diff --git a/.ipynb_checkpoints/PICES_Regional_Ecosystem_Tool-checkpoint.ipynb b/.ipynb_checkpoints/PICES_Regional_Ecosystem_Tool-checkpoint.ipynb
index bc9fc25..9490b76 100644
--- a/.ipynb_checkpoints/PICES_Regional_Ecosystem_Tool-checkpoint.ipynb
+++ b/.ipynb_checkpoints/PICES_Regional_Ecosystem_Tool-checkpoint.ipynb
@@ -97,13 +97,13 @@
     "\n",
     "## Variable ##\n",
     "## Select the variable to analyze from the list above (eg. 'sst','chl','wind_v','current_speed','sla','adt')\n",
-    "var = 'sla' # <<<----- Use short name given above. upper or lower case accepted.\n",
+    "var = 'sst' # <<<----- Use short name given above. upper or lower case accepted.\n",
     "\n",
     "## Date Period to analize ##\n",
     "## Specify the period using the format: ####  YYYY-MM-DD  #####\n",
     "## Data available specified above\n",
     "## All data in monthly resolution\n",
-    "initial_date = '1997-01-01'\n",
+    "initial_date = '1981-01-01'\n",
     "final_date   = '2019-12-31'\n",
     "\n",
     "##############################\n",
diff --git a/PICES_Regional_Ecosystem_Tool_menu.ipynb b/.ipynb_checkpoints/PICES_Regional_Ecosystem_Tool_menu-checkpoint.ipynb
similarity index 86%
rename from PICES_Regional_Ecosystem_Tool_menu.ipynb
rename to .ipynb_checkpoints/PICES_Regional_Ecosystem_Tool_menu-checkpoint.ipynb
index 65e7c42..6a91229 100644
--- a/PICES_Regional_Ecosystem_Tool_menu.ipynb
+++ b/.ipynb_checkpoints/PICES_Regional_Ecosystem_Tool_menu-checkpoint.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "94d6cb099d5a4c6b88827499cbc6ae9c",
+       "model_id": "86603d0c2a0b4fe7a5365ad6405443a4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -64,6 +64,7 @@
    ],
    "source": [
     "import ipywidgets as widgets\n",
+    "from ipywidgets import interact, interactive\n",
     "from IPython.display import display\n",
     "from IPython.display import Image\n",
     "i=Image(\"./utils/figures/PICES_all_regions_mask_ne.png\")\n",
@@ -82,9 +83,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f612c7662f84b38ab432ad414ef73b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(options=('11 - California Current', '12 - Gulf of Alaska', '13 - East Bering Sea', '14 - North Bering…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "lmenames = ['11 - California Current','12 - Gulf of Alaska','13 - East Bering Sea',\n",
     "                '14 - North Bering Sea','15 - Aleutian Islands','16 - West Bering Sea',\n",
@@ -155,13 +171,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e96018aeb1ca4f8f92bc5b9943074d1b",
+       "model_id": "dcbb0f72876440268f4ffaf539b3b025",
        "version_major": 2,
        "version_minor": 0
       },
@@ -175,7 +191,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "da7e9775983e40528b3ab02516135499",
+       "model_id": "9b14130e5f79451c8c4fbd232644b970",
        "version_major": 2,
        "version_minor": 0
       },
@@ -207,15 +223,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11\n",
-      "current_v\n"
+      "Output()\n",
+      "Output()\n"
      ]
     }
    ],
@@ -229,11 +245,11 @@
     "#######################\n",
     "\n",
     "## Region to analyze ##\n",
-    "#region = 11 # <<<----- Use number (11 to 24) based on table above\n",
+    "region = 11 # <<<----- Use number (11 to 24) based on table above\n",
     "\n",
     "## Variable ##\n",
     "## Select the variable to analyze from the list above (eg. 'sst','chl','wind_v','current_speed','sla','adt')\n",
-    "#var = 'current_v' # <<<----- Use short name given above. upper or lower case accepted.\n",
+    "var = 'current_v' # <<<----- Use short name given above. upper or lower case accepted.\n",
     "\n",
     "## Date Period to analize ##\n",
     "## Specify the period using the format: ####  YYYY-MM-DD  #####\n",
@@ -249,118 +265,200 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "metadata": {
     "scrolled": false
    },
+   "outputs": [],
+   "source": [
+    "#### Do not modify ####\n",
+    "  \n",
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.append('./utils/subroutines/')\n",
+    "import pices\n",
+    "from pices import analyze_PICES_Region\n",
+    "\n",
+    "#analyze_PICES_Region(region, var, initial_date, final_date)\n",
+    "\n",
+    "#### End of script ####"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Region = 11 - California Current\n",
-      "Data = CURRENT V\n",
-      "Units = m/s\n",
-      "Period = 1997-01-01 : 2019-12-31\n",
-      "Dataset = DT merged all satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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MRH4RkfQM5UuJyBQRWS0iX4pIrehuSc544IEHaNOmDX369GHZsmV+yzEMo4DgV4t4IDBHVesBc1z6MESkEjAUOBs4Cxga5LDfcXkZuRXYrqp1gWeAx6OgPdcUK1aM//3vf5QvX54uXbrw999/+y3JMIwCgF+OuAOQ7OaTgatClLkUmKWq21R1OzALaAugqgtUdVMYu28DF4uIRFR5HklISOD1119n5cqV9O7d2285hmEUAPxyxAkBR+p+q4YoUx34JSi9weVlxaF1VHU/8CdwTJ7VRpjWrVszaNAgJkyYwKRJk/yWYxiGz0i0nuCLyGzg2BCLBgPJqlohqOx2VT0sTiwi/YFSqvqoSw8B/lHV0UFl0lU1Pij9HXCpqm5w6Z+As1R1awh9PYAeAAkJCU0nT56co+1LT08nPj4+fMFMOHDgAPfeey8//fQTL7/8MjVq1IiY7aww22bbbOef7VatWi1W1WZhC6pqvk/AD0A1N18N+CFEma7AS0Hpl4CuGcqkZ0jPAJq7+TjgD9zFJqupadOmmlPmzp2b43Uysn79eq1YsaI2adJEd+/eHVHbmWG2zbbZzj/bwCLNhk/0KzQxHQj0gkgEpoUoMwO4REQquod0l7i87Nq9BvjI7YwCyfHHH8/EiRP5+uuvGTBggN9yDMPwCb8c8SigjYisAtq4NCLSTEReAVDVbcAjwEI3DXd5iMgTIrIBKCMiG0Qkydl9FThGRFYDfQnRG6Og0b59e3r37s1zzz3HtGmhrkeGYRR24vyoVL2Y7cUh8hcBtwWlJwATQpQbABzRhFTV3UDniIrNB5544gk+++wzunfvzpIlS5g4cSItW7b0W5ZhGPmEvVlXAChVqhSTJ09m3759dO3aleTk5PArGYZRaDBHXECoV68eL730El988YXfUgzDyGfMERcQkpKSuOGGGw6lRQQRISkpyT9RhmHkC+aICwhJSUmoKt999x0A/fr1Q1XNERtGEcAccQHjlFNOAeD5559n48aNPqsxDCPAxIkTo2bbHHEBpFOnTuzfv59HH33UbymGYTii+RDdHHEBpFevXtx2222MHz+eNWvW+C3HMIo8S5cuBWD9+vVRsW+OuIDy0EMPERcXx7Bhw/yWYhhFlqSkJESExo0bA1CrVq2oPEQ3R1xAqV69OnfffTevv/4633//vd9yDKNIkpSUxMGDB2nQoAHw79g85oiLEAMHDqRMmTI8/PDDfksxjCLLihUr+OGHH6JahzniAkzlypXp27cvKSkpLF682G85hlEkSUlJQUTo3Dl6oyeYIy7g9O3bl0qVKvHQQw/5LcUwiiQpKSm0aNGCu+66K2p1mCMu4JQvX54HHniADz/8kM8++8xvOYZRpPjpp59YunQpV199dVTrMUccA9xzzz0ce+yxPPjggxTg4ZUNo9CRmpoKQMeOHaNajzniGKBMmTI89NBDfPrpp8ycOdNvOYZRZEhJSaFp06bUqlUrqvWYI44Rbr/9dk444QQGDx5srWLDyAc2bNjAl19+GfWwBJgjjhlKlixJUlISixcvZurUqX7LMYxCT+A869SpU9TrMkccQ9x44400aNCAIUOGcODAAb/lGEahJiUlhYYNGx56mSOamCOOIeLi4njkkUf4/vvvmTRpkt9yDKPQsmXLFj799NN8CUuAOeKY4+qrr6Zx48YkJSWxd+9ev+UYRqFk2rRpHDx4MF/CEmCOOOYoVqwYjz32GGvWrGHChCO+q2oYRgRISUmhTp06NGrUKF/qM0ccg1x22WW0aNGCRx55hF27dvktxzAKFdu3b2fOnDlcffXViEi+1OmLIxaRSiIyS0RWud+KmZRLdGVWiUhiUP5jIvKLiKRnKN9XRL4XkW9FZI6InBDtbfEDEWHEiBFs3LiRF154wW85hlGoeOedd9i/f3++xYfBvxbxQGCOqtYD5rj0YYhIJWAocDZwFjA0yGG/4/Iy8g3QTFUbAW8DT0RBe4HgwgsvpE2bNowcOZKdO3f6LccwCg2pqanUqFGDZs2a5VudfjniDkDguyPJwFUhylwKzFLVbaq6HZgFtAVQ1QWquinjCqo6V1X/cckFQI2IKy9APPbYY2zdupUxY8b4LcUwCgXp6enMmDGDTp06UaxY/rlHvxxxQsCRut+qIcpUB34JSm9wednlVuCDXCuMAc4880yuuuoqRo8ezbZt2/yWYxgxz/vvv8/u3bvzNSwBINF6XVZEZgPHhlg0GEhW1QpBZber6mFxYhHpD5RS1Uddegjwj6qODiqTrqrxIeq+EbgHuFBV92SirwfQAyAhIaHp5MmTc7R96enpxMcfUXVEyInttWvXcuutt3LdddfRo0ePiNrOKWbbbMe67eHDh7NkyRLeeustihcvnmfbrVq1Wqyq4WMcgU9/ZGcCygLFc7JOJnZ+AKq5+WrADyHKdAVeCkq/BHTNUCY9xHqtgRVA1ezqadq0qeaUuXPn5nidaNm+4YYbtHTp0rpx48aI284JZttsx7LtXbt2aXx8vPbo0SNitoFFmg0flGVoQkSKicj1IvKeiGwBVgKbROQ7EXlSROrl6PLwL9OBQC+IRGBaiDIzgEtEpKJ7SHeJy8tK7xl4Dru9qm7JpbaYIykpiX379jFixAi/pRhGzDJz5kzS09PzPSwB4WPEc4E6wCDgWFWtqapVgfPxHoaNcmGAnDIKaCMiq4A2Lo2INBORVwBUdRvwCLDQTcNdHiLyhIhsAMqIyAYRSXJ2nwTigbdEZImITM+Ftpijbt263HLLLbz00kusW7fObzmGEZOkpqZSoUIFWrZsme91x4VZ3lpV92XMdA4xBUgRkRI5rVRVtwIXh8hfBNwWlJ4AHPH6mKoOAAaEyG+dUy2FhSFDhpCcnMzw4cPtjTvDyCH79u1j+vTptG/fnpIlS+Z7/Vm2iANOWETqiEgpN99SRHqLSIXgMoa/1KhRg7vuuovk5GRWrlzptxzDiCnmzp3L9u3bfQlLQPa7r6UAB0SkLvAqcCJgw38VMAYOHEjp0qUZOnSo31IMI6ZITU2lbNmytGnTxpf6s+uID6rqfqAjMEZV78Pr7WAUIKpWrcp9993Hm2++yTfffOO3HMOICQ4cOMDUqVNp164dpUuX9kVDdh3xPhHpitfD4V2Xl+PYsBF97r//fipUqMCQIUP8lmIYMcHnn3/Oli1bfAtLQPYdcXegOfCYqq4VkROB16Mny8gtFSpU4IEHHuC9997jiy++8FuOYRR4UlJSKFWqFJdddplvGsL1I35ZRDoCv6hqb1X9PwBVXauqo/JFoZFjevXqRUJCAg8++KB9aNQwskBVSU1N5dJLL6VcuXK+6QjXIp4AnA6874aVfEBETs8HXUYeKFu2LIMHD+bjjz9m9uzZfssxjALLwoUL2bBhg69hCQjffW2Bqiap6vnAtcDPwP3uZYkJInJtvqg0ckyPHj04/vjjGTx4sLWKDSMTUlJSiIuL48orr/RVR7ZHX1PVrar6f6p6k6o2BsYCuX3F2YgypUqVYujQoSxcuJDp04vEC4aGkSMCYYmLLrqIihVDfpsi38iWIxaRCu4ljqdF5DkReQ5IVNXHoqzPyAM33XQT9evX56GHHuLAgQN+yzGMAsWyZctYvXq172EJyH6L+H2gFrAMWBw0GQWYuLg4hg8fzvLly5kyZYrfcgyjQJGSkoKIcNVVob5Lkb9k1xEfpap9VfU1VU0OTFFVZkSEzp07c/rpp/Pwww+zb5+9jW4YAVJTUzn//POpWjXUdynyl+w64v+JyO0iUs19+LOS+6acUcApVqwYjz76KD/99BMTJ05k4sSJfksyDN/58ccfWb58eYEIS0D2HfFevCEm5/NvWGJRtEQZkaVdu3acc845DB8+nORku5ExjJSUFAA6duzosxKPcMNgBugL1FXVP6IpxogOIsJjjz3GxRcfMfKoYRRJUlJSOOuss6hZs6bfUoDst4i/A/4JW8ookCQlJR3mhEUEESEpKck/UYbhE+vXr2fx4sUFJiwB2XfEB4AlIvJSoPua68JmxABJSUmoKjNnzgRg/PjxqKo5YqNIkpqaCkCnTp18VvIv2XXEacBjwBdY97WYpXVr7wMmTzzxhPUrNoosKSkpNGrUiLp16/ot5RDZihFbV7XCgYhw4YUX8vHHH5Oamkrnzp39lmQY+cqmTZv44osvCtzdYLjR194RkStDfZdORGqLyHARuSV68oxIM2TIEOrXr8/IkSNtDAqjyJGWloaqFqj4MIQPTdyO98XmlSKyUETeF5GPRGQt3mfrF7sPfBoxQvHixRkwYADffPPNoZixYRQVUlJSaNCgAaeccorfUg4j3Ohrm1V1gKrWATrjfd6+L9BQVduo6rT8EGlElm7dulG9enVGjbIhpY2iw9atW5k3bx6dOnVCRPyWcxg5GX1tnarOV9Ulqmpd2WKYkiVLcv/99zNv3jwWLFjgtxzDyBemT5/OgQMHClxYAnLgiCOJe0V6loiscr8hx6ATkURXZpWIJAblPyYiv4hIeibrXSMiKiLNorUNsc7tt99OpUqVGDlypN9SDCNfSElJ4YQTTqBJkyZ+SzkCXxwxMBCYo6r1gDkufRhuLIuhwNnAWcDQIIf9jss7AhEpB/QGvoyC7kJDfHw8vXr1Yvr06Xz33Xd+yzGMqLJz505mzZpVIMMSEL7XRD8RicY7gB2AQJe4ZCDUOHSXArNUdZuqbgdmAW3h0JdDNmVi+xHgCWB3ZCUXPnr16kWZMmV4/PHH/ZZiGFHlvffeY+/evQUyLAEgWXVhEpFngGuAtcD/AW9FYrwJEdmhqhWC0ttVtWKGMv3wht981KWHALtU9amgMumqGh+UPgN4SFWvFpF5QD9VDTk4kYj0AHoAJCQkNJ08eXKOtiE9PZ34+PjwBXNBftoeO3YsqampvPHGGxx77LERtR1JzLbZzovtoUOHsnz5ct566y2KFctdICA3ulu1arVYVcOHSFU1ywkQ4ELgRWAj8AFwE1AuzHqzgeUhpg7Ajgxlt4dYvz+eUw2khwD3ZyiTHjRfDJgH1HLpeUCzcNunqjRt2lRzyty5c3O8TkG0/csvv2iJEiX0nnvuibjtSGK2zXZubf/9999apkwZvfPOOyNuOxzAIs2GDwp7aXD2PlbVO4GawBjgPuC3MOu1VtVTQ0zTgN9EpBqA+90SwsQGV1+AGu5CkBnlgFOBeSKyDjgHmG4P7LKmRo0adOvWjVdeeYUtW0L9DYYR28yYMYN//vmnwIYlIAcP60TkNGA43kdD9wIP5qHe6UCgF0QiEKo/8gzgEhGp6B7SXeLyQqKqf6pqZVWtpaq1gAVAe80kNGH8y4ABA9izZw/PPvus31IMI+KkpKRQqVIlLrzwQr+lZEq4h3X1RGSIiHwPTMIbCvMSVT1bVcfkod5RQBsRWQW0cWlEpJmIvAKgqtvwHrwtdNNwl4eIPCEiG4AyIrJBRJLyoKXI06BBAzp16sTYsWPZuXOn33IMI2Ls2bOHd955hw4dOhAXl93h1/OfcC3iGcBRQBdVPU1VH1PVNXmtVFW3qurFqlrP/W5z+YtU9bagchNUta6bXgvKH6CqNVS1mPtNClFHS2sNZ5+BAwfy559/Mm7cOL+lGEbE+Oijj9i5c2eBDktA+Feca6vqYFVdll+CDH9o1qwZrVu35plnnmH3buv5ZxQOUlJSKFeu3KEhYAsqfr3QYRRABg0axObNm+27dkahYP/+/aSlpXHFFVdQqlQpv+VkiTli4xCtWrXirLPO4oknnmD//v1+yzGMPPHJJ5+wdevWAh+WgPAP6ybmkw6jACAiDBw4kDVr1vDWW2/5Lccw8kRqaiqlS5embdu2fksJS7gWcaN8UWEUGDp06MDJJ5/MqFGjbOB4I2Y5ePAgqampXHbZZZQtW9ZvOWEJ54jLiMgZItIk1JQvCo18pVixYjzwwAN8++23fPDBB37LMYxcsWDBAjZt2lSgPhCaFeE61lUHRuO95pwRBS6KuCLDd7p27cqQIUMYOXIkl19+ud9yDCPHpKamUqJECa644gq/pWSLcI54taqasy1ilCxZkn79+tGnTx8+++wzzjvvPL8lGUa2UVVSUlJo06YN5cuX91tOtrBeE0ZIbrvtNipXrmwE5OAKAAAgAElEQVSfUzJijm+++YZ169bFTFgCwjviAfmiwihwlClTht69e/Pee+/x7bff+i3HMLJNamoqxYsXp0OHDn5LyTbhHPGD7qvNoaY5+aLQ8I177rmH+Ph4GzjeiClSUlK48MILqVy5st9Ssk04R9wPb1zg4CkFqAMcHV1pht9UrFiRnj17MnnyZNasyfMQI4YRddatW8fKlStjKiwB4ceaWByYgHjgceA6oKeqnpkfAg1/ue+++4iLi+PJJ5/0W4phhOXTTz8FoGPHjj4ryRlhH9aJyKUi8hneFzIeU9XzVdU6mBYRjjvuOBITE3nttdfYvHmz33IMI0umTp1KixYtOO644/yWkiPCveK8EHgJ73t1A4A/7YWOoseAAQPYt28fY8bkZQhqw4gua9asYfv27TEXloDw/Yj/BtLxPiB6TYZl9kJHEaFu3bpcc801vPDCCwwcOJAKFSqEX8kw8pnU1FSAmHTE4WLELVW1VSaTOeEixMCBA/nrr7948cUX/ZZiGIeRlJSEiNC/f38AateujYiQlJTkr7AcEC40MSBovnOGZSOiJcooeJxxxhm0bduWMWPGsGvXLr/lGMYhkpKS2Lx5MyLeSAyBLyMXGkeM10MiwKAMywr+2HJGRBk4cCBbtmzhtddeC1/YMPKRd955J6ZHCwzniCWT+VBpo5BzwQUX0Lx5c5588kkbON4oUKSlpVGrVi1uuukmv6XkinCOWDOZD5U2CjkiwqBBg1i3bh2TJ0/2W45hAPDXX38xe/ZsOnbsSPfu3f2WkyvCOeLTRWSniPwFNHLzgfRp+aDPKGC0a9eOhg0bMmrUKA4ePOi3HMNgxowZ7Nmzh6uuuspvKbkmXK+J4qp6tKqWU9U4Nx9Il8htpSJSSURmicgq91sxk3KJrswqEUkMyn9MRH4RkfQQ61wrIt+LyHciMim3Go3QFCtWjIEDB/Ldd9/x3nvv+S3HMEhLS6Ny5cq0aNHCbym5JlfDYIpIBREZnId6BwJzVLUeMMelM9ZRCRgKnA2cBQwNctjvuLyM69TDe6h4rqo2BO7Ng0YjE6677jpq1arFyJEjY/oBiRH77N27l3fffZcrr7ySuLhwr0UUXMJ1X6spIi+LyLsicpuIlBGR0cAqoGoe6u0ABL7ZngyEuqe4FJilqttUdTswC9dTQ1UXqOqmEOvcDox15VHVLXnQaGRCXFwc/fr1Y/78+Yfe7TcMP/j444/5888/YzosASBZtWhEZC7wMTAfzwleDHwH3KequR54QER2qGqFoPR2Va2YoUw/4ChVfdSlhwC7VPWpoDLpqhoflE4DfgTOBYoDSar6YSYaegA9ABISEprm9OFTeno68fHx4QvmgliwvWfPHrp27Uq9evUODZMZC7rNduGyPWbMGGbMmEFaWhqlSpUqcLpbtWq1WFWbhS0Y6PwcagKWZkj/BpTKap2gsrOB5SGmDsCODGW3h1i/P/BQUHoIcH+GMukZ0u8CU4ESwInABqBCOK1NmzbVnDJ37twcr1PYbI8YMUIB/frrryNuOyNm22xn5MCBA1q9enXt2LFjxG2HIje2gUWaDX+ZndHXKrqHa5WAzXhfdg6ks3LwrVX11BDTNOA3Eanm7FcDQoUQNgA1g9I1gI1h5G4ApqnqPlVdC/wA1Au3jUbuuPPOOylXrpwNHG/4wuLFi/n1119jPiwB4R/WlQcWB01HA1+7+UV5qHc6EOgFkQhMC1FmBnCJuxBUBC5xeVmRBrQCEJHKQH3ARjSPEhUqVOCuu+7irbfeYvXq1X7LMYoYaWlpFC9ePGa+1JwV4bqv1VLV2qp6Yoipdh7qHQW0EZFVQBuXRkSaicgrru5twCPAQjcNd3mIyBMisgGvdb5BRJKc3RnAVhH5HpgL9FfVrXnQaYTh3nvvpUSJEjzxxBNMnDjRbzlGESItLY0LL7yQSpWyvDmPCXLV30NEGgD9VPX23KzvnOPFIfIXAbcFpScAE0KUG0CID5u6mExfNxn5wLHHHkv37t2ZMGECe/fuNWds5As//vgj33//PT179vRbSkQI132tkYjMFJHlIvKoiCSISApe39/v80eiUdDp37+/jT1h5CvTpnnRzFj6UnNWhIsRjwcmAVcDv+PFh9cAdVX1mShrM2KApKQk6tSpc+h1ZxGJubFgjdgjLS2NJk2acPzxx/stJSKEc8SlVHWiqv6gqs8CB4GBqro7H7QZMUBSUhKqyu+//w7Aeeedx4EDB8wRG1Fj8+bNzJ8/v1D0lggQzhEfJSJnBH2jLh1v8B/7Zp1xGJUrVwbgs88+szixEVWmT5+OqhYqRxzuYd1m4OlM0vbNOuMwbrrpJtauXUv//v1p3779IedsGJEkLS2N2rVrc+qpp/otJWJk6YhVtWU+6TAKAd27d6dKlSo0btyYAQMGMGHCER1eDCNP7Ny5kzlz5nDPPfcc+jRSYSBcr4lOGaaOInK+iJTLL4FGbNGwYUP69evHa6+9xieffOK3HKOQ8eGHH7J3795CFZaA8DHiKzNM7YF+wLciYmEJIyRDhgyhVq1a9OzZk7179/otxyhEpKWlUaVKlZgeezgU4UITIb87IiInAG/ijRVsGIdRpkwZxo4dS7t27Rg9ejSDBmX87qxh5Jy9e/fy3nvv0blzZ4oXL+63nIiSq4HhVXU93ghnhhGSyy+/nKuvvprhw4ezZo0N92HknXnz5rFz585CF5aA3H+howGwJ8JajELGmDFjiIuL4+6777YveRh5Ji0tjbJly3LxxUeMjhDzZBmaEJF3OPJrzZWAasCN0RJlFA5q1KjBo48+yr333ktKSgrXXHON35KMGOXgwYOkpaXRtm1bSpcu7beciBOuH/FTGdIKbAVWqao9hTHCcvfdd5OcnEyfPn245JJLOProo/2WZMQgCxcuZNOmTYUyLAHhQxO/AvtV9WM3faKq3wFni0idfNBnxDhxcXGMGzeOTZs2MWTIEL/lGDFKYOzhdu3a+S0lKoRzxGOAv0Lk73LLDCMsZ511FnfeeSfPP/88ixcv9luOEYOkpaXRsmVLKlasGL5wDBLOEddS1W8zZrpxg2tFRZFRKBkxYgRVq1alZ8+eHDhwwG85RgyxcuVKVq5cWWjDEpCNQX+yWFb4IuZG1ChfvjzPPPMMixYtYty4cX7LMWKIwjb2cCjCOeKFInLEVzhE5Fa879YZRrbp0qULbdq04cEHH2TjxnDfgTUMj7S0NJo1a0bNmjXDF45Rwjnie4HuIjJPREa76WO8zxn1ib48ozAhIrzwwgvs2bOHvn3ta1ZGeDZt2sSCBQsKdVgCwn889DdVbQEMA9a5aZiqNlfVzdGXZxQ26taty+DBg5kyZQozZoT7KLdR1Jk+fTpA0XbEAVR1rqr+x00fRVuUUbgZMGAA9evX5+6772bXrl1+yzEKMGlpadStW5dTTjnFbylRJVevOBtGXihVqhTjxo3jp59+YsSIEX7LMQoof/75J3PmzOGqq64qVGMPh8IXRywilURkloiscr8hOweKSKIrs0pEEoPyHxORX0QkPUP540Vkroh8IyLfisjl0d4WI3e0atWKbt268fjjj7Ny5Uq/5RgFkA8++IB9+/YV+rAE+NciHgjMUdV6wByXPgwRqQQMxRtq8yxgaJDDfsflZeQh4E1VPQO4DnghCtqNCPHUU09RtmxZ7rzzThsUyDiCtLQ0qlatyjnnnOO3lKjjlyPuACS7+WQg1CXvUmCWqm5T1e3ALKAtgKouUNVNIdZRIDCYQXnA+kgVYKpWrcrjjz/OvHnzeP311/2WYxQg9uzZw/vvv0/79u0L3djDoRA/WiIiskNVKwSlt6tqxQxl+gFHqeqjLj0E2KWqTwWVSVfV+KB0NWAmUBEoC7RW1ZD9nUWkB9ADICEhoenkyZNztA3p6enEx8eHL5gLipLtgwcP0rt3b3799VeSk5MzHRSooOk229G1/dVXX/HAAw8wYsQImjdvHlHbuSU3tlu1arVYVZuFLaiqUZmA2cDyEFMHYEeGsttDrN8feCgoPQS4P0OZ9AzpvoEyQHPge6BYOK1NmzbVnDJ37twcr2O2Q7N06VItXry43n777RG3nR3MdsGzfccdd2h8fLzu2rUr4rZzS25sA4s0G/4yaqEJVW2tqqeGmKYBv7nWa6AVuyWEiQ1A8Ks0NQgfargV7xNOqOp8vFe07ZvuBZxGjRpx3333MX78eL744gu/5Rg+c/DgQaZNm8Zll13GUUdlNcpC4cGvGPF0INALIhGYFqLMDOASEanoHtJd4vKy4mfgYgARORnPEf8eEcVGVBk6dCg1a9akZ8+e7Nu3z285ho989dVXbN68uUj0lgjglyMeBbQRkVVAG5dGRJqJyCsAqroNeARY6KbhLg8ReUJENgBlRGSDiCQ5u/cDt4vIUuD/gJvd7YFRwImPj+c///kPy5YtY8wYG2G1KJOWlkZcXByXX150ep+G+0JHVFDVrbiWa4b8RXjjWATSE4AJIcoNAAaEyP8eODeiYo18o0OHDnTo0IGkpCSuvfZaTjjhBL8lGfmMqjJ16lRatWpFhQoVwq9QSLA364wCxXPPPQdA7969fVZi+MHKlSv58ccfi1RYAswRGwWM448/nmHDhjF9+vRD49AaRYe0tDQA2rdv77OS/MUcsVHg6NOnD40aNaJXr16kp6eHX8EoNKSlpXHmmWdSo0YNv6XkK+aIjQJHiRIlGDduHL/88gtJSUl+yzHyiV9//ZWvvvqqyIUlwByxUUBp3rw5PXr0YMyYMSxdupSJEyf6LcmIMoGxhzt27OizkvzHHLFRYBk5ciSVKlWiZ8+eJCcnh1/BiGnS0tKoX78+J510kt9S8h1zxEaBpVKlSjz99NMsWLDAbylGlNmxYwcfffRRkRh7OBTmiI0CS1JSEt26dTuUFhFExOLGhZAPPviA/fv3F8n4MJgjNgowSUlJqCp79uwBoFixYtSqVYuLLrrIZ2VGpJk6dSoJCQmcffbZfkvxBXPERoGnZMmSAHz66acUL16cli1b0q9fP3bv3u2zMiMS7N69mw8++IAOHTpQrFjRdElFc6uNmCMxMZEWLVqwZMkS7rjjDkaPHs2ZZ57JkiVL/JZm5JGPPvqI9PT0IhuWAHPERoxw8803A97gQC+++CLvv/8+W7du5ayzzmLkyJEcOHDAX4FGrklLSyM+Pr5Ih5zMERsxyWWXXcayZcvo2LEjDz74IBdccAGrV6/2W5aRQw4cOMC0adO4/PLLKVWqlN9yfMMcsRGzHHPMMUyePJk33niD77//nsaNG/PSSy/Zh0hjiC+//JItW7YU6bAEmCM2YhwR4frrr2fZsmW0aNGCnj170q5dOzZtCvVtWaOgkZaWRokSJYrU2MOhMEdsFApq1KjBhx9+yPPPP8+8efM49dRTeeutt/yWZWRBYOzhiy66iPLly/stx1fMERuFhmLFinH33XfzzTffULduXa699lpuvPFGtm/f7rc0IwQrVqxg9erVRT4sAeaIjUJIgwYN+Pzzzxk+fDhTpkzhtNNOY/bs2X7LMjJQVMceDoU5YqNQEhcXx5AhQ5g/fz7lypWjTZs29O7dm3/++cdvaYZj6tSpnH322Rx33HF+S/Edc8RGoaZZs2Z8/fXX9