diff --git a/Readme.md b/Readme.md
index 621abc9..03aebc7 100644
--- a/Readme.md
+++ b/Readme.md
@@ -18,7 +18,8 @@ If you want to run the code on a local computer, you need to download all files
 * `rmm.txt`. Source of this text file you can [download directly](http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime.txt) from the Australian Bureau of Meteorology. The columns of the file represent the date (from 1974 onwards), components of the Real-time Multivariate MJO index (RMM), MJO phase and amplitude.
 * `OLR_41year_NOAA.npy` This is a `numpy` array with the shape `(14976, 180, 360)`, containing daily averaged (14976 days) Outgoing Longwave Radiation flux values from NOAA PSL data set [olr.day.mean.nc](https://downloads.psl.noaa.gov/Datasets/olrcdr/olr.day.mean.nc) for every cell of a 1°×1° latitude-longitude grid (180×360) starting with 1980-1-1 and ending with 2021-12-31.
 * `DAILY-ENSO34.npy`. This is a `numpy` array with the shape `(4992,)`, containing daily averaged sea surface temperature in the Niño 3.4 region. Here 4992 is the number of days when every third day in 1980–2020 is taken.
-* `DAILY-IP-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) contributions to the ionospheric potential (IP) for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. The gird cell contributions are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
+* `DAILY-IP-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) contributions to the ionospheric potential (IP) for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. The grid cell contributions are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
+* `DAILY-CAPE-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) maximum CAPE values along the vertical axis for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. CAPE are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
 * `vostok_hourly <...>.txt` text files contain two columns, one of which represents the date and time (column `Datetime`) and the other, hourly averaged potential gradient (PG) values on the basis of the measurements at the Russian Antarctic station Vostok (column `Field`, the units are V/m).
 * `sunspot_number_data.csv` contains information about the total sunspot number for every day of the years 1818--2022. You can download this file [here](https://www.sidc.be/silso/datafiles).
 <!-- You can also download a pure Python version of the code without using the Jupyter notebook ([click here to download a zip-archive](https://git260.ipfran.ru/eee/mjo-gec-2022/archive/pure_python.zip)). With `Git 1.7.10` or later you can also clone a specific branch: -->
diff --git a/eof_analysis.ipynb b/eof_analysis.ipynb
index f0f0c70..7e370d4 100644
--- a/eof_analysis.ipynb
+++ b/eof_analysis.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "2c35172b",
    "metadata": {},
    "outputs": [],
@@ -47,20 +47,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
+   "id": "0430e90a-ead2-4744-ad9b-d4d67d6d2784",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "f35c74b8",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "map_contrib = np.load('./data/DAILY-IP-MAP-V4.3.npy')\n",
+    "map_contrib = np.load('%s/DAILY-IP-MAP-V4.3.npy'%path)\n",
     "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
     "# contains IP values (not normalised) depending on (d, lat, lon)\n",
     "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
     "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
-    "# lat (axis 1) describes the latitude (an integer in [0, 180])\n",
-    "# lon (axis 2) describes the longitude (an integer in [0, 360])\n",
+    "# lat (axis 1) describes the latitude (an integer in [0, 179])\n",
+    "# lon (axis 2) describes the longitude (an integer in [0, 359])\n",
     "\n",
     "map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))\n",
     "map_contrib *= 240e3\n",
@@ -68,7 +78,7 @@
     "\n",
     "ip = np.sum(map_contrib, axis=(1, 2))  # total IP values for different days\n",
     "\n",
-    "rmm = np.genfromtxt('./data/rmm.txt')\n",
+    "rmm = np.genfromtxt('%s/rmm.txt'%path)\n",
     "rmm = rmm[::3, [3, 4]]  # RMM1 and RMM2\n",
     "# the array should look like the IP data (with every third day taken)\n",
     "angle_rmm = np.arctan2(rmm[:, 1], rmm[:, 0])  # phase angles of RMM values\n",
@@ -95,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "a39d5c0a",
    "metadata": {},
    "outputs": [
@@ -115,7 +125,7 @@
     "    :return: the same array with ENSO variability removed\n",
     "    \"\"\"\n",
     "\n",
-    "    enso34 = np.load('./data/DAILY-ENSO34.npy')  # mean SST values in the Niño 3.4 region\n",
+    "    enso34 = np.load('%s/DAILY-ENSO34.npy'%path)  # mean SST values in the Niño 3.4 region\n",
     "    assert len(enso34) == a.shape[0]\n",
     "    a_mean_before = np.mean(a, axis=0)  # mean values over days before the procedure\n",
     "\n",
@@ -129,6 +139,7 @@
     "\n",
     "    return a\n",
     "\n",
+    "\n",
     "def remove_seasonal(a):\n",
     "    \"\"\"\n",
     "    Remove the seasonal variability.\n",
@@ -152,6 +163,7 @@
     "\n",
     "    return a\n",
     "\n",
+    "\n",
     "latitude = 15  # the boundary latitude for tropics\n",
     "\n",
     "assert map_contrib.shape[1] == 180  # the next formula assumes this\n",
@@ -179,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "4f117151",
    "metadata": {},
    "outputs": [],
@@ -206,12 +218,13 @@
     "\n",
     "    return eofs, expvars, pcs\n",
     "\n",
+    "\n",
     "eofs, expvars, pcs = eof_analysis(map_contrib)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "47859ce3-8f1d-4cf2-b3aa-d57946eabb44",
    "metadata": {},
    "outputs": [],
@@ -237,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "79db66cc",
    "metadata": {},
    "outputs": [],
@@ -291,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "8b3c241c-d9f4-4502-af62-6ad67a8ccdb6",
    "metadata": {
     "tags": []
@@ -383,13 +396,13 @@
     "\n",
     "ax[1, 0].plot(np.arange(0, 361), np.append(eofs[:, 0], eofs[0, 0]),\n",
     "              linewidth=1.5, color='orangered', clip_on=False,\n",
-    "              zorder = 6, label=f'EOF1 ({expvars[0]:.2%})')\n",
+    "              zorder=6, label=f'EOF1 ({expvars[0]:.2%})')\n",
     "ax[1, 0].plot(np.arange(0, 361), np.append(eofs[:, 1], eofs[0, 1]),\n",
     "              linewidth=1.5, color='olive', clip_on=False,\n",
-    "              zorder = 5, label=f'EOF2 ({expvars[1]:.2%})')\n",
+    "              zorder=5, label=f'EOF2 ({expvars[1]:.2%})')\n",
     "ax[1, 0].plot(np.arange(0, 361), np.append(eofs[:, 2], eofs[0, 2]),\n",
     "              linewidth=1.5, color='mediumvioletred', clip_on=False,\n",
-    "              zorder = 4, label=f'EOF3 ({expvars[2]:.2%})')\n",
+    "              zorder=4, label=f'EOF3 ({expvars[2]:.2%})')\n",
     "\n",
     "ax[2, 0].bar(np.arange(1, 9) - 0.3, phase_avg_pc[:, 0] / 1e3,\n",
     "             width=0.3, color='orangered', label='PC1')\n",
@@ -408,13 +421,13 @@
     "\n",
     "ax[1, 1].plot(np.arange(0, 361), np.append(reofs[:, 0], reofs[0, 0]),\n",
     "              linewidth=1.5, color='orangered', clip_on=False,\n",
-    "              zorder = 6, label=f'EOF1 ({rexpvars[0]:.2%})')\n",
+    "              zorder=6, label=f'EOF1 ({rexpvars[0]:.2%})')\n",
     "ax[1, 1].plot(np.arange(0, 361), np.append(reofs[:, 1], reofs[0, 1]),\n",
     "              linewidth=1.5, color='teal', clip_on=False,\n",
-    "              zorder = 5, label=f'EOF2′ ({rexpvars[1]:.2%})')\n",
+    "              zorder=5, label=f'EOF2′ ({rexpvars[1]:.2%})')\n",
     "ax[1, 1].plot(np.arange(0, 361), np.append(reofs[:, 2], reofs[0, 2]),\n",
     "              linewidth=1.5, color='darkgoldenrod', clip_on=False,\n",
-    "              zorder = 4, label=f'EOF3′ ({rexpvars[2]:.2%})')\n",
+    "              zorder=4, label=f'EOF3′ ({rexpvars[2]:.2%})')\n",
     "\n",
     "ax[2, 1].bar(np.arange(1, 9) - 0.3, phase_avg_rpc[:, 0] / 1e3,\n",
     "             width=0.3, color='orangered', label='PC1')\n",
@@ -448,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "8f4ae8f9",
    "metadata": {
     "tags": []
@@ -522,7 +535,7 @@
     "for j in range(2):\n",
     "    leg = ax[j].legend(bbox_to_anchor=(0., -0.45, 1., 0.2),\n",
     "                       loc='upper center', borderaxespad=0.,\n",
-    "                       mode = 'expand', fontsize='large',\n",
+    "                       mode='expand', fontsize='large',\n",
     "                       framealpha=1, edgecolor='0.', handlelength=1.5)\n",
     "    leg.get_frame().set_linewidth(0.5)\n",
     "\n",
@@ -541,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "36cb8cd3",
    "metadata": {},
    "outputs": [
@@ -567,7 +580,7 @@
     "        for axis in ['top', 'bottom', 'left', 'right']:\n",
     "            ax[i, j].spines[axis].set_linewidth(0.5)\n",
     "        ax[i, j].tick_params(length=6, width=0.5)\n",
-    "    \n",
+    "\n",
     "        ax[i, j].set_xlim(0, 360)\n",
     "        ax[i, j].set_xticks(np.arange(0, 361, 60))\n",
     "\n",
@@ -596,7 +609,7 @@
     "                                    fontsize='large', ha='center')\n",
     "        else:\n",
     "            ax[i, j].set_yticklabels([])\n",
-    "        \n",
+    "\n",
     "        ax[i, j].grid(color='0.8', linewidth=0.5)\n",
     "        ax[i, j].axhline(color='0.', linewidth=0.5)\n",
     "\n",
@@ -606,18 +619,18 @@
     "\n",
     "    ys = np.sum(phase_avg_comp[i, :, :], axis=1)\n",
     "    ax[i, 0].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,\n",
-    "                  color='royalblue', clip_on=False, zorder = 4)\n",
+    "                  color='royalblue', clip_on=False, zorder=4)\n",
     "\n",
     "    ys = np.sum(phase_avg_rcomp[i, :, :2], axis=1)\n",
     "    ax[i, 1].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,\n",
-    "                  color='darkgreen', clip_on=False, zorder = 4)\n",
+    "                  color='darkgreen', clip_on=False, zorder=4)\n",
     "\n",
     "    ys = phase_avg_rcomp[i, :, 0]\n",
     "    ax[i, 2].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,\n",
-    "                  color='orangered', clip_on=False, zorder = 5)\n",
+    "                  color='orangered', clip_on=False, zorder=5)\n",
     "    ys = phase_avg_rcomp[i, :, 1]\n",
     "    ax[i, 2].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,\n",
-    "                  color='teal', clip_on=False, zorder = 4)\n",
+    "                  color='teal', clip_on=False, zorder=4)\n",
     "\n",
     "box1 = TextArea('Total (all EOFs and PCs)',\n",
     "                textprops=dict(color='royalblue', fontsize='large'))\n",
@@ -653,6 +666,14 @@
     "fig.savefig('figures/longitudinal_structure.eps', bbox_inches='tight')\n",
     "fig.savefig('figures/longitudinal_structure.png', dpi=300, bbox_inches='tight')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4af362e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/figures/eofs_and_pcs.eps b/figures/eofs_and_pcs.eps
index 09b94e9..5054d7f 100644
--- a/figures/eofs_and_pcs.eps
+++ b/figures/eofs_and_pcs.eps
@@ -1,7 +1,7 @@
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 %%Title: eofs_and_pcs.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:19:13 2022
+%%CreationDate: Mon Nov 14 00:52:07 2022
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 %%HiResBoundingBox: -19.310156 33.199984 631.310156 758.800016
diff --git a/figures/equatorial_variation.eps b/figures/equatorial_variation.eps
index 61b2ae2..1cd8cfa 100644
--- a/figures/equatorial_variation.eps
+++ b/figures/equatorial_variation.eps
@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: equatorial_variation.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:19:15 2022
+%%CreationDate: Mon Nov 14 00:52:09 2022
 %%Orientation: portrait
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 %%HiResBoundingBox: -17.400000 238.932563 629.400000 553.067437
diff --git a/figures/ip_pg_rmm_variation.eps b/figures/ip_pg_rmm_variation.eps
index e962ca1..06e202a 100644
--- a/figures/ip_pg_rmm_variation.eps
+++ b/figures/ip_pg_rmm_variation.eps
@@ -1,10 +1,10 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: ip_pg_rmm_variation.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:21:17 2022
+%%CreationDate: Mon Nov 14 01:38:17 2022
 %%Orientation: portrait
-%%BoundingBox: -34 32 647 760
-%%HiResBoundingBox: -34.549524 32.832134 646.549524 759.167866
+%%BoundingBox: -34 169 647 623
+%%HiResBoundingBox: -34.549524 169.163563 646.549524 622.836437
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diff --git a/figures/ip_pg_variation_partial.eps b/figures/ip_pg_variation_partial.eps
index 4a83744..dabbf6e 100644
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diff --git a/figures/longitudinal_structure.eps b/figures/longitudinal_structure.eps
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diff --git a/figures/map_of_contributions.eps b/figures/map_of_contributions.eps
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@@ -20776,7 +20786,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 283.295566 563.838333 m
 283.909989 563.335319 l
 283.603096 563.247499 l
@@ -20787,7 +20797,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 279.625287 566.431659 m
 280.09244 566.081892 l
 280.840107 565.104394 l
@@ -20802,7 +20812,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 235.503046 524.985463 m
 233.868251 524.194683 l
 232.531264 523.83886 l
@@ -21013,7 +21023,7 @@ c10
 stroke
 grestore
 gsave
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 167.093099 586.268107 m
 166.860798 584.662321 l
 166.21418 584.223379 l
@@ -21027,7 +21037,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 240.472219 570.345316 m
 242.169093 569.895616 l
 242.729645 568.716339 l
@@ -21040,7 +21050,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 236.622953 568.820496 m
 235.557156 569.103561 l
 235.256799 569.766914 l
@@ -21050,7 +21060,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 238.253683 578.020101 m
 238.364375 577.177999 l
 239.274852 577.042722 l
@@ -21067,7 +21077,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 230.535626 576.016254 m
 232.309004 576.080884 l
 233.833346 577.200711 l
@@ -21116,7 +21126,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 226.475894 558.846448 m
 224.429173 559.926987 l
 225.867209 560.230214 l
@@ -21127,7 +21137,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 228.089969 561.501853 m
 229.116557 561.619239 l
 230.498569 562.184253 l
@@ -21140,7 +21150,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 223.338593 561.770733 m
 224.291386 561.896726 l
 224.673906 561.241102 l
@@ -21154,7 +21164,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 208.266191 564.763075 m
 208.476896 564.214557 l
 211.431225 564.060832 l
@@ -21181,7 +21191,7 @@ c11
 stroke
 grestore
 gsave
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 201.917476 572.993629 m
 202.178864 571.917951 l
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@@ -21220,7 +21230,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 227.306918 594.258551 m
 226.519724 595.433524 l
 227.840853 595.376904 l
@@ -21230,7 +21240,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 230.009023 590.058641 m
 230.396086 590.49065 l
 230.566227 591.447826 l
@@ -21246,7 +21256,7 @@ c10
 stroke
 grestore
 gsave
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 235.854805 587.643147 m
 236.011638 586.618631 l
 236.102486 585.753578 l
@@ -21277,7 +21287,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 209.790374 602.729774 m
 208.525827 603.207804 l
 208.48112 604.534351 l
@@ -21291,7 +21301,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 228.762566 612.28568 m
 227.833919 609.813007 l
 227.173036 608.548022 l
@@ -21304,7 +21314,7 @@ c11
 stroke
 grestore
 gsave
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 223.714817 589.033647 m
 221.663315 587.570349 l
 222.41927 588.648896 l
@@ -21317,7 +21327,7 @@ c10
 stroke
 grestore
 gsave
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 229.62483 602.771691 m
 229.373961 602.132325 l
 229.900404 601.026962 l
@@ -21354,7 +21364,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 229.164372 592.27128 m
 228.925615 593.0052 l
 229.851314 592.527847 l
@@ -21367,7 +21377,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 234.506581 593.423024 m
 234.939705 591.701166 l
 233.749909 592.110303 l
@@ -21385,7 +21395,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 476.842825 588.037621 m
 477.639622 587.98865 l
 478.796665 589.065165 l
@@ -21553,7 +21563,7 @@ c10
 stroke
 grestore
 gsave
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 476.028257 585.806573 m
 476.652681 584.985151 l
 476.896099 583.680878 l
@@ -21633,7 +21643,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 476.028257 585.806573 m
 475.514166 586.251452 l
 475.183804 587.083871 l
@@ -22270,7 +22280,7 @@ c10
 stroke
 grestore
 gsave
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 468.83481 589.418837 m
 469.388547 588.865657 l
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@@ -22290,7 +22300,7 @@ c11
 stroke
 grestore
 gsave
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 485.541966 605.068803 m
 485.73482 605.331666 l
 486.054503 605.362627 l
@@ -22331,7 +22341,7 @@ c10
 stroke
 grestore
 gsave
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 571.061393 604.116169 m
 570.875154 604.882763 l
 571.029078 605.651828 l
@@ -22374,7 +22384,7 @@ c11
 stroke
 grestore
 gsave
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 40.965625 630.139952 m
 41.742667 630.647793 l
 43.227801 631.117177 l
@@ -22611,14 +22621,14 @@ c10
 stroke
 grestore
 gsave
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 596.131582 636.195338 m
 595.701774 635.2876 l
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 stroke
 grestore
 gsave
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 40.965625 634.657808 m
 41.13725 634.40633 l
 40.965626 634.273952 l
@@ -22626,7 +22636,7 @@ c10
 stroke
 grestore
 gsave
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 596.131582 634.273951 m
 595.411218 633.718312 l
 595.077709 632.715911 l
@@ -22677,7 +22687,7 @@ c11
 stroke
 grestore
 gsave
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 40.965626 651.277617 m
 43.030165 651.969096 l
 43.493174 653.232767 l
@@ -22956,7 +22966,7 @@ c10
 stroke
 grestore
 gsave
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 318.548604 674.873602 m
 316.554941 674.187696 l
 314.556467 674.300703 l
@@ -23556,7 +23566,7 @@ c11
 stroke
 grestore
 gsave
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 596.131581 583.199207 m
 595.34874 582.906913 l
 594.491337 582.378079 l
@@ -23606,7 +23616,7 @@ c10
 stroke
 grestore
 gsave
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 318.54862 549.889026 m
 318.867347 549.96035 l
 318.676047 549.21874 l
@@ -23614,7 +23624,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 235.192089 561.663229 m
 236.267926 561.715427 l
 236.749901 561.908042 l
@@ -23633,7 +23643,7 @@ c10
 stroke
 grestore
 gsave
-c11
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 318.548604 549.136338 m
 317.568031 548.756763 l
 316.582483 548.431856 l
@@ -23644,7 +23654,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 315.657993 547.671944 m
 316.040487 547.926225 l
 316.571689 547.48126 l
@@ -23657,7 +23667,7 @@ c10
 stroke
 grestore
 gsave
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 501.013148 591.259889 m
 501.899872 591.460366 l
 502.223719 591.406391 l
@@ -23669,7 +23679,7 @@ c11
 stroke
 grestore
 gsave
-c10
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 356.481644 605.632134 m
 356.755557 605.498385 l
 357.005782 605.291863 l
@@ -23690,7 +23700,7 @@ c10
 stroke
 grestore
 gsave
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 355.56636 606.687396 m
 355.437439 606.502233 l
 354.920533 606.391878 l
@@ -23703,7 +23713,7 @@ c11
 stroke
 grestore
 gsave
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 354.390365 607.323986 m
 354.34242 607.157267 l
 353.516025 607.20205 l
@@ -23712,7 +23722,7 @@ c10
 stroke
 grestore
 gsave
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 352.436551 608.15692 m
 352.565457 608.057854 l
 353.010793 607.548104 l
@@ -23725,7 +23735,7 @@ c11
 stroke
 grestore
 gsave
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 350.369344 608.924869 m
 350.401065 608.565646 l
 350.218169 608.41296 l
@@ -23736,7 +23746,7 @@ c10
 stroke
 grestore
 gsave
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 475.551279 613.544097 m
 476.015259 613.481949 l
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@@ -23748,7 +23758,7 @@ c11
 stroke
 grestore
 gsave
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 474.33434 615.980196 m
 475.05914 616.103566 l
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@@ -23758,7 +23768,7 @@ c10
 stroke
 grestore
 gsave
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 476.169472 616.365728 m
 477.387753 615.67177 l
 477.121659 614.575595 l
@@ -25756,7 +25766,7 @@ gsave
 1.000 setgray
 fill
 grestore
-/c12 {
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 40.965625 417.369574 m
 40.965625 509.897234 l
 596.131582 509.897234 l
@@ -25767,7 +25777,7 @@ clip
 newpath
 } bind def
 gsave
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 40.965625 417.369574 translate
 [556.0000000000001 0.0 0.0 93.0 -1.423220411062281e-14 0.0] concat
@@ -26793,7 +26803,7 @@ fdfffffdfdfdfdfffdfdfffdfdfffffdfdfdfdfffdfdfffdfdfffdfdfffdfdff
 grestore
 1 setlinejoin
 gsave
-/c13 {
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 40.965625 417.369574 m
 40.965625 509.897234 l
 596.131582 509.897234 l
@@ -26803,7 +26813,7 @@ gsave
 clip
 newpath
 } bind def
-c13
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 258.405949 459.623639 m
 261.081597 458.561111 l
 263.932566 457.678605 l
@@ -26874,7 +26884,7 @@ c13
 stroke
 grestore
 gsave
-/c14 {
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 40.965625 417.369574 m
 40.965625 509.897234 l
 596.131582 509.897234 l
@@ -26884,7 +26894,7 @@ gsave
 clip
 newpath
 } bind def
-c14
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 217.082798 470.61288 m
 217.693395 471.189847 l
 219.005359 472.034499 l
@@ -26934,7 +26944,7 @@ c14
 stroke
 grestore
 gsave
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 493.915607 492.18553 m
 494.703837 492.049696 l
 494.981881 491.743361 l
@@ -26947,7 +26957,7 @@ c13
 stroke
 grestore
 gsave
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 476.509356 492.148155 m
 477.54718 492.00989 l
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@@ -26962,7 +26972,7 @@ c14
 stroke
 grestore
 gsave
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 469.263591 499.393202 m
 470.595517 499.283188 l
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@@ -27008,7 +27018,7 @@ c13
 stroke
 grestore
 gsave
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 117.368056 444.40333 m
 117.777432 443.747229 l
 118.159155 442.72869 l
@@ -27061,7 +27071,7 @@ c14
 stroke
 grestore
 gsave
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 298.83528 439.126149 m
 299.803852 438.240217 l
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@@ -27070,7 +27080,7 @@ c13
 stroke
 grestore
 gsave
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 298.181967 439.470098 m
 297.960982 439.89589 l
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@@ -27081,7 +27091,7 @@ c14
 stroke
 grestore
 gsave
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 247.934785 453.000067 m
 247.784646 454.160936 l
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@@ -27092,7 +27102,7 @@ c13
 stroke
 grestore
 gsave
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 296.619533 431.125346 m
 297.88408 430.169205 l
 298.686017 429.45987 l
@@ -27109,7 +27119,7 @@ c14
 stroke
 grestore
 gsave
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 276.356017 457.989243 m
 276.941989 457.495713 l
 277.127113 456.693855 l
@@ -27127,7 +27137,7 @@ c13
 stroke
 grestore
 gsave
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 274.291684 454.626255 m
 273.448228 454.251545 l
 272.65673 453.890622 l
@@ -27153,7 +27163,7 @@ c14
 stroke
 grestore
 gsave
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 290.973857 447.467713 m
 291.405069 446.937764 l
 290.327717 446.947566 l
@@ -27163,7 +27173,7 @@ c13
 stroke
 grestore
 gsave
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 290.296797 448.829006 m
 290.064575 448.544746 l
 288.921638 449.881414 l
@@ -27174,7 +27184,7 @@ c14
 stroke
 grestore
 gsave
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 289.020296 448.408075 m
 288.419421 448.373728 l
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@@ -27186,7 +27196,7 @@ c13
 stroke
 grestore
 gsave
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 287.150889 451.265499 m
 287.513327 450.776192 l
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@@ -27199,7 +27209,7 @@ c14
 stroke
 grestore
 gsave
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 283.295566 452.805142 m
 283.909989 452.302128 l
 283.603096 452.214308 l
@@ -27210,7 +27220,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 279.625287 455.398468 m
 280.09244 455.048701 l
 280.840107 454.071203 l
@@ -27225,7 +27235,7 @@ c14
 stroke
 grestore
 gsave
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 235.503046 413.952272 m
 233.868251 413.161492 l
 232.531264 412.805668 l
@@ -27436,7 +27446,7 @@ c13
 stroke
 grestore
 gsave
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 167.093099 475.234916 m
 166.860798 473.629129 l
 166.21418 473.190187 l
@@ -27450,7 +27460,7 @@ c14
 stroke
 grestore
 gsave
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 240.472219 459.312125 m
 242.169093 458.862425 l
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@@ -27463,7 +27473,7 @@ c13
 stroke
 grestore
 gsave
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 236.622953 457.787305 m
 235.557156 458.070369 l
 235.256799 458.733723 l
@@ -27473,7 +27483,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 238.253683 466.986909 m
 238.364375 466.144808 l
 239.274852 466.009531 l
@@ -27490,7 +27500,7 @@ c13
 stroke
 grestore
 gsave
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 230.535626 464.983063 m
 232.309004 465.047693 l
 233.833346 466.16752 l
@@ -27539,7 +27549,7 @@ c14
 stroke
 grestore
 gsave
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 226.475894 447.813256 m
 224.429173 448.893796 l
 225.867209 449.197022 l
@@ -27550,7 +27560,7 @@ c13
 stroke
 grestore
 gsave
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 228.089969 450.468662 m
 229.116557 450.586048 l
 230.498569 451.151062 l
