghus65pwpXVS3GDAvmvvmRDiemBFsYttM5pSA4yCe5SwPoJBEXIjbEbTYqfcSFEKLrWrctlEnFnbO3heGWg27YDGzU7MxEzdaCY+5luqYQUS1uE3GDjGIWQogus7aMtpdBPp2XmN8DU1Y67voGHMV72jzncBPx3FQTOg2q7G6cHukSJkS0ittEXNoXCiFE1wXrw8fKRs3O0jRtn1Xx/Xl9KthDfGgXE3HDPi0MZVVciGglibgQQohOUUpJ68LDZAkO9jmwTny3tZlmj4/cFBMZ5q632pVR90JEv7hNxKWPuBBCdI2juAx3nQ1TTiaJ+T0iHU5MSgsO9jkwEQ+UpXR1NTwgX3qJCxH14jYRlz7iQgjRNda1gbIU2ajZVZbRQwF/55T9N2webn14QC/pnCJE1IvbRFzaFwohRNe07pgiuiaxdx7GzDTctVaaS8r3ORZsXdiFiZqtSQtDIaJf3GajkogLIUTX2IKDfGSjZlf5N2weWCfu8vjYWduMBgyRRFyIbi8mslFN03ZpmrZB07S1mqatbnksU9O0jzVN29bye0ar83+vadp2TdMKNE07ua1rSh9xIYToPOXzBSdCSuvCw5PWRueUwloHHp+ib3oiSSb9YV0/0MKwstGF2ysNCoSIRjGRiLeYqZQ6Wik1vuXPtwCfKqWGAJ+2/BlN00YC84BRwCnAE5qmHfDVTPqICyFE59kLi/E2NpGY34OEHlmRDiemtTVhc2uI6sMBTHodOckmfArKG6RzihDRKJYS8f2dCbzU8vFLwFmtHl+qlHIqpXYC24Hj9n+ytC8UQojOk7KU0An0Ereu27thM1AffrgdUwKkPEWI6BYry8IK+EjTNAU8rZR6BshVSu0BUErt0TQt0EOrF/Btq+eWtDy2j5qaGqZMmYLBYMDr9XLOOeewcOFCysvLSU5ORq/XY7PZyMnJoba2FqUUOTk5VFRUkJKSAkBjYyO5ublUVVWhaRqZmZlUVVVhsVjwer3Y7Xby8vIoLy/HaDSSlpZGdXU1aWlpuFwuHA5H8LjJZCI1NZWamhoyMjJwOBw0NzcHjycmJmI2m6mrqyMrK4uGhgZcLlfwuNlsxmQyYbVayc7Oxmq14na7g8flNclrktckrykUr2n3l9/7vyj370lJSUm3eE2R+jxlZmait6Tgrq3HtnM39XrFpvIGANI8Nuz2pMN+TWl6f4ewXdWN5Kl6+TzJa5LXFIHX1B5t/7ZJ0UjTtHylVFlLsv0xcB3wjlIqvdU5dUqpDE3THge+UUq93PL488D7Sqm3Wl/zq6++UiNHjjyCr0IIIWLft6dfSf2qDYxf+gjZJ0yMdDgxb9XcRdSsWMUxL9xHyqypnP3P9Rh1Gv938RiM+sN/0/rf6yt45vsyzhyZzcLJfUIQsRCis9asWfPDrFmzxrd1LCZKU5RSZS2/VwLL8JeaVGia1hOg5ffKltNLgNZfbXoDZftfU/qICyFE5/g8Hmw/bQXAIqPtQ6J1nXhgrP3ALHNIknCA/DQZ6iNENIv6RFzTtGRN01IDHwNzgJ+Ad4CLW067GPhPy8fvAPM0TUvQNG0AMAT4fv/rSvtCIYToHPu2InwOJ+Z++ZgyLJEOp1sIdE6xrisI1ocPD1F9OLQe6iObNYWIRrFQI54LLIQvl8wAACAASURBVGuZ3mYAXlVKfaBp2irgDU3TLgOKgfMAlFIbNU17A9gEeICFSinv/heVaXBCCNE51h/9g3zSZDU8ZCxjAyviW9haGdqNmgA9UxPQgIoGJx6fwqCT731CRJOoT8SVUjuAsW08XgPMOshz7gHuae+6Xu8BubkQQoh2WNe1JOLSMSVkzH3zMaan4qquo3h7GRhTGJaTHLLrmww6spONVNndVDa6gl1UhBDRIW7rM6SPuBBCdE6gdaGMtg8dTdOwjPavihu3F5Jk1NE7LbTJsrQwFCJ6xW0iLiviQgjRcT6XG9um7aBppLVsMBShEUjEc0t3MzQnCV2ISycDiXipVRJxIaJN3C4Lx0LbRiGEiBYNmwtRLjfJQ/phSA1d6YTY2zmlx57dJGSHrj48oJesiAsRteI2ETcajZEOQQghYoY1MFFTylJCLlDq06O0mLwQ1ocHSGmKENErbktTpI+4EEJ0nK1lo6bUh4eeuV8+zkQzKY02+vuaQn79YGmKJOJCRJ24TcT1en2kQxBCiJhhXduyIn60TCQOtT0Nbip6+ufQGbcXhvz6PS0mAMobXHh9UpYpRDSJ20RcCCFEx3gdThq37ACdDsuoIZEOp9spqLJT2cufiNvWF4T8+majnqwkIx6fosoug32EiCZxm4hL1xQhhOiYhk3bUF4vKcMGoE9KjHQ43U5BdRMV+X2B8CTiIHXiQkSruE3EZbOmEEJ0zN6JmlIfHg4FlU1U5IdvRRwgv6U8RUbdCxFd4jYR93g8kQ5BCCFiQrBjikzUDDmPT1FY00R9Zg76lCScFdU0V1SH/D6yIi5EdIrbRFwIIUTH2KR1YdgU1TlwehX56YnBwT7hWBXvJZ1ThIhKcdtHXEbcCyHEoXka7TRu24VmNJA6cnCkw+l2Cqr87QqH5SSTNmYYdd/8iG19AT1OmhLS+wRXxEMwXbNs2UcU3PU4yn1k31k2pCYz/E+/ocecqUf0vkKEU9xmo9JHXAghDs22YSsoReqIwegSTJEOp9sJJOJDs5OCPdpt67eE/D49A4l4gxOfUug0rUvX8bk9bP3zEzj3VIUyvA5xVdex9orbmfDmY2RMGH3E7y9EOMRtIi59xIUQ4tD21odLWUo4BBLx4TlJWMz+0hRrGEpTkk160hMN1Dd7qLa76ZHStR+q9vzfxzSXVZI8pD/HvfUYdDGh74ptDzxDycvvsObimzn+3WdIHtjniN1biHCJ20RcCCHEoVnXykTNcHG4veyqc6DTYFB2Egk9ktAnJ+HcU4WzqpaEnMyQ3i/fkkB9s4cym7NLibhSip1PvArAgKvnk9AjK6TxHcrI+3+Lc08VVZ9+ww/zb2Tif58O+d+REEda3G7WlD7iQghxaDbpmBI2hTUOfAr6Z5hJNOjQdDoso/0DkwJ/76HUK+3wOqfUfPE9jZsLSeiRRf65c0IZWofoDAbGPvNnLGOG0bSrlDUXL8bb1HzE4xAilOI2EZc+4kII0T53vY2mnSXoEk2kDB0Q6XC6nb0bNZOCj1nGtNSJbwh9ecrhtjAMrIb3/fV5EdsvYEhO4tiXHySxdx7WNRtZd80fUbKwJmJY3Cbi0kdcCCHaF6hVTh01BJ1RKhlDraDKDuybiKeNCV+d+OEk4rYNBdSsWIU+yUzfi84KdWidktAji/GvPowhLZXKD75k8x8eRSkV0ZiE6Kq4TcSFEEK0z7auZaKmlKWExdbqdlbEw9hLvCuJ+M4nXwOg9wVnYEy3hDSurkgZ2p9xL96PZjJS/I9/s+up1yIdkhBdEreJuPQRF0KI9lnXBgb5SCIearZmD2U2Fwl6jf4Z5uDjyYP6oE8y01xagau6LqT37Nky5r7U5urUCrKjpJzy/3yKptfT//K5IY3pcGROOoYxf78dgII/LaH8nc8iHJEQnRe3ibj0ERdCiPZZZaJm2ARWwwdnJ6HX7W0BqOn1WEYPBUJfnpKaYMCSoMfp8VHb1PHyzF3Pvo7yesk740TMfXqGNKbD1fOskxh6+zUArL/uLuq+WxfhiITonLhNxKWPuBBCHJyruo7mknL0yUkkD+4b6XC6nS2BQT6tylICLGMCo+5D3zklv5Oj7t3WBkpe/i8AA66ZH/J4QmHAwl/R5+Kz8TldrLn4Zhq3F0U6JCE6LG4TcSGEEAcXWA23jB6KJgsXIbe1ZaPm8LYS8dGBRDzyGzZ3/3MZXnsTWdPGB+OKNpqmMeKeG8iZMxV3fQM/zL8JZ1VtpMMSokPiNhGXPuKiNaUUyuvF53LjdTjx2Jtw2xpx1dlwVtXSXFFNc1kljt17aCoqxb5jN25rQ6TDFiJspCwlfJRSrUbbJx9w3BIlnVN8ThdFz/0bgP5RuhoeoDMYGPvkn0g7egSO4jJ+uOC3eOyOSIclxCHF7Y5F6SPePSilKPjTEmq/+gGfxwteH8rnRXl9Lb+8KJ/P/3jLx8rrQ3m8+5yHz9fpextSk5n8yYsk9esVhlcmRGQFJmqmHSMbNUOtyu6mzuEhNUFPvuXAftwpQ/qhNyfSXFKOq9aKKTMtZPfuzFCfsrc/wllRTcqIQWSfMDFkMYSLIdnMuH/9lW9/fgW2dVtYd9UdHPPCfeikOYOIYnH7r1M2a3YPVR9/Fbq2VZqGpteh6fVoOh0EPt7/d50Oj92Bu7aeLXc8yriX/hKa+wsRRQKTHS3SMSXk9q6GJ6Fp2gHHNb2e1KOGUL9qA7b1W0KaBHd0RVz5fOxqaVk44Or5bcYZjRJyMjn21Yf47vQrqfr4Kzbf9ggj7/9tzMQv4k/cJuLynzL2+Vxuttz5GACDblpA7s9moOlaJ8060O39OJBEa4aWx1qOBRPuTvybaC6v4ssp51P54UqqPvmanNmTw/UyhTjimsurcFZUY0hLJam/vOMTalvbGOSzP8uYYdSv2oB1fUFIE/HWvcSVUgf9ulf16Tc0bt1JQs8cep41O2T3PxJSBvdj3Et/YdV5v2H3S8sw985j4HUXRjosIdoUt4m4dE2JfUXPv0nTjt0kD+nHoOsvOaKT/xLzchh80wIK7lrC5j/8jaxp4yM28lmIUAuWpYwdLosWYbAlONr+wPrwgLTAYJ91oe2ckpqgJ8Wkp9Hlpb7ZQ4a57TLNwGp4/8t/ic4Ue6WcGceNYcySO1h7xR/Yes+TJPbOJf/sOZEOS4gDxO1mTRlxH9ucVbUUPvwCAMPv/E1Exm/3u3wuyUP607SzhJ1HYKrbzloH722pxuuTUc4ivPaWpchGzVDzKcW26oO3LgzY28IwtBs2NU07ZHmKde1mar9egyE1mT4XnhnS+x9JeaefyLA/XgvAhkX3UPv1jxGOSIgDxW0iLivisW3bX57F02An+8RJ5MyaFJEYdEYDI++9EYDCv72Io6Q8bPdqcnm57YNCHl25m7c2VIbtPkJA64makoiHWkm9kya3j+xkI1lJB19pTh7SD505AcfuPbhqrSGNIbBBtNTadiK+84lXAeh9wZkYUg++ah8L+l85j36/Pg/lcrPm0ltoLNgZ6ZCE2EfcJuKdGe8roott4zZKXvkvmkHP8D9dF9FYsqaNJ+/0E/E5nMF69XB46Yc9VDf5Nxi/tGYPu+ubw3YvEd+UUljXtZSmHC0bNUNtSzv9w1vTGQykjhwMgO2nrSGNob0V8aaiUsrfXY5miK5x9l2laRrD//Qbcn82A4+1gdXzb6S5ojrSYQkRFLeJuK8L7epE5Cml2PKHR8Hno++l55IypH+kQ2LYndehNydS8e5yqlesCvn1t1Y38Z9NVeg0OCY/BbdX8dCKYilREWHh2F2Ou9aKKSudxF65kQ6n2wmMtm+vPjwgXHXi7SXiu555HXw+ep49h8T8HiG9b6Roej1jlvyRtGNH0VxawZoLfovH3hTpsIQAYiAR1zStj6ZpyzVN26xp2kZN0xa1PH6npmmlmqatbfn1s1bP+b2mads1TSvQNO3ktq4rfcRjU8X7X1D79RqMGRYG3bgg0uEAYO6Vy8AbLgFg820P43OFrjWm16d4dGUxPgVnjcrh9lkDyEwysKnSzjubqkJ2HyECbC2r4ZaxI2SjZhgUtDPafn/hqhPf2znFtc/jrlorpa++C0TvOPuu0iclcuxLfyFpQG9sG7ay9vI/4JO9YiIKRH0iDniAm5RSI4DjgYWapo1sOfaIUuroll/vA7QcmweMAk4BntA07YCCcOkjHnu8zU4K/rQEgMG/uxxThiXCEe014Mp5JA3sg31bEUXPvRmy676zqYpt1Q5yko1cfGxPUhMMLJrSF4B/rN7Dng6OqRaio1p3TBGh5fL62FHjQMPfQ/xQAp8D6/rwrIiXtrQwDNj90tt4Hc1kz5xI6ohBIb1nNDBlZ3Dsqw9jzEyn+rNv2LT4r1KmKiIu6hNxpdQepdSalo8bgM1Ae41tzwSWKqWcSqmdwHbguP1