OnTh//85z80adKEhQsX+i2ryPPLL7+waNEiC0s4zBEbhZ7SpUszZswYZs+ezd9//03z5s1JSkpi3759ADbWsQ8Exh42R+xhjtgoMlx88cUsW7aM66+/nmHDhtGiRQtWrlxpYx37QFpaGg0aNCiSYw+HwhdHLCKVRGSWiKxyvxUzKZfoyqwSkUSXV0ZE3hORlSLynYiMCipfSkSmiMhqEflSRGrlzxYZsUKFChX473//y9tvv83atWs544wzAGwAoXzkr7/+Yt68edYaDsKvFvFAYI6q1gPmuPRhiEglYChwNnAWMDTIYT+lqicBZwDnishlLv9WYLuq1gWeAR6P7mYYscqyZcvYunXrIQdcunRpRISHH37YZ2WFnwULFrB///4i+UmkzPDLEXcAAveDyUCoS+OlwCxV3aaq24FZQFtV/UdV5wKo6l7gayDwyddgu28DF0tRHO7fCEtgrOPA69DnnHMOAJMnT2bKlCkcPHjQT3mFmjfeeINq1apx5pln+i2lwCB+vJcvIjtUtUJQeruqVsxQph9wlKo+6tJDgF2q+lRQmQp4jri1qq4RkeV4znqDW/4TcLaq/hFCQw+gB0BCQkLTyZMn52gb0tPTiY+Pz9E6Zrtg2m7VqhUfffQR8+fP59VXX2XNmjXUqVOHW2+9lXPOOSdPn+6J1X0SLdt79+7l0ksvpX379tx3330Rt1/Q9kmrVq0Wq2qzsAUDrYJIT8BsYHmIqQOwI0PZ7SHW7w88FJQeAtwflI4DPgDuDcr7DqgRlP4JOCac1qZNm2pOmTt3bo7XMdsF03ZiYuKh+QMHDugbb7yhderUUUDPPfdc/fjjj3NtO1b3SbRsT5kyRQH98MMPo2K/oO0TYJFmw19GLTShqq1V9dQQ0zTgNxGpBuB+t4QwsQGoGZSuAWwMSr8MrFLVMaHWEZE4oDywLXJbZRRGAmMdg/ea9PXXX8+KFSsYN24ca9eu5cILL6Rt27YsXrzYP5ExzsMPP4yI0KVLFwDatm1r3x8Mwq8Y8XQg0c0nAtNClJkBXCIiFd1DuktcHiLyKJ6TvTcLu9cAH7mrkmHkiBIlSnDHHXewevVqnnzySRYuXEizZs3o3LkzK1as8FteTLF69epDr5h37doV+PdO3Byxh1+OeBTQRkRWAW1cGhFpJiKvAKjqNuARYKGbhqvqNhGpAQwGTgG+FpElInKbs/sqcIyIrAb6EqI3hmHkhNKlS9OvXz/Wrl3L0KFD+fDDDzn11FO55ZZbWL9+vd/yCjSqyvjx42ncuDErVqxg0qRJTJo0yW9ZBRJfHLGqblXVi1W1nvvd5vIXqeptQeUmqGpdN73m8jaoqqjqyara2E0B571bVTu78mep6ho/ts8ofBx99NEkJSWxZs0a7r33XiZNmkT9+vXp06cPv/32m9/yChy//fYbHTp0oEePHpxzzjksW7bsUGs4MTExzNpFD3uzzjByQJUqVRg9ejSrVq0iMTGRsWPHUrt2bQYPHsyOHTv8llcgmD59OqeddhozZ85kzJgxzJw5kxo1ahxaHhyTNzzMERtGLqhZsyYvv/wyK1asoEOHDowYMYITTzyRUaNG8ffffx8qV5TGsfjrr7+47bbb6NChA9WrV2fx4sX06dOHYsXMzYTD9pBh5IF69eoxadIklixZwrnnnsugQYOoU6cOzz//PHv37i0y41h88cUXNG7cmAkTJjBo0CC+/PJLGjZs6LesmMEcsWFEgNNPP513332Xzz77jAYNGtCrVy8aNGgAeL0GCmvnnb179zJ48GDOP/98Dh48yCeffMKIESMoWbKk39JiCnPEhhFBzj33XFq2bAnAunXrAK/VXKxYMZo0acLkyZPZsiVUt/nYY8WKFTRv3pwRI0Zw8803s3TpUs477zy/ZcUk5ogNI8IMGzYMVT00XsULL7xAp06dWLt2LV27diUhIYHTTz+d+++/n/fff5/09HSfFeeMgwcPHhpk/+effyY1NZVXX32Vo48+2m9pMYs5YsOIEoExKu68805SUlL4448/+Oqrrxg5ciRVqlRh7NixtGvXjooVK3LBBRcwbNgwPv/880MD1hdEfv31V9q2bUvv3r0Pje9so6jlHXPEhhFFgvvMFi9enDPPPJOBAwcye/Zstm/fzqxZs+jXrx+7du1i2LBhnHfeeVSqVIkrr7ySMWPGsHz58kzjy/ndI+PNN9/ktNNO4/PPP2fcuHG88847HHvssfmqobBijtgwokhWfWZLly5N69atGTlyJAsXLuSPP/7g7bffplu3bvzwww/cd999nHbaaVSrVo0bbriB1157jZ9//vnQ+vnVI2PHjh3ceOONdOnShfr167NkyRLuuOOOPI1KZxxOnN8CDMPwqFSpEldffTVXX301AOvXr2fOnDnMmTOH2bNnH3o9uF69erRu3RqAGTNmUKVKlUPTUUcdFREtEydOpGXLlsydO5fExEQ2btzIsGHDePDBB4mLM7cRaWyPGkYB5YQTTuCWW27hlltuQVVZvnw5/fr1Y+bMmaxatQrwRjELJj4+/jDHHG4qW7ZsyJZtcnIylStX5umnn6ZevXrMnz/fBnKPIuaIDSMGEBFOO+00ZsyYAXj9d0uVKsVnn33G77//HnLauHEjS5cu5ffff2fPnj0h7R511FEhHTTA6NGjueuuu3jyyScpU6ZMvm1rUcQcsWHEIIEXJs4999ywZVWV9PT0TB12YFqyZAkbN248bN0XXniBKlWq2HCVUcYcsWHEKNkdxUxEKFeuHOXKlaN27drZXqewvg1YELFeE4YRo9goZoUHc8SGYRyBjRmcv5gjNgzjCKy1nb+YIzYMw/AZc8SGYRg+Y47YMAzDZ8wRG4Zh+Iw5YsMwDJ8R67QNIvI7sD6Hq1UG/oiCHLNtts124bF9gqpWCVfIHHEuEZFFqtrMbJtts22284qFJgzDMHzGHLFhGIbPmCPOPS+bbbNtts12JLAYsWEYhs9Yi9gwDMNnzBEbhmH4jDliw4gCIlJTRK4QkVJ+aymIiPtQntinoAFzxLkmvw6gaNQTLe1BJ1fJaNjPUFeJKNqOxP5pDDwCXJ4f+yOWEJF4/ffhVMV8qjNivi4a54854hwS9CeUzSQ/UvUE/puI/kciIoGTQESuEZGjI2VbVVVEWgM3RNlRNgEGRchW4OKRICKl4dB25On/VNV3gCSgD9A+2s5YRIq734gdh6Fs5dW+Oy5uE5GrROQ24BURKRHFxkE3EamhqgcjUUeG8+dMETkq7yrNEecYd5JeDnwgIkOc44nIyQsgIg1FpJQ7cG7HO1DvEZHz8mobPJ2uno7AXWS4oOQFEWkMXAmsUNV9kbIbgh3A9SJyUV4Nuf/tCuAT4FkRGR6Un+v/U0SOU9VpwBPAPUTBGQddRE4DxovI0ZE6DuHQPmglIjeKyM2BvLzodcfFe0AyMAToEeVj5QxgmIiUyIv2AEHnTx88/Ql5tQnmiHOMa43dhtensAzerec1EJGTtwxwN/CiiFwHXAfMA+oC3USkfR7lB+ppAvQCxqnqJhHJ00dkRaS4iJTH09pQVRe4vEi35uNEpLiqrgGeBhoE6s+DzQZAO+BeYCJQV0RGQ+7/TxGpBjwqIj1U9X3+dcZXRNIZBxwl0AM4BxglIhUicBwGHPxZwEvA8UB/EXkxY5mc2AxyhP8ALwAHgLaBbcmt7TC8CqQDxzjbeT4m3T7vBtyqqjkdoyY0qmpTNiegJrAaeMilj8M7wZ4GrouAfQEa4p24i4E2Lr86cDvwOFAK1/87J3YzpM8GJgMz8AYlOaJMHuz+BdyeWZk87JvTgLfxLlR1gTOBhUClPNhMAH4FJrp0CeAU4A1gbB713oLnxG526cud3s4R3CfN8AarOh+4AXgG72JydF73vdu//wGucelSwGfAf/KouQfwoJs/2Z1Pd7p0B6B2BPZLIl5jqSxeY/O1vOrOYL8LkOzmiwX2MxCXa5uREldUJmA0sAmo59IJwP3uoD02lzYzOrT6wIfAp0CpoLy5QJXc2gZOdxePOLxWzhNue2qG0pFN+62cnRvwHrycBmwHbonAvs64X64C+gHfO8f2PV4MVnKqHTjO/V4H/ASc79LFgpz+ydmwUx14x82fCAwIWnYDMAFIdOkrgHMieCxehLtg4F1EGgIz8Vqb5fJo+25gDTAUiA/a1g9yaxvoDiwDTgzKawaswrvDXB84r/J4nFwMzMK7MPVxx+U0oFFebANHud/GwH+B04OWdQV65np/R+qgKGxT8MmNdwscvNOH4LVuGrj0sbiWZR7/6HOBC/Cu5PWB54HX8W6r2gBfApVzWU8vYIFzvP8HlARqAyPxWm41cqoZaA58hfdQKhnvNrAhnsM/ANyWl/3vfi/FezB3V9CJcLE7qRcC03NhuxpeK6mHS3fDa5md59LFgLI5sPcFXljmArw7jfuDlj3obOf5whRksxZwAt6dwXrgsqBlTwH/w2scHGqt5WB/1wbKuPlOzqG1BOLx7ni+Jpt3IUANd+ye4NIvAR3dfEmgeND2XEuQg87l+XMlcBnQzKWb4TVo3sG788m9o4Q78Boc/YGmwBjgUWCAOxaXA3VzbT9SB0dhnfDihyvwrtgLgeou/wGX3yBC9fTFe2D0X7yWbyPgJHcQ/ebyT8mBvYpB89fg3VYe7U7Uta6OUngXmSQgIYd6TwLmA51cuhZe+OQpl74AuCSP++QKd+Jf6RzdNIIcJF6M/nNcizOHtm9x/+nNLn0jsBm4IAc2igXNT3fHx3l4F6T+Lr8R8G5O/rus6sNr3Y3n3wdFV+OFmG7Ec5QfOWfxZC7sXwYswut29x7enVMfvAv4/+E1Ctpn01YHd3yk4V2s++O1svsDpTPUeUIe9kngAnIX8A3wmDsvHw0q0xEYC5yayzpuxrs7rQ/sxHO8NYHr8e4+Xsyt7UN15PXgKGwTh99q1sW7/T0e77b4b+fQarnlDwHn5rKeY4PmmwDT3Hx/YGbgIONfR1kzB7YvcSfUJUH2q+Fd1WfgOeC5bltKAiWyYbMB3m18IIxxMl5LcG5QmYZ4DvO4oLxshwzcfj7XzVfAa2XXc87mU+cMZuNabK7cw8B92fw/w4UOEoGWOfwfM3PGH+BdOL4DLsrjMZnx1vsi5wDuw3P0lwIf4120T8ML20zHu1Bluv/xwlQnBh1nX+O1iPvh3X0FQhI34V3wOmRTbyvgR7yWYwVn+wv33412yxPwGgifA9VysU+Ox12UgarueD7ZpSvihTvuCyqfq3i524ej3X69CS/0UzLYZiCdp/84rwYK4+QOjplAabwrX1u8q3ppYEU9reAAAAblSURBVArerWaOb6OC7Ldz9qq4dHXnUMbi3UqVcPnt8WJ/YR1lBvu9gN1uG650ecXwbg1buvRw5yyOz4Y9cQfjPmAS3i1aGbxY2XN4LbTieE5zCTl84OLsH40Xe1/Jvw8pj8N7ePa1m68O/