diff --git a/Network Hydraulic Scaling Research App.ipynb b/Network Hydraulic Scaling Research App.ipynb
index 0a2d2bf..5eb7dd3 100644
--- a/Network Hydraulic Scaling Research App.ipynb	
+++ b/Network Hydraulic Scaling Research App.ipynb	
@@ -26,11 +26,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T00:49:12.445796Z",
-     "start_time": "2020-10-16T00:49:12.429851Z"
+     "end_time": "2020-12-18T18:53:02.710602Z",
+     "start_time": "2020-12-18T18:52:57.541991Z"
     }
    },
    "outputs": [],
@@ -90,7 +90,7 @@
    "outputs": [],
    "source": [
     "# Create data frame\n",
-    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/76ebc18852cc41e48d4ee83902bc0a7d/data/contents/ws_metadata.csv')\n",
+    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
     "\n",
     "# Create arrays\n",
     "areaM2 = []\n",
@@ -924,11 +924,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T01:25:57.741520Z",
-     "start_time": "2020-10-16T01:25:52.662992Z"
+     "end_time": "2020-11-27T19:08:15.013064Z",
+     "start_time": "2020-11-27T19:08:09.951809Z"
     }
    },
    "outputs": [
@@ -983,11 +983,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T01:29:55.711367Z",
-     "start_time": "2020-10-16T01:25:58.952360Z"
+     "end_time": "2020-12-19T04:29:08.030539Z",
+     "start_time": "2020-12-19T04:24:53.004320Z"
     },
     "scrolled": false
    },
@@ -1005,7 +1005,7 @@
     },
     {
      "data": {
-      "image/png": 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h+RONgdmdGo+fgzhtihC60dW1dSpSjQucsxqZRESesraO8QoytFjxsidew/DyxcEKOJwYrwsA5AkhOsqfSA+qJdD/0Vl4b/HuWA8jJth9WhNG5foHJvXFyof1vBRF4aBdJvGQRojPcu7azTCx7k92qpOvU5tiGl7+2B/fcGK8VkNvF+Ru6oXMsbHnSFWsh8BEAD3GC3CoChyqvrZRFQ6uZxIPqdLauZyEdX+GdMnm69SmSMPLGweKVziG1wYA+URUAIAACCFEj8gOq2VQ44n9CRAL7D6vyZZBVpyqYgZ3MkyiIN1wgU2y7XUBWw0th6pwAVWb4vHVTxSJFeEYXlcC6A6gNMJjaXHUeH2xHgITAWRwvRWXqpjBnQyTKJiuRn/LcDUqFB83Zqb5kbFdieJq3AugSgjBrsZmRlrgLYWf9+l91mOcyRtxQileLgcrXkziIY0Sq3ESB56aZsVqR3JIgH2Rh9UbBwvgcILruwDYSURLjZ8lkR5Uy6FlXeD5pbUAAsss2BEBUc+4ZMOLSUT8IYLr7TZrWQ0tIsJfZm3F0SpPDEfERAJ5lH1xoGiG62pkIkAcHP+oIo0Rm9tduuIV9FwSG15MAiJtEr+NsxoDFC8irNhzFCv3HMU5A9vHblBMsyNCuM1jRYOKFxE9TUQ5Qoi9wT9E1IaIno3mQO2I3SawppAGl933O6SrkWO8mAQkdOV6e12/1vlI9le1uyrfEqlTvGI/DzemeL0P4D3S7yDrABQCyAZwOgA/gAciPzx709IUL2mM2N3+EBD1VD12NTKJiBZS8YrRYCKE1Y6kFqLKt0TqYrxifwI3aHgJIXYAuMRoij0WQC6AfAB3CyF2Rml8tsZuK8emkKtIuytemgAIHFzPJD6yfpedezUGKF7GHMWGlx1JoHISQohtALZFYSwtDrtNYE0hY7zs3ipJiBDB9aoKt92lPsZ2mK5Ga1ajzSau4KxGoP7CiUl85HH2xcE8HE5WIxMh7DaBNYVUvOzes1CP8Qp8jhUvJhExXY3WJtk2u36DsxoB4Llvt8RqOEyEkEfZGwcLfza8YkhLM7zQUhQvCHY1MrYgtOIVq9FEBi1A8dJ/by2siM1gmIhRl6Eb+3m4ScOLiDKI6CkiepeILiOiXuF8MBGdRkTzQzx/IRH9ZNQEu/k4xmwbrHaXEML2BonfL4wChbEeSWQRQm+KbYUNLyYRCR3jZa8LWISI8WLsxdr9pbjizaUA4iO4PhzF6z0AuwD0AVAA4N2m3kBEDwB4B3pzbevzTgAvATgHesD+LUTUYoulWOevP/xnLa5+Z1nsBhMFfJqAS1Vs72rU71HB5SQIHr8/JuNhmONF2ls+G2c1WvcnuAwMk/iUVHlwsLTGfBzXdbwstBZCvAfAK4RYgvq1IUOxE8BlIZ7vD2CHEKJECOEBsAjA6LBHazOsrsZNh8qxam9JDEcTeTQh4FTJ9sqeEFxOgrEHZssgOxdQtdTityp7Hy3bG4vhMM3I5vxyDHlyLhyWbKd4qOMVVowXEfUzfneGXsOrUYQQXwDwhtiUCaDM8rgCQFYD33kLEa0kopVFRUXhDDPhCGzOSnHRyiCS+DQBl0Ox3cQdjED9fpQuVWXDi0k4/C2hnITlsrT28dtTXBWD0TDNibzXOB11pk6iKF53Qy+mOhTA5wD+cALfVw4gw/I4A0BpqBcKId4SQgwXQgxv06bNCXxl/GI9/KpCtpvQgvFrGpyqYv8CqqJ+cL1DJSzZeSRGI2KY46MltAwKVbkeAJTg1ROTcKS69IpZ1iMZD4pXOHW81gMY2UzftxlAbyLKAVAJYAyAF5rpsxMOq8DVEq5xv4aWoXiJ+sdze2EFth+ujM2AGOY4CZXVaLer1zodBbik4kAZYU4MeTirPXWOunjwLIWT1XiQiLxEdIiIPERUTkTbiWhiuF9CRFcT0S1CCC+AewHMBrAUwHtCiIPHP/zERjSw0rIrdYpX7E/8SKKF6NU4qHN2jEbDNDdr95fGRRHGaCANL5+NFS9rjJdqSUeOh7IDzIkhT9s3f6xrthMPBnU4rsYFAE4SQnSEHhz/XwDnAXiysTcJIfYIIU43/v5YCPGW8fd0IcSpQohhQojXTmz4iU1DErdd8WkCTrUFKF4hNIExvXPRJiMpBqNhmpuLX1uMbzcUxHoYUSFUcL3dLl9NABP6t8WC+8fDodbNw91ap8VwVExzIMWNspq6kHNvHCyawjG8OgshtgKA0aOxq9HH0RfRkbUAApuz2t/w0lpKcL2oq9IvIeImJHaisLw21kOICiHLSdhMsdaEXl+wa+tUcwF8/Rl5vFCyAfJM3XOk2nwuHjwuTcZ4AcgnoucALAEwCkCB4Wb0RHRkNkehoIrJLcDw8mkCSS3C1Vi/nASgtxHSNMFBuzbA7osHSV0B1TqVwG57rvdW1a9Jp3FtKkQt5hjbmVDFfhMixgvAdQAOQXcv7jceVwK4KmKjagEoRC0wxkvA6bB35fqNh/RqKaGOZrJTRa2Pi6jagZZyTzZ7NWrW5+y185q1typJw6vlHGM7E+oYxoOrMRzF679CiHOCnlsaicG0JIiC6ni1FMNLVWznqrBy/suL8JdfDA55PFOcKqo9fjPFmUlc7N59QWLGeNm4V6MIkQzDipc9CHWuJkpwfSkRXURE/YioDxH1ifioWgBEgXW7VPvbXS2mZVCtTwupeKW6VNR4WPFKRGq9fuRNmWE+tvkpbGJmNVpuVnbr1ahZXI11wpe9lfmWQqhEp3i4/4Sz9G4D4B7LYwHgrMgMp+WgUMvLatQ0AafD/jFebq8/ZLJEkkOFm6vXJyTlNYGNOOxmfDSEvFSt7hm77bpueAU+pyr2c6m2REIdwngoBRNOAdXx1sdE5IrccFoOSpDiFZwFZ0e8fg1JqmJ746PW6w8ZXO/kfo0JS3ltoOFl87WDiTQ+rIaX3QwSIaxKl/47OAaXSUxCnavxEFzfpOFFRLdCL3rqhH5+egGwu/EEIbQ8xcvt15DiUgOqCNsJOVHXekO7Gl2qEheBncyxI+sAyTISLeWeLOMxPX4No3vn4pJTOtmuA8P+o9Uhy7/Ewf2ZOUFCXafx4HEJJ8brZgDjAHwL4HoAmyI5oJaCQhTgfW4JipfHpyHFqcaFjz0SeP3S8PKHPJ4uB8HDhldCUl6jly087ZnvANhP9WkIeY/y+DSoCoHIfm7WKV+urxcJpFB83KCZE0MIYHTv3IDn4kHxCsfwKhZC5APIEELMB5AT2SG1DIKzGluE4uXToCiEuZsKYz2UiCDVrJoGXI0uVYGXXY0JSbCrMfZTd3SwuhpVonoLRrtQXOkGALO5fUtxNZZUeQKSRuyGgJ7QZSUeYrzCMbzKiOgSAMJwO7aJ8JhaBIpCsLYCs7vhtbu4Ch6fhiFd7NuzUBpeDbkanaoCdxxc9MyxE6xwtYSbMlDnavT5hal42akcjNwXtzfwugwucG1Xhjw5N9ZDiCjCWqPNIFEUr5sA7AUwBXps120RHVELgRA4edvd0zj+hfnw+DRkpTrR1qatODym4RU6q9HFwfUJS7C93NJcjW6/BodKRn2r2I6pOZE34eDCxmTjOl4bDpa1nIWDEAguZx0PLuRwDC8FQE8AEwGsA9AtoiNqIUjJ/vNVB7BkR3GLaBnk9vmR5FBt6aoA6mK8ZqzPD53VaNPgeq9fs71BKdtADeqUZTyO8YCihOlq9GlQFaVeiESi4zPcDsH19exmYK7eV2L+fcEri7CzqCqGo4keAqhXKiRRFK+vAJwPoD+AfsYPc4LIFdWOw5XYWVRpa1ejlPNrvRqSHPopZ8cVlzV2IFRL7CSbKl63TluFa99ZHuthRBQhBCYP6oAUpwpANz7ypsxAQZm9m2VLI2vOpkI4FLJdElCw4lVXTkJXru3Cpa8vQaXbZz72+DTsOFwRwxFFh1CuRn+CVK4nIcT1Qoipxs+DER9VC6Gw3I1arx9VHr+tWwZ5jVVlpdsHl0OBS1Vsmd1nVbNCHU67Kl5b8suxo6gSN/1zJSqCgtDtgl/TDWd5LkvZ9r3Fu2M3qCgQ0NaMqF7h50RHVuT3+vTf8rLtmJ2ClXuOxkUgdnPh14TpZvtq9QFMeHGBuc2OC2EgsAG6xKfF/pg2aHgRkcsolrqLiEYSUZLlOeYEKavxAABKqz2odvts7WqUk1uV2weXqsCpUlz0y2puPD6BNJeuiITMarSh4uXxaThUVgtVIczbXIjiSk+shxQRNCGQ5FDM81YaH28t2BXLYUUc6z3KoZDtYp/kTTjY49AxOwU/bC3Cu4vsY1gLIeA2lL2KWl/ANrsWtRZIvOD6rQC2QG8P9DGAzZbnmBNkSJdWAPSA7Ep3XfmBshovXvthRwxH1vzIm1VFrQ9JTgUOVbGl4eX1a8hJ19cloYLrnaoCj832+2iVbmjJhYPDhspt3pQZ2HG4EkkO1XQ/xcHcHRUCSt6oZBR+jt14mhs5DzmDmuWmJ+m1xeX5bQd8mjBj2YIDzO1qeFW5ffWKqMZ1cL0QorsQogeAHgDGGH9fYfxmThBFAVqnueD2aqj21J0cP+8twV9mb43t4JoZ6VasNBUve7oa3T4NrdP0jM2WoHj5/JpZ30o2o42H1WQk2HCwDC6HYlZtr/boisGonq1jOayIIwRwap6+SJQxXjYSvODz630aX716aMDzqYZyHQ836ebC5xeoMRYOsoh1/w6Zxn3IPvFsVq57/yd8u6Eg4Ll4WPSHE+P1DwC/Mf6+loj+FsHxtBiE0G/EtT4/DpbWYNqyvfoG+wkGppwvY7ycKsWFn725qfH60cYolREquN6lkq1ivH736Wpc/voSAHU3KDvFxFjx+DVkpTjNx5+s2A8A6N02PVZDigp+IdClVSoA3R2nKPaKB/JpGobn5eAkI1vVDK43lFs7ddnwaXXZx1LhEkIgPdlhW8UrFPFw72myVyOAIUKI2wBACHE3ES1o6g1MeDhVBW6vhpV761J97ZY1BNStMPyaQJJDhUOxZ4xXjcePnFTd1RjsugDsp3it2VeKCiNTSipdXhseV0CPZQvlRq2xqVJwxZtLMaRLNrq2TkWSkclpzxgvEXCtyhABeahttKvw+YV5fVYaMV6aEEh1OWyVwdkU8aDKh5XVSEStjT+yEZ6xxjSBgH4jDl5p2DHI3upWdKoEh02z+2q9fmQk65dHKAPabi5Wl6Nu+vBbjGs74vZpUIhw69jASAu7Nnxfsfsoth+uhCZgltBQFHvFeB2uqIXXr8Gh1L8NyuvXTuezT9PMeVeWlvBrAqku1faK13d/GIv2mckAgNX7SrGzKLaN3sMxop4AsJKIjgLIBnBHZIfUQhBAslOpt9Kwod0VoG4Rkd6z0IbKSKXbhzQjKNfRAhQvq+GVkexAhdtXV27BZniMPqOpzsAp085KgRACmiaQ7NSPs91ivEY8rTc8P7tf23rb5DxsL1ejwAWvLAKgB50DukKd6lJtfR4DQM826ejcKgUF5XrdvVgnATWpeAkhvgHQC8BkAH2FELMjPqoWgIBA+8wUHCytCXzePte5SbC65bBZrJOktNqDVql6HFDDWY322W9peJ0/uAN6t8sAEB+Bq5HA49egEOrVKUtkV+O+I9W44s2lDW73aQKaEKbipSqK2Tx63qZC28R6haqhKBUvO/WltF6bUvHad7TaMLzsMy81hNULYV00xoImv52IfgnglwDOBXCQiO6L+KhaCG0zk+q5Kuy0wpLUM7wUJS787M2JEALltT5kGgHY3hDKlktVsL3QPtWiXao+feSmucz6QHYNrnd7/VCIUB1kaCWyq3H9wTKs2H20we1evwZNAE6HVfHS44Ju+nClbdxTodyJdnQ1Wudh63mb5nKY16+dsXqUnWqcG14A7gMwF8C1ALoCuDCiI2ohCAFkW7KkJHZaYUmkkfXMpYMAwCig2vyTdmOr90iiaQLdp86E16+ZF3QoZat7bhrybdRiRu5rVqrLXDF7bXj+AobipZDZ8koS3OMvkZA34n//tK+B7cKo/K0/VhUCqC7GK5HXiFYjxOr+t7YMAswGBSit9uB/aw9FaXSRQRqRRAhoH5SZ4gx4bFesRXITwfCSd4oKIYQbQEYEx9Ni2FlUaaojVuzigjtcXmte6I/DiBcAACAASURBVF6/hotO7oirT+sKIDIuN00Tja7eI4ncl4MlNebFHSqWKyfdFXOJuzmR+5KV4jTVD7sqXh6f7mpMcqgBzydybIyca56fFbpuoM+vwa/VtVwpLK/VXY3G9kRW59+Yv9P8O9S1KkMFZAbnKU/Mxe8+WR2dwUUIGVeb7FAD9rltZpItO07IxuAL7h8PABjYMQt9jZCI4AVUtAnn23cDWAngPSJ6DIC9u+FGiZJqL5ItB/+us3qhX/sM28QAjXjmO8wyCtf5/CIg2HzWhoIGJ/vjJZb/N6norTtQZhpeoaT7ZIcKd4LHUhRVuM2/5eTVOs2FzfnlAOxbTkITuvspOCg3kWO85HnbKi10FziPX0ATdUaIzzDCpDGSyG64/PI65TlUQoh5mIN2sawmcXuRyvpVMllCkpuehOJKd6i3JDSLthcDqEt0mnpeP8z6/WgACaB4CSGuAzDICLJ/Qwhxe1PvISKFiN4goqVENJ+IegVtf5mIVhnb5hNR1nHvQQIj6+MAeryMQyVbZb3JquZev2bGAwG6kbRmf2mzflc0Da/gyVeqPAXltWY5kFDH0alSQhvWy3cdwalPzzMfSyUkLzcNF53cEUB8FCeMFE5VqZd1nMC2h3neykVCcaXbVPDSXCo255ejxuODanG/KVRXQDWRwyKs81Go/DZ5bgfv4fythyM3qAgjFc5g1bZNehKKK+xneCXL+nPGCUyk16H7xzVD6/XmjDaNNcl+2Pj9CYC3iOhjAC8Zv5viEgDJQoiRAKYA+GvQ9qEAJgkhxhk/Zcc3/MQmxWJ4OR0KHEr9ul6JjIx/8QYpXpEgmkrSyY/PCYiJsBpTjbkaiULVs08cCsoD49Ok4pGe5MDZ/fWUfLtmNQK6azVUtmqiItXJ/UdrUO3xYfhT8/D0jM0AgFSjLEp5rS8g64+ornF2IrsaQxVNBYAReTkY1bN1XVajsY+ya8HSnUeiOMrmQRrKck4KVrxSXCpqbXTfkSQZ++kMqtN23qAOsRhOAI3V8Zpu/H7jOD73TACzAEAIsYyIhssNRKQA6A3dmGsH4F0hxHvH8R0Jy/dbCgHUuWqI9NW0S1ViFqx77t8WYNbvxzTrZ9bKTDctdJHC5iTaSpLHpwF6d6AAY0NVCCO652Bot1ZRHU9zUOX2we3TkNOA6yl4lSj/5x2zk7GlQLoa7TeBS1yqUq/AsVOhgKSKRMKqTlYYlcx3HK7EodIaVLl9GNatFSpqfaZhQtAVA9mXM5FdjdbjJWvvAcDwvBx8fPPppvInbcsebdJw/qAO2HukOqrjbA7kPsgEmGRnoOKldxKx33Urz9tIL/qPh8aaZK8losEAzgZwJYAzABwSQvwYxudmArCqWH4ikmd3GoBXoGdJngvgDuN7AiCiW4hoJRGtLCoqCm9vEoQF23Tfs7yRpbkccKqEZJdqNt+Ndo2cLQXNX+ag2i0VLy0gqPymM7s3+3eFKt8QSayKljdI8frs1pG4+JROId+nEMXtDeu+/6zF2Od/aHB7cDV+t1fD1789A6kuh2mQ2KlMSLXHhx2H666LJEd9V2OyS03YOC+peOWmu0wF90iVGxNf/BHVHj9SXSoq3V4z3okIRoyX/jhez+NwsPbdfPXqIfW2B7safX6Bzq1Ssfdo4hleUrWTxqQMcbnkFD08QFXIVtetRF6q8bgoaszV+EsA7wHYD129qgDwORFdHMbnliMw+1ERQkjfTDWAvwshqoUQFQC+B3By8AcIId4SQgwXQgxv06ZNeHuTIMgJS1ri6UkOOFUFKU7FXHnG4kJobmOv0lId2RqU/ODk/miXmdSs3yUnl2gZrNbgeW+Q4tUYoboVxAuVbp/ZezEUweek2+dHrtEUvDEXa6LyxvydmPBiXWtal0PBpUM64dcju5nPpbrUhC0pIZXazBQnqtw+qAphW2Elqoz9SXaqqPb4TaNabxckMHeTrtgnsuGlEGHqef2w+9nJyEyun11e16vRaP6uCQzpmo3N+eUJF9smhyvnY+lpGZaXA0A3TBL5WDaE3KOEMrwA3A1grBDibSHE/4QQr0B3If4+jM9dDL3SPYjodADrLdv6AFhERCoROY3P/Pm4Rp+gSCNBut/Sk3XDK9mposjILolFrExzG3uyLYWe1Vh3qilK87cdifYq3Frp2adp5mTWlOGV5IjfvmiuJiYo6Y6QhkatVzMzc+V+2yk7KrgmmcuhoEtOKm4eXdevMcWZwIaX4WrMTNbrOAWn2LtUxSijQbhqRFdMGtjeVLGB6Fxrh0prMP6F+c3+uZoQUI2m36Gor3jp13j7zGQUViRWLT55vyk3koKkq7FNehK+uetM2ype0gsS60D6UDQ20/qEEFXWJwyFKpxZ5isAtUS0BMBLAO4honuJ6CIhxGYA/wKwDMCPAD4UQmw8vuEnJvIclypQj9w0tM1IQopTxRGjnkosssOaW62o9lhcjUF+9ua/zI24kwgrXjKrKUDx8gmM6tkaQGIrXk3VGJOLARlk7/b5TbeFDMAurU7cdPtgguO50lx6tIT1/5TiciSkq9Hn17Bohx7ykJXiRGVtfcPLqRJqfRqIgGcvG4RRvXLNUAggOsH1u4ursLu4qukXHiN+Ieq5zq3I89mqeDlUBV1zUhMuzkseplJpeBnH2akSTuqUBYdC8NswGzme400bm2kbGnU4JSg0IcRtQohRQoiRQogtQogXhRD/M7Y/L4Q41dh+PMH7CY5+Jcib9K1je2JMnzZIdqo4UqUrBruKmn+yaQgpnTeX4SWVEWs5CUeQmlLt9jVrSQlpzEZ6/rju/Z8ABCpeXq1u/4Jv1sEkOeO3IW1DkvzM9fn4fkshqoybbkm1vjiwKl7tMpIB2CvGK9iGzkg2DC/L/ynFqSRk26DN+RVYvU+//rJSnKjy+ALKDCybejacqmK2SpJYFaJoKF6Rsu2EqH98G/t+PUGIMKBjJtYdaN5SOJFGKl5yUZRkllmoU6s3HEw8F2pTeHwaHr9oYKyHEZLGjKiBRPRx0M8nAAZEa3CJTrXHh71H6htQ0jgY3DkbQF2bimSnig0H9ewwaYBFA7lyba7MwEq3D1kpTqw/WIbyWi9qvXqgrpUqjx+XvLa4Wb4PqJsgo5XiblW8fH5h3oytGVKhSHbGb0PahhSvO/71M274YCUen74JAHCgRG/sLlUAABjQMRPvX3+qrVbO0siQSpCMBXJaFa84NqQbIzWp7nrMTHGg0u030+8BoH1WMpwOvbyNVcU9u19b8+9oGF5ahK5nTRMhm2MHoyiEvCkz9HAJI2M5Vh0yjhfT8ApWvIz9dygKymq8+Nmo9G4XgpO64onGRnUFgDeDft6AnuHIhMGr3+/A2L/Mr/e8vBBSDGNE3rRlfZWMZH0ijBZyAv1o2d5m+bzyGh/yWqeitNqLh7/agGqPv14Kc3MT7WraAYqXX4NDJaz/0zno1Ta90fclOxSzzEa80dAklW4xJkf3zsUaQykJFvf0tHR7rZoBoI2RQJCZEkLxcjkSUvGyqhtpLgeqGojxqvX6A46z1ViJiuIVoc9tytVofr8lI1BVCP07ZGJbYWWERhUZ5GEqqdKVark4lAsI1VJg1E64gwp3xxONlZP4saGfaA4wkWnIdRc8X0mjRE4ErVJdqKyNXtNSuah85fsdzfJ55bVesw3J0SoPymq89RSvUFzwykK8OOf4WglJwytacnlgVqNepywjRHZUMMlOFbVxeqP+eHnoZslWYeD6M/KwpaAcczYW1Ptf2y1IV+5JepID8+4da96YZPHNHm3S9OD6BFS8pDJ8ySkdkZebZhhegdeoUyXUBrkaAz4jkRUvUb88SiikUV1a4wURIT3JAbfPH9fxQ8EIIZDqUrG/pBpZKU784Zw+AOoKeMvzObgdVqLj8WkB6nQ8EZ+jsgkNSdkVtYEByFalS/4O1esvUjS3e668xovMZCeeuHgg9pdU41/L9wWoJg2x4WA5Vh9n3JfchWgVUt1nqefj8wu4HOFNWsnO+FW8GsJ6Hvdum4E1+0txy7RVOFIV2FjXDmnpT36zqc51aJxUSQ4lQMmUBtj3fxiHFJcSt4Z0Y/g1gaFds/HXK05BepIDlW5fvYrmXr9ASbW3YcMrGm594yumrz3UvB8rRJMxXg9O7mcugK27WljuxtQv1zfwrvhDE0Cn7BSUVnvRLjPJXCBasxsB+7X7Cm5VF0/E56hsQqgJS9ME5hh1cCQyqPn8wXorg/QkR1TrITX3zbK81ovMFAcuPqWTmQHUrXVawGu+umNUSGPsePdbTowVUVIK91sML6l4hUM8x3iN7p0b8nnreZzqUk0VQGZySlSjinsi8+6i3ThUqsewycsiWAmykupyBGT6xTP7j1ab17qm6UH1qqKrOBsPleOnPXqMz5CueuypjE9t6N4VTcWruQ0vfxgxXv07ZAa0BpPM+v1obCts/oLTkUKWzujcKgUFZXWlMOoK4xIuOrlj3Ja5OVZW7D6KPg99C49Pq+c+jxfic1Q2QU5YB0qqsXTnEazeV4KnZ26u9zrphmtrZIalJTmiehE0t3uuvMaHzGQnMg0F78+XD0L33EDDa0jXVmaMm5XjdVXJNibBamKksE5gbp9WTy1oiOQ4DsaWN9LgG2q2pcq3NTu1Y3ZKwOv0tPTEVbyk0b+tsAKFlr6UoWLf9jx3PgD9eNYYhnRZjdeMo4k3Sqs9GP38D/h81X4Aulplds5IcgQEjL94xSkAgHsn9gXQcOyP9VhHqnCxnA/mbCps1nMrHFejqhCqPD50zUkNeL5vuwz4/CKgq0E8oxnxbCmWRV+fdulm8WNAP8ftYngdKq2Bx6/B6xdxWTwVYMMrosjSAmf++Qdc9fYyXPr6EsxYlw8AGNtHr8a/57nz68UGpbjUqCpezR1HoSteThARnrtsECYfQ1PSVXtLzL5/x4IWZcXrh61FqHT7sOFgGao8voCG542R7FDCUry+XnMQ7yzcdaLDPCY0IZCR7DAVH0mH7GT8xqjW7lAIax87BwDqGdMORUnoGC9ptN/20c+4/v2fzH1pLDMq1dIyaPLfF+KS1xfjcHn8Fdh8wshI/eMX61Hr9euKD0nDq+7cbZ+ZbC6YpF3SkIEiF2wLthWh+9SZ2HioDBNe/BGv/dA8saJAYCHp5izOq4XhalSJUFnrQ6vUwPmZiDC0W3ZAV4N4RghAUYz7iqFIz7lnbEDFfqdKtiknIVUujy8xsxqZEySUlC2LTwarBVaSHWpUmz7LWI1hzdTYubzWZ17U/zeia4NB58H/HbkC/2r1wWP+TrnijpbhdXLnLKzZV4oLXlmEhduKkeJqOoYNCL+O1zsLd+OpGfXV0UiiafpqfsPBsoDn/Zowm36rCiErxYnNT5yL28f2DHidU01sV6P13NmUX27uS2NxInrlev19RZVu7D1SjRHPfIf3F++O7GCPEWtJiCNVHmhCBLQsk8y8ezRaGzE/0uBqyECRhqkscHr+y4uw43Al/jL7OBNkNFEvttUad1RWc+Jq9qq9JRBCQNNEk8WOVYVQUu3FrhAFXB+aPMAoeB3/nRqk4tXYeUxx3EP2WJGhEEWVbjNxIN5gwyuCNCZluxu5+aa61Ea3NzeapjfKba6bZnmN10y9b4zgy/zcge1x1YguWLXn2OvJyDmjOSbnxpBB1kO7tcL9n68FAOSX1yLlWFyNYQTXxyI2wS8EzuydixV7AusU+fzCvDlL6T7FpdZbWLTLSkZ+WWzVnvyyGsxcn39c7x0X1JpGqs5JjRzbZEtWY7LlmD0+fROen7XluMYRCbq3qVMniyvcAYqX1eVvdZk3pXjJBVtZjdeMT5X8fd72Yx7jQ//dgF+9swLvLdqNx77eACBQ8WqOrgiX/2MJpq/Lxzfr8pssnyANs1CLuRSXiqtGdDVr20WLwxW1mL2x4Jjec6i0BhW1vkYNTdXS/DzRkcr1qr0lrHi1RBpz4TV2EVgl4eZm6pfr8fj0wA5NfiHMfm3NQXmtN2Tj2WDUoFWWohAuPLkjDpTUmH0ew0f/nEivQHu3Tcd/bhuJTtkpppGxdn8p2mc1rGBa8WsaXv9hZ5Ova+xmHyk0IdA9N62eq9Gn1RlejYkEmcnO4zhuzce+I9UY+ez3uONfx976NdRqf48RXN6YUmBNNjipUxYm9G+HNY9OxNWndcXr85s+zo1x5ZtL8YPRoupESU9y4Poz8pDsVFBc6Q5QfHJSXbh/kh7PlWxJJDAVrwYOut8wiooq3LgwyPB6ad62RntYzt5YgLIgQ2pPcRVW7DmKJ77ZhH8u1WsKyuPSJiMJpdUeHCipxo/bisLe71C8/N12FJTXNpnxJv8/z142KGTiyY2ju2P2xoKoupb/vWI/bp226pjec/k/lmJ3cZWpcIZCofpxehW1XrMLSSJR6fahdZoLfk00mhgTS9jwiiBSQRrRXe8C39Oy6swx6lyFItmpwh2hzLd5mwvxzbpARaCkyoOO2SnNdtMsr/EhM6Vpw8vlUExVYenOI5i+9hCcqoKz+rfFwMdm45kQiQgNoQl9ovzr3G0RjY/zGTcs6SqW7tmJA9qF9f4tBRVhGbhywthWGN7r9eraJ7bfmibQOi0JJUE3RL8m0DpdP1+bUgmIGk/W8GsCP2xpHmMi+Eax/RiCnYP/V6FqcS3crvcybGzVnOKqcx2rCuEP5/RBdqoLz1w6CLnpSSd0TJbvPoqF24qP+/1WvH6BthnJuOGM7iiudOvB9caxdKgKfju+F967bniAkaVQ4O9gpOJ1uKIWbTKSsPCB8Zh+55nm9mvfXY6r3loGTRMBx0oIgVunrcKSnYH7Zo01k0h35uBOWSip9uDMP/+A37y34pj3f//RaszaoCtFOw7rBVCbWtxIwzMz2YlpN55Wb3un7BRcd0YebvpwZYCLdMa6fPzpf5FpPxwqISlc1EYyr4moXnmQZ2Zuxi3TVuGV77Y3a3u3SHOkyoPBnbMAAB2ykmM8mtCw4XUMjHr2u3qrtGCs7jppAHQzsmK6GL+XP3g27pnYp8HPSHGqKKn2IG/KDAD6RJU3ZQbmBpWhOB6CmzR7fBoOldagS04KKmp9x32jEEJgzsYCrN5XYiheTbsanSqZyp68sBUijDMSD95asAur95Vgzf7SkHFRgZO5HhgM4LiC88PF7dOQ7FDRrbV+LJ+7bBD2PHd+k/EiknCD8KWr8ZyXFuDVEIVtX5q7rV4GZ1WY9aQ2HCzDqr312574hUD7rGQcKq0J+N/6NIGsFJeZydcYaS4Hqhtxk286VI7rP/gprHE2xhPTN+GKN5cGPHcwSKlriHUHSjHpbwuw/kAZLnt9Mbx+zVRnWhsLol8O64z/O7ULgCbUaadqdpnQs1vrjm9xpRsjn/s+/J0KQY23eRZDXr8Gp0pwqgr++MV6eHxaPSXrrH6Bi4dwg+sPV7jRNiMZXXJSMci44QG6q2fpriM4528L0H3qTHM+NLNnLefYOwt31TNw86bMwAJD3cpKcYbVv3bepkLc/enqes+vPVCKNxcEKpBNufOlQtRYnNBtY/Q4x3s/W4s9RizY8t1H8MGSPU2OVXK4ojbswPZwir5akfcQoPECqapS39W4YFsxvt9yGH+duw2XvLbY7L0LALM2FEQtgzxc5L1rx+FKXHNaN9wypkeTLdxiBRteQfz6vRXYF6L7/K3TVuJQWS12h+i9KHH7/Oj90LcA9BYTby/UA2wHdswEALRO04NW22UmN9pCJzPFEaA6dJ86EwDw/uLdDVYXD5ckR6Ca1ufhb/H9lsPISnGh2uPHX4Iqx4c7Iew7Wo1bpq3Cpa8vQWF5LVqlNqzoSVwO1TRUZeNlh0I4u387fHH7KFx0ckfc/tHPuOS1xRj25FwA+sW1Zn8pDlfUovvUmeb7r3hzKdKTHGiTkWT2u4wEbq/e065f+0y8+5vhTbYICubyYZ3rPXe0yoPyWi/ypsyA2+fHnuIqFFXUuUzf+HEnHvh8bYAb9e/fbcfP+wJXocGK5ZFKd8gMs8enb8Tl/1gKIQQq3T6zJpGs7ZSe5MCnP+03X+/za2FXtU5PdjQ6IW84pAfuT/77Qrzy3bHHAUkOlFSbdafqnqszvBorb/Dj1iLdPfbqIvy8rxTLdx1FtceHQZ2ysHjKWQCAO8/qhecuHwyg8Ztdp+wUHCzR5wu311/vZl5U4camQ8d/PlY1U+swn1+DU1Vw/Rl5AIAb/7nSLNjcEFLdbGj3pRp1uNxttlWSXDG8M764fSRWPjzBVJh+98lq/LyvBF7DRbm9sBLn/m0Bfvuvn/HUjM0ortTngG/vHm1+ziwjnikzxYldxVUY1bM1ctOT8PaCXVix+2g9t/i8zYX4ek39ml9lNV6s3leK3HQXdj87GUDTdciaangPAK3SXHj+F4MxY10+Lnp1EU5+fA4+NNykTTXTPlLpxsWvLsKIp7/DnE36fu4prjKTFUIh40PDicedEeTZaMzLogQp1ZVuPSZs/n3jMMZYCA/+0xzkTZmBa95Zhts+WoV/W+YIvybw9ZpjT4qyUlBWe9yFw7/8+QB6PfQthBDYcbgSZ/bOxYOT+5/QeCIJG17QJ+lVe0uwaHsxFmwrwhVvLjVdN35N4K5PVmP2Rl1tkv58t8+PIiNIVWIN7N5SoN/M/nhuP0w24h9SXE3/uxc+MB5DurQyFSDrimXJziN48Kv1uP2jVcibMgMvGNlDO4sqsWRncaM1hD77aT/W7i9FslOpFz+243Also2U6Td/rCthsHB7EXo8OBNDn5yL1+fvaPRmdqi0Ls6hbUaS2TKoMTbnl+MZI3Nvu3HzVxWCqhCGdWuFl648xWwWXuXxI2/KDPR66Ftc8tpi3P3JGnOMcqVTVOnGkxcPxLRlewMUMjnueZsKQ7rtfH4NGw6WYeCjs5qsR+Q2ivKphoF4rP3NhnZthTYZSeb3bDhYhqFPzsXgP80BAPR9eBbGvTAfK/eW4LaxPdHDcE9/tvIAhj01D7M25OO/RtanvKHJYNuZ6/NN5Wbt/lL8c+le/GX21nr1hqrcfrTJSEJ+WS3+s3I/znlJT4uXKfbDurXC1C/Xm8afXxONxodYyUh21Gt3VVzpxoJturHzt3nbAOhZg3+duw0TX/wR328JX8mt9fpR5fahUyvd1Wu9SR20GF7LdoVuZFxQVovPVu3HBzeMwLi+bTAiLwcLdxRh7F/mY/3BMiQ7Vfx4/7iAgr+NJWxkpTjN4Gt3iIKNLoeCK99ciqU7jwAIv96VLMraXMVZvX79GGanuvDkxQMxaWC7elmpwdRlNYY+9iXVHnh8Wj2lD9AV6GHdcpCbnoQ9z52Pfu0zMGtjAS57fYk5/0xfdwhbCioww0iGkLXEpHJtJSvFibmbCnGgpAbFlW48PXMzrnhzKUYFKYrSJb4nyHiRx7C40mNes02VdZFqSUETMVz92mdi21PnYcnUszHlvH4A9Dnwzo9X46THZuOKN5bijR934sOle9DvkW+RN2UGCstrsaWgAmsP6AuRR7/eiFe+245xL8zHRa8sghAioFbg9LWH8NXqA2aXhK0FFThcUYt/LtmDdxfVz6Ct9fpx5yc/4+rTuuKyoZ0AAFPO62cuLIJRiAJikg+X16J9ZjLyctPw4Q0j8OP948xti3fo5/KbC3Zh0fZiHK3yoKC8Fnd/ugaLtheb83F5rde8tl/9fnvAYjIU4174Ac/P2npMscY+v4biSjfu/UxPdOo+dSbyy2oj3hv4RIlPHS7KuH0aLv/HEvOxvNA251dg0Y5iTF97CA9N7o8Ktw+b8stR69Pw4pyt2GMoY1/cPhK3ffQzPr5JjwPwawJvL9yFU/Na4UrDXQHoVa6boktOaj3FLdmp4J4JffDst3qW1LdGrMKrP+zAwh3FWGvxvw/unIVh3VphT3EV7jyrF7x+PabmzQW70D03zYy9qvXWNa5eubcElw/rjMuGdMKXqw+ipMqDz1cdwBc/H8CLV5wMVSHMXJ9vKm+f3ToS3XPTzFWuz69hR1Fd49hfhFB1GuLL1Qfx4pWnoLBcvyitNy5VIWx+4lz0euhbnNWvLRQC5m0+jDvG9TQDl2/4YKX5+hqPHyN75OKBz9fhPyv341cj8/D9lkLc8MFKfHzTabjpw5X44PpTMa5vW/M9hytq8e36AjxmxGTsP1qDrq0DCyZa0W+uJ3ZRu1QFXqPN0AWvLGrwdWf1a4sp5/VD74dm4oYzumN3cRVu+6gucPzJbzbhyW82IcO4QTw1YzN+3FaEaTeeht99utrsGjDhxQX45ObTkZHsQIesZBRXujFxQDv8tOdogHvHb8SvyRvOw/9dj9euHgqfJsKuzJ+Z7ERpkKEy/Kl5AIDxfdvUe/32w5W44YOVuG5UHvq1z8B5gzogyzhHX5+/Az1y03HuSe3N1z/69QZsK6zEWf30Yzj+hflY/uDZyElzociiCF719jK8+5vhOLt/nfvM69dwwSuLoCrAkC7Z+OD6EfhucyFu/Kd+Dt1mGCL1uiysPoiXrjwl5P4qCpnZuW6fhqSgCf/+c/pi+e4juOrtZTi5SzbW7i/FM5cOwqierXG02oNajx+jeuXig8W78Z9VB/DSlaegT7sM/PpdPY5JqkAnitdQvADgVyPz8KuReU2+R4qcDakrj369EXuKq+EKMsovH9oZE4JiHv99y0ioKuGcF39EWbUXvdqmmwsHK3eO72Uu2ib0b4d5m/Ubt+nav3wQrn57ecB7pHKbkeyE26shr3Uqxr0wHwsfGI9O2SlQFKpnPM+7dwy65zauVsv5rUcTrwN0A9vlUHDViK4QAshrnYpTumZj1oYCvLVgF577NjDD9bRnvgt4fLjCjb/O1RclFW4f3lqwy5zvbzqzO37cVoTtxv/LpSp4b/FufPlzncJ0wxl5AQbwhoNlEAL446R++HiF7iXJTQ9UJa0oQa7G4koPcjPqFs/dWqdh97OT0X3qTEwc0A5zQiSQVQAAHPZJREFUNxXiwsEdce27+rGQapp8nJ3qxORBHfDx8n345q4z8cKcbXhzwS68ee0wjOqVCyEEthRUYGtBBTYeKjO9Q+8u2o13F+3Gpicm6eU6qjzISXWFTPAQQqDXQ98i1aUGJLmcZsRUxzNseEEPZv/mrjPr3QTnbi7EK99vxw/3jUP33DR8s+4Q7vxYjx/4xbDOpuH1xPRNKKpwmxLzn2dtwYx1+Xj+8sHISXOZvvFw3TXBpRgcioI+7TMAALeO6YFDZbV4+Pz+SHWpWH+gDElOBct3H8WGg2Uor/Fh75FqrNlfisv/ERgDY1UHHp++EUO61tXtyk5x4mLD8FqwvQjvLd6NOfeMMWtwXXxKJ8zdVIilO4+YsTVJDgVrHzsH/R6ZBQB47MIBeHz6JgzokIVjodqjG7QA6q1UHKqCpVPPQgcja1AIASLC6/N3YnDnLKw7UFdzqsbrR1aqE1/eMQoTXlyAA6U12FGoT1bPfKsra5oQ5mcAeu0h60rsotcWYc2j54QcpxACJVWekEHAx0KS0a/RavTsee58VNR6cce/foYQesxbTpr+v9/+9GTzdVsLKjDpb7pC9fHNp+Hqt5ejwrJCXLi9GNOW7qkXl3TV28vMvztkJWNIl2zc/eka8zm/JnTFy/K+2RsLsaWgAj6/FnYMW6fsFOw4XIlT8/TJ75p36r73h616vM7mJ87Fkp3FmLEuHz/tPYr9R2vMmJgpX67HHyb2wbBurfD8LF3RnfG7M1Hj8aNLTipqvbqr2Rrse9oz3+GRCwagtNqD/915BrbkV+CBL9Zh/tYi0/CatSEfH6/YjwsGd8BjFw4wj3+fdvp19ca1wwIMPCvdGjHErbi9/oCSEoB+Q/vLL07G5vxyXP2OflN68KvAPn/3T+pr1r4656UFeOPaoVi5V3ej7gkKbThS6UZmivOYK3L7NHHMNY3IqLTXmEfuvcW70TvI3f7XK06u97osQ1F3OhSM+csPAdsm9G+L5buOosLtM43+Xc9MBpGuYDxx8UAM7doKp3XPwaie9bMLJ7+8CJvzy/Hq1UNQUetDXm4a9hypxujn9e8Z0T0HK3YfxR8m9sFbRlHiXm0zmtx/p6pg4+OTjjlO6OrTupp/Xza0My4d0gml1V4MMcIlNj0xCSv3lODXRpLA787qhZeD4jitIQLvLNodkODg8WsBRhegZ6t/+tN+rHp4AhQi7Cqqwq1jeyAr1dlgyycrwa7G4kp3PUONiLD96fNQ4/Vj5+FKDOnaCg+f3x/Ldh/B1W8vR1aKE2U1Xlx9Wld8vHyfGRYj76sVtT5c/c5yPHx+f/y056jpRQrFgEdn49Ihncyajv/97RlYu78UI7rnoGNWCq58aymuGK6LGtUeP+4+uzfSkxzo2joVE/qHl+gUS9jwMhjQIRNv/3o4fH4Ntxvp6C9/tx0PTe5vVuiefFIHrHgoByOe/g63jumBz1cdAACsPVCGIV2zMW2Zbni9tUC/uM80UpClwRVuYGRwwdFKt89M5091OfDKVUPMbaN66d8xrFuglX+k0o0jVR70aZcBn19DLyP2DAD6tc/AJyv245MVdT76rBQnRvZsjT9fPsi8IQePY+KAdpg4oB3eX7IbQugrfGl0dcpOwZWndsF7i3ejc6vwSitINufXxcCEyjTqYCnVIG+YN57ZHT3bpGPdgfV4+aoh+N0ndQG1vdpm4IvbRwYYnjLuq7zGh+5TZ+Kbu87EgA6ZAUbXmb1ysWhHw1lkFW4fUlxqWMplY8g4uw1HdKNx9u/HAND/3x/eMKJR92Xf9hl49zfD4fULjOqZi+UPnl1v9fzI14EZVTec0R3vWQp65pfV4rxBHXD/5+vM53o+qKuZweeonDTDXTSkJjkw9cv1xspfmG6J3c9Oxs0frsK8zYVIdio4u3870yjanF+Ofu0zUOvVMPjx2ebKH9DP1fNfDlwQyRX3rWN64M0Fu9AmIwmF5bUoqnBjQIdMswXX12sOomebNLzx4y4UlNfit+N74r5z+gb8fzsZ2akNZaVmJjvwUYiMNitFFW54fBq8mghoqQToN7RWaS6M6pWLPc+dbxr9+49Wo3OrFHSfOjOg4Oionq0DVM3Sai+mrz2EC0/uCAAY9tQ83DOhD+6e0Bs7iyrRNSc1LCPsWPqJSuS/yd9E8+TUYzBMrhjexdzfD64/Fc/O3IJ3fnMqAD2sQn6XdQEgBJCXm4Z/3zoSAPCr07uZcy1QN3/IRfHnt43E/K11JSdW7D6K3HQXbh7TA3ed3TvssQJoluBsIkKrNBdWPzIRlW4fUl0OM24K0OPXhndrZRrb5w/qYLpfLzy5I6avPRRg/L5+zdCAkimXnNLRjMkcZqjLAPDF7aMAhHffUYNcjaEML0A3Rp2qYi7aFYVMY7isxouNj0+CTxO4blQeFNJFg69WH8TfLfGcTRWGnjSwHWZvLDSNrkGdsnDJa4vrve6Jbzbhngl98Luzex1zyEes4RgvA0UhTBzQDucN6oB/3jACuelJuHVMD9w8pkfAa9pmJOO2sT3RJScV/77ldHPbPRP0LMXnfzEY/7vzDOx57nyz5IBqGl7hjUVVyJRLM5IcWPTH8UgzbvY3ju4e1me0Tk8yV/PBN4NHLhhgZtetfmQiAH1FSkS4ZEgnnNQpE388t1+Dny0DsHPSXBjQQU8cWPTH8Uh1ObDwgbPCiu8CgDn3jMGADpkBBlKSGp6a9MgFA8yVpXQ7WRnWLQdvXDvUdB+N7dMGN5zRHfd+phuVV729DD0MY0Pyx3P7oUtOCkqrPSHdK/d9trbRwNdwkZmly42Ylr7t61bf4UwgZ/dvZ6oz7SzxMPdP6ot/XDPUfLzwgfHG56dj7aPn4IVf6kpEdqp+/K40Vow9LK1/VCIzDumMXnVNsJtqKCyxuoqla/rnRyaCiPD4xQND7mP/DpkgIqS4VGx58jwAwORB+v7N/N1oLLh/PH64bxx2PK1vkzetJKeKnx+ZiCcuGoi3FuxCSbUXDlUxXYUje7bGC3O2oaC8Fh/eMAL3T+pX77sVRV/FN6TorfvTJDMbuTG+/PlAyH6dwZ8rv79LTqr596l5rbD72cnY89z5+NdNp+HWsT3M7EoAuOuTwCy90hoPbp22Emf/9ccGi8WWVXvNuElAL0R6rCqZPOZNxXGnH4MC/NvxvczwhXF922L2PWMCtodqOVUe5Ca07sf9k/rik5tPx0Und8SyqWfj5auGYHheDl67Wr8O3v3NcNwzoQ++v29czON+WqW5As6l28f1xF1n9cJlQzvj89tHYc49Y/D+dadirOGSn3vPGLxy1RBce7o+z0llMTc9CfPurfu//dK4ji8b0glDumbDpSq4YHAHs9RNOHMKBRVQLa4IbXg1xq9HdkNakgNZKU70aZeBXm0zkJebhnsm9sHGxycBAKbfeSbWPDoR024cEfIzpp7XD/dP6ou3fjXMfG76XWfWU7HO6NUar18zFHedlXhGF8CKV0jG9mmDlQ9PaHC7DKA8rUdr7H52Mm77aBVG985tMN3epSp481fDjim76Y7xvbB89wqM7NkanVulokDR487Sm2EFluxUMLBjJlbuLUGrNBcuPLkjOrfSJ4Qkh4pv7hrd6Ps/NQzOfu0zUev1o98js47r5O+ak2q6GM8f3AEz1uUfc+HQbU+dB5dDCYgHkZx7UgdMGtge87cexj9vGIHr3l8BTdStmL+9ezRKqjy4+p3lGNatFXq3S0eHzBSc8sRcjOvbBh9cr08OB0tr4PVpmLOpEJec0vGY9zOYZIcKt8+PvUeq8PwvBp/w50l+O75XwGM5ybscCrJSnbh0SCe8/N12XDpED7b98y8G48/G9+dNmYEv7xiFFJdqKoz/uul0LNxehF+9G37dJOnOkhmWlw3pZMZ/pIehFKoK4c7xvZCT5sLM9QVQFAqIufv0ltMxsGMmymu8GNunDXLSXPWytZyqglUPT8Bj/9uoZ20a50jDYz7x9WeN129mLVtp6ro4pUs2OmanWDIICVPP64/Te7TG9e/Xld3ImzLDnF/eX7zHfL4hFeupGZvwn1UHsPvZySAis5zEsSBfHax4ZSQ5AtzbaceoAF82tDMuGxo6DjSUshqcDGTtnfh/p3ZB6/QkjOypLxIuMpRBabif1CkrIM4vnghe3PZpl4E+7TIwx0iWkR6Hpy4ZhI+W7cO/bjoNI575DtmpTvRqm4GtT52Lvg/PglNVGi31Es5hV4LqeBVVenBSp/BDRr69e3SjGd5pSQ5cfVpX5OWmIiPZiVPzctC3XQa2FlZg2o0jzDnmVmOh3CM3HVeN6GreH24a3R3zNhdi+p1n4sJXF2Fcn7bH1AM43mDD6wQhIrz5q+FNvmbSwPbYWhB+gUfZRFuu0tplJuHeRmp/hcNff3kyPlu5H3mt0zCqV64pQVtdl+HQr32m+XeyUw2rvlMorCvQeyf2MVepx4K8obocoWcXIsIsw5X37GWDUFnrQ+92GfjNqDz0aptuNt6Vsrz8vPlbizD+hfkorfagpNpr3rQaCrI+FpKcCkqqvfhw6V58/dszTvjzGmN071yc0qWuz+ICQwULxnoMrxuVh/8boa+iG+spGgpZDXzgY7MBAE9fOsjclpXqDOtcuW9SXxwur4U7RCHc03voN1irkRkqGaJ1ehJUhTC6d27E24aM7q1fS8FFg9+4dphpEDTEZ7eODFmuQV6bM353pulqtWY4S2T27tdrDuLuT9dg21Pn4ewX52P/0RoM7ZqNpTuPYFSv/2/v3oOkKs88jn9/3XMfBnEcYEBHBfEGq1EZkQgKKnjLljFxicmyWFqr6BoVjVaSDW4Sy6jExKy39RYTN0KZlGbVaLyyRhBd0Wg0CRrctTBrFpVVIYpkEXCe/eOcbnqauXRPn3O6p+f5VFGc7tOX9+nLmaff877P2xbOaiyyxytsQ34v1GMXHdltNmFUtZKWXTKT9h4KXuaXfZg/YzyfO2RXpn/3yV7L1mQS2XL3cg1E5sd1brmPy0/+K0aFvduZySf1NWkeumA6++ccj3tSSG91OtV9xu17H31MW0vhPV77j+m7DQBX5hwLGmrTPLzgCFaueZ9pE9q44/RDu9X3S6XEVZ/ffvvDxrWy+vLjs+9noWdVKpUnXgkq9FRjTyRxQZHjE3JdfcqBHDtpdLaO1Fdm7wOzB96eqPVXU6g/hYxfGbNTI4Q/4jK/znZpruOnZ20/ZZypJ3bOjL1Yv+ljOvdo5fOH7EpNOtVtUH4patOpbO/n3qOLqwPWk9tP69whQZoejv3rqeJ2f1IpZcex9besSr66vBmfPZ1+K8So4Q38w8y+yx1kjNmpkTeuOnGHaejXzPlUwZMCSrFLcx3rN23Z4TPc22D9XL0lhZlmTxq7vddh/MjmbBHRNVeeyK1PrcnOIM0UV97n0mAs59dP2I/xbc0svH8VN3zpYJ5+/V1OCcsKFCrThvwKGPljhkqdbJKxZ1tzj9fnJ171NWl227mJa089qM+k4vmFx2STlMGkKTued/vrOm/qHgC89p3ju82qzv189GbO5I7skJDe5C+SvWrtB4ws8lRjsdIpMS08Th06rpWb5vb+w1tSNun6xZenFZToVTJPvBJU6DiZjBMPaO/3F3OhvpBT1qISjWopbWmHgZ4uktTtNb76bw6kq4tuFbhzbxuFpa+uY+mr6zhi77aSB+oDO0zdH1Zfww9O3XFm2UAU21s0M6dkxPQJbYmNv5C0w2SQYnt4BiqdSrHhL1sKWp+0ULm1rHZprqM2neJn86cy5YonmH/keFIpMaKplv9ctzHbE7biq0dxzDXLmTt1d86ZsRdbtnUxf/GL2QkStUW+l5n3Lj+BzD+MxV0dvKdxXwAnH9x3IlnqMaVcMgl8T9+dgZSyaaxL07ln3yUWUjljvDZu3srbH2wueoxXKYbV1xR86vBTHSNibk38PPFK0ISRw4rq9bpp7uT+bzTIDfQ0Zb55n96DfSLoPSrkF2SpGmvT/N/WTwqeKVisVeFA1igUm9COHdGYHa+35Mzie9sGo9q0eH/TlqJn8/Zl79Et2e9GTVo01aezvY/nHx2cZm0bVs8/3huUptivvYWO1iZe+87x2T/YdTVBOYRX3vqQL9z6bLcB+4XI/N3P/wyMbKnvNqay2DFexeqvwny1Gd/WzHPfOCbR50ynglmJdzzzBsdOamfCqGElrQvp+uaJV4KOndTOmquiSTRcdwd1jOCgQfJL6ImLZ3D4ol9l61pVskKWTclX7Hpyg106JTZs2tLv6ZyBum1eJ9u6urI9eJnT6qOHBz0S9517ePbUS34vSXN9DVPGtXLJsftkJ9AUqrd3URLrN20vw1JMpfFi3XXmYUUvyzXYSeo2WzkJwxtq+bcX1/Lauo0cvldbj6sHuOh44uVcwsaOaGTW/qM4Y1phpUHKqak+HVtCUS1q08FkiUIWhh+IzKmVzDp2mXFrmT+OE8cO7/cU1HlHD3x8aE/eXB8Ujz5q35Gxzi7L1Cl08Ro7ojG78sNHH2+NZPa8652/us6VQaZoZKWrTad4eEHf5UXyzdp/FO3DkxsfUm7pVNADlD+rMWq1qUyPV5B4jWyp54HzppW8hFVv+jrBd/PfTWbOLc9y4ax9qmLMzVDXVJfOloD5cPO2kic7ub7FMvpUUkrSLZKelbRM0oS8/WdJekHSSkl/HUcbnHPl8dmDdmXhZyaWuxmJGbNTA590lT4ztz+ZyTmZ/yVx4G7xJT1dfSzo3RGetuxtHUc3uDTUprPlWzZu3sYwT7xiFderezLQYGafljQVuAb4LICkduACoBNoAJ6WtNTM+l663DnnKtCZR4znN29uiHRWYyXoq0RLpt5WfnFTNzjlDqQf39bMriN8jFec4kq8pgOPApjZSkm5FUanAM+EidbHkl4HDgR+vePDOOdc5atNpxjRFH/iNWVc32UBotTaXNfv7LotPRS5dYPPzk11NNWlaapLM66tOfYSIUNdXK/ucOCDnMufSKoxs2097NtItqzldpLmA/MBdt999/zdzjlXMa77YnGrPwzU3eFC0Unpa3bdvqNbGNdL0VM3uLQ213H7aZ3U1qQ86UpAXK/wh0BLzuVUmHT1tK8F+HP+A5jZbcBtAJ2dnUOrkItzzlW4/AWu3eDmM0iTE1dp52eAEwHCMV6/z9n3PHCEpAZJOwH7A6tiaodzzjnnXMWIq8frPmC2pP8gqMN3hqSvAK+b2QOSrgdWECR+C81sc0ztcM4555yrGLEkXmbWBZyTd/XqnP0/BH4Yx3M755xzzlWqZFaRdc4555xznng555xzziXFEy/nnHPOuYTI+lgWolJIehf475ifpg14L+bnqCRDKV6PtTp5rNVrKMXrsVanPcxsZE87BkXilQRJL5hZZ/+3rA5DKV6PtTp5rNVrKMXrsQ49fqrROeeccy4hnng555xzziXEE6/tbit3AxI2lOL1WKuTx1q9hlK8HusQ42O8nHPOOecS4j1ezjnnnHMJqfrES1KtpMWSVkh6XtJJkiZIejq87mZJqZzbT5C0w6Ldki6UtCjZ1hen1Fgl7S7p3yUtk7Rc0r7liaR/EcTaLumJ8LZ3S2oqTyT9i/AzfKSkPyXb+uJE8L62Snov/Awvk7SgPJEUJoJ4myXdGd72OUlTyhNJ/yKI9dqc93W1pJXliaR/ER2Ll0t6StL91Xx8kjQujHOFpCWVHGtkzKyq/wFnANeG27sAbwIPADPD624BPhduzwNeAN7JuX8jsAT4L2BRueOJOdafACeH28cB95Y7phhjvRY4Ldz+NnBRuWOKK9bw+g7gF/nXV9q/CN7XWcAN5Y4jwXi/DXw13D4QmFfumOKKNedxaoHngAPKHVOM7+s/A+eG21cA55c7phhj/Tnwt+H2mcCl5Y4p7n9V3+MF3AP8U87lbcBkYHl4+RGCgzXABmBG3v0bgDsJPvyVrtRYLwYeCrdrgM3xNDMSpcZ6EbAk/CXWAayLr6klKylWSQ0EB79z421mJEp9XycDh4S9BfdIGhNnYyNQarzHAVskPRY+zmPxNbVkpcaacT7wuJn9Po5GRqTUWF8Gdg63hwNb42lmJEqNdWJ4G4BngOnxNLNyVH3iZWYfmdlGSS0EmfWlBJMKMrMKNgI7hbf9pZltyrv/BjN7PNFGD1AEsb5nZlvDU4zfBy5LsPlFiSBWA9LAKuAogi98RSo1VuBG4PtmtjaxRg9QBLGuBr5lZjOA+4EbEmr6gEQQbxuws5kdBzxI8L2tSBHEiqQ64GwqOE6IJNb/Ac6T9ApwAkFyU5EiiPVl4KRw+ySgOYFml1XVJ14AkjqAJ4HFZnYX0JWzuwX4c1kaFoNSY5V0FMEfrHlm9lpsDY1AqbGa2VYzmwjMJ+jVrFgDjVXSWOAI4FuSlgGtkn4Wc3NLUuL7+qvwvgD3AQfH0sgIlRjv+wSndSBIvCq6KngEx+JZwFNm9kFMTYxMibF+DzjdzCYBC6jS41PoYuAkSY+G96v6JYWqPvGSNBp4HPiamf04vPolSTPD7ROAFeVoW9RKjTVMuq4DjjezF+Jsa6kiiPWmMF4IfpF19XbbcislVjN7y8z2NbOZZjYTWG9mX4y7zQMVwff1duCUcPsY4MU42hmVCOJ9Gjgx3D4SeCWOdkYhomPxLLaflqpYEcS6Acgkl2+x/bRjxYkg1tnAZWZ2PMFxeGlcba0UVV/HS9J1wKkEpyAyFgDXA3XAH4CzzOyTnPu8Y2bteY9zOrCfmX099kYPUKmxSvotUA+8E+5+zczOTqLtxYog1v0Ixj0ZwZf9PDP7Q0LNL0pUn+G+rq8UEbyv44AfAwI2AWea2dsJNb9oEcTbSpBsjiEYB3Samf0xmdYXJ4rPsaSHgIVm9nIyrR6YCN7XiQRDBNIEn+UFZvZSQs0vSgSxHgb8C/AxwQ+HL5tZJY9pK1nVJ17OOeecc5Wi6k81Ouecc85VCk+8nHPOOecS4omXc84551xCPPFyzjnnnEuIJ17OOeeccwnxxMs5VzEkXZOzCPKb4fY9kg6S9M0In2cXSbdG9Xj9PFe7pBuTeC7nXOXzchLOuYoTd908STcDN5vZ7+J4/B6e7yrgUTNb3u+NnXNVzXu8nHMVT9LMzFJHkl6XdKekZyVdIelGSc9LWhzu75D0iKQnw/878h5rOHComf1O0rGS7snZ94ykMZLmhI//tKRF4b7dJD0oaamk30g6Obx+laR7Jf1U0jRJKyWtkPRAuH4dwF0ERSWdc0NcTbkb4JxzRdoTOBp4G1gPHAacD6yRNIJgAeXrzewRSccAi4C5OfefCmTWIV0KXC9pZ4Lq7+8RVNC+DOg0s79IWixpNsEqB9eY2TJJh4e3uR8YBlxuZi9J+h5wb9iGkwiWetkIvApMi+XVcM4NKp54OecGm/fN7E0ASZvM7NVw+wOgATgA+IakrxEst7Il7/5twDoAMzNJS4AvAeOBHwETgJHAw5IgWOR3PMG6iJdK+nuCJKw25zEzidyVwELgCWAt8Fz4PJ9I2iYpZWYVuy6ocy5+fqrROTfY9DcwdTXBgr0zgbOBn+ft/19gRM7lO4A5BItMPwy8AfwJmB0+xg0ECdTlwJ1mNg94kiCpy8gkU3OBfzWzowjWnZsPoCCD2+ZJl3POe7ycc9XmEuBmSQ1AIzuOrVoJfDdzwczWStoIrDSzbcC7kn4ALJeUBv4I3A3cQ3Ba8h2CxKyth+f+NfATSR8R9LTND68/AHg2ovicc4OYz2p0zg05km4BbjWzl8LLvwQuNLPXY3q+q4EHzWxFHI/vnBs8/FSjc24o+iZwrqRGSS8Cv40x6WoHhnvS5ZwD7/FyzjnnnEuM93g555xzziXEEy/nnHPOuYR44uWcc845lxBPvJxzzjnnEuKJl3POOedcQjzxcs4555xLyP8Ddz4ryjTQV/8AAAAASUVORK5CYII=\n",
