From d9bdf10b8a5317bbd901b0b9ecf4902bed58310a Mon Sep 17 00:00:00 2001 From: Jolene Date: Mon, 23 Oct 2023 13:44:46 +0800 Subject: [PATCH 1/7] cleaned up folders --- .../clean.ipynb | 0 .../new => 01ReformattingGA4}/new.py | 0 .../clean_umami.ipynb | 0 .../parsing urls.ipynb | 0 .../demo.ipynb | 0 .../main.ipynb | 0 .../analysis of the sql data.ipynb | 2 +- .../presenting data.ipynb | 12 +- .../collating fields.ipynb | 385 ------ howard's advisory stuff/getting data.ipynb | 907 --------------- howard's advisory stuff/new/clean.ipynb | 1032 ----------------- huan_yao_code/new.py | 46 - 12 files changed, 7 insertions(+), 2377 deletions(-) rename {huan_yao_code => 01ReformattingGA4}/clean.ipynb (100%) rename {howard's advisory stuff/new => 01ReformattingGA4}/new.py (100%) rename {howard's advisory stuff => 02ReformattingUmami}/clean_umami.ipynb (100%) rename {howard's advisory stuff/exported_csv => 02ReformattingUmami}/parsing urls.ipynb (100%) rename {analysis_application_data => 03AnalysisApplicationData}/demo.ipynb (100%) rename {analysis_application_data => 03AnalysisApplicationData}/main.ipynb (100%) rename {howard's advisory stuff/exported_csv => 04PostgreSQLDumpFIle}/analysis of the sql data.ipynb (99%) rename {howard's advisory stuff => 04PostgreSQLDumpFIle}/presenting data.ipynb (99%) delete mode 100644 howard's advisory stuff/collating fields.ipynb delete mode 100644 howard's advisory stuff/getting data.ipynb delete mode 100644 howard's advisory stuff/new/clean.ipynb delete mode 100644 huan_yao_code/new.py diff --git a/huan_yao_code/clean.ipynb b/01ReformattingGA4/clean.ipynb similarity index 100% rename from huan_yao_code/clean.ipynb rename to 01ReformattingGA4/clean.ipynb diff --git a/howard's advisory stuff/new/new.py b/01ReformattingGA4/new.py similarity index 100% rename from howard's advisory stuff/new/new.py rename to 01ReformattingGA4/new.py diff --git a/howard's advisory stuff/clean_umami.ipynb b/02ReformattingUmami/clean_umami.ipynb similarity index 100% rename from howard's advisory stuff/clean_umami.ipynb rename to 02ReformattingUmami/clean_umami.ipynb diff --git a/howard's advisory stuff/exported_csv/parsing urls.ipynb b/02ReformattingUmami/parsing urls.ipynb similarity index 100% rename from howard's advisory stuff/exported_csv/parsing urls.ipynb rename to 02ReformattingUmami/parsing urls.ipynb diff --git a/analysis_application_data/demo.ipynb b/03AnalysisApplicationData/demo.ipynb similarity index 100% rename from analysis_application_data/demo.ipynb rename to 03AnalysisApplicationData/demo.ipynb diff --git a/analysis_application_data/main.ipynb b/03AnalysisApplicationData/main.ipynb similarity index 100% rename from analysis_application_data/main.ipynb rename to 03AnalysisApplicationData/main.ipynb diff --git a/howard's advisory stuff/exported_csv/analysis of the sql data.ipynb b/04PostgreSQLDumpFIle/analysis of the sql data.ipynb similarity index 99% rename from howard's advisory stuff/exported_csv/analysis of the sql data.ipynb rename to 04PostgreSQLDumpFIle/analysis of the sql data.ipynb index 7bca8ec..ff6aaa9 100644 --- a/howard's advisory stuff/exported_csv/analysis of the sql data.ipynb +++ b/04PostgreSQLDumpFIle/analysis of the sql data.ipynb @@ -3085,7 +3085,7 @@ "outputs": [ { "data": { - "image/png": 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H9G3YZFhgVZ3flyHJO5n4PI5hir5Ksj/wuina9v2qGgXGp2vntPrQzM8FXt0nbQ78aLDINX3aT6YpYkvgb6rqJUn+HdgX+PikOg6kZQOz9kabztQcSZIkSdIdlAFeSZIkSZrZ44EXDN4fBpxcVaf093sCzxrNrKpfzqLMk6pq2eT3Se4CPAI4aZBQuu5guc9W1c3A5dNl3VbVd+nB0Bn8Afh8f30B8LiVNTjJc4CdgEevZNFvV9U1fZ0ltCGkv04LRj+WFng9Cjg4yQeAU4FvAvdN8i/9/RnTtOEZwA7AXjP1VVWdQBtWeXXaOZMPAGdV1dmjJk2xTE0xbeSHVbWkv76g17n8ylXH0ALXrLvZljOVJUmSJEm6gzLAK0mSJEnTSLIBsElVXTuYvC3wD8PFmDqoN5y23qR5v53m/VrAr6pqu2madOOkeqdq82wyeG8aZNAuYyX/N0yyJ/B64NFVdeNMy05q4zJgUZK7A3erqu8meSnwqSRvpgWMX1dVv03yUFow/RXAM4AXTmrD1sBbgEf1YPi0fTXLDN4V2jnTRvX2bgq8dDD5GuDPB+/vRcsIns7kOh2iWZIkSZK0ygzwSpIkSdL0HgN8bdK0twHfH7w/AzgIOBjaEM09i/en/fm93wWeBvxmZZVV1a+T/DDJ06vqpP5c2G2r6uIZVvsNsOGgjNlk8M5aku2BDwF/VVU/W81ift6KymOq6mt9GOIrgH/vwd27AX+oqk8l+QFw3KQ2bAx8EnheVf0cZu6rWWbwzlqSF9OCz4/tGdQjpwAHJfkksAttqO7phmeWJEmSJGlOrLXQDZAkSZKk27Dlnr/bPZv2LNyRfwLumuTSJBfTgsLQno37eeCrTP9M1qnsD7yol3UZ8JSVLL8U+GOSi5Mcsgr1zNY7gbvQhkJekuSUla0wWc8W3hc4og+H/FlaUPzhSfajPbf2zD7vOJbPkAZ4KnAf4MO9DUv69FXtq9V1NHAP4Jxe/5v69NOAq2gB/w8D/2ee6pckSZIk6RaZGJVLkiRJkjSU5EJgl6q6aaHbojuedTfbsjY74D0L3YzblKsX77PQTZAkSZKkOZHkgqraaXXWdYhmSZIkSZpGVe2w0G2QJEmSJEkaMsArSZIkSSLJ64GnT5p8UlUdMcWy2wAfmzT5xqraZb7atyYk+QzwF5Mm/31Vnb4KZfwp8JUpZj22qn5xa9onSZIkSRIY4JUkSZIkAT2Qu0Iwd5plLwG2m9cGLYCqetoclPELbod9I0mSJEm67VhroRsgSZIkSZIkSZIkSZodA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI0JA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI2JRQvdAEmSJEmStKJtNt+Y8xfvs9DNkCRJkiTdxpjBK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI0JA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI0JA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmFi00A2QJEmSJEkruuTH17HFoacudDNW6urF+yx0EyRJkiTpDsUMXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxoQBXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxoQBXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxoQBXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSViLJh5LsluTYJJcl+W6SJy10uwCSbJHk2fNY/suSXJJkSZKvJ3nwapaTJMckubyXt+ss1nlrkj1Xp765lOSE/plfmuQjSdbp05PkvUm+n2Rpkh0Wuq2SJEmSpNs/A7ySJEmStHK7AOcCn66qrYEnA0fd2kKTLJrp/SxtAcxbgBf4f1W1TVVtB7wDePdqlvNIYEtga1p/XrWyFarqTVX15dWsby6dAGwFbAOsD7y4T9+btk1bAgcCH1yQ1kmSJEmS7lAM8EqSJEnSDJI8CLiyqpZV1Wl98nrA7/v8uyT5aM9KXZpk3z79+kEZ+yU5rr8+Lsm7k3wNOHKK9/dL8sUkFyQ5O8lWg/Xem+SbSa5Ksl8vfjGwe8+wPWSW23R9kiOSXJzk3CT3mG7Zqvr14O2dgZqh3D2SnJnk5CRX9MzX9Nl/AO4BrFNVN1TVTwfrrd2379Lej4cMtnm/JDv17VvS51efP2VfzWL7Z2rnVH1wWnXAt4F79VlPAY7vs84FNkmy2TR1bpHkO0k+3LPAz0iy/hTLHZjk/CTnL7vhutlsjiRJkiTpDmZ1fh0uSZIkSXckewNfHL1JshHwMeAf+6Q3AtdV1TZ9/l1nUeYDgD2ralkP/A7ffwV4WVV9L8kuwAeAv+zrbUbLhN0KOAU4GTgUeG1VPbHX/0DgxGnq3aOqfkUL1J5bVa9P8g7gJcA/TdfYJK8A/ha406At09melqV7LfANYDfg68BPgY2A45Ls34OlI9sBm1fVQ3p9mwwLrKrz+zIkeScTn8cxTNFXSfYHXjdF275fVaPA+HTtnFYfmvm5wKv7pM2BHw0WuaZP+8k0RWwJ/E1VvSTJvwP7Ah+ftK3H9O1i3c22nDaYLkmSJEm64zLAK0mSJEkzezzwgsH7w4CTq+qU/n5P4FmjmVX1y1mUeVJVLZv8PsldgEcAJw0SStcdLPfZqroZuHy6rNuq+i49GDqDPwCf768vAB4308JV9X7g/f1Zv28ADphh8W9X1TUASZbQhpD+Oi0Y/Vha4PUo4OAkHwBOBb4J3DfJv/T3Z0xVcJJnADsAe83UV1V1Am1Y5ZlM186ZfAA4q6rOHjVpimVmCsr+sKqW9NcX9DolSZIkSVolBnglSZIkaRpJNgA2qaprB5O3Bf5huBhTB/WG09abNO+307xfC/hVf97tVG6cVO9UbZ5NBu9NgwzaZcz+/4afZOXPmR22cRmwKMndgbtV1XeTvBT4VJI3AzsBr6uq3yZ5KC2Y/grgGcALJ23X1sBbgEf1YPi0fTXLDN4V2jnTRvX2bgq8dDD5GuDPB+/vRcsIns7kOlcYolmSJEmSpJXxGbySJEmSNL3HAF+bNO1twPcH788ADhq9GQzR/NMkD+qByKfNprL+vNsfJnl6Lys98DmT3wAbDsr4blVtN82/X82mHUNJthy83Qf43qqWAfy8FZXH9MzlA2nDHF/Yg7t3A9aqqk/RhrzeYVIbNqYFl59XVT/v2zltX1XVCdNs/36shiQvpgWf/6ZnUI+cAjyv1/1w2lDd0w3PLEmSJEnSnDDAK0mSJEnTW+75u92zac/CHfkn4K5JLk1yMS0oDO3ZuJ8Hvsr0z2Sdyv7Ai3pZlwFPWcnyS4E/Jrk4ySGrUM9sHZTksj6M8d8y8/DMU+rZwvsCR/RyPksLij88yX6059ae2ecdx/IZ0gBPBe4DfDjJkr4crHpfra6jgXsA5/T639SnnwZcRQv4fxj4P/NUvyRJkiRJt8jEqFySJEmSpKEkFwK7VNVNC90W3fGsu9mWtdkB71noZqzU1Yv3WegmSJIkSdLYSXJBVe20Ouv6DF5JkiRJmkZV7bDypSRJkiRJktYcA7ySJEmSJJK8Hnj6pMknVdURUyy7DfCxSZNvrKpd5qt9a0KSzwB/MWny31fV6atQxp8CX5li1mOr6he3pn2SJEmSJIEBXkmSJEkS0AO5KwRzp1n2EmC7eW3QAqiqp81BGb/gdtg3kiRJkqTbjrUWugGSJEmSJEmSJEmSpNkxwCtJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0pgwwCtJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmFi10AyRJkiRJ0oq22Xxjzl+8z0I3Q5IkSZJ0G2MGryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0pgwwCtJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0pgwwCtJkiRJkiRJkiRJY2LRQjdAkiRJkiSt6JIfX8cWh5660M2Y0dWL91noJkiSJEnSHY4ZvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK9uM5J8KMluSY5NclmS7yZ50gzL3zPJyWuyjdO043FJLkhySf/7l4N5O/bp30/y3iRZzTq2TPLtJEuTfHmK+Z/o8w5J8tYke/bpZybZqb/+x9Xdxinqe2WSS5OcluROfdojk7x70nLLkizp/04ZTD+zf75LknwnyYGDeVP22RzWuTTJFUnel2STPm/TJF/v5T91sM7nktxz8P6wJD8elP+EPn2PJNf1aUuTfDnJ3fu8dfv7JUme2ffvB6+kf2/53CZNf36S9813f8zGcD9bhXWuTnK3VVmnr7dHkkfMMP/JSQ5d1XL7uh5bY3psrUbf3nL8zJUkJ/R2vW0w7Y1JnjJ4P9yGJUneNJg36tPLklyc5G+TrNXn7dbLPi/J/fu0TZKcPvrs+rThZ74kE+eeYZ9ekeSDg7L/JMmXknyv/73rPNT5vSSfzkrOd5P68/lJfj7ok5OTbLCqn0sva4skl04xfack712dMvv6R/Y+On4w7blJXj14P+/fC6Zo12FJXjufbZziOF2c5GVJnjcX23BrxHP52J7Ls5Jr/OrKAp/PZmjXHkk+31/P+XVJkiRJknTHYYBXtyW7AOcCn66qrYEnA0dNt3BVXVtV+62pxs3gf4AnVdU2wAHAxwbzPggcCGzZ//3VatZxKPDBqtoWeMlwRpI/Ax5RVdtW1VFV9aaqWuHmJrDKNy6TrD3NrBcD2wIXAY/vN8reCBw+abnfVdV2/d+TJ83bv6q2A3YDjhzdjGT6PpurOrft5dwIfK5P/xvg34Bdgdf1bX8ScGFVXTupjKMG5Z82mH52n7YtcB7wij59e2CdPu/EqnpxVV3OrTef/bFSM+xn82EPYMqbv0kWVdUpVbV4Ncv22BrfY2tBJdkWoLdr9yQbJ9kM2LmqJh9LZw+27a2D6aM+3Rp4HPAE4M193muAfWn718v7tDcCb6uqmlT+/oPyfzaYflTfFx4MbAM8uk8/FPhKVW0JfKW/n9M6e9knAl9Nsimzd+KgT/4APHMV1l2pqjq/ql61Ousm2Zh+TgDWTrJNkvWB5wMfGCy6Jr4XLFQbh8fpoVV1dFUdz8LzXD6+5/I9mOYafyvdFs5nkiRJkiTNGwO8uk1I8iDgyqpaNrgZtB7w+z7/0YNf1l+UZMMMsnPSsiJH83+e5M19+uv6L/eXJnnLKrTnzLQMmG8nuTLJ7tMtW1UXDW5uXQasl5a1uRmwUVWd028kHQ88dYY6j+uZGN9MclWSYfD6D8C9en0/nLTqGcDd+7bv3stZLvCdZDGwfl/mhD7tOX37lqRlT6/dp1+flt3yLWDXtAydy3sfvmtQ7DrABsBNwHOB06rql9Nt3wzuAvwWWDaLPpuTOqvqD8DfAfdO8tBe3vrAusDNSRYBBwPvXNWy+w3VDYFf9iyQjwPb9X6+X5bPFtoryTlJLkxyUpK7TFHeC/o++J+0m7xD89Ufs9nOW/aztMzct/TtuCTJVn36nyY5ox+zHwJGWUbLZdYleW2Sw/rrVw32t08m2QJ4GXDIpH383Um+RrvpfUsGTFqW0af6cX9ekt369BXOIb16j605rHMNHltr9f1uk8H87ye5R5InJflW/5y/nOQeU5S13GeZ5PrB6xWuG0nunOTUtCzbS5M8c7RtaVmxdwKWAW8F3sRq6EGFA4GD+raO+m4D4KYk9wM2r6r/XI3i70S7po4+06fQgjX0v0/tr+eyTqrqRNpx9OxVXbfvK3cetXm6zzUty+4j/dx6VZIVgrdJ7tvXe1iWz56bdt20TOwr0jKcP5GWHXszcKf++axP66/XAe+tqpsG273K3wtWZ/uSvD4t8/DLwAP75Hlr4zSf02GZyBye8rtT2jn/7LRrxIXp2Zr9szgzLVP7irSM+NF14mFp34cu7uVtmGTtJO8cHJ8vHTTFc/kc1rkGz+VbsOI1/odJ1unLbpR2rl+n7yvv6fvFpUl27svcuR8j5/XjZzSCwoKez5Ls3Nt6Uf/7wJWvtdz6ByY5P8n5y264bnWaLEmSJEm6nTPAq9uKvYEvjt4k2YiWTTLKpngt8IqeybA78LvhytWyIrej3bT+BXBckr1omQ07A9sBOyZ5VC//7EwEe4b/hkPOLqqqnWk3sN7M7OwLXFRVNwKbA9cM5l3Tp81kM+CRwBOBYUbiD4BXJ3niFOs8GfhBzzA4e6pCq+pQJjI29k8LqD8T2K332zJg/774nYFLq2oX4HLgacDWPePin/oy76JlW28KfIOW/TPMDBpZr9+cOjeDIf26E5IsBb4LHF5Vy5i5z+aizmGfLAMuBrYC/h/weNo+eBjwf4Djq+qGKVY9qN/E/Uj6sKbd7kmWAP8fsCfwkR6weTETWSs/GC2cNlTxG4A9q2oH4Hzgb4cV9Ru5b6EFdh9Hy8Ibmc/+mO0xMvQ/fTs+SDteoR03X6+q7YFTgHtPV//AocD2fX97WVVdDRzNRBbNaB9/AK3vXjNp/f/bl30Y7Xg8tk+f7hzisTWex9bNtMyypwEk2QW4uqp+CnwdeHjf7z5JC1LMygzXjb8Crq2qh1bVQ4AvVtV3epsuBP4duD+QqrpoiqJ37YGqLyTZerr6q+oq2nezuwNvB46hXYPeBxxByz6bykf78fnGUYCsO6T33U9oP6Ja0qffo6p+0uv8Sa+POapzsguZOK8cNc15ZTjE+jN7m38M/AnwH336TJ/rVrT9bGfgzaPgUK/zgcCngBdU1XlTtG+FddN+hLMvbQSGvwZ26n31m17WRcAPgeuAh9WKGdtDs/1esErbl2RH4FmDNj5sDbQRJgJxS5I8foqypvru9DPgcf0a8UxgOET29n3ZBwP3BXZLy1Q9EXh1VT2Udtz/DngRcF0/vz8MeEmSv+jleC4fz3P51ax4jT8T2Kev8yzgU4MfJ9y5qh7R6/9In/Z64Kt9v3gM8M4kd2bhz2dXAI/qx/SbgLdNU+aUquqYqtqpqnZae4ONV2VVSZIkSdIdxKKFboDUPR54weD9YcDJVTV6Ntg3gHenZVV8uqqumXz/Jcl6wEnAQVX1X0leCexFu8kJLQNiS+Csqpo2I3fg0/3vBcAWK1u437Q/stcJPVtxkslDwk322R64uDwT2Ts70Ibt3B44I8n/AufQbmbeb2XtmsZjgR2B83o/rk+7AQvtJuan+utf07Koj01yKvB5gKr6GH04x7Rs6fcCe6c9h+9HwGv6dty7qq5Ncl/asHaXDIKc+1fV+WlD3X0zyReZoc/mqM7J0su+jn4zsd+M/Hvgr5N8GLgr8M9VdQ4teHl4b9PhwD8DL+xlnV1VT+xl/D3wDlpWynQeTruh/Y3+GdyJ9rkO7QKcWVU/7+WeSAtszmt/9PJnc4wMDY+Xv+6vHzV6XVWnJplNFtFS2k3tzwKfnWG5k/rN58n2BB48OD9slJatO9U5xGNr7uqcbE0cWyfSbpp/lBYEOLEvfy/gxLQfSNyJFuSarb2Y+rpxNvCuJEcCnx8Fiarq4Fs2OPkP4KVJXg88FPhSVX2YFhC4T1Vdn/Y8ys/2Mqcz6rsltPMEPch8bXuZE2mZaa/pAe39q+rHfT//FC1rbzRk7lFV9a4e8Dw5ybOq6pPTVTxHdU65Pb38Q2bY7pETq2qUxfx+WvbpYmb+XE/twckbk/wMGGVtb0r7IcC+VXXZNPVNte4jgc9V1e96X4yCzFTVO2j7IEmOBd6U5MW0/WZpVY0CfKv6vWBVt2934DOjAFsGz1KdxzZC36cG6+86admpvjutA7wvyXa08+ADBst/u6qu6WUt6etcB/xkFJCvql/3+XsB22Yi+3ZjYMt+bvFcPr7n8smOpf3A4bO0/xsMh9z+RG/PWWnZvZvQ9t0np2eS00YruPdt4Hy2MfBvSbbs/bHOSpaXJEmSJGmVmMGrBZdkA2CTWv4ZXtsCXxi9qfZ8zRfTbrCdmz4E7CRH0wI3o+eqBXh7TTxT6/5V9a+9ztlkJ97Y/y5jJT+GSHIv4DPA8wY3ya6hDxfY3Yt2c2kmNw5ej24i7UkLSv+IliVyHHAIbdi9lQWMp20y8G+DvnlgVR3W5/1+FDirqj/SMoY+RRsC8IvLFZLck4nMoDfQsl1upN0YZfSZVstKO5N283U5PXh5IS2YudI+m4s6ezlr055J+Z1Js95Ey/L4G9oN6hfSsy6q6qfVhhG/Gfhw75upnEILbs4ktADQ6DN4cFW9aIrlZvyM56s/ZnmMDE13vEzV/j+y/PVnvcHrfWhBnR2BC9KGgZzKb6eZvhaw66BfN6+q30xzDvHYmuM6ezlr6tg6B7h/D348lYnA0r8A76v2bNGXsvz+NXLLPtgDiaNnW0553aiqK2n75CXA25MsNwxz2pCg59My+x5SVc8Anptkg6r6dVVd37fzNGCdtAz+qfruvrRj6GeDaaF9HofTMiLfTBv6/VW9zB/3v7+hZdmt0HfVst++OOi7n/ZA4mikgOEzJ+ekzoHtmTivzCaDd9TmomXvjto80+c6vHYOz0HX0QJbk4e3H5pq3Zky+OjbMtr/r6Rd+58BPKQHc1bne8HqbN/Krg9z3cbZmOpacAjwU9oPH3Zi4ngbLj9cJ0y9bQFeOTg+/6KqzsBz+bifyydv7zeALZI8Gli7qi4dzp68OO2z2nfwWd272ggLo+1YqPPZ4cDXqo368CSmvhZJkiRJkrTaDPDqtuAxwNcmTXsb8P3RmyT3q6pLqupI2k305QK8SV4BbNiDOCOnAy9Mf65pks3TnolKVe0+uBE0/PdlVlHPHjgV+Id+U4pex0+A3yR5eL+59DxaJtGqugh4SpKNq+oK2vPO/pl2c2pV3JSJYSu/Auw36o8kf5LkPpNX6H23cQ9KHEwbsnTocCaGu1ufdqPtZmCDJHdNsm4v5260G+yXT1HHBrQbZj+YZZ/NRZ3r0Ibu+1FVLR1M3xK4Z7Xns23Qyy36TblRQKR7GjC86Tj0SFrm0EzOpQ1Fef9RPyR5wKRlvgXskfYs23WAp09Rzrz0xxwdI2fRh7RMsjctywfajf679+1alzYkOWnPMv3zqvoaLXtnE1oG5W9oz+ubjTOAgwbbtl3/O9U5xGNr7utcY8dWD9x8Bng38J2q+kVfZmPa8L7QhiidytW0gC20of1Hn9+U140eMLmhqj5OGwZ1h0nb/Gra/rMBEwGItWjPQv2z3uekPTNyLdqjBJbTA9VH04J8wyDGAbQMzl8y0Xejz2XRKFjc2/FEpui7Xv8jmDgvnTLomwNYcV+41XX2+fvSsutGWXeHTHNeWTzV+ix/Lp3N5zrZH2hBt+clWZXnAH8deFKS9fq+sM8UyxxOC3StA6zdp436aBNW/XvBqm7fWcDTkqyflnn4pDXQxtW1MS0j92ZaduTaK1n+CuCeSR4GkPb83UW04/PlmXg+6wPShuL1XD73da7J70lTXeOPp503Pjpp+jN7PY+kDdd9HW2/eOXgPDs5YL1Q57PhMf38afpBkiRJkqTV5hDNui3YGzh50rRnA/8NjIZ0PTjJY2jZHZfTsnuHN5FeS7sxt6S/P7qqjk57hto5/Z7P9cBzmJSpNAcOoj178Y1JRjfU9qr2/NWX0zJJ1u9t/sKUJcygqr6U5OO0rMMbaMM2voD2nOFVGUb3GGBpkgurPV/uDbShDNeiDVX3CuC/Jq2zIfC5tOGvQ8uIASZuoNXE8yb/lZbd9iPac2N3BD6U5GZaQGNxVQ1vIp6Q5HfAusBxVXVBnz5tn81RnTf2Or9MC+wMHUF7lhu0G3ifpQVuRtl67+gBw6IFiF46WHf0bLnQssZezAyq6udJng98YnSzlZZhcuVgmZ8kOYyWqfgTWgbPLTfG10B/3Fpv6dt3IfCftOfuUVU3JXkrLYD9Q9rNfPq2fTzJxrR+PKqqfpU2POrJaVmSr1xJna8C3p/23MJFtCDIy5jiHFJVN3pszWmdC3FsnQicx/I3zw8DTkryY9oPKf6CFX2Y1v/fpgVyftu3/4xprhv3pz3X8WbaZ/ryQVmvoGX63dD3uyS5hJY9+KskB9GCUn+kPUf0WYMA7vp929ahZRV/jBawBm4J7BzAxPC576ZlCv6Blj23LnB6D0ysTev7Dw/adkiS5/TylzLxPM7FwL8neRHtuLzlxyNzWOedaYGSv+wZiLP1zB68WYuWrfj8Pv0wVv65rqCqfpv2XNYvJfktbR9a2TrnpQ15fDHt2D1/uF7ac0vPG2VCJjmnf+ZLq+rifg5Y1e8Fq7R9VXVh2lCzS3obl3u27Dy1cXV9APhUkqfTftA33QgMo237Q5JnAv+SZH3acbMnbejeLYALezDv58BT/Z409ufy5a7x1YbAP4H2PONPTKr/l0m+CWzExNDPhwPvoX126fWOfji2kOezd9CGaP5b4KuzXEeSJEmSpFlLrfbIZdLc6MGfXaoNISlJkrSgktyl2nOTN6D9UOTAqrpwodsl3RGkPWf5KVX13MG0M4HXVtX5C9awBbLuZlvWZge8Z6GbMaOrF0810IEkSZIkaWWSXFBVO63OumbwasFV1Q4rX0qSJGmNOSbJg2lDz/6bwV1pzUjyL7TRfZ6w0G2RJEmSJOm2zACvNEtJHg8cOWnyD6vqaatYzutZ8VmqJ1XVEbemfZIkaW5U1ao8s1fSHKmqKR/FUFV7rOGmSJIkSZJ0m2aAV5qlqjodOH0OyjmC9gwzSZIkSZIkSZIkaZWstdANkCRJkiRJkiRJkiTNzqwCvEn+YjbTJEmSJEmSJEmSJEnzZ7YZvJ+aYtrJc9kQSZIkSZIkSZIkSdLMZnwGb5KtgK2BjZP89WDWRsB689kwSZIkSZIkSZIkSdLyZgzwAg8EnghsAjxpMP03wEvmqU2SJEmSJEmSJEmSpCnMGOCtqs8Bn0uya1Wds4baJEmSJEmSJEmSJEmawsoyeEe+n+QfgS2G61TVC+ejUZIkSZIkSZIkSZKkFc02wPs54Gzgy8Cy+WuOJEmSJEmSJEmSJGk6sw3wblBVfz+vLZEkSZIkSZIkSZIkzWi2Ad7PJ3lCVZ02r62RJEmSJEkAbLP5xpy/eJ+FboYkSZIk6TZmrVku92pakPf3SX6d5DdJfj2fDZMkSZIkSZIkSZIkLW9WGbxVteF8N0SSJEmSJEmSJEmSNLNZZfCmeU6SN/b3f55k5/ltmiRJkiRJkiRJkiRpaLZDNH8A2BV4dn9/PfD+eWmRJEmSJEmSJEmSJGlKsxqiGdilqnZIchFAVf0yyZ3msV2SJEmSJEmSJEmSpElmm8F7U5K1gQJIsilw87y1SpIkSZIkSZIkSZK0gtkGeN8LfAa4e5IjgK8Db5u3VkmSJEmSJEmSJEmSVjCrIZqr6oQkFwCPBQI8taq+M68tkyRJkiRJkiRJkiQtZ1YB3iT/Fzixqt4/z+2RJEmSJEnAJT++ji0OPXVB23D14n0WtH5JkiRJ0opmO0TzhcAbknw/yTuT7DSfjZIkSZIkSZIkSZIkrWhWAd6q+reqegKwM3AlcGSS781ryyRJkiRJkiRJkiRJy5ltBu/I/YGtgC2AK+a8NZIkSZIkSZIkSZKkac0qwJvkyCRXAm8FLgV2rKonzWvLJEmSJEmSJEmSJEnLmW0G738B/wR8s6o+CmyUZOf5a5YkSZIkSZIkSZIkabLZBni3AXYB/qa//w3w/nlpkSRJkiRJkiRJkiRpSotmudwuVbVDkosAquqXSe40j+2SJEmSJEmSJEmSJE0y2wzem5KsDRRAkk2Bm+etVZIkSZIkSZIkSZKkFcw2wPte4DPA3ZMcAXwdeNu8tUqSJEmSJEmSJEmStIJZDdFcVSckuQB4LBDgqVX1nXltmSRJkiRJkiRJkiRpObN9Bi9VdQVwxTy2RZIkSZIkSZIkSZI0g9kO0SxJkiRJkiRJkiRJWmAGeCVJkiRJkiRJkiRpTBjglSRJkiRJkiRJkqQxMa8B3iQfSrJbkmOTXJbku0meNMPy90xy8ny2aTaSPC7JBUku6X//cjBvxz79+0nemySrWceWSb6dZGmSL08x/xN93iFJ3ppkzz79zCQ79df/uLrbOEV9r0xyaZLTktypT3tkkndPWm5ZkiX93ymD6Wf2z3dJku8kOXAwb8o+m8M6lya5Isn7kmzS522a5Ou9/KcO1vlcknsO3h+W5MeD8p/Qp++R5Lo+bWmSLye5e5+3bn+/JMkz+/794JX07y2f26Tpz0/yvvnuj9kY7mersM7VSe62Kuv09fZI8ogZ5j85yaGrWm5f12NrTI+t1ejbW46fuZLkhN6utw2mvTHJUwbvh9uwJMmbBvNGfXpZkouT/G2Stfq83XrZ5yW5f5+2SZLTR59dnzb8zJdk4twz7NMrknxwUPafJPlSku/1v3edhzq/l+TTWcn5blJ/zuozSvLO3mfvnG3Zt1aSg5NsMHh/WlbhnDmprCN7Px8/mPbcJK8evJ/37xZTtOuwJK+d7zYmeV4/J1yW5PJRnbdVWY3rXV9viyTPHrzfKcl757htszkH7d+XWZrkm0keOpj3V/1Y/n76dTTJ+/sxfHmS3w2O8/1WoV2rdb2fpqzr56KcSWWum+SzfR+9KMl9J81/Vdo19IQMvmNMOkaen8F17Fa253Z3vp/lvjmb6+PFSS5M/x44+biaK5m77ysX9f46K8kT56ht1/e/WyS5dC7KlCRJkiTd8cx3Bu8uwLnAp6tqa+DJwFHTLVxV11bVrG82zaP/AZ5UVdsABwAfG8z7IHAgsGX/91erWcehwAeralvgJcMZSf4MeERVbVtVR1XVm6pqhUAVsMpBqCRrTzPrxcC2wEXA4/uNoDcCh09a7ndVtV3/9+RJ8/avqu2A3YAj0wNLTN9nc1Xntr2cG4HP9el/A/wbsCvwur7tTwIurKprJ5Vx1KD80wbTz+7TtgXOA17Rp28PrNPnnVhVL66qy7n15rM/VmqG/Ww+7AFMGeBNsqiqTqmqxatZtsfW+B5bCyrJtgC9Xbsn2TjJZsDOVTX5WDp7sG1vHUwf9enWwOOAJwBv7vNeA+xL279e3qe9EXhbVdWk8vcflP+zwfSj+r7wYGAb4NF9+qHAV6pqS+Ar/f2c1tnLPhH4apJNmVsvBXaoqtfNZuEki+agzoOBWwK8VfWEqvrVqhaSZGP6eQVYO8k2SdYHng98YLDomvhuscbbmGRvWl/u1ff7HYDr5rL9c+1WXO+2AG4JRFXV+VX1qrlq1yqcg34IPLovdzhwTF9/beD9wN60c8TfJHlwVb2inzeeAPxgcJwv+I8q59AzgOv6vvuXwP9Omv9/gCdU1f4zfMd4PrBKAd4ZzkW3q/P9HF8fHwr8A/D2Pn0LBsfVHJqr7yvbV9UDgVcB70vy2HloqyRJkiRJq2zeArxJHgRcWVXLBv9RXg/4fZ//6MEvpS9KsuHwV8xpWZGj+T9P8uY+/XX9l+lLk7xlFdpzZlr2yreTXJlk9+mWraqLBv/xvwxYLy0zYDNgo6o6p98oOR546gx1HpeW5fLNJFdl+UyJPwD36vX9cNKqZwB379u+ey9nucB3ksXA+n2ZE/q05/TtW5KWPb12n359WrbKt4BdkyxOy+JYmuRdg2LXod3svgl4LnBaVf1yuu2bwV2A3wLLZtFnc1JnVf0B+Dvg3mmZLDcB6wPrAjf3G3AHA6ucHdaDYxsCv0zLcvg4sF3v5/tl+czPvZKck5aZcFKSu0xR3gv6PviftIDd0Hz1x2y285b9LC1T5y19Oy5JslWf/qdJzujH7IeAUcbochkISV6b5LD++lWD/e2TSbYAXgYcMmkff3eSr9ECmM/PRGbzpkk+1Y/785Ls1qevcA7p1XtszWGda/DYWqvvd5sM5n8/yT2SPCnJt/rn/OUk95iirOU+ywwyxDLFdSPJnZOcmpZJdGmSZ462LS1L6k7AMuCtwJtYDf2m+YHAQX1bR323AXBTkvsBm1fVf65G8XeiXVNHn+lTaDey6X+f2l/PZZ1U1Ym042iVb8ZnmmtiWvb4nYFvpY2KcJ8kX+mf11eS3Huw/vA8cVxaVtvXenmPTvKRtCy94wb1fjDJ+WnZpaPP/1W0QM7Xenmj897d+uu/7fvFpUkO7tO26GV/uJd1RlqQ9GbgTv0zXp/W568D3ltVNw36bpW/W0y376dlnH0k7fpzVd+e0fa+Pi3b7MvAA/vkeWsjLVDz2tF6VfX7qvpwb8t2Sc7tn+VnMpFpeGaSo9Iy4r6T5GFp2YLfS/JPg/6+Iu374KVp2YN7JvlGX27nQV/ckjHcl91ihs9r8vXuYX2fvDjtPD/6Pnp22jXwlkxDYDEtuLUkbRSIPZJ8vpfzJ2kZpEv7Nm8702eVW3EOqqpvDs7n59KvecDOwPer6qp+7v4k7dywgl7/R9LOjRelZ2EmWTvJu9Ku/UuTvHKw2iuz4veCmfbFFY6jSW1IWvb+pb3MZ/bpayX5QP/cPp+WXb9fkscm+cxg/ccl+XR/+wdg8ySpql8Of6yR5GjgvsAp/XO75TvGYJn9gJ2AE/rnu35a1vp/pmWzn96Pg9H++7a073GvTvL0vg0XJzmrF3l7O9/P6fUR2IiJ7Zl8XJ2dZLvRgv2Y37bvax9L8tV+DnjJYJmp/n84Z99XAKpqCW2bD5rN8knuknYNGx0zUx6LkiRJkiStrvnM4N0b+OLoTZKNaJkgo8y41wKjjILdgd8NV66WFbkd7SbGL4DjkuxFyxrZGdgO2DHJo3r5Z2ci2DP8NxyCb1FV7Uz7z/2bmZ19gYuq6kZgc+Cawbxr+rSZbAY8Engi7QbGyA9oN4WmGurryUxkWJw9VaFVdSgTv4TfPy2g/kxgt95vy4D9++J3Bi6tql2Ay4GnAVv3X+H/U1/mXbSbhJsC36Bl7gyzekbWS7tRfm4Gw511JyRZCnwXOLyqljFzn81FncM+WQZcDGwF/D/g8bR98DBa5sbxVXXDFKse1G8IfST95nO3e5IlwP8H7Al8pAdsXsxEdsIPRgunBQbeAOxZVTsA5wN/O6yo3xx8Cy2w+zhaVsbIfPbHbI+Rof/p2/FB2vEK7bj5elVtD5wC3Hu6+gcOBbbv+9vLqupq4GgmsiVG+/gDaH33mknr/9++7MNox+Oxffp05xCPrfE8tm6mZQk/DSDJLsDVVfVT4OvAw/t+90lawHlWZrhu/BVwbVU9tKoeAnyxqr7T23Qh8O/A/YFU1UVTFL1rv5n/hSRbT1d/VV1Fu9benZatdAztGvQ+4AhadtVUPtqPzzcmyw3Xe0jvu5/QfkS1pE+/R1X9pNf5k14fc1TnZBcycV45aprzynRDrK9wTayWPT465k7s7Ty+H0cnAMMhcCefJ+5Ky9Y7BPgP2ighWwPbDAIEr6+qnWjZ6I9Osm1VvRe4FnhMVT1m2MAkOwIvoI1C8nDgJUm277O3BN5fLVP1V8C+VfUb4FO0jPkf0rJXH1YrZrUNzfa7xUz7/la0Y3Fn4M1J1ultfxZtpIm/Bh7W+3g+2/gQ4IJpyjge+Pv+WV7C8t+9/lBVj6JdDz5Hy+R/CPD8JH/al7k/7Rqwbd/eZ9P2n9cyu5EWVvi8hjPTRkM4EXh1tazCPWnXkp8Bj+vXwGcysQ8eysT1f/KING+h9de2vW3HD+at8Flx689BIy8CvtBfbw78aDBvpu+prwe+2q+tjwHemeTOtB+l/AUT1+0TButM9b1gyu1byXE08te08/Ko79/Zvyf9NS2rcxvad65d+/JfBR6UiREEXgB8tL++CtiRiazQW1TVy5g43qccSahaNvP5TIyY8UfgX4D9qmpH4CO08+fIJlX16Kr6Z1qQ8/F9HxqNhnG7Ot/P0fVx9OO5K2jf5Uajikw+ro6lZVOT5AHAulW1tC+7LbAPbZ94U9rjfaa7zs/F95Up+6O3bf9prn+jzPjfA0/rx8xjgH9eSV8vJ8mB/Tva+ctuuE0PiiBJkiRJWiBzMcThdB5Pu/EychhwclWNnvP4DeDdaRlyn66qayb/nzfJesBJwEFV9V9pWQR70W5QQstm2xI4q6qmzcgdGP3K/wLajaMZ9ZsSR/Y6oWcrTjJ5yLPJPtsDF5dnIvNmB9owedsDZyT5X+AcWmDqfitr1zQeS7uxdV7vx/VpNyihBaQ+1V//mnbD4dgkpwKfB6iqj9GHYkzLln4vsHeS59FuFr6mb8e9q+ratGebfTXJJYMg5/5VdX6/8fbNJF9khj6bozonSy/7OtoNIPqNmr8H/jrJh2kBgX+uqnNoNykP7206HPhn4IW9rLOr6om9jL8H3kHLPJ3Ow2kB22/0z+BOtM91aBfgzKr6eS/3RFrAYl77o5c/m2NkaHi8/HV//ajR66o6NclsMkKX0gKUnwU+O8NyJ/VA4mR7Ag8enB82SsvWneoc4rE1d3VOtiaOrRNpN8o/SgtSndiXvxdwYr/xfydagGq29mLq68bZwLuSHAl8fhTwr6qDb9ng5D+AlyZ5PS0A8aVqmYkXAvepquvTntX32V7mdEZ9t4R2nqDffL62vcyJtEyj1/SA9v5V9eO+n3+KloE9ChYdVVXv6gGik5M8q6o+OV3Fc1TnlNvTyz9khu2eygrXxCnsysQ552O0/WNk8nniP6qqklwC/LSqLqFt4GW06/wS4Blpz65eRAswP5h2XprOI4HPVNVve1mfpv2I5BTgh4Mgyy3fJarqHaN2JjmWFnh4MW3fW1pVox98rOp3i5n2/VN78PXGJD8D7tHb+ZlRACODZ2vPYxunlDYs9CY1kT34b7TvdCOjtl0CXDYKWCW5CvhzWkD2h5M+068MPu8tZqq/m/LzGngg8JOqOg+gqn7d67ozbSjW7Wjn+QfMoq5H0gPIVfXVtBEvNu7zpvqsLmH1z0Gj+Y+hBXgfOZo0Rbum+5z2Ap6ciezn9Wg/2toTOLqq/tjbMxzqeKrvBdNt33TH0TAg+EjgE/2Y/mlaRuzD+vST+rniv9Oz7Ptn/zHgOUk+SjtXPC8tM/s42o87PpLk4Kp6T5LTgNdV1WXT9MFMHkj7wcGX+rV/bVqgdeTEwetv0H6I+u+jPrqdnu8PvmXi6l0ff9eD5yTZFTg+yUOmqPMk4I1JXkf73nDcYN7nqup3wO/6frEzbX9Z4TpfVWdx67+vzNQfJ7D8DyCmWvZt/bO4mfZji3sA/z3DOreoqmPow6+vu9mWK/v/piRJkiTpDmheMniTbEC7qTd8vtG2TGQYUO3ZVy+mBUvOTR/qbZKjaYGb0XPSAry9Jp6RdP+q+tde52yyE2/sf5exkuB2knsBnwGeNwh4XMPEMHj015Of4TTZjYPXo5sCe9KC0j+iZasdR8s+Oq1qhWdkzVaAfxv0zQOr6rA+7/ejG+L9ht3OtBs6T2WQZQ2Q5J5MZPW8gZa5ciMtyEVNDMF4FXAmLZC2nB68vJAWzFxpn81Fnb2ctWnZHt+ZNOtNtCyGv6HdlHwh8LZe5k+rDSN+M/Dh3jdTOYUW3JxJaDe4Rp/Bg6vqRVMst7Kb4vPSH7M8RoamO16mav8fWf58st7g9T60ZwLuCFyQ6Z9X99tppq8F7Dro182r6jfTnEM8tua4zl7Omjq2zgHu3wPZT2UimPAvwPuqPVvxpSy/f43csg+m3Y0fPad4yutGVV1J2ycvAd6eZLlhJtOGUjyflqX9kKp6BvDcJBtU1a+r6vq+nacB66QP7TtF392Xdgz9bDAttM/jcFpG45tpQ7+/qpf54/73N7QMpBX6rtqwul8c9N1PMzF86GbD+uaqzoHtmTivrGoG71TXxJUZHruTzxOj8m6eVPbNwKIkf0HLNHxstWzEU5l6/xmaqV3DOlb4LpGJDMUrad8fngE8JMmWff6qfreYad+fri0ru8bMdRsvox1Lq2rGz27SMpOXGy4z0/Vnxs+L9llP1V+HAD+lBa52YuJ8MpOZgqsrtOPWnIP6/G1pmY5Pqapf9NWuoQXHR2b6nhpaBvro3Hjvalma0/XJcDsm9+VU/Tyb43u6ZWZa96PAc2jXnZP6tXcb4Of9mrYv8MwkrwY2oY2usTpC++HBqH+2qaq9BvNvORdVyxB+A63vl2QiA/12db4flH+rr489sHo32kgjk+fdAHyJNorTM3obb5k9eXFm+P/hwFx8X1muP7LyDN79+/bt2APbP2Xl1x9JkiRJkmZtvoZofgzwtUnT3gZ8f/Qmyf2q6pKqOpJ2k2C5AG+SVwAb9iDOyOnAC9Ofa5pk87RnolJVuw/+Yz/892VWUdozIE8F/qGqvjGaXi275DdJHt5vnjyPNqzgqroIeEqSjavqCtqzoP6ZdvNlVdzUf9UP8BVgv1F/pD0L7j6TV+h9t3G/6XIwbSizocOZGM5tfdqNk5uBDZLcNcm6vZy70YYZXuHGWb/5uD1tKNzZ9Nlc1LkObWi6H9XEMG70G9b3rJZBtEEvt+g3WEY3yLqnAZcytUfSskBnci6wW5L7j/ohbWi5oW8Be6Rl9qwDPH2KcualP+boGDmLPjxxkr1pGRDQblrdvW/XurThV0l7VtufV9XXaEOLbkLLrPgN7dmrs3EGg+edpQ+7Os05xGNr7utcY8dWD8J/Bng38J1B0GJj4Mf99QHTlHM1E0Gmp9CeQQzTXDd68PuGqvo4bUjrHSZt86tp+88GTNxQXov2HNM/631O2nNA16I9SmA5PVB9NC1AN7wpfQAt4+2XTPTd6HNZlInnwK5DO5ZW6Lte/yOYOC+dMuibA1hxX7jVdfb5+9IypT4BLYN3mvPK4qnWn6Vv0jK4oZ1vvn4rytqIFoi5Li1jeO/BvOnOQ2cBT+3n8DvT9t8ph3SfwuG0QMI6tIw/mOjnTVj17xaz2fcnt/1pac8P3RB40hpo49uBdyT5M4C0Z/a+qlq2/y+TjEaPeC4wyuadS1fTj9+0URz+YhXWvQK4Z5KH9fU3TPsR0sa0zN6bae0e9dNM167h9XEP2nDGv56u4lt5Dro37Qcwz+2B4pHzgC2T/EXa8NPPYiJTerLTac/UHZ3LRoH/M4CX9X4gyZ9Mtw0rMZvj6CxaMHbtfr58FPBt2jG/b9qzeO8B7DFaoQdxr6UFMI/rk78HbJVk62oZwy+i9d0pk869KzP8fL8LbJqWaUrasNNTDsffv498q6reBPwPywfZbzfn+8E6c3F93Ip2XP2CqY+rY2mjj5xXy2eRPyXJemlB9D1o+/y0/z/s7+fk+0rajyreSPvRIlV1wjTXv/36KhsDP6uqm9Ky7Vf47ihJkiRJ0q0xX0M07w2cPGnas2lDUo2GdD24/2d3GS2o8AXa0Ikjr6UFWZb090dX1dFpz8M8p987uJ72K/7lfrk+Bw6iPVvqjUlGwZG9qj1/9eW0G0rr9zZ/YcoSZlBVX0rycVrW4Q20IRdfQBvebVWG0T0GWJrkwmrPCn0DbVjatWhDsb0C+K9J62wIfC5t+OvQslSAiZt7NfE8rX+lZZb8iPZsuR2BDyW5mXbDZnFVDQNCJyT5HbAucFxVjZ7JN22fzVGdN/Y6v0wL7AwdQXvOHLQbVJ+l3ZgaZcq8Iy1gWLSbxC8drDt6Tmhozyt8MTOoqp8neT7wiVHgjHYD8srBMj9JchgtU/EntGzM0Y3jNdEft9Zb+vZdSLtR///19t6U5K20APYPaTfN6dv28bRhKkMbbvBXaUP7nZyWBfLKldT5KuD9ac+gXUS7IfwypjiHVNWNHltzWudCHFsn0m7YPn8w7TDgpCQ/pv2QYqoAzodp/f9tWlD+t337z5jmunF/2vMeb6Z9pi8flPUKWtb2DX2/S9qQsKf1/fcg4OVJ/kh7XuezBkGE9fu2rUPLLPwYLWAN3BKkP4CJoW/fTcv6/gMts2hd4PR+E31tWt/fMiQr7ZmMz+nlL2Xi2cqLgX9P8iLacXnLj0fmsM470258/2X1Yebnyatow6y+Dvg5yz/uYZVU1cVJLqJlmV5FG0p15BjgC0l+UoPn8FbVhUmOowWaAI6tqouSbDFTXWnPsT6vB6BIck7fb5b2dryBVf9ucRgr3/eH23th2nCsS2jnqOUCavPRxqo6rQfhvtwDO0V7Xim0/e7ovg9exa34LGfwKdowvUto544rZ158QlX9IckzgX9JG+b3d7SRID4AfCrJ02k/WBxlay4F/pjkYlpfDIcbPoz2XNOlwA2sPCC/Dat/DnoX8KfAB/p57Y9VtVNV/bGfn06nHcsfqemHJz4ceA/tWhfaefqJtMDaA/r0m2jngvetZFtWMN1xNGmxz9CGWb6Ytt/8XVX9d5JP0UaauJT2eX6Ldq0YOQHYdHTNqqpfJjkA+Fjflutowfa3Jzmrqr45y2YfR9tff9fbtR/w3v4dZhGtv6bqz3f2IGJo15+L4XZ7vp+L6yO9rw6oqmW9nFuOq6o6qqouSPJrJp6xPPJt2o9Q7g0cPgr4T3OdH/3/8NZ+X7mIFhj+GfCqqvoKs3MC8B9Jzqedk6+YeXFJkiRJklZNVu2H7bMstAV/dqk2pJgkSZIkzUqSu1R7juuf0oJ6u1XVf/d57wMuqhWH4dXtRFqG+5nAVj2Tnv4Dyeur6l0L2LQFse5mW9ZmB7xnQdtw9eJ9FrR+SZIkSbq9SnJBVe20OuvOSwZvVe2w8qUkSZIkaQWfTxsy/E60TM1RcPcCWkb1axawbZpHSZ5Hy7r921FwV5IkSZIkrWi+hmgeC0keDxw5afIPq+ppq1jO61nxWaonVdURt6Z9kiRJ0h1NVe0xzfQdp5qu24+qOh44forph6351kiSJEmSdNt1hw7wVtXptOeU3dpyjqD90lySJEmSJEmSJEmS5s1aC90ASZIkSZIkSZIkSdLsGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxoQBXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxoQBXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMLFroBkiSJEmSpBVts/nGnL94n4VuhiRJkiTpNsYMXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxoQBXkmSJEmSJEmSJEkaEwZ4JUmSJEmSJEmSJGlMGOCVJEmSJEmSJEmSpDFhgFeSJEmSJEmSJEmSxsSihW6AJEmSJEla0SU/vo4tDj11jdR19eJ91kg9kiRJkqRbzwxeSZIkSZIkSZIkSRoTBnglSZIkSZIkSZIkaUwY4JUkSZIkSZIkSZKkMWGAV5IkSZIkSZIkSZLGhAFeSZIkSZIkSZIkSRoTBnglSZIkSZIkSZIkaUwY4JUkSZIkSZIkSZKkMWGAV5IkSZIkSZIkSZLGhAFeSZIkSZIkSZIkSRoTBnglSZIkSZIkSZIkaUwY4JUkSZIkSZIkSZKkMWGAV5IkSZIkSZIkSZLGhAFeSZIkSZIkSZIkSRoTBnglSZIkSZIkSZIkaUwY4JUkSZIkSZIkSZKkMWGAV7odSfKhJLslOTbJZUm+m+RJMyx/zyQnr8k2TtOOxyW5IMkl/e9fDubt2Kd/P8l7k2Q169gyybeTLE3y5Snmf6LPOyTJW5Ps2aefmWSn/vofV3cbp6jvlUkuTXJakjv1aY9M8u5Jyy1LsqT/O2Uw/cz++S5J8p0kBw7mTdlnc1jn0iRXJHlfkk36vE2TfL2X/9TBOp9Lcs/B+8OS/HhQ/hP69D2SXNenLU3y5SR37/PW7e+XJHlm378fvJL+veVzmzT9+UneN9/9MRvD/WwV1rk6yd1WZZ2+3h5JHjHD/CcnOXRVy+3remyN6bG1Gn17y/EzV5Kc0Nv1tsG0NyZ5yuD9cBuWJHnTYN6oTy9LcnGSv02yVp+3Wy/7vCT379M2SXL66LPr04af+ZJMnHuGfXpFkg8Oyv6TJF9K8r3+967zUOf3knw6KznfTdGneyc5v++/VyR516qsv7p621/bXz9/uH+ugbrXTfLZfnxelOS+k+a/qvfHCRmc7+arzbfHfW8Oj9WLk1yYfk1KskWSZ89Fv0/6DObq3HlR76+zkjxxjtp2ff+7RZJL56JMSZIkSdIdjwFe6fZlF+Bc4NNVtTXwZOCo6Rauqmurar811bgZ/A/wpKraBjgA+Nhg3geBA4Et+7+/Ws06DgU+WFXbAi8ZzkjyZ8Ajqmrbqjqqqt5UVSsEqoBVDkIlWXuaWS8GtgUuAh7fb76+ETh80nK/q6rt+r8nT5q3f1VtB+wGHJkeWGL6PpurOrft5dwIfK5P/xvg34Bdgdf1bX8ScGFVXTupjKMG5Z82mH52n7YtcB7wij59e2CdPu/EqnpxVV3OrTef/bFSM+xn82EPYMoAb5JFVXVKVS1ezbI9tsb32FpQSbYF6O3aPcnGSTYDdq6qycfS2YNte+tg+qhPtwYeBzwBeHOf9xpgX9r+9fI+7Y3A26qqJpW//6D8nw2mH9X3hQcD2wCP7tMPBb5SVVsCX+nv57TOXvaJwFeTbMosJHkI8D7gOVX1IOAhwFWzWXeOPR9YYwFe4BnAdf17xF8C/ztp/v8BnlBV+89wvns+q9jmJIummXW72vfm+Fh9KPAPwNv79C2AOQ/wMnfnzu2r6oHAq4D3JXnsPLRVkiRJkqRVZoBXup1I8iDgyqpaNrg5tR7w+z7/0YPshIuSbDjMHEjLihzN/3mSN/fpr+vZIEuTvGUV2nNmkiPTMvuuTLL7dMtW1UWDm22XAeulZeNsBmxUVef0m5PHA0+doc7j0rLqvpnkqiTD4PUfgHv1+n44adUzgLv3bd+9l7Nc4DvJYmD9vswJfdpz+vYtScueXrtPvz4tU/FbwK5JFie5vPfhMJNqHWAD4CbgucBpVfXL6bZvBncBfgssm0WfzUmdVfUH4O+Aeyd5aC9vfWBd4OZ+0/tg4J2rWnYPjm0I/DIts+jjwHa9n++X5TM/90pyTs8GOinJXaYo7wV9H/xPWsBuaL76Yzbbect+lpaZ+5a+HZck2apP/9MkZ/Rj9kPAKGN0uayfJK9Nclh//arB/vbJJFsALwMOmbSPvzvJ12gBzOdnIrN50ySf6sf9eUl269NXOIf06j225rDONXhsrdX3u00G87+f5B5JnpTkW/1z/nKSe0xR1nKfZXpGWn+9wnUjyZ2TnJqWvXdpkmeOti0tM/FOwDLgrcCbWA09UHUgcFDf1lHfbQDclOR+wOZV9Z+rUfydaNfU0Wf6FFrwiP73qf31XNZJVZ1IO45mGwD7O+CIqrqir//HqvoAQJL7JPlK/1y+kuTeffqU184km6VlLS7pn9nuffrws94vyXHDBvT1dwJO6Ouun+RNfZ+4NMkx/fOZ9rtCkrWTvKufD5emZck/NslnBvU8Lsmn+9s/AJsnSVX9sqp+NVjuaOC+wClpIwnccr5bSZt3TPKfaSOLnN7PAaM2vy3tmvLqJE/v23VxkrN6kbe3fW9Oj1VgIya2ZzEtaLykfz5nJ9lutGCSbyTZNi3T9mNJvpqWYfySwTJTfVeds3MnQFUtoW3zQbNZPsld+nE2uq4/ZeVrLbf+gWmZ+Ocvu+G61WixJEmSJOn2zgCvdPuxN/DF0ZskG9EyYUeZca8FXtGzQXYHfjdcuWdFbke7cfgL4Lgke9Gy1HYGtgN2TPKoXv7ZmQj2DP8Nh5xdVFU7026ovZnZ2Re4qKpuBDYHrhnMu6ZPm8lmwCOBJ9JuGo78gHYjdqrh9Z4M/KBnbpw9VaFVdSgT2Sf7pwXUnwns1vttGbB/X/zOwKVVtQtwOfA0YOue+fJPfZl30bKtNwW+Qctc/sAUVa/Xb/Cdm8EQg90JSZYC3wUOr6plzNxnc1HnsE+WARcDWwH/D3g8bR88jJYtdXxV3TDFqgf1m7AfSR9asts9yRLg/wP2BD7SAzYvZiIj6AejhdOGKn4DsGdV7QCcD/ztsKJ+Q/4ttMDu42iZUCPz2R+zPUaG/qdvxwdpxyu04+brVbU9cApw7+nqHzgU2L7vby+rqquBo5nIUBrt4w+g9d1rJq3/f/uyD6Mdj8f26dOdQzy2xvPYupmWJfw0gCS7AFdX1U+BrwMP7/vdJ2lBw1mZ4brxV8C1VfXQqnoI8MWq+k5v04XAvwP3B1JVF01R9K49gPaFJFtPV39VXUX7fnt3WobgMbRr0PuAI2gZjVP5aD8+3zgKPnaH9L77Ce1HVEv69HtU1U96nT/p9TFHdU52IRPnlaOmOa+MsjgfAlwwTTnvo+072wInAO8dzJvq2vls4PR+HD4UWMIsVNXJtPPxKEv0d8D7quph/bNfv9czMtV3hQOBv2DiXHYC8FXgQZnIZn4B8NH++ipgRyayQofteRlwLfCYqppyVJPJbQb+CPwLsF9V7Qh8hPZZjmxSVY+uqn+mBTkf3zNTR5n5t6t9b46O1dEPea6gXVdGIxwcysQ1/qg+7/kASR4ArFtVS/uy2wL70LJy35T2qJHpzjlzce6csj962/af5lgcPfrk98DT+nX9McA/r6Svl1NVx1TVTlW109obbDzb1SRJkiRJdyDTDSsmafw8nnazc+Qw4OSqGj3n8RvAu9My5D5dVddMvs+UZD3gJOCgqvqvJK8E9qINOwotm21L4KyqmjYjd2CUWXMBbQi+GfUbgUf2OqFnK04yeZjByT7bAxeXp2edJdmBNmzn9sAZSf4XOIcWmLrfyto1jcfSbiaf1/txfWA0zOEy4FP99a9pN/mOTXIq8HmAqvoYfSjqtGzp9wJ7J3ke8CPgNX077l1V16Y9T/CrSS4ZBDn3r6rz+83ubyb5IjP02RzVOVl62dfRbrrSb47+PfDXST4M3BX456o6hxa8PLy36XDgn4EX9rLOrqon9jL+HngHLfN0Og+nBWy/0T+DO9E+16FdgDOr6ue93BNpgc157Y9e/myOkaHh8fLX/fWjRq+r6tQks8kIXUoLUH4W+OwMy53UA4mT7Qk8eHB+2CgtW3eqc4jH1tzVOdmaOLZOpAWnPgo8q7+HlpF9YtoPJO4ETM7MnsleTH3dOBt4V5Ijgc+PAv5VdfAtG5z8B/DSJK+nBRS/VFUfpgVV7lNV16c9H/OzvczpjPpuCe08QQ/4XNte5kRadt9rekB7/6r6cd/PP0XLwD6+l3VUVb0ryTrAyUmeVVWfnK7iOapzyu3p5R8yw3avzK5MnFs+RtsPRla4dtKG8/5I3/bPDgKMq+MxSf6Oll36J7TROv6jz5vqu8KewNFV9UeAqvpfgCQfA56T5KN9e56XZH3gOGDr3t6Dq+o9SU4DXldVl61Gex9IC5Z/qZ+H1qYFWkdOHLz+Bu1Hcf8+2pbb6b538C0TV+9Y/V0PnpNkV+D4tCHFJzsJeGOS19HOYccN5n2u/2Dgd2kjUOxM+2HCCuecqjqLW3/unKk/TqD98GA6Ad7WP4ubaT8Iugfw3zOsI0mSJEnSrJnBK90OJNmAlk0yfKbYtsAXRm+qPW/uxbRgybnpQ8BOcjQtcDN6RmaAt9fEc8nuX1X/2uucTXbijf3vMlbyg5Ik9wI+AzxvEPC4hj70a3cv2g3Lmdw4eD26EbcnLSj9I1q22nHAIbQhVFcWMJ62ycC/DfrmgVV1WJ/3+1HgrN+c3pl2E/WpDLKsAZLcE3hYtWfYvYGWuXgjLcjF6DOtlpV2Ji2QtpwevLyQFsxcaZ/NRZ29nLVpzwX8zqRZb6JlDv0N7Yb9C4G39TJ/Wm0Y8ZuBD/e+mcoptODmTEK7qTz6DB5cVS+aYrkZP+P56o9ZHiND0x0vU7X/jyx/DV9v8Hof4P20IOkFmf4Zkb+dZvpawK6Dft28qn4zzTnEY2uO6+zlrKlj6xzg/j2Q/VQmAm3/Qsu43AZ4KcvvXyO37INpEbDRc4qnvG5U1ZW0ffIS4O1JlhvaNW340vNpWdoPqapnAM9NskFV/bqqru/beRqwTloG/1R9d1/aMfSzwbTQPo/DaRmib6YN/f6qXuaP+9/f0LL+Vui7qrqJto+N+u6nmRiyd7NhfXNV58D2TJxXVpbBexmtn2djeIyucO3sAbJHAT8GPtZ/sDB5van2jeWk/XjsA7Rs2G1o++dwvanOfWHqc99HgefQjoGT+nlgG+Dn/fjaF3hmklcDm9Ay/VdHgMsG+/E2VbXXYP4t589qGcJvAP4cWJLkT28p5Ha07w3Kv9XHag+s3o026sHkeTcAX6KNKPOM3sZbZk9enBm+qw7Mxblzuf7IyjN49+/bt2MPbP+UWRwvkiRJkiTNlgFe6fbhMcDXJk17G/D90Zsk96uqS6rqSNqNueUCvEleAWzYgzgjpwMvTH+uaZLN056JSlXtPriZNvz3ZVZR2jMgTwX+oaq+MZpebejB3yR5eL9h+TzakKKr6iLgKUk2rvZcwnfSMjQ+vorl3NQzaQC+Auw36o8kf5LkPpNX6H23cb/ReTBt+MChw5kYQnF92s3Km4ENktw1ybq9nLvRhhle4WZ1D/BvTxsKdzZ9Nhd1rkMbDvJHNTF0Ikm2BO5Z7Zl/G/Ryi35Tc3RTunsacClTeyQtC3Qm5wK7Jbn/qB/ShnMc+hawR9qzbNcBnj5FOfPSH3N0jJxFH544yd60rCNoN4rv3rdrXfpwp2nPR/zzqvoabVjdTWjZTL+hPXt1Ns5g8IzB9GchTnMO8dia+zrX2LHVg/CfAd4NfKeqftGX2ZgW1IM23PRUrmYikPgU2jOIYZrrRg9+31BVH6cNab3DpG1+NW3/2YCJIM5awJ2S/Fnvc5Ls3KeP2sqgnE1pP1R636QfGBwAnFrtmcijvht9LotGAajejicyRd/1+h/BxHnplEHfHMCK+8KtrrPP35eWnfgJaBm805xXRtfudwL/ODoXpj1reTR0/TdpmdrQzitfn6rOQd33AX5WLTPzX5n4zH6a5EH9fPO0aVYfnnNGQa3/6fvFflOvspwzgJel/0AlyZ/07b+W9sOKNzCR2fk9YKskW1fVb4EX0frhlFX8ocmwzd8FNk3LNCXJOplmaPB+bvxWVb0J+B9aoHfkdrPvDdaZi2N1K1pW9C+Y+vp0LG0khPOqZ293T0myXloQfQ9alvm031X7+zk5dybZlnZ+fz+0DN5pjsXR/r0x7fi5KcljgBWuY5IkSZIk3RoO0SzdPuwNnDxp2rNpw8CNhnQ9uN9gWkYLKnyB9sy9kdfSgixL+vujq+rotOdhntPv111Py5xZLltkDhxEe57bG5OMgiN7VXv+6stpN3HX723+wpQlzKCqvpTk47Sswxtow42+gDak4qoMo3sMsDTJhdWeFfoG2rC0a9GGP3wF8F+T1tkQ+FxaBlNo2Y0AJNm+t280rOC/0rLbfkR7buyOwIeS3Ey7Sbq4qoYBoROS/A5YFziuqkbPXZy2z+aozht7nV+mBXaGjgBe319/gjY846tp2TMA7+gBw6IFiF46WHf0nNAA19GyRadVVT9P8nzgE6PAGe2m/5WDZX6S5DBapuJPaNmYa6/B/ri13tK370LgP2nPQKTfMH4rLYD9Q+CKvvzawMeTbEzrx6Oq6ldpw2menJZ59cqV1Pkq4P1pz6BdRAsyv4wpziFVdaPH1pzWuRDH1om0IMnzB9MOA05K8mPaDyn+ghV9mNb/36YF5X/bt/+Maa4b9wfe2bf/JlpfjryClrV9Q9/vkuQSWib4r5IcBLw8yR9pz35+1iBwt37ftnVoWcUfowWsgVuC9AcwMfT/u2lZ33+gZfOtC5zeA1dr0/r+w4O2HZLkOb38pUw8W3kx8O9JXkQ7Lm/58cgc1nlnWrDpL6sPM78yVbU0ycG088YoAHdqn/0q2hDGrwN+zvKPdZjKHsDrktxE+xxHGbyH0oZE/1Fv312mWPc44Oh+HO3at+8S2r553iw25VjacPpLe/0fpj1XFtqwuJuOjp+q+mWSA2hZxqN9fH9apvhZVfXNWdQ3VZv3A97bz6eLgPfQMqQne2cPIoZ2LFwMt9t9by6OVXpfHVBVy3o5f0xyMe18e1RVXZDk10w8Y3nk27T9+d6056NfC1y7ku+qt/bceREtMPwz4FVV9RVm5wTgP5KcT3t+9RUzLy5JkiRJ0qrJqv2wXdJtUQ/+7FJtGD9JkqTbpSTvAy6aYhhe3U6kZfyfCWzVh06m/1jr+qp61wI2bUGsu9mWtdkB71kjdV29eJ81Uo8kSZIkqUlyQVXttDrrOkSzdDtQVTsY3JUkSbdnSS4AtmXVh4HXmEh71vO3gNePgruSJEmSJGlFDtEsaY1J8njgyEmTf1hV0z3Db7pyXs+Kz1I9qaqOuDXtkyRJt11VtePKl9I4q6rjgeOnmH7Ymm+NJEmSJEm3XQZ4Ja0xVXU6cPoclHME7ZlqkiRJkiRJkiRJdygO0SxJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0pgwwCtJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0phYtNANkCRJkiRJK9pm8405f/E+C90MSZIkSdJtjBm8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0pgwwCtJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNCQO8kiRJkiRJkiRJkjQmDPBKkiRJkiRJkiRJ0pgwwCtJkiRJkiRJkiRJY8IAryRJkiRJkiRJkiSNiUUL3QBJkiRJkrSiS358HVsceuoaqevqxfuskXokSZIkSbeeGbySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI0JA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI0JA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJEmSJEmSJI0JA7ySJEmSJEmSJEmSNCYM8EqSJEmSJEmSJEnSmDDAK0mSJEmSJEmSJEljwgCvJEmSJGneJflQkt2SHJvksiTfTfKkGZa/Z5KT12Qbp5Nk2yTn9HZfkmS9Pn3H/v77Sd6bJAvdVkmSJEnS7Z8BXkmSJEnSmrALcC7w6araGngycNR0C1fVtVW135pq3HSSLAI+Dryst3sP4KY++4PAgcCW/d9fLUQbJUmSJEl3LAZ4JUmSJEnzKsmDgCurallVndYnrwf8vs9/dJIl/d9FSTZMskWSS/v8Ywfzf57kzX3665Kcl2RpkresQnvOTHJkkm8nuTLJ7jMsvhewtKouBqiqX1TVsiSbARtV1TlVVcDxwFNnqPO4nuX7zSRXJVnw4LUkSZIkaTwtWugGSJIkSZJu9/YGvjh6k2Qj4GPAP/ZJrwVeUVXfSHIXeuB3pKpe3Ne7D3A6cFySvWhZszsDAU5J8qiqOivJ2cCGU7TjtVX15f56UVXtnOQJwJuBPadp+wOASnI6sCnwyap6B7A5cM1guWv6tJlsBjwS2Ao4BVhhCOokB9Kygll7o01XUpwkSZIk6Y7IAK8kSZIkab49HnjB4P1hwMlVdUp//w3g3UlOoA3hfM3kx9n2596eBBxUVf+V5JW07NqL+iJ3oQV8z6qqmTJyRz7d/14AbDHDcotoQdmHATcAX0lyAfDrKZatldT52aq6Gbg8yT2mWqCqjgGOAVh3sy1XVp4kSZIk6Q7IAK8kSZIkad4k2QDYpKquHUzeFviH0ZuqWpzkVOAJwLlJ9mRSFi9wNC34O8rADfD2qvrQFHXOJoP3xv53GTP/3/ga4D+r6n962acBO9Cey3uvwXL3Aq5dcfXl3Dh4nWmXkiRJkiRpBj6DV5IkSZI0nx4DfG3StLcB3x+9SXK/qrqkqo4EzqcNYcxg/iuADatq8WDy6cAL+5DOJNk8yd0Bqmr3qtpuin9fZtWdDmybZIMki4BHA5dX1U+A3yR5eFq68fOAz61G+ZIkSZIkrRIzeCVJkiRJ82lvVnzW7LOB/wZ+2d8fnOQxtGzay4Ev0J5XO/Ja4KYkS/r7o6vq6CQPAs7pwzlfDzwH+NlcNr6qfpnk3cB5tCGYT6uqU/vslwPHAev3Nn9hLuuWJEmSJGkqqfKRPpIkSZKk+ZHkQmCXqrppodsybtbdbMva7ID3rJG6rl68zxqpR5IkSZLUJLmgqnZanXXN4JUkSZIkzZuq2mGh2yBJkiRJ0u2JAV5JkiRJ0h1ekscDR06a/MOqetoqlvN64OmTJp9UVUfcmvZJkiRJkjRigFeSJEmSdIdXVacDp89BOUcABnMlSZIkSfNmrYVugCRJkiRJkiRJkiRpdgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JhYtdAMkSZIkSdKKttl8Y85fvM9CN0OSJEmSdBtjBq8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWPCAK8kSZIkSZIkSZIkjQkDvJIkSZIkSZIkSZI0JgzwSpIkSZIkSZIkSdKYMMArSZIkSZIkSZIkSWNi0UI3QJIkSZIkreiSH1/HFoeeOu/1XL14n3mvQ5IkSZI0d8zglSRJkiRJkiRJkqQxYYBXkiRJkiRJkiRJksaEAV5JkiRJkiRJkiRJGhMGeCVJkiRJkiRJkiRpTBjglSRJkiRJkiRJkqQxYYBXkiRJkiRJkiRJksaEAV5JkiRJkiRJkiRJGhMGeCVJkiRJkiRJkiRpTBjglSRJkiRJkiRJkqQxYYBXkiRJkiRJkiRJksaEAV5JkiRJkiRJkiRJGhMGeCVJkiRJkiRJkiRpTBjglSRJkiRJkiRJkqQxYYBXkiRJkiRJkiRJksaEAV5JkiRJkiRJkiRJGhMGeCVJkiRpJZJ8KMluSY5NclmS7yZ50kK3CyDJFkmevQbq2S9JJdlpNddPkmOSXJ7kkiS7zmKdtybZc3Xqm0tJTuif+aVJPpJknT49Sd6b5PtJlibZYaHbKkmSJEm6/TPAK0mSJEkrtwtwLvDpqtoaeDJw1K0tNMmimd7P0hbAvAZ4k2wIvAr41q0o5pHAlsDWtP68amUrVNWbqurLt6LOuXICsBWwDbA+8OI+fW/aNm0JHAh8cEFaJ0mSJEm6QzHAK0mSJEkzSPIg4MqqWlZVp/XJ6wG/7/PvkuSjPSt1aZJ9+/TrB2Xsl+S4/vq4JO9O8jXgyCne3y/JF5NckOTsJFsN1ntvkm8muSrJfr34xcDuSZYkOWSW23R9kiOSXJzk3CT3WMkqhwPvGG3zDOXukeTMJCcnuaJnvqbP/gNwD2Cdqrqhqn46WG/tvn2X9n48ZLDN+yXZqW/fkj6/+vwp+2oW2z9TO1dQVadVB3wbuFef9RTg+D7rXGCTJJtNU+cWSb6T5MM9C/yMJOtPsdyBSc5Pcv6yG66bzeZIkiRJku5gDPBKkiRJ0sz2Br44epNkI+BjwD/2SW8ErquqbapqW+CrsyjzAcCeVfWaKd4fA7yyqnYEXgt8YLDeZrRM2CfSArsAhwJnV9V2VXVUkgcOgqGT/23S17kzcG5VPRQ4C3jJdA1Nsj3w51X1+VlsF8D2wMHAg4H7Arv16T8FNgKOmyKYuh2weVU9pKq2AT46nFlV5/ft2472Wbyrz5qyr5LsP832nzyLdk6rD838XCb2h82BHw0WuaZPm86WwPt7FvivgH0nL1BVx1TVTlW109obbLyyJkmSJEmS7oBWZ/gvSZIkSbojeTzwgsH7w4CTq+qU/n5P4FmjmVX1y1mUeVJVLZv8PsldgEcAJw1ioOsOlvtsVd0MXD5d1m1VfZcWMJ3JH4BRwPYC4HFTLZRkLdpQ1M9fSXlD366qa/r6S2hDSH8dOBl4LPC6XubBST4AnAp8E7hvkn/p78+Ypj3PAHYA9pqpr6rqhP+/vTsPt6So7z/+/rCI4ALuIqggEg0KoiKiiKIQd8EdFRVU4hIQJbjGDXeMa4y7qLggoCCGnxsgEcUEUWQZQEURSESJGhdEjcjy/f1Rdeb23Lnnzp2Ze2fmzLxfzzPPPae6u6pPV29T365q2rDKK7Kes/kA8K2qOn20SjPMU7Msf2lVnds/f7+XKUmSJEnScjHAK0mSJEljJNkE2KyqfjFI3gF45XA2Zg7qDdNuOG3an8Z8Xw/4fe+pOpOrp5U70zrfBTh2zPK7V9XvgWv6cMMA1zH+/4Y3Ae4OnNaDqLcFTkyyV1WdNYd1vA7YIMmtgVtW1UVJngccn+R1wE7AS6vqT0nuQQumHwg8GXj2tN91N+D1wAN7MHzstkqyLy2QPN3FVTUa2nqp9Rzze0Z5vg64FfC8QfLlwO0H37cEhvvKdNPLXGqIZkmSJEmSlsUAryRJkiSN92DgG9PS3gJcPPh+MnAQbbhfktys9+L9ZX9/70XA44CrllVYVf0hyaVJnlRVn+9DGe9QVefNsthVtEDsKI+59OCdk6q6Erjl6HuS04CXzBLcHefXbfE8uKq+keS5wI+Az/Xg7i2Bv1bV8Ul+Chw5XDjJpsAxwDOr6td93cZuqzn24J2zJAfQgs979B7UIycCByU5BrgvbajuK+arXEmSJEmSZuI7eCVJkiRpvCXev9s9jfYu3JE3ATdLckGS82hBYWjvxv0S7Z28yxP02xd4Ts/rQmDvZcy/CLg2yXlJDlmOclaZ3lv4CcCb+3DIX6QFxXdJ8kTae2tP69OOZMke0gCPBe4IfHT0Pt2evrzbakV9CLgNcEYv/7U9/SvAJbSA/0eBf1ig8iVJkiRJWixTo3JJkiRJkoaSnA3ct6quWd3ronXPRptvW5vv954FL+eywx+14GVIkiRJkpaU5PtVtdOKLOsQzZIkSZI0RlXda3WvgyRJkiRJ0pABXkmSJEkSSV4FPGla8uer6s0zzLs98OlpyVdX1X0Xav1WhSQnAFtPS355VZ20HHncAjh1hkl7VNVvVmb9JEmSJEkCA7ySJEmSJKAHcpcK5o6Z93xgxwVdodWgqh43D3n8hrVw20iSJEmS1hzrre4VkCRJkiRJkiRJkiTNjQFeSZIkSZIkSZIkSZoQBnglSZIkSZIkSZIkaUIY4JUkSZIkSZIkSZKkCWGAV5IkSZIkSZIkSZImhAFeSZIkSZIkSZIkSZoQBnglSZIkSZIkSZIkaUIY4JUkSZIkSZIkSZKkCbHB6l4BSZIkSZK0tO232JSzDn/U6l4NSZIkSdIaxh68kiRJkiRJkiRJkjQhDPBKkiRJkiRJkiRJ0oQwwCtJkiRJkiRJkiRJE8IAryRJkiRJkiRJkiRNCAO8kiRJkiRJkiRJkjQhDPBKkiRJkiRJkiRJ0oQwwCtJkiRJkiRJkiRJE8IAryRJkiRJkiRJkiRNiA1W9wpIkiRJkqSlnf/zK9nqFV+e93wvO/xR856nJEmSJGnVsQevJEmSJEmSJEmSJE0IA7ySJEmSJEmSJEmSNCEM8EqSJEmSJEmSJEnShDDAK0mSJEmSJEmSJEkTwgCvJEmSJEmSJEmSJE0IA7ySJEmSJEmSJEmSNCEM8EqSJEmSJEmSJEnShDDAK0mSJEmSJEmSJEkTwgCvJEmSJEmSJEmSJE0IA7ySJEmSJEmSJEmSNCEM8EqSJEmSJEmSJEnShDDAK0mSJEmSJEmSJEkTwgCvJEmSJEmSJEmSJE0IA7ySJEmSJEmSJEmSNCEM8EqSJEmSJEmSJEnShDDAO4GSfDjJrkmOSHJhkouSPGaW+W+X5LhVuY5j1uPvknw/yfn970MG0+7d0y9O8t4kWcEytk3y3SSLknx9hulH92mHJHlDkj17+mlJduqf/2lFf+MM5b0wyQVJvpLkBj3tAUneNW2+65Kc2/+dOEg/rdfvuUl+mOS5g2kzbrN5LHNRkh8leV+Szfq0WyX5ds//sYNl/i3J7QbfD0vy80H+j+zpuye5sqctSvL1JLfu0zbq389Nsk/fv7dbxvZdXG/T0vdP8r6F3h5zMdzPlmOZy5LccnmW6cvtnuT+s0zfK8krljffvqzH1oQeWyuwbRcfP/MlyVF9vd4ySHtNkr0H34e/4dwkrx1MG23TC5Ocl+Qfk6zXp+3a8/5ekjv3tM2SnDSqu542rPNzM3XuGW7THyX54CDvmyc5JclP+t+bLUCZP0nyhSzjfDdte+6f5NdJzunLn5RZjv3Bco+dSzlz2deT7JjkjF4ni5LsM5i2dZIz+7odO8jjNkm+1OvwB0m+soz1WO7z54pK8rb+Oz41SHtGkhcNvi/3fcw8bMskeXOSH6edqw7u6U/o85+e5BY9bZskxwyWvX2Sb/TlLpz2W2bctwfT/6Xvoyv1f4Us43qWZKskTxt83ynJe1ewrMP6MXxBksdNm7Zb3wbnJtki/b447bzzpcHnZR5Hc1yXhbxfSt/HLu7T7jXPZZ6Tdt76VpJHD5ZbyOvuvN0fTFuXBbv2rOR6nZape6NZjxFJkiRJksYxwDuZ7gt8B/hCVd0N2At497iZq+oXVfXEVbVys/hf4DFVtT2wH/DpwbQPAs8Ftu3/Hr6CZbwC+GBV7QD8/XBCktsC96+qHarq3VX12qpaKlAFLHcjU5L1x0w6ANgBOAd4WJIArwHeOG2+/6uqHfu/vaZN27eqdgR2Bd42alRj/DabrzJ36PlcDfxbT38q8EngfsBL+29/DHB2Vf1iWh7vHuQ/bMQ/vaftAHwPOLCn3xPYsE87tqoOqKofsPIWcnss0yz72ULYHZixcTrJBlV1YlUdvoJ5e2xN7rG1WiXZAaCv125JNk2yObBzVU0/lk4f/LY3DNJH2/RuwN8BjwRe16cdCjyBtn+9oKe9BnhLVdW0/Pcd5P+rQfq7+76wHbA98KCe/grg1KraFji1f5/XMnvexwL/nuRWzN2xVXXPvvzhwBeS/O0ylnls/43LMpd9/c/AM3udPBx4T6Yefnkb7fdtC/wOeE5PfwNwSlXdo6q2Y2p7zmhVnT+TbEo/hwHrJ9k+ycbA/sAHBrOuyH3Mym7L/YHbA3etqr8FRgHcQ4FdgE8BowDpm3reI9cCh/bldgEOzFSAf9y+TQ9gPQ74GfDAsRtufmzF1PpTVWdV1cHLm0mS2wP70o7fHWnnwKF9gXf0Y+7nY+6Ld2fMNXSWcjcYM2khz+mPYGo/ey5t35vPMu9ZVXcBDgbel2SPPm0hr7vzHuBdBdceSZIkSZJWKwO8E6Y33v64qq4bNMjcEPhLn/6gwRPo5yS5SVrviAv69CMG03+d5HU9/aVpPZEWJXn9cqzPaWm9Xr6b1rtkt3HzVtU5gwamC4EbpvXa3By4aVWd0RvGP0VrhB5X5pFpPRf+M8klSYaNdH8FtuzlXTpt0ZOBW/ffvlvPZ4kGviSHAxv3eY7qaU/vv+/ctN7T6/f0P6b1LjoTuF+Sw9N6JC1K8o5BthsCmwDXAM8AvlJVvxv3+2ZxY+BPwHVz2GbzUmZV/RV4GXCHJPfo+W0MbARc3xs2Xwy8fXnz7g2DNwF+l9Yr5TPAjn07bzOtd8ND03o3nZ3k80luPEN+z+r74DdpDYdDC7U95vI7F+9nvZfG6/vvOD/JXXv6LZKc3I/ZDwOjnl+Lj93+/SVJDuufDx7sb8ck2Qp4PnDItH38XUm+QWtI3T9TPZtvleT4ftx/L8muPX2pc0gv3mNrHstchcfWen2/22ww/eK0npSPSetleU5a77DbzJDXEnWZ5I+Dz0tdN5LcKMmX03o6XZDWE/EaWt2vB9wAuI4W6HstK6AHSZ8LHNR/62jbbQJck2QbYIuq+uYKZH8D2jV1VKd70wIm9L+P7Z/ns0yq6ljacfS0Zc07ZvlvAB+hbReS/H2vm/P6cb5JWs/EvYC3D86zS803yHbWfb2qflxVP+mffwH8CrhVr5OHAKORQ4bbbXPg8kEei0afk7ysnxfP6+eL6efPeyf5ZlrP2ZP6sTr2PiTJ+kne0fNclOSFs+RzPXCDvu4b99/8UuC9VXXNYH1X9D5mhbZln/wC4A1VdX2fPnpI4Hra+WK0D+4GXDHKp897RVWd3T9fBfwQ2KJPHrdvAzwYuIAWOHzqoI4OS/Lxvs0vSe9N3Kd9sW/TCzPomTmY/sYs2YP4zX35w2nBt3PTRoDYPVM9am+c5BODOnxCr9cj+/nl/CSH9CyvBW4K3Liqrq2qywdlHQA8GXhtWo/OJa6tfZ6tWPoaOu46eViSjyQ5GfhUkrtl6lq2KMm2LNA5vSftDXyqmu8Am/V9cN7KBKiqc2nn6oMGyQtx3V3i/mDcvtL3jW8lOSHtnuBDmRptYab7xIW+9ixT2ogQZ/XjYs7/v+rLPrcve9Z1f75yRVZZkiRJkrSWM8A7eR4BfG30JclNaT1IRk++vwQ4sD8dvxvwf8OFq/WK3JHWOPQb4MgkD6X1AtiZ1uvh3kke2PM/PVPBnuG/4ZCJG1TVzrRGpLk+1f4E4JyquprW2Hj5YNrlTDVAjrM58ADg0bTGwZGfAi/KYEi5gb2An/Yn8U+fKdOqegVTT+vvmxZQ3wfYtW+362i9QABuBFxQVfcFfkDr7XK33lPgTX2ed9B6W98K+A9aj59hb6CRG/ZGnO9kMKxed1SSRcBFwBur6jpm32bzUeZwm1wHnAfcFfgs8DDaPngY8A+0RsY/z7DoQb2h8+NZcujH3ZKcC/w3sCfw8d5odgBTPSh+Opo5bdi6VwN7VtW9gLOAfxwW1Bs2X08L7P4dS/ZQW8jtMddjZOh/++/4IO14hXbcfLuq7gmcCNxhXPkDrwDu2fe351fVZcCHmOqVM9rH/4a27Q6dtvy/9HnvQzsej+jp484hHluTeWxdT+sl/DiAJPcFLquqXwLfBnbp+90xtIDznMxy3Xg48ItqPTTvDnytqn7Y1+ls4HPAnYFU1TkzZH2/tADfV5PcbVz5VXUJ7R7m1sBbacHNFwPvA97Mkr0Yhz7Rj8/XTGugP6RvuytoD1Gd29NvU1VX9DKv6OUxT2VOdzZT55V3jzmvzNbjdfHytBE+7lNV96AF9Z5TVf9JO7+8dHCeXWq+vvxc93X6+u5MC6D8FLgF8PuqurZPHh5D7wc+ljZs8KvSh41N8ghagPG+fV3+eVr+GwL/Cjyxqu4NfJy2zUdmug95LrA1U+fJo8bl04Ofx9N6Jl4KXAncp5bu5Tc01/uYldmWANsA+/Rzyld78BDaNe8k2rF+NO06Ob0n5TDfrWgjZZzZk8bt29CCukcDJwCP7ttt5K60c9XOwOsG057dt+lOwMHpw0YPfKz/dnrA7SnAUbRr2ejaP300mtcAV1bV9r0O/512vtmiqu5erSf1J/q8VwO/pPVk32iYSVUdwdS+vy8zGHMNHXedBLg3sHdVPY0WGP6Xfi3biVb/C3JO7+lb0HpXj4z2t/koc7rheWVBrrvT7w8Yv69A2+8OpfXU3gZ4/Lj7xIW+9iS5y5jz9LmZeqjqVVW1E63n84PSexXPRVV9pKp2qqqd1t9k07kuJkmSJElah4wbVkxrrocBzxp8Pww4rqpG77j6D+BdaT3kvlBVl09vT05yQ+DzwEFV9V9pvVoeSmvYhPZU/bbAt6pqbI/cgS/0v9+nDbU3q95w8rZeJvTeitNMH+Jyui/2wMUP0nudpb2D7JG0BsyTk/wWOIPWSLrNstZrjD1ojXjf69txY1rPGmgBqeP75z/QelEfkeTLwJcAqurT9CEc03pLvxd4RJJn0hrnDu2/4w5V9Yskd6IN03n+IMi5b1WdlTZ0538m+RqzbLN5KnO69LyvBB7V874Z8HJa49pHgZsB76yqM2jByzf2dXoj8E7g2T2v06vq0T2Pl9Ma8p8/plxow0puB/xHr4Mb0Op16L7AaVX1657vsbTA5oJuj57/XI6RoeHx8vj++YGjz1X15SRz6Q2ziNZQ+kXgi7PM9/keSJxuT2C7wfnhpmm9dWc6h3hszV+Z062KY+tYWo+lT9Aayo/t828JHJv2gMQNaIGtuXooM183TgfekeRtwJdGAf+qevHiH5z8P+B5SV4F3IM2ZO9HaY3wd6yqP6a9E/KLPc9xRtvuXNp5gh5k/kX7mGNpPbgO7QHtfavq530/P57W+2z0vtV3V9U7erDquCRPqapjGGOeypzx9/T8Dxkzz2yG++7dk7wJ2IxWNyeNWWbG+ZZjXx89YPNpYL+qun5MEHt0DJ3Uj4uH0x5YOyfJ3Wnno0+MAlFV9dtpy98FuDtwSs9+fVowfmSm+5A9gQ+NAs1V9dte1oz5VNU/0wPLSY6g9fY8gLafL6qq0cMly3UfszLbsuezEfCXqtopyeNpQb7dquoU4JS+3H7AV4C7JHkJrafni0bbM6034/HAi6vqDzOs62JpQ+Y+Ejikqq5KG0nhocCX+yxf7kHtq5P8CrgNLcB4cKbee3t72rH7m8Ubo+qyJL9Jcs++zDlV9ZuZd5fF9qSds0Z5/C7JJcCdkvxrX6eT++SPAYfQhlj+bJIn0R5Y+lNVvX+2QpZR/kzXSYATq2r0ANQZwKuSbEm7bv6E9nDUQp3TZ9zf5uk6Mt3wvLQg192q+q9pP2S2feW7PchKkqNpD3v+hTH3iQt87bmI9sDBbJ6c1qN9A9rDqdvR7t8kSZIkSVpp9uCdIGlDJ25WS75Hawfgq6Mv1d6veQAtWPKd9CFgp/kQrQFq9F67AG+tqXdP3bmqPtbLnEvvxKv73+tYxkMDvfHrBNq75kaNPZfTh37ttqQ1ls/m6sHnUePTnrSg9M9ovdWOpDX2faVqqXcizlWATw62zV2q6rA+7S+jwFlvQN6Z1oD6WAa9rAHSeimNegO9mtZz8WpakGs0JOOoZ8BptEDaEnrw8mxaMHOZ22w+yuz5rE/rKfHDaZNeS+tB9VRao/qzgbf0PH9ZrVfG9cBH+7aZyYks+/1+oTXCjepgu6p6zgzzzVrHC7U95niMDI07XmZa/2tZ8jx9w8HnR9F6w90b+H7GvwfwT2PS1wPuN9iuW1TVVWPOIR5b81xmz2dVHVtnAHfuDeqPZSoY9q/A+6r1gnseS+5fI4v3wR64G70vccbrRlX9mLZPng+8NckSQ2Em2ZvWu+pGwN2r6snAM5JsUlV/qKo/9t/5FWDDtJ5ZM227O9GOoV8N0sJUL8bX9X+fob1Hkqr6ef97Fa2n21LbrtpwvF8bbLtfZmoo4M2H5c1XmQP3ZOq8siI9eBcvTztGD+p1+3pmrttlzresfT1tFJEvA6+uNlQstPfUbjY4Jy1xDFXVb6vqs1X1DNp7RR9I259mO5cEuHCwv21fVQ8dTJ/pvDpTnsvKhx5UAvgx7V7lybRA+LZ9+grdx6zgthzlPXrg5QTafd8w302Y6kn5Vtr54vv0ERH6QwvHA0dV1RcGi47btx8ObAqcn+QyWgDtqYPlhvdf1wEbJNmddp24X7Ue2Ocw8z53BO2dws9iqjfqbJaqw2rDAd+Ddm49kKletXvSHvR6I23bf4D2EMGJrLgZr5N92uJra1V9ljaSxf8BJyV5yLR85vucfjktiD4y033zfJQJS55XgAW57s5k3L4y/Zgu5nCfuBDXniyjB2+SrWkPGexRrQf6lxl/LpYkSZIkabkZ4J0sDwa+MS3tLcDFoy9Jtqmq86vqbbSGjCUCvEkOBG7SgzgjJwHP7j08SLJF2jtRqardBg0mw39fZzmlDVf2ZeCVVfUfo/RqQwNelWSX3lj+TNqQosvrHGDvJJtW1Y9o7xx7J62xfXlck6khB08FnjjaHklunuSO0xfo227T3jD0YpZ+ov+NTA3fuTGtQep6YJMkN0sfTrA3KO1KG5Z2ehmb0BrJfjrHbTYfZW5IazD+WS35rsRtgdtVe9/kJj3fojdcjRqNu8fR3uU3kwcwNQzlON8Bdk1y59F2SPI30+Y5E9g97V22GwJPmiGfBdke83SMfIupxvhH0HraQBtu8tb9d21EG5J8NGTh7au9d/NlTPW+u4r2nr65OJnBe/WS7Nj/znQO8dia/zJX2bHVg/AnAO8CflhVo151mwI/75/3G5PPZbSALbSh/Uf1N+N1ozf8/7mqPkMbzvNe037zi2j7zyZMNdSvR3v/6W37NidtmNr1GPQAHORzK9qDSu+b9oDBfrTehb9jatuN6mWDUYN9X49HM8O26+Xfn6nz0omDbbMfS+8LK11mn/4EWi/Jo6H14B1zXjl8zPIPog1J/NGedBPgil7ucEja6eeIcfONzLav34C2X32qqj4/WqDXyTeA0bubF2+3JA/pxxtpPSG3oQ2hejJtfxpNu/m09biI9n7f+/XpG2aWYVS7k4HnjwLNPc+55PNGWnBsQ1oPXwa/eTNW/D5mubdl90XaO40BHkQLPA+9jDY88Oj9q8N8Q+vZ+sOqete05cbt208FDqiqrapqK9ow1w/Nku9nnm5T4HdV9ee0h4J2GTPfCbQA8n2Y6lU+23Vr+nXqZv2YWq+qjqdtz9E5ZhHw9P75ZbSA79XVHkyaq+nrMuN1croe9Lukqt5L2647DKYtxP3SicAz0+xCG8Z6cY/2+SozbTjh19AeJhua1+tuTxreH8DM+wrAzkm27vdB+9BeNTDrfeJCXXuq6qIx5+kdq+r3tHdC/wm4Mm20oUdMz1OSJEmSpJXhEM2T5RHAcdPSngb8D204PoAXJ3kw7enyH9B69w4bcl5Ca0Q5t3//UFV9KO19mGf09o0/0hrJluipNA8Oor3/6jVJRg1DD632/tUX0HoSbdzX+asz5jCLqjolyWdovQ7/TBtu9Fm09wwvzzC6HwEWJTm72rtCX00blnY92tCbBwL/NW2ZmwD/ljb8dWi9G4Gp3kA19c6vj9F6t/2M1mPq3sCHk1xPa1Q6vKqGjWFHJfk/2jCNR1bV93v62G02T2Ve3cv8Oi2wM/Rm4FX989G0BugX0RrFAf65N4QWLUD0vMGyo3fKhfaewwOYRVX9Osn+wNGZeq/eqxk0clfVFUkOo/VUvILWK2TUML8qtsfKen3/fWcD36QFPKiqa5K8gRbAvhT4UZ9/feAzSTalbcd3V9Xv04YfPC6tp8oLl1HmwcD7096FtwEtyPx8ZjiHVNXVHlvzWubqOLaOpfWW3H+Qdhjw+SQ/pzWQb83SPkrb/t+lBeX/1H//yWOuG3cG3t5//zW0bTlyIK3X9p/7fpck59N6gv8+yUHAC5JcS+sJ95RBAHfj/ts2pPUq/jQtYA0s0Ytx1BvzXbRei3+lBaw2ovWsGwXtvs5UMBTaO3if3vNfxNR7JQ8HPpfkObTjcvHDI/NY5o1oAZaH9F5tc7VPkgfQAhaXAk+o9s5JaMGXM2nH0/lMBa2OAT6a5GBaAHbcfHPZ159M61F4i36OBti/2vDVLweOSRv++Zy+LLTj4329jtcDjqiq7/XydgTOSvJX2nDD/zRal6r6a5InAu/t570NgPcAF86yfY6gDdW/KMk1wEer6n2z5ZP2vtDvVe+BmOSMvo8uqqrz+jlrue9jVnJbHk47bxxCO84OGOR7O2Cnmhr94J20Y/n3tN76u9KGBT9/cN/3T/2BmaX27b5PP4zBeaWq/pTk28BjZtnWX6MF00fvVv3OTDP1evwG7R3No1cHLAKuTXJe337D96O+iXaduoB2TXo9LSD4iX7dAHhl//tM2rn3UNqQve8AnpDkH2cIbo8z/Ro67jo53T7A0/t+9j/AGwbTFuKc/hXaMNoXA39myVe3zEeZ59DOK78CDq6qU0cTF/C6u8T9wZh9Bdp93uG00S++BZxQbWj4/Rl/n7hg157Z9HPGObTzyyW0V2BIkiRJkjRvUis8uqZWtR78uW/vpSFJkiRNhB6UPRt4UrX31EozmmlfSRsK/CXV30u8Ltlo821r8/3eM+/5Xnb4o+Y9T0mSJEnS8kny/araaUWWdYjmCVJV9zK4K0mSpEmSZDtaj9NTDe5qNu4rkiRJkiTNjUM0a94leRjwtmnJl1bV45Yzn1ex9LtUP19Vb16Z9ZMkSdKq04fpvdPqXg+t+cbtK1V1GnDaql4fSZIkSZLWVAZ4Ne+q6iTgpHnI582094hJkiRJkiRJkiRJwiGaJUmSJEmSJEmSJGliGOCVJEmSJEmSJEmSpAlhgFeSJEmSJEmSJEmSJoQBXkmSJEmSJEmSJEmaEAZ4JUmSJEmSJEmSJGlCGOCVJEmSJEmSJEmSpAlhgFeSJEmSJEmSJEmSJoQBXkmSJEmSJEmSJEmaEAZ4JUmSJEmSJEmSJGlCbLC6V0CSJEmSJC1t+y025azDH7W6V0OSJEmStIaxB68kSZIkSZIkSZIkTQgDvJIkSZIkSZIkSZI0IQzwSpIkSZIkSZIkSdKEMMArSZIkSZIkSZIkSRPCAK8kSZIkSZIkSZIkTQgDvJIkSZIkSZIkSZI0IQzwSpIkSZIkSZIkSdKEMMArSZIkSZIkSZIkSRPCAK8kSZIkSZIkSZIkTQgDvJIkSZIkSZIkSZI0IQzwSpIkSZIkSZIkSdKEMMArSZIkSZIkSZIkSRPCAK8kSZIkSZIkSZIkTQgDvJIkSZIkSZIkSZI0IQzwSpIkSZIkSZIkSdKEMMArSZIkSZIkSZIkSRPCAK8kSZIkSZIkSZIkTQgDvJIkSZIkSZIkSZI0IQzwSpIkSZIkSZIkSdKEMMArSZIkSZIkSZIkSRPCAK8kSZIkSZIkSZIkTYhU1epeB0mSJEmSNE2Sq4CLVvd6aJW7JfC/q3sltFpY9+sm633dZd2vm6z3dZd1v25aVr3fsaputSIZb7Bi6yNJkiRJkhbYRVW10+peCa1aSc6y3tdN1v26yXpfd1n36ybrfd1l3a+bFrLeHaJZkiRJkiRJkiRJkiaEAV5JkiRJkiRJkiRJmhAGeCVJkiRJWjN9ZHWvgFYL633dZd2vm6z3dZd1v26y3tdd1v26acHqPVW1UHlLkiRJkiRJkiRJkuaRPXglSZIkSZIkSZIkaUIY4JUkSZIkaQ2S5OFJLkpycZJXrO710cpJcvsk30jywyQXJnlRT795klOS/KT/vdlgmVf2+r8oycMG6fdOcn6f9t4kWR2/ScsnyfpJzknypf7dul/LJdksyXFJftSP/ftZ7+uGJIf0c/0FSY5OckPrfu2T5ONJfpXkgkHavNVzko2SHNvTz0yy1Sr9gRprTN2/vZ/vFyU5Iclmg2nW/VpgpnofTHtJkkpyy0HaKql3A7ySJEmSJK0hkqwPvB94BLAd8NQk263etdJKuhY4tKr+FtgFOLDX6SuAU6tqW+DU/p0+7SnA3YCHAx/o+wXAB4HnAtv2fw9flT9EK+xFwA8H3637td+/AF+rqrsC96DVv/W+lkuyBXAwsFNV3R1Yn1a31v3a50iWrpP5rOfnAL+rqjsD7wbetmC/RMvrSJau+1OAu1fVDsCPgVeCdb+WOZIZzsNJbg/8HfDfg7RVVu8GeCVJkiRJWnPsDFxcVZdU1V+BY4C9V/M6aSVU1RVVdXb/fBUt0LMFrV4/2Wf7JPDY/nlv4JiqurqqLgUuBnZOsjlw06o6o6oK+NRgGa2hkmwJPAo4YpBs3a/FktwUeCDwMYCq+mtV/R7rfV2xAbBxkg2ATYBfYN2vdarqW8BvpyXPZz0P8zoO2MNe3GuGmeq+qk6uqmv71+8AW/bP1v1aYswxDy0Y+zKgBmmrrN4N8EqSJEmStObYAvjZ4PvlPU1rgT7c2j2BM4HbVNUV0ILAwK37bOP2gS365+npWrO9h9bwd/0gzbpfu90J+DXwibShuY9IciOs97VeVf0ceAetJ9cVwJVVdTLW/bpiPut58TI9cHglcIsFW3PNp2cDX+2frfu1WJK9gJ9X1XnTJq2yejfAK0mSJEnSmmOmJ7VrhjRNmCQ3Bo4HXlxVf5ht1hnSapZ0raGSPBr4VVV9f66LzJBm3U+eDYB7AR+sqnsCf6IP1TqG9b6W6O9c3RvYGrgdcKMkT59tkRnSrPu1z4rUs/vABEryKtqrOY4aJc0wm3W/FkiyCfAq4LUzTZ4hbUHq3QCvJEmSJElrjsuB2w++b0kb3lETLMmGtODuUVX1hZ78yz5UG/3vr3r6uH3gcqaG/Buma821K7BXkstow60/JMlnsO7XdpcDl1fVmf37cbSAr/W+9tsTuLSqfl1V1wBfAO6Pdb+umM96XrxMH+57U2YeHlZriCT7AY8G9u3D74J1vzbbhvYwz3n9Pm9L4Owkt2UV1rsBXkmSJEmS1hzfA7ZNsnWSGwBPAU5czeukldDfn/Ux4IdV9a7BpBOB/frn/YB/G6Q/JclGSbYGtgW+24d7vCrJLj3PZw6W0Rqoql5ZVVtW1Va0Y/nfq+rpWPdrtar6H+BnSe7Sk/YAfoD1vi74b2CXJJv0OtuD9t51637dMJ/1PMzribTrh70411BJHg68HNirqv48mGTdr6Wq6vyqunVVbdXv8y4H7tXvAVZZvW8wvz9LkiRJkiStqKq6NslBwEnA+sDHq+rC1bxaWjm7As8Azk9ybk/7J+Bw4HNJnkMLCjwJoKouTPI5WkDoWuDAqrquL/cC4EhgY9r73UbveNNkse7Xfi8EjuoP6lwCPIvW0cZ6X4tV1ZlJjgPOptXlOcBHgBtj3a9VkhwN7A7cMsnlwOuY33P7x4BPJ7mY1ovvKavgZ2kOxtT9K4GNgFNa3I7vVNXzrfu1x0z1XlUfm2neVVnvMfgvSZIkSZIkSZIkSZPBIZolSZIkSZIkSZIkaUIY4JUkSZIkSZIkSZKkCWGAV5IkSZIkSZIkSZImhAFeSZIkSZIkSZIkSZoQBnglSZIkSZIkSZIkaUIY4JUkSZIkSZKkdVySSvLOwfeXJDlsAco5OsmiJIfMd95zKPuyJLdc1eVKkjTfNljdKyBJkiRJkiRJWu2uBh6f5K1V9b8LUUCS2wL3r6o7LkT+kiStK+zBK0mSJEmSJEm6FvgIsFTP2iR3THJq73l7apI7zJZRkhsm+USS85Ock+TBfdLJwK2TnJtkt2nLHJnkQ0lOT/LjJI/u6esneXuS7/Xyn9fT09Mv6OXs09N3T/KtJCck+UHPc6l28CRPT/Ldvi4fTrL+imw0SZJWBwO8kiRJkiRJkiSA9wP7Jtl0Wvr7gE9V1Q7AUcB7l5HPgQBVtT3wVOCTSW4I7AX8tKp2rKrTZ1huK+BBwKOAD/VlngNcWVX3Ae4D/H2SrYHHAzsC9wD2BN6eZPOez87AocD2wDZ93sWS/C2wD7BrVe0IXAfsu4zfJEnSGsMAryRJkiRJkiSJqvoD8Cng4GmT7gd8tn/+NPCAZWT1gD4fVfUj4L+Av5nDKnyuqq6vqp8AlwB3BR4KPDPJucCZwC2AbXsZR1fVdVX1S+CbtAAwwHer6pKqug44eob13QO4N/C9nu8ewJ3msH6SJK0RfAevJEmSJEmSJGnkPcDZwCdmmaeWkUdWsOzp+VbP64VVddISBSSPXM58llgc+GRVvXKF1lKSpNXMHrySJEmSJEmSJACq6rfA52hDI4/8J/CU/nlf4NvLyOZbfT6S/A1wB+CiORT/pCTrJdmG1qP2IuAk4AVJNhzll+RGvYx9+jt6bwU8EPhuz2fnJFv3d+/uM8P6ngo8Mcmte543T3LHOayfJElrBAO8kiRJkiRJkqShdwK3HHw/GHhWkkXAM4AXASR5fpLnz7D8B4D1k5wPHAvsX1VXz6Hci2hDLX8VeH5V/QU4AvgBcHaSC4AP00amPAFYBJwH/Dvwsqr6n57PGcDhwAXApX3exarqB8CrgZP7bzoF2BxJkiZEqpY1moYkSZIkSZIkSQsnyZHAl6rquJXMZ3fgJVX16HlYLUmS1kj24JUkSZIkSZIkSZKkCWEPXkmSJEmSJEmSJEmaEPbglSRJkiRJkiRJkqQJYYBXkiRJkiRJkiRJkiaEAV5JkiRJkiRJkiRJmhAGeCVJkiRJkiRJkiRpQhjglSRJkiRJkiRJkqQJYYBXkiRJkiRJkiRJkibE/wdUlMaosEvzzAAAAABJRU5ErkJggg==\n", 