ILn7IsBVdw2ZdkaIYehg5yetBzpjOcBlfAe6mS7hR2mjkYEPXACLnTb/lCQQ4rDc9IrgdPC2DsJ7yLRGS/sUA/PsV/qjs06rlwL93u3Oy+ah9s/wGDgHjcfCCdVx7uY/uqOn/fxLh65idsmAM/iXTACF4u3Appd+kpgZC5s18PrgXIRLgQD9Ma70/gww3GTGIn/VtUcccY/IfiE+hD42M33Ah5x81c7Z9M4l3W0dQdkW5cuCZTHa+19hIsB490OLQGq5qKOyngPKvoCqUAXl/8y3sOXgXi3ntl+8IfXyv4MqIPX0nsKrxfHRXgtsSV4Ti7XrT+8ePUsd5IGQh4n43WzA6+lOQw4O2ido7L5f0Y1dJChrg9wrfCMy3JgL+MdyEl4vTme5d+W2FXABrw4ZVm8i+PtQP0wtmvhxTRvDcqrgHdBWs+/zu0Cd5xUdek7yeLCHaTrRSApkMe/seCT3L5piBc/zu3zjmJ4oZin8c5NccfiQlzPBZf/eqDubNpth3fRn+qOww3Aqfz7fOYyvPP1Gne8n5SXY+awuiNlqLBMHOmMZ7sD/gXnBOYCTXNpuxJwELjKpevg3X7HuTqm4j2gew7v6XLDHNhuhGtduAP1ceAVdxBNx7sdrIX3gOFNgh4+5qCONGCIm0/Ee7FiEd7t/TIOb7Fl9yFRiaD5S91J3B7vAtIJKAfMcfmbcI6ebDo38iF0kEldU4FeubQT6g4kHu/h01P821PiRLdNp4bSkIX97sCzQcdKE7evhwHf4oVsrsWLuWYrHJHB/sXuvGkaVEcJvLvLt8l9r4t6/PuAXPBavS/w70PXF/HufsY57Tl5ptIWLxZ+YVDeULyeI3Xc/ngNzyd8RJg7jhxvWySNFZYpxAn1/+1dO2gUURQ9D7JRoxKShUAU0dJGQTAriqAYJZ8V0omICCKSNIpYKWlClCBREAtB2xRaGAXBTyOYWMRa/ARTphJBbPw0kmdx7rDjZDfZmfeesxvugVfMzu65w/vcd39vdhG0Tu5AlKgDd7Tr7hYFczl2rwQq5GHE3Kw6OIuggl8Ed+seULnfFeV2ErRay/L9uq2EeH/I810DqyI+ADgHWsQTYPJrCincQdBCmkIlXGJE8UxIPzwHcAD0GPYjZgk7jGeQ0EGVvroKYNSBJ+mB3AQ3115ROG/ApFRf1HcpuA+BYYY+cBN9COZCboMb1TORNxAblzT8G8G8xiRiRosos9eIJZIzzPGvYJhkBBXLeAzAsHxvH1jlUXfoEBUD6bhcr4/dGwdDPa1g+KwLDrXrNZ/BN+FaaYnF+xjAi2r3MnL3y8BfketUMeAanEeEcxzMqD+QxXRa7p8F49ub0yyqhIwu0GX7HU18+TxKXuxCisQL6PougYdXzoOKfTsYOukGazNn4Lj5VRlP59DBKrLWgZuJa7ijmgcyJ8pnMis/GMK4BLrX0+CBkA7wOPAt/JsMzTpXtoIW5SyAGzIvPyGDJ1Zljl8Aw2yPQCv1vqzREUjdfQbuMujVFaMxjN2bRUYvuG75IcmbvWG5ZXzRI/cx2Wnb5dqHMu4FY3xF0KqeBeOKraKAnYr8RUYJdOG6oz7Kuljl9wfBapQyWHr0FrTse8AE4ClIXWiA8cwUOqhTVvZTVit7IANgYnePh2fsTFwfBi3lLS5jGuPbIEr+uih+51JPWTfzMqe3yQb1EsA36aN2B+4B8HBPh1xHSfOncCxPW1V2SPK10ODJ1Vxh4D8nF4Qj56Ds7FHCJXN1Rw3+ApjsOgFPliToIs8Ldwm0pI7KvcwK7X+Pp++G2h5Im2c5BZk37yDhq0ZusmkvoFLV0CF9tcMDd1IZnwETdamT5qnk5t2pzdDgydWswT0ExoydLMsE56Aots7YZ164hasEKWvyyBkdnPHmIeQxngGedZkH4pm/AJ7ofAUpdWyGJgpzARJKCMD9HqwSmQttDVtr9c9D64UxpsVa+ycQ9yZr7Q/PnEOgZbkXfLlVww+0MaYfrCLZaa39HlhWsPH0CXl/7z3wIM60tXYpkIyitfZL4i1pDQ2Z42Ng/NZrv8irUZ+AIaCPPrmrymuSPldkQAgFHxrGmDKAn9bambyfpVEgr6NssdbO5f0sjYaQc9wY02at/RWCe5ksVcSKRkQzWWYKhStUESsUCkXO0H/oUCgUipyhilihUChyhipihUKhyBmqiBUKhSJnqCJWKBSKnKGKWKFQKHLGX9OkH11WwvoHAAAAAElFTkSuQmCC\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb4b7e0c990043b289c5ff91546945cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 360x288 with 1 Axes>"
+       "Button(button_style='warning', description='click me!', layout=Layout(width='200px'), style=ButtonStyle())"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "Mean CURRENT V value =  -0.02 m/s\n",
-      "Median CURRENT V value =  -0.02 m/s\n",
-      "CURRENT V Standard deviation =  0.01 m/s\n",
-      "\n",
-      "\n",
-      "Maximum CURRENT V value =  0.01 m/s\n",
-      "Minimum CURRENT V value =  -0.03 m/s\n",
-      "\n",
-      "\n",
-      "Maximum CURRENT V anomalies value =  0.02 m/s\n",
-      "Minimum CURRENT V anomalies value =  -0.01 m/s\n"
+      "here\n"
      ]
     },
+    {
+     "ename": "TypeError",
+     "evalue": "'NoneType' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "button = widgets.Button(description='click me!',layout={'width': '200px'},button_style='warning')\n",
+    "display(button)\n",
+    "#run_my_script = widgets.Output()\n",
+    "#@run_my_script.capture(clear_output=True)\n",
+    "#print(run_my_script)\n",
+    "\n",
+    "button.on_click(print('here'))\n",
+    "#run_my_script = widgets.Output()\n",
+    "#run_my_script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9f6cd9009c94d5993f78cfb887bb561",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 720x288 with 2 Axes>"
+       "Dropdown(description='Region:', options=('California Current', '12 - Gulf of Alaska', '13 - East Bering Sea', …"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bdc2f60d41b4429bf2bf260b372cad6",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 2160x720 with 1 Axes>"
+       "Dropdown(options=('SST', 'CHL', 'Wind_U', 'Wind_V', 'Wind_Speed', 'Current_U', 'Current_V', 'Current_Speed', '…"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2bc693c928041d6a8580112458e2b40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Button(description='Click Me!', style=ButtonStyle())"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Anomalies calculated based on the entire data period\n"
+      "Output() Output() 1997-01-01 2019-12-31\n"
      ]
     }
    ],
    "source": [
-    "#### Do not modify ####\n",
-    "  \n",
-    "%matplotlib inline\n",
-    "import sys\n",
-    "sys.path.append('./utils/subroutines/')\n",
-    "import pices\n",
-    "from pices import analyze_PICES_Region\n",
     "\n",
-    "analyze_PICES_Region(region, var, initial_date, final_date)\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import display\n",
+    "lmenames = [('California Current',11),('Gulf of Alaska',12),('East Bering Sea',13),\n",
+    "                ('North Bering Sea',14),('Aleutian Islands',15),('West Bering Sea',17),\n",
+    "                ('Sea of Okhotsk',17),('Oyashio Current',18),('R19',19),('Yellow Sea',20),\n",
+    "                ('East China Sea',21),('Kuroshio Current',22),\n",
+    "                ('West North Pacific',23),('East North Pacific',24)]\n",
+    "dropdown_regions = widgets.Dropdown(options = lmenames, value='11', description='Region:')\n",
+    "display(dropdown_regions)\n",
+    "region = widgets.Output()\n",
     "\n",
-    "#### End of script ####"
+    "variables = ['SST','CHL','Wind_U','Wind_V','Wind_Speed','Current_U','Current_V','Current_Speed','SLA','ADT']\n",
+    "dropdown_variables = widgets.Dropdown(options = variables)\n",
+    "display(dropdown_variables)\n",
+    "var = widgets.Output()\n",
+    "\n",
+    "initial_date = '1997-01-01'\n",
+    "final_date   = '2019-12-31'\n",
+    "\n",
+    "button = widgets.Button(description=\"Click Me!\")\n",
+    "output = widgets.Output()\n",
+    "display(button)\n",
+    "def on_button_clicked(button_object):\n",
+    "    print(region, var, initial_date, final_date)\n",
+    "        #analyze_PICES_Region(region, var, initial_date, final_date)\n",
+    "\n",
+    "button.on_click(on_button_clicked)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 96,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2dde5e61cd249c3ac03e2bef7e13c1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Button(description='Click Me!', style=ButtonStyle())"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You pressed Click Me!\n",
+      "California\n"
+     ]
+    }
+   ],
+   "source": [
+    "button = widgets.Button(description=\"Click Me!\")\n",
+    "output = widgets.Output()\n",
+    "display(button)\n",
+    "region='California'\n",
+    "def on_button_clicked(button_object):\n",
+    "    \n",
+    "    print('You pressed {}'.format(button_object.description))\n",
+    "    print(region)\n",
+    "    \n",
+    "button.on_click(on_button_clicked)\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -387,6 +485,384 @@
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+        "17 - Sea of Okhotsk",
+        "18 - Oyashio Current",
+        "19 - R19",
+        "20 - Yellow Sea",
+        "21 - East China Sea",
+        "22 - Kuroshio Current",
+        "23 - West North Pacific",
+        "24 - East North Pacific"
+       ],
+       "index": 3,
+       "layout": "IPY_MODEL_1dcb520180124cd1b23c888568954a37",
+       "style": "IPY_MODEL_9d70cd015ea141d0a1c78208f97c7c14"
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+        "12 - Gulf of Alaska",
+        "13 - East Bering Sea",
+        "14 - North Bering Sea",
+        "15 - Aleutian Islands",
+        "16 - West Bering Sea",
+        "17 - Sea of Okhotsk",
+        "18 - Oyashio Current",
+        "19 - R19",
+        "20 - Yellow Sea",
+        "21 - East China Sea",
+        "22 - Kuroshio Current",
+        "23 - West North Pacific",
+        "24 - East North Pacific"
+       ],
+       "index": 0,
+       "layout": "IPY_MODEL_ddbac5219c7e4b56bf33e83c226846f8",
+       "style": "IPY_MODEL_bf03fe9f3d3c4158a7098b873d0256af"
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+        "CHL",
+        "Wind_U",
+        "Wind_V",
+        "Wind_Speed",
+        "Current_U",
+        "Current_V",
+        "Current_Speed",
+        "SLA",
+        "ADT"
+       ],
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+       "layout": "IPY_MODEL_f3701766334f43cbae15a9fbb8d09bfb",
+       "style": "IPY_MODEL_f8f1a5b93e7840b497a99106af17b65c"
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+        "12 - Gulf of Alaska",
+        "13 - East Bering Sea",
+        "14 - North Bering Sea",
+        "15 - Aleutian Islands",
+        "16 - West Bering Sea",
+        "17 - Sea of Okhotsk",
+        "18 - Oyashio Current",
+        "19 - R19",
+        "20 - Yellow Sea",
+        "21 - East China Sea",
+        "22 - Kuroshio Current",
+        "23 - West North Pacific",
+        "24 - East North Pacific"
+       ],
+       "index": 0,
+       "layout": "IPY_MODEL_01f100f3bd2744529e6ba646132a8b2a",
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+        "11 - California Current",
+        "12 - Gulf of Alaska",
+        "13 - East Bering Sea",
+        "14 - North Bering Sea",
+        "15 - Aleutian Islands",
+        "16 - West Bering Sea",
+        "17 - Sea of Okhotsk",
+        "18 - Oyashio Current",
+        "19 - R19",
+        "20 - Yellow Sea",
+        "21 - East China Sea",
+        "22 - Kuroshio Current",
+        "23 - West North Pacific",
+        "24 - East North Pacific"
+       ],
+       "index": 3,
+       "layout": "IPY_MODEL_e412749262c2406bb7ea951e60d388d0",
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+        "SST",
+        "CHL",
+        "Wind_U",
+        "Wind_V",
+        "Wind_Speed",
+        "Current_U",
+        "Current_V",
+        "Current_Speed",
+        "SLA",
+        "ADT"
+       ],
+       "index": 1,
+       "layout": "IPY_MODEL_e2f185c02c97456f8032ac785bafdfa2",
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+        "11 - California Current",
+        "12 - Gulf of Alaska",
+        "13 - East Bering Sea",
+        "14 - North Bering Sea",
+        "15 - Aleutian Islands",
+        "16 - West Bering Sea",
+        "17 - Sea of Okhotsk",
+        "18 - Oyashio Current",
+        "19 - R19",
+        "20 - Yellow Sea",
+        "21 - East China Sea",
+        "22 - Kuroshio Current",
+        "23 - West North Pacific",
+        "24 - East North Pacific"
+       ],
+       "index": 2,
+       "layout": "IPY_MODEL_3387cb8659a14e8586ab47d06e2f88d8",
+       "style": "IPY_MODEL_918d9028490b465c8bfdf93aabbbd0c1"
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+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,
diff --git a/PICES_Regional_Ecosystem_Tool.ipynb b/PICES_Regional_Ecosystem_Tool.ipynb