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OBULAzekfnlLOke3RqUallGrvGgy8jDHLgbNFZAhwFNAF2ARcZ4xZ0ULjU6rNMlbKK8qaatSNspVqrZZvrWBQ1/xMD0M5XCp7NS4FlrbAWJRytGyvG39QM15KtUY7Kms5/sFPWP270zI9FOVwqbSTUGqfag81XiUTJ1mrGmMuy9EaL6VarVC4HRyYVKuggZdSLUSnGpVSSjU51SgiBcAtQA9gErAgUv+l1B5xeh8v+8zZGJCYRl7ZmvFSqtVy+nFJtR6pZLyeAVYCQ4DNwNNpHZFSbVxtMHlWK0sbqCrVaulUo2opqQRenY0xzwABY8znxJetNEhEDhGRaUkuv1FEvhWRaZF/Q5s3ZNXWOb3Gyw68DCbuzZLt0alGpVqrYMjhBybVaqTSuR4RGRb5vzdWD6+mbn8zcBlQmeTqMcDlxpi5zRinUm2GP5T8LaJTjaqt27irmp4dcjI9jLTQjJdqKalkvK7DaqY6BngV+L8U7rMCOLeB68YCt4rIdBG5NaVRKkdx+uEteuZs4vdq1HYSqq2b8LuPWLu9KtPDSIugBl6qhaTSx+sbYHxzHtQY85qIlDRw9b+Bh4Ey4A0ROd0Y807ijUTkGuAagL59+zbnxyuVUQ2dOfcoymaNQz+0VPtRVhPI9BDSIuz0GgjVajSZ8RKRDSISEJGNIlIrImUiskxETmjuD4tsP/SQMabUGFOLtUrywGS3NcY8YYwZZ4wZV1xc3NwfpVox4/ADnKlLeMXVePXumMO2cn8mhqTUPuPU6XI7Ux3WzJdKs1SmGj8FRhpjegLDgTeBU4B79uDnFQILRSQ/EoQdC2itl3KUhs6cc30eqmqd+aGl2g+nvobtTHXI4SeGKvNSKa7vbYxZAmCMWSEifY0xy0UkmOoPEZGLgXxjzBMichvwMeAHphpj3t2jkas2y+mHNTvwSuzj5fO4qA3pqkbVtjk1MLHfm8GQwevO8GCUo6USeG0Skd8BnwMTgM2Racbaxu5kjFkNHBr5+qWYy18AXtjTAau2z+mpfPvpNZT5MsbEBWRKtWaj757MgrtPYuqiLdYFDnz7GmP43qOfAxAMhwGNvFT6pDLVeCWwEWt6cV3k+wrgorSNSjma01cP2TVsiTVeAD63Zr1U21JWE8QYww//OQdwZhF6/1vrJl60rYRKt1QyXm8aY05MuGxmOgaj2genNypsLOOVl+Wm0h8iy6Nn1KrtiA1GnBZ3JS72cfqJocq8VAKvXSJyJrAUCAMYY5amdVTK0axUvnPV7dVo6qW88rI8VPqDdMrzZWBkSu2Z2GDEaRmvxAyXZrxUuqUSeBUDN8R8b7BWIyq1R5x+YLM/mJLFl3k+D5W1Ka9LUapViMt4ZXAc6ZC4WEAzXirdUmmgekzs9yKip+pqrzj9wBbfxys+5WVNNWrgpdqGZC0WnNaHL/HphBxeCtGeVfqD5GWltFNiWqXSQPXHIrJERFaKyCpgYQuMSzmY82u8TNz/sfJ8Hir8zuyDpJwnENNiweawuKteBt7ppRDtVXVtiP3umpzpYQCprWr8EXA08B7wA+C7dA5IOZd9puz0A1tdHy9DYteI3Cw31TrVqNoIOzsdiFmJ67C4S6ca24mqyHG3Ney8kErgVWqM2QQUGGOmAZ3SO6T24dW561ldWpnpYWSE82u84v+Ppd3rVVsSjARctcG6wMtpxfWJfQWdnpFvr6ojAZc/mPkT/1QCr90icjZgROTHWMX2ai/d9Mp8npmxKtPDaFH28drpBzYT07k+UY7XrYGXajPsk6T4VY2ZGk16JD4fp58Ytlc1gfonEZmSSuB1NbAGmAgMAX6S1hG1I2XVgUwPISOcnsq3D9xhY+o1UM3LckdT3kq1dvY7NXaq0Wk7T8QGWkO65Tu+FKK9qolmvDJ/4ptKeb8LGAiMAhYA/dI6onakvRVZryytACDk8ANbYw1Uc3wetlf4W3hESu0Z+zUcG3g5LSMU+z4d1DXfcc9PWey/a1vJeL0BnAYMB4ZF/ql9wGm1Ek2xU72BdjTVmFhcX1Mb4qEPl2VgVErtgSTlAU47bsUGWm6Xi/Mem8n8dbsyOCKVDvZfuTVs2ZZKxkuMMT9I+0jaofZ2ZmWfNTv9eTeW8dq4u7qFR6PUnrNfy3FTjQ4LvGKfj8dlnSmValbacey/sz+Q+cCrwYyXiPgizVJXish4EcmKuUztA047gDXFznQ5vcarro9X/QaqN5+kCWPVdtiv5dq4qcZMjSY9Yisf3JHAy5WYqlZtnv1x29ozXkuwm29bWwTZXxtgQPqH5nztLO6KyXhl/oWfTrF9vBLl+Nx0Lchq6SEptUfqiuvrXsuJfa/aulCSjNf2ytpMDUeliX08btU1XsaY/saYAVhB1pGRry+I/N8kETlERKYlufwMEflSRGaKyI/2dOBO4PQpt0R24OX8Gq+6/5OdOHvdrlbx5leqKfYKxmBsA1WnBV4xx2GP23rD3vTK/EwNR6WBPxhizpqd0a8zLZXi+keBKyJfXyoiDzV1BxG5GXgKyE643Av8BTgROAq4RkS6N2vEDhJ7prVkc7njCzoDIYPHJY4POGPbSSST69OWEqptaS+rGj2uVD4SVVsRDIX5ZOk2pi7ayu/eWwy08oxXjAONMfcCGGOuA8akcJ8VwLlJLh8OLDfG7DTG1ALTgSNSHazTxPbDmbmilI8Wb83gaNIvEAqT43W3rxqvJBmv3CztXq/ahroar5ipRoe9f2MDr9geXpMWbMrEcNQ+9O3GMq54ZjZed12o01Y614uIdI580YEUVkIaY14DknUHLQR2x3xfDhQ18EOvEZE5IjJn27ZtKQyz7YnNeIVN63hBpFMgFCbb546btnAi+3PJNLCrXZ5mvFQbUbfbRLjeZU4RG0jWxKx4m7d2ZyaGo/ahgmwrXLGnkKF1fM6mEnj9FpgjInOBucBv9uLnlQEFMd8XAEnn14wxTxhjxhljxhUXO3OXonDcNhymVaRA0ykQMu0i42ViMl7U611v7ddY2c6a5zpJa9hkt6XY2aCgg4vrY9f6xL5b7RWOqu2yV6e+9fXG6GWt4XO2ycDLGPMOMAg4FRhqjJm8Fz9vETBYRDpF2lIcCczci8dr02IPYKGwoTbk7AO6PdXotKmKRPbT+/MHS5JeX5DtYWeVrppqq4bd8T6fryjN9DBaRLKmk057/4aMoW+nXMb07RBXGuD0RUDtgX3iMOW7LdHL2kTgJSLnA+cDJwMbROSm5v4QEblYRK4xxgSAG4HJWAHXM8aYDc19PCcQiT/TCrWLjFdkqrGdtJPYVRVIWuP12bJtXPnsly08KrUvfbexLNNDaBF29tbJezWGjWF4jwJe/9lhcf277Gkq1XbZL9UKf11pR1uZarwJmAJcCvQFzkjlgY0xq40xh0a+fskY80Tk67eNMQcZY8YaYx7ew3G3eW6RuKLOcNi0ihdEOllTja64aQunWby5rMnGuI9fNo4eRdmN3ka1bu0lG1JX4xVfj+ok4bCJBlx23HXlhBIGdc3P4KjUvpDsWNwaEhypBF41kf/LjTF+4mu01B5yJbRVCIVbxwsindrDVOPJD30WF0AnqxLZr2eh1o+0McYYPlpcN13RXnadsN+qcVONDnvuobDB5bIDL+t/j0vazd/YyZL9DdtKH69VwBzgGRG5C/givUNqH1ySuKqxHUw1BsPk+JxfXF9eE+Sqw/o3eH221+347KbTbK+s5arn5kS/d/LJQyx7Za6TpxpDxuC2M16Ry9ztoN9ge5CsqiXQClbVp9Ia4koRyTfGVIjIl8aYLU3dRzXNJRK3LDtsTKvYQyqd/MEwuT6P41spVNQEyfY2fk7ji3Sv93m0YWNbsK3c2jS5vWz0brM/uGJPFJyWCQqGTDQDbU81imjg5QTJXqutoUygsU2yb4/8/zLwhIi8BPwl8r/aS25JnGp0fo1XZW2QohyvYzN79odyhT8QrRlp6G+a7XVR3Y7aErR1W8qsiosTHvwEcF7w0ZBoA9Wgc6caL39mdjTgst+3bpfz+pUl8+XqHazbUZXpYaSNMXDssK5xl7WGz9nGMl5vR/5/rCUG0t7Uq/Eyzg68dlcFqK4N0b9LHk9PX5Xp4aSFHUiV1wTJKXADsLs6WR/hSC1JOziwO8WuKuvvuHq79SHl9OnyRLF1MU6aarRXbVZHdpKITjWKOC7ATOb8x6xuTqt/d1qGR5IeYWPiVqpC66ilbmyT7PkiMho4DrgQOAzYaIz5pKUG52Rul2CMYVdVLdW1IcJhZ9d47f/bD6isDTGqdxHFBVmZHk5a2I01F20ujxbpbthZnfS2QsOd7VXrkxhoOSn4aIyd8fIHYvt4ZWo0+579dy2vscof7Pdt4omxkzw3Y1W7aQIcMobEdUytocarsanG84FngHXA+1jb+7wqIme10NgczRU5ozr+wU+44T9fR1Y1OvPNYJ9VVvmD5Pk8SVf6OYH94TR/3S5cIhRkeeL6x8QScc5UxnMzVvH4JysyPYy0CocNw3sUclBJR8AqDfj5S19RXpM8o+kU9mv08xXbo5c5aZrV/hC2s9V2ciQcNmyvcE6T45KJk6JZy7vf/o6pi7Zy5j+mZ3hU6WdaacarsanG64CjjDGV9gUi8hzwFvC/NI/L8cLGUFpmvbHX7ayie1G2Y4vr7ee1o6qWXJ8br9uFPxgiy+PO8Mj2rdiaLZdAjs/dYB2XS5yzXP1PHyzF53Hx0IfLmDHxWDrl+TI9pH0uGLaabK4qtQ6HIWOimyg/fPGYTA4trezX6IZd1fUucwL7Q9jOAEnktPCJz1ZSEwhz3fGDMza2fa0mEI4GIbNXbWfB+rptk4OhMB638xb6hA24Ep5Wq854AcHYoAvAGFMOODMt08I65HijX1fVhqLtJKpqg7w5z1nN/O2NZ3dU1pLjc5PldTmynq0mEKJ7odUY1eUSK/Cqbfjt4oSPrx2VtVT4g3jdQnUgRGUDGb627KInZrFxVzV5Pg+bd1tF9vY0lB18OVWy16iTpuBqE1ap2tNSsZtlO0UwFKYiMqVaGTku2as5nbp37A3/+ZppS7bFXdYaEhyNBV4Njc55YXEGdCvMpku+lRkorwmyeXcN/mCYL1bt4Pr/fJ3h0e1bdop7R0UteT4P2R53XM2IU1T6Q/TtlAtYUxb9u+SRn5U8qSwJ7UTaosWby/hsmXVQs59Lazib3NdmrtzOzJXbyc/2sCkSeP37y3UAHD+8a2N3bfOMMRQmbJ3joLgrmvGys3j2rFSHXG9Dd2mzakPhaOmDHYAN6JJH/y55lPudOWW+fmc1VQknv63hpL+xqcb9krSOEGBEGsfTbhgM2V5rqq20ws8H320hx+vG48CO5naQVe4PxmS8nHeGVV4ToE+nXGav3oHX5eKRS8ZQVt1AjRdtv7j+B89+GQ1E7ICrNZxNpkNVbYiimCy1/YHdtdCZWz/d9sY3DO9RyPDuBQzqms9Xa3dFr3PSwgL771iX8bKOvy9dfSin/u0zXpmzjvPH9cnY+Pal2mA4GnTsrLLKXIJhQ8dcb4O1qE5xQJ8OfL3Oeg23hpPDxrJXFwCPJ/x7DGuFo9pLYQO5vvgap5pgCE/ihLQDxJ5hZHlcZHlcjkzl764ORFdset1Crs9D9wb2ZHQ5oE9Qjrfu9WuvDnNiJhOsaWSPS7hyQknc5fZquLaqJhBK+kH00hdr+WjRFkJhQ15C1tYpbRa2lfujdU5HDC62Loyc99p1iku3lGdiaGlRGwzzv6+tMha7Pcqq0ko65vqiGTCnev2nE9i/dxHQyovrtW1Emhno2SGHpVsq6i4y1Fv66gSxS5dFhCyP25EZr63lfroVWoFXU3sxCm1/qjEncuIwqGs+PYqy+WxZaatI46dDVW0Qt0volpDhasurGtdsr+SoP07jrAN68tfvH1jv+mDYEAybuEwfOCfjddB9HwJw9NBi7j5zP6CuuN4+/20NXc73FX8wzMMfW6uP7YwXQIdcH+UOz3i5XBI9JreGwMt56ZU2wmDo3yWv3uVOOZuMZX8YDyy2nm92morrn8lQY1ZjDL98eR47KmujZ8pNZfSs/qlt+29tZ7yOHdY1Gly3hoNaOlTVWhmvxCx1W8542VMvy2JO/mL5A+HoxvaxnLCqMTZ4jKmplXkAACAASURBVM3S2udL7iZ2nmiLYp+LnfEC6JTnZWelc1pn2IKRTO7IXoUA0dmk1rAAKC2Bl4i4ROQxEZkpItNEZFDC9X8TkbmR66aJSFE6xtGaGUPSRqJBh5xhTV9WGs0G+IMhThjRjXeuPQKwGjBujWzBsi89mqFeUv5gmLfmb2Tltgq8kSXZiQWdiYS2X6ScG5mC6l6YzZerdwI4MpMJ1sHaXqkaqy1nvOyAI5hsJ2Gs0odAyNRrM+CEQ9QTn62Mfh27d6xdXG9nR+xp2BnLSxlzz5SWG2Aa2CdFOV53XC1mzw450VpNJ3l59loAnrhsHAAed+Rv2goOvOnKeJ0NZBtjxgMTgT8nXD8GOMkYc3Tk3+56j+Bwc9bspCDJijendBS+9Okvokvt/YEwHXO90Q+tDxdt4Sf/+iqTw9srOxLODu0zyU+XldYFXoHGz6qsVY2ZPwA0x+rSSo7507To99mRDb4Hdc2PXuakDEGssAGvy1Uv49WWa2NqIkGy3SKjrCbA7kgmxOd2sWD9bqoDIXyRD6z8LA+HDersiKnGjTF9ySpjTpLqphrjSwW+21jGjsraNp3Rtf/eiTV7PYpy4vq0OYV9LPJFjlN9O+VGkx2ZPvamK/A6HKvbPcaYWcA4+woRcQGDsTbeniEiV6VpDK1e4hsAyNjGyatLK5u+UTPZK2VaqllqS5XHjblnSlymwx/5m4XCJnpW1Vj/LmibnesXbNgdbSAKdSvB+nXO5W8XWTVCTs14gVXTlhh4teU9G+2MV1lNkJpAiCuemc0Fj1t79xVG6rq2ltVEM16HDujEjScMcUQfr+zYhSGhhqcabfe9uwiAi56clf7B7WOJ+1EWJLQH6ZjrjQbcTpIVCbjs/+85eySf/OpoPC7J+Ps2XYFXIRCbxQqJiP3XzgP+DlwKnAz8LLInZBwRuUZE5ojInG3btiVe7QjJAq+yyBl0S0fkR8dkMvaVsmp7qjEcffEDDOtesM9/FrRsQ9LYwCo2y+N1uXjrF4dx3XGNd7xujWso/j51GT987ssGr09sdVJZG+TCcX3o0zEXbysqXN1XKvxBFm0qi36f63OT441/z7qk7e7pF5td315Zy5rtVSzZUk7JxEmUVviZMLAz2yr80SyutfjHGTsuxC4YuCtSWA9E5xrtqcbE92ns66GtsF+f9olwXlb8yUO215mLneyspX3S73W7yPV58Lgz/55NV+BVBsR+urqMMXZOvgr4qzGmKtIJ/yNg/8QHMMY8YYwZZ4wZV1xcnKZhZlZi0arbJeyOrDbJxGqafT2FsDNyFuUPhOPOMN/95RF0TcNG2ULLrbiK7XsTe9DyuoXRvTvQOb/x5yet8ANs9uodTF28tcHr7XoX+3dc6Q9x/QmD41YMNdS3rC16/JMVnPLXz6Lf5/jc9Wq88rLcbbIH0tayGv48ZSkAPYuy2VFRW28lbn6Wh52VtXgjWVxDywde2yv8XPvyvH3+uD63i4mnDOPb35zEMUPrmuDav4KEhBcjehQy+fojKcj2NJnNbm3sBVt2iURiU+csrzPb+wQiJ4H269fmcbky3ssrXYHXDOBUABE5FPgm5rohwHQRcYuIF2tasu0W/OwFnyf+15+f5WF3JEtU04JnIHZ2bV//zB2RILImGIrLeLlckpbslNftItBAofC+8v7CzUD8Fhs1gTCH9O8EkPJ+Z65WONWYOI2WyM7srd9p1YNU+IPRg7g38vddv7MqjSNsWYkBRmG2t97vqCDbG83stiXbY+oUe3TIYXuln2xv/Gs3x+emvCYYk/EyuF0tmy1YtrWCt+dv3OePGwwbvG5XvVkHu8bLnmq0n2nYGIoLshjVqwPfbGhbJcn2IdGu5cvPim8Pku3Q9j7VgTBetyCSGHg5N+P1BlAjIp8DfwFuEJEbReRMY8wi4EVgFvAJ8Lwx5ts0jSOjQmGTdNWTnTHwJnxIF+Z4olmi+et21btfutivwS1l/n3yePYUxprtlYTDhqraUL1MwbZyPyf+Zd+2ivO4Je2rQn/yr7lA/YxXx1yrjUSqOw8I6Qk+90auL3lbv6ue+5KSiZN4LLJqdM0Oq86r0h8kL3KfET2sJdtOKq5PrPMpLsiql6UuyPa0yZYSrpjn1qMomx2VtWTH1GF++evjyfZYgZcnJuPl87j4eMk2fvfe4hYZZ1P98PZUKBxO+l5NXNVoDPz+/cXRconjhnfl3W/a1v6cdsarLvCq+zsfP7wb2V63IzNe1YEQvzlzZL3LPW5XxvuzpSXwMsaEjTE/McZMMMaMN8YsNsY8aIx5K3L9H4wxB0WufywdY2gNnvxsJaPu/qDe5fYbITHlW5DljfZX2dmCxY72cvJj9lGd17ZyPyN7FbJwQxm/fec7yqoD0WLdWEsT+gdt2FUdzfjtCXcLFk3GBl41gTDZXhdPXzGO4ZEApEnS+vohbW+gl8/sVTsAWLmtkpG9CvkoMh1pqKuj6FaYzfNXHeyoGi/7U9huilucn1VvD7/CbG+bbCkRSGgnsKOyNq4coLggK5LxCuCLOUEcVGytYH2shVq3pKuhdChcf+Vi7M+zsyTGGB6dtgJ/IITP4+LUkT34eEnD0/HptnhzGU/FtMJIhZ3dWb3dOmHq2SEnep0xhiyPM7dwqwmEyPHVD3GcnPFSWPUJybw4aw0AQxOKzDvn+/hw0RaAaK1XS4jNEpXtgw+RreU19Iq8uees2cEj01ZQmN30prOH/e4jbnl1wR7/XJ/b1WIf/Jtj+pDZqzaPG96t3vRxQ4TMTjUGk9Q4fLo0+SKW2BqJ+84exdRFWymZOIlt5fGvb5/Hhb8V7IO2Nz5Zuq2uTjDyB8rzeZh7+/G4XEJBtpfPbj4mevu2kvF6evoqSiZOip7YBEJhDu7fiY9vOppBXfPZXlmbtPan3F831di7Yw4ul3DssMY3Bi+rCTQ7OGiIHQAt28db9zSc8Yq/zD452lUdwOMSinK9bCv38/LstTyegb6BUxdt5d5Ji5p1n3DYUNI5l8Wby+lRlM11x9ct/DHguIyXMYaaQIjq2lC9DDVYMyNOrfFSgNtVVxthR9jrdlRx99vfJb39VYf1j37dkm+E2CzRy1+s3evH21rmp2tBNgvuPpGFG6xVQPv1jM8E/SZ2JVGMHXsRcHbI9bJrD+4fCIX5et0urnhmdsr3+WZ93VRwTSBMlrd5byVrqie9kdfOyloG3Dqp3uULN+zmxy/MrXd574459S4D4lqB9O6Yw9odVh1X4upUn8cVLWhti4wxXPHMbFZsszKx9tvC45a4xRL27gQQCbz8rT/jNWe1lbW88T9fY4y1FVCXfB/9u+TROc/HOws2MnPldqCuL5vX5aK6NoTHLXx28zH8+tQR1uXuumxQMnPX7OTeSYuimdJkUu1XaE+Jnv3wjJRun6pg2CSdxkwsqrcbIVfVhqJB2YMX7M+tr3/DA+8tbvHV53lN1GEmEzKGbK+bXh1yqKgJxr2fww7MeH2ydBvD7ng/UuJSv3xCM16tlDGGV+eu3+vHsQ9Qx/35Ewbe9i4lEyc1+OF+5YQSRvaqa+Dfkv28Yl+EA4rzG7llaraW++lakEVhtpfeHXP4388Po0+n3LjbXDGhJOnKxtmrdrBm+571FCsuyGZbA1nGhkz5bgun/PUzzn54BlvL/SkXh/93znpqAiG+21hGWXWgXm+cpjTVx2vump183MgKw1Qs3lyetDv+4G75LNy4u96HRp+O1t+oNOF3GBuQ+TwuLj6kL0C9zIfP7YrriN3W2K1cfvT8HO7638JoSUDixvV5WR4W33MyX9x2HAXZ3mjG64+TF/PAe4tYt6Mq42fUiexM8NTFWymtqCUQDEczWZ3yfKzbUddA85FLxgBWwFlVG8LrdtGnU260TtO+n31yuHJbBcf+aRqrSyv5y5SlvDhrDYO75nPLawuSbs9ijGHYHe+n1JrBfo1W1ob2Sa+pz1eUEg5bJ8KJNXwARw4u5qwDegJwwbjeVNbWH//JI3tw5BBrpf2uqgAffreF7z8xkwenLOV/X2/grIdnsN+d7+/RSWBTaiJ/R3vnj9e/Wt/kZ1UoEmQW5XijezK+9KNDAOvkwuWSNr+LRiz7/VgTaCDj5XI1uFtDS9HAK4ktZX5uemV+g1OFTQmFDUs2l0fPqFbGNJ1cmdCodPXvTgOsouTYaaqW7GAfDIdxu4QLx/XZJwHf1vIaukbqYqbfciz79+mQ0v3srsKPf7pn0xTF+Vn1pr+acuvr37B8q5Xh6Jzni1ut2JjeHXN4fuZqTv3bZ3y4aEtKU6mxRBrfMui3b3/LDxrpqdUY+8Nq2dbk0zNZHjceV/39MkNhwxGDu0RXbtpcItEPY5/Hxf3njOKTXx3NtcfG9yrL9rozvtQ+HDZ7/IFnB7qrt1fxz5lrou9Bj7v+B3S21023wuy4qcYnP1vF45+s5Ig/fMwZf58eLWZuDUbEZJzX7qgiEFnVB9A5L/4EyH7eXreV8Upcjm8vJLGPFe9+s4mVpZUc/adp/HXqMj5ctJXjR3SjuCCL/e6aXO84ahc2ryqt5PMVpdHLP168lRdmrmZ7hT86JRpbB7mlvIanp6/ixv98vce/h4uf/IIXZ6/lpdlrk/5dR/Yqim4YfuKI7izfWkHH3Prv7X/+4CCOH96NGStK+cPkxcxauYNgKMw7CzYxf90uKmtDHPDbKXHtSPbWx4u3Rhc1zFmzk79NXcbtby7kplfms6WshgXrky/IsgOv2P5dEwZ2AVJfDNSW2MH+ki3lSQOvF68+hEFd09NLMlUaeCVh79317cY9a5a3uzrAL1+eF12anIraYDiuiDVdGa/HP1nBszPqNpOurg0RDBn261lIcUEW1UnO8Jpra5k/6T6UydhBgjGGUb2KeOzSMXy0aOsefWj17JDd7K0vYtsDbC2vabJGbMLAzvzxvNFcdHBf7n/XOgh+uGgr3Yuym/VzrVWNDUde+c3MoNm1eSu2VdD/1ncBWLvdyt4l622W7XXFbQ4MEAiHOX10D2ZFppxsVYFgtMDcfo3265xXb6Vq18IstjYz8N2XNu+uYcBt73LAb5u/p144bLg+4QN95TbrJKmxD6eCbG/0dz8gsun9dccNZvHmcg59YGqzxxHrjjcX8tXanXv1GDYR4b5zRnL5+H6s3VFJlT8Y/VDqUlA3dfrOtYczKpJ597qFLvk+ivPjX9v9OluZUfs4uWl3DQ9deEDcbcqqA/z3x+O575yRXPnsl3FZVPvY9vpXG7j4yS+iC1We/Gwld/zvWw6+fyr7/8ZalGTXnw4szmPT7hrueec7Xp+3IaXn/NyMVWxJsifsHW8ujGuT0RCPW9hS5k+60ElEuPqI/vzipXnRRUI3nTiUJy8fx5Bu+fzoiP7k+tx71HD1s2Xb+HJ1/WnaD76z6n/7dMrhZy9+xYNTlkanQg+5fypn/mMGp//9M8579HPmrql73Zz7yOcsWL876fONfWknZsA/X14aPSltLcprAnyzvvF2HqUVfnK8bpZvrajXLBaSL6poaRp4JWGfkdn9ipqrJhAi2+uK9sWyD2T2LunJ1IbiM15V/hCPTFse/f6t+Rv3OAMX65+fr+ZvU5dFvx9+5/tM/nYzhdleuhdlM2tlw3UZTSmP7PVmTTU2HYh4Y5b1vjV/Ix8t3kqnvCzOOqAnhz4wlZKJk9hVVZtyFmVgcT5/eH9Js7Je9tn87F8fx8Di/CZrHWqDVlGy3T7h3DG9OHVUd04Z2SPlnwlNTzXahc5n/H16tD4n1vKt5XEHytF3f4A/GGLaEqtA3hhDdSDEb8/ar17dCkQKaiPP1Q7MQmFDSec81u2In26tqg3Rp1MuJ4zoVq/4OFZhZNqtsbqX0go/h//+owavT9XCDbv5JGExQEMZvoZU14Z4K9Ijyp6Cid0/1X78HkXJa98gvri+c76PD244khtOGMKqB04F2KseVC/MWsPrX+19yQNY21rl+tyUdM7jiU9XMXPl9mitWq7Pw+rfncZ/fzyekb2Kon9jj8vFpeP7cfjgLnGPZZcm2O/LLWU1lHTJY/otx/DOtYcDdVOblxzSj54dsjni9x8z/oGp/PmDJdQEQgzumh9dSLRqWyXz1+2KdpO3H//VueujQU+PohwWxwQxJRMnMXXRlnq1OlvLapi5wjpxuPvt76KtHz74djMnP/Rp3G2T7RwSK3aK+cMbj6p3/aEDrJMwm/2B/t51R3LrKcN57gcHA83fheTqf87hllcXsLW8hkuesrYo+u+cdayNtHEZ1Eg5SG0wzJw1O/neo58D1uvPXgiUGHgN7VZAv87WyUKyMoGLn/qC4x/8hJKJk/j+EzPjTuA27qrOyJ6dU77bwhn/mM6SzQ2/15dvreB7Y3sB0LtjboO3yyQNvBJc8/wcVpVWMLxHIcePqKthMcZQMnFSvQ2SY9UEQpRMnERNIIQBHp1mrXo5fbT1oTykkfRmbTAUV+y5rcLPH95fwurSSg7//Uf88uV5XPzkF9z1v4VU+oNs3FXdrAJBYwzGGDrk+uqdwc1Yvp3uRdl0yvPxxrwN9T54U7G7OsCouz/goidnsaq0MuUMkD0tNG+tlSb3uIWfHDWQiw/pS7/OuRx8/1SG3/k+4+79kCWby1m8uYzHP1nBmu2VDL39vegBvmTiJIoiUwIfLd6S9Pmv2FZR7yA4uGsBN588lK4F2WR5XNEzyIaU1wTJy/JwUP9OVtbrnFE8csnYlFcz2qxNsq2g5+35G3lj3npKJk5ixbYKPl6ylRWRbMs3G3Zz3mMzuemV+SyMadx4/IOfRj9g7APg+p3V7Iy8PjeX1eAPhulakJ30NWutZAoxY3kpt75u9TcOhgz9u+SxqzrA/e8uiv6uLj64L51yfTx5+bh6j5OoY25dS5Rk3vp6I+t3VjP67snc9Mp8KvzBPVqJ+uCUpfXqJbdX1D3PVD4ULnlqFr98eR5zVu9g2pKtuATe+PlhAEy54cjo6sXCnIY/oGMDrwp/KPphbgcv1748jzdTzNAkE/ucUhEIhfn5i1/Va3Hx+KcrqfSHOHG/bizaVMbzM9cwpFv88ejgSBNgm7eBvnh27Zv9Qb1pdw09irLp3TE3Wqcae6/vjelNdSDEpt01/P2j5fzkX3NxifD4ZWPJ87k54x/TOevhGdEP09tPGw7ATa/Mj/bNKy7IYsnm8mhtFcAP/zmHIxKC+IemLuOiJ2dFA2p7/Bt2VbM48vjL7jsFaLpVRezxOHYj+Fjnj+vDlBuOrHc/l0s4uH8n+nfJizve1gbD0ffo9go/x/xpGve/u4hw2OAPhnh/4Sb8wTDrd1azbEsFM5Zb7/GbX10Q/XpgksBr/IDO/N8JQ/jghqNYcf+pdC3I4oNvN8d1/e/TKSdu6u3d647g16dav+vshO71u6pq6VaYxR/OG43P42LWyh0MiNQpf+/Rz5nwu4/41xdrorcPhMI8FzOTsic+XryVjbuqGw1U7b/nSQlB9PMzV3Psn6dRVRvky9U7+dWJw/jy18c3+7jcUpo3n+FQwVCYy56eTUG2hw++28IH323hmKHFcVkbeyXXks3ljB/YOe7+28r9vLdwE0cPsQK16kCIBZF06KOXjOGg/p144L3F0cAg0cs/OpQeCYFKReRgbu+h2CHXy5It5SzZUs4/Z1ov+Gyvi2uPHWx9cCzdxgnDu3HU0GL6dspl0+4ajhxsHaQWby7j128s5OSR3aMfItOXlbJ/H+sg+eGiLfzimEEc0KcDo3sX0TFm1VYy4bCVTbE/ZIwxfBlZwdS1MIsR+YV0aWLLHLAOhuc9NpNPbz4mmpL3ulx0zPNx/zmjALjhP1/zxrwNlFb4495sD0RqHUbc+X60/1BNbYjXfjqe7z06kz6dcqN1DP/4aBlet4sH3lvMs1cexDExReHVgVD097RuZzWXPzM7WneXqLo2xI6qWjrm+nC7hAfOHdXkc2yIYNWv/OC5L+MyN8f9ua6p7K9OGorP7eK+dxfx6lyriHZAl7xoneC1L8/jxP26Rw/klz31RXQaZ/wD1gdSrs/Ny7PXcveZ+1GU46Ug24PX7aIg20NphZ9h3Qu4KzKdFYrU/Tx9xTiOf/BTJgzszNFDu3L1EQNSfl49OuSwcXd13Gvo/ncX8cLMNdx95ggejSzBL6sJ1j2n4jxuPWU4vTvmMKA4L7rq6tuNu+mU54vLOE1dtIUtZX6Gdi/go8VbuezpL3j00rHket1xGa+D7vuQN5Ms6oj1j4vHcNMr8znvMWtj6E55PgZ1zWfeHSfEjT+xuD5WbB8vq6Fs/NTG4YO68MB7i3jgvUX84tjBzF+3ix8cVkJJ5zz8wTDlNQH6dc7jq7U7+W5jGSeP7E6X/Cyenm59iG1OMlXWGGPgjP178m1k0ceJ+3WPXlcTCNG7Yy6v/GQ84bDhkAGdG3kkq9FksrYjsRlSsI5/ie/32O9P3K87Pz9mIP065eHzuLj+P19z7bGDOGm/7tx95n78KtJCxn5dX3pov3otEwR4fd4GbjxhSFzbk427a9hVVcsLM9dw7XGDo7WWv4wEHO8t3MTzs1Zz0oi634PX7aJXh5wmyyGS1YAlM7hbAYvvOTnpdaN7FzHmnvpT3+eN7c3MFdvZsKuaJz5dyfMzV5PtdUdPWmpDYS556guAelNrAyNB4LljevH6V1ZQ/8glY6KvWbdL2Fru55oX5nLn6SPYWu7nsU9WcPPJw7juuCHRx4kNLLOi3eut39+3G8s4uH9nLhjXhwvG9WHz7hoe+nAp//5yXXQa887/fcsjH6/g4P6d6NUxh0enreC373zHhQf1YVSvDgzpls9LX6zl1lOHc/5jnzPlxqPYvLsm7j25bEs5ZTUB/jVrLW/M24ArUvs6+fojGdq9AGNMXJY9sd3R/e8uYnDXfO78n9WDfcSdk+mQ623ws7a10MAL6wAzpl8HHv64ri/Lx0vipzG8bhcjexWyZnsl4wd2pqwmwHMzVjNzxXa8HhefLt1Glsc6WNhvmD+cN5pTRvWIvliykxT6AfUCOaBeI9Hff280P35hLpcd2o+8LA8njOhGdW2IuWt2sl/PIgzWpsWvzV1PXpaHldsqufnVBXjdEp3Osw/mAJc+/UVcTVlJlzx6dsjhrV8c3uTva/K3m/npi9YuTy9dfQgXR57v3WeMYNI3m7j5pGFNPoZt7Y4qKvxBvogEbom1TX+58ABOG9WDYT0KyPN5WL6tgnH9OtL/1nfpku+jtKI2WiOyuayGc8f05r8/Hs8Fj8/kmKHF9Oucx3Ofr44+XuIqpTXbK6Or9v76/QM45aHPIlPF9f9WB933IRX+4D7ppm0fS+yg6+s7T2BruZ/r//013Yuymbd2J6eM7M6A4nxOG92DHkXZbCv3M2NFKTf8Zz4AJ4zoxsuz69p/bExSF3fOgb146MNlcc1xl957CieM6MaFj89i1m3HsasqwNIt5eyoqsXtlmiz2yuf/ZK/XXQgRw0pjttUuDE9i7J5de569utpBfVn/mN69CTklteszNobP5vAok3lTP52M58s3cbKbZVc+/JX0TPu44d35cC+Hfnj5CUA/PyYgeRleeic5+OPk5fG1Qt9tqyUkXdN5oFzRzFv7S5uOXkYu6pqefzTlVzw+Eym33Jsg3+vnh1yOKBPBz5fsZ0/nDc6mplOPPFo7AM4NuNV6Q/Wm746+8Be9O+Sx5TvtnDHmwsBeO+bTVTWhvBEGv4ePbQ4OkV8+5sLuXx8P56PnFwtTNieZvaqHQwozqNLfhbnPfo5d5+5X9xqaJ/Hxckju/PmvA0s3LA7LvCyX9MHlcRnthqS7U2eAbZjsWAkS+OJ2asTYP6dJ9ar//tVzDHhjP17Rm9/zoG9+M+X65gT+TC/5eRhZHvdvPnzwxjUNZ+Rd00G4OJD+jJ//S6uPXYQb8/fyLKY2iO7pu/PU5aS43UzqldRdFufryKZ9Kemr+LFqw+JZuJnTDy2yedvj/HwQV2auGXDx/aHLjyA208bwYzlpdEawscuHcOyLRVxqxFrAuEGWwid8Y/pcd/3j9QSnjiiOwvW72b51op6jX0BThnZnasO788Tn1qfa4XZXmhgIiKx5nPT7hp6dqi7cfeibH73vdF8b2xvuhdmM3PFdk4b3YN5a3fx+YpSHonM7oSN9dl12xt1uwTaNXmDf/0eAKeN7sE363dzxYQS3vtmU/Rvb98frIzWsO4F0SzlRQf3JRQOM31ZKb065LBhV3XcseUXxwzCJdZr8twxvZM/yVZEA6+IXxwzmKMiGasrn51d74DTs0MOt506nEc+XsH3D+5LlT/Eg5FNZv/vhCGU1wSi02W7qgKcN7Y3J0UOenaAYy9fjg2GGhIbeF12aD+6FVpvgvPH9WZ077pVgon1F7aaQIhKf5DO+VkYY6IF12C9iF+evZbaUJgjhxTz6dJtjO5dlPRxkulaWPeGtIOuhy8ew6mjunPG/j3j+hw1pjDbQ0G2l6uerVu9l6wtw/EjukW/PijP+tD4388PoyjHy9F/msZ71x3BKX/9LNqR+eD+nZh7+/Ec9+An9QLoa1+ex+mjreXi/mCIk0f2oENklVbvjrn86uShSQOvXVVWgPf+9Uek9Nya4hKJHni/vvMEOuT66JDr493r6j++/by6FmZzzoG9OWxgF0LG0KMohzvPGMGIOydHb3vBuN78d471uMcO60r/Lnn87aID+W5jGS/MWs2732xm465qLhzXB6/bRWG2l9IKPyf+xcomelzC+p3VXDmhhOc+X80vX57Hyft157HLxqb0vPKzPDw1fRV3nbEff5y8mAXrd3PCiG48efk4XvxiDb9+YyEH9u3IgX07RttS2Hs+hsOGZz9fzT3vfMeHi6wVhr85cz9e/GINm3bXcPTQrpRW+LnqsP48EzOtceiATuyK1BZ+/6A+fL1uF6tKKxnTr2OT00k3nzwMETh/bO8G69cam64oiGS8wmFDIGTqDw1OGAAAEldJREFU1dGEjWFsv46M7deRiafUBR/VkW20SiZOYtqSbRxc0okzDuhJcX5WdHoNrHrTkomTWHzPyWR73Vzw+EyunFDCQSWdmLNmJ4s3l8cFXrYOud56JQUXHtSn8V9Ggn6d86J/h1j2VGM4bNha5qdbQra+qWxDbJDmcbt49acTAKtcwJ52PiBhJXTXgmym/t/R0eexdkdVNDjt1zmXNdur6JTno7wmwIs/OoRzHp7Bim2VnDe2N6/OXc+/rzmUQ5vI8NV7npEo4JgmGsY2RkQoLsji7AN7cfLI7tSGwhRmezl5JDw9YxW7qgL864eHcOnTX0TvM+2mo6kJhpi0YBNn7t+TE/4SP63WtSCLpfeeEg2yk7nmyAFcMM76e7sbydjaXvvphLjX/5ayGroX1o/S7KDdzlodPrgLhw/uEg28Vtx/KqGw4Z6zAvg8LsLGWr36p8lLmL68lJLOuSzaVMbaHVXc807yfpbnj+3NK3PXR4Ouv110YDSDCfDDw/tTXhPgv3Osv+sh/Ts1WnvaGmngFZHjc0drHGbccixrktQ5jR/QOTrX370om/16FrJ8awU1wRDXHz8El0BJZytzFJ/GdXHrKcOiwdyy+05tcjz2FMOpo7pzz9kjWRVJwyfWZTQk2+uOBg+JL8pDB3TirjNGUF0bomOej6raYIP79CUztl9Hvv3NSeyuDtA538e6HVXR5bmdU5hitD195UE8O2MVc1bXnfGk2g/LblEx5YYjGdytoN70YOf8LL6+80SMMZz76Oe8+pMJvP7V+riizCyPO+7DEODy8SVJf57bJbx49SEM657ilkApeO7z1dx+2vBo4Jeq2MA39u9m/w4uH19CXpYnemYMVlb1wL4d+GjxB3y6bBuXjy+JHphn3nosR/1hGn88fzQ5Xjdj+nZkTN+O3Hn6CN5esLFZndntzNjHS7by8McruHx8P357lrVf2pn79+TXbyysdx97IYHLJfzw8P5sLa+hX6c8bnvjG66YUMIVE0qitz1+eFcOG9SFzvk+xvXryKtz1zOkWwHbK2spqw7QIdfLoQM6s3+fDimfAPyqkQztaz+dEPd7TGRnvDaV1dQrF4CGC6vtjNDZB/Skb+c8bjyhbgpo1QOnMm3pNn4Qc0Jy0H0f8qfz9wes142dxU3c2Nq2X88ifv3GQjbsqqZnUTZ//f4BTa7iS3Rgnw7846ID611uTzUGwyZa37Wv2G1oYl11WH/6dq6bnrr6iAEYY3h+5hpW3n8qLpfUm5J677ojGXvvFP50/v7R31tz2UHg0BSPuU2JPSaDtR+mx2Vt4rz6d6cxf90uRveuW9xgH2vs93VNIMSwO97HJdJk7dJtkdotSK1lROJnxLodVU3uUBDrhR8ezNh+HXFHsp+xnwMH9OnAv64+hJKJk3j+qkPo2zmXNdsrOeqP0wD48ZED4loIHT20K7efPoJbX1+Az+3izP17IlgnzXeePoLTR/ega2E2fzhvz/6urYEGXkl0zPMlrXMSkbi6r/vOGcVDHy5t9MBt3+/HRw2MW02YKvuN2iHygZa1F8WCT1w2lmtemEtVbSjuINCcoMuWl+WJTqvsaU+Uwmwvu6sDdMrz8a+rD0k5qIw1uIn7iAhv/MwqmD5/XPPO+GMVZHs5LIUph1TZGc0z9u+5zx7TliwDAtZrafE9p9S7vEdRDkvvq3+5yyWcdUCvZv1se5rSDhruOqNuh4KCbG+D9XOxbj1lOKGw4bBB9TMU9nh+fswgAA4Z0Jl3FmxkzY5KXrz6EESEHJ+73lTXnhrbr2Oj13vdLoJhw7odVfRNUk/WVI3/Q9+vH9iISDQ7/r+fH8ZZD8+gvCYYt9vAr04aSp7PHc3eJiouyOLIIcX88uV5vPbTCc3+O9rjSMbOeIXChvLaIEU5zTtxaMiSe0+OK3+oG0fysS259+ToSsLEsfo8Lr65+6S9Go8deDU0q7C3EgPhpvodZnvdvPWLw6LtPFI1tl9HDumf2vSy7ZsNu/nJUQNTvv0Rg4ubvI2dtQUrm/r6zyZw7iOfc8vJw5gwqAtXPDObb39zErk+NyLCI5fUZdlLIqsvrzq8f9LHbms08NoLo3sV1etf05g9qQ2yX6j2B9qeplTvOXskh0ZqyZJ1k86Eohxr37OlWyqSNrpzst3VAVxCdAp5byy995SUO+6n26mjenDXW1ah67kH9trjeji3S6JL3ZtyYN+OzF2zs8kgPF1cYheYxwcgRTnepCvQmqNDrpcz9+/JhQf14cC+Hfjrh8sYWJzPBZFpw1krt/P79xfz2k8m1OtPdM9Zybfl2lthY5BIPU0gFN6rk8FYsVvZxGpokVtDt99XhnQv4KghTQcULSm2zCRVI3sV8Z8fj0/59rurA2zcVd3owpQ9kVi+MaZvx+iJmJ0pb6jFx6jeRSmdtLUVGnjtBZdLmjVN1NxtZQCGR/bDc7uEmbc2XRDakMsO7Rf9OtPdxW2FOZ5o88HmNiBt6+xM377g87j2yVZP+0JxQRZHDO7CZ8tKebAZJyV7