P0umi/qWL/RQ9+waO4jJShg2gz0XRtZNfl2BixN03ALD9oX/QXH74q9XVdhcv/bAHgIWTe2M2+n+enNQvjZmDMnB6fDz8ZbF8IxEhFRxtLxM1Q25nrQO3T9E7LYFk06EbBiQP7Y8u0YSjqAx3vS1kcaSbDSQZddhdXhqcXsC/0FH0vH+c/YBrfhWye0Wb5AG9OfZff0GXaKLklf+y49GXIh2SiHMx1Udc07T+wDHAd8AU4FpN0y4CVuNfNa/Dn6R/2+ppJbSRuNfW1jJlyhQMBgNer5dzzjmHhQsXUl5eTnJyMnq9HpvNRk5ODrW1tSilyMnJoaKigpSUFAAaGxvJzc2lqqoKTdPIzMykqqoKi8WC1+vFbreTl5dHeXk5RqORtLQ0qqurSUtLw+Vy4XA4gsdNJhOpqanU1NSQkZGBw+Ggubk5eDwxMRGz2UxdXR1ZWVk0NDTgcrmCx81mMyaTCavVSnZ2NlarFbfbHTwe66/JonRsf8TfrnDQH65hd2lp1L2mpkE9SZt5HNbl37Nm8V8Y8/gfD+vz9I8tLprcPo7uYWJcbiJFRUXB13R6b8WaEj3r9jTyyrfbOWdsr6j4PHXHf3vx9Jp0mkZ9y4q4PcfC7t27Y/41RdPn6ZuCEgAGphspKirq0GtKHNyPpp+2Uf3DT7gG9wrZa8pO1Ch2ww9bixiWbabpf1/hqq4jaeQgPEP7UFRU1G0/T9bsVPrecz27fvtXtt3/DGSlkTJncky/pu74eepOr6nd3DZWVtM0TUsBvgDuUUq9rWlaLlANKODPQE+l1AJN0x4HvlFKvdzyvOeB95VSb7W+3ueff67Gjh17ZF+E6LINN9xL6WvvkjNnKsf+M3pHyjcVlbFyxnx8zS6Oe/txMicf06XrfFts5Y6PdpBo0PHcL0bQI+XAYUFf7Kjjns92kWTU8cy5bZ8jRGfYC4v5cso8EvKymbn2nUiH0+389YsiPt5Wy8JJvTlzVE6HnrNx8V/Z/dIyht5+DQOvvSBksdz96U5W7Kxn8Qn9OHFgOiunz8e+vZgxT94ZN4Nvdj33Bltu/xua0cD41x4ma+r4SIckuqk1a9b8MGvWrDb/gcVEfYamaUbgLeAVpdTbAEqpCqWUVynlA55lb/lJCdCn1dN7A2X7X9NgiKk3A+KadX0BpUvfQzMaGH5nZNsVHkpSv3wGXusfpbzp1oe6tBnI4fay5OvdAFx8bM+DJtjTB6QzpV8aTW4fj67cLSUq4rAFy1KkbWFYbA1O1Dx0fXhAoE7ctiG0GzZbd06p/Ggl9u3FJPbKJe+0E0N6n2jW/9dz6X/lPJTbw4+X/p6GzYWRDknEoahPxDX/tv3ngc1KqYdbPd6z1WlnAz+1fPwOME/TtARN0wYAQ4Dv97+utC+MDf52hX8Dpeh32XkkD+xz6CdF2ICFF2Dum0/jlh0Uv/DWoZ+wn5fXlFPZ6GZQlpmz2lk10zSNa6f0ITVBz6oSG59srz2csIXY2z9cNmqGXJPLS3F9MwadxsAsc4efF67OKa0T8cAAn/5XzovIlOJIGvbHa8k9bSaeBjs//OommvdINypxZEV9Io6/FvxC4MT9WhX+RdO0DZqmrQdmAjcAKKU2Am8Am4APgIVKKe/+F5VEPDaUv/MZdd+tw5SVzqAbL410OB2iNycw4s+LANj+l+dwVnU8Qd5R4+CtnyrRgEVT+qDXtd8+LivJyFXH+7dAPPlNKTVN0g1IdN3e0fayIh5q26qbUMDATDMmfce/9aYMG4guwUTTzhLctsaQxRNIxO1rNlL//XoMaan0/tXpIbt+rNB0OsYsuYP048bQXFbJDxf8Fk+DPdJhiTgS9Ym4UmqlUkpTSo1p3apQKXWhUmp0y+NnKKX2tHrOPUqpQUqpYUqp/7V1XekjHv28DicFd/nbFQ655QqMlpQIR9RxOXOmkn3iJDwNdgr+/ESHnuNTike/8vcMP31kNsN7dGy09OzBmUzobaHR5eWxr6RERXSN8nqxrfdPcJQV8dDrTP/w1nRGQ7CVYChXxQO9xHu+/z4AfS8+G0Ny52LrLvSJCYx78QGSBvWlYeM2frz8Nnxu6TEujoyoT8TDRfqIR79dT71Kc2kFqaOG0Ht+bK3UaJrGiLuvRzMZKXvjfepWbTjkc97fUsPmyiYykwxcOj6/U/daNLUPSUYdXxdZWbGz/nBCF3GqcVsR3iYH5j49MWWlRzqcbqegZaLmoUbbt8USmLAZwkQ8M8lAj7oq+m9ci2Yy0veyX4Ts2rHIlJnG+FcfwpSVTs3n37Pxdw/IooY4IuI2EZc+4tGteU8VO/7+LwCG37UITX/onrvRJnlgHwZcfT4Am299COU9oEIqqLbJzfOr/HuKrzm+d4d6DLfWI8XE5RP9JSpLvi6h3iE/aIrO2VuWIqvh4VBQ5S936OyKOIBlzFAgtIN9NE1j8nefoylFys9nkZibHbJrx6qkfr0Y968H0ZkTKF36HoUPvxDpkEQciNtsVBLx6Lb1nifxOprJ/fkJZE0ZF+lwumzgby4msVcutg1b2f2v/xz0vKe/K8Xu8jKht4VpA7q2GvmzYVkcnZ+CtdnDk9+WdjVkEaeCEzWlY0rI1TW5qWx0Yzbq6JOW2Onnh2NF3FlVS//vvgKg+RfRNSAtktLHjeTop+4CnY7tf32Opfe9wjPflfL6ugo+KKjh22Irmyvt7Glw4nB7ZdVcHLb42h7diqcLbeXEkVG/ZiNl//4AzWRk2B0LIx3OYTEkmxl+53Wsvfx2tt3/NHmnn3jA2/6rS2wsL6wjQa9x7ZTe+BsFdZ6madwwtS9XvL2F5YV1zBiYzuR+UmIgOmZv60JZEQ+1QFnKkKykQ27Abkvq8IFoJiNNO3bjtjWGZL9M8Ytvo3O7KRx2FOmZuYd9ve6kx8nTGHTX9RTe/jCpjz3Jp/VQNGRkm+cm6DXSzAbSEg2kJxpJMxtIT2z5FXi85fe0RENwQrIQAXGbiEsf8eiklGLzH/4G+FtpJfU7YChqzMk9bSZZ0ydQs2IVW+97iqMevCV4zOnxBXuG/2pcHj1TEw7rXj0tCSwY35Mnvy3l71/tZnReCqkJ8m9dtM/n9tCwcRsAltHDIhxN91PQhf7hrelMRlKHD8K2fgsNP23r8qCwAG9Tc7C16uqpsxlpcx7W9bobp8fHU3ljSZ92EhO+/Jhzlj5L5Z23UzFwCPUON9ZmD9ZmD/UOD06vorLR/44HOA557QSDbt8kvSVBDybwZgMZZiMDM81d+qFNxJ64/Q4t7Quj0563P8L6w0YSemQxaNFFkQ4nJPwbN2/gqxMvpOSV/9LnV2eQdox/deW1teWU2Vz0z0jkF6NDsyp15qgcVuysZ2OFnWe+K+Wm6f1Ccl3RfTVuKcTndJE0qC/GtNRIh9PtBOrDu5qIA1jGDsO2fgvW9VsOOxEvff093LVWjKOGUtp/MOmSiAd5fIp7PtvJhvJGss48l5N76Kl96wN63fsAZ/37MdJm7H3HSClFs8dHvcNDfavk3P+7P2Gvb/1Yswenx0dFo4uKxvbHnh+Vm8zdJw8iqZP7hUTskURcRA2P3UHB3f5Wf0N+fyWGlI6174sFKUP70/+Keex84hU23fowx7/3DLutLt5YXwnAoql9MIRo9UOnadw4rS9XLdvCh1trmTEwg/G9LSG5tuiegmUpslEz5JRSrVbEu/41LW3MMEo4/Dpx5fWy6+mlAPS+cj7YNMokEQf8LWQfXlHEt8U2UhP03PfzIfQ7/zbWedyU/+dTVp9/A8e9/TipwwcC/kUWs1GP2ainp+XQ72YqpXC4ffsk6P4E3o21VTK/rdrBTxV2bv2gkHtPkWS8u4vbHYvSRzz67Hz8FZx7qrCMGU6vX/4s0uGE3KAbLyEhLxvrj5soee1dHv1qNx6f4tRhWYzKDW2P9D7piVw8zj989pEvi7G7Dt6xRQhJxMOnvMFFg9NLeqKBHild/76zd8Pm4XVOqfjfCpp2lWLul8/gc07EqNeoc3ji/muEUoqnvyvlk+11JBp03H3yIPpnmNH0esYs+SM5J03BXWtl9dxF2HeWdOkemqaRZPIn7SN6JDOpXxqnDsti3tg8rjy+N4tP6M+9pwzm72cMJSfZyKZKfzLeFOefm+4ubhNx6SMeXRwl5ex84mUARvx5EVo37GpjSElm2B+vBWDjXU+wbUcFaYkGLpvQ8Z7hnXHu6B4MzU6iyr63NaIQbbFJx5Sw2dKqPryrG7GhZcOm0YC9cDeexq5NflRKtRpnfz56g4H8ln0pe+J8Vfy1tRUs+6kKg07jjtkDGNFqoJrOaODoZ+8mc+qxOCtrWPWL63CUlIctlp6WBB78+RBJxuNE98t2OkjaF0aXrfc8ia/ZRd4Zs8iYODbS4YRNz7NOwjLxaLDamPzpu1x1fC8sieGpENPrNG6a3heDTuPdzdWsK2sIy31EbPM2O2nYXAg6HalHDYl0ON3O1hDUhwPoEkz+kgilsG3Y2qVr1H23DuuajRgzLMF3HQOj7uO5POXdzdW8+MMeNGDxCf3aLOXTJyYw7qUHSB9/FM2lFayauwhnZU3YYpJkPH7EbTZ6OCsTIrTqVm1gz7KP0SWaGPaHayIdTlhpmsbqX5yPT6dj7PdfMt5RFdb7Dcg0M/9o/ybQh78sxuGWL+RiXw2bClEeLylD+sXtiPNw6upo+7ZYxvg72nS1TjywGt73knMxJJsByLeYACiN00T8ix11PPaVv3PVdVP6MGNgxkHPNSQncewrD5F61BCaduxm1dxFuGqtYYtNkvH4ELeJuLedKYfiyFE+H5tvfwSAAVfPx9ynZ4QjCq91ZQ2860xh/fEz0JRi860Ph30gxC/H5jIwM5E9DS5e+mFPWO8lYo8M8gkfr0+xrcbf0u5wNmoGBOvEN3Q+EW/ctouqj1aiSzTRd8G5wcd7tQwYiscV8dUlNh74vAgFXDq+J6eNOPR0UWNaKhOW/o3kIf1p3LKD1effgKeha6VCHSHJePcXt4m49BGPDmVvfoBt3RYS8rIZcO0FkQ4nrFxeH4+2rLz0ueFSTNkZ1K/aQNmbH4T1vka9jhun90OnwbKfqthUEb5vGiL22Nb5E3HLWEnEQ62orhmnx0deqom0EJSgpbWsiFvXdT4R3/XUawD0mvszEnIyg48HVsTLbO230+tuNlfa+dMnO/H4FOcclcO8sR1vH2vKzmDCG49i7puPbd0Wfrjwt3ibmsMWqyTj3VvcJuKyIh55HnsTW+99CoCht13d7d8Wf2N9JSVWJ73TEjhv8kCG/cE/NbTgz4/jtjWG9d5Ds5OYOyYXBTy0ogiXR9p3Cj+ZqBk+gYmah1sfHpAyYhCaQY99exEee1OHn+esrKH0zQ9A0+h/5bx9jsVjjfiuOge3f1iI0+PjpCGZXDGxV6fLVRN75jDhzb+T0DOHum/X8eNlv8fnDN8PM5KMd19xm4iHuxxAHNqOv/8TZ0U1aceMJP/ckyMdTliVWpt5ba1/l/2iKX0w6XXkn3cK6eOPwlVVy/YHnw97DBcck0eftAR2W528/GP4dvyL2OGxO2jcugvNoCd15OBIh9PtBAf5ZIcmEdcnJpDSsmGz4adtHX5e0fNvolxuepwyjeRBffc5lpNswqDTqGlyx8UekvIGJ7//XyENTi+T+qZx47S+6Lq4ZyypXz4T3ngUU1Y61cu/Y93Vf8Tn8YQ44r3aSsbjve1kdxC3ibj0EY+spqIydj3lHyox4u7ru2W7wgClFH//ajdur+KkIZmMzfdPLtR0OkbedxNoGsXP/9vfuSKMTAYdN03vhwa8sb6CrdUdX1ET3VPDT1vB5yN1xCD0iYceSCI6Z2ugdWGP0A0ns4xuKU/pYD9xj72J3S8tA2DANb864Lhep5GX6i9PKW/o3uUpdU1ubvlfITVNbkbnpXDrif0Pe4x8ypD+jH/jUQxpqVS8/wU/XX8PKowDA/dPxm+TZDzmdd/s5xCkj3hkFfz5cXxOFz3PnUP6sUdFOpyw+qywjh/LGklN0HPFxF77HLOMHkafi85Ceb1svu2RsL9TMzI3mbOPysGn4OEVRbi9UqISzwJlKRYZ5BNyTo+PHbUOdBoMzjKH7LqBOnFbB+vES157F3d9A+kTRpMxYXSb5/RqKU/pzp1T7C4vt35YSJnNyeAsM3fNGUiCITQpkGXUEMa/+hD6JDNl//6QTbc8FNav5YFkvEeKJOPdQdwm4nq9jIyNlNqvf6Ti3eXozYkMu617tytscHp4+ttSAC4/rlebG7aG3HIlxsw0ar9eQ/l/Pgl7TJeMzyffYmJHbTOvr6sI+/1E9Ap2TJFEPOS21zThU9AvPRGzMXTfbwI/NHWkhaHP46Ho6dcBf1eqgwnWiVu7ZyLu9Pi446MdFNY46GVJ4J5TBpEc4rHx6ccexbh//RVdoond/1xGwZ+WhD0Z/+vPJBnvDuI2EReRobxeNt/xNwAGXHsBifk9IhxReD2/qoz6Zg+j81I4eWhmm+eYMiwMvfUqALbc+VinNmF1RaJBxw1TSdJx+wAAIABJREFU/XWir66tYGetI6z3E9Fr70ZN6ZgSaltD2D+8tdQRg9H0ehq3F+Gxt/9/t+Ld5Th27yFpYB96nDz1oOfld+MVcY9Pcc9nO9lQ3kh2kpH7Th1Ehjk8palZU8ZxzHP3ohkN7HrqNQof+kdY7hMgyXj3ELeJuHRNiYySpe/R8NM2EnvltrtC0x1srGjk/S01GHQai6b0aXdXfu/5p5N29Aic5dUUPvxC2GMbm5/KacOz8fgUD60oxuuTzcvxxm1rpKmwGF2CiZRhAyMdTrezd7R96OrDAfTmBFKGDQCfj4ZN2w963j7j7K86H62dd4G7a+cUn1I8vKKIb4ttpCboue/UQeSlhncvRM7syYx94k7Q6dj+4PPsbGkbGS6SjMe+uE3EZbPmkedpsLPtvqcBGPaHa9AnJUY4ovDx+BSPrvT3DD9vTA/6ZrT/WjWdjhH3+jdu7nrmdRq37Qp7jJcdl09OspGt1U289VNl2O8nokugtCF15GB0Jvl6GGrBjZohXhGHVhM21x18w2btV2uwrS/AlJVOr/NObfd6vdK6XyKulOLp70r5ZHsdiQYdd588iH4ZoavVb0/e6Scy+pFbASi48zF2/+v/wno/ScZjW9wm4p4wthgSbSt85EVc1XWkHzeGvDNnRzqcsHprQyW76prJt5iYf3Reh56TPm4kveefhnJ72Hx7+DduJpv0XN9SovLSD3vYXR++gRQi+shEzfBpcHootTkx6jUGZIY++QtM2LS2UyceHGd/2Xnoze2vAuemmNBrUGV34+wmMwZeW1vBsp+qMOg0/jh7ACNC2LmmI3r98mf+xRVg481/pezf4R3cJsl47IrbRFwcWfadJex61r9paMRdizo9PCGW7Glw8vIa/yj5ayf36dTO/KG/vwpDWio1X6yi4v0vwhVi0IQ+FuYMycTtVTz8ZTE+6a8fN2zSMSVsAqvhg7PMGA6zPV5bLGOGAmA7SAvDhs2FVH/2DTpzAn0vPvuQ19PrNHJbSjbKG2J/VfzdzdW8+MMeNOCWE/pxbG9LROLot+Bcht52NSjFhkX3hP1ruiTjsSluE3EZcX9kFfzpMZTbQ69f/qxbr8AppXj86xKcXsXMQRmM7+Q3AFN2BkNvuQKALXc8GtaxyQFXHt+LTLOBjRV23tlUHfb7iegQ3KgpiXjIFYSpPjzAMnII6HQ0bt3V5teIwDj73vNOw5SV3qFrdpdR91/sqOOxr/xlgddN6cP0gRkRjWfgdRcy8PqLUV4va6+6g6rl34b1fpKMx564TcSlj/iRU/Plaio/+BJ9chJDWrqDdFdf7qrn+902kk16rtyvZ3hH9bnoLFKPGkJzaQU7HvtniCM8UGqCgd9M7QP4u7zs6QYrYqJ9rpp6HMVl6M2JpAztH+lwup1Qj7bfnz6p5fPm82HbtO+EzeY9VZS9/RHodPS/al7bF2hDd+glvrrExgOfF6GAS8f35LQR2ZEOCYAhi6+g3+VzUS43Py74PbXf/BjW+0kyHlviNhGXPuJHhs/jYfMf/O0KBy66iMTc6PjCGA52l5cnvikB4LIJ+WQmdW0DnKbXM7KltnDH469g31kSshgPZnK/dE4YmI7T4+ORL4vDXp8uIiswldEyZli73TRE1wRH24cpEYe9deK29Vv3ebzouTdQbg95Pz+BpH4dXwyI9c4pmyvt/OmTnXh8inOPymHe2NxIhxSkaRrD71pE7/mn43M4+eHC32H9cVNY7ynJeOzocCKuaVqmpmn3apr2vqZpK1r/CmeAIraVvPJfGrfswNw3n/5X/DLS4YTVi6vLqG3yMKJHEj8bnnVY18o4bgz5552KcrnZ0vKDTLj9f3t3HidXWeb9/3PV1t3pLb0lnQWydQIkLIGECBNFBUaQQQg4ODCOwiNuGB11nnEelfn5OC9/Puo4Om6j/kZEXEFGhOAjiCC4DLKHNWTrzr50p7f0Wt1VXXX//qiqTqfp7nR3qrqW832/XvVK9zl1qs65Ozl95a77uq4PXbiQyuIALxzq5cHt7TPynpIdWh+eOW19ETr6hygL+YeD20wY7rA5Yp34UE8f+3+UqNCxeIrlYYdriedhU589nWH++aEmBofi/OXyat73ugU5l4dkZqz68j9Rv+FSYr39PPu3/0DP1qaMvue8iiK+rA6cOW8qM+I/Ay4EfgV8f9Qj76iOeOZFu3rY+aXvAXDaZzbiL85s/dZs2t6aWF/tM/jo+lPxpeGXwGn/z4cIlJfS+sifOfLb/07DWU5sdkmQjRcuBOA/nzrIkd78Xisq41PFlMxJ1Q9fXjsrLfeB8YzVYXP/T+9nqKePqgtWM/u8lVN6vXydEW/uGeRTDzbRMxjjwlMr+Yc3pOf+mwnm93P2Nz9D3VteT7Szm2fe8VH6du3P6HvOK1cwnuumEoj/BXC5c+47zrkfjnxk6uQySXXEM6/xq7cT7ThK1QWrmftXb8r26WRMLFkz3AFvP3MOS2vSU66saE4NDZ94LwBb//lrxAYy/wvyjUtn8xeLKumPxvnG4/u1RKVAKVEzc1IVU07P4LIUSNR/x+ejd/tuYuFB4tEh9n7vbgCWfOidU369+vIQPoPWvgiRWH6UMOzsj/LJB5to749ydn0Zn754Mf4MVKlJJ18wwOr//Bw1b1hLpLWDZ677e8L7D2f0PRWM57apBOIvAQszdSIzTXXEM6u3cS/7vv8LMOOMzxV2ucJNr7bS2B5mblmIvztvcjXDJ+vU97ydstOXEt53iN3/8dO0vvZYzIyPrD+FspCfp/d387vGzoy/p8ysgZY2Bg+3EigvZdaSgrml54zU+vB0t7YfLVBaQlnDIlwsRs/WRpo3PcLAwRZKly+m7tILp/x6Qb+POWUh4g5aenL/07C+SIxPP9TEoe5BGmpK+Je3LJ1Sqdhs8hcXce4dX2L2+WcxcLCFZ97xUQZaMluxSsF47prwb62ZvSf1AB4FfmNmnx65PblP5DjbP/tN3FCMhX97JRVnnZbt08mYI70R7ng2MZux8S8WUhJMb+KbLxDgjM//AwC7vvkj+vdlduYEoGZWkA9ekEjy+s6TB+joV4WhQjJyfbj58iNwyRdx59jRFgbg9AyVLhwp1WGz64Vtww18ltxyw7R/rvmyPGVwKM5nfruLpvYwCyqK+PzlyygN5VfScaC0hDU/+TcqzlpB/+4DPPuOjxLp6MroeyoYz00n+tf6rhGPNwAHgL8ctf3vMnmCmaI64pnT+tiTtD7yZ/xls1j+yQ9k+3Qy6ttPHGBgKM7rF8/mglMrM/IeNevPo37DpcQHImz99Ffo2drEQHMr8cHMzVr95fJq1i4sp2cwxjce30/XwBCxuJapFIKuF7QsJVMOdg3SF4lRMytITWnmlz9WnJMIxPfedjc9rzZSNKeG+W+/bNqvlw+B+FDc8flHd/Nycy+1s4J88a0NVJXk51LTYGU5a+/8d0qXL6Z3+26evf7jRLt7M/qeCsZzz4TRqHPuzTN1IulkZpcDXwf8wG3OuS+Ofo7qiGdGPDrEts98A4CGj/8Piuqqs3xGmfPE3i7+vLeLWUEfH7pwejXDJ+v0//0RWn/7OK2P/JnWR/48vN0/q4RgdSWh6kqCVRUEqyoJVc8mWFVBqKqSYHJ7YlsloeoK/KWzTrhUyMz42OtP5f33bOXPe7v4896XASgN+ako8lNeFKC8yE95kZ+K4sDx3xcd/315USDn1216SfeLyUTNc5SomW7HGvlkdllKSmWyhGF/MuHv1Pdeh68oNO3Xy/VAPO4cX/3jXp7c1015kZ8vvHUZc8unf725IFRbxfn/9XWeuvoWul/axuZ3fYI1P/sqgdL05BqNJRWMf+LXO4eD8Xz8VKFQnHBa2My+DfwB+KNzLvOfi58kM/MD/0Fi5v4A8IyZ3e+cO75oZxzaDqmLYLq13vMb+nbuYdbiBSx673XZPp2MCUdjfOvPiV9+N66ZR21pZn8ZFM+r46yv3cre239BtKOLSGcX0c4uYv1hYv1hBg40T/q1LBRMBOnDgfvIYH3EtupK/nFpgDsbe+kcjNE3GCPWB50kHlNRGvJTFvJRVhSgLORPPJJBelnRsX3locT3pUEfPgXvGXE0tTRFFVPSbqYD8fIzl4MZOId/VgmnvnvDSb1eLjf1cc7x/z11kEcaOykO+Ph/L1vGoqrMBaszqbi+jvP/65s8veEWOp96keff80nO/uZnMlrjvwb4woVz+OzDTeza3ctn/6uHf7joFIJ+LVebaZNZnxEA/gVYYWa7SAblJALz3Zk8uWlaBzQ653YBmNldwNXAcYH4wI7dPPvWW7Jwet6w/DMfOamZmVz3483NtPZFWV5bwlUr62bkPeuvupj6qy4e/t45x1BPH9HOrkRw3pEIziOdXUQ7uhNfj9zW2U20o4tYeIDBljYGJ5kc9LZMXdAIcaA7+ZDMGyovo+SU9CYWe4Vzjvb+KAe6BjnQNcjBroHEn92DwzPJMxWIB0pnUdqwiL6de1j4d1cRnF1xUq+Xy23uH97Zwb2vtBLwGf/70iWcMSfza/Bn0qxT57H27q/z9IYP0f6HZ3js7Jm488LI6bIXZ+QdvWnOA98ad98JA3Hn3PsBzGwOcBHweuCjwG1m1gz8wTmXS+vEFwAjC3MeAF43+klxn49wyYibpRmafzt5Dmg67Sw6Z8/lTYcPU15eTnt7O1VVVYTDYQYGBqivr6e5uZni4mJKSkro7OykpqaGnp4eIpHI8P6SkhJCoRBdXV3U1tbS1dVFNBod3l9aWorf76e7u5u6ujo6OjpwzlFXV0dLSwtlZWUA9Pb2MnfuXFpbWzEzqquraW1tpaKiglgsRl9f3/BrBoNBKisraWtro7KykkgkQjgcHt4fCoXopYh7XzmCAe86o5QD+/dl75q6OjG/UX3aYo62tlJRcTqh5DWdOs41DXT30NvcSlWwiJZde7DeMIGBKN2HmgkMDjHQ1kmk/Si+8AADbZ24vjA4cC6O+XyJkobO4fP5iMfjyX87NuF+gHjcgRlx53AusfwlHne45N+bRKlEI45LbJCMiGO88ro3sfrQIWJDQ1n/95Sr94g9h1rotRIOdg2yr7OfrniIvR19HOmPMxgb/y/o0tlBFpXC3r17Z+SaKt9+KfFf/4H5N7+dvXv3ntTPKdrZipGozd20ew8L58/LiZ/TrNJSfrY58YH8B8+fyxzXxYED3YX3d6/Yz5k//CJbPvZ/iLV2Ag43+l5rhovHMfPhGONefKL9Y9yrMR/90RiqVpsdNp06wWZ2JnAl8DGgzjmXMwuLzOw64DLn3HuT378LWOec+8jI5z3++ONu5cqpNTyQE3t4Zztf/sM+6kqD3H7dyrwpJzUVn/vdbv60+yiXrajmf160KNunIzIl1//sZTr6h/jBdWewoLI426eTVQNDcQ51DXKge4CDwzPcidntroHxS9xWFgdYUFHEwsoiFlQWsbCymIWVRcyrKKI4z+9577zzFVr7ovzgupUsqMyNJmzP7O/m1oeaqC0N8qO/WUVAy9Ykz2zevPm5Sy65ZO1Y+yazRtyA80jMhr+RxOzyfuBPwIeAzLf8m5oDwCkjvl8IHBr9JCVrZsbFy6q55+VWdnWE2bSllXecMzfbp5RWW4/08afdRynyGzeumZft0xGZsuU1s3iqv5vG9rAnAvFY3NHcE+Fg98BrlpO09o3/e6A44Dsu0F5Qkfh6QUURFcWFW3VrfkURrX1RDnUP5kwgfu+WIwBctbJWQbgUnMncTY4CB4GfA98E3umc68voWZ2cZ4DlZraExHlfD/zt6CcVcoOZbPL7jPeum8+nf9PEnS+2cPlpNQXzS8s5x38+dRCAa8+ak/EETZFMWFZTwlP7u2ls6+eNS6uyfToZ89Pnm/ldYweHuwcZbyWJ32BecmZ7YWVxIuiuSHxdPSvgyd8TCyqLePFwb85UTtl3dIBnD/RQ5DeuOK0226cjknaTiZDuJrEu/N3AEmCxmf3JObcjo2c2Tc65ITP7MPAQifKFtzvntox+nj+D2chet2ZBOefOL+f5Qz3c+UIzH7igMLr3Pb63iy0tfVQWB3jH2YU10y/e0VCbyI3Z2R7O8plkTjga48ebD5MqfT+nLMiCiuJkwJ2a2S6mvjyk0pqj5FoJw01bWgG4uKG6YCZ1REaaTLLm+wDMrI7E8pQ3ABvNrB74M/An59zXM3qWU+ScewB4YKLnqMV95pglZsU33red+19t4+pVddSX58ZHnNM1FHfc/kxihdO7zqtXvVXJW8trEoF4U3sY51xBzvo2tYeJO1hcVcw3rj4t79dtz6RcCsR7B4d4eGcHABtWzUx1KpGZNum7k3Ou1Tl3j3PuYyRmx79BYqb8q5k6uUzSjHhmLa+dxcXLqojG3XAL+Hz2wLY2DnQNsqCiiCtO18ejkr/mlAUpL/LTNTA04RrpfLazLVHP+/S6UgXhU5RLtcR/s72dgaE4584vY0l1YdQMFxnthHcoS1hjZh83s3vNrJXEOuwrgO8Db830SWbCdKrFyNTctHYeQZ/xaFPn8C/GfNQfifHjzYmGOTefP1/JQpLXzIyGmkRQ09iev/8uJ5K63yyvVfA2VfXJTpXNPRFi8ez9nozFHZteTfQ62LBqTtbOQyTTJjNVcBT4PfBXwPPAXwOznXMXOedudc79NoPnlzHxeDzbp1Dw6suLuGplYvb4tqcP5u1/fu5+qYWugSFWzill/eLKbJ+OyElrSC5PaWwrzHXiO5PXtbx2ZhrrFJKSoJ+aWUGG4o7Wvuw19nliXxctvRHmlYdYd8rJNSoSyWWTyXy4DGgDRv6LnDtyXaFzbl+azyvjgsFgtk/BE25YXc9DOzp4/lAvzx3sYe3C/LqhtvdFueflROms971ufkGupxXvSSVsFuKMeDgaY3/XAH6DpVrOMC3zK4po70+UMMxWfs99rySSNK9eVaeEWilok5kRfxzYDuwG9ozxyMU29yekOuIzo6I4wPXJWuK3PX0oqx91TscPnzvMYMzx+sWzWTW3LNunI5IWw0tTCnBGPJWouaiqhJDWh09LqtX9wa7srBNvau/npeZeSoI+LltRk5VzEJkpk7lLvQzsBP4ZWAQERz3yspiyz6cb9Ey5elUddaVBdnWEebSpI9unM2m7O8L8dmc7foObz1fzHikcCyqLKAn6aOuP0hkurEkJrQ8/edmunHJfsmThW5bXqEKVFLwTRqPOudUk1oVXk+ii+QCJJjkh51zMORfL7ClmhgLxmVMU8HHT2kQge8ezh4kM5cf6/O8/c4i4gyvPqPVEB0LxDp8Zy5LLNpoKrJ54KhBfofXh07ZgOBCf+TXiR8NRHm3qxIANq1ShSgrfpKJR59wrzrlPkGjo81XgSuCwmZ2XyZPLJNURn1kXL6tmaXUxrX1R7nu1Ndunc0LPH+rh6f3dzAr6eOe59dk+HZG0G27sk8cVjcaiRM2Tl2ptn40Z8Qe2tRONOdadUqEJEPGEqU4LLwfeCFxIooJKZ9rPaIYEAurQNZP8PuO96xYAcNcLLXQP5O5/hOLO8b1kK/u/OWcus0uU2CuF51gJw8KZEVeiZnrMSyZoHuoZJD6D1a6G4o5fbU2VLFQDH/GGydQRrzazjWb2NHAf0Atc5Jx7s3MuLxM1QeULs2HNgnLOnV9GbyTGXS+2ZPt0xvVYUyeN7WFqZwW55kzVr5XC1DDcYbNwZsR3KVEzLWaF/FSVBIjGHG0z2PTpT7s7ae+Psmh2MectKJ+x9xXJpsncqQ4BHyYRhG8EngQazOzi1COTJ5gpCsRnntmxWfFNW1pp7sl+57bRIkPx4U6gN66dp658UrBOrSom6DcOdUfoHczdT6imYocSNdMmGwmb944oWahSseIVk4kymoFi4H0kOmmOftyWsbPLINURz47ltbO4eFkV0bgbDnhzyaZXW2npjbCkqphLG6qzfToiGRPwGUuqCithU4ma6TPTgfjWI31sa+2nvMjPpct17xXvmEzVlMXOuSUTPJbOxImmm+qIZ89Na+cR9BmPNnXSmEOJYt0DQ9z5QmLJzHvXLVATCSl4DbWFtU5ciZrpM9OBeKpk4VtPq9EnkeIpnv3brvKF2VNfXsRVKxNlqb739KEsn80xd77QTG8kxrnzy1m7UOsTpfANt7ovgHXiStRMr1QgPhNNfdr6IvxxVyc+g6tWKklTvMWz0agC8ey6YXU9ZSE/zx/q4dkD3dk+HQ73DHL/q20Y8L51amUv3lBIHTaVqJleC2ZwRvxXW9uIOVi/eDZzyvKyR6DItHn2bqU64tlVURzg+nPmAnDb04eIxWeuRNZY7nj2MNG445KGquH6yiKFbkl1CT6D/V0DDORJo63xKFEzvVJt7g91D+IyWMIwMhTngW3tAFyjkoXiQZ4NxFVHPPuuXlVHXWmQXR1hHm3qyNp57Gjt57GmToJ+46a187N2HiIzrSjgY9HsYuIOdnfk96z4sdb2+o90OpQVBagsDjAYc3T0Z27i6rFdnXQNDNFQU8KquaUZex+RXOXZQFzlC7OvKODjxjXzgMSMdCQLM3LOOf4z2bznmlV1+lhUPKdQOmymEjVVMSV9UrPiBzO0PMU5N1yycINKFopHKRCXrLqkoZql1cW09kW579XWGX//p/Z381JzL+VF/uGlMiJeUgjrxJWomRmZrpzycnMvuzrCzC4O8KZlVRl5D5Fc59lAXHXEc4Pfd6zJz10vtNA9MHNr92Nxx/eTVVveeW49ZUVariTek5oRz+fKKUrUzIxMB+Kp2fArz6gl5NfPTbzJs3/zVUc8d6xZUM6588vojcS468WWGXvfh3a0s/foAPPKQ7ztjNoZe1+RXLIsOYO8p3OAaCw/PylUomZmZDIQb+4Z5Il9XQR8xpW6/4qHeTYQV/nC3GF2bFZ805ZWmnsyXy4rHI3xo+cSnT3fc/58gpqNEY+aFfKzoKKIobhjb+dAtk9nWna2q5FPJmQyEL//1TbiDt64dDbVs/QJtXiXZ6MPJYXkluW1s3jzsiqicccPkwFyJt3z8hE6wkOcVjeLi5bMzvj7ieSyfO+wubNVFVMyIVVL/GCaSxiGozEe3J4qWTgnba8rko88G4jHYrFsn4KMctPaeQR9xu8aO2nMYAWHjv4od790BID3rVug/5SJ5y3P4w6bStTMnPIiP2UhP+FonKPh9OXvPLyzg75IjJVzSllRp/88ibd5NhBXHfHcM6+8iKtWJtYK3vbMoYy9z082NzMwFOfCUys5e15Zxt5HJF8sy+PKKSMTNYuUqJlWZpb25Slx57hvSyJJ85oz1cBHxLN3Lc2I56YbVtdTGvKz+WAPzx7oTvvr7zs6wAPb2/AZ3LxOzXtE4FjllKaOcNa73E6VEjUza0HlseUp6fDcgR4OdA1SWxpk/WItCxTxbCCeyZa9Mn0VxQFuSNbzvu3pQ8TT/HP6/jOHiDt462k1nDq7OK2vLZKvKosDzCkLMjgU52BX5pOl00mJmpmV7hnxe7cklgVetbKWgE/LAkU8G4irjnjuunpVHbWlQXZ1hHm0sTNtr/tycy9P7O2iOODjXefNS9vrihSChuQ68Z15tk5cre0zK9VdMx2B+L6jAzx7oIciv3HFaSpZKAIeDsRVRzx3FQV83LQmESjf8dwhIkMnX9t4ZCv7d5w9R+WyREY51mEzfwLxcDTG/qMD+JSomTHHZsQjJ/1am5Jrwy9uqKaiWHlaIuDhQNzv92f7FGQClzRUs7S6mCO9UTa92nrSr/fH3UfZ3tpPdUmAt5+lclkiox3rsJk/CZupRM3FStTMmPlpKmHYOzjEwzs7ANiwSkmaIim6c0lO8vuMm89PNPm584UWugemXzorEotze7IKy7vXzKMkqP+EiYx2rIRhOG9yaJSomXmziwPMCvroi8ToGZx+kYPf7OhgYCjO6vllLNGnFyLDcjoQN7Mvm9k2M3vJzO41s9nJ7YvNLGxmLyQf3x1xzBoze9nMGs3sGzZOkWhVTcl9axeWc+78MnojMe56sWXar/PrrW0c7olw6uxiLltRk8YzFCkc1bMCzC4O0BeJ0dxz8ssQZoISNTNvZAnD6VZOicXd8LIUNfAROV5OB+LAw8CZzrmzgR3Ap0bsa3LOrU4+Pjhi+3eA9wPLk4/Lx3phJWvmPjPj5nWJWfFNW1pp7pn6L4HewSF+8nwzAO9dNx+/svRFxmRmeddhU4maM2M4EJ9mRZ0n93XR0hthXnmIdadUpPPURPJeTgfizrnfOudSaxKeBBZO9HwzmwdUOOeecInPVn8EbBjruUND6esSJpmzonYWb15WRTTu+OFzh6d8/M9fbKFnMMY588p4nX4BiExoeHlKHiRsKlFz5pxsCcNUA5+rV9VpMkRklJwOxEd5D/DgiO+XmNnzZvYHM3tDctsC4MCI5xxIbpM8dtPaeQR9xqONnVMKEI70Rvhl8heAWtmLnFgqYTMfShgeS9QsVqJmhqWa+kwnEN/VHubFw72UBH1aGigyhqzXDzKzR4D6MXbd6pzblHzOrcAQ8NPkvsPAqc65djNbA9xnZquAsSKtMbOOjh49yvr16wkEAsRiMa699lo2btxIc3MzpaWl+P1+uru7qauro6OjA+ccdXV1tLS0UFaWaIve29vL3LlzaW1txcyorq6mtbWViooKYrEYfX191NfX09zcTDAYpLKykra2NiorK4lEIoTD4eH9oVCI8vJy2tvbqaqqIhwOMzAwMLy/uLiYkpISOjs7qampoaenh0gkMry/pKSEUChEV1cXtbW1dHV1EY1Gh/fn8zVFurq4fHklv9p+lG/9sYkv/dWKSV3Tj7YNEI051tWHOLXcx/79+3Pmmgrx56Rryv9rqgskSoXubOtn//79ADl7TU/tTJQjXVQRYO/evZ76Oc30NRUlq/3ubuumu7t7Stf046d2AfDGRWW0HT4AOXJNhfhz0jXl7jVNxHI9O96oicsrAAAcwUlEQVTMbgQ+CFzinBtzmsbMfg/8I3AQeMw5d3py+w3Am5xzHxh9zO9//3t3zjnnZOy8Jb26B4a48e5X6YvE+D+XL2PtwomXmTS29bPxvu0EfMZt153BvPKiGTpTkfzlnOPaH79MXyTGnTecSU1p7ubS/Osf9vLIzg4+/BcLuWqlyuFlUnt/lBt+9goVRX5+8a6zJ33c0XCUd961haGY4/brzmBBpboZizdt3rz5uUsuuWTtWPty+vM8M7sc+F/AVSODcDOrMzN/8uulJJIydznnDgM9ZnZBslrKu4FNY7226ojnl4riANefMxeA254+RHyC/0A65/je04dwJNooKwgXmRwzG27sk+vLU5SoOXOqSwIUBXx0D8boGZx8ftUD29oTn0qeUqEgXGQcOR2IA98CyoGHR5UpvAh4ycxeBH4BfNA515HcdwtwG9AINHH8unLJYxtW1VFbGmRXR5hHGzvHfd5zB3t4/lAPZSE/N6wea9WTiIwnHzpsKlFzZpkZC5Kt7g9PssPmUNzxq61tgBr4iEwk62vEJ+Kcaxhn+z3APePsexY480SvrTri+aco4OOmNfP4tz/u447nDnHRktmERiVpxeKO7yVb2d+weq7aKItMUT502Ewlai6tVqLmTJlfUcSujgEOdg+you7En0L8aXcn7f1RFs0u5rwF5TNwhiL5ybN3MNURz0+XNFSzpKqYI71RNr3a+pr9v2vsYHfnAHPLQlytdaMiU3asw2buzojv0LKUGTfVEob3vnKsZKEqVomMz7OBuOqI5ye/z3hvssnPnS+00D1w7Oc4MBTnjmcTtcZvWjvvNbPlInJiCyqLKAr4ONIbPe7fVy5RR82ZN5XumluP9LGttZ/yIj+XLq/O9KmJ5DVFKpJ31i4sZ/X8MnojMe56sWV4+72vHKGtP0pDTQlvXlaVxTMUyV9+n7GsOtVhMzdnxZWoOfOGZ8Qn0V0z1cDnrafVUKwJEZEJefZfSCCgtcP5yuzYrPimLa209EQ4Go7y82RQ/r7XLcCnj0JFpm241X1b7q0TV6Jmdky2qU97X5Q/7urEZ/C2M7Q8UOREPBuNRqPRbJ+CnIQVtbN487IqHmvq5IfPHaI05Kc/GmfdKRWcO1+JQSIno6Emdzts7upQomY21MwKEvIbRweG6IvEKA2NXQL4V1tbiTl4/eLZzC0PzfBZiuQfz97FVEc8/920dh5Bn/G7xk7+79Y2fAY3nz8/26clkveOlTDMvRnxHa1alpINPjPmJZenHB5nVjwyFOfX29oBuOZMzYaLTIZnA3HJf/PKi3jbylocEHPwluU1LNFH1SInbVFVMQGfcbB7kL5IbpV6VaJm9pyocspjuzrpGhiioaaEM+eWzuSpieQtzwbiqiNeGP52dT0VRX5mBX3cuGZetk9HpCAE/T4WVyU6Ie7qyK1ZcSVqZs+CCSqnOOeGSxZuUMlCkUnz7Bpx1REvDBXFAb59zenEnKOmVD9TkXRpqJlFY3uYxrZ+zqovy/bpAErUzLaJZsRfbu5lV0eY2cUB3qSqVSKT5tlAXMmahWNOmRKCRNKtobYEduRWh00lambX/GSb+0NjtLlPzYZfeUYtIb9+NiKT5dl/LfrYTERkfKmlH41tuVM5ZWeb1odn03gz4s09gzyxr4uAz7jyjNpsnJpI3vJsIK6qKSIi41tSXYLPYO/RAQaH4tk+HUCt7bOtrjREwGe090cJR4/lWd3/ahtxB29cOpvqWVoiKDIVng3E1eJeRGR8xQEfp1QWE3ewpzM3lqcoUTO7/D6jPlkb/HByeUo4GuPB7cmShavmZO3cRPKVZwNxzYiLiEws1WFzZw7UE1eiZm5YMGp5yiM7O+iLxFg5p5QVdfoPkshUeTYQd85l+xRERHJaqsNmYw502Ewlai6uUqJmNs0f0eo+7hz3bUkkaaqBj8j0ePZuFo/nxppHEZFclUsdNpWomRtG1hLffLCH/V2D1JYGWb94dpbPTCQ/eTYQVx1xEZGJLUsG4rs7wwzFs/spotaH54aRlVNSJQuvWllLwKdKZCLT4dlAXHXERUQmVlYUYH5FiGjMsa9zIKvnooopuSEViO9o6+eZA92E/MYVp6lkoch0eTYQ9/k8e+kiIpO2LAfWiStRM3fMLQvhNwhHE8s7L2mopqLYs70BRU6aZ6NRBeIiIic2vE48ix02laiZO/w+Y2550fD3G1YpSVPkZHj2jqY64iIiJ5YLHTaVqJlbUq3uV88vY4k+oRA5KZ4NxAMBfZQmInIiqYTNpo4w8SyVfVWiZm5Zd0olQZ/xztX12T4Vkbzn2WhU5QtFRE6sqiRI7awgbf1RDnUPsrCyeMbPQYF4btmwqo4rz1ClFJF08OyMuAJxEZHJyWaHzXA0xj4lauYcBeEi6eHZQFx1xEVEJme4w2YW1okrUVNECpln72qqIy4iMjmpGfFslDBUoqaIFDLPBuIqXygiMjnDM+LtYdwMJ2xqfbiIFDLPRqMKxEVEJqeuNEhlcYCewRhHemf200QF4iJSyDwbjaqOuIjI5JjZcBnDnTO4PEWJmiJS6DwbiKuOuIjI5C1P1ROfwQ6bqUTNRbOVqCkihcmzdzaVLxQRmbyGLHTYTCVqrqjTshQRKUwKxEVE5IRSCZszuTRF68NFpNDldCBuZp81s4Nm9kLyccWIfZ8ys0Yz225ml43YvsbMXk7u+4aZjdl1QHXERUQmb15FiFlBHx39Q3T0z0zCpgJxESl0OR2IJ/27c2518vEAgJmtBK4HVgGXA982M3/y+d8B3g8sTz4uH+tFVUdcRGTyfGYsGy5jmPlZ8YGhuBI1RaTg5UMgPpargbucc4POud1AI7DOzOYBFc65J1yi2O2PgA1jvYDKF4qITM1wY58ZaHXf1N6vRE0RKXj5cHf7sJm9ZGa3m1lVctsCYP+I5xxIbluQ/Hr09tcYZ8WKiIiMo6Fm5jpsqqOmiHhB1mv4mdkjQP0Yu24lsczkc4BL/vkV4D3AWFG0m2D7a7S1tbF+/XoCgQCxWIxrr72WjRs30tzcTGlpKX6/n+7uburq6ujo6MA5R11dHS0tLZSVlQHQ29vL3LlzaW1txcyorq6mtbWViooKYrEYfX191NfX09zcTDAYpLKykra2NiorK4lEIoTD4eH9oVCI8vJy2tvbqaqqIhwOMzAwMLy/uLiYkpISOjs7qampoaenh0gkMry/pKSEUChEV1cXtbW1dHV1EY1Gh/frmnRNuiZd08leU10gAiTWbu/fvz+j1/T8niMAnFLmY+/evfo56Zp0TbqmvL2midhMtyueLjNbDPxf59yZZvYpAOfcF5L7HgI+C+wBHnPOnZ7cfgPwJufcB0a/3n//93+7VatWzci5i4gUgljccfUPXyQSc9zzrrMoL8rcXM7779nKns4Bvn7VCs6YU5qx9xERybTNmzc/d8kll6wda19OL01JrvlOuQZ4Jfn1/cD1ZlZkZktIJGU+7Zw7DPSY2QXJainvBjaN9dqxWCyDZy4iUnj8PmNJdWp5SubWiStRU0S8IqcDceBfk6UIXwLeDHwcwDm3BbgbeBX4DbDROZeKrG8BbiORwNkEPDjWC+fLJwEiIrlkebJySlMGG/vsaldHTRHxhqyvEZ+Ic+5dE+z7PPD5MbY/C5x5otdWHXERkalLVU7ZmcEZ8R2qHy4iHuHZqQbVERcRmbqZaHWvRj4i4hWeDcT9fv+JnyQiIsdZXFWM3+BA1yDhaGZybVKB+Io6BeIiUtg8G4iLiMjUhfw+FlWV4IBdHelfnqJETRHxEs8G4qqaIiIyPcsz2GFTiZoi4iWevcspWVNEZHqWJSunZKLDptaHi4iXeDYQHxoayvYpiIjkpeU1maslroopIuIlng3ERURkepbWlGDAno4wkVg8ra+tGXER8RLPBuKBQE6XUBcRyVklQT8LK4uIOdjTOZC21z0uUbNGiZoiUvg8G4irjriIyPSl6omns8PmyETNYiVqiogHePZOpzriIiLT11CT/g6bWpYiIl7j2UBcRESmLxMdNhWIi4jXeDYQVx1xEZHpW5ZstrO7I0ws7tLymqqYIiJe49lAXHXERUSmr6I4wNyyEIMxx/6uk0/YVKKmiHiRZwNx1REXETk56eywqURNEfEi3e1ERGRa0tlhU+vDRcSLPBuIq464iMjJSeeMuAJxEfEizwbiqiMuInJyGkbMiMfdySVsKhAXES/ybCCuOuIiIienelaQ6lkB+qNxDndHpv06A0Nx9ipRU0Q8yLOBuIiInLzUrHjTSawTTyVqnqpETRHxGM/e8VRHXETk5KWjw2ZqWcoKLUsREY/xbCCuOuIiIicvHR02tT5cRLzKs4G4kjVFRE5eaka8sT2Mm2bCpgJxEfEqzwbiZpbtUxARyXtzy0KUF/npGhiirX/qExxK1BQRL/NsIK6qKSIiJ8/Mjs2KT6OeuBI1RcTLPHvXU4t7EZH0OJkOm1qWIiJe5tlAXDPiIiLpcTIdNlUxRUS8zLOB+HSTikRE5HipWuI7NSMuIjIlng3E4/F4tk9BRKQgLKgsoiToo60vytHw5BM2lagpIl7n2UBcdcRFRNLDZ8bS6mNlDCdLiZoi4nWevfOpjriISPo0TCNhU8tSRMTrPBuI+3yevXQRkbSbTsKmAnER8TrPRqMKxEVE0mdZzdSXpqhiioh4XU5Ho2b2czN7IfnYY2YvJLcvNrPwiH3fHXHMGjN72cwazewbNk4LTdURFxFJn0VVJQR9xqHuQfoisRM+f1CJmiIiBLJ9AhNxzv1N6msz+wrQNWJ3k3Nu9RiHfQd4P/Ak8ABwOfDg6CcFAjl96SIieSXgM5ZUl7CjrZ+m9n7Onlc+4fN3dSQSNRdXKVFTRLwrL+5+yVntdwB3nuB584AK59wTLlEo/EfAhrGeq/KFIiLp1VA7+eUpO1q1PlxEJF+mhd8AtDjndo7YtsTMnge6gX92zv0JWAAcGPGcA8ltr9He3s769esJBALEYjGuvfZaNm7cSHNzM6Wlpfj9frq7u6mrq6OjowPnHHV1dbS0tFBWVgZAb28vc+fOpbW1FTOjurqa1tZWKioqiMVi9PX1UV9fT3NzM8FgkMrKStra2qisrCQSiRAOh4f3h0IhysvLaW9vp6qqinA4zMDAwPD+4uJiSkpK6OzspKamhp6eHiKRyPD+kpISQqEQXV1d1NbW0tXVRTQaHd6va9I16Zp0TZm+pvqixATH9iO97N0bnvCant97BIBTy/3s3bs3Z6+pEH9OuiZdk65pZq9pIpbtDpNm9ghQP8auW51zm5LP+Q7Q6Jz7SvL7IqDMOdduZmuA+4BVwGnAF5xzlyaf9wbgn5xzbxv94o8//rhbuXJlRq5JRMSLth3p4+/v38GiqmK+9/YzJnzuB+7Zyu7OAf79bctZNbdshs5QRGTmbd68+blLLrlk7Vj7sj4jngqax2NmAeBaYM2IYwaBweTXz5lZE7CCxAz4whGHLwQOjfW6qiMuIpJeS6pL8BnsPzrAwFB83LXfIxM1l9VoaYqIeFc+rBG/FNjmnBtecmJmdWbmT369FFgO7HLOHQZ6zOyC5LrydwObxnpRlS8UEUmvooCPU2cXE3ewu2P8deKpRE111BQRr8uHO+D1vDZJ8yLgJTN7EfgF8EHnXEdy3y3AbUAj0MQYFVNAgbiISCY0JJMvG9vG77CpRj4iIglZX5pyIs65m8bYdg9wzzjPfxY480SvqzriIiLpt7ymhEd2Tlw5RRVTREQSPDstrDriIiLpl1rz3dg+mRlxNfIREW/zbCCuOuIiIumXanW/p2OAaOy191klaoqIHKNAXERE0qY05GdBRRHRuGPf0YHX7FeipojIMZ69CwaDwWyfgohIQZqow6YSNUVEjvFsIK464iIimdFQM37lFAXiIiLHeDYQV/lCEZHMaEiuE9/Z9toZ8WMVU5SoKSLi2Wg00e9HRETSLVVLvKkjTCzuhrcrUVNE5HieDcRjsVi2T0FEpCBVFgeoKw0yOBTnYPfg8HYlaoqIHM+zd0LVERcRyZyxOmxqfbiIyPE8G4hrRlxEJHOW17y2cooCcRGR43k2EHfOnfhJIiIyLWN12FRHTRGR43l2fYbqiIuIZE4q2G5sC+OcIxJz7OlUoqaIyEieDcRVR1xEJHNqZgWZXRzg6MAQzb0RjoaHiDtYVKVETRGRFM/eDf1+f7ZPQUSkYJnZcIfNprbw8LKUFVofLiIyzLOBuIiIZFaqw+bO9n4laoqIjMGzS1NUNUVEJLMaRqwTb++PAErUFBEZybOBuJI1RUQya3lyRnx7ax+9kZgSNUVERvHs0pShoaFsn4KISEGrLw9RGvLTPRgj7uAUddQUETmO7ogiIpIRZkZDzbGlKFofLiJyPM8G4mpxLyKSeSMDcVVMERE5nmcDcdURFxHJvJFrwpWoKSJyPM8G4qojLiKSeangW4maIiKvpfUZIiKSMafMLuayFdVUlwSVqCkiMopnA3HVERcRyTyfGf/zokXZPg0RkZzk2ekJ1REXERERkWzybCCuOuIiIiIikk2eDcRFRERERLLJs4G46oiLiIiISDZ5NhBXHXERERERySbPBuKqIy4iIiIi2eTZQFxEREREJJs8G4irjriIiIiIZFPWA3Ezu87MtphZ3MzWjtr3KTNrNLPtZnbZiO1rzOzl5L5vmJkltxeZ2c+T258ys8Xjva/qiIuIiIhINmU9EAdeAa4F/jhyo5mtBK4HVgGXA982s9TC7u8A7weWJx+XJ7ffDHQ65xqAfwe+NN6bKllTRERERLIp64G4c26rc277GLuuBu5yzg0653YDjcA6M5sHVDjnnnDOOeBHwIYRx/ww+fUvgEtSs+WjHT16NK3XISIiIiIyWkdHR+14+3K5mPYC4MkR3x9Ibosmvx69PXXMfgDn3JCZdQE1QNvoFz969Cjr168nEAgQi8W49tpr2bhxI83NzZSWluL3++nu7qauro6Ojg6cc9TV1dHS0kJZWRkAvb29zJ07l9bWVsyM6upqWltbqaioIBaL0dfXR319Pc3NzQSDQSorK2lra6OyspJIJEI4HB7eHwqFKC8vp729naqqKsLhMAMDA8P7i4uLKSkpobOzk5qaGnp6eohEIsP7S0pKCIVCdHV1UVtbS1dXF9FodHj/TF7Ttm3bOP300wvqmnLl57Rjxw7OO++8grqmXPo5pf7uFtI15crPaceOHZxzzjkFdU258nPavn0769evL6hryqWf0/bt2znrrLMK6ppy5efU2NjIBRdcUFDXNNbPqbu7u268YNcSk8qZZWaPAPVj7LrVObcp+ZzfA//onHs2+f1/AE84536S/P77wAPAPuALzrlLk9vfAPyTc+5tZrYFuMw5dyC5rwlY55xrH/3GP/jBD9yGDRtGb5Y0+NjHPsbXvva1bJ9GQdLYZpbGN3M0tpmjsc0sjW/meGVsf/nLX/bffPPNpWPtm5FAfDLGCMQ/BeCc+0Ly+4eAzwJ7gMecc6cnt98AvMk594HUc5xzT5hZAGgG6twYF/nAAw/0FBUVjbUkRk5Sd3d3ZUVFRVe2z6MQaWwzS+ObORrbzNHYZpbGN3O8MraDg4OnXXHFFeVj7cvlpSn3Az8zs68C80kkZT7tnIuZWY+ZXQA8Bbwb+OaIY24EngD+Gnh0rCAcYLwBERERERGZCVkPxM3sGhKBdB3wazN7wTl3mXNui5ndDbwKDAEbnXOp4t+3AHcAJcCDyQfA94Efm1kj0EGi6oqIiIiISM7JmaUpIiIiIiJekvXyhelgZpcnm/40mtknk9uqzexhM9uZ/LNqssdO5fhCN87Yfs7MXjKzF8zst2Y2f7LHJrdrbJMmGKOPJLdvMbN/neKxGl/G/bt7jpk9kWwI9iszq5jsscntGlvAzG43syNm9sqIbV82s23Je8O9ZjZ7nGM1thMYZ2w/a2YHk/fcF8zsinGO1dhOYJyxXW1mTybH9VkzWzfOsRrbCZjZKWb2mJltTf7e+mhy+7hNG0cd793xdc7l9QPwA03AUiAEvAisBP4V+GTyOZ8EvjTZY5P7Tnh8oT8mGNuKEc/5e+C7Gtu0ju+bgUeAouTz5mh80za2zwBvTD7nPcDnNLbTGt+LgPOAV0ZsewsQSH79Jd1z0zq2nyVRzGCi4zS20xvb3wJvTX59BfB7je20xnYecF7y63JgR/KeewZwGvB7YO04x3p6fAthRnwd0Oic2+WciwB3kWjsM7K5zw851vRnMscyyeML3Zjj45zrHvGcUmCs9U0a2xMbb4xuAb7onBsEcM4dmcKxoPGF8cfnNI518X0YePsUjgWNLQDOuT+SyMMZue23zrmh5LdPAgvHOFRjewJjje0kaWxPYJyxdUDqk7FK4NAYh2psT8A5d9g5tzn5dQ+wFVjgxm/aOJKnx7cQAvHhJj5JqQY/c51zhyHxFwSYA2Bm883sgRMcy3jHe8y442Nmnzez/cA7gc8kt2lsp2a8MVoBvMHMnjKzP5jZ+aDxnaLxxucV4KrktuuAU0BjmwHvIZlEr7FNmw8nl/3cnvp4XmObFh8Dvpz8ffZvwKdAY3syzGwxcC6JynbjPUfjm1QIgfhYLezHzUB1zh1yzqXW103pWA8ad3ycc7c6504Bfgp8OLlNYzs1441RAKgCLgA+AdxtZqbxnZLxxuc9wEYze47Ex6cR0N/ddDKzW0lUuvopaGzT5DvAMmA1cBj4Cmhs0+QW4OPJ32cfJ1F9TWM7TWZWBtwDfGzUp+fH0fgeUwiB+AGSs1pJC0l8tNRiZvMAkn+O9fH+eMcyyeML3UTjk/Izxv54X2N7YuON0QHgly7haSAO1E7yWND4wjjj45zb5px7i3NuDXAniXWJkzo2+bXGdgJmdiNwJfBO59xYv0g1ttPgnGtxzsWcc3HgeyQ+yh9NYzs9NwK/TH79X2hsp83MgiSC8J865355oueP4OnxLYRA/BlguZktMbMQidrh93OsuQ/JPzdN4VgmeXyhG3N8zGz5iOdcBWyb7LHJfRrbhPHG6D7gYgAzW0EieaVtkseCxhfG/7ubWqLmA/4Z+O5kj03u09iOw8wuB/4XcJVzrn+cp2lspyEViCRdQ2KJ1Wga2+k5BLwx+fXFwM4xnqOxPQEzMxKfJmx1zn11iod7e3yznS2ajgeJTOcdJGa3bk1uqwF+R+If1e+A6uT2+cADEx070fFee4wztveQ+EXwEvArEgkZGtv0jW8I+ElyjDcDF2t80za2H01u2wF8kWO9FDS2UxvbO0kskYiSmM26GWgksc7zheTjuxrbtI3tj4GXk/fc+4F5Gtu0je3rgedIVOp4ClijsZ3W2L6exHKSl0bcA64g8R/HA8Ag0AI8pPE9/qGGPiIiIiIiWVAIS1NERERERPKOAnERERERkSzI+0Dcxm5ZO9mWqneY2V+f4PVvMrNvpfOcRURERETyPhAH7gAuH7XtFeBajnXQExERERHJKXkfiLuxWy1PpqXqccxsj5nVJr9ea2a/T99ZioiIiIgcL+8DcRERERGRfKRAXEREREQkCzwViJvZD8zsBTN7YIzdQxwbj+IZPC0RERER8aBAtk9gJjnn/scEu/cAa4AHgbfPyAmJiIiIiGfl/Yy4md0JPAGcZmYHzOxmM7vGzA4AFwK/NrOHxjk8QKLtKsC/AF83sz8BsYyfuIiIiIh4mmdb3JuZD3gGeLdzbku2z0dEREREvCXvZ8Snw8zmk6g1/qSCcBERERHJBs/OiIuIiIiIZJMnZ8RFRERERLJNgbiIiIiISBYoEBcRERERyQIF4iIiIiIiWaBAXEREREQkCxSIi4iIiIhkwf8PuLGXEyulmZEAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -1251,19 +1249,17 @@
     }
    ],
    "source": [
-    "fig,ax = plt.subplots(1,1)\n",
-    "fig.set_size_inches(12,6)\n",
+    "fig, ax = plt.subplots(figsize=(12,6))\n",
     "\n",
     "# TASK\n",
     "p_storage = network.storage_units_t.p.sum(axis=1)\n",
     "state_of_charge = network.storage_units_t.state_of_charge.sum(axis=1)\n",
     "\n",
-    "p_storage.plot(label=\"Pumped hydro dispatch\",ax=ax,linewidth=3)\n",
-    "state_of_charge.plot(label=\"State of charge\",ax=ax,linewidth=3)\n",
+    "p_storage.plot(label=\"Pumped hydro dispatch\", ax=ax)\n",
+    "state_of_charge.plot(label=\"State of charge\", ax=ax)\n",
     "\n",
     "ax.legend()\n",
-    "ax.set_ylabel(\"MWh\")\n",
-    "ax.set_xlabel(\"\")"
+    "ax.set_ylabel(\"MWh\")"
    ]
   },
   {
@@ -1275,7 +1271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 68,
    "metadata": {
     "scrolled": false
    },
@@ -1295,10 +1291,9 @@
    ],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
@@ -1327,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1345,10 +1340,9 @@
    ],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
@@ -1384,7 +1378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1393,7 +1387,7 @@
        "-10.6195570131184"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1413,7 +1407,7 @@
        "10000048.6725254"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1424,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1713,7 +1707,7 @@
        "[8 rows x 585 columns]"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1724,7 +1718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -1741,7 +1735,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1753,7 +1747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -1770,7 +1764,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1791,12 +1785,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Hint:** Set the cost associated with transmission expansion to zero and rerun LOPF."
+    "> **Hint:** For testing what would happen if there were unlimited transmission capacity, set the cost associated with transmission expansion to zero and rerun LOPF."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -1816,17 +1810,12 @@
     "carriers =  [\"Wind Onshore\", \"Wind Offshore\", \"Solar\"]\n",
     "\n",
     "n_graphs_g = len(carriers)\n",
-    "\n",
     "n_cols_g = 3\n",
-    "\n",
     "n_rows_g = 1\n",
-    "\n",
-    "    \n",
-    "fig, axes = plt.subplots(nrows=n_rows_g, ncols=n_cols_g)\n",
-    "\n",
     "size_g = 6\n",
-    "\n",
-    "fig.set_size_inches(size_g*n_cols_g,size_g*n_rows_g)\n",
+    "   \n",
+    "fig, axes = plt.subplots(nrows=n_rows_g, ncols=n_cols_g,\n",
+    "                        figsize=(size_g*n_cols_g,size_g*n_rows_g))\n",
     "\n",
     "for i,carrier in enumerate(carriers):\n",
     "    i_col = i % n_cols_g\n",
@@ -1836,7 +1825,7 @@
     "    capacity = network.generators.groupby(\"carrier\").sum().at[carrier,\"p_nom\"]\n",
     "\n",
     "    # TASK\n",
-    "    p_available = network.generators_t.p_max_pu.multiply(network.generators[\"p_nom\"])\n",
+    "    p_available = network.generators_t.p_max_pu.multiply(network.generators.p_nom)\n",
     "    p_available_by_carrier = p_available.groupby(network.generators.carrier, axis=1).sum()\n",
     "    p_curtailed_by_carrier = p_available_by_carrier - p_by_carrier\n",
     "\n",
@@ -1882,7 +1871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1899,7 +1888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1915,7 +1904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1932,7 +1921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -1953,30 +1942,30 @@
       "               '2011-07-11 20:00:00', '2011-07-11 21:00:00',\n",
       "               '2011-07-11 22:00:00', '2011-07-11 23:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.046204 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050791 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.048102 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049523 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050063 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.061711 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.116377 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.100883 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.087967 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.091155 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.093765 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.088268 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.057591 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.051321 seconds\n",
       "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050346 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.051144 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050054 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049094 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049674 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049722 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049412 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.056933 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.058284 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.056710 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.057439 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.057810 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.057223 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.058978 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.058891 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.065794 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.060435 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.059115 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.061703 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.053669 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049420 seconds\n",
-      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.052922 seconds\n"
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050484 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049948 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050004 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.051482 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049862 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.048923 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.048950 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049731 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.050084 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049121 seconds\n",
+      "INFO:pypsa.pf:Newton-Raphson solved in 4 iterations with error of 0.000000 in 0.049745 seconds\n"
      ]
     }
    ],
@@ -1993,7 +1982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -2011,10 +2000,9 @@
    ],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
@@ -2053,16 +2041,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0240a46a0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fca75847cc0>"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2086,7 +2074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -2095,15 +2083,22 @@
        "1.4176201471846963"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# TASK \n",
-    "(network.lines_t.p0/network.lines.s_nom*contingency_factor).abs().max().max()"
+    "(network.lines_t.p0 / network.lines.s_nom*contingency_factor).abs().max().max()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorial-5/notebooks/tutorial-5-task-3.ipynb b/tutorial-5/notebooks/tutorial-5-task-3.ipynb
index c2ec41933256367a35d946f4c7f560ad9ef50fcd..6c572c81e838590880927d3617d6a3026a934491 100644
--- a/tutorial-5/notebooks/tutorial-5-task-3.ipynb
+++ b/tutorial-5/notebooks/tutorial-5-task-3.ipynb
@@ -63,8 +63,6 @@
     "\n",
     "* The marginal costs are illustrative, not accurate.\n",
     "\n",
-    "* Only the first day of 2011 is in the github dataset, which is not representative. The full year of 2011 can be downloaded [here](http://www.pypsa.org/examples/scigrid-with-load-gen-trafos-2011.zip).\n",
-    "\n",
     "* The ENTSO-E total load for Germany may not be scaled correctly; it is scaled up uniformly by factor 1.12 (a simplification of the methodology in Schumacher, Hirth (2015), which suggests monthly factors).\n",
     "\n",
     "* Biomass from the EEG Stammdaten are not read in at the moment.\n",
@@ -109,7 +107,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some general settings (You may have to adjust this path to where you downloaded the network data!):"
+    "Some general settings:"
    ]
   },
   {
@@ -209,6 +207,7 @@
    "source": [
     "network.lines[\"s_nom_original\"] = network.lines.s_nom\n",
     "\n",
+    "# extendable, but no reduction of line capacities\n",
     "network.lines.s_nom_extendable = True\n",
     "network.lines.s_nom_min = network.lines.s_nom\n",
     "\n",
@@ -236,7 +235,7 @@
    "metadata": {},
    "source": [
     "***\n",
-    "## (a) Describe the nework as well as its regional and temporal characteristics."
+    "## (a) Describe the network as well as its regional and temporal characteristics."
    ]
   },
   {
@@ -260,7 +259,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### (a)-(ii) | Plot the total generation capacities grouped by generation technology. Why is the share of capacity for renewables higher than the share of electricity produced?"
+    "### (a)-(ii) | Plot the total generation capacities grouped by generation technology. Why is the share of capacity for renewables higher than the share of electricity produced?\n",
+    "> **Hint:** Use the pandas functions `groupby`."
    ]
   },
   {
@@ -269,8 +269,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig,ax = plt.subplots(1,1)\n",
-    "fig.set_size_inches(16,6)\n",
+    "fig, ax = plt.subplots(figsize=(16,6))\n",
     "\n",
     "# TASK\n",
     "???"
@@ -290,10 +289,9 @@
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                        subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
@@ -340,16 +338,14 @@
     "    n_rows_g = n_graphs_g // n_cols_g\n",
     "else:\n",
     "    n_rows_g = n_graphs_g // n_cols_g + 1\n",
-    "\n",
     "    \n",
     "fig, axes = plt.subplots(nrows=n_rows_g, ncols=n_cols_g,\n",
+    "                         figsize=(width_factor*n_cols_g,height_factor*n_rows_g),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
     "width_factor = 4\n",
     "height_factor = 4\n",
     "\n",
-    "fig.set_size_inches(width_factor*n_cols_g,height_factor*n_rows_g)\n",
-    "\n",
     "for i,tech in enumerate(techs):\n",
     "    i_row = i // n_cols_g\n",
     "    i_col = i % n_cols_g\n",
@@ -434,6 +430,12 @@
     "# TASK\n",
     "p_by_carrier = ???\n",
     "\n",
+    "#reorder\n",
+    "cols = ['Nuclear', 'Run of River', 'Brown Coal', 'Hard Coal', 'Gas',\n",
+    "        'Storage Hydro', 'Waste', 'Wind Offshore', 'Wind Onshore', 'Solar']\n",
+    "p_by_carrier = p_by_carrier[cols]\n",
+    "p_by_carrier_gw = p_by_carrier / 1e3 # convert MW to GW\n",
+    "\n",
     "colors = {\"Brown Coal\" : \"sienna\",\n",
     "          \"Hard Coal\" : \"dimgrey\",\n",
     "          \"Nuclear\" : \"deeppink\",\n",
@@ -445,23 +447,20 @@
     "          \"Waste\" : \"forestgreen\",\n",
     "          \"Storage Hydro\" : \"darkmagenta\"\n",
     "          }\n",
-    "#reorder\n",
-    "cols = ['Nuclear', 'Run of River', 'Brown Coal', 'Hard Coal', 'Gas',\n",
-    "        'Storage Hydro', 'Waste', 'Wind Offshore', 'Wind Onshore', 'Solar']\n",
-    "p_by_carrier = p_by_carrier[cols]\n",
     "\n",
-    "fig,ax = plt.subplots(1,1)\n",
+    "fig, ax = plt.subplots(figsize=(16,8))\n",
     "\n",
-    "fig.set_size_inches(16,8)\n",
+    "p_by_carrier.plot(\n",
+    "    kind=\"area\",\n",
+    "    ax=ax,\n",
+    "    linewidth=0,\n",
+    "    color=[colors[col] for col in p_by_carrier.columns]\n",
+    ")\n",
     "\n",
-    "(p_by_carrier/1e3).plot(kind=\"area\",ax=ax,linewidth=0,color=[colors[col] for col in p_by_carrier.columns])\n",
-    "\n",
-    "ax.legend(ncol=5,loc=\"upper left\")\n",
+    "ax.legend(ncol=5, loc=\"upper left\")\n",
     "\n",
     "ax.set_ylabel(\"GW\")\n",
     "\n",
-    "ax.set_xlabel(\"\")\n",
-    "\n",
     "ax.set_ylim(0,100);"
    ]
   },
@@ -480,19 +479,17 @@
    },
    "outputs": [],
    "source": [
-    "fig,ax = plt.subplots(1,1)\n",
-    "fig.set_size_inches(12,6)\n",
+    "fig, ax = plt.subplots(figsize=(12,6))\n",
     "\n",
     "# TASK\n",
     "p_storage = ???\n",
     "state_of_chage = ???\n",
     "\n",
-    "p_storage.plot(label=\"Pumped hydro dispatch\",ax=ax,linewidth=3)\n",
-    "state_of_charge.plot(label=\"State of charge\",ax=ax,linewidth=3)\n",
+    "p_storage.plot(label=\"Pumped hydro dispatch\", ax=ax)\n",
+    "state_of_charge.plot(label=\"State of charge\", ax=ax)\n",
     "\n",
     "ax.legend()\n",
-    "ax.set_ylabel(\"MWh\")\n",
-    "ax.set_xlabel(\"\")"
+    "ax.set_ylabel(\"MWh\")"
    ]
   },
   {
@@ -511,10 +508,9 @@
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
@@ -548,10 +544,9 @@
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
@@ -624,7 +619,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Hint:** Set the cost associated with transmission expansion to zero and rerun LOPF."
+    "> **Hint:** For testing what would happen if there were unlimited transmission capacity, set the cost associated with transmission expansion to zero and rerun LOPF."
    ]
   },
   {
@@ -636,17 +631,12 @@
     "carriers =  [\"Wind Onshore\", \"Wind Offshore\", \"Solar\"]\n",
     "\n",
     "n_graphs_g = len(carriers)\n",
-    "\n",
     "n_cols_g = 3\n",
-    "\n",
     "n_rows_g = 1\n",
-    "\n",
-    "    \n",
-    "fig, axes = plt.subplots(nrows=n_rows_g, ncols=n_cols_g)\n",
-    "\n",
     "size_g = 6\n",
-    "\n",
-    "fig.set_size_inches(size_g*n_cols_g,size_g*n_rows_g)\n",
+    "   \n",
+    "fig, axes = plt.subplots(nrows=n_rows_g, ncols=n_cols_g,\n",
+    "                        figsize=(size_g*n_cols_g,size_g*n_rows_g))\n",
     "\n",
     "for i,carrier in enumerate(carriers):\n",
     "    i_col = i % n_cols_g\n",
@@ -773,10 +763,9 @@
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols,\n",
+    "                         figsize=(size*n_cols,size*n_rows),\n",
     "                         subplot_kw={\"projection\":ccrs.PlateCarree()})\n",
     "\n",
-    "fig.set_size_inches(size*n_cols,size*n_rows)\n",
-    "\n",
     "for i,timestep in enumerate(timesteps):\n",
     "    i_row = i // n_cols\n",
     "    i_col = i % n_cols\n",
diff --git a/tutorial-5/worksheet/sheet05.pdf b/tutorial-5/worksheet/sheet05.pdf
index 67f2587e83835109901e10641da35c27f717d3fb..ac7804a4c35b6968d24b699ac8389946150d3807 100644
Binary files a/tutorial-5/worksheet/sheet05.pdf and b/tutorial-5/worksheet/sheet05.pdf differ
diff --git a/tutorial-5/worksheet/sheet05.tex b/tutorial-5/worksheet/sheet05.tex
index b92e9d20e651499598db97af7c4bca32c63d0240..d2af89a75b1a10d5b0071d7ecce9438f52e54b0c 100644
--- a/tutorial-5/worksheet/sheet05.tex
+++ b/tutorial-5/worksheet/sheet05.tex
@@ -134,7 +134,7 @@ electricity $\pi$ is never below 15 \euro/MWh and averages 20
 \paragraph{Problem V.2 (anal./prog.) -- duration curves and generation investment}~\\
 %=====================================================================
 
-Let us suppose that demand is inelastic. The demand-duration curve is given by $D=1000-1000z$. Suppose that there is a choice between coal and gas generation plants with a variable cost of 2 and 12~\euro/MWh, together with load-shedding at 1012\euro/MWh. The fixed costs of coal and gas generation are 15 and 10~\euro/MWh, respectively.
+Let us suppose that demand is inelastic. The demand-duration curve is given by $D=1000-1000z$, where $z\in [0,1]$ represents the probability of time the load spends above a certain value. Suppose that there is a choice between coal and gas generation plants with a variable cost of 2 and 12~\euro/MWh, together with load-shedding at 1012\euro/MWh. The fixed costs of coal and gas generation are 15 and 10~\euro/MWh, respectively.
 
 \begin{enumerate}[(a)]
  \item Describe the concept of a screening curve and how it helps to determine generation investment, given a demand-duration curve.
diff --git a/tutorial-5/worksheet/solution05.pdf b/tutorial-5/worksheet/solution05.pdf
index b75eb3a23c56cb94932f7b23f45db59bc8224436..13cf334ce6692c87cf7178e7ab14a8afabf21544 100644
Binary files a/tutorial-5/worksheet/solution05.pdf and b/tutorial-5/worksheet/solution05.pdf differ
diff --git a/tutorial-5/worksheet/solution05.tex b/tutorial-5/worksheet/solution05.tex
index 5ece42173f5bb6d0c09e3d368d72d1f1d8957ed1..ea2b761afc48a3c3a51e92f27e4594d4c36aaec8 100644
--- a/tutorial-5/worksheet/solution05.tex
+++ b/tutorial-5/worksheet/solution05.tex
@@ -206,13 +206,13 @@ electricity $\pi$ is never below 15 \euro/MWh and averages 20
   \int_{1000}^{1200} \m_T(K) dK
  \end{equation*}
  Since  $\m_T$ is constant as we expand $K$ from 1000~MW to 1200~MW, the extra profit would be per year: (average$(\pi)$-10) \euro/MWh * 200 MW * 8760h/a = \euro 17.52 million/a.
- At or below this annualised capital cost, it would be worth investing. An extension beyond 1200~MW would not bring anything, because the generator constraints would be then binding.
+ At or below this annualised capital cost, it would be worth investing. An extension beyond 1200~MW would not bring any benefit, because the generator constraints would be then binding.
 \end{enumerate}
 %=============== ======================================================
 \paragraph{Solution V.2 \normalsize (duration curves and generation investment).}~\\
 %=====================================================================
 
-Let us suppose that demand is inelastic. The demand-duration curve is given by $D=1000-1000z$. Suppose that there is a choice between coal and gas generation plants with a variable cost of 2 and 12~\euro/MWh, together with load-shedding at 1012\euro/MWh. The fixed costs of coal and gas generation are 15 and 10~\euro/MWh, respectively.
+Let us suppose that demand is inelastic. The demand-duration curve is given by $D=1000-1000z$, where $z\in[0,1]$ represents the probability of time the load spends above a certain value. Suppose that there is a choice between coal and gas generation plants with a variable cost of 2 and 12~\euro/MWh, together with load-shedding at 1012\euro/MWh. The fixed costs of coal and gas generation are 15 and 10~\euro/MWh, respectively.
 
 \begin{enumerate}[(a)]
  \begin{shaded}
@@ -238,8 +238,7 @@ Let us suppose that demand is inelastic. The demand-duration curve is given by $
 
  \begin{shaded}\item Plot the screening curve and find the intersections of the generation technologies.\end{shaded}
  First we work out the intersection points of
- the generators as a function of their capacity factors (percentage of
- time that they operate at full power per year), then we work out the
+ the generators as a function of their capacity factors, then we work out the
  capacities $K_*$ of the generators.
 
  The screening curves tell us above which capacity factor it costs less