@@ -27563,7 +27573,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 223.338593 450.737542 m
 224.291386 450.863534 l
 224.673906 450.207911 l
@@ -27577,7 +27587,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 208.266191 453.729884 m
 208.476896 453.181366 l
 211.431225 453.027641 l
@@ -27604,7 +27614,7 @@ c14
 stroke
 grestore
 gsave
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 201.917476 461.960438 m
 202.178864 460.88476 l
 202.716145 460.02417 l
@@ -27643,7 +27653,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 227.306918 483.225359 m
 226.519724 484.400333 l
 227.840853 484.343712 l
@@ -27653,7 +27663,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 230.009023 479.02545 m
 230.396086 479.457458 l
 230.566227 480.414634 l
@@ -27669,7 +27679,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 235.854805 476.609956 m
 236.011638 475.58544 l
 236.102486 474.720387 l
@@ -27700,7 +27710,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 209.790374 491.696582 m
 208.525827 492.174613 l
 208.48112 493.50116 l
@@ -27714,7 +27724,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 228.762566 501.252488 m
 227.833919 498.779816 l
 227.173036 497.51483 l
@@ -27727,7 +27737,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 223.714817 478.000455 m
 221.663315 476.537157 l
 222.41927 477.615704 l
@@ -27740,7 +27750,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 229.62483 491.7385 m
 229.373961 491.099134 l
 229.900404 489.993771 l
@@ -27777,7 +27787,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 229.164372 481.238088 m
 228.925615 481.972009 l
 229.851314 481.494656 l
@@ -27790,7 +27800,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 234.506581 482.389832 m
 234.939705 480.667974 l
 233.749909 481.077111 l
@@ -27808,7 +27818,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 476.842825 477.004429 m
 477.639622 476.955459 l
 478.796665 478.031974 l
@@ -27976,7 +27986,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 476.028257 474.773382 m
 476.652681 473.95196 l
 476.896099 472.647687 l
@@ -28056,7 +28066,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 476.028257 474.773382 m
 475.514166 475.21826 l
 475.183804 476.05068 l
@@ -28693,7 +28703,7 @@ c13
 stroke
 grestore
 gsave
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 468.83481 478.385645 m
 469.388547 477.832465 l
 469.35699 477.50573 l
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 stroke
 grestore
 gsave
-c13
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 485.541966 494.035612 m
 485.73482 494.298475 l
 486.054503 494.329435 l
@@ -28754,7 +28764,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 571.061393 493.082978 m
 570.875154 493.849572 l
 571.029078 494.618636 l
@@ -28797,7 +28807,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 40.965625 519.10676 m
 41.742667 519.614601 l
 43.227801 520.083985 l
@@ -29034,14 +29044,14 @@ c13
 stroke
 grestore
 gsave
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 596.131582 525.162147 m
 595.701774 524.254409 l
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 stroke
 grestore
 gsave
-c13
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 40.965625 523.624617 m
 41.13725 523.373139 l
 40.965626 523.24076 l
@@ -29049,7 +29059,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 596.131582 523.240759 m
 595.411218 522.685121 l
 595.077709 521.682719 l
@@ -29100,7 +29110,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 40.965626 540.244425 m
 43.030165 540.935904 l
 43.493174 542.199575 l
@@ -29379,7 +29389,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 318.548604 563.84041 m
 316.554941 563.154505 l
 314.556467 563.267512 l
@@ -29979,7 +29989,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 596.131581 472.166016 m
 595.34874 471.873721 l
 594.491337 471.344887 l
@@ -30029,7 +30039,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 318.54862 438.855835 m
 318.867347 438.927159 l
 318.676047 438.185548 l
@@ -30037,7 +30047,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 235.192089 450.630038 m
 236.267926 450.682236 l
 236.749901 450.87485 l
@@ -30056,7 +30066,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 318.548604 438.103147 m
 317.568031 437.723571 l
 316.582483 437.398664 l
@@ -30067,7 +30077,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 315.657993 436.638753 m
 316.040487 436.893034 l
 316.571689 436.448068 l
@@ -30080,7 +30090,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 501.013148 480.226698 m
 501.899872 480.427174 l
 502.223719 480.3732 l
@@ -30092,7 +30102,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 356.481644 494.598942 m
 356.755557 494.465193 l
 357.005782 494.258672 l
@@ -30113,7 +30123,7 @@ c13
 stroke
 grestore
 gsave
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 355.56636 495.654205 m
 355.437439 495.469041 l
 354.920533 495.358687 l
@@ -30126,7 +30136,7 @@ c14
 stroke
 grestore
 gsave
-c13
+c14
 354.390365 496.290795 m
 354.34242 496.124076 l
 353.516025 496.168859 l
@@ -30135,7 +30145,7 @@ c13
 stroke
 grestore
 gsave
-c14
+c15
 352.436551 497.123729 m
 352.565457 497.024663 l
 353.010793 496.514912 l
@@ -30148,7 +30158,7 @@ c14
 stroke
 grestore
 gsave
-c13
+c14
 350.369344 497.891678 m
 350.401065 497.532454 l
 350.218169 497.379768 l
@@ -30159,7 +30169,7 @@ c13
 stroke
 grestore
 gsave
-c14
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 475.551279 502.510905 m
 476.015259 502.448757 l
 476.555004 501.168791 l
@@ -30171,7 +30181,7 @@ c14
 stroke
 grestore
 gsave
-c13
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 474.33434 504.947004 m
 475.05914 505.070374 l
 476.076944 505.024111 l
@@ -30181,7 +30191,7 @@ c13
 stroke
 grestore
 gsave
-c14
+c15
 476.169472 505.332536 m
 477.387753 504.638579 l
 477.121659 503.542404 l
@@ -32179,7 +32189,7 @@ gsave
 1.000 setgray
 fill
 grestore
-/c15 {
+/c16 {
 40.965625 306.336383 m
 40.965625 398.864043 l
 596.131582 398.864043 l
@@ -32190,7 +32200,7 @@ clip
 newpath
 } bind def
 gsave
-c15
+c16
 
 40.965625 306.336383 translate
 [556.0 0.0 0.0 93.0 0.0 5.713359635775202e-14] concat
@@ -33216,7 +33226,7 @@ fdfffdfdfffffdfdfdfdfffdfdfffffdfdfffdfdfdfdfffdfdfffdfdfffdfdff
 grestore
 1 setlinejoin
 gsave
-/c16 {
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 40.965625 306.336383 m
 40.965625 398.864043 l
 596.131582 398.864043 l
@@ -33226,7 +33236,7 @@ gsave
 clip
 newpath
 } bind def
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 258.405949 348.590448 m
 261.081597 347.527919 l
 263.932566 346.645414 l
@@ -33297,7 +33307,7 @@ c16
 stroke
 grestore
 gsave
-/c17 {
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 40.965625 306.336383 m
 40.965625 398.864043 l
 596.131582 398.864043 l
@@ -33307,7 +33317,7 @@ gsave
 clip
 newpath
 } bind def
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 217.082798 359.579688 m
 217.693395 360.156656 l
 219.005359 361.001308 l
@@ -33357,7 +33367,7 @@ c17
 stroke
 grestore
 gsave
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 493.915607 381.152339 m
 494.703837 381.016504 l
 494.981881 380.71017 l
@@ -33370,7 +33380,7 @@ c16
 stroke
 grestore
 gsave
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 476.509356 381.114963 m
 477.54718 380.976698 l
 478.36645 380.606331 l
@@ -33385,7 +33395,7 @@ c17
 stroke
 grestore
 gsave
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 469.263591 388.360011 m
 470.595517 388.249996 l
 471.807149 388.232624 l
@@ -33431,7 +33441,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 117.368056 333.370139 m
 117.777432 332.714037 l
 118.159155 331.695498 l
@@ -33484,7 +33494,7 @@ c17
 stroke
 grestore
 gsave
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 298.83528 328.092957 m
 299.803852 327.207025 l
 299.295419 327.00421 l
@@ -33493,7 +33503,7 @@ c16
 stroke
 grestore
 gsave
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 298.181967 328.436906 m
 297.960982 328.862699 l
 297.929026 330.044287 l
@@ -33504,7 +33514,7 @@ c17
 stroke
 grestore
 gsave
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 247.934785 341.966876 m
 247.784646 343.127744 l
 248.058466 343.682 l
@@ -33515,7 +33525,7 @@ c16
 stroke
 grestore
 gsave
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 296.619533 320.092154 m
 297.88408 319.136014 l
 298.686017 318.426678 l
@@ -33532,7 +33542,7 @@ c17
 stroke
 grestore
 gsave
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 276.356017 346.956052 m
 276.941989 346.462521 l
 277.127113 345.660664 l
@@ -33550,7 +33560,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 274.291684 343.593064 m
 273.448228 343.218354 l
 272.65673 342.85743 l
@@ -33576,7 +33586,7 @@ c17
 stroke
 grestore
 gsave
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 290.973857 336.434522 m
 291.405069 335.904572 l
 290.327717 335.914374 l
@@ -33586,7 +33596,7 @@ c16
 stroke
 grestore
 gsave
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 290.296797 337.795815 m
 290.064575 337.511555 l
 288.921638 338.848223 l
@@ -33597,7 +33607,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 289.020296 337.374883 m
 288.419421 337.340536 l
 287.473879 337.496573 l
@@ -33609,7 +33619,7 @@ c16
 stroke
 grestore
 gsave
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 287.150889 340.232307 m
 287.513327 339.743001 l
 287.578674 339.43308 l
@@ -33622,7 +33632,7 @@ c17
 stroke
 grestore
 gsave
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 283.295566 341.77195 m
 283.909989 341.268936 l
 283.603096 341.181116 l
@@ -33633,7 +33643,7 @@ c16
 stroke
 grestore
 gsave
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 279.625287 344.365276 m
 280.09244 344.015509 l
 280.840107 343.038011 l
@@ -33648,7 +33658,7 @@ c17
 stroke
 grestore
 gsave
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 235.503046 302.91908 m
 233.868251 302.1283 l
 232.531264 301.772477 l
@@ -33859,7 +33869,7 @@ c16
 stroke
 grestore
 gsave
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 167.093099 364.201724 m
 166.860798 362.595938 l
 166.21418 362.156996 l
@@ -33873,7 +33883,7 @@ c17
 stroke
 grestore
 gsave
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 240.472219 348.278933 m
 242.169093 347.829233 l
 242.729645 346.649956 l
@@ -33886,7 +33896,7 @@ c16
 stroke
 grestore
 gsave
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 236.622953 346.754113 m
 235.557156 347.037178 l
 235.256799 347.700531 l
@@ -33896,7 +33906,7 @@ c17
 stroke
 grestore
 gsave
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 238.253683 355.953718 m
 238.364375 355.111616 l
 239.274852 354.976339 l
@@ -33913,7 +33923,7 @@ c16
 stroke
 grestore
 gsave
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 230.535626 353.949871 m
 232.309004 354.014501 l
 233.833346 355.134328 l
@@ -33962,7 +33972,7 @@ c17
 stroke
 grestore
 gsave
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 226.475894 336.780065 m
 224.429173 337.860604 l
 225.867209 338.163831 l
@@ -33973,7 +33983,7 @@ c16
 stroke
 grestore
 gsave
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 228.089969 339.435471 m
 229.116557 339.552856 l
 230.498569 340.11787 l
@@ -33986,7 +33996,7 @@ c17
 stroke
 grestore
 gsave
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 223.338593 339.70435 m
 224.291386 339.830343 l
 224.673906 339.174719 l
@@ -34000,7 +34010,7 @@ c16
 stroke
 grestore
 gsave
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 208.266191 342.696692 m
 208.476896 342.148174 l
 211.431225 341.994449 l
@@ -34027,7 +34037,7 @@ c17
 stroke
 grestore
 gsave
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 201.917476 350.927246 m
 202.178864 349.851568 l
 202.716145 348.990978 l
@@ -34066,7 +34076,7 @@ c16
 stroke
 grestore
 gsave
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 227.306918 372.192168 m
 226.519724 373.367141 l
 227.840853 373.310521 l
@@ -34076,7 +34086,7 @@ c17
 stroke
 grestore
 gsave
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 230.009023 367.992258 m
 230.396086 368.424267 l
 230.566227 369.381443 l
@@ -34092,7 +34102,7 @@ c16
 stroke
 grestore
 gsave
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 235.854805 365.576764 m
 236.011638 364.552248 l
 236.102486 363.687195 l
@@ -34123,7 +34133,7 @@ c17
 stroke
 grestore
 gsave
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 209.790374 380.663391 m
 208.525827 381.141421 l
 208.48112 382.467968 l
@@ -34137,7 +34147,7 @@ c16
 stroke
 grestore
 gsave
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 228.762566 390.219297 m
 227.833919 387.746624 l
 227.173036 386.481639 l
@@ -34150,7 +34160,7 @@ c17
 stroke
 grestore
 gsave
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 223.714817 366.967264 m
 221.663315 365.503966 l
 222.41927 366.582513 l
@@ -34163,7 +34173,7 @@ c16
 stroke
 grestore
 gsave
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 229.62483 380.705308 m
 229.373961 380.065942 l
 229.900404 378.960579 l
@@ -34200,7 +34210,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 229.164372 370.204897 m
 228.925615 370.938817 l
 229.851314 370.461464 l
@@ -34213,7 +34223,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 234.506581 371.356641 m
 234.939705 369.634783 l
 233.749909 370.04392 l
@@ -34231,7 +34241,7 @@ c17
 stroke
 grestore
 gsave
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 476.842825 365.971238 m
 477.639622 365.922267 l
 478.796665 366.998782 l
@@ -34399,7 +34409,7 @@ c16
 stroke
 grestore
 gsave
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 476.028257 363.74019 m
 476.652681 362.918769 l
 476.896099 361.614495 l
@@ -34479,7 +34489,7 @@ c17
 stroke
 grestore
 gsave
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 476.028257 363.74019 m
 475.514166 364.185069 l
 475.183804 365.017488 l
@@ -35116,7 +35126,7 @@ c16
 stroke
 grestore
 gsave
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 468.83481 367.352454 m
 469.388547 366.799274 l
 469.35699 366.472538 l
@@ -35136,7 +35146,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 485.541966 383.00242 m
 485.73482 383.265283 l
 486.054503 383.296244 l
@@ -35177,7 +35187,7 @@ c16
 stroke
 grestore
 gsave
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 571.061393 382.049786 m
 570.875154 382.81638 l
 571.029078 383.585445 l
@@ -35220,7 +35230,7 @@ c17
 stroke
 grestore
 gsave
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 40.965625 408.073569 m
 41.742667 408.58141 l
 43.227801 409.050794 l
@@ -35457,14 +35467,14 @@ c16
 stroke
 grestore
 gsave
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 596.131582 414.128955 m
 595.701774 413.221217 l
 596.131582 412.591426 l
 stroke
 grestore
 gsave
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 40.965625 412.591425 m
 41.13725 412.339947 l
 40.965626 412.207569 l
@@ -35472,7 +35482,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 596.131582 412.207568 m
 595.411218 411.651929 l
 595.077709 410.649528 l
@@ -35523,7 +35533,7 @@ c17
 stroke
 grestore
 gsave
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 40.965626 429.211234 m
 43.030165 429.902713 l
 43.493174 431.166384 l
@@ -35802,7 +35812,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 318.548604 452.807219 m
 316.554941 452.121313 l
 314.556467 452.23432 l
@@ -36402,7 +36412,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 596.131581 361.132824 m
 595.34874 360.84053 l
 594.491337 360.311696 l
@@ -36452,7 +36462,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 318.54862 327.822643 m
 318.867347 327.893967 l
 318.676047 327.152357 l
@@ -36460,7 +36470,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 235.192089 339.596846 m
 236.267926 339.649044 l
 236.749901 339.841659 l
@@ -36479,7 +36489,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 318.548604 327.069955 m
 317.568031 326.69038 l
 316.582483 326.365473 l
@@ -36490,7 +36500,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 315.657993 325.605561 m
 316.040487 325.859843 l
 316.571689 325.414877 l
@@ -36503,7 +36513,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 501.013148 369.193506 m
 501.899872 369.393983 l
 502.223719 369.340009 l
@@ -36515,7 +36525,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 356.481644 383.565751 m
 356.755557 383.432002 l
 357.005782 383.22548 l
@@ -36536,7 +36546,7 @@ c16
 stroke
 grestore
 gsave
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 355.56636 384.621013 m
 355.437439 384.43585 l
 354.920533 384.325495 l
@@ -36549,7 +36559,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 354.390365 385.257603 m
 354.34242 385.090884 l
 353.516025 385.135667 l
@@ -36558,7 +36568,7 @@ c16
 stroke
 grestore
 gsave
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 352.436551 386.090537 m
 352.565457 385.991471 l
 353.010793 385.481721 l
@@ -36571,7 +36581,7 @@ c17
 stroke
 grestore
 gsave
-c16
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 350.369344 386.858486 m
 350.401065 386.499263 l
 350.218169 386.346577 l
@@ -36582,7 +36592,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 475.551279 391.477714 m
 476.015259 391.415566 l
 476.555004 390.1356 l
@@ -36594,7 +36604,7 @@ c17
 stroke
 grestore
 gsave
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 474.33434 393.913813 m
 475.05914 394.037183 l
 476.076944 393.990919 l
@@ -36604,7 +36614,7 @@ c16
 stroke
 grestore
 gsave
-c17
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 476.169472 394.299345 m
 477.387753 393.605387 l
 477.121659 392.509212 l
@@ -38602,7 +38612,7 @@ gsave
 1.000 setgray
 fill
 grestore
-/c18 {
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 40.965625 195.303191 m
 40.965625 287.830851 l
 596.131582 287.830851 l
@@ -38613,7 +38623,7 @@ clip
 newpath
 } bind def
 gsave
-c18
+c19
 
 40.965625 195.303191 translate
 [556.0000000000001 0.0 0.0 93.0 -1.423220411062281e-14 0.0] concat
@@ -39639,7 +39649,7 @@ fdfdfffdfdfffdfdfffdfdfdfdfffffdfdfffdfdfffdfdfffdfdfdfdfffdfdff
 grestore
 1 setlinejoin
 gsave
-/c19 {
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 40.965625 195.303191 m
 40.965625 287.830851 l
 596.131582 287.830851 l
@@ -39649,7 +39659,7 @@ gsave
 clip
 newpath
 } bind def
-c19
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 258.405949 237.557256 m
 261.081597 236.494728 l
 263.932566 235.612222 l
@@ -39720,7 +39730,7 @@ c19
 stroke
 grestore
 gsave
-/c20 {
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 40.965625 195.303191 m
 40.965625 287.830851 l
 596.131582 287.830851 l
@@ -39730,7 +39740,7 @@ gsave
 clip
 newpath
 } bind def
-c20
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 217.082798 248.546497 m
 217.693395 249.123464 l
 219.005359 249.968116 l
@@ -39780,7 +39790,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 493.915607 270.119147 m
 494.703837 269.983313 l
 494.981881 269.676978 l
@@ -39793,7 +39803,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 476.509356 270.081772 m
 477.54718 269.943507 l
 478.36645 269.57314 l
@@ -39808,7 +39818,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 469.263591 277.326819 m
 470.595517 277.216805 l
 471.807149 277.199432 l
@@ -39854,7 +39864,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 117.368056 222.336947 m
 117.777432 221.680846 l
 118.159155 220.662307 l
@@ -39907,7 +39917,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 298.83528 217.059766 m
 299.803852 216.173834 l
 299.295419 215.971018 l
@@ -39916,7 +39926,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 298.181967 217.403715 m
 297.960982 217.829507 l
 297.929026 219.011096 l
@@ -39927,7 +39937,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 247.934785 230.933684 m
 247.784646 232.094553 l
 248.058466 232.648808 l
@@ -39938,7 +39948,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 296.619533 209.058963 m
 297.88408 208.102822 l
 298.686017 207.393487 l
@@ -39955,7 +39965,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 276.356017 235.92286 m
 276.941989 235.42933 l
 277.127113 234.627472 l
@@ -39973,7 +39983,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 274.291684 232.559872 m
 273.448228 232.185162 l
 272.65673 231.824239 l
@@ -39999,7 +40009,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 290.973857 225.40133 m
 291.405069 224.871381 l
 290.327717 224.881183 l
@@ -40009,7 +40019,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 290.296797 226.762623 m
 290.064575 226.478363 l
 288.921638 227.815031 l
@@ -40020,7 +40030,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 289.020296 226.341692 m
 288.419421 226.307345 l
 287.473879 226.463381 l
@@ -40032,7 +40042,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 287.150889 229.199116 m
 287.513327 228.709809 l
 287.578674 228.399888 l
@@ -40045,7 +40055,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 283.295566 230.738759 m
 283.909989 230.235745 l
 283.603096 230.147925 l
@@ -40056,7 +40066,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 279.625287 233.332085 m
 280.09244 232.982318 l
 280.840107 232.00482 l
@@ -40071,7 +40081,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 235.503046 191.885889 m
 233.868251 191.095109 l
 232.531264 190.739285 l
@@ -40282,7 +40292,7 @@ c19
 stroke
 grestore
 gsave
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 167.093099 253.168533 m
 166.860798 251.562746 l
 166.21418 251.123804 l
@@ -40296,7 +40306,7 @@ c20
 stroke
 grestore
 gsave
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 240.472219 237.245742 m
 242.169093 236.796042 l
 242.729645 235.616765 l
@@ -40309,7 +40319,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 236.622953 235.720922 m
 235.557156 236.003986 l
 235.256799 236.66734 l
@@ -40319,7 +40329,7 @@ c20
 stroke
 grestore
 gsave
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 238.253683 244.920526 m
 238.364375 244.078425 l
 239.274852 243.943148 l
@@ -40336,7 +40346,7 @@ c19
 stroke
 grestore
 gsave
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 230.535626 242.91668 m
 232.309004 242.98131 l
 233.833346 244.101137 l
@@ -40385,7 +40395,7 @@ c20
 stroke
 grestore
 gsave
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 226.475894 225.746873 m
 224.429173 226.827413 l
 225.867209 227.130639 l
@@ -40396,7 +40406,7 @@ c19
 stroke
 grestore
 gsave
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 228.089969 228.402279 m
 229.116557 228.519665 l
 230.498569 229.084679 l
@@ -40409,7 +40419,7 @@ c20
 stroke
 grestore
 gsave
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 223.338593 228.671159 m
 224.291386 228.797151 l
 224.673906 228.141528 l
@@ -40423,7 +40433,7 @@ c19
 stroke
 grestore
 gsave
-c20
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 208.266191 231.663501 m
 208.476896 231.114983 l
 211.431225 230.961258 l
@@ -40450,7 +40460,7 @@ c20
 stroke
 grestore
 gsave
-c19
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 201.917476 239.894055 m
 202.178864 238.818377 l
 202.716145 237.957787 l
@@ -40489,7 +40499,7 @@ c19
 stroke
 grestore
 gsave
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 227.306918 261.158976 m
 226.519724 262.33395 l
 227.840853 262.277329 l
@@ -40499,7 +40509,7 @@ c20
 stroke
 grestore
 gsave
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 230.009023 256.959067 m
 230.396086 257.391075 l
 230.566227 258.348252 l
@@ -40515,7 +40525,7 @@ c19
 stroke
 grestore
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diff --git a/figures/olr_sn_cape_variation.png b/figures/olr_sn_cape_variation.png
new file mode 100644
index 0000000..e1a08ed
Binary files /dev/null and b/figures/olr_sn_cape_variation.png differ
diff --git a/figures/rmm_diagram.eps b/figures/rmm_diagram.eps
index a4c92f2..0cfee7a 100644
--- a/figures/rmm_diagram.eps
+++ b/figures/rmm_diagram.eps
@@ -1,7 +1,7 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%Title: rmm_diagram.eps
 %%Creator: Matplotlib v3.5.1, https://matplotlib.org/
-%%CreationDate: Fri Sep  2 14:21:16 2022
+%%CreationDate: Mon Nov 14 01:38:16 2022
 %%Orientation: portrait
 %%BoundingBox: 141 233 471 559
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diff --git a/map_of_contributions.ipynb b/map_of_contributions.ipynb
index 9ed5b95..425fae2 100644
--- a/map_of_contributions.ipynb
+++ b/map_of_contributions.ipynb
@@ -54,17 +54,27 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "02837325-92a2-471e-a2c4-20a0e78b38d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "56b259ed",
    "metadata": {},
    "outputs": [],
    "source": [
-    "map_contrib = np.load('./data/DAILY-IP-MAP-V4.3.npy')\n",
+    "map_contrib = np.load('%s/DAILY-IP-MAP-V4.3.npy'%path)\n",
     "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
     "# contains IP values (not normalised) depending on (d, lat, lon)\n",
     "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
     "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
-    "# lat (axis 1) describes the latitude (an integer in [0, 180])\n",
-    "# lon (axis 2) describes the longitude (an integer in [0, 360])\n",
+    "# lat (axis 1) describes the latitude (an integer in [0, 179])\n",
+    "# lon (axis 2) describes the longitude (an integer in [0, 359])\n",
     "\n",
     "map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))\n",
     "map_contrib *= 240e3\n",
@@ -72,7 +82,7 @@
     "\n",
     "avg_map_contrib = np.mean(map_contrib, axis=0)  # long-term means of contributions\n",
     "\n",
-    "rmm = np.genfromtxt('./data/rmm.txt')\n",
+    "rmm = np.genfromtxt('%s/rmm.txt'%path)\n",
     "rmm = rmm[::3, [3, 4]]  # RMM1 and RMM2\n",
     "# the array should look like the IP data (with every third day taken)\n",
     "angle_rmm = np.arctan2(rmm[:, 1], rmm[:, 0])  # phase angles of RMM values\n",
@@ -105,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "b2a2b2a9",
    "metadata": {},
    "outputs": [
@@ -132,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "87d48691",
    "metadata": {
     "tags": []
@@ -218,6 +228,14 @@
     "fig.savefig('figures/map_of_contributions.png', dpi=300, bbox_inches='tight')\n",
     "fig.savefig('figures/map_of_contributions.eps', bbox_inches='tight')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "560b26b1-7abd-4145-9126-f49bfb33b456",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/variations_ip_pg_rmm_with_mjo_phase.ipynb b/variations_ip_pg_rmm_with_mjo_phase.ipynb
index 7d0536d..ef3aac6 100644
--- a/variations_ip_pg_rmm_with_mjo_phase.ipynb
+++ b/variations_ip_pg_rmm_with_mjo_phase.ipynb
@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Variations of the IP, the PG and the RMM with the MJO phase\n",
-    "Here we plot variations of various parameters with the MJO. In particular, we plot the ionospheric potential (IP), the fair-weather potential gradient (PG) and the two components of the RMM index. We also plot the sunspot number and outgoing long-wave radiation (OLR)."