index bc9fc25..9490b76 100644
--- a/PICES_Regional_Ecosystem_Tool.ipynb
+++ b/PICES_Regional_Ecosystem_Tool.ipynb
@@ -97,13 +97,13 @@
     "\n",
     "## Variable ##\n",
     "## Select the variable to analyze from the list above (eg. 'sst','chl','wind_v','current_speed','sla','adt')\n",
-    "var = 'sla' # <<<----- Use short name given above. upper or lower case accepted.\n",
+    "var = 'sst' # <<<----- Use short name given above. upper or lower case accepted.\n",
     "\n",
     "## Date Period to analize ##\n",
     "## Specify the period using the format: ####  YYYY-MM-DD  #####\n",
     "## Data available specified above\n",
     "## All data in monthly resolution\n",
-    "initial_date = '1997-01-01'\n",
+    "initial_date = '1981-01-01'\n",
     "final_date   = '2019-12-31'\n",
     "\n",
     "##############################\n",
diff --git a/User_Data_And_Figures/PICESregion11_ADT_anomalies_1981-01-01_2019-12-31.csv b/User_Data_And_Figures/PICESregion11_ADT_anomalies_1981-01-01_2019-12-31.csv
new file mode 100644
index 0000000..f524a94
--- /dev/null
+++ b/User_Data_And_Figures/PICESregion11_ADT_anomalies_1981-01-01_2019-12-31.csv
@@ -0,0 +1,314 @@
+,Year,Month,ADT
+0,1993,1,-0.015758097
+1,1993,2,-0.0031516552
+2,1993,3,0.018360972
+3,1993,4,0.025972068
+4,1993,5,0.02090615
+5,1993,6,0.020585
+6,1993,7,0.022493422
+7,1993,8,0.020595133
+8,1993,9,-0.00016480684
+9,1993,10,0.0043587685
+10,1993,11,-0.00030624866
+11,1993,12,0.0078055263
+12,1994,1,0.0062620044
+13,1994,2,-0.0008711815
+14,1994,3,0.0020409226
+15,1994,4,0.0013152957
+16,1994,5,-0.001827538
+17,1994,6,0.0005712509
+18,1994,7,-0.0015356541
+19,1994,8,0.0005488992
+20,1994,9,0.006913841
+21,1994,10,0.0005161166
+22,1994,11,-0.019078434
+23,1994,12,-0.024601996
+24,1995,1,-0.008408785
+25,1995,2,0.0030270815
+26,1995,3,-0.005659163
+27,1995,4,-0.009569943
+28,1995,5,-0.011084259
+29,1995,6,-0.0056503415
+30,1995,7,-0.018078744
+31,1995,8,-0.02236867
+32,1995,9,-0.005178869
+33,1995,10,-0.006558895
+34,1995,11,-0.005931139
+35,1995,12,-0.01326704
+36,1996,1,-0.016027927
+37,1996,2,-0.008127689
+38,1996,3,-0.01764667
+39,1996,4,-0.0028584003
+40,1996,5,0.0018705726
+41,1996,6,-0.004290879
+42,1996,7,-0.010348141
+43,1996,8,-0.009862721
+44,1996,9,-0.00463289
+45,1996,10,-0.005264282
+46,1996,11,-0.025252998
+47,1996,12,-0.023625672
+48,1997,1,-0.013467848
+49,1997,2,-0.0054525733
+50,1997,3,-0.014964521
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+60,1998,1,0.03162706
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+62,1998,3,0.031635225
+63,1998,4,0.02545607
+64,1998,5,0.024240375
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+70,1998,11,-0.00562042
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+83,1999,12,-0.034637332
+84,2000,1,-0.04591468
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diff --git a/User_Data_And_Figures/PICESregion11_SLA_anomalies_1981-01-01_2019-12-31.csv b/User_Data_And_Figures/PICESregion11_SLA_anomalies_1981-01-01_2019-12-31.csv
new file mode 100644
index 0000000..1caae13
--- /dev/null
+++ b/User_Data_And_Figures/PICESregion11_SLA_anomalies_1981-01-01_2019-12-31.csv
@@ -0,0 +1,314 @@
+,Year,Month,SLA
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diff --git a/User_Data_And_Figures/PICESregion11_SLA_anomalies_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion11_SLA_anomalies_1981-01-01_2019-12-31.png
new file mode 100644
index 0000000..0c4f7f0
Binary files /dev/null and b/User_Data_And_Figures/PICESregion11_SLA_anomalies_1981-01-01_2019-12-31.png differ
diff --git a/User_Data_And_Figures/PICESregion11_SLA_climatology_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion11_SLA_climatology_1981-01-01_2019-12-31.png
new file mode 100644
index 0000000..1ac381c
Binary files /dev/null and b/User_Data_And_Figures/PICESregion11_SLA_climatology_1981-01-01_2019-12-31.png differ
diff --git a/User_Data_And_Figures/PICESregion11_SLA_densityplots_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion11_SLA_densityplots_1981-01-01_2019-12-31.png
new file mode 100644
index 0000000..6a138e5
Binary files /dev/null and b/User_Data_And_Figures/PICESregion11_SLA_densityplots_1981-01-01_2019-12-31.png differ
diff --git a/User_Data_And_Figures/PICESregion11_SLA_timeseries_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion11_SLA_timeseries_1981-01-01_2019-12-31.png
new file mode 100644
index 0000000..940050e
Binary files /dev/null and b/User_Data_And_Figures/PICESregion11_SLA_timeseries_1981-01-01_2019-12-31.png differ
diff --git a/User_Data_And_Figures/PICESregion11_SST_anomalies_1981-01-01_2019-12-31.csv b/User_Data_And_Figures/PICESregion11_SST_anomalies_1981-01-01_2019-12-31.csv
index 60ea002..998b46a 100644
--- a/User_Data_And_Figures/PICESregion11_SST_anomalies_1981-01-01_2019-12-31.csv
+++ b/User_Data_And_Figures/PICESregion11_SST_anomalies_1981-01-01_2019-12-31.csv
@@ -1,454 +1,454 @@
 ,Year,Month,SST
-0,1981,12,-0.16722012
-1,1982,1,-0.3584919
-2,1982,2,-0.23809528
-3,1982,3,-0.30632114
-4,1982,4,-0.4612646
-5,1982,5,-0.6555233
-6,1982,6,-0.7051754
-7,1982,7,-0.5170069
-8,1982,8,-0.53189564
-9,1982,9,-0.08567619
-10,1982,10,-0.17338467
-11,1982,11,-0.11692333
-12,1982,12,0.010899544
-13,1983,1,0.30308723
-14,1983,2,0.40404987
-15,1983,3,0.5697193
-16,1983,4,0.26360607
-17,1983,5,0.103279114
-18,1983,6,0.1498661
-19,1983,7,-0.028788567
-20,1983,8,0.43234062
-21,1983,9,0.36276245
-22,1983,10,0.4372425
-23,1983,11,0.5197964
-24,1983,12,0.17498207