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", 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" ] diff --git a/howard's advisory stuff/presenting data.ipynb b/04PostgreSQLDumpFIle/presenting data.ipynb similarity index 99% rename from howard's advisory stuff/presenting data.ipynb rename to 04PostgreSQLDumpFIle/presenting data.ipynb index 3135aed..2d8111a 100644 --- a/howard's advisory stuff/presenting data.ipynb +++ b/04PostgreSQLDumpFIle/presenting data.ipynb @@ -59,7 +59,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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38okgmFFau7LVtjUFCQkbv98t99DEheLrW3FWr/MOOVXvDS6MVFeWhfJ2vC/Ze9IkUp4jOaEsSUrtjZOYIuGEagn7cZzkkPpTqh03qVgiifvdsmBYShJ8ziF/ApUJiVrhT9RKf5JT6Uv0VUiiv9JN0opwklaFg1oVTqQ6nCChSMAJh31OOOLzuxFHJOS6hF1XwgoRRUIuRNRHRB2JqA9XA7gaQEnwa8gf0IgvoGF/gLAvSFgCEg4nEIokEA4HCUUSCIWDEooECUWSCblBQpGg1LhBQm4CYTdIjQYlpEEOvwhKSBPMfk0gJAkSJoGwEyDsBgg7nvL2+eWwMnb8uH7HKGK/g+tzUL+g/tq/IXzBGP4d0zDhojVAd+D3QF9Mx5wAXAwsxwRCfILxhz0CBERkG/ArYA1wAfAYZoT/eWC+134Oxs5/CLgG+Ni7ns+7BsC3ROQe73oB4EWMEjpXVZcAqGqpiPwRM4voIiIHVPVe7/z/JyJpwOUYZdQf2Oztu09EcoEP69z3VlVd4b3vg5nhTANSVXWBt307MNAzXy2rPdFTOmWq+ivv82rgMlXdVue5DhaRLcBaVb0s+uIicjdmFhUEXq+9FxH5CXA9sAOjmB/yts8C7lLVj0QkB/hIVfNExIfxpXwGUOBpVX3Me56XA0nAAuA7GCV3FvC8iFQC5wHvRLX7FYx5ToCZqvo/3rXLgN9iFHYlcIWqfkoDdDgFcL5vjYstsHHc7PH59ny/W+7m9QmBsX0L2XbtvMig/44/o9Td9/fiSxMn7t/Ud7bvX2OvL07YEJHLa15hdcoin7PdL3mfZJJZMxw30EOLkzRS4atISBSXzEBPega6+M8IdtOgP81XHcCpdNxAqVMZKnbKQ4ek0gkH9gqJJeIklPn9ieUkBstDwWB5JCGlwklIqHTwaaDCl+iUO8m+Ckn2H5L0UAnp1aWkh0rICJWSHjpEWlU5Kf5Kkv1VpCZWkJAUxu938SWjmkSEaiJaJRG3krC6ElYl7IYl7IYJa5iQG5Gw1ioZIeJi/qqZzkTwGwWjAVxNQAmiJGJ+vMnS8joUsq3pY/Ix3+1qVZ0sIj/FmFn2AzcCLma0uAV4Frgf+AvwA+/8AGaUnwI8helMJ2BG/MqR2XOi97cKeBmTrno4MBj4KfCIqt4rIqVAGvA+cE4dWX8CLPbaniAiw4Gwt2+/qp4pIvuAm7z7ApinqveJyGTg5qi2qqPev4151r8F/ui1uxG4F6MExnFk1hIr+ZiO9bK6O0TkUswMZAzm+bwpIuOBcuDLwChMP3oghuvcDJwCjFLVsIhkedsfV9X7vOs9h1FQr4jIbZ5cH3n7amXqiVEko4Ei4D0RuVJV38D8bxep6k88E99NwAMNCdThFABG61laSLlI2U9ys5e+n5x0NiIXBMJa9YtnI6GKpG4HxPH1La45WDa2Kt2/Z+fwmksOFZwWGXVw50NlP/Unrz7o3pH2kvYfs8j3Ysa+yM7yhPSxqykds8HxB2Rg8GD2iLJd6T2cTTU1gYi7PhwJ74hoZH9yqi8pPSMht/y0YM/yzGB/Ny2QlRT0Jee6kF3mVBWVSGVRsZSXFzvlNaVSGS6XqsRqwilhwtl+f40/K1hZ3CO4vyyYuL08MVgeCiaWuQkJleGEhErx+2siPl8Ix4kkACkIvkp/clWZPzV0iLTqUtIrS0mvLiGjpoT0yCHS3VLSpIw0p4KUYBWJwWqSE8MEkiL40hRJQ6TxkbqrNYS1kohbIWGtJqI1EnarCbshMQom7CmYiIQVwi6EFfyyI8Z/USaQ6Y0KqzBmmoeAeZjOIBtTDW88EMGYhGrXxtwFrPKOewcTepqAGakXY+z/92IUSTKwF9PxBTA+hL9i/AyISDdMhxjGzDpGicg679wnvGvfhTGvVAJvASHMKPo1EbkMM1P5vNe2YMw6qOpMzzIHcBUwQERWerLXVvt72GtrFsb8VOO97sJ0yt1E5OcYxZYpIuu9ZzQAeFREHgeSRGQT8Gvvnm7AM1l59/cExt+SAUzEmLUGYBRiEFiqqhXeubV/U4BhwAsiUoPxp9RyMfCEqoa9+zzobb9QRH7sPfMszCztLRrmbGCWqhZ613we8/9+w3sGtf6PpcAljbRjFYDFEIHI7zIzFjyTnjZIRSbUbv/ZC5EPg2HG7+k5aj7QV5yUAwv3vZXzmZ7fOOXFQ+UrQ4vTRv169J1r/3zBzZGfHrgjI6GgKHzFoXn6ZODvNZsGVaQ/dV566Vr/xl4jtm789OIVWjxqu2a4vpyU/TkjdhTmXHqoLLlP4j6tyNlbvTPslq2o1MheV91DPkGTUgOZRVkJ3cNZwR7hXsFuTqq/X0rQl9RVcHqJiBOpdqsPVVQeKpEKt1gq3GIpjxQ7FU65VCdWEUoLE8kBchAEVP3+mpKEhIqiYLDiUDCxvCInWBbqlbg1EkyocAIJVX6/vybR5wulOU4kHcgSOfb3UaOB6nJSS8tIKyulS0UJGVWlpNeUkB4uJT1SKl2kLNBFygMpvkqSEqpJTKwhkBghmBPBScWYP+pjQQPbo+mP6bDXqupQEXkD88PfhjFZ/hlj838f07GWYzqtTzGj6D8DdwI7gVeAH2I63d2YDq6Xd85mzEj1x8AXMKP5lRjzUAj4BmYmUobpQ0Lecb/AdGIPYZTHJMxsxAVmYhTW7cC/MIpkv3etQu/+tM79DsA4nLeq6ggRGQn8xztnPUYZjPau+ylGiQz07vFmb98e7zoDgGu9++yPsflXYhTG3RgFGM3vMA71LcAm4O+qWiIiWZ4P5G7gbhHJVtXo0f9nvfv8uqp+KCKDo/ZJ3XsUkUTgDxjFs8MzWSXSOI35OUOqWnuNCE308VYBWHgjNWXJ/dlZWTWOjIvefvEyd9GgXYwHKMwZ7gKIL7uqqGbLwMpw6eKrq889/QXmFq/48PJzvzb45dmX5byW+MuJP4/849PLq99YfUHyaSU7ku+veKZklKxLeDcjpebPV3QJPBjw98ksKyq/cNUHOm71+/4eB+mmTkLwQNbgg4W5oyqLMs731ySkd1FqqqrCu0t2hj6p+KRqtaORudlQcwqQ5OCr7pKQvTMzofuB7GCPyoxgV6e7Pzct4PQ5RZDuEuVBjODWlEnVvhKpOFgSKi8rrqoIlTgVlFOVeEBC2WEi2Qq5SN2IOFW/v6Y4IaGiKJhYfigxWF4RDJbXBBPLNZhQQXZCcaCb/9Mkny/cpTGFURdXnUg5KYfKSD10iC4Vh+hSWUJ6TQT/DmPSb5TRmA52gIjsx3SipZhRvgL9MOaQWhNoGkZhbMKMWv8XM5rv4u1/BGNH/iVmlnAIuNczP0TMU9BfichNGP/ApcBWjAIZ4bVb+50Zh+mQFTMrCGNMMjUYJfQh5rd5E3AhxgH9eYwp47fAz4CvAQ+IyOcwCm048KKq/sS7hgK7MDOOxcDPMUrqsahncSmmo3cwimWPd+9bvXs/BTNSfh9j1y/AzEQidZ71JIx9fwLGlPY3b/tPROSzXlvZwFARWcaRhXwFniz3i8gDHF3u8z3gFhGZFWUCqp3p7BeRVIzv5RVv2yHM/7Aui4Hfev6FIuAr3jNoNh1RAZTHW4CThWXBhHU/7JZbVeTznV13X9di3XXTu+6g2s/lqT17Aji+7gE3tIXFhTO6XNTz6xlfqDn7wBsJH5atXzd+Yteum5c8efr1/ad3u7ngg24Xn712Z/qq69b/rFeqWxm4I/JK5cuH/t3HdSLyclrqx387Jy3htbG+kQK+IdvD6y5ZvqpkxJaV2UOqGQJIWWrvLYU5I0v3Zw91ylPH9lIncIqqG9HI/i1ueMfe0tAnoeLyzSlbywoGgXarldMn/or0hNydWQk9DmQFe1RnJOT6U/zpXbo4WT36Sk69q8Rd3HCZVO8pkYqDJVJeViwVNSVSQZlbFawKJadVlmd29ZREI/Z9Vb+/uighWFkcDJaXJgbLK4OJxocRTKiQQEJVgt9fnejzhVNTnUMZqRzq2UP2RLf35xj+ZZWY0e9ATCexAjOiHoPpLLpEvVyMyWMUMBZjVhiNsfsvqv23xnBNMCP9PEy458+Ab2PMNksxI+tRGPPGeapa4TlBq7xr+jjiZI7Q8Oi1BhjvdaazMU7so0bMqrpSRJZjIpiGYTr1+zCd71LvuRwAngSGquplXsjouxjz1ncxClI54ldwMTOYsNfW9mihVPU9bxS/0DPv5Hj3ucZzGj+HmY1UecdvFLN6+h+YWc/yqOb+hDFfrRKREMYJ/LiIPI1RHNs4Et4LMB14IsoJXCvTHhH5X0wElwBvq+o/G3iujSJHZgsdhKnpAcwPxRaFb4Bdft/u27rlbv04EBhbN94OwHE18qdHI2tSqxkOUBNIOThv7IOZiEgktH11qOzVoQCX9/nukmR/l7M3+HZ/ONe/7mwESU4u3jZy1NvhT33dAj/nF0VlpA3zbTm00L/5UJ6o9rrcWbjs/wVecLtz8KwSn1M8PT1t9StpqV1KHGc4IpJaoUXjV+v6iQVupG8hAx2lW60MB7KHbt6XM6q8JP2UzLA/5TS8eHB1ywvd8M5P3ND2Mje8269uaVcIn0KdAU7ACZZkJOTuygr2LM5K6F6TnpAbSPanZfoloZeYuPUGcdFIOVX7Sp2KA8VSUVYs5dUlUkGZVCVUSSgtRCRL0a7EMAswqPr91cXBYGVRQrD8kN8X+smttz47s7EzvBjw2ZgOfp13f+mYDq0PphNagunURgD/xJg7Znj7v4uJ7OmLGZGfhhn5P4yJ9rkC00FmYWL/+3rnzQBSveuMwsw43sKMxp/DjLZzMErpEU+GYkxH/FNMiOq1mJH4Zap6g4i8Cbysqn8Tke8CD6tqap37PQN4HdPhHogyv0wHZqjqK95xZaqa6jls7wcuUtUyEemF6dyTveOHescfPl9E8mr31fEBvIhxpj7qRe+kYGYu31bVy0VkEEYBf1ZVZ3lRVmdhZlwHVbVKRK4EblDVKxv7v8aTjjcDmFoSYmr6J5ipniWKQyKl/9M1Z/ncpMRziFpgUpcfvebOS63msB9gf/bwjxEZA+D4uvau3b64cGbyhT2+wumRnmN2Owdnb/Z9OqGiIiNv8aJry0adObPgiaRvnvsyX5335qlXDY+ckpbk31Ay581Pxg55q3psbn/Ztf1+/zPbfnBw7cgfFpWk7/b79jyZkb7xnZTk7m+Pcc57e4yxyAzYpRsvWeHuGb2pPL373sUje+xdHABwxQmVdDl1bWHuqP0HsgYHKpMG5PkSTj+cUE01XK2RTzdFQtsPuOGdEY3szwi5VXmFVTuHFFbtPOaeg07ygYxg1z3ZwR7FmQndw+kJOcEkX2qWTwK9RSTFQXxpJPVIc5N69Gog27iibjnVn5Y6FfuLpeJQsZTXlEiFlklVoFJqopVEAETC4cTMcDgxs7w8E8xUvikUY1tPxXS6pwL/xZhKyjEd9wccMSuAiQK6BRNnL5iO/hBmtPw7jH3/Iczv5WrMwOlNzCj/GlV9xOuse2LCK0MYx+zFGPNKGsZc8ivgi8CZmBnGIEyCuypMmOhdGNt/LT/AOEp/ALxa782aUfYvgNmeSWo5Jja+/odz9GgdzMzl6xxr3omFHwBPiciN3vnf9eS/RURWYUxYi+o5bxjwsOfEDnnntVs63gwAYGr6ezTh/e5MhCH866yM+c93SRuqIo3WShizwV1+52vuCIlaJb5i2PdmH8w+47BCqCr6zQG8mgtf6HPr0iR/6miAlxMWLCx1Kg9PVQeetmBWt26bxx2QnH338cCOA5I7hrBbFlhb/JGzp3KUQHoKlWU/8L+6bIrv372CEuoPsD4hsPmJjPSds5OT8sIi/WrbS6zRsrFrde2klW71qXs5xe9yWBkBVCZm7dmfM2JbYc7w0KHUvjkRX/A0RI4a5LiR4p1ueMduN7S9QiN7k9Qt6wFuHxpxrCX50vZmBrvtzQ72OJSZ0D3SJZCVlOhLzXHE11uaigqqg6JaQc3+UqkoLHbKDxVLRXWJVKiLO+Xm+3+wvbFzvbDAn2FmABsw/4Ms4J+q+lVvxDsFoxguwSiDGRjn7gCMCacM6Ab8r6pOixo9n4bp4HZhOvvewDOqeos3Yp4AXKqqm0TkY4wd/TngMVUd78l3EXCrql7ljYg/xISR7gB+oaoXN+dZWdqejqoA/ogZ9XR6Xk5LXTwtO7NrSKTJGVF6ue5/8rFIpNbsUsuc8x8uCAeSh9V+rir+/Sq0ejhA96RTV03ofu1wgBDh8ueDc3eGxT299ticnO3LBg2ekydC1ttctvAFpgxUcXKoiRQFCopWOfurx4hxHjLZWbTsJ4HnIz04MFo8p+zixOCaJzLSDyxNDA5Ws0joMH326daLV7g7ztmgyZllnFHbTi0RJ6HiYObpGwtzR5UUZZyWXB3MGIBIZt37Vq0udcO7t7mh7cVueJdo5GA2hE6hTnv14Kb6M/ZkBrvvzQr2KM9M6EZtGKuD01vqKJ/G2gESe08bF2rsIBG5FzMyVYyN/EpMBzsaM9rcjxnxZ2PMOT0wDtipGBPMOZiZQhiYpqoPRimAuzCmoGrvnCcwiiAEfMs77yngNYxiKfZeJZj0FDdjlFEiZnax2XtfjLHvF2GcvL/HOF0rgJtUdX2Mz8jSBnRUBVD7Ze60fJgYXHNH15xIqc83PKYTVPWPj0c+yi7jKIdwxPFXzR73qINIQu226tLn5mmk8ILaz1f2vX1F0Jc8EqBUKna+nLAwCTliJ0lMLN155uiZZT5feFApaQd/wX3rdkrf8wGoCu9NWHFwk5SEzhPPJHmK7P7kAf8zW8c6a0aKkA4mTPU/Kckrn07vUrEhITAck27gMIGwVp29UddcvELLTtupvRMi9D/mFkHLU3pu25c7cteB7GGUp/To4Yr/1Pr8IJ7DeZsb/uRTN7Sjxo3sS0Mr+oDGVG9CkHBaIGtnZrB7YXawZ2VmQldSA5mpCU5iV8HpKSLRUUc7ek8b17fJNo1z8d/A51X1HRG5GmO2OR+jBJ7FjNznYtIwfA+zbuAzmGibv2KcnD/GhIBe7DltB2AUxxDvdaP3+tBr9z2M+ed1VS2OmgH8FOMzuMpbnTodo3RmYEIxF2LMO9swJqIIcIs3izgH+KWqNivDqKV16agK4EqM86jTsd3v33Fbt9yd2wL+c+vr2Bripncisy9ZoRPqbt+fdcaqVcO/d5QSCZW/OytSs2Zi7edeyQOXX9DtqlGHZXAKV/w7sGpotEPUccKVI0e9sywlpfh8gHmM/+hJbuvpiq8ngJSFtgdWHNwl5eHzxDPHJFNV/n3/a0tv8L3bK9EzDwFUC1Wvp6aueDY9jZ1+/6j6Fmd1LdZdF61wt4xdqwldSxgsR0IfjyLkTy7enz3048KckeUl6aemhwKpAzHRHvXiOZx3uKHtpW54d4K6JV0hkkcz/GkmjDVnZ1aw+/7sYI/KFH/G5tG//XqTeV48h+WHmLh9B+PAVcwovfb9PIx5aD9m1J+HWbT0vPc5jDERfRsT5liD6cQnYYInumOe/wZMTqGRmNnGSEyEzdWYqJo3VPUuz+xTO1uoxswI3sXEw0crgEcwTtINUbcUxETyfNVm/owPHVUBDMOsGuw0lDhO8V1ds1cuSkw8L3q0HguDP9G1U5+PDJR68hytHfSN2Xu7n3uUYghXr1wcrnj/qKX/X+z7g1UJvsTDimKRf+Oc1f4d4+u2d+qpS2b37LV+rAiBSpIOPcRPl2/k9HG1ykqKazYmrDhYItWRo2Yin3UWL/tp4PlwL/afJVEx+6WOlDzfJa3gpS5pyQccZwQmYuMoHFfDI7fo2ouXa9HQ7dotGOJ0acDmr0ikpMspG/d1HVV4MHOIvzIpt586vl4NPz3P4RzeuzUS/qTQDe9QjRxIR6vyMBE6sfDknS/NaNJkGR2x4n3+NbBRVZ+sc9xEzPL/S6PCMqdGR6uo6v6o4y/HdMJf8c49nBbB83FchEl70FtVJ9UThbMVuNIL07wBmOhF+kzHzAZex0TRbFBVW/msHdFRFUAynWQ9QA3UPJSdufDltNThWo99uymSqrT0z7+NFPld+tW3f/65DyypTsw8qjN2w/s21xz621Emlr4pgz86r+sXjsrB9EbCh3P3O4eOWlwGkJm1c+UZZ3zQU8RUb1vOmSsf5cddwhI47Kdw9letCqw6qBLSEdHn5smeHff5p28Z5xQcNg/Vss/n2/en9C7r30xLyS53nDMauueMMi28cJVuHLfalZ4HON2hgbAej6pg5p7CnOHb9+eMqC5N65sT8SWehkiTSQHdSNFON7xzlxvaXqWRvUF1y3qB25tjlc8P73xpxm8ba8uLQX8LEw++GRPuWIRZRFWDmRGswUTbjMaYYPwYu/xpmFSjlZhcOn0xs4BHMX6OEMZ0NN77W2si2g0kq+o+ETkPY1pa7R3zsOdD+DomK+cGzIg/D+NIvgYT/pmNiWcf5p33CGaB1p0YE9I/8RSSiFzvya/AKlX9hhi/zxOezAA/VNXadQWW46RjKgCAqem76eB1Vv/WJXXhr7Mye4VFmrQfN8QjT4UX9DrA2Pr2KegHEx4voU5lJtVITXXxb2szph7mqn4/XBNwgoc7Xhc39Lfg3HU1Ej7GD5GQUL539Flv7ff7Q0MBaghU/Y67Fi9n9PnRkTvO7oqPAmuL0ySip0efn0R1xe3+15d+y/dOj0QJDajb/taAf/sfM9K3vZ+S3KdG5NS6+2sRVXfIJ7r+4uVaOHKLZidXM7iphG4RJ1BVlHHaxsLcUUUHM09Lqg5mnoo4OY2dU4txOO/a5oY+KXLDuxzjcOa2O196/YPGzvPs/dcAw7yY9XSMyWUPZlFWbYbQJzD2/n2YjniFt/0FTOjm5zCdaQ1Q4a1IvRjjM+iKifuPYMInl2A66ExM5/2sFxU0wbuOetd/DdNxJ2A6/V2YaKR7MKtUqzGRRxcDv8FLaIdZJ/At73M3r53zPWVQG/O/ArhNVeeJ+Z6/q6qDvWcSxCR+648xbV2tqlsaeH6pmJw/F2NCUw8Ad6vqYjFZPb/q3bcLfMfbPosjoaw1GKf1isb+TycbHVkBzMWkve1wzE9KLLgrN4cynzOs6aMb5tq5kXnXztMGn1Fpap+PPzor/5jOFaCq6NFPwD1K8eSlDv3wnNzJY6K3VVBd+PfgvJAKx6w7EInUDB/x3qIuXfYfNhVt5PT107iHakk8vAoZVfVtL1/o31jSU5S8uu18xlmy4qf+56p7y/6z5ZiUDrAimLDhjxnpexclJZ7metWyGiK1UovHrdZ1EwvcSN99DPAp3Rs7vpby5O7b9+WO3LE/e5iWp/Ts4TqB/s3wweTc+sSkRrNJemGa72Kifmao6lwRGUE9YZiYhG5PqOr5ddqYiGfeEZE+mHUAAzEdeQATOfdL4EDdzJhiCqb8BONIfs1z5N6GSSWxzzssCZMzZ6qIhIGgqka885/CrFl4BZNf5wxVPRS1gOorQPeotA+113U52pybCwzyzv0GxpE9RczsVxvyJXgLu7Zi6hm4YgYFgzHJ9H6DMVtVe+kVElR1txyd1vmbGDNZhwov78gKoMOFgm4J+Lff2i13z85A4NzjbavvPt3y8J8j3aWRYuQfn3rF3E/6XnqMCQegqvjJj9DyY9JuX93vjvV+J2FQ9La9UrxuRsLSPKT+sMp+/VbM7dO3YIwIQYAITvhpvjd/LhPPwSTLMrga9m8uXejbUjZA6pnd9ZO9O+/zT/94nLNqpCNk1N2voLOTklY9mdGldE0wYWgsJrP+u3XTJcvd3Wdt0vS0SoaIGeU2ScifVHIg64yPC3NHlhWn9+8SCqQNxIxC67L91icm5TXWlmf+eRmT6ycdY958AbMo7DeYjm2Jd8zNmDQET3rbqzE2/NF4nTsm5cEMzKj2E4wp5i+YRVbHKIBaxYEJQX0cM7DagFk8NgtjkvocZuR/uaqGvI77YczqWTC5ar6IWcx1ByYa6QDGPHWmd+1vYGaVisnzczZm1fEqYLWqfq3Oc7kOk1voEm2kIxOR/pgkcgNqFVLUvqswhWgur+e8WRxRAIMwiq9VK3LFm463EvgIc+kgCqDIcQ7+qGtOwUeJwbFELYxqKV6K53BjnT/AgexhDZpCHF9muRs+1s2y4uAHJWflfOaobd01Y/CY8IAFHwY+rtfUtH37yHHFxd3XDBv+nywR7eHD9d/C4xMm8+bW+/X+Q+WSakxIjvjDA9PHhU/tUuVfXzzbt7NimBgbt2lHu/eeEsrvnUR1xa3+N+be6Hune5LUDKzdLyATKytHTKysJAShGakpS/6S3iW0LeAfiUi9z2JzTxm4uadvIJiFaOet0xWTVrpV/fdwit+lT0PPJxCuTO++76PR3fd9BBjncmmXfhv35YzaezD7DH9FUtc+6vj6cGzxk/qoTdd8tZdi4KscyUVfiulY78F07C9ismMewkT6bMCYtKL/l+sx2Uefw0TtPB6DDMmYkftizMrjFzAK5lXMyPjHYsow3oBZFwBQqqpjPNv+dZhUEA4mFPRlEfk2XipmzMrhvphO+oCInKKqr4rIncDfVPVhMIXmo8wwWzii2GprCtTHGcCKup2/x3vAPSKyEaMkXlLV2fUc91lM6GuHoiPPAHpjViCetNRA9QM5WYteT00ZSRO5aprD/X8Nzzndy/LZGB+M/91OdXy969sXqpgzJ1L9UX1t6NX9fvSx3wkMrLvjX4EVs3f6DhwTalpLIFBZOPqst3YFAtUjDzcG+jxT5r7D5aOOSaUccksDa4qWOZ9WnSUmPcIxXOJ8tOJn/ueq+0jhWdJAMrcKkfIXu6SufL5LWmCfzzeq7urhhuhdqNsuXuF+cu56Tc4sY0hTCrUu1Qnp+4ozBvx04py/Pd3YcSJyIaajKsJ02MWYkf9vMGaZ2zEzAweTpuExjBP3MYxZphIzor4HM+r+LmZUnYJxAFdiVgjfQMMzgD9i7OPZGKVzridHNcb5XJsP6CVVvcObAQxQ1S1iHOZ7gb9jbP4LMDb/BI7kG3oXY8+/HmOLX+5FEpVhkqoNxgxY53h+iCRMmOolmNnLv7y8PW9jbPtrouT/AmaU/8UGnq8Pk830Qkw1rnxVnR7lA0jBKNAzVXVP/f+lk5MmFYCILFDVekduUceMwziFQpjETe0jJfPU9M0cKWh90qCg09PTFvw2M6NfRKTeDrilXLLMXXTTu26TJqSqYOaeBec90KC9PFKzaXmo/K1R9e07rctZC0ZlX3TMd0ZR9+/B+UsrpPqY7KNHcMPDhv9nfkbGp0cpin103XUfv9hTJFnHVnurjuwPrDq4xjlYc66Y2PJj6CP7dv3cP33TRGflcEc0q75jAA46zoFn0rusfS0tJb3UcYbFasf3FqKtvWiFHjq9gYVoDXD24PXrPmrsAC/88x3MyP4WjDJ4E/hdPfb/qcAfVI/27dSx/0/HLB77Cma9wCw1JQsPH9PIuVOB8qgReZl6SdwkqvyiZ9u/UFW3egpgj6rmeJ3qb1T1Ta/dqao6UUwW0C+p6sd1rn24/TrbxwAPeeemYEbvL2JmGudHm4Q8E9C/MQrJrdtWnXavAaaoSfg2C2P6WokpG3mKql7V2PknG60yAxCRJ4DFqvrM8YvUikxNf4ZGkke1R2YlJ634n9zshArHaXVbY9di3fXYHyMpwrH28brs6H3hwk0Drjmvof0aKd1TXfqnhhSEe03endt84j9G+VYTKnk+OPegK9poaorevVfPzztl+ZlSx2/wJl+c/zJfHaziHNOJS0V4V2DFwS1yKDS2oUieRKorv+t/86ObfDO7JkvN6fUdU8tOv2/XkxnpH/8rJblHleOc1tixdelarLsmrXS3nL9WA12LGSz1rwkoBbIGr1/XaLIyr7N7DpN8bALGYTsUE4HzZVV9Q0RmYxy6RZjcP5eoKdBehknhcBnGXDbY+zwQM7L+Oib6ZxLmmf0MMzKvXQx3m/f5l5homBzv+Osws4qxGFPQa16bZ2NmKZmYUofTvFDR67xOdTkmo+ZSEXkG06lOFFPjN1FVf+jdc6aqFolIEdBVVY9Kk+E5fddhMn+uEZMpdQVwj6oeUyBeTDH5Dd5+FZGBmFXP6wFXVTd5xz0AZKjJCDqLIz6AJIyv4yJVXdfY/+tkIpYZQG2ukImY0cV+zJdvKebLcyMmm2AJZmr3de/z5zDOnAdU9SWvrR9jHD0u8I6q5oup8vMEZvq8GfiW94+fhXEYjcZ4/q/HFLQYhplm/rTJu5uafj1meXy7Z0MgsOX27rn79/j9Y5o+uvk4rob//GhkXUo1MUUOLR15x5ySjAENmolUVauLH6ngSEdxFIPSz5k/Imvi+fXtOyhlW19LWJyFNL5QKi2tcMPwEe8mO44eZWsvJqPwfu7/eK/0rFdByaHQlsDyA59KZeTchhZ8AUxylq281//Xyr6y7+yGzEO1rE0IfPzHjPRd85KTTg2bCJqYcVwNj9iiay9eoUVDt2m3xCML0d4avH7dF5o637OhP4tJ5VyMic+fi/mtJXIkHfTpmMpYCzErn6sw9vobMfbyJzEj4ecxBVGKMOaTmzH56B/ArAG4yvM1DMSYbe7CKIDhGPv+Toz55Q2MMsriSOWsGRhLwP0Y+/7nMeahr6jqxyJyBWYtwC6MQjvbUwCpmDxBozEmoJ+r6msi8iCm2tiyepzAn/HkEkz/83vv8/V6pFh87bFdPHkmYfIQHcBUAnMxprIMTCjpx8DNXijqLI6uyXsnMERVb2zs/3Uy0VwF8E+MQ2U3psjD3V587nSO5Ne+GjNN/SxmtLAEk4RqJGZ0UZt/pDbOdxVwu6rOFpH7gC6q+kPv4S9W1f8RkzL2fzBfjoMYRTFCjy7FdixT07thRkkxp0Q40RxwnP0/6Ja7dmUwYWystueWcPcrkdlnbzo21UNDzBr3m/WuLziosWOqin63AcL1jqIFiVydd+dOn/jqdVpvcvYsmR1YO/rYSlxH4/dXFY0ePWNzQrDyGNPPB1y8+M/c3E/FV2+ophRVr0tYcbBCatzR9e2vpbcU7v65f/qmC50VwxozD9WyMDFx9ROZXQ4uDx6boC4W0r2FaIN26p+/NnNtk7NmzwQ0R1X7ep8nAd/HdFp3YUb8V6vq9d7+GzFhlj8Sk0Y5qCbe/1RMJMtI7/f1E1WdL6b+7XxVHeCtL3gc83uNAKeparL3+79XVS/0rjHRO/8S7/McTIbR+Z58M4C+GrXi2NL+aPTHVw8fqupOz462Ao6NycaEiP1dVSOq+immgMXZmAUYz6hXRNnr/NMx061ar/uzcJRz8k3vbwGwRlX3qGo1ZrTR9ChsasmnmCRW7Y4qkcr/zc2eNbFvr4SVicHxbdn5j9ngLj9rk9YbzlkfYV/wkOskHOPErYs4qQcb2qeob13xwgad8APdHmcPjPSY06Qs4cTMxYuvPvPAgV6z6u67kP+c8yQ3JJ2qm+ZSz0hGM4ODqy/sMbpmVNYK9cvqhq6xU3N73hi6e8Lg6meSHw1fNbdCgxsaOhbgvKqqoc/u2Td++bYdWQ/u2790YE3NfFQPNXUvtZSkSu4bY53zp33JNzfWczi2Xm705+YMcKLPq62KFV079g7MLGIERwqc1FI37Ks66r3L0VW2LCcBzVUA0f/whgoON/RlPKq8WzOvF/3lqv0ca4f5djOv2aa44D6Z3mX+Of16F89ITZlYN6tla5Nervt/9LrbU5rxvz6Qdcam+nLq1EV8OdWN7V9bvOBcVyPHVl/xmBAeMjHdTY6hGLo4a9dMmvjxpjGLVDmqo02hIv1+8sfdwUMrfBquN5++2zVpZPVFPYeGzsj4UB02NXSVahISHw1fM25I9TOnf7Pm7lVb3W4LVQk3dLwPfJ8vrxj92q695y/ZvtOff+Dgop6h8GLMIKUp1hdMKfi46cMO01dMOgYwztt5Ufv6AVeKSI4X0fIVzMALzP/9Gu/9V+ucVx9nYBZCuRhzrc8zQdWLiHxBROoLwfyvHf23f5qrAGJhDnCdiPjETI/HY2Kd3wO+JV68tWcCKgGKvCgiMF+4+mJwj4d3Wrm9FvPv5KRl5/TrvenxrIzzm1qR2iqo6oN/iWyrm9+/KfZ1PTOm0azj797ooihF/RtKltS7NL+WL9aMGeVXX0xOtT17Tj93+bLJ+1zX2Vp331l8OOppvtF1mK6YTf3x3kR6p4ypvrhn/9DALvNVGg8R/sAdNfzCmkfOu6D6t4X/jpw5y1Vp1NyYqJr0tdKyc9/dufuceZ/srLqlqGReZiSynIajTt5orL16WAdM8UymWZiwzFpKMHb/DzARK8v0SI3YcuAMEVmKsX/f19AFxMxC9wDXiMgizCKtclX9a0PnqOqb9TldLScHbWF2eB2TsGolZsT/Y1XdC/zLc/h+JCI1mJH5/8PkDHnCUwxbMFWLWpPFmKXqMeVxbwvWJgQ+vr1bbvE+v//YEMY25OZ/uXOyyojZ7l9LcfqAmNYcOL4eGU0ds7po7rmnp4/Z44hTr8Lz40u6uuac9JcSFhTiJYdrjPLyrP6LFl5beuboGR8mJpYf5TAPUpOUz/0T1nLG2of1J4EaCR5rxhJxIqemnR/JSw35N5XO8W0rGySNfDd2kdvjptBdPYLUVH3HN2Ped/xvZadI9eDGZEx3Nf3W4pILbi0uYa/Pt/dPGV02vJWaklsnsqveMogN8CgmVn8c8FtVfcpLTdAT49jciHHAfgnzu8s3tyrJmOCK+zAJ2X6P+R0eLsbi+e8OYiJ5XsKEh0YwzuW3gWoRucsL7RwuImsxztK1XljoDZhkbhNFZLqIlGJMR91F5BrPL+hg/AoTvOs4wF/UyyZqiR8ddyFYNFPTH8REN5xQ9vl8+77fLWfDmoSEsbGYVFqTIdt17b0v1J/iuTFcccKzxv+upqGVsdGoVpdWF/++SRPWyKxJs09PP7tRRbTDObDq3cCKQUhsqRZA9fRB82bn5m4bX1/+nzC+0B/5/oJFnN94euyIWxFYW/Khs7tiVAOhmscw3lm5aqr/2fJTZO/ZEnMReNgc8G/7Y0b69nnJSbLom2tiVsxRARNJmKCKz2BG/KMxo/8PMAunbhORfwKPquoHYlIl/E1VAyLyPvUUY/EUQA5whapGomP5vWsf/iwiuzFhm9UikqGmOMwNHCmkPh0TFXYdZmXvm55j+RrMArDLMMp2HUYBWQUQZ9rCBNQeeYrm+x9aTIVI+V252bMv6tMzZU0wOO5Ed/5JVVr60xcjqc3t/AFKupy6MZbOH0Ak2AWksKnjVhXNPkfV3dfYMX3c7OEjI3mLY5UTRDasHzdx44bzl6pSUnevn0jgdh6Z8Avu3pGk5Q06gPE5yaFhmROrJ/Ugkps4S02IYKPMcUcMn1Tzm/POr/5d4buR0bNdlZhs3f1D4bxfFR6YsGj7zuaaOb8vIisxYZN9MKbSWapaqKo1mJF7LS9hOmAw6SI+74VYjgX+ISa75pMcnUvpH9qA2awOq4Dnvbj+hnwjb6iqq6pr4bDp8QLvGq5nDWg086nlxNE5FMDUks2YuOc2xQX3sYz0eef16132bmrKhMYqS7Ul//dsZLXfPZw/vVns6zq6yQ79KCS4u6lDXI0kbj60skk7/1nh/uO6uulNRgZFs2/fqWcv/egLxZGIr17nbh5b+z/FlCGX6DtzUG24RkTASQ+dmT2xekL3MjcjYY6aWPZG2U1Oj++E7pwwqHp62q9C184v18RYFwj9LcbjasMtL8assB+BWRuznoYHNG8CnxORLMwM4b+Y33mxqo6MekWbsWKtnTGZI7H6S6X+yLVoB7jU+WtpZ3QOBWB4sulDWs7MlOSPxvTrvfmpzPQLXBNXHRe+NCcyr9fB+vP7x8KBrMH1plJoCHHSS2M5bsXB/54dS1TIZTVnjg1qYEVzZKisTO+3aOG1PSsqutQbUeSgzg38afyvua0oXYuXNtpYoq9rzTm542su6LbXTfXP1xhCGmsIBB+PfPH8M6r/MvgbNfmrN7s9Fqg2qEDmMbVkY9N3dZh0zELI74nJSHkuJr/PRBHJ9tIsXFt7sKqWYYIunsN0+hFVLQW2isi1AGIYUfdCHoeAtLobPTt+H1X9AGNOzaCB/Ev1MA+4WkQcb83BxBjPs7QxnUkB/BMT39yqrAombJjYp9fS/K45Z1U7TpOx821Jv09189Xz9czjaaMqMTvWHDYAOL6uMcV8RzScvLWsoGFTTG17OP5rqs/tIyoNho/Wh+sGUpZ+dMXYPXsGzFalXnNGd/b2/gM3jr5aX5xHEzVoNcXfp+b8bufXnJu7WRN9MZum5rrDh15U8+uxY6sfO/CvyNmzXD3GRNZo4rd6+Bfmd/ojzOraRZhInakYP8B/OHaty0uYFbi3R237GnCjZ0paA1zRwPXeAr4oIiuiovPApIn4m4gUYGYhj2jsdXxfxaweXo0ZiC2GY812lhNP53AC1zI1/f8w6SSOmz0+357bu+Vu3pAQGIsZHcWVQFir/vJIZEcwTIuVUHlyt08Wj7mnWaajcHXBh+GKf8eUvsIvgbKr+t1R45knGmWflGx4M+GjPkjzMmwCZGd/snzwkNl9RRou9VhE5r6f88CWQukeU20F50DV6sDKopCE3HoT4DVEgHDNt30zl9zq/2dmqlR1A3oztaSqqfPEVKm6HpPRthCTeuV1jAkmF+OrqI3kuRZTBCYClKjqeDk6gVsuJn1zNsaJ/FmMGScVEyY9D+Mj2IVxBrd6MkcRSVXVMhHJxsxQzvf8AZY4EveO6wTzNMfpDC4TOfT9rjmzL+3TM31DMOGC9tD5A9zzQuTD4+n8Afbljmp2+mzH3z3mVAhhDaV+Ur5uVdNHQldNP/288GkrmysPwIEDfUd9tOTKqkjE36BNPpOiro9y67k36FOLpAkHNYCbnTi0elKPUTUjMpeqT9bGKksIf8IfI1ecP7T6L0OurL7v0Rg7/9Ecyfd/FWYlPZhghttVdTQmBcQfvO33AJ/xfAT15Ra6F7Mw60yMEolW8gOB36vqGXg1B2K9t2Yyw3NAzwXut51/+6BddF4njKklWzEL0ppNBCK/zsyYO7Zf76oPUpInxBopcyK4ZJm7KJb8/k2xP2dEs5WjOFm9aTgi5BiWHnhvlLcAsEnOiPQ5r18kp0ULA6uq0notXHjtKWVlmY2ufL2Ed899gm8m9NWtTa2QBcDtnjy6+qIeQ0KD0xepQ6OL3OoQXqEDYk1MOA54XVUrPPv9m5i4/IYieeYD00XkJurPgnoBJlUyqvovTBK4WrbqkQIrS6k/vctxo6oTPefzEFWd3hbXsDSfzqUADM12Br+emvLhmH59tk3P6DKuJcm/2pJuRbrz2++6jS5MipWylB69mnuOiC8ATsz2+pBbnb6rYuPyWI+/ODR8fIomxlI16xjU9ScuX3bZBTt3DJnTiFOWVMoyfsldF9ymv1nqaLjpexEh0jf13OqLe/YL90+bp8KuGMR5edu0yc2ZYdVVxg1G8qjqLZiyjn2AFZ6Z5SiJG7lOLOldLB2UzqgA3sLUQW2SpcHgunF9e624Jzd7TI0jzXKOnggcV8PTnokUxbqAqTFqAqkH1Ak0mqO/QSS5SRNKNEv2/2uExpg8TRC5uvqcwT51NrdINmDr1tHj16yetE5VGg0COI/5o5/m+szBunpOIykcooQTX3hAlwuqL+qZE+6bMltNqvT6UExBkViZg3HEJonI/2Hi/iuAsJcx96hIHhHpr6qLVfUejAx1EyXOw6wSRkQuxeTqb5UV9yIyVUTuao22LCeezqcAppaEMYUsGmSn37fryl7dF9zQo+ugYp9v5IkRrPnc9ao7P9b8/k2xP2d4iztYx5fVLKdhjVuVuadyc+PhmFEk4E+7smaMH6W42cJ5FBX1Gv7h4qsIhwMFjR2XSHXKT7l3fD73rQloTWzPxCfB8OCMCdUX9UiMdE+apabQSzRvbZs2udHrRqOqyzCRPCswi7pqcx+NB8bUE8nzsIgUiMhqjPKo6zv5OXCpmKpbn8OkRrkhVnksHZfOpwAMz2IW0xxFqSMl3+2WO/tzvXtmbzbpG9rtApZz1rvLRn8ce4rnptiXO6rFkR/i797s79GHhe8MrU0NHguZmtLvwtDQzTQQ4hkLNTXJ3RYtvPb00tKcJhebDWPVsKf5Ru+zdPFs6lSjahC/kxoakTWx+sLu4Uh2cLaagixKEwOOWkTkehFZ5XXwg1T1dEw8/8teaoZ7gT95zt5kIFFEFmKculMwUTyXAd9R1VnAl70UEO9iUjD8HHgZk+L5VExCune8a9+NUTZXiUi98orIZ0VkmYis9NqtZYiIzBKRLSLy/ajj3xCRpSKyRkRujtpeJiK/8NpZ5K0NQET6e5+XiMh9YqqZ1Z5zt7d9VUPyWZpP51QAU0simOI0AIQgNC0rY/YFfXuH5yUnTUAkMY7SNUl6mRbe8YbbqzkpnpuipMspDYZMNoXj697slNbVbkXOp1XblzTnnP5ut9GDIr1ictY2hKovYeWKz43fvm34PNWj7N/HECAcvIOHJtxH/tZErYw58ocEX1borJwJ1eO7FUVygr/dNm1ykzUpROQMTIH3SV4H/4MYrrRDVc/DRNZMx6R9PpcjGT+rgC9izD+K6fx/h1EWmz0/wt2eWWggMAZTCGa0iBwVVOCFkj6NKTwzgqjFZ5i8P5/xzr/XW5wGprrfaExyuO9H+SZSgEVeO3OAm7ztv8UkuzsbU3Sq9tpNymdpGZ1TARheBT56KS118Tl5fXY9n95lgh7rPGt/qOqDz0S2NzfFc2NEHH9VxJfYrJq30Tj+bvVW5GqKxYUzB6tqk2GR0VwQHjQh0005LiUA8MknIy5YterSzarSZCqL/nx82tNcf/pE/c9smhMjn+TvGhqd80SMR08CXqldLa2qDRbbiSK6YNJiVT2kqoVAlYhkYJy//4f5rrve63JMTp9oLvVeyzGLygbBMSHF52Kqkm2tR76Zqlrtyb6PIzmA6uYwqm2zBlMxDI6OPDoP+If3/oVmymdpAZ1XAUwt0c/37nHnAzlZ54RMyb2Tgu+8487JKqNV00oXZQza2GjGzCYQJ60bEJNTN5qqSFnXwqqdzUgAZ7ii5uyzAupb09zz6lJa0m3I4kVXJ4Rqgk1GJTm4vpv444SH+MG+VC1dEeMl/rT3wpGNVheLoi0KJn0Ns2hstKqOxKyEr292K5jsoLXRRQNU9c/NkO+YSKIGchjVXjukR1agxhJ5FIt8lhbQeRUA8Pa318+h+YU54sYZ2901k1Zqi/P8NMS+rmcWNX1UUwSalbqhlsWFb52msVXQOowfX+LV1edmix5/ao9QKCln0aJrhhcVdY9pvUEvdvV7gm+OuFxfm4uJ0W+IEqLMjDHwPvClWjNJLKulYyAd2KeqIRG5EFM5DI7N9/MuplhTqnftXiJSt0bCQmCCiJwSo3zpQJGa+t+1OYyaYhFHFqJ9uZnyWVpAp1YAHndC47bg9kBSlZb+5EU3rSUpnpviYObpx521VJy0FimRisihHgeqdzd7FpBKYvfPhkYV0oQdPzYc3+qCSyZs2Tx6gWrT6aAF5Ms8P+633FKerYUNrVGYuvfCkTFnVlXVNcAvgNme2eQ3UbvHScsWHj4PnCUiH2FmA+u9ax0A5ovIahF5WFXfw5hcFnq5fl6hTkI4z7R0M/CaJ190Cur6+BdmJrCKIzmMmuKHwI9E5EPMIrcS79pNymdpGZ0rF1ADDHt22DTgf+ItR2M8+mR4Qc/jyPLZEIq4H0x47BAix7WWoKZsxiw3tHFiS85N8WfsnNz75m5RzsOYWebbOm9ZYMsFLblufaSm7d84YsS/Eh1HY86J9DaXLXyBKQNVnBxv02pg1N4LR8a8QrohxNSS2IwputKha+x6Sq5SVVVEvgx8RVUbSlpnaQXsDMDwAEdirdsd182OzGuLzh/gUFqfzcfb+QM4/h4t9iGUh4t7F9V82uxZAMCZkVMu6O5mNKuGQGOUHco5bfGia7tUVyd9FOs5n2fGeX/gW04v/WQBxv5+U6ydf32hkl6Y5H0ishgTGdQT+EBEPhBTa3u6N3ovEJE7WnCb7ZXRmJXMq4DvYWbnljbEzgA8hj077HxMQfoTWr2rKfL26uYHn4n0EJqfFTMWPj71yrmf9L3kuNcTuOFd62sOvTSopeenBbK2f67Xt3tLC6qnubjhF4LzVlVJ6LhSYR+NuoOHzJ6Tnb1jgkjsBU2WcM7P8ye9MDXW4+XYco8TMKt5r1PVl71jtuHNALxEcdNU9RJvX0Yz0jJbLEdhZwAeBVMK5tO85fptTiCsVfc/F3HbqvMH2J89tFUUnvhyex/P+YdCB/uVhPbHYic+Bq+GwCmOSkwpPmJDnHVrJ078eNM5H6rGHOG05WwWP9zMC9UXKhmh4aLxW4BTReQxEfksx646tlhixiqAo5mKGYW1C+59PrLkeFM8N0VlUtd+TR/VNCIJqSDHleJ30b43e2osOXjqIZGEzMtrzqpGKWv66NjZu/e0c5Yvu6zQdZ2mMn+6wDcvmrQ51vKKDZV7TASqGqrRq6pFwAhgFnAr8KdYr2ex1MUqgCgKphSEga8TQ2HwtubSpe6i03bTaqke6qMymLVHHV+zM4A2iCTuOZ7TS0L7TzkUOtiiWQBArnYZeEF40Br0+Go+1KW8PPPURQu/lFtVldKYn+KhiyZtbq4vItZQycNhmyKSAziq+iomzLQVzV6WzoZVAHUomFKwEVN+L250K9KdN77XOimeG6Mwd+S21mxPnPTjHn0vKnwrV4/DMTUo0uucU9yureYUriUSCaQt+fCLY/Z9esos1WPqBH+IKcrSXGINlXwKeEdEPgB6AbO8mgDTaaUKd5bOiVUA9VAwpeBJTNroE05rpnhuisKcEbElOYsRx9+tReabaIpqPh1YHi5uUURQLZNCQ8enuYktnkk0jMiGDRdM3LD+gmV6JDNpMfCViyZtbvRZRic28z7fAPxaVT8HvIYp9pKBqfJ1U/SxqvqYqg5S1QtVdaWqnhm1Kvad1rgzS+fEKoCGuZE2KCLfFHe96s5rrRTPTXEorU+Lcvg0hOPr3irO6kWFMzKP53xB5Kqac4b61NnUGvLUpbDwlLOWfvSF0kjEtx64/qJJm5tTGawhHvHSNVwBPNmSNREWS3OxCqABCqYUFGIyHtacqGueu85dNvpjPSFZDsO+xFLXSRjQmm2Kv3urLM8/UL379PJwaYuqgNUSwJ96Vc05iSixJFVrNpWV6X0XLrjuhYsmbW7VmaKqbsL4oI5LCVossWAVQCMUTCmYC3z7RFwrvUwLf/hPt3drpnhujAPZZ2xs7YL24mT2ppUU5uLCGanH20a6Jve5ODRsOxp7zeJm8Iqq74FmHJ8kIitqXxxJ2XwUInImsElVm1VlzWJpCVYBNEHBlILnMDla2g5Vfegvke2OcsISXH2ae2bM4YqxIuL4wNeipHB1KazaMaQyfCjm1bgNked2HXVGpPeC1pApihXAlKlTpzbHWV0ZXc+XY53Gd4jIBmAxJhzZYmlzrAKIjZ8BL7ZV4995x52dWd66KZ6boiR9QNs4mZ2UmBOgNcXi/W8HW6Od88Knj8920+a2RlsYv9AVU6dObe1Q4Ue8CmDXAX+Vdl6UyNIxsAogBgqmFCimitJ/WrttL8Xz+a3dbmO44oRCgZQWF4BpDMfJalaBl8b4tHLbsKpIRZO5+mPhCzVnnZOg/pjr8jZAEXDp1KlTW3HF8dGo6mvAR5jvm8XSprRLBSAiU0XkLu/9DSLSM94yFUwpqMGU1ztus0QtyVVa8pMX3S5tkeK5MUrS+2+kZemFm6Ql9YEbY8n+d1qlPR9OwtXV53YTpaWL1Q4Bn506dWrdalptwX2YtMjt8vdp6TicDF+wGzDZEONOwZSCMuBzQKxVnhrl/6ZH1vpd+rRGW83h09zRbZZW2PF3b1XT0u6Kj0dURypXtkZbKQS7fr7mzCKU5s5SKoHLpk6d2uLIJFVNrfN5uqre5r2f6hV9r923VFVPb2laDIslVmJSACJyvYisEpGVIvKciPQTkfe9be+LSF/vuOki8jsRWSAiW0TkGm97DxGZ40VArBaRcd72sqhrXCMi0+tc9xpMQennvXOTROQeEVnitfOUiIh37CwReVBEPhSRjVHX8InIr7zUuatE5HYRuUhEXo+6ziUi8losz6JgSsF+TP3W1bEc3xBfnh2Z27OI846njZZyMGtwm9mXHV+3Hq3d5tID77VaFE8PzRxydrj/0macUomx+bf66mKLJd40qQBE5AxMTvJJXsKqHwCPA39V1eGYqkO/izqlB3ABcBlHsmt+FXjXi34YgYmiaBJVfQVjcvmaFz1RCTyuqmer6lAgybtOLX5VHYOpLHSvt+1m4BRgVJS8/wUGi0iud8w3gWdikQmgYErBbmA8MD/Wc6LJ26ubv7hAT6jTN5qqxKxT26ptcVJy8So5tRY7ytePrnGrjkvhRjMiknd+r0hWLCUgy4HJU6dO/XdrXdtiaU/EMgOYBLxSW41IVQ8C52FKtAE8h+nwa3lDVV1VXQt087YtAb4pIlOBYara7ALiUVwoIou90nCTgDOi9tWO4pcCed77i4EnVDVcK7+Xa+Y54OsikuHdT7OW1BdMKSgCLgFmNOe8qBTPSc05r7UoS+6xDXFymz7yeEholVDQaJYd+E9la7b3mdDIC5I0oTF/Tq3N/4Om2qqb5qE1iPaDWSxtRSwKQKDJ7IrR+6NrtAqAqs7BjJh3Ac+JyPX1nNekWcILjfsDcI2qDgOernNe7bUjgL8J+Z/BZP78CvCPWgXRHAqmFFRiHMN/jfWcqScgxXNjFOaOavXOuS7ipBW3dpvby9acHXJr1rVWew7iu6b63IGOyrZ6dh/ARPvMa63rWSztkVgUwPvAl0QkG0wFI2AB8GVv/9eARn8oItIP2KeqTwN/5kgK209FZLAX7fDFBk4/nAqXI539fhFJBa6JQf73gFtExB8lP6q6G9gN/BSTVbFFeCmkb+DoIt718pmP3IUD2zjFc1MU5rR9miHx57bFyltWHPzv8cwcjyFIIP2KmrMj6FFFVdYBY6ZOndqiZHIicrfno1olIj+P2v4zEVkvIv8Wkb9HRbnd5B2/UkRelTaKzrJY6qNJBaCqazArYWd7lYt+A3wfY9JZBXwD4xdojImYWp/LgauB33rb8zEmlP9Cg+F504EnvOXz1ZhRfwHwBrEVb/kT8AmwypP/q1H7ngd2eOaqFlMwpUALphTcibmfeul2UHd+69/ukOO5TmtQntKz9fL/N4Dja3l94MbYcmjl2WE3tLE128zWtP7jw4PXY1I8/xs4b+rUqS1K7iYil2Iqeo0BRgKjRWS8iJyF+d6PAq6Coxb9veb5tEZglM+NLb8bi6V5dOqawCLyOLBcVf/cWm0Oe3bYN4AnibLxO66G//xoZH1KNUNb6zotoSaQtn/e+dNy2vo6bnjPxppDf2+ThWYDu5y58MzsS1o9emqhf+O9a/w7/m/q1KnNnr2ISJmqporIrzCz0mJvVyrwS8wMNlNV7/WO/w2wW1V/JSITgAcwqaBTMcESt3j+srLo8FCLpbU5GdYBtAkishQYDvytNdv1cgedAxweqd79qjs/3p0/QGHO8M0n4jriy+lN036jFrGpdNk5ETfcmvdRCUy59oEb72tJ518HAX4ZlfNngDe4aKyo/HTgNs+n9XNi8IVZLK1Fp1UAqjpaVceranXTRzePgikFBZhp/svnrXOXnnmCUjw3RWHuqFZL09AYIoFkkOMqD9kITkHx3OOqPRzFFmBs72njYnbiN8G7wLc8/xQi0ktEumJ8ZJeLSKK3b3LUOWnAHi///9daSQ6LJSb8TR9iaQkFUwoOAdctGTn422J8HnF37pV0yWtz889hJGkvWtEmK7g3liw5d1jm+G0+8eUdRzNvAjf0njauqJXEQlXfE5HBwEJvfWIZ8HVVXSIibwIrge2YtS21ayV+hskAuh3j20o7pmGLpY3o1D6AE8W6QYMHYRzOcSvgHXEClbPHPeLnBFWaqi59cY5GdrfZzGdIxtj5wzLHtSSJXhHwg97Txj3X2jI1hoikqmqZF+UzB7hZVZedSBkslrp0WhPQiWTw+nXrgXMxK6PbJESyKQ5mDtpwojp/AMfftU1HFuuKF57jamRHM0+bAZxxojt/j6e8SLZlwKu287e0B6wJ6AQxeP26EPC/6wYNrk2dceGJvP6+3DNbNT1DUzj+nimR6hVt1r6i/vUli7cNyRgbSzK9fcCPek8b93ybCdQEqvrVpo+yWE4sdgZwghm8ft3qwevXTQK+BDR3BNtiijJPSzlR1wIQX7dWLThfH2uK5p/jqrurkUNc4I/A6fHs/C2W9opVAHFi8Pp1/wAGAfdDs9MTNwtF3JqE9BOafkKcjJ608X25uAkbSz9qKCR0GXBe72njvtd72rjitpTDYjlZsQogjgxev65i8Pp19wBDgH+21XVK0/p9jEjblIBsAJPeo3XqAzdGwcE557jqRoeFbsSs9j6797RxLc7fb7F0BqwCaAcMXr9u6+D1667EZDdtMvtkc9nXdVRrxc03C3FS26zwTC0ukeDmQys2Alsxab2H9J427u+9p4074cVUvJoUZ3nv3/YyzVos7RbrBG5HDF6/7gPgg3WDBp+HqcEwuYlTYuJA1tC4/J/Fl1Wtbpv7njcVFM15ZmCXM5/vPW1cqK0vFiuq+vl4y2CxNIWdAbRDBq9ft3Dw+nWXYVJVPMPRKbabTWVybl5ryNVcHF93Xxs2vwi4Dhj0/b+/Or2lnb+I5HlZOv/kVZl7XkQuFpH5IrJJRMaISIqI/MXL2rlcRK7wzk0SkRe9zJ8vEZX/SUS2iUiO9/4NEVkqImtE5OZWuHeLpVWwM4B2zOD16wqAb60bNDgf+C5wC9Cs6JrKxOzdKr641FQWf4+MVm6yGJO76ak7X5pR0IrtDgCuxVSPW4LxIVwAfAH4f8Ba4L+q+i3PrPOhiPwH+A5QoarDRWQ4xvFcH99S1YMikgQsEZFXVfVAK8pvsbQIqwBOAgavX7cP+Pm6QYMfwKTW/jImrXBWU+cW5ozcBsRFATi+rq113QXAU8DLd740o1Urg3lsVdUCABFZA7yvqupVncsDegNfiKrQlQj0xRQ5+h2Aqq7y0qPXx/dFpLbeRR9MymirACxxxyqAk4jB69dFMAV63l83aPD3MCUpvwxcAXSp75zC3BFxWXkMIE5yFnCQGBRVPSwDXgFevfOlGa1aA6Aeok1sbtRnF/MbiQBXq+qG6JO8fD+NrngWkYmYsqTnqWqFiMzCZvy0tBNOagUgInnADK9A/PG0cwNwlqreJiJXAhtri8R4P9i7VLWx+rEnHG9l8dvA2+sGDU4EPodZXHYxcDjp26HUPm2+IKtRJLgbrY5FAVQDs4G3gBl3vjRjW5vK1TzeBW4Xkdu9mcEoVV2OyenzNeADERmK8dnUJR0o8jr/QZiUIBZLu+CkVgBtxJWYnDHHVSWsMUTE35IaxA0xeP26KuB14PV1gwYLMBSYGHESznedQFxTUYvTpVgjhfXtKgEWYsw784HFd740o/xEytYM7gcexVSVE2AbcBlmlfEznulnBVDfuoN/YUqSrgI2YJzXFku74KTOBurNAN7B5Fsfiyk6fwXG5v17IBeoAG5S1fUicjmmBnACxgb7NVX9tHYGALyA6fxLvNfVmBrGizG5ezKAG1V1roj4gAeBz2DMAE+r6mMicg9wOSYiZAHwHW/UOMv7fD4mFfEsTHnNVGA/cIOqtkkO/d/f8t88zMjzHOAMoD/Ght3mA4Ca8n/NdmvWjsSUO1yLSYU8D1hz50szTnisvsViOUJHUAAfY8w3K0TkZUzn+k3gFlXdJCLnYKo0TRKRTKDY65C/DQxW1TvrmICmY8xKr3jXmAUs9Y77PPAjVb1YRL6LMbdcp6phEcnyIj2yVPWgd+5zwMuq+pbXzlpV/Z5X/GM2cIWqForIdcBnVPVbJ+bJwe9v+a8f6AecilEI/b33PTFmiy7eK5X6K1pVAaX1vIowue03A1vc8J5Nt//pa5+26c1YLJYW0RFMQFtVdYX3fikmamMs8A/PSQcQ9P72Bl4SkR6YWcDWGK/xWp32wXT+T9Sacmo7feBCEfkxpgBMFrAGY9cGeMn7ezrGTPNvT0Yf0FYVtOrl1icmhTGd9GZMMfQG8ZRFAuY5CnDo1icmtZtFV7HiKfr3VHV3vGVpCBH5AjBEVafV9Uc1cs5U2qB+cHPaFZGRQE9VfbuZ18gDxqrqCy2R0XJ8dAQFEB3BEQG6YUb5I+s59jHgN6r6phedMbWZ14hw5JkJdSJARCQR+ANmNrHD+wFFR3yUR527RlVbvbh5W+ApizDGnHYycwOwGohZAbS2v6YpVPVNzCwWToA/qhUZiTGjxqwARMSPGVB9FWN+tZxgOuJK4FJgq4hcCyCGEd6+dIyfAGBKA+cfIrayfO9hnHt+7zpZHOns93u1X69p4NwNQK6InOedGxCRM2K4ZqdDRB4Uke9FfZ4qInd67+/2VueuEpGfe9vyRGSdiDztrbx9z1uxew2mg3peRFZ426JX657lmelqr/GUiLwH/FVEckXkVe9aS0TkmEpkIuITkV+JSIEnz+3e9nu8c1Z7bYq3fZaIPCoiC7x9Y7ztN4jI4yIyFrMQ7WFP3v4icpPX1kpPnkbLjIrI5SKyWMzq5f+ISLeo+/uLJ8MWEfl+1Dk/EZENYha6nd5Au9d6Mq8UkTkikgDcB1znyXqdmBXUC7xrLxCR06Pu7x8i8hbmNzQNGOedd0ft/Udda4Y3WENEykTk1yKyTETeF5Fcb/v3RWSt99xfbOyZWOqgqiftCzN6WB31+S7MqP4UTPTFSszo6R5v/xWYQuBzgYeBWd72G4DHvffne+csx9jFZ2FG9GDCK7d57/0YJ+5a7zq3edsfwPgl/oNJ4zDV2364He/zSEwY4UqMmeimeD/P9vgCRgGzoz6vxTiwL8UsDhPMQGYGZmFWHma2MtI7/mVMXd76/gfbgBzv/VlR34epGHNfkvf5BeAC731fYF09cn4XeBXwe5+zov96758DLo+S5Wnv/fja73Gd7+J04Jqo87Oj3j8A3B4l7131yJTJET/ft4FfRx2/AGPSy8EERASA0Zi6xMkY/8/HDbRbAPTy3mfUldv73CXqWVyMqYJWe9zOqOczEeNzo4F2ZgATvfeKCdwAuCfqOe0GgtHy2Fdsr5PaBKSq2zC29NrP0bbKz9Zz/D+pJ+2yqk7H/NhQ1fmY9My1TIw6bj+eD0CNWeBH3iu6rZ9iIo3qXmNinc8rMD98SyOo6nIR6SoiPTFRXUWq+ok3ar0Uo6jBOKsHAp9Qv1+oubypqrWrji8GhsgRn1IXEUlT1UNRx7fEJ/R379g5ItJFms4eOlREHsBEo6Vi1ic0RmM+r5mqWg1Ui8g+jOl0HPC6qlYAiClkXx/zgeligi5ea+CYdOBZERmI6bijy5H+O+r5NAeXI360v0VdexVmZvcG8EYL2u20dEQTUJviTZ/vauKYK0VkSGPHHKcM94nIxW3VfjvkFYw57TqgdoovmOiukd5rgKr+2dtX1y/U0EAnzJHfQN3VudFrEhzMSt7aa/Wq0/nXytOQT+gaVR0GPF3nOnVD8JoKyZuOmWkOA35ej8x1eQwzSh6GyVsUfXxDz6jJsEBVvQUzyOkDrBCR7HoOux/4QM0izcup3xdWH9H/E2j8HmtlnYwJ+x4NLBXPLGtpGqsA2oYrOXoW0aqo6j2q+p+2ar8d8iIm5cU1GGUAZvT7LTG+FkSkl4h0baKduv6dbZhOA8yaj4Z4D7it9oOYiJf6jmmuT+g679gLgBJVrZs7u668acAeMWHEX2tE3lpi8XlFMwf4oucfScN03McgIv1VdbGq3oNZw9KnHlmjr31DI9es738yUkQcEekDjIna53DkGX4VmCciDtBHVT8AfsyR2ZElBqwCiIGGHGP1OeVa6rwTkQne8Ss8x1mat/3HYhyLK0VkmrdtuhinJiIyWkRmi0k3/K433a91Mj4oIh+KyEYRGedtb8hZWW877QFVXYPpJHapt1hOVd/D2OYXikna9gpNO++nA094zzgJM4r+rYjMxYyCG+L7wFne81qLycpalz9hzE+rRGQl8FVVLcaM+gswpokldc4pEpEFwBPAjfW0+SJwt/d96A/8DLMo8d/A+ibuFYyt/x/e/TVZnEdVl2FMLCsw/oy5DRz6sPf9Wc0RP9YHGDPZCjHrWh4Cfiki8zFhzg2xCgh73+87MOalrZhn9iuOzrBaDpwhIksxxZPu89r+m/cdWA484j13SyzE2wnR3l804hijYafcdGJw3tW5zlvA+d77VMyU/HMYZ12ytz0run2MXXUBkOttvw74i/d+Fkecfp8H/uO9P8ZZ2Vg79tVm36tZRDmk7SumZ1YWbxk62svaypqmMcdYrE65WI6bD/xGRJ4HXlPVnZ6d/5naa+uxjrOmFpTFtIBNTCKzuC5Ms1gsJx6rAGKjIcfYdOBKVV0pZpXpxJYep2bl50zMaH2R1/kf41isQ1MLymJawBZDO5ZWRutEhVmaRlWtbb+VsT6ApmnMMdaQU67ZzjvPsVagqg9iEqYNwjgWv1XrM/Aci9G0ZEFZfc5KuzDNYumEWAXQBNq4Y6whp1xLnHc/FG91JVAJvKOq/8KkBfhIRFZgFrpFy1aD8QU86J23ApMHqTHqc1a2pB2LxXKSc1JnA7VYLBZLy7EzAIvFYumkWAVgsVgsnRSrACwWi6WTYhWAxWKxdFKsArBYLJZOilUAFovF0kmxCsBisVg6KVYBWCwWSyfFKgCLxWLppFgFYLFYLJ0UqwAsFoulk2IVgMVisXRSrAKwWCyWTopVABaLxdJJsQrAYrFYOin/H7nnUPdvIIAZAAAAAElFTkSuQmCC\n", + "image/png": 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", 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\n", 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", 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" ] @@ -248,7 +248,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", 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\n", 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" ] diff --git a/howard's advisory stuff/collating fields.ipynb b/howard's advisory stuff/collating fields.ipynb deleted file mode 100644 index 3acca0a..0000000 --- a/howard's advisory stuff/collating fields.ipynb +++ /dev/null @@ -1,385 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 108, - "id": "f7ebf578", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os \n", - "filesdir = os.path.dirname('C:/Users/howar/Downloads/Telegram Desktop/umami_preprocessed/')\n", - "files = os.listdir(filesdir)\n", - "file = 'present.csv'\n", - "filedir = os.path.join(filesdir , file)\n", - "data = pd.read_csv(filedir)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "9a8a8df6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2/2780012%NaNNaNNaNNaNNaN
3/?size=n_20_n&filters[0][field]=industries&fil...136001%Banking and FinanceNaNNaNNaNNaN
4/?current=n_2_n&size=n_20_n136001%NaNNaNNaNNaNNaN
...........................
187/?size=n_20_n&filters[0][field]=industries&fil...520%SecurityNaNNaNNaNNaN
188/?size=n_20_n&filters[0][field]=organisation&f...510%NaNMcKinsey & CompanyNaNNaNNaN
189/?current=n_2_n&size=n_20_n&filters[0][field]=...510%NaNBoston\\n Consulting GroupNaNNaNNaN
190/?current=n_4_n&size=n_20_n&filters[0][field]=...510%Data\\n Science & AnalyticsNaNNaNNaNNaN
191/?current=n_5_n&size=n_20_n&filters[0][field]=...510%Data\\n Science & AnalyticsNaNNaNNaNNaN
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search
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gic110