o1eHnLjMWkvLy/KwbmdVvSz5/LtO3OPHtM+v+nW2VgIGQmFyfR5ujZlCAnjjqw0cMbg4aVNITzOnFlM18eRh7KoMsHRzOVO+2xJdHZ0ujTUaTqdeHXL451UHZ+RnZ9KOyloGdy3YJ4uIUrV/7yK+uXvP3y9tTVoCLxFxAY8A+wN+4GpjzPKY638E/BgIAvcaY95Jxzham4sO7ssxQ5ueN+/XOZfuhdl8sWoHl8YETI01cmyOPQkA08HOcl10cJ9m15+o1quF9w3OuIJsL2u3V8Vty7O3Yne9iG00nOj3MQ08W4qI4HELXQp87K4OpL1XUnt7PWWaS+CooS2b6RMRCpq57Vpblq5P4LOBbGPMeBE5FPgzcBaAiHQHfgmMw1rcOl1EphhjMrfXSAuxN5ttjN05291A7cLeWvibk1rNtJ6IcNSQ4uj8fXvyw8P771Hj0LaguCArpT5uTlGY7WHDrup9WgMYK8vjanWvFY9LyPa4uWx8v3qbqu9rze38rvZOv8550RpKlR7pCrwOB94HMMbMEpHYltcHAzMigZZfRJYDo4E92xHYYboWZtEx10eX/KwGVyztjfwmtsloaT075KRcy+MkzSlcbWseOHdUve1HnOz644cwqDi/0Q73zTWmXwfuOdtaDerzuKgNtY7yAFtRjhe3SwgkbHW2rz16yZgGF4so1VZJOs4mROQp4DVjzHuR79cCA4wxQRG5FBhljLklct3zwPPGmA8THuMa4BqAvn37jl2zZg3tgV107XYJ1bWhZrVnaItWl1bSMc+XcoNOpdqb0go/WR5Xq5yKeWXOOgxEW5MopSwiMtcYk3SftXSlP8qA2CVGLmNMsIHrCoBdiQ9gjHkCeAJg3Lhx7SbXHBuA7Embh7ampJEeSUqp/2/v/mO9qus4jj9fIgaZP0Ba0KLSsWk0WmVly9RLQqh/MFtz/XA4XQaNZGi2bEEqcxalFgKFP8oSmDVxSKCikAFeSCAM/I2LlcNQTJQc0kqQd3+czx3fXbDbvefH99zvfT22O8/3nPM95/3y+4P3Pefc86HWp23z3KLFrK8q6xjxOuA8gHSN15MNyzYCZ0gaIOk44EPAoXdVNDMzM2sxZR1SuRcYK+mPZOObXiLpW8C2iFgqaTbQTtb4TYuI7o0Ea2ZmZtYLldJ4RcQB4BudZm9tWH47cHsZ+zYzMzOrK988yczMzKwibrzMzMzMKuLGy8zMzKwipdzHq2iSXgHKvpHXEGBXyfuok76U11lbk7O2rr6U11lb0wci4rBjL/WKxqsKkja93c3OWlFfyuusrclZW1dfyuusfY9PNZqZmZlVxI2XmZmZWUXceB10W7MLqFhfyuusrclZW1dfyuusfYyv8TIzMzOriI94mZmZmVWk5RsvSf0lLZDULmmjpPGSRkham+bNk3REw/ojJB0yaLekyyXNrLb67smbVdL7Jf1e0mpJaySd3JwkXSsg61BJD6d175b0zuYk6VqB7+EzJb1QbfXdU8DrOljSrvQeXi1panOS/H8KyHu0pPlp3Q2SPtWcJF0rIOushtd1q6T1zUnStYK+i9dIekTSklb+fpJ0YsrZLmlhnbMWJiJa+ge4BJiVpk8AtgNLgbY07xbgC2l6ArAJ2Nnw/IHAQuAvwMxm5yk5653A+Wl6HLC42ZlKzDoLuChNXwtc0exMZWVN84cDv+s8v24/BbyuY4A5zc5RYd5rge+k6Y8AE5qdqaysDdvpD2wARjU7U4mv60+ByWn6emBKszOVmPUe4Ktp+lJgerMzlf3T8ke8gEXA9xse7wdOBdakx8vJvqwBdgNndXr+AGA+2Zu/7vJmvRK4P00fCfy7nDILkTfrFcDC9JvYcODl8krNLVdWSQPIvvwml1tmIfK+rqcCH09HCxZJGlZmsQXIm3cc8Kakh9J2Hiqv1NzyZu0wBVgREU+WUWRB8mbdAgxK08cC+8opsxB5s45M6wCsAz5bTpn10fKNV0S8ERF7JB1D1llPJ/ujgo6/KtgDHJfWvS8i9nZ6/u6IWFFp0T1UQNZdEbEvnWK8EZhRYfndUkDWAPoBTwGjyT7wtZQ3KzAXuDEidlRWdA8VkHUrcE1EnAUsAeZUVHqPFJB3CDAoIsYBy8g+t7VUQFYkHQVMosY5oZCsfwcuk/Q0cC5Zc1NLBWTdAoxP0+OBoysou6lavvECkDQcWAUsiIi7gAMNi48B/tmUwkqQN6uk0WT/YE2IiOdKK7QAebNGxL6IGAlMJDuqWVs9zSrpvcAZwDWSVgODJf225HJzyfm6/iE9F+Be4GOlFFmgnHlfJTutA1njVeu7ghfwXTwGeCQiXi+pxMLkzHoDcHFEfBiYSot+PyVXAuMlPZie1/JDCrV84yXpPcAK4KqIuCPN3iypLU2fC7Q3o7ai5c2amq6bgXMiYlOZteZVQNafp7yQ/UZ24O3WbbY8WSPixYg4OSLaIqINeC0ivlx2zT1VwOf1F8AX0/TZwGNl1FmUAvKuBc5L02cCT5dRZxEK+i4ew8HTUrVVQNbdQEdz+SIHTzvWTgFZxwIzIuIcsu/hlWXVWhctfx8vSTcDXyI7BdFhKjAbOAp4Fvh6RLzV8JydETG003YuBk6JiO+WXnQP5c0q6XHgHcDOtPi5iJhURe3dVUDWU8iuewqyD/tlEfFsReV3S1Hv4f81vy4KeF1PBO4ABOwFLo2Ilyoqv9sKyDuYrNkcRnYd0EUR8Xw11XdPEe9jSfcD0yJiSzVV90wBr+tIsksE+pG9l6dGxOaKyu+WArKeBvwM+A/ZLw7fjIg6X9OWW8s3XmZmZmZ10fKnGs3MzMzqwo2XmZmZWUXceJmZmZlVxI2XmZmZWUXceJmZmZlVxI2XmdWGpJsaBkHenqYXSfqopKsL3M8Jkm4tantd7GuopLlV7MvM6s+3kzCz2in7vnmS5gHzIuKJMrZ/mP39EHgwItZ0ubKZtTQf8TKz2pPU1jHUkaRtkuZLelTS9ZLmStooaUFaPlzSckmr0n+Hd9rWscAnI+IJSZ+XtKhh2TpJwyRdkLa/VtLMtOx9kpZJWinpz5LOT/OfkrRY0m8knS5pvaR2SUvT+HUAd5HdVNLM+rgjm12AmVk3fRD4HPAS8BpwGjAF+Kuk48kGUJ4dEcslnQ3MBC5seP6ngY5xSFcCsyUNIrv7+y6yO2jPAD4REf+StEDSWLJRDm6KiNWSPpPWWQK8C7guIjZLugFYnGoYTzbUyx7gGeD0Uv5vmFmv4sbLzHqbVyNiO4CkvRHxTJp+HRgAjAK+J+kqsuFW3uz0/CHAywAREZIWAl8BTgJ+CYwA3g08IAmyQX5PIhsXcbqkr5E1Yf0bttnRyP0AmAY8DOwANqT9vCVpv6QjIqK244KaWfl8qtHMepuuLkzdSjZgbxswCbin0/J/AMc3PP4VcAHZINMPAH8DXgDGpm3MIWugrgPmR8QEYBVZU9eho5m6EPh1RIwmG3duIoCyDm6/my4z8xEvM2s13wbmSRoADOTQa6vWAz/qeBAROyTtAdZHxH7gFUk/AdZI6gc8D9wNLCI7LbmTrDEbcph9/wm4U9IbZEfaJqb5o4BHC8pnZr2Y/6rRzPocSbcAt0bE5vT4PuDyiNhW0v5+DCyLiPYytm9mvYdPNZpZX3Q1MFnSQEmPAY+X2HQNBY5102Vm4CNeZmZmZpXxES8zMzOzirjxMjMzM6uIGy8zMzOzirjxMjMzM6uIGy8zMzOzirjxMjMzM6vIfwEQpknJ9UGZHAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 720x216 with 1 Axes>"
       ]
@@ -1029,18 +1029,7 @@
     "\n",
     "a = 0.016  # Pre-factor per Whiting and Godsey 2016, eq. (3)\n",
     "\n",
-    "# Correction factors for discharge per Whiting and Godsey 2016\n",
-    "name_coef = {\n",
-    "    'Blackbird Ck.': 0.054,\n",
-    "    'Meadow Ck.': -0.174,\n",
-    "    'Johnson Ck.': 0.003,\n",
-    "    'Middle Fork Salmon': 0.001,\n",
-    "    'Thompson Ck.': -0.094,\n",
-    "}\n",
-    "\n",
     "# Function to get data\n",
-    "\n",
-    "\n",
     "def getUSGSdata(siteno, startdate, enddate):\n",
     "    sitename = {}\n",
     "    sitename = ulmo.usgs.nwis.get_site_data(\n",
@@ -1054,8 +1043,7 @@
     "    dPio = dPio.resample('15min').mean()\n",
     "    return dPio\n",
     "\n",
-    "\n",
-    "# Convert discharge in cfs to cms\n",
+    "# Correct values as per Whiting and Godsey 2016 and convert discharge in CFS to CMS\n",
     "dPios = []\n",
     "for value in name_id.values():\n",
     "    site = str(value)\n",
@@ -1063,25 +1051,27 @@
     "    enddate = pd.to_datetime('2020-05-29')\n",
     "    series = getUSGSdata(site, startdate, enddate)\n",
     "    dPio = pd.DataFrame()\n",
-    "    dPio['CFS'] = series\n",
-    "    dPio['CMS'] = dPio['CFS'] / 35.3146662126613\n",
-    "    dPios.append(dPio)\n",
+    "    dPio['CMS'] = series \n",
+    "    \n",
+    "    if value == 13306336:\n",
+    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),0.0212,(dPio['CMS']*0.054/35.3146662126613)) \n",
+    "\n",
+    "    if value == 13310850:\n",
+    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),-0.0144,(dPio['CMS']*(-0.174)/35.3146662126613))\n",
+    "\n",
+    "    if value == 13313000:\n",
+    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),np.nan,(dPio['CMS']*0.003/35.3146662126613))\n",
     "\n",
-    "# Run interpolate function on datasets used to reconstruct Pioneer Creek discharge\n",
-    "interps = []\n",
-    "for dPio in dPios:\n",
-    "    interps.append(dPio.interpolate())\n",
+    "    if value == 13310199:\n",
+    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),np.nan,(dPio['CMS']*0.001/35.3146662126613))\n",
     "\n",
-    "# Create gap-filled datasets and use correction factors per Whiting and Godsey 2016\n",
-    "correcteds = []\n",
-    "for i, dPio in enumerate(interps):\n",
-    "    corrected = dPio * list(name_coef.values())[i]\n",
-    "    correcteds.append(corrected)\n",
+    "    if value == 13297330:\n",
+    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),np.nan,(dPio['CMS']*(-0.094)/35.3146662126613))\n",
+    "                \n",
+    "    dPios.append(dPio)\n",
     "\n",
     "# Add corrected values per Whiting and Godsey 2016, eq. (3)\n",
-    "final = a + correcteds[0] + correcteds[1] + \\\n",
-    "    correcteds[2] + correcteds[3] + correcteds[4]\n",
-    "final = final[final > 0]\n",
+    "final = a + dPios[0] + dPios[1] + dPios[2] + dPios[3] + dPios[4]\n",
     "\n",
     "# Plot reconstructed Pioneer Creek discharge time series in  mm/day\n",
     "final['mm15min'] = final['CMS'] * 1000 * 60 * 15 / (15.8 * 10 ** 6)\n",
@@ -1103,13 +1093,16 @@
     "d = {'date': final_daily_date, 'q_mm_per_day': final_daily_q}\n",
     "dPioDaily = pd.DataFrame(data=d)\n",
     "\n",
+    "dPioDaily['q_mm_per_day'] = np.where((dPioDaily['q_mm_per_day'] < 0),np.nan,dPioDaily['q_mm_per_day']) # Convert any negative values to NaN values\n",
+    "\n",
+    "dPioDaily.to_csv('q_pioneercreek.csv') \n",
+    "\n",
     "# Plot data\n",
     "%matplotlib inline\n",
     "fig, ax = plt.subplots(figsize=(10, 3))\n",
     "ax.plot(dPioDaily['date'], dPioDaily['q_mm_per_day'], linewidth=0.9)\n",
     "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Pioneer Creek')\n",
-    "labels = ['2011', '2012', '2013', '2014', '2015',\n",
-    "          '2016', '2017', '2018', '2019', '2020']\n",
+    "labels = ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']\n",
     "ticks_to_use = dPioDaily.index[::365]\n",
     "ax.set_xticks(ticks_to_use)\n",
     "ax.set_xticklabels(labels)\n",
@@ -1696,11 +1689,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T02:05:40.733875Z",
-     "start_time": "2020-10-16T02:05:21.491543Z"
+     "end_time": "2020-11-06T18:24:35.712154Z",
+     "start_time": "2020-11-06T18:24:13.817021Z"
     }
    },
    "outputs": [
@@ -1731,7 +1724,7 @@
     "\n",
     "# Get watershed metadata\n",
     "dMeta = pd.read_csv(\n",
-    "    'https://www.hydroshare.org/resource/76ebc18852cc41e48d4ee83902bc0a7d/data/contents/ws_metadata.csv')\n",
+    "    'https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
     "\n",
     "# Get Köppen Classification System per Chen and Chen, http://hanschen.org/koppen/#data\n",
     "dKoppen = pd.read_csv(\n",
@@ -1864,11 +1857,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T01:43:19.757658Z",
-     "start_time": "2020-10-16T01:43:16.715825Z"
+     "end_time": "2020-11-06T18:25:20.652054Z",
+     "start_time": "2020-11-06T18:25:17.973919Z"
     }
    },
    "outputs": [
@@ -1878,24 +1871,12 @@
      "text": [
       "Unable to open EPSG support file gcs.csv.  Try setting the GDAL_DATA environment variable to point to the directory containing EPSG csv files.\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
     "# Create data frames\n",
     "dMeta = pd.read_csv(\n",
-    "    'https://www.hydroshare.org/resource/76ebc18852cc41e48d4ee83902bc0a7d/data/contents/ws_metadata.csv')\n", 
+    "    'https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
     "d = pd.DataFrame(\n",
     "    {'Watershed': ['Bull Creek', 'Caspar Creek', 'CWT12', 'Duke Forest', 'Elder Creek', 'FNW37', 'HB13', 'HB42', 'Pioneer Creek', 'Providence', 'River Ray', 'Sagehen Creek', 'Upper Studibach', 'Yellow Barn'],\n",