+    "Here we plot variations of various parameters with the MJO. In particular, we plot the ionospheric potential (IP), the fair-weather potential gradient (PG) and the two components of the RMM index. We also plot the sunspot number, outgoing long-wave radiation (OLR) and equatorial convective avaliable potential energy (CAPE)."
    ]
   },
   {
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "26804089-74df-41d6-8c8e-b60af3173175",
    "metadata": {},
    "outputs": [],
@@ -50,6 +50,16 @@
     "## Loading the data"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d5a5c63-feb9-44e4-8398-3902c4e4d18b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './data'"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -59,13 +69,13 @@
    },
    "outputs": [],
    "source": [
-    "map_contrib = np.load('./data/DAILY-IP-MAP-V4.3.npy')\n",
+    "map_contrib = np.load('%s/DAILY-IP-MAP-V4.3.npy'%path)\n",
     "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
     "# contains IP values (not normalised) depending on (d, lat, lon)\n",
     "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
     "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
-    "# lat (axis 1) describes the latitude (an integer in [0, 180])\n",
-    "# lon (axis 2) describes the longitude (an integer in [0, 360])\n",
+    "# lat (axis 1) describes the latitude (an integer in [0, 179])\n",
+    "# lon (axis 2) describes the longitude (an integer in [0, 359])\n",
     "\n",
     "map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))\n",
     "map_contrib *= 240e3\n",
@@ -81,7 +91,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "map_olr_source = np.load('./data/OLR_41years_NOAA.npy')\n",
+    "map_olr_source = np.load('%s/OLR_41years_NOAA.npy'%path)\n",
     "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
     "# contains OLR values depending on (d, lat, lon)\n",
     "# d (axis 0) is the number of a day starting with 0 and ending with 14976\n",
@@ -111,6 +121,48 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "a9ce8214-a1ea-4aae-8093-f96440e18815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cape_map = np.load('%s/DAILY-CAPE-MAP-V4.3.npy'%path)\n",
+    "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
+    "# contains mean CAPE values in J/kg depending on (d, lat, lon)\n",
+    "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
+    "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
+    "# lat (axis 1) describes the latitude of the cell (an integer in [0, 179])\n",
+    "# lon (axis 2) describes the longitude of the cell (an integer in [0, 359])\n",
+    "\n",
+    "eq_cape = np.zeros((2, 4992), dtype=float)\n",
+    "eq_cape[0] = np.sum(\n",
+    "    np.mean(\n",
+    "        cape_map[:, 75:105, :], axis=2\n",
+    "    ) * (\n",
+    "        np.cos(np.arange(75, 105) * np.pi / 180) -\n",
+    "        np.cos(np.arange(76, 106) * np.pi / 180)\n",
+    "    ), axis=1\n",
+    ") / np.sum(\n",
+    "    np.cos(np.arange(75, 105) * np.pi / 180) -\n",
+    "    np.cos(np.arange(76, 106) * np.pi / 180)\n",
+    ")\n",
+    "# equatorial CAPE for different days (15°S–15°N)\n",
+    "eq_cape[1] = np.sum(\n",
+    "    np.mean(\n",
+    "        cape_map[:, 60:120, :], axis=2\n",
+    "    ) * (\n",
+    "        np.cos(np.arange(60, 120) * np.pi / 180) -\n",
+    "        np.cos(np.arange(61, 121) * np.pi / 180)\n",
+    "    ), axis=1\n",
+    ") / np.sum(\n",
+    "    np.cos(np.arange(60, 120) * np.pi / 180) -\n",
+    "    np.cos(np.arange(61, 121) * np.pi / 180)\n",
+    ")\n",
+    "# equatorial CAPE for different days (30°S–30°N)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "9fd8402a-3763-47d5-b510-413eb03ee3aa",
    "metadata": {
     "tags": []
@@ -123,8 +175,7 @@
     "pg = np.full(((d_end - d_start).days, 24), np.nan)\n",
     "\n",
     "# hourly data obtained from 10-s data\n",
-    "with open('./data/'\n",
-    "          'vostok_hourly_from_10_s_without_calibration_and_empty.tsv') as file:\n",
+    "with open('%s/vostok_hourly_from_10_s_without_calibration_and_empty.tsv'%path) as file:\n",
     "    tsv_file = csv.reader(file, delimiter='\\t')\n",
     "    next(tsv_file)  # skip the header\n",
     "    for line in tsv_file:\n",
@@ -137,8 +188,7 @@
     "            # negative PG values and PG values greater than 300 V/m are omitted\n",
     "\n",
     "# hourly data obtained from 5-min data\n",
-    "with open('./data/'\n",
-    "          'vostok_hourly_from_5_min_without_calibration_and_empty.tsv') as file:\n",
+    "with open('%s/vostok_hourly_from_5_min_without_calibration_and_empty.tsv'%path) as file:\n",
     "    tsv_file = csv.reader(file, delimiter='\\t')\n",
     "    next(tsv_file)  # skip the header\n",
     "    for line in tsv_file:\n",
@@ -162,14 +212,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "dabe9bd5-2e61-48ff-b63f-cd2d0010326c",
    "metadata": {},
    "outputs": [],
    "source": [
     "sn = np.full(((d_end - d_start).days), np.nan)  # sunspot number\n",
     "\n",
-    "with open('./data/sunspot_number_data.csv') as file:\n",
+    "with open('%s/sunspot_number_data.csv'%path) as file:\n",
     "    # the file downloaded from https://www.sidc.be/silso/datafiles\n",
     "    csv_file = csv.reader(file, delimiter=';')\n",
     "    for line in csv_file:\n",
@@ -185,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "c45b721f-6f09-4c8e-85ed-528bc15e02d0",
    "metadata": {},
    "outputs": [],
@@ -196,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "c61cb9c0-5ead-4abf-a725-726b35ea055f",
    "metadata": {},
    "outputs": [],
@@ -216,12 +266,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e9d4adde-6492-40ee-b11b-5c47cc0e9dd9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "rmm_source = np.genfromtxt('./data/rmm.txt')\n",
+    "rmm_source = np.genfromtxt('%s/rmm.txt'%path)\n",
+    "\n",
     "\n",
     "def phase_separation(rmm, val):\n",
     "    \"\"\"\n",
@@ -256,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "8f99af7a-43bf-4bd9-8535-1374a4534dc7",
    "metadata": {
     "tags": []
@@ -269,12 +320,14 @@
     "phase_avg_rmm1 = phase_separation(rmm, rmm[:, 0])[0]\n",
     "phase_avg_rmm2 = phase_separation(rmm, rmm[:, 1])[0]\n",
     "\n",
+    "\n",
     "# phase separation for the IP for the whole period of 1980—2020\n",
     "rmm = rmm_source[::3, [3, 4]]  # RMM1 and RMM2\n",
     "# the array should look like the IP data (with every third day taken)\n",
     "\n",
     "phase_avg_ip, phase_avg_sqr_ip, counter_ip = phase_separation(rmm, ip)\n",
     "\n",
+    "\n",
     "# phase separation for the IP for 2006–2020\n",
     "# (when the PG data are available)\n",
     "d_start = dt.date(1980, 1, 1)  # first date (for the IP)\n",
@@ -288,6 +341,7 @@
     "\n",
     "phase_avg_ip_pg, phase_avg_sqr_ip_pg, counter_ip_pg = phase_separation(rmm, ip_pg)\n",
     "\n",
+    "\n",
     "# phase separation for the PG\n",
     "rmm = rmm_source[np.ix_(fw_days_ind, [3, 4])]  # RMM1 and RMM2\n",
     "# the array should look like the PG data\n",
@@ -295,6 +349,7 @@
     "\n",
     "phase_avg_pg, phase_avg_sqr_pg, counter_pg = phase_separation(rmm, pg_fw)\n",
     "\n",
+    "\n",
     "# phase separation for the sunspot number for 2006–2020\n",
     "# (when the PG data are available)\n",
     "rmm = rmm_source[np.ix_(fw_days_ind, [3, 4])]  # RMM1 and RMM2\n",
@@ -303,11 +358,25 @@
     "\n",
     "phase_avg_sn, phase_avg_sqr_sn, counter_sn = phase_separation(rmm, sn_fw)\n",
     "\n",
+    "\n",
     "# phase separation for OLR for 1980—2020\n",
     "rmm = rmm_source[np.ix_(olr_days_ind, [3, 4])]  # RMM1 and RMM2\n",
     "# the array should look like the OLR data\n",
     "\n",
-    "phase_avg_olr, phase_avg_sqr_olr, counter_olr = phase_separation(rmm, olr)"
+    "phase_avg_olr, phase_avg_sqr_olr, counter_olr = phase_separation(rmm, olr)\n",
+    "\n",
+    "\n",
+    "# phase separation for the equatorial CAPE for 1980—2020\n",
+    "rmm = rmm_source[::3, [3, 4]]  # RMM1 and RMM2\n",
+    "# the array should look like the IP data (with every third day taken)\n",
+    "\n",
+    "phase_avg_cape = np.zeros((2, 8), dtype=float)\n",
+    "phase_avg_sqr_cape = np.zeros((2, 8), dtype=float)\n",
+    "counter_cape = np.zeros((2, 8), dtype=int)\n",
+    "\n",
+    "for j in range(2):\n",
+    "    phase_avg_cape[j], phase_avg_sqr_cape[j], counter_cape[j] = \\\n",
+    "        phase_separation(rmm, eq_cape[j])"
    ]
   },
   {
@@ -322,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "befd58ab-d5e8-4ebd-88e5-6e909da21585",
    "metadata": {
     "tags": []
@@ -346,6 +415,7 @@
     "    phase_avg_ip_part[i], phase_avg_sqr_ip_part[i], counter_ip_part[i] = \\\n",
     "        phase_separation(rmm, ip_part)\n",
     "\n",
+    "\n",
     "# phase separation for the PG for 2006–2015 and 2011–2020\n",
     "phase_avg_pg_part = np.zeros((2, 8), dtype=float)\n",
     "phase_avg_sqr_pg_part = np.zeros((2, 8), dtype=float)\n",
@@ -380,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "3e2b96da-d6db-429d-9f8c-8bc844718c9c",
    "metadata": {
     "tags": []
@@ -412,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "cbfb26db-80ae-4bd7-bb7e-8f16015adf32",
    "metadata": {
     "tags": []
@@ -436,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "a9648327-d770-4534-823b-733ffe35b5e4",
    "metadata": {},
    "outputs": [
@@ -458,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "814e19ad-7a71-4aef-95c9-6a471db693ec",
    "metadata": {},
    "outputs": [
@@ -480,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "4ef049d7-9d36-461f-a837-01c00423836b",
    "metadata": {
     "tags": []
@@ -504,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "a046c8d5-6f7c-45ba-82c2-fca9d4456c57",
    "metadata": {
     "tags": []
@@ -536,7 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "d9108a37-3893-439d-a59a-c645e6ee653b",
    "metadata": {
     "tags": []
@@ -564,10 +634,10 @@
     "                          phase_avg_rmm2[np.newaxis, :] *\n",
     "                          np.sin(np.reshape(np.arange(360), (360, 1)) * np.pi/180))[0, 1:]\n",
     "olr_rmm_corr = np.corrcoef(phase_avg_olr[np.newaxis, :],\n",
-    "                          phase_avg_rmm1[np.newaxis, :] *\n",
-    "                          np.cos(np.reshape(np.arange(360), (360, 1)) * np.pi/180) +\n",
-    "                          phase_avg_rmm2[np.newaxis, :] *\n",
-    "                          np.sin(np.reshape(np.arange(360), (360, 1)) * np.pi/180))[0, 1:]\n",
+    "                           phase_avg_rmm1[np.newaxis, :] *\n",
+    "                           np.cos(np.reshape(np.arange(360), (360, 1)) * np.pi/180) +\n",
+    "                           phase_avg_rmm2[np.newaxis, :] *\n",
+    "                           np.sin(np.reshape(np.arange(360), (360, 1)) * np.pi/180))[0, 1:]\n",
     "ip_rmm_angle = np.argmax(ip_rmm_corr)\n",
     "pg_rmm_angle = np.argmax(pg_rmm_corr)\n",
     "olr_rmm_angle = np.argmin(olr_rmm_corr)\n",
@@ -591,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "92ca748a-eec7-4731-8d67-2c3d138cc0c5",
    "metadata": {},
    "outputs": [
@@ -645,7 +715,7 @@
     "    ax.text(0.5 + 0.4 * np.cos(np.pi * (1 + i/4 + 1/8)),\n",
     "            0.5 + 0.4 * np.sin(np.pi * (1 + i/4 + 1/8)),\n",
     "            f'Phase {i + 1}', fontsize='large', ha='center', va='center',\n",
-    "            transform=ax.transAxes, zorder = 5,\n",
+    "            transform=ax.transAxes, zorder=5,\n",
     "            bbox=dict(edgecolor='1.', facecolor='1.', boxstyle='square, pad=0.'))\n",
     "\n",
     "ang = np.pi * 7/8\n",