-25,1984,1,0.44760704
-26,1984,2,0.49402428
-27,1984,3,0.3690281
-28,1984,4,-0.06564045
-29,1984,5,-0.20188999
-30,1984,6,-0.5746908
-31,1984,7,-0.4695654
-32,1984,8,-0.06847954
-33,1984,9,0.15566444
-34,1984,10,-0.20660019
-35,1984,11,-0.5009937
-36,1984,12,-0.46383953
-37,1985,1,-0.34766483
-38,1985,2,-0.525157
-39,1985,3,-0.6673784
-40,1985,4,-0.4571352
-41,1985,5,-0.4649048
-42,1985,6,-0.1393156
-43,1985,7,0.13444614
-44,1985,8,-0.09166336
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-46,1985,10,-0.31567764
-47,1985,11,-0.90954113
-48,1985,12,-0.664052
-49,1986,1,0.0016355515
-50,1986,2,0.24821949
-51,1986,3,0.57078266
-52,1986,4,0.07930279
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-59,1986,11,0.1430788
-60,1986,12,0.4269743
-61,1987,1,0.40539837
-62,1987,2,0.30700588
-63,1987,3,0.16343594
-64,1987,4,0.24830818
-65,1987,5,0.2674532
-66,1987,6,-0.31043053
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-73,1988,1,-0.46154213
-74,1988,2,-0.28797436
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-84,1988,12,-0.3425169
-85,1989,1,-0.8283825
-86,1989,2,-1.1446562
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-88,1989,4,0.17700863
-89,1989,5,0.28946877
-90,1989,6,-0.0773592
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-92,1989,8,-0.18343735
-93,1989,9,-0.274889
-94,1989,10,-0.021370888
-95,1989,11,0.16043186
-96,1989,12,0.6270752
-97,1990,1,0.25006294
-98,1990,2,-0.53477764
-99,1990,3,-0.40481853
-100,1990,4,0.24679947
-101,1990,5,-0.057247162
-102,1990,6,0.04586029
-103,1990,7,0.30123806
-104,1990,8,0.58247375
-105,1990,9,0.9045601
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-111,1991,3,-0.34272957
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-114,1991,6,-0.9919338
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-126,1992,6,1.0016813
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-129,1992,9,0.21895218
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-131,1992,11,0.7526026
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-165,1995,9,0.4633751
-166,1995,10,0.2391386
-167,1995,11,0.3137226
-168,1995,12,0.7962017
-169,1996,1,0.71585464
-170,1996,2,0.70090294
-171,1996,3,0.5245342
-172,1996,4,0.6957855
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-174,1996,6,0.34595108
-175,1996,7,0.022912025
-176,1996,8,-0.13740921
-177,1996,9,-0.008659363
-178,1996,10,-0.17180157
-179,1996,11,-0.27357578
-180,1996,12,-0.0010023117
-181,1997,1,0.27003098
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diff --git a/User_Data_And_Figures/PICESregion24_SST_anomalies_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion24_SST_anomalies_1981-01-01_2019-12-31.png
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diff --git a/User_Data_And_Figures/PICESregion24_SST_climatology_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion24_SST_climatology_1981-01-01_2019-12-31.png
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index 0000000..ee9a389
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diff --git a/User_Data_And_Figures/PICESregion24_SST_densityplots_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion24_SST_densityplots_1981-01-01_2019-12-31.png
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diff --git a/User_Data_And_Figures/PICESregion24_SST_timeseries_1981-01-01_2019-12-31.png b/User_Data_And_Figures/PICESregion24_SST_timeseries_1981-01-01_2019-12-31.png
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diff --git a/utils/subroutines/__pycache__/pices.cpython-37.pyc b/utils/subroutines/__pycache__/pices.cpython-37.pyc
index c38a9a9..5049ff2 100644
Binary files a/utils/subroutines/__pycache__/pices.cpython-37.pyc and b/utils/subroutines/__pycache__/pices.cpython-37.pyc differ
diff --git a/utils/subroutines/pices.py b/utils/subroutines/pices.py
index 8fc0f9b..3d0f828 100644
--- a/utils/subroutines/pices.py
+++ b/utils/subroutines/pices.py
@@ -214,16 +214,20 @@ def select_propervar(dtmean, dtclim, dtanom, var):
 def print_var_info(dtmean, var, lmei, lmename, initial_date, final_date):
     # short and long name for variable
     svar = var.upper()
+    #print(dtmean.attrs)
     
     if (svar=='SST'):
-        lvar = dtmean.attrs['long_name']
-        units = dtmean.attrs['units']
+        print(dtmean)
+        lvar = dtmean.attrs['title']
+        #units = dtmean.attrs['units']
+        units = 'C'
     elif (svar=='SLA') or (svar=='ADT'):
         lvar = dtmean.attrs['comment']
         units = dtmean.attrs['geospatial_vertical_units']    
     elif svar=='CHL':
         lvar = dtmean.attrs['parameter']
-        units = dtmean.attrs['units']    
+        #units = dtmean.attrs['units']  
+        units = 'mg m-3'
     else:
         lvar = svar.replace('_',' ')
         svar = lvar
@@ -247,7 +251,7 @@ def make_plot(plot_type, ds, ds2, var, svar, units, lmei, lmename, initial_date,
     import seaborn as sns
     import os
     home_dir=os.getcwd()   
-    
+
     if plot_type == 'timeseries':
         plt.figure(figsize=(10,4))
         plt.plot(ds.time,ds[var])
@@ -347,6 +351,9 @@ def analyze_PICES_Region(region,var,initial_date,final_date):
     # print information
     svar, units = print_var_info(dtmean, var, lmei, lmename, initial_date, final_date)
 
+    if var=='chl':
+        var='CHL1_mean'
+        
     # displaying time series data
     make_plot('timeseries', dtmean, dtanom, var, svar, units, lmei, lmename, initial_date, final_date)