     "     'PointColour': ['#fcfe04', '#cecc08', '#63fd32', '#c5fe4b', '#cecc08', '#63fd32', '#3cc4f8', '#3cc4f8', '#3cc4f8', '#cecc08', '#63fd32', '#c900c4', '#63fd32', '#3cc4f8'],\n",
@@ -1973,11 +1954,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T01:43:27.780762Z",
-     "start_time": "2020-10-16T01:43:25.248949Z"
+     "end_time": "2020-12-19T04:52:54.691100Z",
+     "start_time": "2020-12-19T04:52:21.320346Z"
     }
    },
    "outputs": [
@@ -1986,27 +1967,27 @@
      "output_type": "stream",
      "text": [
       "Watershed         alpha              beta\n",
-      "Bull Creek        2.9472 +/- 0.0789  0.187 +/- 0.0195   \n",
-      "Caspar Creek      1.271 +/- 0.0866   0.3125 +/- 0.0372  \n",
-      "CWT12             4.7027 +/- 0.1393  0.0753 +/- 0.0184  \n",
-      "Duke Forest       3.7912 +/- 0.1824  0.1781 +/- 0.0152  \n",
-      "Elder Creek       3.3695 +/- 0.4457  0.1806 +/- 0.0374  \n",
-      "FNW37             1.7965 +/- 0.0634  0.1721 +/- 0.0239  \n",
-      "HB13              7.6794 +/- 0.4451  0.1393 +/- 0.0307  \n",
-      "HB42              5.5818 +/- 0.185   0.1992 +/- 0.0256  \n",
-      "Pioneer Creek     0.8318 +/- 0.0133  0.1978 +/- 0.0288  \n",
-      "Providence        1.7853 +/- 0.1134  0.3882 +/- 0.0639  \n",
-      "River Ray         2.399 +/- 0.0935   0.1495 +/- 0.0157  \n",
-      "Sagehen Creek     1.1197 +/- 0.0735  0.3202 +/- 0.0757  \n",
-      "Upper Studibach   6.1268 +/- 1.671   0.3119 +/- 0.1418  \n",
-      "Yellow Barn       5.4044 +/- 0.1585  0.1041 +/- 0.0314  \n"
+      "Bull Creek        2.94724 +/- 0.07893   0.18699 +/- 0.01948   \n",
+      "Caspar Creek      1.27105 +/- 0.08656   0.31253 +/- 0.03715   \n",
+      "CWT12             4.70266 +/- 0.1393    0.07534 +/- 0.01836   \n",
+      "Duke Forest       3.79121 +/- 0.18244   0.17806 +/- 0.01518   \n",
+      "Elder Creek       2.33444 +/- 0.14761   0.17266 +/- 0.03691   \n",
+      "FNW37             1.79648 +/- 0.06343   0.17214 +/- 0.02391   \n",
+      "HB13              7.67939 +/- 0.44509   0.13925 +/- 0.03066   \n",
+      "HB42              5.58181 +/- 0.18499   0.19925 +/- 0.0256    \n",
+      "Pioneer Creek     0.83179 +/- 0.01334   0.19785 +/- 0.02881   \n",
+      "Providence        1.78529 +/- 0.11339   0.38823 +/- 0.06389   \n",
+      "River Ray         2.39902 +/- 0.09345   0.14948 +/- 0.01565   \n",
+      "Sagehen Creek     1.11966 +/- 0.07348   0.32018 +/- 0.0757    \n",
+      "Upper Studibach   6.12678 +/- 1.67099   0.31193 +/- 0.1418    \n",
+      "Yellow Barn       5.40442 +/- 0.1585    0.10407 +/- 0.03143   \n"
      ]
     }
    ],
    "source": [
     "# Access watershed metadata\n",
     "dMeta = pd.read_csv(\n",
-    "    'https://www.hydroshare.org/resource/76ebc18852cc41e48d4ee83902bc0a7d/data/contents/ws_metadata.csv')\n",
+    "    'https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
     "\n",
     "# Create arrays\n",
     "alpha = []\n",
@@ -2035,12 +2016,12 @@
     "    popt, pcov = scipy.optimize.curve_fit(model, Q, L)\n",
     "\n",
     "    # retrieve parameter values\n",
-    "    alpha = round(popt[0], 4), '+/-', round(pcov[0, 0]**0.5, 4)\n",
-    "    beta = round(popt[1], 4), '+/-', round(pcov[1, 1]**0.5, 4)\n",
+    "    alpha = round(popt[0], 5), '+/-', round(pcov[0, 0]**0.5, 5)\n",
+    "    beta = round(popt[1], 5), '+/-', round(pcov[1, 1]**0.5, 5)\n",
     "\n",
     "    # Print results table body rows\n",
     "    print(str('{:<18}'.format(dMeta['ws_name'][i]) +\n",
-    "              '{:<25}'.format(str(alpha)) + '{:<25}'.format(str(beta))).replace('(', '').replace(')', '').replace(',', '').replace('\\'', ''))"
+    "              '{:<28}'.format(str(alpha)) + '{:<28}'.format(str(beta))).replace('(', '').replace(')', '').replace(',', '').replace('\\'', ''))"
    ]
   },
   {
@@ -2053,12 +2034,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 36,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T01:44:07.015901Z",
-     "start_time": "2020-10-16T01:43:30.091747Z"
-    }
+     "end_time": "2020-12-19T07:17:36.652130Z",
+     "start_time": "2020-12-19T07:17:00.871490Z"
+    },
+    "code_folding": []
    },
    "outputs": [
     {
@@ -2066,43 +2048,43 @@
      "output_type": "stream",
      "text": [
       "Watershed        Q_mean     Q_std      L_mean     L_std     \n",
-      "Bull Creek       1.7878     3.5187     0.7879     0.2084    \n",
-      "Caspar Creek     1.3609     4.3229     0.1156     0.0714    \n",
-      "CWT12            2.725      2.5469     39.6182    4.5599    \n",
-      "Duke Forest      1.1791     5.3538     65.207     54.6525   \n",
-      "Elder Creek      3.5822     8.2197     0.2078     0.0668    \n",
-      "FNW37            1.7448     3.1404     4.4371     1.6947    \n",
-      "HB13             2.5776     5.1343     57.4087    13.9366   \n",
-      "HB42             2.3926     4.8902     13.1899    4.1828    \n",
-      "Pioneer Creek    0.2771     0.208      0.0392     0.0068    \n",
-      "Providence       0.9351     1.6973     0.3574     0.1884    \n",
-      "River Ray        0.4412     1.2623     0.0794     0.0437    \n",
-      "Sagehen Creek    1.0692     1.9752     0.0354     0.0145    \n",
-      "Upper Studibach  4.813      7.8766     11.8245    5.2543    \n",
-      "Yellow Barn      1.5067     2.3166     0.053      0.0058    \n"
+      "Bull Creek       1.78775    3.51865    0.78789    0.20841   \n",
+      "Caspar Creek     1.3609     4.32294    0.11563    0.07142   \n",
+      "CWT12            2.72504    2.54695    39.61816   4.55994   \n",
+      "Duke Forest      1.17915    5.3538     65.20698   54.65253  \n",
+      "Elder Creek      3.58219    8.21966    0.14304    0.04281   \n",
+      "FNW37            1.74482    3.14041    4.43711    1.69469   \n",
+      "HB13             2.5776     5.13427    57.40866   13.93659  \n",
+      "HB42             2.39261    4.89017    13.1899    4.18283   \n",
+      "Pioneer Creek    0.31733    0.27786    0.04071    0.00462   \n",
+      "Providence       0.93506    1.69729    0.35742    0.18841   \n",
+      "River Ray        0.44117    1.2623     0.07943    0.0437    \n",
+      "Sagehen Creek    1.06915    1.97522    0.03545    0.01449   \n",
+      "Upper Studibach  4.81301    7.8766     63.67032   28.29222  \n",
+      "Yellow Barn      1.50672    2.31656    3.56591    0.39108   \n"
      ]
     }
    ],
    "source": [
     "# Create data frame\n",
-    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/76ebc18852cc41e48d4ee83902bc0a7d/data/contents/ws_metadata.csv')\n",
+    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
     "dMeta = dMeta.drop(['ws_name_sub', 'gauge_id', 'gauge_name', 'gauge_lat_decdeg', 'gauge_lon_decdeg', 'q_url', 'gauge_elev_m', 'q_start', 'q_stop', 'ws_elev_avg_m', 'ws_soil', 'ws_bedrock', 'ws_climate', 'ws_precip_mm_per_yr'], axis=1)\n",
     "dMeta = dMeta.reset_index()\n",
     "\n",
-    "Q_series = ['https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_bullcreek.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_casparcreek.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_cwt12.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_dukeforest.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_eldercreek.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_fnw37.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_hb13.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_hb42.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_pioneercreek.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_providence.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_riverray.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_sagehencreek.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_upperstudibach.csv',\n",
-    "            'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_yellowbarn.csv']\n",
+    "Q_series = ['https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_bullcreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_casparcreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_cwt12.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_dukeforest.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_eldercreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_fnw37.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb13.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb42.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_pioneercreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_providence.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_riverray.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_sagehencreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_upperstudibach.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_yellowbarn.csv']\n",
     "\n",
     "# Print results table header\n",
     "print('{:<16}'.format('Watershed'),\n",
@@ -2129,10 +2111,133 @@
     "\n",
     "    # Print results table body rows\n",
     "    print('{:<16}'.format(dMeta['ws_name'][i]),\n",
-    "          '{:<10}'.format(str(round(qMean[i], 4))),\n",
-    "          '{:<10}'.format(str(round(qStd[i], 4))),\n",
-    "          '{:<10}'.format(str(round(lMean[i], 4))),\n",
-    "          '{:<10}'.format(str(round(lStd[i], 4))))"
+    "          '{:<10}'.format(str(round(qMean[i], 5))),\n",
+    "          '{:<10}'.format(str(round(qStd[i], 5))),\n",
+    "          '{:<10}'.format(str(round(lMean[i], 5))),\n",
+    "          '{:<10}'.format(str(round(lStd[i], 5))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-19T08:45:47.003434Z",
+     "start_time": "2020-12-19T08:45:03.153534Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Watershed        R-squared \n",