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" - ], - "text/plain": [ - " value\n", - "search \n", - "\"harish kumar malaga\" 83\n", - "gic 110\n", - "google 266\n", - "harish 112\n", - "healthcare 123\n", - "investment banking 136\n", - "product 52" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.read_csv(filedir)\n", - "data = data.drop(data[data['search'].isnull()].index) #dropping Nan for column, industries onwards\n", - "data = data.loc[:,['value', 'search']]\n", - "data.iloc[:,0] = data.iloc[:,0].apply(int)\n", - "data = data.groupby(by='search').sum()\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "bc639281", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'value': {'\"harish kumar malaga\"': 83,\n", - " 'gic': 110,\n", - " 'google': 266,\n", - " 'harish': 112,\n", - " 'healthcare': 123,\n", - " 'investment banking': 136,\n", - " 'product': 52}}" - ] - }, - "execution_count": 156, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.to_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45bcd10d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/howard's advisory stuff/getting data.ipynb b/howard's advisory stuff/getting data.ipynb deleted file mode 100644 index ef0a829..0000000 --- a/howard's advisory stuff/getting data.ipynb +++ /dev/null @@ -1,907 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "a53dc1ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['2020w1.csv',\n", - " '2020w2.csv',\n", - " '2020w3.csv',\n", - " '2021w1.csv',\n", - " '2022.csv',\n", - " 'E-Scholars Guidebook AY2324.pdf',\n", - " 'umami_dumpfile_v2',\n", - " 'umami_preprocessed',\n", - " 'umami_preprocessed.zip']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#gettin files\n", - "import os \n", - "filesdir = os.path.dirname('C:/Users/howar/Downloads/Telegram Desktop/')\n", - "files = os.listdir(filesdir)\n", - "files" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e75f9905", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mentor 1 2\n", - "Mentor 2 4\n", - "Mentor 3 10\n", - "Mentor 4 540\n", - "Unnamed: 4 1045\n", - "dtype: int64\n" - ] - }, - { - "data": { - "text/plain": [ - "{'-',\n", - " '.',\n", - " '1.\\tJayasutha Samuthiran',\n", - " 'A',\n", - " 'Aaron Ng',\n", - " 'Aaron Tay',\n", - " 'Adrian Chew',\n", - " 'Adrian Liew',\n", - " 'Akshay Maheshwari',\n", - " 'Alayana Ling',\n", - " 'Alayna Liang',\n", - " 'Alayna Ling',\n", - " 'Alayna Ying',\n", - " 'Alvin Seo',\n", - " 'Alvin Tan',\n", - " 'Alvin Tan, LinkedIn',\n", - " 'Alvona Loh',\n", - " 'Alvona Loh (Doctor)',\n", - " 'Alvona Loh (Doctor, Institute of Mental Health)',\n", - " 'Alvona Loh (Institute of Mental Health)',\n", - " 'Alvona Zhang',\n", - " 'Amra Naidoo',\n", - " 'Amra Naidoo, Co-Founder, Accelerating Asia',\n", - " 'Andrew Pang',\n", - " 'Andrew Tan',\n", - " 'Andrew tan',\n", - " 'Andrezj Surzyn',\n", - " 'Andrzej Surzyn',\n", - " 'Ang Eng Hieang',\n", - " 'Anira Perera',\n", - " 'Arrchana Muruganantham',\n", - " 'Arrchana Muruganatham',\n", - " 'Bhargav Sriganesh',\n", - " 'Bianca Stringuini',\n", - " 'Biance Stringuini',\n", - " 'Billy Loh',\n", - " 'Brain Liu',\n", - " 'Brian Liu',\n", - " 'Brian Liu: Lazada Group',\n", - " 'Bryan Low',\n", - " 'C',\n", - " 'Carol Chuah',\n", - " 'Carol Soon',\n", - " 'Celine Teoh',\n", - " 'Celine Toh',\n", - " 'Celine Toh, McKinsey',\n", - " 'Cheah Wui Ling',\n", - " 'Chia Lih Wei',\n", - " 'Chin Hui Lim',\n", - " 'Chin Hui Lin',\n", - " 'Choon Hong Tay',\n", - " 'Chow Yeh Wah',\n", - " 'Chow Yew Wah',\n", - " 'Choy Mun Kit',\n", - " 'Chris Chia',\n", - " 'Chris Tan',\n", - " 'Christian Cadeo',\n", - " 'Christian Caedo',\n", - " 'Christine Kim',\n", - " 'Chua Ruo Mei',\n", - " 'Chua Seng Tat',\n", - " 'Chua Seng Tat-',\n", - " 'Chun Huey Yei',\n", - " 'Cindy Chng',\n", - " 'Clarence Quek',\n", - " 'Cliff Hartono',\n", - " 'Clinton Yip',\n", - " 'Clinton yip',\n", - " 'Colin Lee',\n", - " 'Crystal Koh',\n", - " 'Damian Ngiam',\n", - " 'Damian Ngiam (Tiktok)',\n", - " 'Damson Capital',\n", - " 'Daniel Teh',\n", - " 'David Chua',\n", - " 'Davis Tan',\n", - " 'Davis Tan, Senior Associate, Rajah & Tann',\n", - " 'Debby Caroline',\n", - " 'Derrick Sim',\n", - " 'Desmond Koh',\n", - " 'Desmond Koh (Managing Director (SEA) at BNP Paribas Wealth Management)',\n", - " 'Desmond koh',\n", - " 'Divya Jagtiani',\n", - " 'Dr Alvona Loh',\n", - " 'Dr Carol Soon',\n", - " 'Dr Cheah Wui Ling',\n", - " 'Dr Geraldine Tan',\n", - " 'Dr Hamid Rahmatullah Bin Abd Razak',\n", - " 'Dr Hamid Razak',\n", - " 'Dr Janice Soo',\n", - " 'Dr Li Jingmei',\n", - " 'Dr Mala Satkunanantham',\n", - " 'Dr Neo Mei Lin',\n", - " 'Dr. Alvona Loh',\n", - " 'Dr. Cheah Wei Ling',\n", - " 'Dr. Cheah Wui Ling',\n", - " 'Dr. Geraldine Tan',\n", - " 'Dr. Neo Mei Lin',\n", - " 'Duo Geng',\n", - " 'Duo Geng Goh',\n", - " 'E',\n", - " 'Edmond Twohill',\n", - " 'Edmund Teo',\n", - " 'Edmund Twohill',\n", - " 'Edmund twohill',\n", - " 'Edward Yee',\n", - " 'Edwin Lee',\n", - " 'Edwin Lee from DXC Tech',\n", - " 'Eileen Lee',\n", - " 'Emil Tan',\n", - " 'Esther Seah',\n", - " 'Eugene Wee',\n", - " 'Felicia Ng',\n", - " 'Felicia ng',\n", - " 'Fong Yoong Kheong',\n", - " 'Foo Wan Xuan',\n", - " 'Foo Wan Xuan (Wildlife Reserves Singapore)',\n", - " 'G',\n", - " 'Genie Gan',\n", - " 'Geraldine Tan',\n", - " 'Geraldine Tan (Director and Principal Psychologist)',\n", - " 'Goh Duo Deng',\n", - " 'Goh Duo Geng',\n", - " 'Goh Duo Seng',\n", - " 'Grace Lee',\n", - " 'Grace Lee Khoo',\n", - " 'Grace Lee-Khoo',\n", - " 'Grace Ng',\n", - " 'Grace Ng (Senior Scriptwriter)',\n", - " \"Grace Ng (Senior Scriptwriter, Mediacorp Studios' English Drama Productions)\",\n", - " 'Grace Tong',\n", - " 'Grace Zhu',\n", - " 'Grace zhu - ministry on social development (disability)',\n", - " 'Guo Duo Sheng',\n", - " 'Hamid Razak',\n", - " 'Hannah Lim',\n", - " 'Harsh Raghuvir',\n", - " 'Hemaa Sakar',\n", - " 'Hemaa Sekar',\n", - " 'Heng Kai Le',\n", - " 'I Naishad Kai Ren',\n", - " \"I couldn't really find anyone specifically in the biotech/microbio area, it was more data science, but if there are others available I dont mind!\",\n", - " 'Irnina Wong',\n", - " 'Isabel Lee',\n", - " 'Jacklyn Seow',\n", - " 'Jacky Yap',\n", - " 'Jaclyn Seow',\n", - " 'Jaclyn Seow (Head of ESG and Impact at Openspace Ventures)',\n", - " 'Jacyln Seow',\n", - " 'Jamie Kloor',\n", - " 'Janice Loh',\n", - " 'Janice Soo',\n", - " 'Jared Kang',\n", - " 'Jared Kang, State Counsel, AGC',\n", - " 'Jaslyn Seah',\n", - " 'Jason Ong',\n", - " 'Jason Ong (Hubspot)',\n", - " 'Jayasutha Samuthiran',\n", - " 'Jeanne Tai',\n", - " 'Jee Soo Lee',\n", - " 'Jeremy Chia',\n", - " 'Jerviel',\n", - " 'Jerviel Human resource - awwa',\n", - " 'Jerviel Lim',\n", - " 'Jessica Tan',\n", - " 'Jiang Xin Yu',\n", - " 'Jiang Xinyu',\n", - " 'Jiaxi Zhang',\n", - " 'Jillian Lye',\n", - " 'Jimmy Ong',\n", - " 'Jimmy Sia',\n", - " 'Jimmys Sim',\n", - " 'Jocelyn - pathlight',\n", - " 'Jocelyn Low',\n", - " 'Johanna Tay',\n", - " 'John Wu',\n", - " 'Johnathan Kuek',\n", - " 'Jonathan Ang',\n", - " 'Jonathan Kuek',\n", - " 'Jonathan Lee',\n", - " 'Jonathan Lee, Associate General Counsel, Facebook',\n", - " 'Joshua AU',\n", - " 'Joshua Au',\n", - " 'Joshua Au (Head of the Data Centre)',\n", - " 'Joshua Tan',\n", - " 'Juhi Ramireddi',\n", - " 'Justin Ho',\n", - " 'Justin Ho (Vice President, JP Morgan Chase)',\n", - " 'Kamil Haque',\n", - " 'Kanitha Jagatheson',\n", - " 'Karen Sim',\n", - " 'Karen Sim (Senior Sustainability Strategist at Forum for the Future)',\n", - " 'Karpagam Venkkatesan',\n", - " 'Kenneth Bok',\n", - " 'Kenneth Lau',\n", - " 'Kenneth Law',\n", - " 'Kenneth Tay',\n", - " 'Kenny Sng',\n", - " 'Kevin Low',\n", - " 'Kevin Low (Infrastructure Asia)',\n", - " 'Kia Liang Fua',\n", - " 'Koh Ching Ching',\n", - " 'Kok Ching Ching',\n", - " 'Kristene Chan',\n", - " 'Kush Sagar',\n", - " 'Kush Sagar, Facebook',\n", - " 'Lai-Yee Soh',\n", - " 'Lavinia Thanapathy',\n", - " 'Leanne Thachil',\n", - " 'Lee Boon Pin',\n", - " 'Lee Hui Min',\n", - " 'Lee Jee Soo',\n", - " 'Lee Jee Soo, BCG',\n", - " 'Lee Keng Leong',\n", - " 'Lee Sing Kok',\n", - " 'Lee Sing-Kok',\n", - " 'Lee Sing-Kok (SK)',\n", - " 'Lee Wei An',\n", - " 'Lee Wei An from Uber Eats',\n", - " 'Lee Zhihan',\n", - " 'Leon Khee Pay',\n", - " 'Leon Khee Pay (Multi-Asset Product Executive, Schroders)',\n", - " 'Leon Toh',\n", - " 'Leslie Lim',\n", - " 'Li Jingmei',\n", - " 'Li Jingmei (Senior Research Scientist)',\n", - " 'Lim Boon Pin',\n", - " 'Lim Boon Pin (Policy & Planning at Municipal Services Office)',\n", - " 'Lim Huishan',\n", - " 'Lim Wei Jie',\n", - " 'Lim Wei Jie (Foreword)',\n", - " 'Ling Han',\n", - " 'Lionel Chong',\n", - " 'Lionel Choong',\n", - " 'Lionel Tan',\n", - " 'Loke Jia Li',\n", - " 'Low Siew Ling',\n", - " 'Mala Satkunanantham',\n", - " 'Malminderjit Singh',\n", - " 'Mark Lee',\n", - " 'Mark Tang',\n", - " 'Mark Yong',\n", - " 'Matthew Wong',\n", - " 'Mavis Tan',\n", - " 'Mavis Tan, Senior Consultant E&Y',\n", - " 'Mdm Eileen Lee',\n", - " 'Mdm Jayasutha Samuthiran',\n", - " 'Mdm Vivian Yeong',\n", - " 'Meera Sachdeva',\n", - " 'Melissa Low',\n", - " 'Melissa Low ( Energy Studies Institute )',\n", - " 'Melissa Luki',\n", - " 'Melissa Luki (Cistri)',\n", - " 'Michael Ngo',\n", - " 'Michelle Carvalho',\n", - " 'Mike Brown',\n", - " 'Minh (Mark) Vu',\n", - " 'Miss Grace Tong',\n", - " 'Miss Janice Soo',\n", - " 'Miss Jayasutha Samuthiran',\n", - " 'Miss Jeanne Tai',\n", - " 'Miss Jillian Lye',\n", - " 'Miss Johanna Tay',\n", - " 'Miss Mala Satkunanantham',\n", - " 'Miss Nicole Teoh',\n", - " 'Miss Shennon Ho',\n", - " 'Miss Stephanie Siow',\n", - " 'Miss Valerie Lim',\n", - " 'Mr Aaron Ng',\n", - " 'Mr Adrian Liew',\n", - " 'Mr Akshay Maheshwari',\n", - " 'Mr Alvin Seo',\n", - " 'Mr Alvin Tan',\n", - " 'Mr Andrew Pang',\n", - " 'Mr Andrzej Surzyn',\n", - " 'Mr Andrzej Surzyn (Cerberus Capital Management)',\n", - " 'Mr Bhargav Sriganesh',\n", - " 'Mr Billy Loh',\n", - " 'Mr Brian Liu',\n", - " 'Mr Chris Tan',\n", - " 'Mr Christian Cadeo',\n", - " 'Mr Cliff Hartono',\n", - " 'Mr David Chua',\n", - " 'Mr Desmond Koh',\n", - " 'Mr Edward Yee',\n", - " 'Mr Emil Tan',\n", - " 'Mr Eugene Wee',\n", - " 'Mr Hamid Razak',\n", - " 'Mr Jared Kang',\n", - " 'Mr Jimmy Sia',\n", - " 'Mr John Wu',\n", - " 'Mr Jonathan Kuek',\n", - " 'Mr Jonathan Lee',\n", - " 'Mr Kenneth Lau',\n", - " 'Mr Kenny Sng',\n", - " 'Mr Kevin Low',\n", - " 'Mr Kush Sagar',\n", - " 'Mr Leon Toh',\n", - " 'Mr Leslie Lim',\n", - " 'Mr Lim Boon Pin',\n", - " 'Mr Lim Wei Jie',\n", - " 'Mr Lionel Choong',\n", - " 'Mr Loke Jia Li',\n", - " 'Mr Mark Yong',\n", - " 'Mr Naishad Kai-Ren I',\n", - " 'Mr Nakul Asjia',\n", - " 'Mr Navjeev Singh',\n", - " 'Mr Nesh Sooriyan',\n", - " 'Mr Rahul Daswani',\n", - " 'Mr Raymond Tay',\n", - " 'Mr Rondy Krish',\n", - " 'Mr Sanjay Nair',\n", - " 'Mr Simon Phua',\n", - " 'Mr Tay Choon Hong',\n", - " 'Mr Teddy Low',\n", - " 'Mr Xu Si Han',\n", - " 'Mr Yujie Tag',\n", - " 'Mr Yuvan Mohan',\n", - " 'Mr. Alvin Tan',\n", - " 'Mr. Bhargav Sriganesh',\n", - " 'Mr. Davis Tan',\n", - " 'Mr. Desmond Koh',\n", - " 'Mr. Kenneth Tay',\n", - " 'Mr. Leon Toh',\n", - " 'Mrinalini Vekatachalam',\n", - " 'Mrinalini Venkatachalam',\n", - " 'Ms Alayna Ling',\n", - " 'Ms Alvona Loh',\n", - " 'Ms Amra Naidoo',\n", - " 'Ms Carol Chuah',\n", - " 'Ms Celine Toh',\n", - " 'Ms Cheah Wui Ling',\n", - " 'Ms Chin Hui Lin',\n", - " 'Ms Christine Kim',\n", - " 'Ms Crystal Koh',\n", - " 'Ms Eileen Lee',\n", - " 'Ms Esther Seah',\n", - " 'Ms Foo Wan Xuan',\n", - " 'Ms Genie Gan',\n", - " 'Ms Geraldine Tan',\n", - " 'Ms Grace Ng',\n", - " 'Ms Grace Zhu',\n", - " 'Ms Jaclyn Seow',\n", - " 'Ms Jamie Kloor',\n", - " 'Ms Janice Soo',\n", - " 'Ms Jaslyn Seah',\n", - " 'Ms Jessica Tan',\n", - " 'Ms Jiaxi Zhang',\n", - " 'Ms Jillian Lye',\n", - " 'Ms Jocelyn Low',\n", - " 'Ms Juhi Ramireddi',\n", - " 'Ms Kanitha Jagatheson',\n", - " 'Ms Karen Sim (Forum for the future)',\n", - " 'Ms Koh Ching Ching',\n", - " 'Ms Lavinia Thanapathy',\n", - " 'Ms Leanne Thachil',\n", - " 'Ms Lee Jee Soo',\n", - " 'Ms Li Jingmei',\n", - " 'Ms Ling Han',\n", - " 'Ms Mala Satkunanantham',\n", - " 'Ms Mavis Tan',\n", - " 'Ms Meera Sachdeva',\n", - " 'Ms Melissa Low',\n", - " 'Ms Nadia Yeo',\n", - " 'Ms Neo Mei Lin',\n", - " 'Ms Nicole Teoh (Changi Airports International)',\n", - " 'Ms Quek Jiahui',\n", - " 'Ms Rashi Tulshyan',\n", - " 'Ms Regine Chan',\n", - " 'Ms Ruo Mei Chua',\n", - " 'Ms Samantha Thian',\n", - " 'Ms Samyukta Venkatraman',\n", - " 'Ms Serene Goh',\n", - " 'Ms Stephanie Siow',\n", - " 'Ms Sulin Tan',\n", - " 'Ms Valerie Lim',\n", - " 'Ms Velda Wong',\n", - " 'Ms Vivian Yeong',\n", - " 'Ms. Celine Toh',\n", - " 'Ms. Felicia Ng',\n", - " 'Ms. Vicki Wong',\n", - " 'Muhammad Ashiq Chu',\n", - " 'Musarrat Maisha Reza',\n", - " 'Musarrat Maisha Reza (lecturer)',\n", - " 'N.A',\n", - " 'N.A.',\n", - " 'N.i.l',\n", - " 'NIL',\n", - " 'Nadia Yeo',\n", - " 'Nadia Yeo, Deputy Director (Legislation and Policy Advisory, Ministry of Home Affairs (MHA)',\n", - " 'Naishad I',\n", - " 'Naishad Kai Ren',\n", - " 'Naishad Kai-Ren I',\n", - " 'Nakul Asija',\n", - " 'Nakul Asija, the founder of The Gosto Foods Co.',\n", - " 'Nakul Asjia',\n", - " 'Nanthinee Jevanandam',\n", - " 'Nanthinee jevanandam',\n", - " 'Narjeev Singh',\n", - " 'Nathan Ong',\n", - " 'Nathan ong',\n", - " 'Navjeev Singh',\n", - " 'Neo Mei Lin',\n", - " 'Neo Meilin',\n", - " 'Nesh Sooriyan',\n", - " 'Nicole Teoh',\n", - " 'Nicole Teoh (Asset Management Analyst, Changi Airports International)',\n", - " 'Nil',\n", - " 'None',\n", - " \"None as I'm looking for a PM mentor and she's the only one\",\n", - " 'Not sure yet',\n", - " 'Peng Jing Kai',\n", - " 'Peng Jingkai',\n", - " 'Professor Cheah Wui Ling',\n", - " 'Quek Jia Hui',\n", - " 'Quek Jiahui',\n", - " 'Rahul Daswani',\n", - " 'Rashi Tulshyan',\n", - " 'Raymond Tay',\n", - " 'Rebecca Tan',\n", - " 'Regine Chan',\n", - " 'Remus Tan',\n", - " 'Rick Liu',\n", - " 'Rondy Krish',\n", - " 'Ruo Mei Chua',\n", - " 'Samantha Thian',\n", - " 'Samantha Thian (Seastainable)',\n", - " 'Samyukta Venkatraman',\n", - " 'Sanjay Nair',\n", - " 'Sarah Song',\n", - " 'Sarkunan Chandra',\n", - " 'Seah Wen Yan Jasyln',\n", - " 'Seastainable',\n", - " 'Serene Goh',\n", - " 'Shankar Venugopal',\n", - " 'Shanthakumar Bannirchelvam',\n", - " 'Shanthakumar Bannirchelvam (Managing Partner at Global Impact Partners)',\n", - " 'Shennon Ho',\n", - " 'Shermin Ho',\n", - " 'Simon Gwozdz',\n", - " 'Simon Phua',\n", - " 'Simon Phua, Facebook',\n", - " 'Soh Lai Yee',\n", - " 'Soh Lai Yi',\n", - " 'Sophia Tan',\n", - " 'Stephanie Siow',\n", - " 'Stephanie Sutanto',\n", - " 'Stephanie Sutanto: Rakuten Viki',\n", - " 'Sugidha Nithiananthan',\n", - " 'Suhaimi bin Zainal Shah',\n", - " 'Suhaimi bin Zainal Shah- Educational Technology Officer- Ministry of Education Singapore',\n", - " 'Sujatha Selvakumar',\n", - " 'Sujatha Selvakumar (Legislative Assistant, Singapore Parliament)',\n", - " 'Sulin Tan',\n", - " 'Sulin Tan: Carousell',\n", - " 'Tag Yujie',\n", - " 'Tan Chia Boon',\n", - " 'Tan Su Lin',\n", - " 'Tan Wei Xiong',\n", - " 'Tay Choon Hong',\n", - " 'Teddy Low',\n", - " 'Teddy Low, Assistant Director for Training Programmes, MFA',\n", - " 'Three is enough',\n", - " 'Tristan Theurier',\n", - " 'Valerie Lim',\n", - " 'Valerie Lim (Gojek)',\n", - " 'Velda Wong',\n", - " 'Vera Chng',\n", - " 'Vera Chng (Communications and Engagement Planning Officer [Strategic Communications], Ministry of Education)',\n", - " 'Vicki Wong',\n", - " 'Vivian Yeong',\n", - " 'Vivian Yeong @Verz Design',\n", - " 'Vivian yeong',\n", - " 'Walton Zhang',\n", - " 'Wendy Cheong',\n", - " 'Wendy Cheong (Duke-NUS Medical School)',\n", - " 'Wilson Tang',\n", - " 'Wong Yoke Yong',\n", - " 'Xin Yu Jiang',\n", - " 'XinYu Jiang',\n", - " 'Xingyu Jiang',\n", - " 'Xinyu Blair Jiang',\n", - " 'Xinyu Jiang',\n", - " 'Xu Si Han',\n", - " 'Ye-Her Wu',\n", - " 'Yeher Wu',\n", - " 'Yip Ren Kai',\n", - " 'Yu Jie',\n", - " 'Yu Jie Tag',\n", - " 'Yuan Ning (Mock)',\n", - " 'Yuan Ning Mock',\n", - " 'Yujie Tag',\n", - " 'Yujie Tag (Associate, McKinsey and Co)',\n", - " 'Yujie Tang',\n", - " 'Yuvan Mohan',\n", - " 'Yuvaraj Anandan',\n", - " 'alvona loh',\n", - " 'celine toh',\n", - " 'christine kim',\n", - " 'edward yee',\n", - " 'g',\n", - " 'geraldine tan',\n", - " 'grace tong',\n", - " 'jacky yap',\n", - " 'jonathan kuek',\n", - " 'justin ho',\n", - " 'kamil haque',\n", - " 'karen sim',\n", - " 'mala satkunanantham',\n", - " 'miss alvona loh',\n", - " 'mr hamid razak',\n", - " 'na',\n", - " nan,\n", - " 'nil',\n", - " 'regine chan',\n", - " '–'}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#getting list of mentors\n", - "\n", - "import pandas as pd\n", - "file = '2020w1.csv'\n", - "filedir = os.path.join(filesdir , file)\n", - "data = pd.read_csv(filedir)\n", - "print(data.isnull().sum())\n", - "data.dropna(how='all', inplace=True)\n", - "mentors = set()\n", - "\n", - "for column in range(4):\n", - " for mentor in data.iloc[:, column]:\n", - " mentors.add(mentor)\n", - " \n", - "mentors" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e132af95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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01
0NaN548
1Regine Chan85
2Alayna Ling78
3Celine Toh75
4Geraldine Tan72
.........