@@ -667,15 +737,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "306b70f0-69aa-4f0a-9b7a-7cceacd0b57e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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GmEjx6o7qehFpqqprgOVALxHZCuw/kouJSB2gBc7s2hwpIqLATOB+Vd0qItWAesBCn/MWApcGuGYizqO6PHr37k1qauqRVDP2tOgUmuvY92lijKr+WsCxDcDIMFanQNbWRRdt3r1I54fy3yiSsYti0KmDinR+qOoZqbimYF51VJ8FWgFrcBrsz4AywB1FvZC7ktUk4D1V/UtEEoDTgAVADeBV93gPIMEtttPnEjuBSv7XVdVkINl/f1JS0oj69esXtZqxafmc0Fzn2rtDcx1jooSIFDopVFWjorNqbV102bTy30U6v37vh2MidlG8Nf+tIp3/5uA3i3VcUzBPOqqqOt7n9XT3TmcZVU0vynVEJA6YiHMndoh7vXRgvnvKZhEZAmwUkUpAzvUrAxk+r3cf4UcxUSgxMRGA5OTkiNbDlGhJwDLgvwRORWWz/o0xJgRCuTKVqKq6r/3HvmYBWe5Y1exgrwe8A9QBLixg6dWcXwhxqpomIhuBdjhDAnBfLw5Y0hhjjszdQF+ccfMTgPfdR/7GGGNCKJSTqXwft2cBB/y2nH3Beh1n+EBPVd2Xs1NEzhCRliISJyI1gP8DklU1J/4EYJiIVBOR44GBwPgj/EzGGHMYVX1JVU8BrgKqAz+JyEwRuV5EyoajDiJSSkTKAfFAvIiUExHPcmMbY0wkhLKj2trndTPgGL8tZ1+hRKQJMBhoD2zyyZd6nXuNGTiP8xcBmUAfn+IjgJVACjAbeC6WU1MlJibmPgo3xoSXqi5R1QeA5sD/cP4o7hCm8MOAfcCDOEtY73P3GWNMzAjlEqrrfN5eparP+5/jJsIeE8S1Uih4CcIPCyibCdzobsYY4xkRaYWTEu8anD+QbwJ+CkdsVU3CGStrjDExy6vHRMOBwzqqOH/tF9pRNSVc0mUFH1+zKLjzkr4MTX2M8SMit+OMUa2AM+Gzo98f68YYY0IgpB1VETnXfRkvIl3Ie1f0GGz2vTEmNryEM+t/PnAC8IQz//MQVe0bgXoZY0xMCfUd1Xfc/5YDxvnsV2ATcHuI48U+u7toTDQaiaWgMsYYz4W0o6qqzQBEZILdTTDGxCp3fKgxxhiPeZXwP7eT6p9TNdg8qsbkJ7l/x0hXwZRwIlLo+sKqGqKl3YwxpuTypKMqIifjLG3aFmcYADjjVRUn558xxhRnycA/OKvm5bcyVeNwVsgYY2KRV7P+3wOm4qSI2utRDIPdXTQmQqYAZ+K0cxNU9dcI18cYY2KSVx3VJsAjOUuqGmNMLFHVy0SkOs5iI/8nIlVw0lRNsDRVxhgTOqFcmcrXl0B3j65tjDERp6rbVfVVVT0D6AXUBVaJyNkRrpoxxsQMr+6olgO+FJG5OGmpclk2AGNMrBAneWp3nNWpzgXeB1ZFtFLGGBNDvOqoLnE3Y4yJOSLSBmdlqt44bd0E4CZV3RfRihljTIzxKj3VY15c1xhjosRCnJWp3gJScZ4i9fFdnUpVxwUuaowxJlhe3VFFRLoB1wC1VbWniJwKVFbV772KaYwxYTIHJwXVefkcV/KuzmeMMeYIeJVH9XbgTuBt4Ep39z7g/wCbaGCMKdZUNTHSdTDGmJLAq1n/dwFdVfVpIGclqr+Alh7FM8aYqCAitUXkuUjXwxhjYoFXj/4rATm5BHNyqZbGWcXFGGOKNXe2/41Ae+Bv4DWgIjACGAjMjljlTFB+fzKxSOef/HCyJ/WIZjIw0KJr+dOxgVOnF/W7LolC9V3HIq/uqM4BHvTbdwcwy6N4xhgTTs8DjwG1gQeA8cB8oBZwpqpeFLmqGWNM7PDqjurtwFQRGQhUEpFlwG7gYo/iGWNMOF0NdFLVVSJyPE6KqqtV9bMI18sYY2KKV+mpNorIacBpOMuprgPmqWp2wSWNMaZYqKKqqwBU9S8R2WudVGOMCT2vZv1PUdVewDx3y9n/hape7kVMY4wJIxGRZkDOwLIsv/fkdGSNMcYcOa8e/XfJZ3+iR/GMMSacKuJMovKdAbHS57UC8WGtkTHGxKCQdlRFZKT7sozP6xzHACmhjGeMMZGgql5NRDXGGOMj1HdUG7n/jfN5Dc7dhXVAUojjGWOMMcaYGBXSjqqqDgAQkZ9UdWwor22MMcYYY0oWr2b9jxWR44A+QANgA/Chqq7wIp4xxhhjjIk9noyzEpGewG/A8cB2nKVT54vIJV7EM8YYY4wxscerWf9PAr1UNXclKhFJBF4BvvIopjHGGGOMiSFedVQbAj/47Zvr7jfGmGJLRNbhTBAtkKo2DkN1jDEmpnnVUV0A3As847PvHne/McYUZ9f7vD4N6Af8H076vSbAEGBCBOpljDExx6uO6r+AqSJyJ05aqsbAHqCnR/GMMSYsVHV2zmsReRXooaobfPZNB2YAoyNQPWOMiSlezfr/S0RaAWcC9YFU4FdVPeBFPGOMiZD6QLrfvnScbCfGGGOOkperq6jflu1hLGOMiYSvgK9EpJuItBKR7sCX2KRRY4wJCa/SU7UFVgCfAPcDnwIrRKRdkOXLisg7IpIiIrtFZIGIXBDgvOEioiLS1a/sOBHZJSKbROSeEH0sY4zxdwvwM/AG8Lv731/d/Z4Tkeoi8qWI7HHby2vDEdcYY8LFqzGq44BXgTGqqiIiwN3u/lOCrNc6oDOwFrgQ+ERE2qjqGgARaQ5cBWz0K5sEHIczqaEuMEtElqjqjKP9UMYY40tVM4AH3S0SXgX2A3WA9sA3IrJQVRdHqD7GGBNSXnVUWwAvqqoCuJ3Vl3A6kYVS1T1+534tIqtxOrlr3H2vAg8Ar/kV7wf0V9U0IE1ExgL9cSY3GGNMSIlIS6AdkOC7X1XHeRy3InAFcKKqpgNzReQr4AYi13E2xpiQ8qqjOg24BGesVo6ewDdHcjERqYPT+V3svr8KyFTVac7N2tzzqgH1gIU+xRcClwa4ZiKQ6L+/d+/epKamHkk1vdGiU2iucySfKVKxI/mZC3DllVcC8Nlnn4X0uqb4EpGHgeE47cxen0OK8wTJSy2ALFVd7rNvIc6TKN86JlJIW7fxh/FFCvzWjpQinQ8woueIgPu9jp1f3Lr9PyjSdQa/ObhYxA1l7A2Fn5JHfr87IxW3qD9bGx4rWuSC+gqRjB1rvOqoxgMfichvOI/wG+HcDZ0iIrn5BVW1b2EXEpHSwCTgPTebQCWcla+6BTg9547GTp99O4FK/ieqajKQ7L8/KSlpRP369QurVvgsnxOa61x7d/GJHcnPXIAyZcoAEFU/HybS7gJOV9U/IhA7Adjlt++w9i6Ytm7Tyn8XKfBbq2cXfpKfNwe/GXC/17Hzi1tUb81/q1jEDWXs4q6oP1v1ez8cE7FjjVcd1UXulmMJ8G1RLyIiccBEnDFYQ9zdScDEnLGqfnLSxFQGMnxe7y5q7KJKTEwEIDk52etQxpjosQ/4K0Kx03HaN19hae+MMSZcvMqj+tjRXsOdgPUOziSBC31ysJ4HNBSRW933tXAmWj2jqs+IyEac8WIz3ePtcIcMGGNMiD0KvCwiScBm3wOq6nVKvuVAKRE5TlVXuPusvTPGxBSv7qiGwutAK6Crqu7z2X8eUNrn/X9xlmed7r6fAAwTkfk4ndyBwADvq2uKvaTLCj6+ZlFw5yV9WfBxE0vGu/+92Wef4IxRjfcysKruEZEvgJEicjPOrP9ewNlexjXGmHCKyo6qiDQBBgOZwCafCVODVXWS37kHgTR31ivACJxObgrOY7lnLDWVMcYjzSIc/1acSVv/ANuAf1lqKmNMLInKjqqqpuDclQjm3KZ+7zOBG90tdOxumzHGj9tWRTL+dgJkNTHGmFgRlR1VY4wpLkTkEpyUUDXx+QM7mKwmxhhjCubVEqr/JyJn++07W0Re9CKeMeGQ3L8jyf07RroaJoqIyAjgTZy29Cqcx+89gB0RrJYxxsQMTzqqQB9gvt++3wBbh9oYE0tuBLqp6t3Afve/PYGmEa2VMcbECK86qhrg2vEexjPGmEioqqo5OaP3i0hpVZ2H3+pQxhhjjoxXY1R/AEaJyFBVzXYT9ye5+2OSPRI2pkRaKSKt3Zn2i4B/iUgakBbhehljTEzwqqN6J/A1sFFEUoDGwEacR2LGGBMrhgE13NcPAh/gLG16a74ljDHGBM2rlanWi8jJwBlAQ2AdMC8MK7UYY0zYqOo0n9fzgGMjWB1jjIk5nqWncjulP3t1fWOMMcYYE9tC1lEVkaWq2sp9vQ5nQtVhVLVxqGIaY4wxxpjYFco7qgN9Xl8fwusaY4wxxpgSKGQdVVWdCyAi8Ti5BQe5y5kaY4wxxhhTZCHPa6qqB4HugE2cMsaUOOLoFOl6GGNMLPAqAf8LwGMiUtqj6xtjYlhiYiKJiYmRrsaRKgPMinQljDEmFng16/92oC5wj4hswWdilU2mMsYUdyLSt4DDZcJWkRhw8sPJRSswUDypR7TSsQHnJRtTYnjVUbXJVMaYWPYu8BsQaBx+zPekrPNkioMi/xFkopJXCf9ne3FdY4yJEiuAB1T1sEf8IlIO2Bv+KhljTOzxZIyqiJQVkSdEZJWI7HT3dReRIV7EM8aYMJsNHJ/PsYPucWOMMUfJq0f/LwANgOuA6e6+xe7+VzyKaYwpLpIuK/j4mkXBnZf0ZWjqU0SqOriAYweALmGsjjHGxCyvOqqXAceq6h4RyQZQ1Q0i0sCjeMYYY4wxJsZ41VHd739tEakFbPMonjHGhI2IjCvsHFW9MRx1McaYWOZVR/VT4D0RuRtAROoBLwIfeRTPGGPCqT+wDPgK5w9zY4wxHvCqo/ow8AzwJ1ABZ4bsWGCkR/GMMSacLgP6uttkYIKq/hzRGhljTAzyKj3VfuBu4G73kf9WVbXEe8aYoCT37xjpKhRIVacAU0SkGnANMEZEqgMTgVdUdUck62eMMbHCqzuqiEgVoCWQ4L4HQFW/9yqmMcaEk6qmAa+LyCRgGJAE/IgtoWqMMSHhSUdVRPoDrwLp5E18rcAxXsQ0xphwEpE4oAfQD+gMfAOcZwueGGNM6Hh1R/UJ4EpVnV7omcYYU8yIyGjgapxx+BOA/qqaEdlaGWNM7PGqo1oK+LdH1zbGmEi7G1gJVAJuBW7NGd6UQ1U7RaBexhgTU7zqqD4DDBORx1U126MYxhgTKQMiXQFjjCkJQtZRFZF1OGNQAQSoCwwVkTxJ/lW1cahiGmNMJKjqe5GugzHGlAShvKN6fQivZYwxUUtE+hZ2jqpOCEddjDEmloWso2ozXY0xJch44G9gE84TJH+KM8nKGGPMUfAqPVUZnJyCfYD6QCrO8qlP2MxYY0wMeAm4CtiN0yGdrKqZka2SMcbEnjiPrvs6cC5wB3Ca+99E4LXCCopIWRF5R0RSRGS3iCwQkQvcYyeIyHwRSXO3/4jICT5lRUSeEZFt7vaM+E/FNaYYSkxMJDExMdLVMC5VvRtogtOmXQ6sEZGxInJOOOKLyBC3LcwUkfHhiGmMMZHgVUf1UuBiVZ2uqkvcfKq93P2FKQWsw0mgXQXnzuwnItIU587slUB1oCbwFc6d2hyD3BjtgLZAT2DwUX8aY4zxo6oHVfUbVe0NHA+kAcki0iUM4VOBUcC4MMQyxpiI8aqjugmo4LevPLCxsIKqukdVk1R1japmq+rXwGrgFFXd4e5XnHFhB4FjfYr3A0ar6npV3QCMBvqH4PMYY8xhRKSKiAwGpgOXAY8DC7yOq6pfqOpkYFth5xpjTHHmVR7VicAMEXkZWA80Am4DJojIuTknqer3hV1IROoALYDFPvt2AAk4He3hPqe3Bhb6vF/o7jPGmJARkZ5AX+AcYApwv6r+GNlaGWNM7PGqo5rzuP1hv/23uBs4s2KPKegiIlIamAS8p6p/5exX1aoiUhHnDmqKT5EEYKfP+51AgoiIexfW99qJOONm8+jduzepqamHV6ZFiBaZCXTtgkQqbiRjl7S4AMkfF3h4/z/rnUt/8ELB10nsXfTYkRDJ7zo0pgDLgPeBfUAPEenhe4KqDg9UMNyCaeu0efciXTNgGxkmg04dVKTzQ1XXSMU1xVNx+n8q2nnSUVXVZkd7DRGJw7kzux8YEiDGHhF5A9giIq1U9R8gHajsc1plIN2/k+qWTwaS/fcnJSWNqF+//uEVWj7niD7HYa69u2jnRypuJGOXtLhBxC6zdwcA9Qur45HEjoRIftehMQHnj+2aob6wiCTjjNEP5EdVLdKErWDauk0ri7bidf3e/vcgwuet+W8V6fw3B79ZrOOa4qk4/T8V7by6o5qHO7ngoKoG9dvJnan/DlAHuFBVD+RzahzOWNgGwD84wwPaAfPc4+3wGTJgjCmanEwDycnJEa1HtFHV/h5eO9GraxtjTHHjyWQqEZktIh3c1w/gzMz/UESC/ZPhdaAV0FNV9/lct5uInCQi8SJSGRiDM9N2qXvKBOAeEWkgIvWBe3EScxtjTFiISBsR+dTjGKVEpBwQD8SLSDkRCcuNB2OMCSevGrYTgV/c1wOBLjiJsX8EniyooIg0wRnjmgls8kmDOhhnGMDLQEOccWHzgPN9FhF4E2fc65/u+7fdfcYUa8n9O0a6CsaHiFQAHgLaAyuAJJxhAKOBbsB7HldhGDDC5/31wGNuPWKajj1sJJcxJoZ51VGNA1REmgOiqksARKRaYQVVNYXASxLmyPdOhTsWdai7GWOMV14FTgK+BS4A2uDkUn0PGKiqW70MrqpJlIBOaTSxDrIxkeFVR3Uu8ApQD/gSwO20etp4G2NMmPQA2qvqP24avrVAZ1X9IcL1OiInP5wc6SoYE1Ps/6nQ8aqj2h9nfOgW4Dl33/E462MbY6JF0mUFH1+zKLjzkr4MTX2KjwQ30wiqul5E0otrJ9UYY6KZV+mptuGXQ1VVv/EiljHGREApN5tJ7jAl//fBLGhijDGmYJ50VN1E/cOAG4D6OOtSTwSeUNX9XsQ0xpgw+gcY5/N+m9/7Qhc0McYYUzivHv0/C5yOswpVCtAEeBQnAX8xyUhujDGBqWrTSNfBGGNKAq86qlcB7dwhAADLROR3YCHWUTXGGGOMMUHwqqOaX3qpgtJOGWOijOVvNcYYE0ledVQ/BaaKyGM4aVua4IxZ/cSjeMYYY0zUWLt2LWPHjiUuLg6fhWuMKRJVJTs7m4EDB9K4ceNIVycivOqoDsXpmL7KoclUHwKjPIpnjDHGRIW1a9cyevRonn32WcqWLRvp6phiLjMzk6FDh3LvvfeWyM5qnBcXVdX9qjpcVY9V1Qrufx9V1Uwv4hljjDHRYuzYsdZJNSFTtmxZnn32Wd56661IVyUivLqjioi0BNoBCb77VXVc4BLGGGNM8RcXF2edVBNSZcuWJT4+PtLViAiv8qg+DAzHmeW/1+eQkjfXoDHGGBNTbEyq8UJJ/bny6o7qXcDpqvqHR9c3JdjuzAMAVCpbukTFNsYYY0oarzqq+4C/PLp2xH22ZAOn1KtKs2oVSdu3n9un/8H0vzcD0LNFXV46vy1VynnTkVmzYw9Nq1bMff/xovV8tjQVVbj0+Hpc37aRJ3EB/r3yH8YvWMviLbvYnZlFpbKlaF2rMgPaN6Zb89qexHz515V0b16bljUrsW7nXvp8Pp+f1m1HBM5pXIMPLj+VBpXLx1zsyX+l8vf2PVzbpiFVypZm+KylrNqxl67NanHb6eFf8CgrO5tBUxcwrtfJnlxfVfPcLfh53Xa+WJqKAr1a1qVjk5qexDXGGBPdPJlMhbMK1csiUk9E4nw3j+KF1T3fLqKq2xG9c8afZGRlM/OGDsy8oQP7srK5c4Z3N5Lbvj4r9/Ub81dz97eLOLVeVU5vUI0H/7OYV+et8iTuCz//Tb/Jv3Fs9YoM79SSt3q2Z0Tn4zmuekX6Tf6dl35Z6UncJ+cup6HbGRwy/Q9OqVeVrUMvZMv9F3Jy3arcOm2hJ3EjGfvpucsZ9v1SJixcR6d35/L8T39Tu2JZTq1XlSd+WM4zc5d7ErcgB7OV9xau9ez6VZ7+Jvf1F0tT6fH+T6Tvz2LvgYNc/OEvfLp4g2exjQmH359MDMlWFE2bNqV8+fIkJCRQt25d+vfvT3p6OgD9+/dHRJgyZUqeMnfffTciwvjx4wEYP348IsLdd+ddq2fKlCmICP3798/dN2jQIFq2bElcXFxu+SMlAyUkW1GE8/tavnw5vXr1olatWlSvXp0ePXqwbNmyI/uyYpxXHcfxwEBgPXDA3bLc/xZ72/ftz71j+t3qLUy47GROrleVk+tV5d1eJzH97388i60+r1/972o+v/p0HjinBUM7HMcXvc/g1f+u9iTucz/9zax+5zCySyuuOKEBXY+pzeWt6vNYl1Z8368DT//oTecpff9BypZyfkx/WZ/Gc91PpHr5MlQvX4Znu7Vm7tpthVyh+MV+87c1zLyhA9OvO4tVaXu49Ph6PHBOCx7p1JLJ15zBuwu86TCe+97cfLfu7//kScwcvj/XT81dzsdXnsbrF7fntYva8elVp/NkBDrnxsSCqVOnkp6ezoIFC/jf//7HU089lXusRYsWTJgwIfd9VlYWn3zyCc2bN89zjebNm/PJJ5+QlZWVu++9996jRYsWec5r164dr732Gief7M2Tl3AI1/e1Y8cOLrnkEpYtW8bmzZs5/fTT6dWrl4efrPjyqqPazN2O8dly3hd7LWsm8J9VWwCoUb4MG3cfyrq1OT0TVc2v6FHz/ftw4+4MzmxYLff96Q2qsX7XPk/i7jmQRf1K5QIeq5tQlr0HDnoS9+R6VfhsSSoAx1avyKJ/duUeW7p1N+VLeTcLMlKxt+/bT71K5WhQuTwVy5SiXd0qucdOb1CN1N0ZnsT9dUMa5zarxXVtGh629TmxoScxc/j+XK9O20uPYw8NJel2TC1Wp+3xNL4xsa5u3br06NGDBQsW5O7r2bMnc+fOJS0tDYAZM2bQtm1b6tate1jZNm3a8O233wKwfft2fvrpJy655JI85912222cd955lCsX+HdFceL193X66adz0003Ub16dUqXLs3dd9/NsmXL2LbNu5svxZUnY1RVNQXAfdRfB9isqtlexIqEZ7q25rovfuOWU5py6fH16DbxR248qQkA7y5I4bbTvOuPZ2QdpO+XvwFwUJXNezKpm+A0Cjsy9lMm3pu/Pa5oVZ+eH/7C8E7H07ZOZaqUK82uzAMs3LSLUT8s48pW9T2J+0KPNlww6Wem/72Z0+pXpeuEH7ns+HqICF/+lcqT557gSdxIxq5StjT7DhykfOl4hnXMe8fCy3/j9nWrcHzNBK48ocFhxzKzDnLrN94Ns8g8mM3wWUtz3+/MOEC18mUA2HvgYImd7WpMqKxfv57p06dz7rnn5u4rV64cvXr14qOPPuJf//oXEyZMoG/fvrz66quHle/bty8TJkzgoosu4qOPPqJXr14xnYIr3N/XnDlzqFu3LjVq1PDk8xRnnvzGE5HKIjIByAA2APtE5D0RqVJI0WKh6zG1mdWvA3+n7WHSn+vYsDuD539awb9X/sOwji0ZkXi8Z7Ef6diS5tUq0rxaRe46ozk7Mg6NppiTso3uHk1qeuPi9pzdsDp9J/9GneenU/6Jr6jz/HT6Tf6NMxtU4/WL23sS9+R6VVly63k0rFSeP//ZRa2KZflt4w72H8zms6tOZ/CpzTyJG8nYfU5smHtn/IFz8nZUP1mcymkNqnoS964zmlPd7Rz6Kx0fx7seTaQC6HNiA9bt2se6Xfvo2bIuKTsPZbWb8fdm2tSu7FlsY2LZpZdeSqVKlWjUqBG1a9fmsccey3M8p0O1Y8cOZs+ezaWXXhrwOpdddhnJycns3Lkzt4MWiyLxfa1fv57bbruNMWPGhPKjxAyvZv3/H1AROBFIAZoAT7j7+3kUM6xOqFWZSZefGva4BXWCL2lZj0ta1vMkbpn4OJ7q2pqnurZmR8Z+0vcfJKFMPFXLBe7YhFKNCmV44jzv7pxGW+xnurXO99jAk5sw8OQmnsS9qvXhd1JzxInQr713S/eNv/SUfI9dcUIDrghwl9cYU7jJkyfTtWtXZs+ezbXXXsvWrVupWrVq7vFzzjmHLVu28MQTT3DxxRdTvnzgTCbly5fnoosuYtSoUWzbto0OHTowffr0MH2K8An397Vlyxa6d+/OrbfeSp8+fbz6WMWaVx3V84FjVDXntshyERkAeDM1PArs2Z9Ftqrn+TX/t3EHJ9Wr6mmMgmRkHWTdzn3sPXCQhpXLU7X4D0UK2rKtu/n3SmeiXI9j69CiRkIhJY7O8m3pLP5nF7v3Z1GpTCla167secxIxd2+b3++d3ONMUevc+fO9O/fn/vuu4/JkyfnOXb99dczcuRIZs2aFbiwq2/fvpx77rmMGDHCw5pGh3B8X2lpaXTv3p1LLrmERx55JFRVjzledVQzgFo4d1Nz1AQyA59evDwxZxmPdGoJwLa9+7nui/n8e+U/iECXprX44IpTqV3Rm7E7p7yVTPPqFenbthF92zWmSdUKnsTxl74/iyHTFvLx4g3sP3houHGDSuUZ0bklN53c1JO4m9IzGDDld35at53WtSrzfPfWnN3o0Bieyk99za6HLvYkduL4H3ihRxtOqleVL5am0m/y73RpWhMRGDZrKRMvO8WTO9hrd+6l92f/ZeGmXTSvXpEqZUuxKzOLlWl7aFenCh9deSqNq4T+3z1ScQFqPzedxKY16d++MZe3qkeF0p6t7mxMiXXXXXfRtGlTFi7MO978jjvuoGPHjnTq1KnA8p07d2bmzJmcdNJJAY/v37+f7OxsVJUDBw6QkZFBmTJliIsrnpkpvfy+du3aRY8ePejQoQNPP/10SOsda7z6bfA2MFNExnDo0f/dwFsexQurZ35ckdtRvX/mIiqVLcXGe88HnLyqQ2cuKvBR5tFwJti05L2Faxk5ZxkdG9egf/vGXNGqPhXLePfLfdDUBew9kMX3fc8hW5Vnf1rBuU1r0apWJe799yLS9x/kzjObF36hIrpzxp/UTyhHcr9zmJ2ylZ4f/sLLF7Tl2jbOwgbe5VeAhZt30d6dcf/Y7L/45toz6eQmnv9x7TYGf73Ak47qgCm/07FxDb7r2yFPh23P/ixGzl5G/8m/832/c2ImLjhDSzo2rsGI5KXc+s1CrjyhPv3bN879vo0p7k5+ODnSVaBWrVr07duXkSNHUqlSpdz91atX57zzziu0vIgUeF737t2ZPXs2AD/99BODBg1i1qxZJCYmFrmuOtbL1j04Xn5fX375Jf/9739ZvHhxnpyzS5YsoXFj74ZZFUde9WyeAFKBa4H67utngXEexQsr3/99/rNqC78NSqSWewf11Qvb0faN7z2LHe+OFezXvjEpO/YyYeFaRs1Zxm3T/uCKVvXo374xiU1rhTzutBWbSL33/NwOzITalWnz+vesvbsHH195Khd98IsnHdXkNVtJuas75UrFc1K9qpzbrBYXTvqZPfsPMvCUpng5F7xUnJCWcYDq5cuQujuDsxtVzz12ZsPqpOz0JhXYr+vTmH7d2YfN7q9YphQjuxxP9WenxVRccL7rEYnHMyLxeOakbGXCwnX0/PAXapQvQ992ztODY6pVLPxCxphca9asOWzf66+/Xmi5uXPn5r7u379/nqT+vkaNGpXnfXJyclGqF3XC+X3169ePfv1iYsqO5zy5H6+OcaraVVVPcP/7jnqZYDSMBGfJx4PZiuJMuMlRvXxpdmVm5Vs2lJpUrcCjnY9n+e3dmHHdWZSJj+Pyj+d5EqtKudJ5MgzsyDhAfJzTTTyhVmW27PFmVEdWdjZZ2Yd+bNrWqUJy/3N4cu5yxvz8tycxc1x1QgOGTPuD9P1Z3NC2EU/9sBxVJVuVp+cu92wmeqMq5fl6+aaAx6at2EzjKt4s2xqpuP46NanJ25ecxKZ7L+DxLq34eX0ax7/yn7DENsYYE11CekdVRM4t7BxV9e52Y5ik78+i1ONTUAURWLBpJye7E5xWbN9DrQreTQrJr6ffoXENOjSuwcsXtPUk7qCTm9J1wk/cdFJjFHj79xT6t3MeT6xK25O71GionVKvKt/+vTnPrO9jqyeQ3O8czp0wlz0HvPujYHT3Exk49X80HDODxlUqsOifXTzxg7NCUuMq5ZlyzZmexH3lgnZc8cmvjPn5b9rVqZKbs3bBpp0s3rKbz68+PabiQuCf6/Kl47mubSOua9uI1N3e3L02xhgT3UL96P+dQo4rMbA61eo7u+d5X6vioY7pjowDPOlhOqM3L25X4PGyHq2W9EinljSsXJ6vVzh33O4/+9jcCVTVypVmah9vOm2Pd2lFWsbhK+82qVqBHwZ05O3fUwKUCo3ypeN5//JTWbZ1N/M2pLF+VwblS8fRrk4VOjapQSmPJgicd0wtVt7RnS+WprJ4yy7+2ZNJpTKl6N++MZceX4+aFbyZqBepuAAP++WL9Ve/Unju5hpjjIkuIe2oqqp32dejyPpd+zimWkXqVSpHZtZBRs1ZxrS/NwNw8XF1cydaeaFJlQps3J0RMHbPFnV5uGNLz1YuyhkbC7A6bQ+vzluFAucfW5vjPEpflJWttKvjTGjK7/N65ce12zimWkVa1qxEk6oVGDVnGZ8u2YzIOk+/65y4A09pSkbOZ16xmTlrt7J+176YiwvwkN8KXKvT9jBtxebcn69jq3uflssYY0z0KZ45IyKs/5Tfc1/f9+9FzFqzlUc6tmRYx5bMWrOVB2Yujkjs71d7F7vVq4fGCM5es5V2b8zi6xWbmLZiMye9mcz3q7d4Eje/z/uIx5/XP/bQmYtJXrOVYZ28/65LWlyI3M+XMV7Izo6ZFcNNFCmpP1eWrPAI5NzRBJiybBMLbumSm6w8sWlNWr/2PS+c3yamYq/flZH7etispbxyYVv6umNUJ/2xjmHfL+Wnm0KfbaCgz9sljN/15L82hu27LmlxIXI/X8Z4oVmzZowfP55+/foh4mVuElMSqCrjx4+nWbMS8dD6MNZRPQKNq1TgvxvSOK1BNcqWisszKz0rW9mXdTDmYvs2tcu27qbPiQ1z319zYkNun/6HJ3FL4ndd0uJC5H6+jPHCgAED+PHHHxk2bBilSpWyzqo5YqpKVlYWF110EWeffXakqxMR1lE9AsM7t+Tqz/7L8E4tufmkJlz8wc/ccYaTQ/Tleau41ueXbKzEPpCdzbv/S0FxkhjvP5hNaXe8YlZ2Ngc9yjxWEr/rkhYXIvfzZYxXOnToQIcOHSJdDWOKPeuoHoFrTmxIjfJlSJr9F/NTd3DgYDZ9v/yNhpXLM6B9Yx7t7N0En0jFPqNBNSb8sQ6AE2pVYsmW3ZzWoBoAs1O20dKjyVQl8bsuaXEhcj9fxhhjolvUdVRFpCzwGtAVqA6sBB5S1ekicibwOHAKcBBIBu5Q1Y1uWQGeBm52L/c28KAXCw10a16bbs1rk63K5vRMypeOo2o57/KnRjp2cv+O+R47o0E1pl13lmexS9p3XRLjRvLnq7gpqI2MaMWMMcYDUddRxanTOqAzsBa4EPhERNoA1YC3gG+BLOAV4F3gfLfsIOBSoB1OztaZwGrgDa8qGyeSOwEl3CIZ21eVcqXDEqckftclLW4g4fr5KkbybSNVdU0kK2aMMaEWdR1VVd0DJPns+lpEVgOnqOrnvueKyCvAbJ9d/YDRqrrePT4aGIiHHVVjjAmngtpIYE0k6mSMMV4RD56Kh5SI1AFSgPaq+pffsbuAa1T1TPf9TqC7qv7qvj8VmKWqlQJcNxFIDBDyNOC/R1jdpkTmF0Wk4kYydkmLG8nYxTFuQ1W9ufDTir+C2kj3eCKx09ZFMnZJixvJ2CUt7tHGju32TlWjdgNKA/8B3gxwrC2wHejos+8gcLzP++NwhgBImOqbFKHvKSJxS+Jntu869uMWp62gNjJW/21K2s+jfdexHzfSsaN9i9qVqUQkDpgI7AeG+B07FpgO3KmqP/gcSgcq+7yvDKSr+1NgjDHRTkSSRUTz2eb6nJdvG2mMMbEi6saoQu7s/XeAOsCFqnrA51gTnDsIj6vqRL+ii3EmUs1z37dz9xljTLGgqomFnVNQG2mMMbEkWu+ovg60Anqq6r6cnSLSAPgeeEVVA02QmgDcIyINRKQ+cC8wPgz1NcaYcArYRhpjTKyJujuq7h3TwUAmsMln6bnBwLHAMUCSiCTlHFDVnGzgb7rH/3Tfv+3uM8aYmFBQG6mqkyJWMWOM8UDUdVRVNYW8S3/7e6yAsgoMdbdISC5hcSMZu6TFjWTskhY3qgXRRoZDcgmMXdLiRjJ2SYsb6dhRLerTUxljjDHGmJIpWseoGmOMMcaYEs46qsYYY4wxJipZR9UYY4wxxkQl66iGgIgMEZH5IpIpIuPDFLOsiLwjIikisltEFojIBeGI7cZ/X0Q2isguEVkuImFdvk1EjhORDBF5P0zxkt146e62LBxxfeJfIyJLRWSPiKwUkY4ex0v32w6KyMtexvSJ3VREpolImohsEpFXRCTqJn6WRJFo69y4EWvvSlpb58aMWHsX7rbOjWntXRSzjmpopAKjgHFhjFkKWAd0BqoAw4BPRKRpmOI/BTRV1crAJcAoETklTLEBXuXI1yk/UkNUNcHdWoYrqIh0A54BBgCVgE7AKi9j+nzOBKAusA/41MuYPl4D/gHqAe1xfsZvDVNsU7BItHUQ2fauJLZ1EIH2LhJtHVh7F+2soxoCqvqFqk4GtoUx5h5VTVLVNaqarapfA6uBsDSgqrpYVTNz3rpb83DEFpFrgB3Ad+GIFwUeA0aq6i/uv/UGVd0QxvhX4DSkPxR2Yog0Az5R1QxV3QTMAFqHKbYpQCTaOjduxNo7a+vCKtJtHVh7F3WsoxojRKQO0IIwLhkrIq+JyF7gL2AjMC0MMSsDI4F7vI4VwFMislVEfhSRxHAEFJF44FSgloj8LSLr3UdD5cMR39UPmKDhy2X3InCNiFRwV6O7AKfxNgYIf3tXAts6CHN7FyVtHVh7F3WsoxoDRKQ0MAl4T1X/CldcVb0V5/FMR+ALnJVyvPY48I6qrg9DLF8P4Kx61gB4C5gqIuG4q1IHKA1cifM9twdOwnn06Tl3FaTOwHvhiOeag3NHYRewHpgPTA5jfBPFItHelbC2DiLT3kW0rQNr76KVdVSLORGJAyYC+4Eh4Y6vqgdVdS7QEPiXl7FEpD3QFXjByziBqOqvqrpbVTNV9T3gR+DCMITOWcf9ZVXdqKpbgTFhig1wAzBXVVeHI5j78zwDpzNQEagJVMMZt2ZKuEi2dyWlrYOItXeRbuvA2ruoZB3VYkxEBHgH5y/RK1T1QASrUwrvx20lAk2BtSKyCbgPuEJEfvc4biBKGJaxVNU0nL+yfR9DhXM5ub6E9+5CdaAx8Ir7S3Ib8C7h/WVlolAUtXclra2DMLR3UdDWgbV3Uck6qiEgIqVEpBwQD8SLSLkwpZd4HWgF9FTVfYWdHCoiUttNIZIgIvEi0gPog/cD/t/C+QXR3t3eAL4BengZVESqikiPnH9XEbkOZzZquMYRvQvc7n7v1YC7ga+9DioiZ+M8+gvX7FfcuyirgX+533VVnDFjf4SrDiZ/EWzrIALtXUlr6yDi7V1E2jqw9i6qqaptR7kBSRyaDZqzJXkcs4kbJwNI99muC8PnrQXMxpmNugv4ExgYoe/9/TB93v8Cu93P/AvQLYyfszROCpMdwCbg/4ByYYj7JjAxAv+u7YFkIA3YCnwC1Al3PWwL+G8T9rbOjRuR9q6ktXU+nzki7V2k2jo3trV3UbqJ+0UZY4wxxhgTVezRvzHGFEBEkkRERcT+qjfGmDCzjqoxxhhjjIlK1lE1xhhjjDFRKeiOqoj0F5GfRSRdRPaJyFJ3NqAxxsQEEakkIuNFZLe7Ks9onAkexhhjIiCotCIi8hxOHjdwZgKuwlmf9jScFUKMMSYWvAVc477egpNXMdxLOBpjjHEVOutfRBrj5PmKA+YCF6nqLjeX3rGqusj7ahpjjLdE5Bjgb5zE5u+r6g0iUgn4DTgOQFU9X+TBGGPMIcE8+j/d57yXVHUXgKpmWCfVGBNDTuTQ6jufAKjqbsKUcNwYY8zhbDKVMcYYY4yJSsF0VOcB2e7r291HYYhIGRFp7VnNjDEmvBZxaG3xKwFEJAG4KGI1MsaYEq7QjqqqrgXGuG87AetFZCHORIOBHtbNGGPCRlVXcWid774ishJnfH6TyNXKGGNKtqAe/avq/cAA4FcgHmgBpALzvauaMcaE3UBgIs468tVxOq4vRrJCxhhTkhU6698YY4wxxphIsMlUxhhjjDEmKllH1RhjjDHGRCXrqBpjjDHGmKhkHVVjjDHGGBOVrKNqjDHGGGOiknVUjTHGGGNMVIrKjqqIDBGR+SKSKSLj/Y5VEJHXRGSriOwUkTk+x0REnhGRbe72jIjIYQGMMSYKiEhZEXlHRFJEZLeILBCRC9xj14lIus+2V0RURE5xj1t7Z4wpkvz6V9Hc3kRlRxVnMYFRwLgAx97CScTdyv3v3T7HBgGXAu2AtkBPYLCXFTXGmKNQClgHdAaqAMOAT0SkqapOUtWEnA24FVgF/O6WtfbOGFNUAftX0dzeRGVHVVW/UNXJwDbf/SJyPHAJMEhVt6jqQVX9zeeUfsBoVV2vqhuA0UD/MFXbGGOKRFX3qGqSqq5R1WxV/Rpn2dZTApzeD5igh1ZpsfbOGFMk+fWvAoia9qZUOIKE0OlACvCYiNwAbASSVPVz93hrYKHP+QvdfYcRkUQg0X9/165dR7Rt2zZ0NTbGhN2YMWPeUdWbI12PohKROjhLVC/2298E6ATc6LM7qPYuv7aubdu2I5o1a3a0VTbGRNiUKVPWEfgJdLKqJhf1ekfT3nihuHVUGwInAp8D9YGzgG9EZImqLgUSgJ0+5+8EEkREfP4qAMD9x0v2D5CUlDQiKSnJk8obY8JjzJgx6yNdh6ISkdLAJOA9Vf3L73Bf4AdVXe2zL6j2Lr+2LjExccTkyZNDU3ljTMSIyCpVTQrhJY+4vfFCVD76L8A+4AAwSlX3q+psYBbQ3T2eDlT2Ob8ykO71l2iMMUdDROKAicB+YEiAU/oC7/nts/bOGOOFqGpviltH9Y8A+3y/pMU4A31ztMPvEZoxxkQTd+bsO0Ad4ApVPeB3vAPOE6TP/Ipae2eMCalobG+isqMqIqVEpBwQD8SLSDkRKQXMAdYCD7nndAC6AN+6RScA94hIAxGpD9wLjA//JzDGmKC9jpPFpKeq7gtwvB/wuaru9ttv7Z0xpkgK6F/liLr2Jio7qjgpWvYBDwLXu6+HuXcaegEX4oyPGAv09RnP9SYwFfgTWAR84+47apmZmdx00000adKESpUq0b59e6ZPn557/LvvvuP444+nQoUKdOnShZSUlDxlb7zxRipXrkzdunUZM2ZMnmvv3buXW2+9lZo1a1KlShU6depUaH2ee+45TjzxRCpVqkSzZs147rnn8hxfs2YNXbp0oUKFChx//PH85z//yT323nvvccopp1C5cmUaNmzI0KFDycrKyj2+fft2LrvsMipWrEiTJk344IMPivx9GWMK505aGAy0Bzb55DC8zj1eDriawx/DgUftnbV1xsS0gP0riEx7E4yonEzlDgpOyufYYpxJVIGOKTDU3UIqKyuLRo0aMXv2bBo3bsy0adO4+uqr+fPPP0lISODyyy/n7bffpmfPnjz66KP07t2bX375BYCkpCRWrFhBSkoKmzZtokuXLpxwwgmcf/75AAwaNIisrCyWLl1K9erVWbBgQaH1UVUmTJhA27ZtWblyJd27d6dRo0Zcc801APTp04ezzjqLadOmMW3aNK688kpWrFhBrVq12Lt3Ly+++CJnnHEGW7Zs4ZJLLuH555/nwQcfBOC2226jTJkybN68mQULFnDRRRfRrl07WrcOywQ/Y0oMVU0B8k2araoZQNV8jnnS3llb501bt3btWsaOHUtcXBy2LoMJlqqSnZ3NwIEDady4cSiul0T+/auwtzdBUVXbfLYRI0ZosNq0aaOfffaZvvnmm3rWWWfl7k9PT9dy5crp0qVLVVW1Xr16+u233+YeHzZsmPbu3VtVVZcuXaqVKlXSnTt3Bh03kNtvv12HDBmiqqrLli3TMmXK6K5du3KPn3POOfr6668HLDt69Gi9+OKLc+teunRpXbZsWe7x66+/Xh944IGjqp8x4YSTti7i7Uk0b507dw76+7S27uikpKToHXfcoRkZGSG/tol9GRkZescdd2hKSkrA4zhpqCLepni1Reuj/6i3efNmli9fTuvWrVm8eDHt2h0aY1yxYkWaN2/O4sWLSUtLY+PGjXmOt2vXjsWLnTHI8+bNo0mTJowYMYKaNWvSpk0bPv/888PiFURV+eGHH3LvAixevJhjjjmGSpUqBYzpb86cObllly9fTqlSpWjRokVQZY0xsc3auqM3duxYnn32WcqWLRvya5vYV7ZsWZ599lneeuutSFclIqyjegQOHDjAddddR79+/Tj++ONJT0+nSpUqec6pUqUKu3fvJj09Pfe9/zGA9evXs2jRIqpUqUJqaiqvvPIK/fr1Y+nSpUHXJykpiezsbAYMGABQYH38jRs3jvnz53Pffffllq1cuXJQZY0xsc3autCIi4uzTqo5KmXLliU+Pj7S1YgI66gWUXZ2NjfccANlypThlVdeASAhIYFdu3blOW/Xrl1UqlSJhISE3Pf+xwDKly9P6dKlGTZsGGXKlKFz58506dKFf//73yxfvpyEhIQ82w033JAnziuvvMKECRP45ptvchvCgurja/LkyTz00ENMnz6dmjVrFqmsMSa2WVsXOjYm1YRCSf05so5qEagqN910E5s3b+bzzz+ndOnSALRu3ZqFCw+tLLZnzx5WrlxJ69atqVatGvXq1ctzfOHChbmPnwIt15rzw9iiRQvS09PzbBMnTsw9b9y4cTz99NN89913NGzYMHd/69atWbVqVZ47A74xAWbMmMHAgQOZOnUqbdq0yd3fokULsrKyWLFiRb5ljTGxzdo6Y0zUiPQg2WjbCppMNXjwYD3jjDN09+7defb/888/WrlyZf3ss8903759OnToUD3jjDNyjz/wwAPaqVMn3b59uy5dulTr1q2r06dPV1XV/fv3a/PmzXXkyJF64MABnTt3riYkJOROTsjP+++/r3Xq1NElS5YEPH7GGWfovffeq/v27dMvvvhCq1Spov/884+qqn733XdavXp1nT17dsCyvXv31muuuUbT09N17ty5WrlyZV20aFGB9TEmmuDxZCrgWeAEL2N4vRU0mcrautAqyiTdkmrWrFnaoEGDSFfDE4MHD9aRI0cGdW7nzp117NixAY/l93NEjE+mingFom3L7wdhzZo1CmjZsmW1YsWKudv777+vqqozZ87Uli1barly5bRz5866evXq3LIZGRk6YMAArVSpktauXVtHjx6d59qLFi3SM888UytUqKCtWrXSL774ImAdfDVt2lRLlSqVpy6DBw/OPb569Wrt3LmzlitXTlu0aKEzZ87MPZaYmKjx8fF5yp5//vm5x7dt26a9evXSChUqaKNGjXTSpEmF1seYaBKGjuqXQAbwG3A7UNPLeF5s+XVUra0LvYC/V0ZcGpqtCJo0aZLn+4kmkeqorl69WgE9cOBASK737rvvaocOHY64vHVUD9+iMo9qNGrSpEnOL6iAunbtyl9//RXwWNmyZRk3bhzjxo0LeLx169b8/PPPRarP6tWrCzzetGlTkpOTAx6bNWtWgWWrV6/O5MmTi1QfY0oSVb1MRKoBfXCSZj8nIt/iJMqeqn7LoBYn1tYZY6KJjVE1xpgjoKppqvqaqp4NtAEWAs8BG0XklcjWzpiCZWdnM2rUKJo0aULt2rXp27cvO3fuBJzVvkSE9957j8aNG1OzZk2eeOKJ3LKZmZncdddd1K9fn/r163PXXXeRmZkJwNatW7n44oupWrUq1atXp2PHjmRnZwPOHxVPPfUUJ5xwAtWqVWPAgAFkZGTkqdfo0aOpXbs29erV49133w3684gI//d//8cxxxxDzZo1uf/++3PjFvRZc1ZHq1q1KgkJCbl/SI0bN45WrVpRrVo1evTokWcFNhHhjTfe4LjjjqNq1arcdtttqCpLly7llltu4eeffyYhIYGqVasC0L9/f4YNGwZAWloaF198MbVq1aJatWpcfPHFrF+/PujPWRJZR9UYY46Sqq4ARgEPAbuBWyJbI2MKNn78eMaPH8+sWbNYtWoV6enpDBkyJM85c+fOZdmyZXz33XeMHDkyN5XYE088wS+//MKCBQtYuHAh8+bNY9SoUYDT0WzYsCFbtmxh8+bNPPnkk3lmq0+aNIlvv/2WlStXsnz58txyAJs2bWLnzp1s2LCBd955h9tuu420tLSgP9OXX37J/Pnz+f3335kyZUrunf2CPuucOXMA2LFjB+np6Zx11llMmTKFJ598ki+++IItW7bQsWNH+vTpkyfW119/zX//+1/++OMPPvnkE7799ltatWrFG2+8wVlnnUV6ejo7duw4rI456dVSUlJYu3Yt5cuXP+x7N3l52lEVkcoiUt938zKeMcaEm4icLSJvABuBJ4B3gWMjWytjCjZp0iTuuecejjnmGBISEnjqqaf46KOPyMrKyj1nxIgRlC9fnnbt2tGuXbvcjA6TJk1i+PDh1K5dm1q1ajFixIjcLA2lS5dm48aNpKSkULp0aTp27JinozpkyBAaNWpE9erVeeSRR/jwww9zj5UuXZrhw4dTunRpLrzwQhISEli2bFnQn+mBBx6gevXqNG7cmLvuuiv32sF8Vl9vvPEGDz30EK1ataJUqVI8/PDDLFiwIM9d1QcffJCqVavSuHFjunTpEtRywAA1atTgiiuuoEKFClSqVIlHHnmE2bNnB/0ZSyJPOqoi0lVEVgFpwHqfbZ0X8YwxJpxEpJmIDBeRv4FvgHigl6oep6ojVXVNZGtoTMFSU1Np0qRJ7vsmTZqQlZXF5s2bc/fVrVs393WFChVyF3UIVDY1NRWA+++/n2OPPZbu3btzzDHH8PTTT+eJ26hRo4DlwOnElSp1aOqMb8xg5HftYD6rr5SUFO68806qVq2aO4RBVdmwYUPuOfl9N4XZu3cvgwcPpkmTJlSuXJlOnTqxY8cODh48GPTnLG5EZJWIrC5gW1NQea/uqL4DPAlUAUr7bGU8imeMMeG0HDgLeBSop6oDVXVuhOtkTNDq16+f5w7h2rVrKVWqFHXq1DmisvXrOw9MK1WqxOjRo1m1ahVfffUVY8aM4bvvvss9d926dQHLhUJ+1y7oswZKot+oUSPefPNNduzYkbvt27ePs88+u9A6FJaUf/To0Sxbtoxff/2VXbt25Q49KGgCYwy4GbjJ3W4Eavi9r11QYa86quWAd1U1XVUP+m4exTPGmHBqoqoXALNVNcP/oIi0D3+VjAlenz59eOGFF1i9ejXp6ek8/PDD9O7dO88dzYLKjho1ii1btrB161ZGjhzJ9ddfDzhjN//++29UlSpVqhAfH09c3KGuxquvvsr69evZvn07TzzxBL179w7ZZ3ruuedIS0tj3bp1vPTSS7nXLuiz1qpVi7i4OFatWpV7nVtuuYWnnnqKxYsXA7Bz504+/fTToOpQp04d1q9fz/79+wMe3717N+XLl6dq1aps376dxx577Cg/dfRT1e99tllAlv/7gsp7lZ7qBWCoiDytMf5nQqQlJiYC5JuexRgTeqqa87zy3yLSSVW35xwTkdOAr4B6EamcKR6Svoxo+BtvvJHU1FQ6depERkYGPXr04OWXXw6q7LBhw9i1a1fuamNXXXVV7qz2FStWMGTIELZs2UK1atW49dZb6dKlS27Za6+9lu7du5OamkqvXr1yyxXmySef5IcffmD69On5ntOrVy9OOeUUdu7cSf/+/bnpppsK/awVKlTgkUceoUOHDhw4cIAZM2Zw2WWXkZ6ezjXXXENKSgpVqlShW7duXHXVVYXW89xzz6V169bUrVuXuLg4tm7dmuf4XXfdxbXXXkvNmjWpX78+9957b0lMkbZTRBqp6joRqQfsKehk8aIfKSLHAd8CNYE8/0qqekzIA4ZQUlKSJiUlRboaQbOOqjGHE5HHVDUpDHHuAPoCiaqaLiJnA58DN6nqNK/jH43ExES1diM8kpKSKE6/V7zStGlT3n77bbp27Rrya4sIK1as4NhjY3ceY34/RyIyW1UTw16hIyQiLwNdgGnABcB8VR2Q3/le3VH9DPgB+BTY51EMY4yJKFX9PxGpCkwTkaeA8cB1qvqfiFbMGGOi1304qfxOBqbipPbLl1cd1WbASaqa7dH1jTEmKqjqSBGpAnwMXKyqcyJdJ2OMiSYi0gK4GqgPpAKTgn3q5VVHdQpwLmB3FYwxMUVE1gH+Y6bi3O39nFm/qto4zFUzJqqtWbPGs2vbdJjoJSK9gPdxUvmlACcCD4rItar6VWHlveqolgW+EpEfgDyJylS1r0cxjTEmHK6PdAWMMaYYeRK4xJ3hD4CInAv8H87E0wJ51VFd7G7GGBNTVNWWkTHGmODVx5m35Gu2u79QIe2oikhXnLyCsZ8YLEzOvXVtgccXrsgM6rzvX7OnkMYYY4wJuwXAXcDzAOKMj7rH3V+oUN9RvQ/4UER+xBmLME1VNxRSxhhjjDHGxKZbgakichewDmiEkxHq4mAKh7Sjqqrni0gF4DzgQmCYiOzAyZX1DfCTZQIwxhgTjSwvtTGhp6pLReR4nGWnGwIbcPqDBa5IlSPkS6iq6l5Vnaqq/1LVJsB1QBpOnqyNIvKRiJwR6rjGGFPciEhZEXlHRFJEZLeILBCRC3yOVxCR10Rkq4jsFJE5PsdERJ4RkW3u9owUttC4MVFo0qRJdO/ePahzk5KScpdrjYSmTZvyn/84CY2efPJJbr755ojV5UiIyBARmS8imSIy3u+YZ+2Nqmap6g+q+qGqzlHVLBGZEEzZUI9RraqqO/wqtwhYBDzr5hrsDlQKZVxjjIkWIjIOmAu8p6oHCzm9FM6jsM7AWpwnUZ+ISBtVXQO85Z7TCtgOtPcpOwi4FGiHky5rJrAaeCNEH8V4qLB5BcEqyvyDpk2bsnnzZuLj46lYsSIXXHABr7zyCgkJCYWWC9WKUmvWrKFZs2YcOHCAUqWcLsh1113Hddddd9TXDreHH344JNcJ9J14KBXnxmEPoLzfMU/aGxG5BHgQqAv4dm4bi0hHAFVtll/5UN9R3SQin4rIxSIS739QVXeq6qe2aosxJoYJcC3wR2EnquoeVU1S1TWqmq2qX+M0/qe4j8ouAQap6hZVPaiqv/kU7weMVtX17lyA0UD/kH8aE1OmTp1Keno6v//+O/Pnz2fUqAIXBYppBw8W9ndk7FHVL1R1MrDNd7/H7c3LwDjgZuAmd7sRyPB5n69Qd93PwMkx+CZQSkQ+wLmrsCDEcYwxJirlrFktIqWLWlZE6gAtcNL7nY6THPsxEbkB2Agkqern7umtgYU+xRe6+/yvmQgk+u9PTExk2bJlRa1iTDvvvPMAQv697N27N6TXC4UGDRpwwQUXsGjRIgC++uorHnroITZs2ED79u15/fXXadWqFTfccANr166lZ8+exMfHM3z4cIYOHcovv/zCPffcw5IlS2jSpAkvvfRS7hjfxMREOnbsyPfff88ff/zBWWedxQcffEDNmjXp1KkTAFWrVgVg5syZLFu2jLfffpu5c+cCcOedd/LFF1+wc+dOjjvuOF588UU6duwY1Od69tlneeGFFxARRo4cycCBA1mxYgXHHnss/fv3p3z58qSkpDB79mymTJlCZmYmw4YNY+XKlVSpUoWbbrqJpKSk3OtNnDiRYcOGkZ6ezj333JMnVlJSEn///Tfvv/8+QEi/k7POOuuwz7Z37978fjarikhSgP3Jqpoc1BcXovYmH5VU9W3/nSJyUFW/L6xwqCdTLQQWisgDQDfgBmCuiKwC3sNZMmtTKGMaY0w0UtUDRTnf7dhOwvnj/i8RuRxnBZfPcfINngV8IyJLVHUpkADs9LnETiBBRER9lulxf1El+8dLTEwc0bJly6J9qFiSdNlhu74b76R6fPTg7wHO//KIQ1WoUOGIy3pl3bp1TJs2jcsvv5zly5fTp08fJk+eTGJiIi+88AI9e/ZkyZIlTJw4kR9++CHPo/8NGzZw0UUXMXHiRM4//3y+++47rrjiCv766y9q1aoFwAcffMD06dNp1KgRF1xwAc8//zxPP/00c+bMoVmzZuzYsSP3Mbd/5+u0005j+PDhVKlShZdeeomrrrqKNWvWUK5cuQI/04wZMxgzZgzfffcdzZo1Y9CgQYed88EHHzBt2jS+/vpr9u/fzy+//MKECRNo3bo1ixYtolu3brRv355LL72UJUuW8K9//Ytp06Zxxhln8NBDD7F+/fqAsUP9nQRSoUIF8vl/dkewy5EWoCEhaG/ykd9t+5HBVCzkk6kA3EdY36rq9UAdnNxZQ3B66yaETur1MSf1+jjS1TCmxBCRdSKytrCtiNeMAyYC+3HaSnDStxwARqnqfnehgVk44/wB0oHKPpepDKQH8UvDlGCXXnopVatW5ZxzzqFz5848/PDDfPzxx1x00UV069aN0qVLc99997Fv3z5++umngNd4//33ufDCC7nwwguJi4ujW7dunHrqqUybNi33nAEDBtCiRQvKly/P1VdfzYIFC4Ku4/XXX0+NGjUoVaoU9957L5mZmUHd5f7kk08YMGAArVu3pkKFCnnujObo1asXHTp0IC4ujnLlypGYmEibNm2Ii4ujbdu29OnTh9mznTU9PvvsMy6++GI6depE2bJlefzxx4mLC9xt8vo7CQPP2htVHSMiDUXkQRF5VUSGikh9VX0+mIp5OmpXROrjDAXoC9QAPvAyXiRZWhNjSoyQTjl2Z86+g/NH/YU+d2IDjXH1/aWwGGdiwzz3fTtsRUBTiMmTJx82KSo1NZUmTZrkvo+Li6NRo0Zs2BA4DXpKSgqffvopU6dOzd134MABunTpkvu+bt26ua8rVKhAenp60HV8/vnneeedd0hNTUVE2LVrF1u3bi20XGpqKqeeemru+0aNGh12jv++X3/9lQcffJBFixaxf/9+MjMzueqqq3Kv53t+xYoVqVGjRsDYXn8nYeBZeyMipwFTgKk4/cFPgD9E5GJV/aWw8iHvqIpIeeAKnIG3nXCWzXoG+FxVo2+gjjHGFIEHS6i+jjPLtquq7vPZPwcnE8BDIvIUzhyALsBQ9/gE4B4RmYbzC+VenEkLxhRJ/fr1+fPPP3Pfqyrr1q2jQYMGAPhnIWrUqBE33HADY8eOLXKswjIa/fDDDzz77LN89913tG7dmri4OKpVq0YwDwrq1auX59H8unXrCo1/7bXXMmTIEKZPn065cuW46667cjvF9erVY+nSpbnn7t27l23b8sxByuXldxJKIlIKp+8XD8SLSDkgC2/bm+eA/qr6bxG5SlX7iUgPnAlZHQorHNJH/yLyHrAZeATnlvGxqtpVVScWpZOaX54vEWkqIioi6T7boz7Hy4rIOBHZJSKbROSegAGMMSZERKS9iNwuIo+JyMicLciyTYDBOGlgNvm0a9e5d1Z74aSs2gmMBfqq6l9u8Tdx7lD8iZMC8Bt3nzFFcvXVV/PNN9/w3XffceDAAUaPHk3ZsmU5++yzAahTpw6rVq3KPf/6669n6tSpfPvttxw8eJCMjAySk5PzHb/pq1atWsTFxeW5nq/du3dTqlQpatWqRVZWFiNHjmTXrl1Bf453332XpUuXsnfvXh5//PFCy+zevZvq1atTrlw55s2bxwcfHHrwe+WVV/L1118zd+5c9u/fz/Dhw8nODrxmkZffSYgNw3nM/yDO06F9wDCP25s2QJ5sT6r6LXBCMIVDfUd1L9A9mFu5hSgozxdA1XxWNEgCjgOa4OTrmuUOBJ5xlPUxxpjDiMgg4AXg38AFwHScMV1TgimvqinkzSvof3wxzqSGQMcU527H0EDHTXQrSv5Tr7Vs2ZL333+f22+/PXfW/9SpUylTpgwADz30ELfffjtDhw5l2LBh3HfffUyZMoWhQ4fSp08f4uPjOf3003n99dcLjVWhQgUeeeQROnTowIEDB5gxI++v5x49enD++efTokULKlasyN133x3wEX4gF1xwAXfccQddunQhLi6ORx99lAkTJlC2bNl8y7z22mvce++9DBkyhM6dO3P11VezY8cOAFq3bs2rr77Ktddey549e7jnnnto2LBhwOs0atQopN/JmWeeGdRnLip30lVSPse8am+ygDI46agEQETOA9YEU1iiedy9iIwCGqpqf/d9U5wcg6UDdVRFJBX39rL7/nHgOFW9JtiYSUlJGmgAdmG8GqMaiaTQxhR3IvJYCGbBBhPnb2CAqv4gImmqWk2claWuUdV+Xsc/GomJiVqix9QHmPVf8PlHPus/KSkp4MQe462lS5dy4oknkpmZGY5E+p7L7+dIRGaramLYKxQkEZkKPK+qs0VkN/AjcCpwRTBDqYrrv1yKiOSsjHC/qm4VkWpAPQ7P83VpoAvkl1uwd+/epKamFrlC+/fvBziisgXpdtKekFwn1PUyxgBQW1V/cF9ni0icqk4XkUkRrZUxJdSXX37JhRdeyN69e3nggQfo2bNnTHRSi7l/ATljJkYCm4DrVDXwgF8/xe1fbytwGrAAJ4vAqzh5B3vg5PiCw/N8BVyuNb/cgklJSSPq169/2PmF3dlcmuLcmb5+VKARCYcU9c7mzP8VfL1gPTTw8M9kjDlq60Wkqbvc6XKgl4hsxUkzZYwJszfffJP+/fsTHx9P586dee211yJdpRJPVdf7vH6uqOWLVUdVVdOB+e7bzSIyBNgoIpVwcnyBk9srw+f17vDW0hhTgjyLM2N/Dc6dgs9wxmLdEcE6FXuW7s8cKf8xrybyROR78hmLr6pdAu33FbaOqog0BjaoaigX180ZYBunqmkishEnt9dMd7/lFTTGeEZVx/u8nu4OQSrj/lFtDEBQqZWMKUwx/jl63+99XWAA8GEwhcN5R3UNsEZEnlLVAhONFZDn6xRgB7ACqAb8H85atjmP+ycAw0RkPk7y7IE4X4YxxoSE75KB7opSvrKALHesauA8NqbEyc7OJjMzs8DZ58YUJDMzk4MHQ3mfL3xUdZz/PhH5GHgPGF5YeU+WUM1HM5wVCWoHcW7APF/AMcAMnMf5i4BMoI9PuRHASpylWmcDz1lqKmNMiPmOg8/CWXbQd8vZZwwAAwcOZOjQoWRmZka6KqYYyszMZOjQoQwaNCjSVQml9UDbYE705I6qiJyhqr/67lPVFBGpo6pPFFa+oDxfFHCrWFUzgRvdzRhjvNDa53WziNXCFBuNGzfm3nvv5fHHHyc+Pj6sKxGZ4k1VOXjwIPfeey+NGxfPNJMi4p+qrwJwNfBTMOW9evQ/E2cik78ZQHWPYkbUSb0+jnQVjDFhoKq+6zJeparP+5/jroo3Jny1MtGucePGjBo1KtLVMCYSbvJ7nw78DBzWdgYS0o6qO15LnJci5J3l1RznkZgxxsSK4QRubIdhHdXgBEq8v2ZR/seOIvG+MSb8VLXT0ZQP9R3VLA7NxPfvlGYDhT72N8aYaCci57ov40WkC3n/KD8GS4sXVpbOypjoJiLlgVrkbSvnAJ1xVmhNya9sqDuqzdxKzAZ8e9AKbFHVfSGOZ4wxkfCO+99ygO+MVsVZdeX2sNfIGGOikIgkAY8QeAL/Spx+Y76T+0PaUfXpETcJ5XWNMSVLtN8hU9VmACIyQVX7Rro+xhgTxW4FTsDplOZQYBdQlUNP4gPyatZ/deA+oD2HljZ1anaUYxWMMSZa+HZS/XOqWh5VY4wBoJSqrvDf6aakLjQ5rFez/j8AygKfAHs9imGMMRElIicDr+LkAyyXsxvnDkF8pOpljDFR5Px89ncLprBXHdWzgVpuXlNjjIlV7wFTcXI32x/lIZLcv2Okq2CMCZ1WItIqn2O/AojItar6QaATvOqo/gE0JO94BGOMiTVNgEe0GC/CXdyce+vaw/YtXJGZ77HvX4v+JOnRPibbmKPkn0c1h+D8sQ/OyqVh7ah+D8wQkXdxZsDmCrTmqzHGFFNfAt2BbyNdEWOMiUbBzE1S1fyGB3jWUe2Is46r//gDJW8qF2OMKc7KAV+KyFwO/6PcsgEYY0o8d6JpDyBdVX8oanlPOqqq2sWL6xpjYkOgR7S+CnqU6ysKHusucTdjjDGBjcXpqJYWkedU9XkRuRY4XVXvKqywV3dUEZEawIVAXVV9TkTqA3Gqut6rmMYYE06q+lik62CMMVHuSqAFUAP4DGfZ6WnA08BdhRXOdyWAoyEinYFlwHU4a2EDHAe87kU8Y4yJFBHpJiLviMhU9/2pPkusGmNMSZcOpKnqEqCeu28XUCWYwl7dUX0R6K2q34lImrvvV+B0j+IZY0zYicjtwJ3A2zh3DQD2Af+Hk6avxIjkzPWTen0c9phHLOmyw/etWZT/saQvva2PMd57AXgQGOmz705geTCFveqoNlXV79zXOWlb9nsYzxhjIuEu4DxVXSMiD7j7/gJaRq5KxhgTVW4FGonIXUAlEdkOZAJXBVPYk0f/wBIR6eG3ryvwp0fxjDEmEioB69zXOX+Ul8b5w7xQIlLWHTaQIiK7RWSBiFzgHmsqIioi6T7bo35lx4nILhHZJCL3hPKDGWNij4gMEZH5IpIpIuN99nvZ3tyMM5nqSpxsUJ2BJqo6N5jCXt3hvBf4WkS+AcqLyJtAT6CXR/GMMTGkGD3KnYPzSOsJn313ALOCLF8Kp6PbGViLMwH1ExFp43NOVVXNClA2CWfsfxOgLjBLRJao6owifQJjTEmSCozC6TiWD3A85O2Nqn5/5NX1Lj3VLyLSDmcy1Tichvh0m/FvjIkxtwNTRWQgziOtZcBu4OJgCqvqHpxfADm+FpHVwCnAb4UU7wf0V9U0IE1ExgL9AeuoGmMCUtUvwJn0ibOCaLCOuL0RkRFB1i1gFhXPxoyq6gbgWa+ub4wxkaaqG0XkNOA0nDsN64B5qpp9JNcTkTo4aVwW++xOEREFZgL3q+pWEamGM3t2oc95C4FLA1wzEUj035+YmMiyZcuOpJp8Mefwj7d2414Anhq7NGCZyzvlM9LsrBuKFLvb2j1FOj/fz1jEuBzhdxVM3DfdfQEjhCquiWVVRSQpwP5kVU0u4rWOqr3JR/MgzpH8DoSsoyoib6nqIPf1RA6N18rDVmsxxsQKEZmiqr2Aee6Ws/8LVb28iNcqDUwC3lPVv0QkAacDvAAn/+Cr7vEeQIJbbKfPJXbijJnNw/1Fley/PzExcUTLlkc252vmS4cvxJCWHu8c+1/FgGUeGpjP4gwfPli02P+8VKTzQxWXHiGafR+puCaW7VDVpKO8xlZC0N4EcrT9vlDeUV3t8/rvEF7XGGOiVX6r8CUW5SLuEoMTcSZhDQFQ1XRgvnvKZhEZAmwUkUo4eQkBKgMZPq93FyWuMcZAdLc3IeuoqupTPq9ttRZjTMwSkZx8gGV8Xuc4BkgpwrUEeAeoA1yoqgfyOTXnKVWcqqaJyEagHc4jOtzXiwOWNMaYooma9iaUj/6DWonlaGd/GWNMFGjk/jfO5zU4jfs68k6QKszrQCugq6ruy9kpImcAO4AVQDWcRQSSVTXn8dsEYJiIzMfp5A4EBhT1gxhjSg4RKYXT94sH4kWkHJCFM4FzB1HY3oTy0f87fu8b4DTa23DGOwiwHudugzHGFFuqOgBARH5S1bFHeh0RaQIMxkl+vcm5uQruvmzgSaA2znKDM4E+PsVH4HRyU3BWw3omkqmpilFKMWNKsmE4bUeO64HHcObyRWV7E8pH/81yXovIwzid00dVda+IVMBZOmtbqOIZY0ykqepYETkOp0FvAGwAPlTVFUGWT6GA2a7AhwWUzQRudDdjjCmUO+kqKZ/DnrU3InI+cDVO9oBU4NNgO7perUx1N/Cgqu4FcP/7EGArpxhjYoaI9MTJd3o8sB1n6dT5InJJRCtmjDERJCLTReRS9/UDOE/dU4EpwCZgnIgMDeZaXuVR3QOcDvzos+80YK9H8UwEJCYmApCcnBzRehgTQU8CvVQ1dyUqN2/pK8BXEaqTMcZE2lkcWo30LqCLqv6Vc9BNY/o9QeTb96qj+igwQ0Sm4kwsaISzUsttHsUzxphIaAj84LdvLkVb8cUYYw5TzG8GZQEVcVLuHcRZItrXGpxx+IXy5NG/qk4EzgCW4uTa+gs4091vjDGxYgFwr9++e9z9xhhTUiUD40XkeOBx4DUROVZEyohIc2AseSd15cvLJVSXAEu8ur4xxkSBfwFTReROnKdHjXGGPvWMaK1CoJjfzTHGRNZtwGvAnxy6KXoDzuTRnByt13F4xqjDeNZRdScTdAZq4jOr1ZZQNcbECnep01bAmUB9nMkCvxaQtN8YY2Keqm4GrnBX3asHlD3Sa3ny6F9ERgBvute/CictVQ+cZLLGGBNL1G8LatyVMcbEOlXNVtUNqroq0BbMNby6o3oj0E1VF4nIAFW9W0Q+xEk0Wyh3jdn+QBucnIT9A5wzHCdJbTdV/Y+7ryxOQtorcTIMPKuqY0LweUqkc2/1H/uc18IVmUGd9/1rjUNWJ2OiiYi0BSbj3C3YgDOJKkNELlPVhZGsmzGm+Aj0e7Sg37HF6feqiLyb37GcxVMK4lUe1aqqush9vV9ESqvqPJyhAMFIBUYB4wIddAfiXgVs9DuUBBwHNAG6AEPdJLPGGOOFccCrQENVPR0n6f8r5NN2GWNMCbTOZ1uPMxz0UpwVrgrl1R3VlSLSWlUXA4uAf4lIGpAWTGFV/QJARE4lcJqXV4EHcAbq+uoH9FfVNCBNRMbi3JmN2LKCxpiY1gJ4UVUVQFVVRF4i/5VfolPSZYfvW7Mo/2O85Gl1TPjYpDnjNVUd7r9PRM7ESWVaKK86qsNwllAFeBD4AEgAbj3aC4vIVUCmqk7zWRcbEamGM2DX93HbQpxee6DrJAKJ/vt79+5NamrqYed3O2nPkVfaR6BrFyRScYOJvea7g0GddySxTWyL5M91iE0DLgG+9NnXE/gmMtUxxpjop6q/iEinYM4NeUfVneGVAfziVmYecGyIrl0JZyWYbgEOJ7j/3emzbydQKdC1VDUZJ89XHklJSSPq169/2Pkz/5dVtMrm46GBh1+7IJGKG0zstPR497yKIY9tYlskf65DLB74SER+49DiJqcAU0RkQs5Jlu3EGGMOEZEyQC8RiVPVAieghryjqqrZIjJFVQN2EI9SEjBRVdcEOJbu/rcyTkc55/VuD+phjDHgDG1a5PN+CfBthOpijIkhJ/X6ONJVCAkRuRxnqGY6cK17A/NMoFxhnVTw7tH/HBE5U1V/CfF1zwMaikjOEIJawCci8oyqPiMiG4F2wEz3eDtgcYjrYFyx8j+RMUdKVR+LdB2MMSbKjQHuB6oBTwPn4qQtfRFn+FSBvOqopgDTRWQKzuOwnFUIAg6q9Scipdy6xQPxIlIOZ93Y84DSPqf+F2e5wunu+wnAMBGZD9QBBgKFpj4wxhiTV3L/jpGuggm1ok6aS/ry8H3GFF01VZ0oIqVx05Sq6mI3g1OhvEpPVR4nt6DizNpv5LMFYxhO2oIHgevd18NUdZuqbsrZgINAmqrmPPYfAazE6SjPBp5TVZvxb4wxxpiISkxMzM2yUMLMFpHT3RX7ygCIyIkEuQiUJ3dUg0ngWkj5JIJI76KqTf3eZ+IsNnDj0cQ3xhhjjDEhsQaYKiIfAGVE5AXgGuCNYAp70lEVkWPyOZQJbAxm8KwxxhhjjCn2quJMMq0BfIqzzPRgVf0qmMJejVH9m0PjUsXnNUC2iHwF3Kqqmz2Kb4wxnhOR/wM+UtWffPadDVytqndFrGLGGBMljjY9n1djVAfiJPlvAZQDWgITcRL+t8HpIL/qUWwT40rwOB8TffoA8/32/QZcG4G6GFNkyf072sQ54zkROUFEhovI6yLyqIicEGxZrzqqjwEDVXWlqu5X1b9xOqmPqupfOMuaJnoU2xhjwkU5vB2ND7DPxCD7o9mYwonIDTiLQB0P7AJaAb+6+wvl1aP/OKAp8JfPvsY4DTjAHg9jG2NMuPwAjBKRoe5iJ3E4E0F/iGy1jDERVdRUYLzkaXUibBRwvt8QqQ7AJJyn7QXyqrP4IvC9iLyLk0e1IU4+0xfd4xcCP3sU2xhjwuVO4Gtgo4ik4PxBvhHoGdFaGWNM9KiAk/fe1zyg4PXXXV6lp3pWRP4ArgJOxmm4b8rJaaqqk3HyrBpjTLGlqutF5GTgDJw/yNcB8yyziTHG5BoDPCUiw1V1r4hUAB4HXgimsGfjqFR1hqrepKoXqOqNlnjfGBOLVDVbVX9W1U9V9ZeidFJFpKyIvCMiKSKyW0QWiMgFAc4bLiIqIl39yo4TkV0isklE7gnVZzLGxCYRGSIi80UkU0TG53NOqNubgcAdQJqIpAJpwO3AQBFZnbPlV9jGiRpjTBGIyFJVbeW+zrNEtC9VbRzE5Urh3IXtDKzFGRb1iYi0UdU1bozmOE+nNvqVTQKOA5oAdYFZIrLEbgp449xb1x62b+GKzHyPff9aMP/8xoRdKs6Y0R44q4jm4VF7c/PRVNg6qibqBGr0fRX0y8GX/aIwHhno8/r6o7mQqu4h7yp8X7t3Fk7BWc0FnFR+DwCv+RXvB/RX1TScOxVjcTKqWEfVGBOQqn4BICKn4gxX8hfy9kZVvz+aOltH1RhjikBV5wKISDzOcs2D3OWbj5qI1MHJP73YfX8VkKmq00TE97xqQD1goU/xhcClAa6ZSIB0gImJiSxbtsx5c1ZQWWJydVu7p0jnA4di+fM4tpdx13x30Dl20uHHQhWX/K5zJCIZu6QJ8F2/6e4L9K0e5c91VRFJCnBasqomB3vNULQ3+Vx3RH7HVPUx95yHVPWpQOdYR9UYY46Aqh4Uke44ywEeNREpjZOu5T1V/UtEKgFPAt0CnJ7g/nenz76dQKUA9UwGkv33JyYmjmjZsqXz5sMHi1TXmf8UPZXOQwPzecLhcWwv46alOxkXZ/7v8MnLoYpLjy+Ldn5BIhm7pAnvz/UOVU0q0gX8hKq9yUfz/ML6vG6WX+GQdVRFZCL5jNXydbRLaRljTBR5AXhMREao6oEjvYibf3UisB8Y4u5OAibmjFX1k+7+tzKQ4fN695HWwRTdSb0+jnQVjAmVJDxqb4Lp96nqoPyOhfKO6t8hvJYxxhQHt+NMLLhHRLbg88d6kJOpEOcZ2ztAHeBCnw7veUBDEbnVfV8LZ6LVM6r6jIhsBNoBM93j7XCHDBhjTBF52t6ISEOcMf2NgBRgkqpuCKZsyDqqOeMMjDGmBDmqyVSu13GWFOyqqvt89p8HlPZ5/1/gHmC6+34CMExE5uN0cgfiLKxijDEBiUgpnL5fPBAvIuWALDxsb0TkNGAKMBXoC3wCLBSRi1X1l8LKezZGVUTKAC2BmviMQzja2V/G2OM2Ey1UdfbRlBeRJsBgIBPY5DOBYbCqTvI79yCQpqo5j+FG4HRyU4B9wDOWmsoYU4hhOG1HjuuBx/zHuIa4vXkOJ2PAv0XkKlXtJyI9gNFAh8IKe9JRFZFzgE+BsjjjGHbhDLpdBxzjRUxjjAk3ESkLDAf6ADVUtYo7waqFqr5SWHlVTSHvhIKCzm3q9z4TJ+vAjUWttzGmZHI7pElBnNfU7/3RtDdtgP/4Xe9bEfkomMJerUz1AvCsqlYHdrv/fZzD83IZY0xx9gJwInAdh8anLgb+FbEamZiXmJhIYmJipKthTLCygDLuawEQkfM4lCu6QF49+m8B+OdaeBpYDTzvUUxjjAm3y4BjVXWPiGQDqOoGEWkQ4XoZY0y0mAecAcwGSonIDOBU4IpgCnvVUd2J88h/B7BRRE4AtnEoF5cxpghy7p4kJydHtB7mMPvxa0dFpBZOe2eMMcZ5wpSTb3oksAm4TlWDaie96qh+gbNm9QfAOGAWcAD4zKN4xhgTCZ8C74nI3QAiUg94EQhq7JUxxsQ6VV3v8/q5opb3pKOqqnf5vH5eRH7FuZv6rRfxjDEmQh4GngH+BCoAK4CxOHcNjDGmxBOR78ln0qiqdimsfFiWUFXVH8IRxxhjwklV9wN3A3e7j/y3qmqhK/QZE6xzb1172L6FKzLzPQbw/WtBrTVhTLi87/e+Lk4O1g+DKRzKJVRnqOr57usfyGc5VVXtFKqYxhhv2djYwolIFZyc0Qnue8ByRhtjDICqjvPfJyIfA+/hpPcrUCjvqE7wef12CK9rTMzL785IjsLuoOSwOynhJSL9gVdx1sLe63NIsZzRxhiTn/VA22BODOUSqh/4vP1LVX/1P0dETg9VPGOMiQJPAFeq6vRCzzTGmBJIRPr57aoAXA38FEx5r8aozsRJT+VvBlDdo5jGGBNupYB/R7oSxhQ3NqyoRLnJ73068DNB5tUPaUdVROJwZnaJOAO1fGd5NcdZncAYY2LFM8AwEXlcVbMLPdsYE1HWQQ6/o52bFOo7qlkcmkTl3ynNxnlMZowxxZaIrONQOyc4M1iHikie5NWqagOGjSdO6vVxpKtgTJGISHmgFnlvYM4BOgOqqin5lQ11R7WZW4nZgG8PWoEtqrovxPGMMUfBJnEdkesjXQFjjCkuRCQJeASIC3B4JU6/MdAxIMQdVZ8ecZNQXteYks7uoEQPVZ0d6ToYU9LYI/ti7VbgBJxOaQ4FdgFVySedaQ5PJlOJSHXgPqA9bm7B3JpZHlVTTFlDafyJSBlgGNAHqA+k4iyf+oSqZkSybsYYEyVKqeoK/50ioqp6sNDC3tSJD4CywCfkzS0YFBEZAvQH2gAfqmp/d/8JOPlam7un/gbcoapL3OMCPA3c7B5/G3jQVooxxnjkdZxk/3cAKThPkx4GGgA3RrBexkSPpMsO37dmUf7Hkr70tj4m3M7PZ3+3YAp71VE9G6ilqplHWD4VGAX0AMr77b8S5xdCHHAbzt2LnKSxg4BLgXY4t5JnAquBN46wHsYYU5BLgeaqusN9v0REfgX+xjqqxkSWdZAjTkQeAWaLSBP/CVOB8u0Hku/g1aP0B9DwSAur6heqOhnwn0W7Q1XXuHdIBTgIHOtzSj9gtKquV9UNwGicO7PGGOOFTTjJq32VBzZGoC7GmBBLTEzMHfZljsg/OE/CVx/pBby6o/o9MENE3sVpyHMFWvO1qERkB87Y1zjyrhPbGljo836huy/QNRKBRP/9vXv3JjU19bDzu5205whrm1egaxckUnEjGTuSn7kg+/fv9+S6EL3f9ZrvDgZ1Xqz8Gx+BiTht3cs4SwI2wnnSM0FEzs05SVW/j1D9jCl2AmUZKSgDSQnLOlKsqOpYERmHMxzqiHjVUe2I02j7jz9Q4Kg7qqpaVUQq4txB9b2VnADs9Hm/E0gQd8Su3zWSgWT/ayclJY2oX7/+YTFn/i80axU8NPDwaxckUnEjGTuSn7kgZcqUASDQz8fRitbvuul5n7nnhTdusEL9b3wEBrv/fdhv/y3uBk67d0zYamSMMVHEnTBVcI7DAnjSUVXVLl5c1y/GHhF5A9giIq1U9R+cZbl8l26tDKTbZCpjjBdUtVmk62BMcZTcv2Okq2CKCa/uqCIiNYALgbqq+pyI1AfiVHV9CMPE4YwPa4AzDmIxzkSqee7xdu4+Ywplye/N0RKRLsBBVZ0T6boYY0ws8CqPamfgc2A+0AF4DjgOJ7dqzyDKl3LrFg/Ei0g5nCVZuwBbcSZrVcTJDJAGLHWLTgDuEZFpOI/b7gVeDtkHM8YYHyIyG3hYVX8UkQeAe4AsEXlVVZ+McPWMMUVgY2Ojk1ez/l8Eeqvq+TgdTIBfgdODLD8M2Ac8iLNc4T53X1XgQ5yxpytx8qme75NY+01gKvAnsAj4xt1njDFeOBH4xX09EOeP6TM5ND61QCJSVkTeEZEUEdktIgtE5AL32AkiMl9E0tztP24u6ZyyIiLPiMg2d3vGzSVtjGciNQv+pF4fh2SFvuT+HUv0sAMRGeK2K5kiMt5nf0TaGxE5U0ReL+gcrx79N1XV79zXOeND9wcbT1WTgKR8Dn9aQDkFhrqbMcZ4LQ5QEWkOiM/iI9WCLF8KWAd0xplscCHwiYi0wfJGG2NCL+J56kWkEXCDuzUACkxg69Ud1SUi0sNvX1ecO53GGBMr5gKvAM/jNrZup3VrMIVVdY+qJrn5obNV9Wucxv8UyxttjAm1SOWpF5EKInKDiPwHZ0GULsATQB1V7VdQWa/uqN4LfC0i3wDlReRNnLGpvTyKZ4wxkdAfp73bgjMWH+B44KUjuZiI1AFa4DMJ9GjzRueXMzoxMZFly5Y5b866oUj17La26Hlwc2P58zh2SYsbytgEuM7evXsLjhGCuNH0XReUS7rA7yC8n7mqiCQFOC3ZTcUZtFDkqQ9wzXeBK3CeGk0A+qpq0EmwvUpP9YuItMUZXzoO59HW6SGe8W9MWIVifJSJLaq6Db8cqqr6zZFcS0RKA5OA91T1L5/rHVXe6PxyRicmJo5o2bKl8+bDB4tU15n/FL0f/tDAfCaeeBy7pMUNZWx6HP5EtkIFZyG23J+dYBTj77qgXNL5xg1R7IL4xd7hDpk8aqHIUx/AtThDC54FPlfVIvXKvZr1f5+qPu9Wynf/Pao6xouYxhgTbm7nchjOWKv6OI3xROAJVd1fhOvEueX2A0P8j1veaBMJNgu+ZPKgvWkCXIeT+ek1EZmM097NVNXswgp7NUZ1eD77h3kUzxhjIuFZnPH3t+BMMrgFOBd4JtgLuDNn3wHqAFeo6oF8TvXNGw2H8kbnsLzRxphQCVl7o6qbVHW0qrYFzsEZKjUBWCciz4lIu4LKh/SOqs/a1vFu4mvf1AXHALtDGc8YYyLsKqCdOwQAYJmI/I4zfuvuIK/xOtAK6Kqq+3J2ikg3LG+0MSaEIp2nXlUXAAtE5D7gApwhBr8C5fIrE+pH/++4/y2HMzY1t27AJuD2EMczxphIyi+PYFD5BUWkCTAYyAQ2+aQlHIwzDOBloCFOLul5HJ43+hgOZVN5G8sbbYwp2DBghM/764HHcO6OhrS9EZGuQDXg36rqO74VVT0IfI0z8b5qQdcJaUc1Z91rEZmgqn1DeW1jjIlCnwJTReQxnBmtTXB+EXwSTGFVTaHgTq3ljTbGhEyY89TvBm4GXhGRpcA0YJqqLvK79o6CLuLVrP++7u3ls3HGN6wHflbVrIJLGmNMsTIUp2P6KocmU32I89jMGGNKLFX9FbjGnSx6NnARMMm9gzrN3b5T1b0FXcerWf8tcW7plsdJTdUIyBCRnqq6tMDCxhhTTLgz+4eT/wRSY2KKpekzReXO7J/rbg+JSAOcVfhuAsaJyG+qen5+5b1K+P868BbwfE7qAnfg7Gs4A3aNMSYmuH+Yt8PJM5hLVccFLmGMMSWXu7LVWGCsm+Kvc0Hne9VRbQ9088uv9SLwiEfxjDEm7ETkYZy7qQsB38dXSt4JpcYYUyKJyIj8jqnqY8B/CirvVUc1FaeH/L3Pvo7u/pgWHwfP3l6be1/6x5Pr16kez+btB3PfJ55Sgc4nOSuF/PjHXv4zr8ChHkds7MN1Sf59LzN/3cM/aQcLLxAi5coK/S+qQpN6pZm3eB9T5qTzyIAanH5CeVZt2M8zE7aTutWboc8icHliJRrUKsXUuemk7TrIXX2qU69mKX7/K4N3vtpBlgdfRaT+jQG6n1GRC86uSNN6pSlXRtiy4yB/rdnPxOk7WbfZuyHmkfr5CoG7cFbd+yPSFTHGmCjV3O99HZw+4dfBFPYq4f/DwFci8pGIPCMiHwFf4bfUYCyKi4O2x5X17PpvP1Iv93XPjgncekVVlqVk8ldKJgN7VaVXp4QCSh+5RnVKc/Lx5Zg0sj6j76xNt9MrULZ0UBl4jsrd11TnmAal+fnPfXRoW4Gnbq3F7j3Z3DlmM6tSD3D71dU8iz34sqqc1aY8DeuU4rk7atOrcyWSf9/Lp9/t4vTW5RhwcRVP4kbq37jvhZW54YLKzFuSwWff72brjoN8P38vm7Zn8eI9dTi5pXc/15H6+QqBfcBfhZ5ljDEllKr29dt6AJfgpOUrlFez/r8SkZOA3jgzYRcBw1V1uRfxwu39kfXzPRbn8e9W8bl+r04JJI3dypLVzkqNC5dnMrRvDabMSQ953ANZyr0v/kOd6vF0P6MiN1xYhTt6V+eHBXuZ8cse/lgR1M9bkZ16QjmuH57Kvkzl+//u4ctnG3Lxm+vJPKC8/vkOPn4i/3+Lo9XllArcOGoj8XHCF880YOave3Lv3q5Yu58nbq3N2Ck7C7lK0UXq3/iSjpW47blNuXdzZ/22l5GDanLzE5tY9Hcmgy+rxuCnN4U8LkTu5ysEHgVeFpEkYLPvgWCWBjTGmBLqe+CLYE4M9cpUFXBStZwI/A48papR+xvmSFWuEMcbX6Sxcdvhj0JLxwtP3FrLs9i+o35rVInP7cAA/JWyn1pV4z2LDbB5+0EmTt/FxOm7OLF5WbqfUZFRg2uxe2821w0P/ciOODn0mZ1ZeZDt7vB6SfPy5eLYs08BZW+m5hlikLIpi6oJ3jyQiNi/scD2XYceu2/fdZAqCU6s/y7NYMTNXo0UOiTcP18hMN79780++wTnx9Xb/xmNMaYYEJHz/HaVx1nVb3Uw5UP9m+dV4FRgOnAlUIMYXI1qxbr9ZB5Q/rfs8D546VJBLklzhMqUFh7sVwOAuDihWuU40nY5N24qlheyDnrTeZMAH2rRykwWrczk5U+2c067Cp7E/X1ZBkkDa/LjH/s4q015/rcsg39dXo2pc9O5sEMCi1d593fQrvSDVCgn7M1QXvxwe55jVRLiyMj05ruO1L/xvMX7eLBfDT769y5EhD7dK/P7MmdRkoTycezJ8O4GYaR+vkKgWaQr4KXX/ruKW087psjlGtUpdVRjmrfv20/18mWCPr9cWaFR7VKkbs1y/7g8cgV95mMalGbVhgNHdf0cqsq8DWls2O38P9agUjlOb1ANCfQ/QwCVK8axa8/R/z/5/+2dd3hU1fa/35XeCElIJaFIE5CmiCKiIhaK/YpeVESxixUvilcsWO4X1KtcBbEgSNMrIP6wooKIgkqTchEBpbdAOqmTTDL798eZhEkygZkwM2eS7Pd55knm7HPmc84+e9ass/faa7tb156isKycP7MLaRcbQUzYyfU9eY9dueZOrUNIjA3EZoMDGVavxuhX4sn25WdMr/E+HmO51kGuHOxpR3UwcJZSKl1EpgA/0Qgd1TlfH8NS5vyLYi2Hx7w0kQrgw2+ODzUvWm4lKvy4E9OzQxjrt1nqOvSU2LKzbofQWm4ME3uDyR/lcOc1MfTrHs7inwr4a38Zz90Vz5SxSWzfW8ar87JP/iH15P+tKCQ6MoBiixGr6Ui/HuGs2VpSx5Gnhln3+K2FuTxwQywvP5gIwLo/LExZYDjoUeHCWwtzvaIL5rWvU8W+shT2hNZJwNGGOuT/+q87q71XSjFx1V9Yyo3Leey8Di5/1isPJXLT0671gP+8P5u7vthIgAgzrz6Tp3/Yxu7cIsoqFAuG9eG8VnG1jnlkeCxvfGy0x27tQxk/qgWHs8pJTQhi8kc5rNnq2nfE3Wt+95/JpGeV88P6YpavL2Lfkfo5L9/tymD0V5vp2CKS1GbhABzML2FnThHTrujJ5e0Tq+1/TtcwHhkeR1ZeOVMW5PLU7S0ICRaCg4RJc7Kddpo4oz513SopiNHDYlEKpi7IZcSQaPr3jOBghpVJc7LZ72IdjP5qM9Ou6AnAqv3Z3LxoPe3jItmZU8S7V/ZkaMfkavt76h6/9NMOnr7wdAD+yMzn2o/XYLUplFLMH9aHc9OqX3OPjqHc/7cYCksUnVqH8PuuUppFBFBeoZg4O5tMFyd7bjl6jLu/2MShAgtDOiTy8qVnEGt3js+ZvoK1dw+odYyn2pe/oZSq9uQnxtPYk8D1wKSTHe9pRzVSKZVuP7EDIuKd2SYms/kk8XLejKeb83V+nWW/bCnhly3ecZ7+OS3TK597MgpLVJWxquSR1733IODIoh8K6ixb8ksRS34p8oquWfe4yKJ4ZW6O07L07ArSs72jC+a1r1NFRKKBqcBwDHtqtU8efbjm2tb+znMrtjO0YxJnJDSrCj+pUIqCUuc/lg/e4Hwio4jRA+8qY77dwoJhfSgsq+CKj1azePi59G/dgg3peTy05H/8fMeFtY7p2vb4xL5RVzXn2Xcz+euAlZQWgTx7V7zLToy717z7kJWJs7IZ2CeCl+5LwFKmWG53KhwzdZyMR775H8tG9qNtTGS17Xtyixj60a9se+DSatvvuiaGf07LICo8gFcfTuSpaZls21tG6+Qgxt8e73LseH3q+h83xzF/WQHhocJrjyTy3uI8Xpmbw3ndw3n4xjjGvumaPV598LhteeaHbSwefi5npcSwO7eIGxeuq+Woeuoef7rtcJWj+vjSrbwxuAdDOiax9lAuj36zhV/urH7NDwyL5YkpGRwrtJHcIpDR18fy8GtH6d05jMdHxPHEFNds1f1fbWbCgM70TYvl/Q376P/BSj4f3pf2cZFYbc47ujzVvvwdpZQSkVcwMkH53FENEpGLOT76XfM9SqnlTo9sgCS3COS0liGEhgiZueXsPWylyOLduMkOacHsPOj7oQGzdCtJSwyibUow4WEBlFhs7Dls5VCm7582oyMDiI8JZP8Rq1dSUzliRvtypFViEGd3DQOM3tWDGd6rb7Pb1ynwJhCJEZe/D2gD/Mu+/TYTz8ttto4eyD+++52isgqeG3A6EcFBzN68n+cGdHa6/+DzInl7US5WJ81i4Nmuh2pYbYruSUafRkJkCP1bG2EvZ6XEUGI9+ZcsMiyAvw4YbSc9u4IAF4fOwf1rRsHedCszPz/GzM+P0blNCBefHcGb/0giI7eCh/591PlxNSi3KdKiw2ttT40Ox+oktEcpqnouS62KbXuNuPX9R8qdhs3URX3qOjwsgF/tD8ejrmxeNbrx65YSbr+ifn1R+aVWzkqJAaBdbGTVvIO6OJV77MjhAgtDOiYBcE5qLCVOjHhAABwrNHrUM3IqSIoz3KTftlt4YFiMy1oFZeUM7mBoje3Xkd4pMQz+8BfmXte77vBAD7Uvf0NEasbrh2M83Lv0tOFpRzWD6kmus2u8V4D7AU9+Rlx0AE/fEU+PDsYTn1JQXKoIDIBFywv44EvvdaS882Qyh7PK+W5NEUvX+O4pyyzdxNhAnrkznvZpwRzOLKeoxEZEeAAt44PYfcjKizOyvJZ3s3VyEM/cEU/L+CDmfZvP7oNlPHlbC0JDhIIiG+OmZrI33fPOlVnt6/VHE5n2SS47D1q5oFc440a2YNOfFpSCUVfFMGlWttd6c81qXx5gMNDOYa3qP0VkFLDLxHOqF62bR7DwhnP4bHs6l839hTF9a6Y+rM6OfWXsTbeydXdZrbLb3HBgHJ2UiZd0rVZWVuE8iqJVchDTxycjQHKLIKLChcIShQgEufGr5u411/Qwtu8rY/u+Mt5elOdWWsI7erWhz/QfGX5GKq2aGw7rgWMlfLz1EHee2abW/oUlNq7sH0VkmFBQbGPYwGas+K2YszqHUVLqeqRJfeo6wKFzfOHy6qNM7tT19qxCery9HAXszSsmt6SM2PAQbEo51fbUPd6dW8TV/12NQnEwv4RiazkRwcYHWJ3o/rmvjLEj4ti4w0K/HuFs+svwpUKDhQA30/ocs1hpHhYMwMWnJbDoxnO4fsFackpqf2cAj7UvP6SM6lenMB7s73TlYI86qkqptp78PH/l8REt2HfEykszswgQ4dah0aRnlbNyUwmP3RLHHVc1Z+YX3nEmSq2KeUvyGdQ3kpFDm7NlZynfrC5i5cbiOuNmG7LuE7e2YMuuUsa+kUGp9bhOWIhw69DmjBvZwmuLKzx4Qyxf/1KIzWb8/+aCXK574hABATD6+ljuvjaG8W97fsjarPbVPi2kqldz5NDmPDUtk//ZY0fPaBfCYzfHec1RNat9eQALkIBhdCuJx8X8gP7INZ1TuLRdAhNWbHfa61fJhOlZlFmd3xt3MjS8eHGXKufh2s7H083tyiliZM/WTo8Z9UJ6tfcl9omNzSICmFWPBzlXr3n+0rrDctwJ+frnBZ24pnMyn+84wq8HjCHx1OhwPvxbb7omRNfaf9KcbEYMjkYBT0zJ4JKzI3n5oQSO5lTw2kfOw3WcUZ+6/uzHQsJCBUup4nOHtHgtE4yFT1xl2wPVJ35HhRjuR05JGS9c3KXW/jXvcaUdcPcefza8b7X3FfZh96OFFu4/u/ZcyNc/yuGK/lGccVooG7ZbqkK8FDBuquu/NePO78i2rAL6OsTA9khqzvcjz+fFH3c4PcZT7csPaVXjfaFSqu6LrYH38800Qrq1D2X8O5nY7A9jb32Sx4cvtOTjpQW8PDubqY8nec1RtdnguzVFfLemiKS4QC47N5IRg6N55O+x/LSxmG9XF500hrYh6XZpG8KTb2XUGma3lClmfZnH4lfTPK5ZScdWITwxJdNwTIfF8u1qw2DZbDDzizzmTPBODlez2ldFhaJZRAAFxTZaNA/kd4eMCtv2lFUNgXkDs9qXB3gfWCoir3N86H8M8J6pZ3WKRIYE8erl3U64T0GxZ+aMXX16itPt7eMieeL8jk7L6upxzy+ysXJT/R6mXLnmmpMqT4WuCdFOnVJnZOZWMPm/x2P1P1lewCfL646hr4v61PWXq5znbD6cWc5bn+S5rN0mxnk4SHxEKH/rUtuWeuoeX9Q23un2pKgwHjin9gBvhY1qDjkcz7DgzkjPzd1r+mYGrZtHMP3qM52WebJ9+RNKqVPKLagd1XqQW1BBasLx9Cst44MotqfuycitICLMWwt+VedoTgXzluQzb0k+Z7QLYVDfKJ6/J55rHz/UaHQz8io4r3u4U8N07hnhXl1uszIMymYzHGPH3qOSUuW1lZPMal8/bizm4b/H8tpHOSxdW8TNg6KZtyQfEbhpUDR7DvsmhtSsdl1P/oUxIeBmjMVNDgOvUD3kqUHwzc6jVTF1eZYyxn63lbWHcumWGM3kQd1Iigqrtn9YqDD8smgu7BVOfGwQ5eVGruEvVhZWPdS5wpFCC8+v2E6ACC9c3IUpa3exaFs6XeKjeGNwD1KahdU65t0nk1m5qZjl64u9toTykA9/Yckt/byim19qZeLKPzlYUMLQDsnc1P34A7fj7PhKAgJgaL8ozu8ZTrw9j3JWXgU/by5hyS+F1DFqX4v1h3N5fOlWUpuFMfGSrtzx+UbWHsqlU4sopl/Vi17JMbWOCQ0Wrh0QBQo+XVHAwLMjuaBXOPuPlhsZcDyQps9ZXXuqfR2zWJm46k8Wb08no6gUEUiMDOWa01N4sn/HWqmx7r6mOfOXFZBfZKNT6xCevSseZVMEBQoT52S73LPpri74pl03RLSjWg8+/i6fyWOS+HFDMQJc1DuC2fahiLYpwaR7sYHVFUO+dXcZW3fnMGVB49J9c34Oz9+dwLCBZew6ZKWoxEZkWADt04Jp2zKECe95b7b44axyEmMDycit4JqxB6uVtUsNJuuYd5xks9rX24vy+MctcSz4VypHc8s5LSWYEYONWMOjOeU886736tqs9nWqKGPViZk0QMe0Jk99/8fxyR/fbSU5KpQvburLp9sOc++Xm1hcYwh1/O0tWLW5hCemZjLgrAjCQoUf1hczYkg0aYlBzPjctV7/2xdv4IqOSRRZK7h49ipu6Z7G1zf3ZfH2dO77alOtoVuAqIgAoiICeO3RRHLzjfRxP/xWTLab38kN6XlOtysFm47UPn9P6Y76bAMd46K4vktLZm7czyfbDvHR384mNCiw2uz4Sp66rQWFJTbmfHWMzDxDKyEmkEF9I3lqVAtenOFamr7RX23m+QFdyLNY6TdzJZMHdWPprefz/e5M7v9qM7/eeVGtY8aNjCMjt4LQEGHi6ET2H7Eyf2kB/XqEM2Z4HBNnu6btbl17qn3d+Mk6BraNZ8Xt/Um2P2wdKbQwe9N+bly4ju9uPb/a/ud2C69acfDev8Xw4owsduwrIy0xiPGjWnD/y65NaHJXFzzXvhob2lGtB1//UsShzHL6djNimV6amVWVx+5QppVHX/fezLzXTxKP5GwGbkPW3bijlBHPHeaCXuG0TQkmplkgllIb364uYtXmLI8kvK6LSbOzKSxx/vnNIwP44Is8r+ia1b7KrIqJs7JplRhE57YhxMcGUVam2H2ojM07S6tCEbyBWe2rvojIwJPt05AznKw/nMum+4xLHHNeB2Zv3l9rn+QWQVU9W58sL2DauCTmLcnnlbk5fPBMisuOxNGiUh4615jENG3dHsb17wTAQ+e2Z8bGfU6PKSy28c6nebzzaR7d24cysE8E7z6ZzL6jVpavK+Krn13rceszfQUXtYnHWZ9gnqX2CIKndHflFLHoxnMBuLZzS/710w4GzvmZz5045QAdW4dw2/PVYzaz8irYtreM2c85H853htWmqma9j1u2lWFdUwG4pF0CY5c6/4KnJQbzgt0R/mRialU6qi27Spk+PtnpMc5wt6491b725hVXtamqz44KY1z/TszcVLtdBwYKAQHGSFposLBjnzHx6WBGOcFBro+iuasLnmtfjQ3tqNaT9Kxytu4uZW+6tVraHms5XNAr3GuxJsvXF5MYG0in1iG1tMFIC+MNbbN0wZj5X1Bs4/3PjmGtUFxzYRRtU4LJzq9gze/eSX4PRuqXDmnBtEwIYs3vFqzliqsvjKJlfBAbdlj4aaP38oqa1b4SYwNpkxLMtr1lHFxbXcOb99jM9lVPZpykvMFlOMkoKuX1X3eilCK/tBylVNUqSc7SPlrKFN3ah/L7rlL6dQ+nwP7QqFTdPeTOcJyJPrJn9bg+VxZh27KrlC27SpkyP5feXcK4uHeEyz/oXeKb8e6VvejYIqpWWavJ33pNt7TChk2pqjRL4y88ndToMC6ctZLCstpPZQXFNi46M5yfNpVU5XsVgYvOiqDQjVjhsKAAvtuVwTGLFRFYvP0w13ZuyY97swh04abVWuTEjVF/d+vaU+2rTfNwXvn5L27r2aoqfOVooYVZm/bTysnEuc9/KmDSAwn899t81v1h4YEbYlm5qZgzO4Wxy40Ueu7q1uRU2ldjQzuq9aBP1zCevTOeI9nlpCYaT31T5udWGfMxN8V57UfVLG2zdIf0i+SOq2JQSnHT5RWs3FRCYmwggQHCM3fEM3VhLt/86p0v74m0n/aidlO7x2Zr1welVKNbOvXus9pWJbq/rWdrsorLSIgM5UihhV7JtdNNTf5vDmNviSM1MZi9h8v49zyjV7x5VACLf3R9os81p6dQWFZOVEgQLw08njJpZ04hpztxasBY0rImNmXk+133h+sPrxMGdK4zh+eUId29pntVp2SW78nk0nbHV6C6vVcbkqPCeGjJ/2rt/+KMLO65LoaHh8dVOaZREQFs+tPCizOzXNZ954pePLFsKwEC347ox9vr9nD74g2kRocz/apeTo/Zsb+satb/q/OOj3w4xs67grt17an2NX9YHyat+ouLZq3iaFEpAiRFhXJ1pxQW3NCn1v7/b0Uhuw9ZufrCKNISgwkMgLSEIFZtLmbeEtcnsbqrC55rX40N7ajWg7uujuGlD7JY87uF2GYB/PP2Frx4XwLPvZdJeYV7T3sNRdss3b9fGs2YyUcR4INnU5i6MLcqb+OPG4u597oYrzmqZmk3tXtstrbG4LkBndmeVcChfAvnpsVWpQ9Kjgrj5u61s2vsPmQsoRnfPJA/9pZVTao5Vmhza3GIFy7u4lS3Q1wUd51VO6cowEszs2mVFER8TCDbHLTBeOhx9Ud9WNdUtmcV8P3uzGraAGFBNXOUe073lcu6OdUd3CGJNwf3qLX/0ZwKFn5fwMJlBRzOKqdVUhBntAtlX7qVI9muxy/2TG7OfwZ153CBhbTocN4Y0oM3hhh63+x0HlL02oc5dG4TgsLIndsmOYg+XcM5kGF1a5VAd+vaU+0rNjyEUWe25rL2CfRNi6um6ziB0JGc/Aq+XFV4Sve4Prqeal+NDd9MT29kpNiHggFyC2w8+VYmJaU2Jj6Q6LWZ4GZrm6Ub1zyQgxnlHMgop9SqqiUX/227xaspk8zSbmr32GxtjcGUNbu45uM1TFm7i27Tvuez7cdjIp/6/o9a+183IIoX7kng2gHNmPl0Cv16HB/OvOvqGK/pVmq/dF8C152i9pv1uGZP6J7omscvr607cmg0D94QyyPDY7nx0maMujKGkGDhpsujuWWwaymuwLje6+a7V9cjh0bz4I2xPDo8lruuac7Df48jLFS46TL3tc1oX5W6U9fu9uk9dlfXk9qNDd2jWg8Ki20kxAaSaU+NZLMZT0KPj4jj348kVlvJo7Fom6VrKbURGGDktquZkiQ0WE669F5D1G5q99hsbY3Bexv28ds9A4gKCWJvXhHDFqxjb14xj/Rt7zQU8Yrzo7jv5SNYShVJcYFMuDue5BZBfPpDQa0VdjypW6l976RT155ej2v2hK6713zhmRHc839HCA4SPpmUyt/HH6LYoliwrIC3nkjiw29cy50+fcM+1rtZ157UNqN9mXWP3dX1pHZjQ5v/evDbdguD+0bW2v7qvBx2HSojxI2ZgQ1F2yzdjTtKSU00nqfenJ9braxv93B2H/Jebk+ztJvaPTZbW2NgU6pqeLJtTCQrbu/Pkp1HeezbLSgnD2UBQtXQ5NGcCsb8J4NzuoZx//Uxbv2muqtrprZZuhU2I1ax1GrkEi22GPuUWRXKjWwc9alrs7Sb2j32pHZjwy8dVRF5UETWi0ipiMxy2N5XRJaKSI6IZIrIQhFJcSgXEXlZRLLtr5dFPB/d9sbHOSxY5jyY+z//zeXmZ05pEQa/1DZLd+LsbPYfcR6PtHGHhQnTXZ9I0FC0m9o9NlvbTEQkVERmiMg+ESkQkU0iMsRe5lN7lxQZyqYjeVXvo0KC+PLmvmQVl7Elo3avWW6BjfZpwVXvLaWKp97OpHlUIKelBtfa31O6ZmqbpVtefnyBkfsmHanaHhkmuDOwU5+6Nku7qd1jT2qfCH/3r5zhr0P/h4GXgEGAYx6HWIylCb8FyoGpwAfAYHv5PcC1QE+MxBlLgT3AO548ufIKKD9B3hRvrpZklraZ11wXxwq9mNjTRO2meI/9sX35iCDgAHARsB8YCiwQke742N7Nua43QQHVf3eCAgKYc11v7u3dttb+E2dnV62bXonNZuQf/mKl67Oy3dU1U9ss3UcnH63KJezoHAYGCpPmuJZwvz66Zmo3tXvsSe2T4Nf+lTP80lFVSn0KICJnA2kO25c47iciU4EfHTbdBrymlDpoL38NuBsfVKRGo9G4i1KqCJjgsOlLEdkD9FZKLXLc19v2Lu0EuR3Pb92i1rasvLofHhwnHnpa10xts3TrWvAiv8jm1qIn9alrs7Sb2j32pPaJaIj+ldQVK+EPiMhLQJpS6vY6yh8Fhiul+trfHwMuV0qtsb8/G/hBKdXMybEDgAFOPrYPsK6ep9wW2FvPY08Fs3TN1G5qumZqN0TdNKXUXZ47Fd8gIknAPqCXUmp7jbJHqYe9O4GtuwPYXc9TjQHy6nnsqWKWdlPTNVO7qemeqnY7nC/jvEIptcLZAd70rzyOUspvXxjd07PqKOsB5AAXOGyrADo7vO+I0UUtPjrfCSbVkym6TfGadV03fl0T728wsAx410mZX9k7/T1o/LpN8ZqbUl03JP/KLydTnQwR6QAsAR5RSq10KCoEHBO7RQOFyl6rGo1G44+ISAAwFygDHqxRpu2dRqPxCf5obxqcoyoibTB6HV5USs2tUbwVI9C3kp72bRqNRuOX2GfOzgCSgOuVUlaHMm3vNBqNT/BXe+OXjqqIBIlIGBAIBIpImH1bKrAcmKqUchbAOwd4TERSRaQl8A9gls9OXKPRaNznbaALcJVSqqRyo7Z3Go3G0zRE/8ovZ/0DTwPPObwfATyPEQ/RDpggIhMqC5VSUfZ/37WXb7G/f9++TaPRaPwOew/GvUApcMQhLeG9QAe0vdNoNJ6lwflXfj3rv6EhIgNUHTPsGqOumdpNTddM7aamqzk5+nvQ+HXN1G5qumZr+zvaUdVoNBqNRqPR+CV+GaOq0Wg0Go1Go9FoR1Wj0Wg0Go1G45doR1Wj0Wg0Go1G45doR1Wj0Wg0Go1G45doR9UDiMiDIrJeREpFZJaPNENFZIaI7BORAhHZJCJDfKFt158nIukiki8if4qIT9dVF5GOImIRkXk+0lth1yu0v3b4QtdBf7iIbBORIhHZJSIXeFmvsMarQkSmeFPTQbutiHwtIrkickREpoqIv6bSa1KYYevsuqbZu6Zm6+yaptk7X9s6u6a2d36MdlQ9w2GMdXNn+lAzCDgAXAQ0x8iNtkBE2vpIfyLQVikVDVwNvCQivX2kDfAWsM6HegAPKqWi7K/TfSUqIpcBLwOjgGbAhcBub2o6XGcUkAyUAAu9qenANCADSAF6YbTx0T7S1pwYM2wdmGvvmqKtAxPsnRm2DrS983e0o+oBlFKfKqUWA9k+1CxSSk1QSu1VStmUUl8CewCfGFCl1FalVGnlW/urvS+0RWQ4kAd87ws9P+B54AWl1Gr7vT6klDrkQ/3rMQzpypPt6CFOAxYopSxKqSPAN8AZPtLWnAAzbJ1d1zR7p22dTzHb1oG2d36HdlQbCSKSBHTCh2t9i8g0ESkGtgPpwNc+0IwGXgAe87aWEyaKSJaI/CwiA3whKCKBwNlAgojsFJGD9qGhcF/o27kNmKN8l3T5P8BwEYmwL+s3BMN4azSA7+1dE7R14GN75ye2DrS98zu0o9oIEJFg4ENgtlJqu690lVKjMYZnLgA+xVgG0tu8CMxQSh30gZYj4zCWj0sF3gO+EBFf9KokAcHAMIx67gWciTH06XXEWOLzImC2L/Ts/ITRo5APHATWA4t9qK/xY8ywd03M1oE59s5UWwfa3vkr2lFt4IhIADAXKAMe9LW+UqpCKbUKSAPu96aWiPQCLgUme1PHGUqpNUqpAqVUqVJqNvAzMNQH0iX2v1OUUulKqSzgdR9pA9wKrFJK7fGFmL09f4PhDEQC8UAsRtyapoljpr1rKrYOTLN3Zts60PbOL9GOagNGRASYgfEker1Symri6QTh/bitAUBbYL+IHAHGAteLyAYv6zpDAeJ1EaVyMZ6yHYehfLnu8Uh827sQB7QGptp/JLOBD/Dtj5XGD/Eje9fUbB34wN75ga0Dbe/8Eu2oegARCRKRMCAQCBSRMB+ll3gb6AJcpZQqOdnOnkJEEu0pRKJEJFBEBgE34f2A//cwfiB62V/vAF8Bg7wpKiIxIjKo8r6KyC0Ys1F9FUf0AfCQvd5jgTHAl94WFZF+GEN/vpr9ir0XZQ9wv72uYzBixv7nq3PQ1I2Jtg5MsHdNzdaB6fbOFFsH2t75NUop/TrFFzCB47NBK18TvKzZxq5jAQodXrf44HoTgB8xZqPmA1uAu02q93k+ut51QIH9mlcDl/nwOoMxUpjkAUeAN4EwH+i+C8w14b72AlYAuUAWsABI8vV56JfTe+NzW2fXNcXeNTVb53DNptg7s2ydXVvbOz99ib2iNBqNRqPRaDQav0IP/Ws0Go1Go9Fo/BLtqGo0Go1Go9Fo/BLtqGo0Go1Go9Fo/BLtqGo0Go1Go9Fo/BLtqGo0Go1Go9Fo/BLtqGo0Go1Go9Fo/BLtqGqaNCLSVkSUD5OWazQajSloe6dpiGhHVWMKIrJXRMpEJL7G9o12Q9rW/n6CiMxzKBcReVxE/hKREhHZLyITRSTUx5eg0Wg0LqHtnUZTf7SjqjGTPRjLEQIgIt2BiJMc8yZwD8aazM2AIcAlGKt5aDQajb+i7Z1GUw+0o6oxk7kYBriS24A5de0sIh2B0RjLJv6qlCpXSm0FrgcGi8jAOo5bYe+FWCsi+SLymYjE1djtFntvRZaIjHc49hwR+VVE8kQkXUSmikiIvUxEZLKIZNg/d4uIdLOXhYrIv+2feVRE3hGR8PpUkkajaRRoe6fR1APtqGrMZDUQLSJdRCQQGA7MO8H+lwAHlVJrHTcqpQ7YP+uyExw7ErgDSAHKMXoqHOkPnG7XeFZEuti3VwBjgHjgPHv5aHvZ5cCFQCegOXAjkG0vm2Tf3gvoAKQCz57g/DQaTeNG2zuNph5oR1VjNpW9DJcB24BDJ9g3HkivoyzdXl6njlLqd6VUEfAMcKP9x6KS55VSJUqpzcBmoCeAUuo3pdRqe2/GXuBd4CL7MVaM4bjOgCiltiml0kVEMIbrxiilcpRSBcD/YfwwaTSapou2dxqNm+iZfxqzmQv8BJzGCYbB7GRh9BA4IwUjBqwuDjj8vw8IprqhP+LwfzEQBSAinYDXgbMx4smCgN8AlFLLRWQq8BbQRkQ+BcYCYfZ9fzNsOAACOP5QaDSapoe2dxqNm+geVY2pKKX2YRjcocCnJ9l9OdBKRM5x3CgirYC+wPcnOLaVw/+tMXoHslw4xbeB7UBHpVQ08BSGEa48/zeVUr2BrhhDX4/bP7cEOEMpFWN/NVdKRbmgp9FoGina3mk07qMdVY0/cCcw0D5MVSdKqT+Bd4APRaSviASKyBnAImCZUmrZCQ4fISJdRSQCeAH4RClV4cK5NQPygUIR6QzcX1kgIn1E5FwRCQaKAAtgU0rZgOnAZBFJtO+bKiKDXNDTaDSNG23vNBo30I6qxnSUUruUUutPtIvD/w8C72NMQigEvgFWYMyEPRFzgVkYQ15hwMMunt5Y4GagAMMYz3coi7Zvy8UYXssGXrWXjQN2AqtFJB9YhjF5QaPRNGG0vdNo3EOUUiffS6MxCRF5HQhQSj16Cp+xApinlHrfU+el0Wg0nkbbO42mNrpHVeO3iEgMMAg4Ue+DRqPRNHi0vdNonKMdVY1fIiJXAruANehVWDQaTSNG2zuNpm700L9Go9FoNBqNxi/RPaoajUaj0Wg0Gr9EO6oajUaj0Wg0Gr9EO6oajUaj0Wg0Gr9EO6oajUaj0Wg0Gr9EO6oajUaj0Wg0Gr/k/wN41kT+Egu4yQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 720x864 with 7 Axes>"