+      "Bull Creek       0.70553   \n",
+      "Caspar Creek     0.65243   \n",
+      "CWT12            0.31192   \n",
+      "Duke Forest      0.20263   \n",
+      "Elder Creek      0.55377   \n",
+      "FNW37            0.41202   \n",
+      "HB13             0.41343   \n",
+      "HB42             0.54601   \n",
+      "Pioneer Creek    0.83855   \n",
+      "Providence       0.79517   \n",
+      "River Ray        0.28341   \n",
+      "Sagehen Creek    0.77407   \n",
+      "Upper Studibach  0.77213   \n",
+      "Yellow Barn      0.58146   \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import r2_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Create data frame\n",
+    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
+    "dMeta = dMeta.drop(['ws_name_sub', 'gauge_id', 'gauge_name', 'gauge_lat_decdeg', 'gauge_lon_decdeg', 'q_url', 'gauge_elev_m', 'q_start', 'q_stop', 'ws_elev_avg_m', 'ws_soil', 'ws_bedrock', 'ws_climate', 'ws_precip_mm_per_yr'], axis=1)\n",
+    "dMeta = dMeta.reset_index()\n",
+    "\n",
+    "Q_series = ['https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_bullcreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_casparcreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_cwt12.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_dukeforest.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_eldercreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_fnw37.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb13.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb42.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_pioneercreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_providence.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_riverray.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_sagehencreek.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_upperstudibach.csv',\n",
+    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_yellowbarn.csv']\n",
+    "\n",
+    "# Print results table header\n",
+    "print('{:<16}'.format('Watershed'),\n",
+    "      '{:<10}'.format('R-squared'))\n",
+    "\n",
+    "r_squared = list()\n",
+    "\n",
+    "for i in range(len(dMeta)):\n",
+    "    data = pd.read_csv(Q_series[i])\n",
+    "    alpha = dMeta['alpha_units_vary'][i]\n",
+    "    beta = dMeta['beta'][i]\n",
+    "    Q = data['q_mm_per_day']\n",
+    "    Q = Q[np.logical_not(np.isnan(Q))] # Remove NaNs\n",
+    "    L = alpha * Q ** beta\n",
+    "    \n",
+    "    # APPROACH 1 TO FINDING R-SQUARED\n",
+    "    correlation_matrix = np.corrcoef(Q, L)\n",
+    "    correlation_xy = correlation_matrix[0,1]\n",
+    "    r_squared = correlation_xy**2\n",
+    "\n",
+    "    # Print results table body rows\n",
+    "    print('{:<16}'.format(dMeta['ws_name'][i]),\n",
+    "          '{:<10}'.format(str(round(r_squared, 5))))\n",
+    "    \n",
+    "    # APPROACH 2 TO FINDING R-SQUARED\n",
+    "    # Creating linear regression model\n",
+    "    slope, intercept, r_value, p_value, std_err = stats.linregress(Q,L)\n",
+    "    def linefitline(b):\n",
+    "        return intercept + slope * b\n",
+    "    line1 = linefitline(Q)\n",
+    "\n",
+    "    # Plot data, linear model, and y-mean line\n",
+    "    plt.scatter(Q,L)\n",
+    "    plt.plot(Q,line1, c = 'g')\n",
+    "    line2 = np.full(len(L),[L.mean()])\n",
+    "    plt.plot(Q,line2, c = 'r')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # Calculate variance in linear model\n",
+    "    differences_line1 = linefitline(Q)-L\n",
+    "    line1sum = 0\n",
+    "    for i in differences_line1:\n",
+    "        line1sum = line1sum + (i*i)\n",
+    "\n",
+    "    # Calculate variance of target variable\n",
+    "    differences_line2 = line2 - L\n",
+    "    line2sum = 0\n",
+    "    for i in differences_line2:\n",
+    "        line2sum = line2sum + (i*i)\n",
+    "    \n",
+    "    # Explained model variance is target variable variance minus variance in linear model\n",
+    "    explModVar = line2sum - line1sum\n",
+    "    \n",
+    "    # Explained model variance of the model / target variable variance\n",
+    "    print('r-squared', explModVar / line2sum)"
    ]
   },
   {
@@ -2145,11 +2250,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 70,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-16T04:03:06.455417Z",
-     "start_time": "2020-10-16T04:01:32.955859Z"
+     "end_time": "2020-12-19T09:24:32.156242Z",
+     "start_time": "2020-12-19T09:23:22.921505Z"
     }
    },
    "outputs": [
@@ -2159,19 +2264,19 @@
      "text": [
       "Watershed         CVQ       CVL_emp   CVL_botter\n",
       "Bull Creek        1.9682    0.2645    0.5069    \n",
-      "Caspar Creek      3.1765    0.6471    1.2691    \n",
-      "CWT12             0.9346    0.3154    0.084     \n",
-      "Duke Forest       4.5404    0.3259    1.3115    \n",
-      "Elder Creek       2.2946    0.3411    0.6083    \n",
-      "FNW37             1.7998    0.4942    0.4242    \n",
-      "HB13              1.9919    0.5798    0.4102    \n",
-      "HB42              2.0439    0.5361    0.5574    \n",
-      "Pioneer Creek     0.7506    0.5577    0.1615    \n",
-      "Providence        1.8152    0.5681    0.8058    \n",
-      "River Ray         2.8612    0.6331    0.6874    \n",
-      "Sagehen Creek     1.8475    0.72      0.7081    \n",
-      "Upper Studibach   1.6365    0.7532    0.6039    \n",
-      "Yellow Barn       1.5375    0.7276    0.2256    \n"
+      "Caspar Creek      3.1765    0.6177    1.2691    \n",
+      "CWT12             0.9346    0.1151    0.084     \n",
+      "Duke Forest       4.5404    0.8381    1.3115    \n",
+      "Elder Creek       2.2946    0.3214    0.5789    \n",
+      "FNW37             1.7998    0.3819    0.4242    \n",
+      "HB13              1.9919    0.2428    0.4102    \n",
+      "HB42              2.0439    0.3171    0.5574    \n",
+      "Pioneer Creek     0.8756    0.1135    0.1934    \n",
+      "Providence        1.8152    0.5271    0.8058    \n",
+      "River Ray         2.8612    0.5502    0.6874    \n",
+      "Sagehen Creek     1.8475    0.4088    0.7081    \n",
+      "Upper Studibach   1.6365    0.4444    0.6039    \n",
+      "Yellow Barn       1.5375    0.1097    0.2256    \n"
      ]
     }
    ],
@@ -2199,7 +2304,7 @@
     "    return hydrograph\n",
     "\n",
     "# Create data frame for watershed metadata \n",
-    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/76ebc18852cc41e48d4ee83902bc0a7d/data/contents/ws_metadata.csv')\n",
+    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
     "dMeta = dMeta.reset_index()\n",
     "\n",
     "# Print results table header\n",
@@ -2207,38 +2312,38 @@
     "      '{:<10}'.format('CVL_emp') + '{:<10}'.format('CVL_botter'))\n",
     "\n",
     "# Create arrays\n",
+    "area = [3.58, 8.48, 0.124, 0.033, 16.8, 0.366, 0.134, 0.424, 15.8, 4.01, 18.6, 27.2, 0.7, 101.01] # Areas written out as Upper Studibach and Yellow Barn watersheds use proxy watershed areas for calculations and this is not reflected in the ws_area_km column of the ws_metadata table\n",
     "CVQ = []\n",
     "CVL_emp = []\n",
     "CVL_botter = []\n",
-    "hydroL = list()\n",
+    "#hydroL = []\n",
     "\n",
-    "# Find CVQ, CVL_emp, and CVL_botter\n",
+    "# Find CVQ, CVL_emp, and CVL_botter   \n",
     "for i in range(len(dMeta)):\n",
-    "\n",
     "    # Import hydrograph data\n",
     "    hydrograph = pd.read_csv(\n",
-    "        'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_' + dMeta['ws_name'][i].replace(' ', '').lower() + '.csv')\n",
+    "        'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_' + dMeta['ws_name'][i].replace(' ', '').lower() + '.csv')\n",
     "\n",
     "    # Set to date index\n",
     "    hydrograph = hydrograph.set_index('date')\n",
     "    hydrograph.index = pd.to_datetime(hydrograph.index)\n",
     "\n",
     "    # Remove NaN values that cause error in CV calculation\n",
-    "    hydrograph['q_mm_per_day'] = pd.to_numeric(\n",
-    "        hydrograph['q_mm_per_day'], errors='coerce')\n",
+    "    hydrograph['q_mm_per_day'] = pd.to_numeric(hydrograph['q_mm_per_day'], errors='coerce')\n",
     "    hydrograph = remove_nan(hydrograph)\n",
     "\n",
     "    # Calculate L timeseries\n",
     "    alpha = dMeta['alpha_units_vary'][i]\n",
     "    beta = dMeta['beta'][i]\n",
     "    \n",
-    "    for i in range(len(hydrograph)):\n",
-    "        hydroLItem = alpha*hydrograph['q_mm_per_day'][i]**beta\n",
-    "        hydroL.append(hydroLItem)\n",
-    "\n",
+    "#    for i in range(len(hydrograph)):\n",
+    "#        hydroLItem = (alpha*(hydrograph['q_mm_per_day'][i]**beta)) \n",
+    "#        hydroL.append(hydroLItem) \n",
+    "        \n",
     "    # Calculate coefficients of variation\n",
     "    CVQ = CVQ + [np.std(hydrograph['q_mm_per_day']) / np.mean(hydrograph['q_mm_per_day'])] # CVQ\n",
-    "    CVL_emp = CVL_emp + [np.std(hydroL) / np.mean(hydroL)] # CVL empirical\n",
+    "    CVL_empItem = dMeta['l_sd_km_km2'][i] / dMeta['l_avg_km_km2'][i]\n",
+    "    CVL_emp.append(CVL_empItem) # CVL based on empirical values\n",
     "    CVL_botter = CVL_botter + [botter_CVL(beta, CVQ[-1])] # CVL from David Dralle's formula\n",
     "\n",
     "# Print results table body\n",
@@ -2286,7 +2391,7 @@
     "height": "calc(100% - 180px)",
     "left": "10px",
     "top": "150px",
-    "width": "423.767px"
+    "width": "346.23px"
    },
    "toc_section_display": true,
    "toc_window_display": true