511Jimmys Sim1
512Celine Teoh1
513Johnathan Kuek1
514Christian Caedo1
515Leon Khee Pay (Multi-Asset Product Executive, ...1
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516 rows × 2 columns

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" - ], - "text/plain": [ - " 0 1\n", - "0 NaN 548\n", - "1 Regine Chan 85\n", - "2 Alayna Ling 78\n", - "3 Celine Toh 75\n", - "4 Geraldine Tan 72\n", - ".. ... ...\n", - "511 Jimmys Sim 1\n", - "512 Celine Teoh 1\n", - "513 Johnathan Kuek 1\n", - "514 Christian Caedo 1\n", - "515 Leon Khee Pay (Multi-Asset Product Executive, ... 1\n", - "\n", - "[516 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#finding frequency of mentors\n", - "\n", - "frequency_dic = {x:0 for x in mentors}\n", - "print(type(frequency_dic))\n", - "for column in range(4):\n", - " for mentor in data.iloc[:, column]:\n", - " frequency_dic[mentor] += 1\n", - "\n", - "sorted_data = pd.DataFrame(sorted(list(frequency_dic.items()),key=lambda x: x[1], reverse=True))\n", - "sorted_data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "bb5b80df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " 0 1\n", - "0 NaN 548\n", - "1 Regine Chan 85\n", - "2 Alayna Ling 78\n", - "3 Celine Toh 75\n", - "4 Geraldine Tan 72\n", - "5 - 55\n", - "6 Desmond Koh 52\n", - "7 Janice Soo 51\n", - "8 Alvin Tan 51\n", - "9 Alvona Loh 51" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted_data[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fed40cc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/howard's advisory stuff/new/clean.ipynb b/howard's advisory stuff/new/clean.ipynb deleted file mode 100644 index b37bdce..0000000 --- a/howard's advisory stuff/new/clean.ipynb +++ /dev/null @@ -1,1032 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reformating data from GA4" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from urllib.parse import urlparse, parse_qs, unquote" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('data-export.csv')\n", - "df.head(15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ignore Rows with Metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Landing pageSessionsUsersNew usersAverage engagement time per sessionConversionsTotal revenue
0NaN373582121521361105.798329700
1/%3Fsize=n_20_n43730724036.3821510300
2(not set)23415114.52564102600
3/%3Fsize=n_60_n1710000
4/%3Fcurrent=n_2_n&q=software%20&size=n_20_n169980.87500
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" - ], - "text/plain": [ - " Landing page Sessions Users New users \\\n", - "0 NaN 37358 21215 21361 \n", - "1 /%3Fsize=n_20_n 437 307 240 \n", - "2 (not set) 234 151 1 \n", - "3 /%3Fsize=n_60_n 17 1 0 \n", - "4 /%3Fcurrent=n_2_n&q=software%20&size=n_20_n 16 9 9 \n", - "\n", - " Average engagement time per session Conversions Total revenue \n", - "0 105.7983297 0 0 \n", - "1 36.38215103 0 0 \n", - "2 4.525641026 0 0 \n", - "3 0 0 0 \n", - "4 80.875 0 0 " - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_cleaned = df[10:]\n", - "\n", - "# take first row as heading and reset index\n", - "df_cleaned.columns = df_cleaned.iloc[0]\n", - "\n", - "# remove index from header row\n", - "df_cleaned = df_cleaned[1:]\n", - "df_cleaned = df_cleaned.reset_index(drop=True)\n", - "df_cleaned = df_cleaned.rename_axis(None, axis=1)\n", - "\n", - "df_cleaned.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Landing pageSessionsUsers
0NaN3735821215
1/%3Fsize=n_20_n437307
2(not set)234151
3/%3Fsize=n_60_n171
4/%3Fcurrent=n_2_n&q=software%20&size=n_20_n169
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" - ], - "text/plain": [ - " Landing page Sessions Users\n", - "0 NaN 37358 21215\n", - "1 /%3Fsize=n_20_n 437 307\n", - "2 (not set) 234 151\n", - "3 /%3Fsize=n_60_n 17 1\n", - "4 /%3Fcurrent=n_2_n&q=software%20&size=n_20_n 16 9" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop columns\n", - "df_cleaned = df_cleaned.drop(['New users', 'Average engagement time per session', 'Conversions', 'Total revenue'], axis=1)\n", - "df_cleaned.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Landing pageSessions
0NaN37358
1/%3Fsize=n_20_n437
2(not set)234
3/%3Fsize=n_60_n17
4/%3Fcurrent=n_2_n&q=software%20&size=n_20_n16
\n", - "
" - ], - "text/plain": [ - " Landing page Sessions\n", - "0 NaN 37358\n", - "1 /%3Fsize=n_20_n 437\n", - "2 (not set) 234\n", - "3 /%3Fsize=n_60_n 17\n", - "4 /%3Fcurrent=n_2_n&q=software%20&size=n_20_n 16" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop number of users\n", - "df_cleaned = df_cleaned.drop(['Users'], axis=1)\n", - "\n", - "# we will be exploding by sessions later" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Landing pageSessionsquery_params
0nan37358{}
1/%3Fsize=n_20_n437{'size': ['n_20_n']}
2(not set)234{}
3/%3Fsize=n_60_n17{'size': ['n_60_n']}
4/%3Fcurrent=n_2_n&q=software%20&size=n_20_n16{'current': ['n_2_n'], 'q': ['software '], 'si...
\n", - "
" - ], - "text/plain": [ - " Landing page Sessions \\\n", - "0 nan 37358 \n", - "1 /%3Fsize=n_20_n 437 \n", - "2 (not set) 234 \n", - "3 /%3Fsize=n_60_n 17 \n", - "4 /%3Fcurrent=n_2_n&q=software%20&size=n_20_n 16 \n", - "\n", - " query_params \n", - "0 {} \n", - "1 {'size': ['n_20_n']} \n", - "2 {} \n", - "3 {'size': ['n_60_n']} \n", - "4 {'current': ['n_2_n'], 'q': ['software '], 'si... " - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_preprocessed = df_cleaned.copy(deep=True)\n", - "\n", - "# expand landing page links to utm query params\n", - "# example: /%3Fcurrent=n_2_n&q=software%20&size=n_20_n\n", - "\n", - "# Define a function to extract query params from a URL\n", - "def extract_query_params(url):\n", - " url = unquote(url)\n", - " query_params = parse_qs(urlparse(url).query)\n", - " return query_params\n", - "\n", - "df_preprocessed['Landing page'] = df_preprocessed['Landing page'].astype(str)\n", - "df_preprocessed['query_params'] = df_preprocessed['Landing page'].apply(extract_query_params)\n", - "\n", - "df_preprocessed.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'current': ['n_2_n'], 'q': ['software '], 'size': ['n_20_n']}" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_preprocessed.iloc[4]['query_params']" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Landing pagequery_params
0/%3Fsize=n_20_n{'size': ['n_20_n']}
1/%3Fsize=n_20_n{'size': ['n_20_n']}
2/%3Fsize=n_20_n{'size': ['n_20_n']}
3/%3Fsize=n_20_n{'size': ['n_20_n']}
4/%3Fsize=n_20_n{'size': ['n_20_n']}
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" - ], - "text/plain": [ - " Landing page query_params\n", - "0 /%3Fsize=n_20_n {'size': ['n_20_n']}\n", - "1 /%3Fsize=n_20_n {'size': ['n_20_n']}\n", - "2 /%3Fsize=n_20_n {'size': ['n_20_n']}\n", - "3 /%3Fsize=n_20_n {'size': ['n_20_n']}\n", - "4 /%3Fsize=n_20_n {'size': ['n_20_n']}" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# explode by sessions for easier analysis\n", - "\n", - "df_preprocessed['Sessions'] = df_preprocessed['Sessions'].astype(int)\n", - "\n", - "df_preprocessed = df_preprocessed[df_preprocessed['Landing page'] != '(not set)']\n", - "df_preprocessed = df_preprocessed[df_preprocessed['Landing page'] != 'nan']\n", - "\n", - "df_preprocessed['copy'] = df_preprocessed['Sessions'].apply(lambda x: list(range(x)))\n", - "df_exploded = df_preprocessed.explode('copy').drop(columns='Sessions')\n", - "df_exploded.drop(columns='copy', inplace=True)\n", - "\n", - "# reset index\n", - "df_exploded = df_exploded.reset_index(drop=True)\n", - "\n", - "df_exploded.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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visitor_urlquery_params
0/%3Fsize=n_20_n{'size': ['n_20_n']}
1/%3Fsize=n_20_n{'size': ['n_20_n']}
2/%3Fsize=n_20_n{'size': ['n_20_n']}
3/%3Fsize=n_20_n{'size': ['n_20_n']}
4/%3Fsize=n_20_n{'size': ['n_20_n']}
.........
663/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...
664/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...
665/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...
666/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...
667/%3Fsize=n_60_n&filters%5B0%5D%5Bfield%5D=indu...{'size': ['n_60_n'], 'filters[0][field]': ['in...
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668 rows × 2 columns

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" - ], - "text/plain": [ - " visitor_url \\\n", - "0 /%3Fsize=n_20_n \n", - "1 /%3Fsize=n_20_n \n", - "2 /%3Fsize=n_20_n \n", - "3 /%3Fsize=n_20_n \n", - "4 /%3Fsize=n_20_n \n", - ".. ... \n", - "663 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "664 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "665 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "666 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "667 /%3Fsize=n_60_n&filters%5B0%5D%5Bfield%5D=indu... \n", - "\n", - " query_params \n", - "0 {'size': ['n_20_n']} \n", - "1 {'size': ['n_20_n']} \n", - "2 {'size': ['n_20_n']} \n", - "3 {'size': ['n_20_n']} \n", - "4 {'size': ['n_20_n']} \n", - ".. ... \n", - "663 {'size': ['n_20_n'], 'filters[0][field]': ['sc... \n", - "664 {'size': ['n_20_n'], 'filters[0][field]': ['sc... \n", - "665 {'size': ['n_20_n'], 'filters[0][field]': ['sc... \n", - "666 {'size': ['n_20_n'], 'filters[0][field]': ['sc... \n", - "667 {'size': ['n_60_n'], 'filters[0][field]': ['in... \n", - "\n", - "[668 rows x 2 columns]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# rename landing page to visitor_url\n", - "df_exploded = df_exploded.rename(columns={'Landing page': 'visitor_url'})\n", - "df_exploded" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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visitor_urlquery_paramssizecurrentqfilters[0][field]filters[0][values][0]sort-fieldsort-directionfbclidamp;amp;size
663/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...[n_20_n]NaNNaN[school][London School o]NaNNaNNaNNaN
664/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...[n_20_n]NaNNaN[school][Nanyang Polytechn]NaNNaNNaNNaN
665/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...[n_20_n]NaNNaN[school][Nanyang Technolog]NaNNaNNaNNaN
666/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho...{'size': ['n_20_n'], 'filters[0][field]': ['sc...[n_20_n]NaNNaN[school][National Universi]NaNNaNNaNNaN
667/%3Fsize=n_60_n&filters%5B0%5D%5Bfield%5D=indu...{'size': ['n_60_n'], 'filters[0][field]': ['in...[n_60_n]NaNNaN[industries][Data Science%]NaNNaNNaNNaN
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" - ], - "text/plain": [ - " visitor_url \\\n", - "663 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "664 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "665 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "666 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=scho... \n", - "667 /%3Fsize=n_60_n&filters%5B0%5D%5Bfield%5D=indu... \n", - "\n", - " query_params size current q \\\n", - "663 {'size': ['n_20_n'], 'filters[0][field]': ['sc... [n_20_n] NaN NaN \n", - "664 {'size': ['n_20_n'], 'filters[0][field]': ['sc... [n_20_n] NaN NaN \n", - "665 {'size': ['n_20_n'], 'filters[0][field]': ['sc... [n_20_n] NaN NaN \n", - "666 {'size': ['n_20_n'], 'filters[0][field]': ['sc... [n_20_n] NaN NaN \n", - "667 {'size': ['n_60_n'], 'filters[0][field]': ['in... [n_60_n] NaN NaN \n", - "\n", - " filters[0][field] filters[0][values][0] sort-field sort-direction fbclid \\\n", - "663 [school] [London School o] NaN NaN NaN \n", - "664 [school] [Nanyang Polytechn] NaN NaN NaN \n", - "665 [school] [Nanyang Technolog] NaN NaN NaN \n", - "666 [school] [National Universi] NaN NaN NaN \n", - "667 [industries] [Data Science%] NaN NaN NaN \n", - "\n", - " amp;amp;size \n", - "663 NaN \n", - "664 NaN \n", - "665 NaN \n", - "666 NaN \n", - "667 NaN " - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert query params to columns\n", - "df_exploded = pd.concat([df_exploded, df_exploded['query_params'].apply(pd.Series)], axis=1)\n", - "\n", - "df_exploded.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "# what is the amp;amp;size column?\n", - "df_exploded['amp;amp;size'].value_counts()\n", - "\n", - "df_exploded.drop('amp;amp;size', axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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visitor_urlquery_paramssizecurrentqfilters[0][field]filters[0][values][0]sort-fieldsort-directionfbclid
663/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=London%20School%20o{'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['London School o']}[n_20_n]NaNNaN[school][London School o]NaNNaNNaN
664/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=Nanyang%20Polytechn{'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['Nanyang Polytechn']}[n_20_n]NaNNaN[school][Nanyang Polytechn]NaNNaNNaN
665/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=Nanyang%20Technolog{'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['Nanyang Technolog']}[n_20_n]NaNNaN[school][Nanyang Technolog]NaNNaNNaN
666/%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=National%20Universi{'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['National Universi']}[n_20_n]NaNNaN[school][National Universi]NaNNaNNaN
667/%3Fsize=n_60_n&filters%5B0%5D%5Bfield%5D=industries&filters%5B0%5D%5Bvalues%5D%5B0%5D=Data%20Science%{'size': ['n_60_n'], 'filters[0][field]': ['industries'], 'filters[0][values][0]': ['Data Science%']}[n_60_n]NaNNaN[industries][Data Science%]NaNNaNNaN
\n", - "
" - ], - "text/plain": [ - " visitor_url \\\n", - "663 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=London%20School%20o \n", - "664 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=Nanyang%20Polytechn \n", - "665 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=Nanyang%20Technolog \n", - "666 /%3Fsize=n_20_n&filters%5B0%5D%5Bfield%5D=school&filters%5B0%5D%5Bvalues%5D%5B0%5D=National%20Universi \n", - "667 /%3Fsize=n_60_n&filters%5B0%5D%5Bfield%5D=industries&filters%5B0%5D%5Bvalues%5D%5B0%5D=Data%20Science% \n", - "\n", - " query_params \\\n", - "663 {'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['London School o']} \n", - "664 {'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['Nanyang Polytechn']} \n", - "665 {'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['Nanyang Technolog']} \n", - "666 {'size': ['n_20_n'], 'filters[0][field]': ['school'], 'filters[0][values][0]': ['National Universi']} \n", - "667 {'size': ['n_60_n'], 'filters[0][field]': ['industries'], 'filters[0][values][0]': ['Data Science%']} \n", - "\n", - " size current q filters[0][field] filters[0][values][0] sort-field \\\n", - "663 [n_20_n] NaN NaN [school] [London School o] NaN \n", - "664 [n_20_n] NaN NaN [school] [Nanyang Polytechn] NaN \n", - "665 [n_20_n] NaN NaN [school] [Nanyang Technolog] NaN \n", - "666 [n_20_n] NaN NaN [school] [National Universi] NaN \n", - "667 [n_60_n] NaN NaN [industries] [Data Science%] NaN \n", - "\n", - " sort-direction fbclid \n", - "663 NaN NaN \n", - "664 NaN NaN \n", - "665 NaN NaN \n", - "666 NaN NaN \n", - "667 NaN NaN " - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option('display.max_colwidth', None)\n", - "\n", - "df_exploded.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "# export data\n", - "df_exploded.to_csv('data-preprocessed.csv', index=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "vscode": { - "interpreter": { - "hash": "26663b30a87f8b6b521f0a61964c10b31d4c5e2632109eb7f9afdda680fa86bd" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/huan_yao_code/new.py b/huan_yao_code/new.py deleted file mode 100644 index 386cff1..0000000 --- a/huan_yao_code/new.py +++ /dev/null @@ -1,46 +0,0 @@ -from urllib import parse -import csv -store=[] -processed=[] -with open('data-export.csv', 'r')as basefile: - for i in range(13): - heading = next(basefile) - for row in csv.reader(basefile): - store+=[row[0]] -for index in range(len(store)): - store[index]=store[index].replace('%3F','?') - processed+=[parse.parse_qs(parse.urlsplit(store[index]).query)] -####print(processed) -keystore=[] -uniquekey=[] -valuestore=[] -unwanted=['size','current','fbclid','sort-field','sort-direction','amp;amp;size'] -for dictionary in processed: -## print(dictionary) - keys = [] - values = [] - for key in dictionary: - if key in unwanted: - continue - keys.append(key) - values.append(dictionary[key]) - if key not in uniquekey: - uniquekey.append(key) - keystore+=[keys] - valuestore+=[values] -####print(uniquekey) - -with open('data-export - Copy.csv', 'w',newline='') as file: - with open('data-export.csv', 'r')as basefile: - cursor=csv.writer(file) - ##hardocding skipping 13 lines - count=0 - for row in csv.reader(basefile): - count+=1 - if count<14: - cursor.writerow(row) - continue - for i in valuestore[count-14]: - if len(i[0])>1: - row+=[i] - cursor.writerow(row) From ea4bd2e6231edc06806b70feaeb534ae9cb527f3 Mon Sep 17 00:00:00 2001 From: Jolene Date: Mon, 23 Oct 2023 13:49:48 +0800 Subject: [PATCH 2/7] feat: prevent adding of files without extensions --- .gitignore | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.gitignore b/.gitignore index c824773..709de35 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,13 @@ +# ignores files without extensions (avoids pushing compiled db files) +# except gitignore and license files +* +!*/ +!*.* +!.gitignore +!LICENSE + + + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] From 210db323c49e30439c1dd7fc952677c50c8c7815 Mon Sep 17 00:00:00 2001 From: Jolene Date: Mon, 23 Oct 2023 13:52:45 +0800 Subject: [PATCH 3/7] feat: updated folder names --- .gitignore | 2 -- .../analysis of the sql data.ipynb | 0 .../presenting data.ipynb | 0 3 files changed, 2 deletions(-) rename {04PostgreSQLDumpFIle => 04URLsInPostgreSQL}/analysis of the sql data.ipynb (100%) rename {04PostgreSQLDumpFIle => 04URLsInPostgreSQL}/presenting data.ipynb (100%) diff --git a/.gitignore b/.gitignore index 709de35..f395e59 100644 --- a/.gitignore +++ b/.gitignore @@ -6,8 +6,6 @@ !.gitignore !LICENSE - - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/04PostgreSQLDumpFIle/analysis of the sql data.ipynb b/04URLsInPostgreSQL/analysis of the sql data.ipynb similarity index 100% rename from 04PostgreSQLDumpFIle/analysis of the sql data.ipynb rename to 04URLsInPostgreSQL/analysis of the sql data.ipynb diff --git a/04PostgreSQLDumpFIle/presenting data.ipynb b/04URLsInPostgreSQL/presenting data.ipynb similarity index 100% rename from 04PostgreSQLDumpFIle/presenting data.ipynb rename to 04URLsInPostgreSQL/presenting data.ipynb From 8caa9873a0c2a1469237957623f2a7251dae321a Mon Sep 17 00:00:00 2001 From: Jolene Date: Mon, 23 Oct 2023 16:25:17 +0800 Subject: [PATCH 4/7] feat: added script to task 03 --- 03AnalysisApplicationData/main.py | 139 ++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 03AnalysisApplicationData/main.py diff --git a/03AnalysisApplicationData/main.py b/03AnalysisApplicationData/main.py new file mode 100644 index 0000000..384dfc3 --- /dev/null +++ b/03AnalysisApplicationData/main.py @@ -0,0 +1,139 @@ +# +# Script to process mentorship data from CSV files and save to CSV +# # TODO: NOT TESTED YET (getting credentials) +# +# 1. Set up /data with Application from different waves and their respective files +# 2. Create env file with your credentials for Elastic Cloud +# - CLOUD_ID= +# - PASSWORD= +# - USER= +# 3. Run script, which produces mentors.csv and mentors_per_application files in /data +# +# Written by: Jolene +# + + +import pandas as pd +import numpy as np +from elasticsearch import Elasticsearch +import re + +class ProcessApplicationData: + def __init__(self, user, cloud_id, password): + self.es = Elasticsearch( + cloud_id=cloud_id, + http_auth=(user, password) + ) + self.mentors = {} + self.unknown_mentors = [] + + def search_documents(self, index, query_body): + result = self.es.search(index=index, body=query_body) + return result + + def get_mentor_info_by_name(self, name): + search_options = { + 'query': { + 'bool': { + 'should': [] + } + } + } + + # Add match query for the name + search_options['query']['bool']['should'].append({ + 'match': { + 'name': name + } + }) + + # Search by organization if name includes organization information in brackets + organization = re.search(r'\((.*?)\)', name) + if organization: + organization_name = organization.group(1) + # Add match query for the organisation + search_options['query']['bool']['should'].append({ + 'match': { + 'organisation': organization_name + } + }) + + result = self.search_documents('enterprise-search-engine-mentorship-page', search_options) + exact_matches = [doc for doc in result['hits']['hits']] + + if len(exact_matches) == 0: + return None + + if 'organisation' in exact_matches[0]['_source']: + return { + 'name': exact_matches[0]['_source']['name'], + 'industries': exact_matches[0]['_source']['industries'], + 'organisation': exact_matches[0]['_source']['organisation'] + } + else: + return { + 'name': exact_matches[0]['_source']['name'], + 'industries': exact_matches[0]['_source']['industries'], + 'organisation': None + } + + def check_same_name(self, name): + search_options = { + 'query': { + 'match': { + 'name': name + } + } + } + result = self.search_documents('enterprise-search-engine-mentorship-page', search_options) + documents = result['hits']['hits'] + if len(documents) > 1: + return True + else: + return False + + def process_mentors_data(self, dataframes): + df = pd.concat(dataframes) + df['year'] = np.concatenate([np.full(len(df_i), year_i) for df_i, year_i in dataframes]) + df.columns = ['mentor_name', 'year'] + df = df[df['mentor_name'] != '[INSERT NAME LIST OF WAVE 3 MENTORS]'] + + df['industries'] = "" + df['organisation'] = "" + + for index, row in df.iterrows(): + name = row['mentor_name'].lower() + year = row['year'] + + if name in self.mentors: + mentor = self.mentors[name] + else: + mentor = self.get_mentor_info_by_name(name) + + if mentor is not None: + row['industries'] = mentor['industries'] + row['organisation'] = mentor['organisation'] + else: + self.unknown_mentors.append(name) + + return df + + def save_data_to_csv(self, df, filename): + df.to_csv(filename, index=False) + +if __name__ == '__main__': + CLOUD_ID = "" + PASSWORD = '' + USER = "jolene" + + mentorship_system = ProcessApplicationData(USER, CLOUD_ID, PASSWORD) + + # Load CSV dataframes + df_2020w3 = pd.read_csv('data/2020w3.csv') + df_2021w1 = pd.read_csv('data/2021w1.csv') + df_2022 = pd.read_csv('data/2022.csv') + + # Process and save mentor data to CSV + dataframes = [df_2020w3, df_2021w1, df_2022] + mentors_df = mentorship_system.process_mentors_data(dataframes) + mentorship_system.save_data_to_csv(mentors_df, 'data/mentors.csv') \ No newline at end of file From e2e84b3960cb01596b83ede29a3a2dc4b9e0334b Mon Sep 17 00:00:00 2001 From: raven0205 <73115813+raven0205@users.noreply.github.com> Date: Sat, 11 Nov 2023 18:32:37 +0800 Subject: [PATCH 5/7] Create wave2.py --- 03AnalysisApplicationData/data/wave2.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 03AnalysisApplicationData/data/wave2.py diff --git a/03AnalysisApplicationData/data/wave2.py b/03AnalysisApplicationData/data/wave2.py new file mode 100644 index 0000000..d25889c --- /dev/null +++ b/03AnalysisApplicationData/data/wave2.py @@ -0,0 +1 @@ +## this will contain the application data for wave 2 From ca26054ff5a9a98c6fb9d4aea0fbb52fe669d049 Mon Sep 17 00:00:00 2001 From: Jolene Date: Sun, 12 Nov 2023 17:44:24 +0800 Subject: [PATCH 6/7] rebasing to remove data files --- 02ReformattingUmami/parsing urls.ipynb | 2073 ++++++++++++++++++++++++ 1 file changed, 2073 insertions(+) diff --git a/02ReformattingUmami/parsing urls.ipynb b/02ReformattingUmami/parsing urls.ipynb index fe4c034..02e27ba 100644 --- a/02ReformattingUmami/parsing urls.ipynb +++ b/02ReformattingUmami/parsing urls.ipynb @@ -2,18 +2,27 @@ "cells": [ { "cell_type": "code", +<<<<<<< HEAD "execution_count": 23, +======= + "execution_count": 11, +>>>>>>> d5fed27 (Update parsing urls.ipynb) "id": "4ba88477", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", + "import numpy as np\n", "from urllib.parse import urlparse, parse_qs, unquote" ] }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 24, +======= + "execution_count": 12, +>>>>>>> d5fed27 (Update parsing urls.ipynb) "id": "c20ad040", "metadata": {}, "outputs": [], @@ -23,7 +32,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 25, +======= + "execution_count": 13, +>>>>>>> d5fed27 (Update parsing urls.ipynb) "id": "e731453c", "metadata": {}, "outputs": [], @@ -40,7 +53,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 26, +======= + "execution_count": 14, +>>>>>>> d5fed27 (Update parsing urls.ipynb) "id": "c7471d8e", "metadata": {}, "outputs": [], @@ -50,12 +67,2068 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 27, +======= + "execution_count": 49, + "id": "5604d86c", + "metadata": {}, + "outputs": [], + "source": [ + "x2 = x.assign(industries=lambda x: np.nan, \n", + " school=lambda x: np.nan,\n", + " course_of_study=lambda x: np.nan,\n", + " organisation=lambda x: np.nan) #create copy with the columns that i want" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "33cd1847", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + 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"20853630300\n", + "21018679200\n", + "21212859100\n", + "21388865100\n", + "21575672800\n", + "21758869500\n", + "21933173500\n" + ] + } + ], + "source": [ + "def isnotNaN(num):\n", + " return num == num\n", + "\n", + "columns = ['view_id', 'website_id', 'session_id', 'created_at', 'url', 'referrer', 'query_params',\n", + " 'q', 'size', 'current', 'sort-field', 'sort-direction', '_sm_au_', 'v', 'fbclid', 'trk',\n", + " 'amp;amp;size', 'industries', 'school', 'course_of_study', 'organisation'] #columns that i eventually want\n", + "\n", + "\n", + "for i in range(len(x2)): #go through original DF\n", + "\n", + " row = x2.iloc[i] # for each row,\n", + "\n", + " temp ={0:[None,list()], #theres 4 possible fields [field, list of values]\n", + " 1:[None,list()],\n", + " 2:[None,list()],\n", + " 3:[None,list()]\n", + " }\n", + "\n", + " for j in range(4): #identify which field corresponds to reach index\n", + " if isnotNaN(row.loc[f'filters[{j}][field]']):\n", + " temp[j][0] = row.loc[f'filters[{j}][field]'][0]\n", + " else:\n", + " break\n", + "\n", + " for k in range(4): #condense all the values for each of the fields into list of values\n", + " for l in range(20):\n", + " if f'filters[{k}][values][{l}]' in row.index and isnotNaN(row.loc[f'filters[{k}][values][{l}]']):\n", + " temp[k][1].append(row.loc[f'filters[{k}][values][{l}]'][0]) \n", + " else:\n", + " break\n", + "\n", + " for z in range(4): #add the new column:values to dataframe\n", + " field = temp[z][0]\n", + " values = temp[z][1]\n", + " if field is None:\n", + " break\n", + " else:\n", + " x2.loc[i,field] = str(values)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 61, +>>>>>>> d5fed27 (Update parsing urls.ipynb) "id": "57f88523", "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD "x.to_csv('data-preprocessed.csv', index=False)" +======= + "x2.to_csv('data-preprocessed.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "473214f9", + "metadata": {}, + "source": [ + "testing\n", + "\n", + "


























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+ "cell_type": "code", + "execution_count": 147, + "id": "a5c9804c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "view_id 7688\n", + "website_id 2\n", + "session_id 1392\n", + "created_at 2022-05-29 06:50:26.338+00\n", + "url /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", + "referrer /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", + "query_params {'size': ['n_20_n'], 'filters[0][field]': ['in...\n", + "q NaN\n", + "size [n_20_n]\n", + "filters[0][field] [industries]\n", + "filters[0][values][0] [Banking and Finance]\n", + "filters[0][type] [all]\n", + "filters[0][values][1] NaN\n", + "filters[0][values][2] NaN\n", + "current NaN\n", + "sort-field NaN\n", + "sort-direction NaN\n", + "filters[1][field] [school]\n", + "filters[1][values][0] [National University of Singapore]\n", + "filters[1][type] [any]\n", + "filters[0][values][3] NaN\n", + "filters[0][values][4] NaN\n", + "filters[1][values][1] [Singapore Management University]\n", + "filters[1][values][2] NaN\n", + "filters[1][values][3] NaN\n", + "filters[1][values][4] NaN\n", + "filters[1][values][5] NaN\n", + "filters[2][field] [course_of_study]\n", + "filters[2][values][0] [Business Administration]\n", + "filters[2][values][1] NaN\n", + "filters[2][values][2] NaN\n", + "filters[2][values][3] NaN\n", + "filters[1][values][6] NaN\n", + "filters[2][type] [any]\n", + "filters[0][values][5] NaN\n", + "filters[0][values][6] NaN\n", + "filters[0][values][7] NaN\n", + "filters[0][values][8] NaN\n", + "filters[0][values][9] NaN\n", + "_sm_au_ NaN\n", + "v NaN\n", + "fbclid NaN\n", + "filters[3][field] NaN\n", + "trk NaN\n", + "filters[3][values][0] NaN\n", + "filters[3][values][1] NaN\n", + "amp;amp;size NaN\n", + "Name: 7485, dtype: object" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row = x.iloc[7485]\n", + "row" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "2335961c", + "metadata": {}, + "outputs": [], + "source": [ + "def isnotNaN(num):\n", + " return num == num\n", + "\n", + "temp ={0:[None,list()],\n", + " 1:[None,list()],\n", + " 2:[None,list()],\n", + " 3:[None,list()]\n", + " }\n", + "\n", + "for i in range(4):\n", + " if isnotNaN(row.loc[f'filters[{i}][field]']):\n", + " temp[i][0] = row.loc[f'filters[{i}][field]'][0]\n", + " \n", + "for i in range(4):\n", + " for j in range(20):\n", + " if f'filters[{i}][values][{j}]' in row.index and isnotNaN(row.loc[f'filters[{i}][values][{j}]']):\n", + " temp[i][1].append(row.loc[f'filters[{i}][values][{j}]'][0]) \n", + " \n", + "for i in range(4):\n", + " for j in range(20):\n", + " if f'filters[{i}][values][{j}]' in row.index:\n", + " row = row.drop(labels=f'filters[{i}][values][{j}]')\n", + " \n", + "for i in range(4):\n", + " if isnotNaN(row.loc[f'filters[{i}][field]']):\n", + " row = row.drop(labels=[f'filters[{i}][field]', f'filters[{i}][type]'])\n", + " \n", + "for i in range(4):\n", + " field = temp[i][0]\n", + " values = temp[i][1]\n", + " row[field] = values" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "e393f951", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "9c3dba3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['industries', ['Banking and Finance']],\n", + " 1: ['school',\n", + " ['National University of Singapore', 'Singapore Management University']],\n", + " 2: ['course_of_study', ['Business Administration']],\n", + " 3: [None, []]}" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "b57d4ca9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "view_id 7688\n", + "website_id 2\n", + "session_id 1392\n", + "created_at 2022-05-29 06:50:26.338+00\n", + "url /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", + "referrer /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", + "query_params {'size': ['n_20_n'], 'filters[0][field]': ['in...\n", + "q NaN\n", + "size [n_20_n]\n", + "current NaN\n", + "sort-field NaN\n", + "sort-direction NaN\n", + "_sm_au_ NaN\n", + "v NaN\n", + "fbclid NaN\n", + "filters[3][field] NaN\n", + "trk NaN\n", + "amp;amp;size NaN\n", + "industries [Banking and Finance]\n", + "school [National University of Singapore, Singapore M...\n", + "course_of_study [Business Administration]\n", + "None []\n", + "Name: 7485, dtype: object" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "5d91fe23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([ 'view_id', 'website_id', 'session_id',\n", + " 'created_at', 'url', 'referrer',\n", + " 'query_params', 'q', 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"output_type": "stream", + "text": [ + "['view_id', 'website_id', 'session_id', 'created_at', 'url', 'referrer', 'query_params', 'q', 'size', 'current', 'sort-field', 'sort-direction', '_sm_au_', 'v', 'fbclid', 'filters[3][field]', 'trk', 'amp;amp;size', 'industries', 'school', 'course_of_study', None, 'organisation']\n" + ] + } + ], + "source": [ + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "b21898f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "view_id 7688\n", + "website_id 2\n", + "session_id 1392\n", + "created_at 2022-05-29 06:50:26.338+00\n", + "url /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", + "referrer /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", + "query_params {'size': ['n_20_n'], 'filters[0][field]': ['in...\n", + "q NaN\n", + "size [n_20_n]\n", + "current NaN\n", + "sort-field NaN\n", + "sort-direction NaN\n", + "_sm_au_ NaN\n", + "v NaN\n", + "fbclid NaN\n", + "filters[3][field] NaN\n", + 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" + ], + "text/plain": [ + " view_id website_id session_id created_at \\\n", + "7485 7688 2 1392 2022-05-29 06:50:26.338+00 \n", + "\n", + " url \\\n", + "7485 /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust... \n", + "\n", + " referrer \\\n", + "7485 /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust... \n", + "\n", + " query_params q size \\\n", + "7485 {'size': ['n_20_n'], 'filters[0][field]': ['in... NaN [n_20_n] \n", + "\n", + " current ... _sm_au_ v fbclid filters[3][field] trk amp;amp;size \\\n", + "7485 NaN ... NaN NaN NaN NaN NaN NaN \n", + "\n", + " industries \\\n", + "7485 [Banking and Finance] \n", + "\n", + " school \\\n", + "7485 [National University of Singapore, Singapore M... \n", + "\n", + " course_of_study None \n", + "7485 [Business Administration] [] \n", + "\n", + "[1 rows x 22 columns]" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "403e6169", + "metadata": {}, + "outputs": [], + "source": [ + "x = ['view_id', 'website_id', 'session_id', 'created_at', 'url', 'referrer', 'query_params',\n", + " 'q', 'size', 'current', 'sort-field', 'sort-direction', '_sm_au_', 'v', 'fbclid', 'trk',\n", + " 'amp;amp;size', 'industries', 'school', 'course_of_study', 'organisation']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "36451395", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['view_id',\n", + " 'website_id',\n", + " 'session_id',\n", + " 'created_at',\n", + " 'url',\n", + " 'referrer',\n", + " 'query_params',\n", + " 'q',\n", + " 'size',\n", + " 'current',\n", + " 'sort-field',\n", + " 'sort-direction',\n", + " '_sm_au_',\n", + " 'v',\n", + " 'fbclid',\n", + " 'trk',\n", + " 'amp;amp;size',\n", + " 'industries',\n", + " 'school',\n", + " 'course_of_study',\n", + " 'organisation']" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" +>>>>>>> d5fed27 (Update parsing urls.ipynb) ] }, { From 81df9a94bc99ec8562c7ac5d3d08a75a95edd77a Mon Sep 17 00:00:00 2001 From: Jolene Date: Sun, 12 Nov 2023 16:43:19 +0800 Subject: [PATCH 7/7] optimized code and added comments w more details --- 02ReformattingUmami/parsing urls.ipynb | 2164 ------------------------ 04URLsInPostgreSQL/parsing urls.ipynb | 385 +++++ 2 files changed, 385 insertions(+), 2164 deletions(-) delete mode 100644 02ReformattingUmami/parsing urls.ipynb create mode 100644 04URLsInPostgreSQL/parsing urls.ipynb diff --git a/02ReformattingUmami/parsing urls.ipynb b/02ReformattingUmami/parsing urls.ipynb deleted file mode 100644 index 02e27ba..0000000 --- a/02ReformattingUmami/parsing urls.ipynb +++ /dev/null @@ -1,2164 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 23, -======= - "execution_count": 11, ->>>>>>> d5fed27 (Update parsing urls.ipynb) - "id": "4ba88477", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from urllib.parse import urlparse, parse_qs, unquote" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 24, -======= - "execution_count": 12, ->>>>>>> d5fed27 (Update parsing urls.ipynb) - "id": "c20ad040", - "metadata": {}, - "outputs": [], - "source": [ - "original_df = pd.read_csv('v1_pageview')" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 25, -======= - "execution_count": 13, ->>>>>>> d5fed27 (Update parsing urls.ipynb) - "id": "e731453c", - "metadata": {}, - "outputs": [], - "source": [ - "def extract_query_params(url):\n", - " url = unquote(url)\n", - " query_params = parse_qs(urlparse(url).query)\n", - " return query_params\n", - "\n", - "new_df = original_df.copy() \n", - "new_df['url'] = new_df['url'].astype(str)\n", - "new_df['query_params'] = new_df['url'].apply(extract_query_params)\n" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 26, -======= - "execution_count": 14, ->>>>>>> d5fed27 (Update parsing urls.ipynb) - "id": "c7471d8e", - "metadata": {}, - "outputs": [], - "source": [ - "x = pd.concat([new_df, new_df['query_params'].apply(pd.Series)], axis=1)" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 27, -======= - "execution_count": 49, - "id": "5604d86c", - "metadata": {}, - "outputs": [], - "source": [ - "x2 = x.assign(industries=lambda x: np.nan, \n", - " school=lambda x: np.nan,\n", - " course_of_study=lambda x: np.nan,\n", - " organisation=lambda x: np.nan) #create copy with the columns that i want" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "33cd1847", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1019300\n", - "198379000\n", - 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" for l in range(20):\n", - " if f'filters[{k}][values][{l}]' in row.index and isnotNaN(row.loc[f'filters[{k}][values][{l}]']):\n", - " temp[k][1].append(row.loc[f'filters[{k}][values][{l}]'][0]) \n", - " else:\n", - " break\n", - "\n", - " for z in range(4): #add the new column:values to dataframe\n", - " field = temp[z][0]\n", - " values = temp[z][1]\n", - " if field is None:\n", - " break\n", - " else:\n", - " x2.loc[i,field] = str(values)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 61, ->>>>>>> d5fed27 (Update parsing urls.ipynb) - "id": "57f88523", - "metadata": {}, - "outputs": [], - "source": [ -<<<<<<< HEAD - "x.to_csv('data-preprocessed.csv', index=False)" -======= - "x2.to_csv('data-preprocessed.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "id": "473214f9", - "metadata": {}, - "source": [ - "testing\n", - "\n", - "


























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"cell_type": "code", - "execution_count": 147, - "id": "a5c9804c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "view_id 7688\n", - "website_id 2\n", - "session_id 1392\n", - "created_at 2022-05-29 06:50:26.338+00\n", - "url /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", - "referrer /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", - "query_params {'size': ['n_20_n'], 'filters[0][field]': ['in...\n", - "q NaN\n", - "size [n_20_n]\n", - "filters[0][field] [industries]\n", - "filters[0][values][0] [Banking and Finance]\n", - "filters[0][type] [all]\n", - "filters[0][values][1] NaN\n", - "filters[0][values][2] NaN\n", - "current NaN\n", - "sort-field NaN\n", - "sort-direction NaN\n", - "filters[1][field] [school]\n", - "filters[1][values][0] [National University of Singapore]\n", - "filters[1][type] [any]\n", - "filters[0][values][3] NaN\n", - "filters[0][values][4] NaN\n", - "filters[1][values][1] [Singapore Management University]\n", - "filters[1][values][2] NaN\n", - "filters[1][values][3] NaN\n", - "filters[1][values][4] NaN\n", - "filters[1][values][5] NaN\n", - "filters[2][field] [course_of_study]\n", - "filters[2][values][0] [Business Administration]\n", - "filters[2][values][1] NaN\n", - "filters[2][values][2] NaN\n", - "filters[2][values][3] NaN\n", - "filters[1][values][6] NaN\n", - "filters[2][type] [any]\n", - "filters[0][values][5] NaN\n", - "filters[0][values][6] NaN\n", - "filters[0][values][7] NaN\n", - "filters[0][values][8] NaN\n", - "filters[0][values][9] NaN\n", - "_sm_au_ NaN\n", - "v NaN\n", - "fbclid NaN\n", - "filters[3][field] NaN\n", - "trk NaN\n", - "filters[3][values][0] NaN\n", - "filters[3][values][1] NaN\n", - "amp;amp;size NaN\n", - "Name: 7485, dtype: object" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "row = x.iloc[7485]\n", - "row" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "2335961c", - "metadata": {}, - "outputs": [], - "source": [ - "def isnotNaN(num):\n", - " return num == num\n", - "\n", - "temp ={0:[None,list()],\n", - " 1:[None,list()],\n", - " 2:[None,list()],\n", - " 3:[None,list()]\n", - " }\n", - "\n", - "for i in range(4):\n", - " if isnotNaN(row.loc[f'filters[{i}][field]']):\n", - " temp[i][0] = row.loc[f'filters[{i}][field]'][0]\n", - " \n", - "for i in range(4):\n", - " for j in range(20):\n", - " if f'filters[{i}][values][{j}]' in row.index and isnotNaN(row.loc[f'filters[{i}][values][{j}]']):\n", - " temp[i][1].append(row.loc[f'filters[{i}][values][{j}]'][0]) \n", - " \n", - "for i in range(4):\n", - " for j in range(20):\n", - " if f'filters[{i}][values][{j}]' in row.index:\n", - " row = row.drop(labels=f'filters[{i}][values][{j}]')\n", - " \n", - "for i in range(4):\n", - " if isnotNaN(row.loc[f'filters[{i}][field]']):\n", - " row = row.drop(labels=[f'filters[{i}][field]', f'filters[{i}][type]'])\n", - " \n", - "for i in range(4):\n", - " field = temp[i][0]\n", - " values = temp[i][1]\n", - " row[field] = values" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "e393f951", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "9c3dba3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: ['industries', ['Banking and Finance']],\n", - " 1: ['school',\n", - " ['National University of Singapore', 'Singapore Management University']],\n", - " 2: ['course_of_study', ['Business Administration']],\n", - " 3: [None, []]}" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "b57d4ca9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "view_id 7688\n", - "website_id 2\n", - "session_id 1392\n", - "created_at 2022-05-29 06:50:26.338+00\n", - "url /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", - "referrer /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", - "query_params {'size': ['n_20_n'], 'filters[0][field]': ['in...\n", - "q NaN\n", - "size [n_20_n]\n", - "current NaN\n", - "sort-field NaN\n", - "sort-direction NaN\n", - "_sm_au_ NaN\n", - "v NaN\n", - "fbclid NaN\n", - "filters[3][field] NaN\n", - "trk NaN\n", - "amp;amp;size NaN\n", - "industries [Banking and Finance]\n", - "school [National University of Singapore, Singapore M...\n", - "course_of_study [Business Administration]\n", - "None []\n", - "Name: 7485, dtype: object" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "row" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "5d91fe23", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index([ 'view_id', 'website_id', 'session_id',\n", - " 'created_at', 'url', 'referrer',\n", - " 'query_params', 'q', 'size',\n", - " 'current', 'sort-field', 'sort-direction',\n", - " '_sm_au_', 'v', 'fbclid',\n", - " 'filters[3][field]', 'trk', 'amp;amp;size',\n", - " 'industries', 'school', 'course_of_study',\n", - " None],\n", - " dtype='object')" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "row.index" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "d3b752a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.series.Series" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(row)" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "043c5d16", - "metadata": {}, - "outputs": [], - "source": [ - "l = list(row.index)\n", - "l.append('organisation')" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "7787dc1e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['view_id', 'website_id', 'session_id', 'created_at', 'url', 'referrer', 'query_params', 'q', 'size', 'current', 'sort-field', 'sort-direction', '_sm_au_', 'v', 'fbclid', 'filters[3][field]', 'trk', 'amp;amp;size', 'industries', 'school', 'course_of_study', None, 'organisation']\n" - ] - } - ], - "source": [ - "print(l)" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "id": "b21898f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "view_id 7688\n", - "website_id 2\n", - "session_id 1392\n", - "created_at 2022-05-29 06:50:26.338+00\n", - "url /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", - "referrer /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...\n", - "query_params {'size': ['n_20_n'], 'filters[0][field]': ['in...\n", - "q NaN\n", - "size [n_20_n]\n", - "current NaN\n", - "sort-field NaN\n", - "sort-direction NaN\n", - "_sm_au_ NaN\n", - "v NaN\n", - "fbclid NaN\n", - "filters[3][field] NaN\n", - "trk NaN\n", - "amp;amp;size NaN\n", - "industries [Banking and Finance]\n", - "school [National University of Singapore, Singapore M...\n", - "course_of_study [Business Administration]\n", - "None []\n", - "Name: 7485, dtype: object" - ] - }, - "execution_count": 158, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "row" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "id": "a3a0f1c7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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view_idwebsite_idsession_idcreated_aturlreferrerquery_paramsqsizecurrent...sort-direction_sm_au_vfbclidtrkamp;amp;sizeindustriesschoolcourse_of_studyorganisation
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view_idwebsite_idsession_idcreated_aturlreferrerquery_paramsqsizecurrent..._sm_au_vfbclidfilters[3][field]trkamp;amp;sizeindustriesschoolcourse_of_studyNone
74857688213922022-05-29 06:50:26.338+00/?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust.../?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust...{'size': ['n_20_n'], 'filters[0][field]': ['in...NaN[n_20_n]NaN...NaNNaNNaNNaNNaNNaN[Banking and Finance][National University of Singapore, Singapore M...[Business Administration][]
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1 rows × 22 columns

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" - ], - "text/plain": [ - " view_id website_id session_id created_at \\\n", - "7485 7688 2 1392 2022-05-29 06:50:26.338+00 \n", - "\n", - " url \\\n", - "7485 /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust... \n", - "\n", - " referrer \\\n", - "7485 /?size=n_20_n&filters%5B0%5D%5Bfield%5D=indust... \n", - "\n", - " query_params q size \\\n", - "7485 {'size': ['n_20_n'], 'filters[0][field]': ['in... NaN [n_20_n] \n", - "\n", - " current ... _sm_au_ v fbclid filters[3][field] trk amp;amp;size \\\n", - "7485 NaN ... NaN NaN NaN NaN NaN NaN \n", - "\n", - " industries \\\n", - "7485 [Banking and Finance] \n", - "\n", - " school \\\n", - "7485 [National University of Singapore, Singapore M... \n", - "\n", - " course_of_study None \n", - "7485 [Business Administration] [] \n", - "\n", - "[1 rows x 22 columns]" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 167, - "id": "403e6169", - "metadata": {}, - "outputs": [], - "source": [ - "x = ['view_id', 'website_id', 'session_id', 'created_at', 'url', 'referrer', 'query_params',\n", - " 'q', 'size', 'current', 'sort-field', 'sort-direction', '_sm_au_', 'v', 'fbclid', 'trk',\n", - " 'amp;amp;size', 'industries', 'school', 'course_of_study', 'organisation']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "id": "36451395", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['view_id',\n", - " 'website_id',\n", - " 'session_id',\n", - " 'created_at',\n", - " 'url',\n", - " 'referrer',\n", - " 'query_params',\n", - " 'q',\n", - " 'size',\n", - " 'current',\n", - " 'sort-field',\n", - " 'sort-direction',\n", - " '_sm_au_',\n", - " 'v',\n", - " 'fbclid',\n", - " 'trk',\n", - " 'amp;amp;size',\n", - " 'industries',\n", - " 'school',\n", - " 'course_of_study',\n", - " 'organisation']" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" ->>>>>>> d5fed27 (Update parsing urls.ipynb) - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afede6fa", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/04URLsInPostgreSQL/parsing urls.ipynb b/04URLsInPostgreSQL/parsing urls.ipynb new file mode 100644 index 0000000..7fd5c6b --- /dev/null +++ b/04URLsInPostgreSQL/parsing urls.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parsing URLs with data from PostgreSQL dump file\n", + "\n", + "Input dataframe:\n", + " - view_id\n", + " - website_id\n", + " - session_id\n", + " - created_at\n", + " - url\n", + " - referrer\n", + "\n", + "Output dataframe:\n", + "- industries (this will contain a list of all the values in this field)\n", + "- course_of_study\n", + "- organisaton\n", + "- school\n", + "\n", + "Summary of insights gained: (if any)\n", + "\n", + "Written by: Howard and Jolene (only mostly optimizations)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ba88477", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 113589 entries, 0 to 113588\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 view_id 113589 non-null int64 \n", + " 1 website_id 113589 non-null int64 \n", + " 2 session_id 113589 non-null int64 \n", + " 3 created_at 113589 non-null object\n", + " 4 url 113589 non-null object\n", + " 5 referrer 99529 non-null object\n", + "dtypes: int64(3), object(3)\n", + "memory usage: 5.2+ MB\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from urllib.parse import urlparse, parse_qs, unquote\n", + "\n", + "original_df = pd.read_csv('v1_pageview')\n", + "original_df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e731453c", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_query_params(url):\n", + " url = unquote(url) # make it human readable, not percentages\n", + " query_params = parse_qs(urlparse(url).query)\n", + " return query_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Explaining process_query_params\n", + "\n", + "What does the url look like when navigating the page?\n", + "```\n", + "// filter by industry Information and Communications Technology\n", + "filters[0][field]=industries&filters[0][values][0]=Information and Communications Technology\n", + "\n", + "// filter by organization Google and SAP\n", + "filters[0][field]=organisation&filters[0][values][0]=SAP\n", + "filters[1][field]=organisation&filters[1][values][0]=Google\n", + "\n", + "// filter by school \n", + "filters[1][field]=school&filters[1][values][0]=National University of Singapore\n", + "filters[1][type]=any # we will ignore this part, not sure what it as, its always in 'any'\n", + "filters[2][field]=course_of_study\n", + "filters[2][values][0]=Economics%2C Psychology\n", + "filters[2][type]=any\n", + "```\n", + "\n", + "To summarize, the number in filters[0][field] is the first/second/third filter applied etc. while the second value which is either 'field', 'value' or 'type' shows what the text after = is. `filters[1][field]=school&filters[1][values][0]=National University of Singapore` this means the second filter applied is a school filter, the value of the school filter applied is `filters[1][values][0]=National University of Singapore`.\n", + "\n", + "So we can make use of this to extract the filters applied to each vistor URL." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def process_query_params(params):\n", + " '''\n", + " Parameters:\n", + " params -> {'size': ['n_20_n'], 'filters[0][field]': ['industries'], 'filters[0][values][0]': ['Information and Communications Technology'], 'filters[0][type]': ['all']}\n", + " \n", + " Returns:\n", + " {\n", + " \"search_query\": \"search term\",\n", + " \"filter_name\": [\"filter value 1\", \"filter value 2\"]\n", + " }\n", + "\n", + " Note:\n", + " - The filter_name is the name of the filter, e.g. industries, school etc.\n", + " - size and type are ignored\n", + " '''\n", + " result = {}\n", + " current_field = ''\n", + "\n", + " for key, value in params.items():\n", + " if 'filters' in key:\n", + " parts = key.split('[')\n", + " field_or_value = parts[2].strip(']')\n", + "\n", + " if field_or_value == 'field':\n", + " # if its a field then use it as a key\n", + " current_field = value[0]\n", + " result[current_field] = []\n", + " elif field_or_value == 'type':\n", + " # there's a type of all in all queries, not sure what that is and whether its relevant\n", + " pass\n", + " else:\n", + " # if its a value then add it to the list by using the last saved field\n", + " result[current_field].extend(value)\n", + " elif key == \"q\":\n", + " result[\"search_query\"] = value[0]\n", + "\n", + " result = dict(result)\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Main" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process URLs to extract query params" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df = original_df.copy(deep=True)\n", + "df['url'] = df['url'].astype(str)\n", + "df['query_params'] = df['url'].apply(extract_query_params) # gets all the query params and makes it a dictionary " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# x = pd.concat([new_df, new_df['query_params'].apply(lambda x: pd.Series(x, dtype=\"object\"))], axis=1) #separate each of the key:value pairs into it's own column \n", + "\n", + "# x2 = x.assign(industries=lambda x: np.nan, \n", + "# school=lambda x: np.nan,\n", + "# course_of_study=lambda x: np.nan,\n", + "# organisation=lambda x: np.nan) #create copy with the columns that i want\n", + "\n", + "# def isnotNaN(num):\n", + "# return num == num\n", + "\n", + "# columns = ['view_id', 'website_id', 'session_id', 'created_at', 'url', 'referrer', 'query_params',\n", + "# 'q', 'size', 'current', 'sort-field', 'sort-direction', '_sm_au_', 'v', 'fbclid', 'trk',\n", + "# 'amp;amp;size', 'industries', 'school', 'course_of_study', 'organisation'] #columns that i eventually want\n", + "\n", + "\n", + "# for i in range(len(x2)): #go through original DF\n", + "\n", + "# row = x2.iloc[i] # for each row,\n", + "\n", + "# temp ={0:[None,list()], #theres 4 possible fields [field, list of values]\n", + "# 1:[None,list()],\n", + "# 2:[None,list()],\n", + "# 3:[None,list()]\n", + "# }\n", + "\n", + "# for j in range(4): #identify which field corresponds to reach index\n", + "# if isnotNaN(row.loc[f'filters[{j}][field]']):\n", + "# temp[j][0] = row.loc[f'filters[{j}][field]'][0]\n", + "# else:\n", + "# break\n", + "\n", + "# for k in range(4): #condense all the values for each of the fields into list of values\n", + "# for l in range(20):\n", + "# if f'filters[{k}][values][{l}]' in row.index and isnotNaN(row.loc[f'filters[{k}][values][{l}]']):\n", + "# temp[k][1].append(row.loc[f'filters[{k}][values][{l}]'][0]) \n", + "# else:\n", + "# break\n", + "\n", + "# for z in range(4): #add the new column:values to dataframe\n", + "# field = temp[z][0]\n", + "# values = temp[z][1]\n", + "# if field is None:\n", + "# break\n", + "# else:\n", + "# x2.loc[i,field] = str(values)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# optimized version of the code above\n", + "df_processed = df.copy(deep=True)\n", + "df_processed['query_params'] = df_processed['query_params'].apply(process_query_params) # use only 1 for loop\n", + "df_processed = pd.DataFrame(df_processed['query_params'].values.tolist())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why are there so many additional columns?" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "indust\n", + "[] 4\n", + "Name: count, dtype: int64\n", + "ind\n", + "[] 22\n", + "Name: count, dtype: int64\n", + "i\n", + "[] 3\n", + "Name: count, dtype: int64\n", + "cou\n", + "[] 3\n", + "Name: count, dtype: int64\n", + "sch\n", + "[] 1\n", + "Name: count, dtype: int64\n", + "o\n", + "[] 1\n", + "Name: count, dtype: int64\n", + "course_of\n", + "[] 1\n", + "Name: count, dtype: int64\n", + "wave_id\n", + "[n_2_n] 8\n", + "[n_1_n] 7\n", + "[n_3_n] 5\n", + "[n_0_n] 2\n", + "[n_0_n, n_2_n] 1\n", + "[n_3_n, n_1_n] 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# ['i', 'ind', 'cou', 'indust', 'o', 'sch', 'course_of']\n", + "print(df_processed['indust'].value_counts())\n", + "print(df_processed['ind'].value_counts())\n", + "print(df_processed['i'].value_counts())\n", + "print(df_processed['cou'].value_counts())\n", + "print(df_processed['sch'].value_counts())\n", + "print(df_processed['o'].value_counts())\n", + "print(df_processed['course_of'].value_counts())\n", + "print(df_processed['wave_id'].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the additional columns all contain empty list, so we can safely drop them. As for wave_id, we will leave it in for now." + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 113589 entries, 0 to 113588\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 search_query 20959 non-null object\n", + " 1 industries 29829 non-null object\n", + " 2 course_of_study 7103 non-null object\n", + " 3 organisation 13074 non-null object\n", + " 4 school 3657 non-null object\n", + " 5 course 1 non-null object\n", + " 6 wave_id 24 non-null object\n", + "dtypes: object(7)\n", + "memory usage: 6.1+ MB\n" + ] + } + ], + "source": [ + "# drop unused columns ['i', 'ind', 'cou', 'indust', 'o', 'sch', 'course_of']\n", + "df_processed = df_processed.drop(['i', 'ind', 'cou', 'indust', 'o', 'sch', 'course_of'], axis=1)\n", + "df_processed.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export to CSV" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "57f88523", + "metadata": {}, + "outputs": [], + "source": [ + "df_processed.to_csv('data-preprocessed.csv', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}