+       "<Figure size 720x504 with 5 Axes>"
       ]
      },
      "metadata": {
@@ -685,10 +755,10 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10, 12),\n",
+    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 7),\n",
     "                       constrained_layout=False)\n",
     "\n",
-    "for i in range(3):\n",
+    "for i in range(2):\n",
     "    for j in range(2):\n",
     "        for axis in ['top', 'bottom', 'left', 'right']:\n",
     "            ax[i, j].spines[axis].set_linewidth(0.5)\n",
@@ -699,7 +769,7 @@
     "        ax[i, j].set_xticklabels(np.arange(1, 9).astype(int),\n",
     "                                 fontsize='large')\n",
     "\n",
-    "        if i == 2:\n",
+    "        if i == 1:\n",
     "            ax[i, j].set_xlabel('MJO phase', fontsize='large')\n",
     "\n",
     "        ax[i, j].grid(color='0.8', linewidth=0.5, axis='y')\n",
@@ -737,36 +807,19 @@
     "ax1.set_ylim(120, 170)\n",
     "ax1.set_yticks(np.arange(120, 171, 10))\n",
     "ax1.set_yticklabels([f'{y:d}' for y in np.arange(120, 171, 10)],\n",
-    "                      fontsize='large')\n",
+    "                    fontsize='large')\n",
     "ax1.set_ylabel('Potential gradient, V/m', fontsize='large',\n",
-    "              rotation=270, va='bottom')\n",
-    "\n",
-    "ax[2, 0].set_ylim(249, 252)\n",
-    "ax[2, 0].set_yticks(np.arange(249, 253, 1))\n",
-    "ax[2, 0].set_yticklabels([f'{y:d}' for y in np.arange(249, 253, 1)],\n",
-    "                         fontsize='large')\n",
-    "ax[2, 0].set_ylabel('Mean OLR for 15° S–15° N, W/m²', fontsize='large')\n",
-    "# thin spaces after '°'\n",
-    "\n",
-    "ax[2, 1].set_ylim(30, 50)\n",
-    "ax[2, 1].set_yticks(np.arange(30, 51, 5))\n",
-    "ax[2, 1].set_yticklabels([f'{y:d}' for y in np.arange(30, 51, 5)],\n",
-    "                         fontsize='large')\n",
-    "ax[2, 1].set_ylabel('Average daily sunspot number', fontsize='large')\n",
+    "               rotation=270, va='bottom')\n",
     "\n",
     "for j in range(2):\n",
-    "    fig.align_ylabels([ax[i, j] for i in range(3)])\n",
+    "    fig.align_ylabels([ax[i, j] for i in range(2)])\n",
     "\n",
     "for j in range(2):\n",
     "    ax[0, j].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
     "                  transform=ax[0, j].transAxes, fontsize='large')\n",
     "    ax[1, j].text(0.02, 0.98, '2006–2020', ha='left', va='top',\n",
     "                  transform=ax[1, j].transAxes, fontsize='large')\n",
-    "ax[2, 0].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
-    "                  transform=ax[2, 0].transAxes, fontsize='large')\n",
-    "ax[2, 1].text(0.02, 0.98, '2006–2020', ha='left', va='top',\n",
-    "                  transform=ax[2, 1].transAxes, fontsize='large')\n",
-    "                    \n",
+    "\n",
     "ax[0, 0].bar(np.arange(1, 9), phase_avg_ip / 1e3,\n",
     "             yerr=std_error(phase_avg_ip, phase_avg_sqr_ip, counter_ip) / 1e3,\n",
     "             width=0.6, color='coral')\n",
@@ -787,14 +840,6 @@
     "        yerr=std_error(phase_avg_pg, phase_avg_sqr_pg, counter_pg),\n",
     "        width=0.4, color='royalblue', label='Potential gradient')\n",
     "\n",
-    "ax[2, 0].bar(np.arange(1, 9), phase_avg_olr,\n",
-    "             yerr=std_error(phase_avg_olr, phase_avg_sqr_olr, counter_olr),\n",
-    "             width=0.6, color='teal')\n",
-    "\n",
-    "ax[2, 1].bar(np.arange(1, 9), phase_avg_sn,\n",
-    "             yerr=std_error(phase_avg_sn, phase_avg_sqr_sn, counter_sn),\n",
-    "             width=0.6, color='darkgoldenrod')\n",
-    "\n",
     "leg = ax[0, 1].legend(fontsize='large', framealpha=1, edgecolor='0.',\n",
     "                      ncol=2, columnspacing=1., loc='lower right', handlelength=1.5)\n",
     "leg.get_frame().set_linewidth(0.5)\n",
@@ -807,22 +852,22 @@
     "leg.get_frame().set_linewidth(0.5)\n",
     "\n",
     "for p in range(1, 9):\n",
-    "    ax[0, 0].annotate(str(counter_ip[p-1]), xy = (p, 221),\n",
+    "    ax[0, 0].annotate(str(counter_ip[p-1]), xy=(p, 221),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='0.')\n",
-    "    ax[1, 0].annotate(str(counter_pg[p-1]), xy = (p, 121),\n",
+    "    ax[1, 0].annotate(str(counter_pg[p-1]), xy=(p, 121),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='1.')\n",
-    "    ax[1, 1].annotate(str(counter_ip_pg[p-1]), xy = (p - 0.2, 221),\n",
+    "    ax[1, 1].annotate(str(counter_ip_pg[p-1]), xy=(p - 0.2, 221),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='medium', color='0.')\n",
-    "    ax1.annotate(str(counter_pg[p-1]), xy = (p + 0.2, 121),\n",
+    "    ax1.annotate(str(counter_pg[p-1]), xy=(p + 0.2, 121),\n",
     "                 ha='center', va='bottom', rotation=90,\n",
     "                 fontsize='medium', color='1.')\n",
     "\n",
-    "for i in range(3):\n",
+    "for i in range(2):\n",
     "    for j in range(2):\n",
-    "        ax[i, j].text(-0.25, 1.05, chr(ord('a') + 2 * i + j), fontsize='x-large',\n",
+    "        ax[i, j].text(-0.25, 1.05, chr(ord('a') + 2*i + j), fontsize='x-large',\n",
     "                      fontweight='semibold', ha='left', va='bottom',\n",
     "                      transform=ax[i, j].transAxes)\n",
     "\n",
@@ -834,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "c169197e-495c-475c-9a97-3bc3ca7494b8",
    "metadata": {},
    "outputs": [
@@ -856,10 +901,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "63796d95-d604-49d4-b4de-1174787e526b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x756 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10, 10.5),\n",
     "                       constrained_layout=False)\n",
@@ -935,28 +993,28 @@
     "\n",
     "for j in range(2):\n",
     "    ax[2, j].bar(np.arange(1, 9), phase_avg_pg_part[j],\n",
-    "                          yerr=std_error(phase_avg_pg_part[j],\n",
-    "                                         phase_avg_sqr_pg_part[j],\n",
-    "                                         counter_pg_part[j]),\n",
-    "                          width=0.6, color='royalblue')\n",
+    "                 yerr=std_error(phase_avg_pg_part[j],\n",
+    "                                phase_avg_sqr_pg_part[j],\n",
+    "                                counter_pg_part[j]),\n",
+    "                 width=0.6, color='royalblue')\n",
     "\n",
     "for p in range(1, 9):\n",
-    "    ax[0, 0].annotate(str(counter_ip_part[0, p-1]), xy = (p, 211),\n",
+    "    ax[0, 0].annotate(str(counter_ip_part[0, p-1]), xy=(p, 211),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='0.')\n",
-    "    ax[0, 1].annotate(str(counter_ip_part[1, p-1]), xy = (p, 201),\n",
+    "    ax[0, 1].annotate(str(counter_ip_part[1, p-1]), xy=(p, 201),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='0.')\n",
-    "    ax[1, 0].annotate(str(counter_ip_part[2, p-1]), xy = (p, 221),\n",
+    "    ax[1, 0].annotate(str(counter_ip_part[2, p-1]), xy=(p, 221),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='0.')\n",
-    "    ax[1, 1].annotate(str(counter_ip_part[3, p-1]), xy = (p, 221),\n",
+    "    ax[1, 1].annotate(str(counter_ip_part[3, p-1]), xy=(p, 221),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='0.')\n",
-    "    ax[2, 0].annotate(str(counter_pg_part[0, p-1]), xy = (p, 121),\n",
+    "    ax[2, 0].annotate(str(counter_pg_part[0, p-1]), xy=(p, 121),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='1.')\n",
-    "    ax[2, 1].annotate(str(counter_pg_part[1, p-1]), xy = (p, 111),\n",
+    "    ax[2, 1].annotate(str(counter_pg_part[1, p-1]), xy=(p, 111),\n",
     "                      ha='center', va='bottom', rotation=90,\n",
     "                      fontsize='large', color='1.')\n",
     "\n",
@@ -973,6 +1031,119 @@
     "fig.savefig('figures/ip_pg_variation_partial.eps', bbox_inches='tight')\n",
     "fig.savefig('figures/ip_pg_variation_partial.png', dpi=300, bbox_inches='tight')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "70eebfb7-2a9e-4a2d-9012-09627684ec85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 7),\n",
+    "                       constrained_layout=False)\n",
+    "\n",
+    "for i in range(2):\n",
+    "    for j in range(2):\n",
+    "        for axis in ['top', 'bottom', 'left', 'right']:\n",
+    "            ax[i, j].spines[axis].set_linewidth(0.5)\n",
+    "        ax[i, j].tick_params(length=6, width=0.5)\n",
+    "\n",
+    "        ax[i, j].set_xlim(0.5, 8.5)\n",
+    "        ax[i, j].set_xticks(np.arange(1, 9))\n",
+    "        ax[i, j].set_xticklabels(np.arange(1, 9).astype(int),\n",
+    "                                 fontsize='large')\n",
+    "\n",
+    "        if i == 1:\n",
+    "            ax[i, j].set_xlabel('MJO phase', fontsize='large')\n",
+    "\n",
+    "        ax[i, j].grid(color='0.8', linewidth=0.5, axis='y')\n",
+    "\n",
+    "ax[0, 0].set_ylim(249, 252)\n",
+    "ax[0, 0].set_yticks(np.arange(249, 253, 1))\n",
+    "ax[0, 0].set_yticklabels([f'{y:d}' for y in np.arange(249, 253, 1)],\n",
+    "                         fontsize='large')\n",
+    "ax[0, 0].set_ylabel('Mean OLR for 15° S–15° N, W/m²', fontsize='large')\n",
+    "# thin spaces after '°'\n",
+    "\n",
+    "ax[0, 1].set_ylim(30, 50)\n",
+    "ax[0, 1].set_yticks(np.arange(30, 51, 5))\n",
+    "ax[0, 1].set_yticklabels([f'{y:d}' for y in np.arange(30, 51, 5)],\n",
+    "                         fontsize='large')\n",
+    "ax[0, 1].set_ylabel('Average daily sunspot number', fontsize='large')\n",
+    "\n",
+    "ax[1, 0].set_ylim(650, 710)\n",
+    "ax[1, 0].set_yticks(np.arange(650, 711, 10))\n",
+    "ax[1, 0].set_yticklabels([f'{y:d}' for y in np.arange(650, 711, 10)],\n",
+    "                         fontsize='large')\n",
+    "ax[1, 0].set_ylabel('Mean CAPE for 15 °S–15° N, J/kg', fontsize='large')\n",
+    "# thin spaces after '°'\n",
+    "\n",
+    "ax[1, 1].set_ylim(470, 510)\n",
+    "ax[1, 1].set_yticks(np.arange(470, 511, 10))\n",
+    "ax[1, 1].set_yticklabels([f'{y:d}' for y in np.arange(470, 511, 10)],\n",
+    "                         fontsize='large')\n",
+    "ax[1, 1].set_ylabel('Mean CAPE for 30 °S–30° N, J/kg', fontsize='large')\n",
+    "# thin spaces after '°'\n",
+    "\n",
+    "for j in range(2):\n",
+    "    fig.align_ylabels([ax[i, j] for i in range(2)])\n",
+    "\n",
+    "ax[0, 0].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
+    "              transform=ax[0, 0].transAxes, fontsize='large')\n",
+    "ax[0, 1].text(0.02, 0.98, '2006–2020', ha='left', va='top',\n",
+    "              transform=ax[0, 1].transAxes, fontsize='large')\n",
+    "for j in range(2):\n",
+    "    ax[1, j].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
+    "                  transform=ax[1, j].transAxes, fontsize='large')\n",
+    "\n",
+    "ax[0, 0].bar(np.arange(1, 9), phase_avg_olr,\n",
+    "             yerr=std_error(phase_avg_olr, phase_avg_sqr_olr, counter_olr),\n",
+    "             width=0.6, color='teal')\n",
+    "\n",
+    "ax[0, 1].bar(np.arange(1, 9), phase_avg_sn,\n",
+    "             yerr=std_error(phase_avg_sn, phase_avg_sqr_sn, counter_sn),\n",
+    "             width=0.6, color='darkgoldenrod')\n",
+    "\n",
+    "ax[1, 0].bar(np.arange(1, 9), phase_avg_cape[0],\n",
+    "             yerr=std_error(phase_avg_cape[0], phase_avg_sqr_cape[0], counter_cape[0]),\n",
+    "             width=0.6, color='orangered')\n",
+    "\n",
+    "ax[1, 1].bar(np.arange(1, 9), phase_avg_cape[1],\n",
+    "             yerr=std_error(phase_avg_cape[1], phase_avg_sqr_cape[1], counter_cape[1]),\n",
+    "             width=0.6, color='orangered')\n",
+    "\n",
+    "for i in range(2):\n",
+    "    for j in range(2):\n",
+    "        ax[i, j].text(-0.25, 1.05, chr(ord('a') + 2*i + j), fontsize='x-large',\n",
+    "                      fontweight='semibold', ha='left', va='bottom',\n",
+    "                      transform=ax[i, j].transAxes)\n",
+    "\n",
+    "fig.subplots_adjust(hspace=0.25, wspace=0.3)\n",
+    "\n",
+    "fig.savefig('figures/olr_sn_cape_variation.eps', bbox_inches='tight')\n",
+    "fig.savefig('figures/olr_sn_cape_variation.png', dpi=300, bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b848fba-d5b0-432b-9121-4eaab5cad412",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {