{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Running DplPy with Jupyter Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This Jupyter Notebook must be run within the `dplPy` directory. Refer to `README.md` for instructions on how to run Jupyter Notebook and the required `conda Environment` on your OS." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import packages and load test data from the `test` directory.\n", "> **Note**: choose your own example file from `/tests/data//`. In this example, the test file is `rwl/ca533.rwl`. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Attempting to read input file: ca533.rwl as .rwl format\n", "\n", "\n", "SUCCESS!\n", "File read as: .rwl file\n", "\n", "Series names:\n", "['CAM011', 'CAM021', 'CAM031', 'CAM032', 'CAM041', 'CAM042', 'CAM051', 'CAM061', 'CAM062', 'CAM071', 'CAM072', 'CAM081', 'CAM082', 'CAM091', 'CAM092', 'CAM101', 'CAM102', 'CAM111', 'CAM112', 'CAM121', 'CAM122', 'CAM131', 'CAM132', 'CAM141', 'CAM151', 'CAM152', 'CAM161', 'CAM162', 'CAM171', 'CAM172', 'CAM181', 'CAM191', 'CAM201', 'CAM211']\n" ] } ], "source": [ "# Import statements\n", "# loading test example as data\n", "# Find examples in `tests` folder\n", "\n", "import os\n", "directory = os.getcwd().split(\"/\")\n", "if directory[-1] != 'src':\n", " os.chdir(\"./src\")\n", "import dplpy as dpl\n", "data = dpl.readers(\"../tests/data/rwl/ca533.rwl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summarize data using the `summary` function." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CAM011CAM021CAM031CAM032CAM041CAM042CAM051CAM061CAM062CAM071...CAM151CAM152CAM161CAM162CAM171CAM172CAM181CAM191CAM201CAM211
count454.000000551.000000628.000000549.000000301.000000446.000000737.000000627.000000459.000000947.000000...749.000000229.000000504.0000001000.000000758.000000797.000000781.000000791.000000593.0000001343.000000
mean0.4395810.4244650.3491560.2932240.5256480.4391480.2730120.4622810.4419390.249071...0.4456480.5337990.3394640.3967100.4502640.4822960.2826380.3662710.4739290.356813
std0.2218010.1853970.2136660.1629300.2225680.3477050.1396910.2017850.1883890.109357...0.2725610.1949470.1489160.1840570.2098480.2490020.1488530.3357880.1809670.182086
min0.0000000.0500000.0000000.0000000.1000000.0700000.0000000.0000000.0000000.000000...0.0000000.0600000.0000000.0000000.0800000.0800000.0000000.0000000.0000000.000000
25%0.2900000.2900000.1800000.1800000.3500000.2700000.1800000.3350000.3300000.180000...0.2400000.4100000.2300000.2600000.3000000.3100000.1700000.1700000.3500000.220000
50%0.4000000.4000000.2900000.2600000.5300000.3600000.2500000.4700000.4500000.250000...0.3900000.5200000.3300000.3700000.4000000.4200000.2500000.2500000.4700000.340000
75%0.5400000.5200000.5100000.3900000.6800000.4600000.3300000.6000000.5800000.320000...0.6100000.6600000.4300000.5100000.5800000.5900000.3800000.4550000.5800000.470000
max1.3600001.1100001.0300000.8500001.3800003.0300001.3200001.0900000.9200000.620000...1.6400001.2500000.9000001.0400001.5400001.9800000.8000002.5400001.4900001.100000
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8 rows × 34 columns

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" ], "text/plain": [ " CAM011 CAM021 CAM031 CAM032 CAM041 CAM042 \\\n", "count 454.000000 551.000000 628.000000 549.000000 301.000000 446.000000 \n", "mean 0.439581 0.424465 0.349156 0.293224 0.525648 0.439148 \n", "std 0.221801 0.185397 0.213666 0.162930 0.222568 0.347705 \n", "min 0.000000 0.050000 0.000000 0.000000 0.100000 0.070000 \n", "25% 0.290000 0.290000 0.180000 0.180000 0.350000 0.270000 \n", "50% 0.400000 0.400000 0.290000 0.260000 0.530000 0.360000 \n", "75% 0.540000 0.520000 0.510000 0.390000 0.680000 0.460000 \n", "max 1.360000 1.110000 1.030000 0.850000 1.380000 3.030000 \n", "\n", " CAM051 CAM061 CAM062 CAM071 ... CAM151 \\\n", "count 737.000000 627.000000 459.000000 947.000000 ... 749.000000 \n", "mean 0.273012 0.462281 0.441939 0.249071 ... 0.445648 \n", "std 0.139691 0.201785 0.188389 0.109357 ... 0.272561 \n", "min 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n", "25% 0.180000 0.335000 0.330000 0.180000 ... 0.240000 \n", "50% 0.250000 0.470000 0.450000 0.250000 ... 0.390000 \n", "75% 0.330000 0.600000 0.580000 0.320000 ... 0.610000 \n", "max 1.320000 1.090000 0.920000 0.620000 ... 1.640000 \n", "\n", " CAM152 CAM161 CAM162 CAM171 CAM172 \\\n", "count 229.000000 504.000000 1000.000000 758.000000 797.000000 \n", "mean 0.533799 0.339464 0.396710 0.450264 0.482296 \n", "std 0.194947 0.148916 0.184057 0.209848 0.249002 \n", "min 0.060000 0.000000 0.000000 0.080000 0.080000 \n", "25% 0.410000 0.230000 0.260000 0.300000 0.310000 \n", "50% 0.520000 0.330000 0.370000 0.400000 0.420000 \n", "75% 0.660000 0.430000 0.510000 0.580000 0.590000 \n", "max 1.250000 0.900000 1.040000 1.540000 1.980000 \n", "\n", " CAM181 CAM191 CAM201 CAM211 \n", "count 781.000000 791.000000 593.000000 1343.000000 \n", "mean 0.282638 0.366271 0.473929 0.356813 \n", "std 0.148853 0.335788 0.180967 0.182086 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.170000 0.170000 0.350000 0.220000 \n", "50% 0.250000 0.250000 0.470000 0.340000 \n", "75% 0.380000 0.455000 0.580000 0.470000 \n", "max 0.800000 2.540000 1.490000 1.100000 \n", "\n", "[8 rows x 34 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summarizes input data\n", "\n", "dpl.summary(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show general statistics of `data` through the `stats` function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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seriesfirstlastyearmeanmedianstdevskewginiar1
1CAM011153019834540.4400.400.2221.0290.2730.698
2CAM021143319835510.4240.400.1850.9460.2370.702
3CAM031135619836280.3490.290.2140.6900.3410.809
4CAM032143519835490.2930.260.1630.7170.3090.665
5CAM041168319833010.5260.530.2230.4880.2380.710
6CAM042153819834460.4390.360.3483.6780.3240.881
7CAM051124719837370.2730.250.1401.8360.2620.705
8CAM061135719836270.4620.470.202-0.1110.2470.510
9CAM062152519834590.4420.450.188-0.2660.2400.529
10CAM071103719839470.2490.250.1090.0270.2470.578
11CAM072111419838700.3090.290.1630.6980.2920.735
12CAM081108119839030.3270.310.1240.5550.2110.723
13CAM082977198310070.2850.290.1140.3120.2230.771
14CAM091146019835240.5320.520.2550.4250.2670.632
15CAM092159119833930.3490.340.2260.3370.3690.561
16CAM101172719832570.5680.560.2600.2540.2590.716
17CAM102166519833190.6040.620.2610.0820.2430.677
18CAM111144619835380.6250.620.2490.1960.2250.625
19CAM112147119835130.5700.560.2110.2230.2070.583
20CAM121100019839840.2590.260.1060.0420.2310.594
21CAM122100019839840.2710.270.1090.3460.2230.653
22CAM131695197012760.5520.530.1980.3300.2020.788
23CAM13271012325230.3970.380.1480.8710.2030.810
24CAM141103019709410.6270.600.2040.6950.1770.746
25CAM151122219707490.4460.390.2731.0680.3320.765
26CAM152122114492290.5340.520.1950.2970.2030.695
27CAM161110616095040.3390.330.1490.6330.2430.794
28CAM162971197010000.3970.370.1840.6470.2590.840
29CAM171121319707580.4500.400.2101.2500.2500.799
30CAM172117419707970.4820.420.2491.6220.2680.847
31CAM181119019707810.2830.250.1490.7060.2930.805
32CAM191118019707910.3660.250.3362.3590.4290.876
33CAM20199015825930.4740.470.1810.7720.2080.709
34CAM211626196813430.3570.340.1820.5130.2860.683
\n", "
" ], "text/plain": [ " series first last year mean median stdev skew gini ar1\n", "1 CAM011 1530 1983 454 0.440 0.40 0.222 1.029 0.273 0.698\n", "2 CAM021 1433 1983 551 0.424 0.40 0.185 0.946 0.237 0.702\n", "3 CAM031 1356 1983 628 0.349 0.29 0.214 0.690 0.341 0.809\n", "4 CAM032 1435 1983 549 0.293 0.26 0.163 0.717 0.309 0.665\n", "5 CAM041 1683 1983 301 0.526 0.53 0.223 0.488 0.238 0.710\n", "6 CAM042 1538 1983 446 0.439 0.36 0.348 3.678 0.324 0.881\n", "7 CAM051 1247 1983 737 0.273 0.25 0.140 1.836 0.262 0.705\n", "8 CAM061 1357 1983 627 0.462 0.47 0.202 -0.111 0.247 0.510\n", "9 CAM062 1525 1983 459 0.442 0.45 0.188 -0.266 0.240 0.529\n", "10 CAM071 1037 1983 947 0.249 0.25 0.109 0.027 0.247 0.578\n", "11 CAM072 1114 1983 870 0.309 0.29 0.163 0.698 0.292 0.735\n", "12 CAM081 1081 1983 903 0.327 0.31 0.124 0.555 0.211 0.723\n", "13 CAM082 977 1983 1007 0.285 0.29 0.114 0.312 0.223 0.771\n", "14 CAM091 1460 1983 524 0.532 0.52 0.255 0.425 0.267 0.632\n", "15 CAM092 1591 1983 393 0.349 0.34 0.226 0.337 0.369 0.561\n", "16 CAM101 1727 1983 257 0.568 0.56 0.260 0.254 0.259 0.716\n", "17 CAM102 1665 1983 319 0.604 0.62 0.261 0.082 0.243 0.677\n", "18 CAM111 1446 1983 538 0.625 0.62 0.249 0.196 0.225 0.625\n", "19 CAM112 1471 1983 513 0.570 0.56 0.211 0.223 0.207 0.583\n", "20 CAM121 1000 1983 984 0.259 0.26 0.106 0.042 0.231 0.594\n", "21 CAM122 1000 1983 984 0.271 0.27 0.109 0.346 0.223 0.653\n", "22 CAM131 695 1970 1276 0.552 0.53 0.198 0.330 0.202 0.788\n", "23 CAM132 710 1232 523 0.397 0.38 0.148 0.871 0.203 0.810\n", "24 CAM141 1030 1970 941 0.627 0.60 0.204 0.695 0.177 0.746\n", "25 CAM151 1222 1970 749 0.446 0.39 0.273 1.068 0.332 0.765\n", "26 CAM152 1221 1449 229 0.534 0.52 0.195 0.297 0.203 0.695\n", "27 CAM161 1106 1609 504 0.339 0.33 0.149 0.633 0.243 0.794\n", "28 CAM162 971 1970 1000 0.397 0.37 0.184 0.647 0.259 0.840\n", "29 CAM171 1213 1970 758 0.450 0.40 0.210 1.250 0.250 0.799\n", "30 CAM172 1174 1970 797 0.482 0.42 0.249 1.622 0.268 0.847\n", "31 CAM181 1190 1970 781 0.283 0.25 0.149 0.706 0.293 0.805\n", "32 CAM191 1180 1970 791 0.366 0.25 0.336 2.359 0.429 0.876\n", "33 CAM201 990 1582 593 0.474 0.47 0.181 0.772 0.208 0.709\n", "34 CAM211 626 1968 1343 0.357 0.34 0.182 0.513 0.286 0.683" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# General statistics of input data\n", "\n", "dpl.stats(data)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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htwI4DsBIQshyAN8H0AYAlNKpAD4K4MuEkAqArQDOpnXymprwbyG9Ex5jy89Z6Nzazd0YNaijaPGU6K64OO3qp3Dk7iOCYxQUrkuxtbuKfm0lX+ZaWfrm5b67qQtD+rVF4r9lg2S9N0a/5LZZ3HXRK/NMzZPufHPNFqza6A3Q8cGGBn1LZgmL7VZUwjW+2DSzOL4fpG2zLEpfGb2TuiQPbGtLsa17PyufD4lKn1J6TsL53wP4fWESGSCLguHHIXEBy5X/mIdfnbV/EIVhgqpLM6czYC/19CUhvUQp8KnrnscLS9YGqyrnv70RrksLWUzl1ev9N53ZdFWqmPzjR/HRg8fgV2cdEBwXQyS9smtA72TdgzaHKEn2yjFXPRZei6jSqroU5ZJ83152nofssW7c1oMn30iXCbUQSz+lDu/OQu+oone4w3+athAvLF6LTx62M46fuJ22PJZlVbUDWRbbs5YRbs2CllyRKyoD3cNls4GIpV+O/uz7Zq8wSt7G12PiqFOXEz/mUopnFnqsWH8/5e31Ty/G1GkLjco0QVp6h71MD859Wzge/+21eElkzfnOhm247qnF2rprzemL1waho5RqFZGokGWW/rVPLo4dS5SDexxplH6eOH2ZpU8pxS3Pv6ncXF7N6YfHf/rAfDz66qrIvgoqlBS5l1izvrelB796+LXEcnj01GLK2mRItPSbEaKlaWK58tNXUekDMLLy+Xp7qi4620qx8+0GVrnsBeMP8XnOX/GTyxWBLPSODGKuG6A2cfqydvrSX2YmpobO46hO0zQ0+B8qL5bETNZG4lgp6ymrNqaL9Qeis4o08psO/kvXbMbOIwZEjsks/X/PX4X/unsuXn9nI37woXjktjp6JxvCWZXckSsaByZgCQ9t9E6TQZw6y+gGBhmnL8tP0r/DQOlXeaWfneKQvWz8vbx8fCbEvBDj2nlnqAwqRS6nd/LJJgtflNW/fmtP4nVJL+xld8zG9QqFkOZlF8VLonfEY3e99Bb+8uySyLE0FCMDU8Alh6QafF03pJh0fffYqx7HvBVR40Nm6W/Y5j2b9xSpu4uO3lGl4cjjyNXpkt6C1lT6gsLVWfrhzlnhNR1SS9/By2+tx1+fXxpJb8yDt25MHHUqsOk4/+Lwd/K+gnKBORO2CXvPXnLbLHz4f59Wy6k4LuWra2Dpm5YoXudSYPTQfrjg2N0iOeMZ7pi5HFf+Y560rDSzBCooraobDthSo0BS9nfveUVaZhqwftSvrZRq8HU5Okq8T5wNihEyMqXP3kvWf2csWRtdbCjucBY4crP1nSROPwsC3dKLdX9L0jtMuTLVaDI6y0I2eVAKfPB/ngIAbOmq4ovH7Bq7ppKk9HNY+vzLzmcjNMmaaAq2QvGaaYsiq5ZVUK2UbCS9I6tHFkVDiNzie/mt9do60yigx15bhfGX34/PHTE+qLdNF71jtPDLuPoALF9TZ5uTylINghGq8XaNt330u4zeYfeUHYLl723BR6c+izMmjcZvP36gV59QZhiyaSxyBGxWFeP0sxUHQG7Q9Da0pqWfgdPnn6WMMuFf0nVb5dPTKKcvs+TMeq9MmfG38pZ+UfQO//vmvrUet01flnhPuPgpKoPUiV0nR66smljIJuVTaofnKlU3GNhVSDN2MflueGYJAJ/eCXL8Z5sJUlAMH9COPbY3z4vfHSj9Uip/TZWGdFTM0he+i+0itfTZfg0OwYat3n4Lr74d0kJimRXXxa0vvKmcWSeBvSff+n9zsKWb298hA7/DXjn2XvdiQ781lb7I4elHZ391XkKZfHbLTo5XpZTi4VdWolJ1o3SMRDuYWllJjtyo0lc/og/9/ins8Z0HlefHX35/kA99ZcpkYCo5gfqFbJrOHmTROw7xvATR1arJZeX5HZ4SVVMORkqfxkM5k9qBpRMxoXf4slw3nJkkWfpiuTKl73JKX7b5uzgg/W36Mnz7rrmY+sQivdAKsAH2rXVbcfv0ZVi6xtuCMYuZxKgiNgC9smI9ZqRM59EqaEmlz5QO60IyuoFBtiJXZljwnZiPnnlg7kp86S8zce1TiyO5emQvl7GlL6mfly/K6au78Jzl6xMXybCdj7Ikn1L9nLqFbGacabvUUzaERAd7E4We53dQStGeIk5fBu+S6DNX3UcpxfjL78dvH/Gecb/2UuJv5E9XabjWJO4XEWZPoJGBQUbvsIGu7DhBPXyqCfF3rN3sO34z9E0AKHGF3/DMEhx71eO49PbZuPult1KXxYpiMj69YA0+OvXZ1OXcO3uFEXXaSLQ0p886ptaR6/+nEaUf77BfufWl4DOv9Fdu8Czkleu3YWu33tI35fTTWPoyR2QWZFH67DeKw45skM0bBirdLlEyP5M1sUxhEXgRQWl9DbksfTe09KVtZFQ2jVn6FZdCFtTDinvKVzKdZQOlz8vjAm1OmCwuKmu8Lr5oXSCDytJXp1bO1uZlrqGWrPEczXfm3NUrb5z+V309suTnp+Uqp5ZoaUtf/C9DaOmHx5IoRJ7eYS9D2SER3lBWo2n0juw6qrL0nWyPSHyJi7L039vcjb88t1RybfGmvun7F6N3kC1rJ7s3K3qqFD9/cH7wmWHa66sx/vL7sWLd1sQyXDcuu6pfib+nX3spsQ+Kq3dLAqd//5y3ceYfnsEyIVrHpTRyr2yGyZR6mVP6/E8R+2RRjtwi4CjCP3sjWtrSZ5tnmDyoKLer72V8Z+KdU1sTct0bh2wmWKt89E7Wji1aVdmUflzQr98+C4+/Fs8gmfXFvWfWW7jktlnobIsPbuYDSXidt4sUVdA7BiUJdW7uqqBfW8koFcZzi8I8g7wlfJMfi/+SYk0EXycF9Wco4XnVDFI8OqizjJ4q1abuiJTr0mCBIbO2r3tqEV58c500yinJcGL933FIIBsxsfQzGgy8pe+QfMEEzNB6bH72lO+tgta09Kvmlj4Dr0CSwrJkuUxKDonEHMv6qanSjy7EIrFjTiRkM6PSF2RRRSTpIFO6azbJy8lq6d/qbzHI7yPAIB8c9ZQPpWzRkcyRm47TX7+1B/t8/2H89tHX1Tdw4PuhLMe9SrmJzmZRX4s7g4XXRo8P6mwDoM+LI/ZtpjjZ4Z7AkIqWTWlyjp8qNytmv5X/LWKZ//BTn2RV1jynLwvDTgPWDrxh11vRkkqfWfbsv9aRG+TW5l7IhIkBPyawsrd0V7H8vXB6Luv0t77wptHGLLxCCDNqcjJzL4pJnP74y++PHRPl29xVVW7Pp4JMR6mi4bKyO7r7knwfsmMUYZy+KKzJDk38c1i/xXM0/n2WmWOQV2qR3bgk5yP3CQMEISQytKmMGrEtBnd6E/cujXNftPSZD0LcWD227zCoMLjGZWL3OIQE7cgbMKpnndXS5w2ivOyiKuNpb0RLKv0egQtMy+knOR35l5B9vuGZJfjx/a8Gx2Wd7JonF+Oqh5ITPPGdnOXO54/x/S/rilxRwWztrgaWoCliERyUYs5y+eKmrJa+7i5W5JX3zcNDL2s2yOFKcSn1Of14YgczS99soJFBF2Xj1S+/j5eLgsYG1mcXrcHb6+P+AFGuQb7S10V08W3FrytgZTEZxd/iuqKlL/kdnKVf5QYAvj4Zslv6xSn9rBlz06BZ9uluSaXPprth9I66k8tCNpPoINf1eOE7Zy7H9MXyWF3V82NRBDrwM42AU42UF+UqTRBf4h5+3tpdxebuCgZ2pHPhiM2kS/mb2ZFrYOlf//RiXHCzt0FOoqVPWZw+f4wmVcXVGX7OY/zx97Iy1ZZu9LNY71dvfQkn/mZa7D6xzZkBIaN3KKV48o3Vkb7vUnCLs/x3yj8vRudQiEpfbemXSpwjl6d3FA2Qte/056Ls8iZIq7WlP3/lBuzy7QeaIva/JZV+EKfPLH2D3Bvs2mVrtySGdbnUW/Bx6R2z8ewi+SZgqo66cVs8IZjuXjZF5Tst3/9MN6sQBzK+jnc3dWFLdzWwBEUkRof48qyTJDsLrzUSU12HBJQCm7oqyvPBdZHPFK6vOMO8S8l1hXVmt/R58PMMdrvK0otuW+g5ocVrZe0g/h5mQHRJeOl/znsHn77uhUiiuapL0eawxVnRMuOcPo3OliU/hRlfJc6DzpTpm2u2pDag0iDvivBaW/qz3lwHAPjdo2/UtB4TtKTSDzl9eQflwV4+1pmfWZi8cKJKqdSxyENVo4mS4uUNEl5x1fHd74f3zcOqDcmraUXnNG9Vrdncjc1dFQxW0Duq/U7je4/qkSVWX3eHSyne8X+7qSEWWvok9Odw5aWRpyjjj7WjiaXrUvMVpWJpLAJKZumzFdlL3t0cqTdcnBWdPcuMiEiUkdSR6/2PcPq+hjnmqsdwx0y5saWy0tds6tJuch8dhJKfra5/lmus9NmzL2pDpDxoSaXPOqSqg8rA+sRqf6s7HVyXpop35rFxm4Flyt0qi94Rlc2y95Lju2MvKfd91YZt2NJdxUCFpa9S+mITJCnBLGlpdS+rS2mwd/DIgertLGPRO9Tn9IUMqyYWpWmStyww4bQpzLYBBeKO6faSZ+nLZoesSH5SzKeCDnLbK2bPVTcq54/vfxV/fGKhcE2Yi162OEv5OxTNe9bUZ3GWZlWs+NyToOuftVbGLCX4oJQUay3QkkqfvTzsQQdZN2XPTeD02dJvHVxKE8M6VYogLb0jWwYfd0HG64otdOFe0l//8zVM+em/gu9L1mzG5u4K+reXpNNYVWpaUQHKct5HZMhi6WtuoTTcVIQpfSn9wrXPXt97CFXX4/SzOXKTZTZBlNP3ClVRdeIOVqYzDPH3sJThsugdViRfV8WlwT1i9I7oJ6u6bqztf+YvROPL88oIy5ENYH/+/CHa38GwiJuVyJB+L+Hw+pXrt+F/H1sQ/KZSimnd2+u34sf/mKft790VN7I2hin9tZu78f17Xs6cZK4ItKTSFy19ZqnqrAr2eEyebdWliX4CVX9LooWA6DS/rEh49ey336ctQ5zC93Av6f/8e0Hk3KLVm7G1u4r+7SWp0lQro+j3ZEs/y2bZOksfnKXfrikk+rWn6kpDJk3GpEgkDZUWnxqsHFX8fER55aJ31JY+e3iiogwSrvnfWd8UreKKSxPbj3dYC+6gaJ3CKvOkcmsx07rktpdw1cOvYf7KjQDSUXmX3TEH1z61WEs9ffXWl3DQjx4JvrOso88uWoMbn10abI3aCLSk0lfl3pHN0AILJ0XHodQgwidHP+Q7sRgyB3gdMLqDUvyHidYc36HFdli7uRs9VS8mW27px5XEtU8uiu2qlfRemDjURcjuOHjnYRg9tB8ox+mzSA3Z9eIxz9IncUeuCQ3If6bR/2kQGXSY0lcMrpE4fVDjSJJ/z18V+d7BOH1JPeyxi9YpW9QUhJW6cZnYfXInt3fs9unLgp2zvLBZ9TspLjhMUuqmaSiSwL/TbBEWayuVI3dLdwX/fGVl5Bj7bSpaFAAe8u9hv22DwAA0cl1ASyp9fhrJf5fRFGx6mSaCo0qTOf081gffV8LFMeExgmQFK8rHK1xxdSKF77QjRLrYS2aB/vj+V/Ffd8+NHKsXp3/hcbthxMB2uJRi9SbP0tfNhsVn0RMo/XhkVBLElAhp72fgaQ12v2rRFC8+y70jq/GyO2YHtMAfn1iIb94xO3I+pHfi0TtiQAMDS+gXhsT6lr7wLqks/dtnLMOSdzfjW3fOwf3+CltKaTB4yJRbm9g/E5pXmYYi5WPhB32m5FmfVTly/+uuuTj/LzPx+jsbuXudyL2y8oNj/iExoqoe6wJUaEmlHziMFOFl/DmRyzSx9jx6JylVg6m0snvDm9ucuGLinZDwz4rQ7RMsZuak/iDmEHnn1lksgKcAvDbWd9Rs9E78GEuh4NJQNt0gG7f03Yio6UI2w895wwDFMpU0mqGlf8fM5Zjnb0oi8ulAODuUcvqSRYpAXAGrFmdVqq60/Z5btFZaho7TF/tn0nNRdau0ln6EVg0SrPkzEoUSXrB6EwBvrQvghZ5Oe93LzxNzdkvkYf1XfCaNXADckkqfNbaKfwTiysSl0f86UEoTrdYwIiSDdcuHbCp2LkpymoovAj9IdbRF8/BSiiAJl8zS76lQ4fro9609VZxzzXOJHVUsxwSy5vNi7L1UBIGzXtPOiwWHX6XqO3IFec04ff4ze8bx69IYaux2VYoOXsEm+fd0tIBq+0AgHANj9I7QH8J3Km5UqJSszGEehGxKxO0U+meyr0BF7+jvExGlQJml7/1OVasyXcOuP/XqJ7lzaopVPNYl+PqSDK1aojWVfmC1e99lK3LFFYGsMxvRO25yJAo7m8Ua5O+ROXJJjN+Jd8k0lj5Li2tK78gGvBcWr9XmdAHUSk0H2fNwCIFDgJlL1uLBlz1uVIzY4iGmhuipulLlmHZxlm4lb6rnnmDp82JVXdezjhXla5W+o1H6kpXpQEgJhbLI6R2P04/XOfet9bEyXcrNtAVxyw6JzTaTmlI94Ke09P12WbOpK5gxJaV3DiKv/HeEX4cj5t6XtTtrxy7hHTNddFkLND5oNAPi0TsSS5/99z+wUdmU008aicUQtzTg75E6ciHSO5IyBPH4Didy+i4Nd0mSpWpmv7XqW3Mqh+xXuY1mZEgaFEzhKX2Czd3xVNYm/HrFFRZn+beYzMpkq07T3M8g8w2o2ofvDz3V+CYqPHTbK5R0Sh/yc6IRIPrJGFSW/oJVm7BsbXQdCb96VxykSg6J8dlJ7aqiZNOyiey3/+zB+cF6GmaBq2SoBJZ63KCJWfqSMphxJt5f1LuSBS1p6YecPvuupncootewjqILAWQcuA6iQmAwCgmNKP1onDQrI8kWEi19fpCKWW/wfrfjEOlOXMzqOPtPz2LCFQ9Gwj/TIEtHlqbopXFeO41IlSqNpmEIVpsm3xu19MVz5jLwlyZG70Ri511tH9LFkzMFLrWMFZZ+3ECgMZnYd1X7rRfSc0Sjd6Lylp24QZPoyFVUnNbBzn7bIp+nB8I+q/ptrO5tkllspUrx7MI1QTJAWQpsNmiIz95a+inBLNHX3tmIe2a9Jef0WeSFMBtwKcVOQzoxYmAH3lXkhq+4FNOXvKeVIcycSIXj6nu+f8/LWMw5ggCVpZ+8KlOsV2fpU0o9S5/oLX32m7OEXgLZ6B1Ze3VX3BijxRSZieKtujR7lk3p9elndezSh15eGVAJakdu+NnzRxClOtOtHGUWtMwyDgMaosf5vnLF3XODfiS+U1WFpe8hPiu4Z9YKr16R3pGEDSe1qzp9hfa2GNhv4n/zJbe9hKMmjFTKwN4F2fqbiktxzjXPAfC2R5Rb+vJZnlX6KcEruP+8cw6+ceIesWtES59ZxlXqKQTdFPqvzy3FhoR0CuGU31hs3Pjs0tgxMcshILP0ZZx+tGKe4opP2f3oHZUjV+T0MzqZirL0uytu7PmkyetTcaP3p3lWukySWfw3F9w8M/jcVXFR4tIOy+phC8tUeyPrOH02C5AZQcyIEBUTX89fn38z+Hzf7BUxGU02gQG8mP33/L0IZJa+OFtJXpwlP556RW6wniesv+JS3Pzc0kRLX2bQiLNtWR+tKKJ3RI6/nmhReod/MfXRO4FjinMEOo7aYio7RKvw2W15Yrh5BCsieUs/FrIZh2z6zSBaUsGG1YIjly2S6RYs+6y7B5lYL9NeX40/PB7mbJG1XlelGlMWMivqilP3wu7bDYwd76kKi7P84xs0WUIDeWj8c2hApLD0Jdd2V10pPSNy+oQQDBuQbu8DgN/nNV530uKsJLgaekc8zhQ+EO+L5RKJvXtZF2elffVYH4rTS+oBjUFm6Yu+RJkeqigGDWvppwTfuN0VF/fOWhG7JqR3/Hs4R27JdxQy7DC4Eyv9lZ8dZQeVbrXSK5ccdFfcYJqss1KeXbgGzy1agwWrNuFjh4yVlyeL04cYshmvRHwRIjy8cDmbopacqGXntQFFj9ABs6Z/NbH0P3P9CwCALx+3m1RWQB59E/hQuGPxGZGH9Vt7Im3IXuiP/+m5RPlkaRh0oZsqqK51HABC94rs1Oa66CAORgyQJ5jTKSfWl2QWsJh8jsFY6VP1bxIHOELCa8XnU3ac2MCXldNPvSLXfw9E4s9LJicviw1a2xIcudt6qlI5t3ZX8YuH5scSMWahQotCSyp9sXFZ/gweopXGBgq2RF/lEHP86AJVR2tnSt8veN/vP6yUk/F9AHD/XPnOT7IVuZDEmIuIWfoKHt4hYWiZ4xCMHd4fL/g5Q1iHFumdu18y2x5QhCzCIQm81MP6t+G9LT1wqSTGPnDExxWMDERi6adFnN5JY+nL4TnuBVqAK7dSpSCA0tLXGRniKlOT+1U0kogqVXP64uESIagEVnU8eke09BMXZ6nq1d4lL2dTVwVPLYimV1+zuUvZrmw2/MP75uHm56L0LN/Oe373ITz+zeNi9982/U3c/NybseONtPRbkt4xWflJg/8+vVMNrTWiUaoOIegvLB7hEW56kgyTSB5VyKZQUuy+2IpJ7jtveZUdJ7K5xVETRgTn2MBX1EIR3YbcKvAv9DdOmojzjtoFHzlodExZsN/L18GHZYrIuspdxukHfSkfkwdALleE3nG9Wc6+Ow1RlKAWgs2OZNyyKtLN1NKnGqUvHucpHfH3lktxf1qiI1dp6Wtvk5bz0wdejRzr11bCTc8uxYJVm6T38L9l4eroIkDxvZFRkKpU61bpp4RJCl9xSs4GCrZxBE8f8PqlJAkp46HKisnAK9wdBncmyql05EZkSKZ3+IGQF63kEI7eIfjwgaPxndP2AhBywEXFDIurDk3Ay3rMhJH47gf3Rke5pFQMkc3HNYM3fzwrLXPG/z0DANjcVcFrKzdmit4RIcu58rMHwpQKLNz044eMxQ8/tI9xuUBoQMgsfTFJIYOxpa9YnCVDVOlLHLkpF2fpBps06QyqLsVmYZOjMcP6ae/RzZri+wjHr5XRQmOG9cORu4/U1ltLtKTSV+V/5xGL3qmGSsMh8Y7H4BCiLb9d4nhVYfgATTpgHyGnH4IgOQufLuFapPwSiaSeJoTg5H12AMDTOwWYsMhv6e88YkDwOc7pR2dsgD4TEJ9wLQ0PIHvHe6oUJ/9uGp5fpE6lG4eeI+bBb8nJHLmEEBy264jYtTp7hw3iovK568XluOz/zQEgWZxlaOnreO+Ypc89O3ExWXvZiffthOdz+Z1z5Sdoujz4VZfGotdk0Ww8RH9X5Jzw3sjeI5lBddnJE63STwujzToCTt9XFtxya1l0zF0XHoG7LzwCDtHTR6pcOSKeXbjGaPqpmjkkdWVZ6lsZ2kpOGL3jKwX23ymY3slr6fMQdaOU3tFwOCxpG5Au6kZnzS9Zo9/Ug0caS59HxQ33Ahg7PG6FJv2WskNifeMeLtAhq6Uv7pHLQ+w+0Xcr+nsHdpQT4/TZTJRhxtL3pPW6lKba8apKaSytc9KgJ0a28RBDm2V64/HXVseOiYsn642WVPpmnH6U3uGzNZac+Mt30LhhmDRuGEqO3tJvCxyv6msWrNqIc655Dq/6i3J0KCussyinnRynH83JHoL/PY6g9Bk9UoTSd4g8B1ISVDSZyOmzn8vnbSGS68L74xujmwmkPpVmRqRU+gmWKVucBQD928s4db8dpOUqZ6oOifUNtncuIHHkls2Ups6RG99lK7xOFHNgRzkxTv8LR+8qpW0emfcO/vp86EylNJ3vxnXDFfAMHQmDXnelirJDgv0ceIjtbMrTR/fKqD9aUumbWPoivcNzmiy3iwxJtIq405AMJlsyiuWJHUjHen76uudjGzsoLX2HBIMke0F4h197yclEy4goO06qfPouN/OSQcbpV10h+6lk1W14KilPqUIuzQiRdnCUcbxJlmlPVZ+GIfRVKegjQmL18kpG7CdJCuii43fz61XXKTZLT0TpR3/MgI6yUfSO7D384k0zcMXdL+NpP/qGhV+bouK6sRXpbQmDXk+V4uwpY/Gzj+wXO8fn2AfM6U1T53mt0JJK30S5iC9HmI4ZsRW5/GPnDQGZIzaI3tEohzQOP9YJRStS1Ze7Ky6efONd3PrCsshx/mWO7MzF0zt+ocP6t2HPHQbhF2fuj/aSkyklsoiSxMLUoeJSrN/So6Qr4rHUNGZJydIn8+cYCjL0Uyl9CiqN5ig5RLs5dsWNOifFdhCNGRFlh8SUcNTSF5W+XgWcMWk0dhs1QLs4K5bxNRJhFb12oOS3y14XnTL/5LXPe/ch3YbmLo1z+kn0Vk/VRdlxpNy/uOWhKb1p6Z2UcA2jCKjwv8fl6B2ituj5znb/V4/CZSdPjJznV9DKvfUuVm/sShbQB3up5yxfFz2ukE+1WlY10JQl9E655OChrx2DE/beHm1lB39+ZjHOMVi4pEOZixIywf1zV+CAK/+JdzbI20p0ALoujS1o0WUjjeyclWIQ1g3YaWZElMpnXyWHYO4PT44dP2Gv7QGEgQZBOQr1rl78RWJ0C2/Ni302ycfAgh5cSpWpMMR1MvxlolIeO7x/7H7Z8zEx4KkfiSfDwTsPw0/PiFrnVTe+VWOSI7fiUpQV6UtEyCJ1ZGh6eocQcj0hZBUh5GXFeUIIuZoQsoAQMocQclDxYoYwtSbD1ZTef1N6Z8maLcHnkkPwiSnjIuf5VY+qRFBfSUhBDACn7bcjgNBKYnnjZeCr2apYLRyx9Hl5SySwvGTWU1uJgNJoBEkWlEtxZSOCt9SfWaCvLxanT2ksEsIh6s1mPL7f+0wBzFuR7F8B9Px/mhkRhXwAUVmwptx60ixS5sjlLf0V67dFzokb7ohg4c26LJu3cDl7ZPIA3h7HgzrL+MJRu8SukZVrsp2gS9Xt+fkjx+MTh0bf3arrxjh9k+ilUkmeqFCEaehzR1vzW/o3ADhFc/4DACb4f+cD+EN+sdQwitwB78j1/vdoQjZVVrUjidlv41bQmsoiw4AO72Uzcf7wtWzpli/2iNI74XGea5e9SCYWjAlKjhPbVELEI/PeCeVKqFeWhkGcPuvj9KPDAb/jkQ66WUEaeselFJfcNit2XKXM4ukxVPIBb7wTX4Ee3CtZTa6jQLYf1IERmtBipvR1nL4O/Mr4sw8ZK33usnYV22DGkni4LKVU7cj3n/59Fx+F284/DIDc0lftjcujzXHQptvIwIe5pd/kSp9SOg2ALkD5dAA3UQ/PARhKCNmxKAFFGO/DKvTPMPeORx2o3qsvHbtr8Fm24pO9QCY593VgUzwj/4R/zeauijTlBADc8MwS6UtZLoVcu+zlNw3ZS0LZIcpUEIDXXlOfWBi5XgdZls35K6PWuno9rsDpp3hMuseRRumv2dQdGeRCuVSWfvgcdNQGBXDib6cpz5dIXOnraLeSQ3D2FHleKHbecfQzWxNUNeGVMmUpHntaMjN0qZdPSgZW1X5jhmDkwI5ABn7QueHzhxj1DdXmQ0kyq9D09I4BRgPgvYrL/WMxEELOJ4TMIITMWL06Hr9qAlNF63IWBn8fTaB3dh4eLhAqSTJ6sWdPqTnVJAN7yU3SGLNaPnv9C7jwry9Kr1n+3la8+OZ7seN8HiE5vVOUpa935E5f8h7mvrU++J70Eolnq5Ti+qcXAwidgYTncAQ43DQgVXZMHaefYuWy0sei+N1tCktfHNaS6J2SxJGrG6yI5l1g5ZWIx+lnWofh/6/63LgMMlpE7EuydqNQ0zviKntPBjfSmsdN3M4o6MLj9A2UvnHIZpNb+gaQtYa0JSmlf6KUTqaUTh41alSmyow5fSH18aJ3NweZ8HQrcvnOJZsRsPso1I4tEzClr1v8wcD6pWqRCkN3hf3mEPwLLdPvecPHdhzSiV+ddQDaEjh9UfEkW/rx6J3t/Wiqo/zVjLqNZiLjdYrHpLs0jSNXpUxUCpZX+rqWSdJRJYkjN2mGomvHku8Qr7pUuntUkiy80aVS0CbvtGzApZrZA/+bWL1VN1zPcsDYoQCALZqMusH9pWIduY0O2Swiy+ZyAPz8cAyAeK7jgmAaIcJziQwvLn3Pp3fUUR/8iF6SxHoHMe45Lf0Jfh74sQm5PwBzS9Uh3gs+e9m64Ni6Ld3cebkjNw+u+cxk7Dt6CP7w+AItpy929CROX1RElHpO7D13GBQ43wjUCjJrlk2dUk0TnfSagoZTGRsdEXpHHb2TxKuXHAJRzO4EB7Ru/GV72rqUSnPKJ8nCG0dpwitFyAYulkdLBv4oo+N/eO8rGNLfy176l/OmAADWbJbvnsejzXFiDmAZTNunN1j69wL4jB/FcxiA9ZRSeR7hAmDK6bNuzneMikt9ekc9LeQfrpd8LXpdsCVdTk7/jEmjcfuXDseHJ0mZsAhMqVTHIbjmyUWRY+LvEVEUvVN2HC2nL9aTltOvuhRbuqvo314KXmidb0YX2aNDUZa+aitOVb+LWPoJnL4OJSe+OCvJ0jeid9z0m+uUfUufzXrSLKQSIfsN3opcDb0XyOG17cauCpa/523iPqDds3fXbk4Ory4Z0jtJOfL32nGwJ09B71xWJFr6hJBbARwHYCQhZDmA7wNoAwBK6VQADwA4FcACAFsAfL5WwgLmnP6sN9dh9NB+wVLtKrwXhm26raR3hKiemKUfOHJTOJUlIIRgyi7DjSgiY6VPgHVboquBIxkPZY7cnFYHe7fSLs4yiQ/nUaUUm7srGNhRFhYv6eUC0mbZLCZ6RwVl9E5ZTu+oFmcpyyck1i+Tlb6mPH9WXKU09X4JoYHkfc+h86UpMFzfgJOBN8xlRjq7b9eRA7Fs7VZt3eUSMVLUSZb+BcfuitMPTDbyag2T6J1zKKU7UkrbKKVjKKXXUUqn+gofftTORZTS3Sil+1FKZ9RSYFPFctEtnsOTgqdkKFzX+65yqIkPN8bpcwt+cuh8ZfkymNI7hJDY1LEs0FUi8lr6TCm1leLKhofIMyc9RlFU16XY0hW19CnVhNtG6J3sMzIeReRAVyl9lSNXRFpH7uauSixdgAjWhqOH9sN9Fx8VOccMJEqpMWftlendSykN2l/nO0iCytJX0zvqfu+t5PaOXX3OpMT0yg4hRqGdskHxxnOnRMppBrTcitzUlAo/BaShdaCaTouDgWhphY7cfJZ+WHdyRzC1lBxCYiGYfGeV0zvFdETdbmNAnA9PilqKJ1yj2NJTwYD2MrfSVm3pR7JsprL01ecKSUynVPr8DFN9f1pH7nk3To9t/iGC1bff6CHYb8yQyLkytzhrW098w3oVyv4MgXIy51F6aemdyDstmPr8PUP6tWHyzsMi5//77ANxwl7bBd/Xb+0xmhHLopD2Hx22p1X6GZHGmQZ4Vh7Pw7MVuSoLVxzRVdE7rpuP008DquBEzxHiqx0Sp2t0G1oA5pb+4p+dKj3Oiiw7TuTZ3D59GcZffn+QkkKM8U6ascUTrgFrN3Wjf0cp4qBVL86Krsg1BQXFk2+sllr1RSSmU42x/AxNuzgr4dc4giP3OYM9AFh9MhqEbW/oUi86pTNhBS9fJgEinH4OP65UobJ3WQbeaBB/l9gXRQNj1KAOnLZ/uNTo3U1dRpa+bCbEy5fn9xeJllP6aRUtn37V64BeJ1B1liQvPVO8LpVvj1YLsFpiOw4J1VMajwyITHNzLM7SpTBmZfPP5rbp3tL8pX4O+thG7omWfvzY5u4q+reXg9/kUqpJw6BeunXNZyYr652x5D18+roX8LtHX4+dy5OYLmwneXtHBt8cln5ZcOQOkKQEZmBhi6xbyJ4xS07oUoqtPVX0M1T6ZT8IgiJM35DH0hXTj1z31GLMe3uDZjOk8HPSexPz2wlrF7b1eP0uCbKQVsI/1iax9FtuY/S0lAofvcNb+qpRV6Q7xI4aOHKRftaRGX41SZ23Smkslwovvuw353bk+q9MuUS0cdyiZZ/UdiqF3Vl2wt9EoVSQUUdutK5dRsaTfjGsWO859Zau3RI715ODzhvYXsbGropyBakpp5/WkTuws4zNklj0W75wKA7yaQ1Wnyq6hi3O2tbjmlv6TtzSz6PzxMihH/1jnleP6vlzHSNxsBFOR3ZdA3DpSRPR2eb4g5+6GJkjV7UtayPR+y19RGPrXX9xluoBJDpy/dN50zCkgSrkTRbDraNrZHyy6vrjJ3qL507Yazss+MkHlGWG9E7U0o8lTPOV/MXH7w4gmd5RPZ+ONs6Rq90kHMHLLCpKkxdXFkudx5E7sNOzr5SOXEX0johkegeRAIMBijTOAzrKgQJnz0qpQInnHO6pusY+IBb1QwFQXx6d8v3JGftKj3/5OC+fv8qJPHv5eulx/rckKX3RwPCc0OH3kQM7/CAJ/YAnC9lMI0e90HJKP+2CKNflFaE31XSIegSOcfpCeey+vGkY0iBwhCVY+uy3Ra7hPqeJ3jlmD0/pe5FO6m7CSiwJnL4I1lYjB3rJvRIduYrj7SUn8gxU1+k2UdFFwDDlIlP6jJL69VkHpOZnmfJV0juOvD+KSOpyXoK9sG07FYpqWP8wyRqrTplu3IGfT998e8KyP+qacvoDFPQJew5p1wggomwTLo1Z+vK2EGW49MQ9It+TLH3L6WeEqFgOGT9Me31PlQa747gsesdRK4RY9E4s3CtKFWXB6KH6EDGxvyk5feE+WfpbntqQRu8oUvqy35k4M+Ys/VUbw7S9IqXCZgGMfsqaGqCjLaR3tI5cTRk6liZU+nFlyWLFh/RrM9r/mAfLF6QylE3pnaQtOEVHrqqdRwwMlT7rFiqF7vj0ji5aRnaPdynl6B31vaq62WxElVJcV7/sswwST4aRgj553+hWluJshM1qTeWoF1pP6Qtv7PF7bqe40kOVs/QpR++otIXoyFVZ+i414/S3H9wR+X7ZyRNx78VHJt7HgylQ0frccUh0Zy/ZRhe87pV1OpUjl11quqp12hur8e6mbjzjb2UnlsOc3v0CpW9O7/AvYAfH6XuWvoKaiHD60VM6imTDVi91NU9j7DyiP/bYfiDW+kv2DVbkx8CUvkq58caGrsV//uB8bT1lh2Dt5i585voXsGrDNuzHhQzy4Pd8ZTKpFzqRYI9cU2u15BtWt76wDOf/Zaa2fN25Tr/Pp00BkYZLl1n6qhmZqg4gKuOkcUPxzZMnWk6/CIg8elIoVY/rBrmwPeucTd/k14ucpaxDAJ7iMOH0p37qYOw6Mszcuf+YIRgxsENzRxysGnGGcOFxu+PqcyYF3/mpdHCM+5wmDQMxtPSZimKJq95aF13dGCbc8l4IZrmJ/OeXjtk18p0fbPiB2HM8+/QOwq0FY74YroS470PyK/yLWVgmPyjddv5hGBqhQ9K/vex3ixQbG7jbI+tGuGtSVuUQgmVrt2La66tx7VOLMbhfGwZ3xqkTElFGJLhXhhIJaRpTa5XvazP9RIFJid1kYDPDtPROpAk1PjxAxukTowAH0QjjF2eVgjYNz1tLPyMYN8wUoMpRxVB1Q3qHIozmUfOXCfROyjQMk8YNw63+Jg5A1vwj3m+uUoqjJ4wMjnaUHXzogJ2C7zJ6x43QO/GSVUq/JFH6LEkcD/Hn8LQBDzYrYhYmH3d92ckT8e1T91KWy1vBHeVS1NIn7HjcAc9fx4MfPH/0Yc+BKD4VfqpOhOl+2kd4+5cOD9o+vvjPQ5thPv0k8I9z2dotmLVsXWx2MWX8cKkM6tXNYd8yDTsc1NkmMZjCA788c/+oDIpy2fu9fmuP9LwKon2jU7gyw07msP7wgTtFvo8d3j8yWPPRa+HsyVr6ucGs6/8++0D83ycPwm6j4oqIR6XK0zthx+Xb/zcfOzD4nLRYKVQk5tE7SflvRMji7wHvt5Q1ZSVtdCGldzhlwyuDIHaba6k7LzwC/7702Mj97OxN/nJzkUIO6B2/rfpJlL5stsYf4duvo+xw6y7COH0xlNDhlT53/IxJozGM2ymKrcYUlQ5vWTokeb2DDiUnvEd8BqzeyHPlL0npO+BnRQ++vBKzlq0DASI0z0Xvi3LNjD7U0Tsep29O7wzuLMd38OLu/dgh0YWFqjYd2s/Liql610YNUs2aBWNNI3c8bj+ezgQAfnf2JMz4zgmRY58+fOfgMz87ZM8zSlM2h9ZvOaW/w5BOnH7gThgzrD9O3W/HxIasuG6U0/c7LlOYFx63Gw7hlJ1pnvc0qZV5617WuZf8/DTt/ayWiku1XCOlcQcq/1W+OEtuiQScPndscGcbdhUGWaa0gt2JFG3C2opx+nz4o0wuVURVe9kJFDDvyO1sk9E78XJF+itwWAvXRSx9oY+lna3xi33iM0nvfzSffnblIDMqHEJwxwWHB1apeEmwyY7OkesyatRMtiH92iR7GqvvVb12g32lL8NlJ0/EvwQjRFVemjYlREN7Ct9VwRylQOlbSz83Dho3DP999iTs4HOhSZZH1aVBZ2cRN/xLK+qopI2i2Z1iauWPHKTOnueorDhDsI5VdfVx0lVX5sgNvyelYZDtG5w0nWdnw92JvPpERy37DZ3GSp8fKEMZRXqHQQxN5MXm2yBm1UkGNwDYyjnlRPHSW/pEY+nHy8xF70juJcRrd6ZAxUEracWstzgrNJiu+cxk/A/nS5JhsETp636XagY8RKP0B3eWlSuEY/02RZsSol60KNvnQQZZW1pLvyAkKaWeKg0UJYve4beIEy3jgR1lXHriHrFwKwY+RpwPh/vlmfsH+bJFJOW/SUJA77jqTSMARu8I9yrkYFCFCppas6LSqlKKpWs2c1sjEsxbsSFw9IacPuf0SqR3ws8dZScy8DKI9A7hZOPbQHxHZTQW4GWnDMoSLf2USt8hJGjP+DqQcAYgsw5FZbXH9no6Uz4TjFINsmR2TE4ejDpxHPjRO969J+69Pf7jgCi/LWJIv7bYYja9pS8/J3NCB/doQq/jlr45dLm50lr6YrnNgJZLwyDCxNJvi1j6bLrtnZc9sq+8f4KyPBWnL9u8IjiXQO8kgZWq22cU8Cy2tPROPy50L8I/+n0+SVpeaXkyupHN29ds6gpC9gCO3qnqLX1VyGV72QmUFp9aWaR3+B8TaQNRGShexPe2qHdUSrsDVLlEgntU9A4bGKpcRFIWyHRV3KkZ/R5mwfT+v/TdE1EuEQzqbAtkoyk5/UESZa2LglQZGTp6p0TUARlp6BwxuouQ5FBmhjTbYtrFWQVBN3qu3dztcfr+VC2M3gmjAkwTSEXrZKmVvQc+7bLjvaXqqg7Ab+iQydL3yu2uuFpOX7ZgjP8uq3sHLtZfbp2YyVgOlH5UyYpT/HCxjd6Rq2on3sFGEY4NckufcFd6+M4H945cp1I2/GY04kCaltMvcZa+OGDwi+DYo9W1eZIbSdY/RHHF5xxY+v7xYQPaA4UPeO/Ru5u68drKjZHn8tcvHKqUQ2Yp6xSxOoTaURo6um1P8wycBOqQTdNNbaTBCVbpFwOd5XzQjx5BpRpy+pRLuPbxQ8biW6dMDHJ7mIIgXKHIwhBZB7lCCDtkSNqyMAmUAs8vWoO312/DKyvW47BdoyF3Fx3v/QbPzxC/V1c3H77Iv9ATt/eoqmMn6jewDyxVztLnlayovJkM724Kt6mTZndU1NfRVopQc+xWcQUtIWEZbIu8S94/IXA4y+TjxeD3Fhbf65JDYgvjdHAcztJXcPqUi4GPKBYxBDdB68sMVFkceqRMGpVFBDMMVm3sirQXy9Ipg6wsndLTrtbV+Bp0m+hkBQVVZ58ViuXTWURkS/BTNRItr/ST2rHihpy+l3DNa/y2koMLj9vdOGugWKdLwwVH7AEfv+d2uP+rR8Wu559/FqX/tb/NCjjy+Ss34ubzDsX8H50SnD9j0hgAYZoJHhFOX9JYozglyIu2906DMfv7JwVl8/jcEeNjx1SWvriWQWYBjZIsVlM91/aSEznHPoohdvxL/7k/TwegjxKioJH3WadbHUJw94Xmq6pLhATKOEbv+P8plS/oEZG4c5YBrSB+V3H6DJ88dFwor6TtZZBZ9Ul78WoKS32PWFWayFdKo+smdOVe/L7d8cWjd4ldp4qiaga0vNI3aUgxZDPXDoG+deFSGtA7kV2PZJk8OBmzPvjtBnvW1rlH7oJyyYkMVqx/bdzWg+cWrYncx1MTRMb3OgQfmzxGKpsqcuIHH9onoMV4ThrwBkL+BYvFagsvw78uPVaaSkNFBXS0ORFFqVo5TCTHkkJDZc+m5JDY7KDkkAgtlgS+XvZ59vdOwkvfPTH0TyBsG50jl6cQv3nSHsEsLyxfRu/IZ1tBmSxkU9E3+ZmqafeV6eMsIZu6c2F7patL3GNAti5GyekL3zvbSvj8kRKlL5VJKVJd0aeUPp9PPw/YsnRZfHNSyo4slj4QZqX8DLcYRCzzirtfxpNvRHPfRJ2Y8roPHDsMQDbOUVxcJCr5pPxEMoefTpaOshO87N4mKlE5+PvFgSMpjE5W50US+i+t0eA4JFCc7FkN6d+GYQPao5a+Qd/gJ07DBrTjc0dEFY6U3vGLZaWL7SBy+iLaFLt6dbaVMGKAnN5I68jUOcdVBkA4M9IP5jy+/YE9MecHJ6sFgTfrU4VGm+4+J7vbWvoFwaQdGefOr8jNXB8QLEtnsei8JZT0YFWKd/oVJ+C8o+IWAwNTnrIN3U2310vaZSjLoqBAkfiFxDZLScqbr6hTJUl7ibP0EVdowf0k7uSTR7boZ2myDpb25WX7zMruZXvSDuwsB89BVz5P70gpFAMHYjwChdUrr5NP+xwJ63UIZn73ROk9ck5fZ+lHz+06agB3Tn4Pe55pnka55BgNrkk7xfGQOX3TDET1RsuHbJpkN2ajdtI01hSO420YInL6QPIUTlX1qEEdGK6wmoBw1yapVaGpMynLZtL9KgTK1v/P707G11lNyE+krFspK8HeO3lO5j22H4gZS9YqLxcPJVqfBmUA2eL02WAtivDTM/bDJw8dh9FD+0UieVSIblQTv1ae0kJP79AETp9PZmfaV2RKU9ds4jv58NeOSUzJrGsvVVUmj06nU2QDrYwKkgcnNIfWb3lLP2knISBUlNUEi8YEhHgvDeU4ff5FS5pFZKd34nWZlJm0OAtQ8+I6iC9GmbP0+Wcis/SP2G1EWLeifJ0opx84Go9+41i8b8/tgysJgNd+fAo+erDnn5D9Fl1EhZemWSKHf5BqIpKSwC+8EumvzrYSDt55eEQ+05BNgrjMZo7c6IGkTU742aV5Pn3ZMfW94qm2khNEZLFTh+86InJNsJgthTJN8+y+dOyuuOHzh2jlBKKW/qcOG+fXI6m7SbRtk4hRWwRK37c60y6uEeEQLya/6kbz+LBzDN8QdtYBsit9tvpXtouVlt6JxOnLr8nDNYb57L3/f3hsoWDpx5X++UIaZWm5CSLt7mf85B3JHeVSmOgKMnpHrxDlFmO6QVaGEiFKn0dUFjb4GtI7ROakVVudYVtFz7MwX1W9bRH6UilatE7ZMZ2lb2C4HL7bCJwxKUx3Erx3JjKlMPhYE3/7A3vhuIn6/TqAqOyjh3r7L6d1LtcTvYbe2XXUABy1+0i0lRxc99TiyDWM3mFWZ14l5xC2ZyiNbbrCd6qvSlb2Zq1bFilkUiavYpQ8ZSaJWJnef6bUNnZVsHFbmMJAtlmKMnc8B9N2IrEPvFzRg4mOXGnkVfxcanrH4aObNEqfcdSm9I5EYp0jN/wup3dUvyuTpZ8yZNHEcGH77jIE6b+NJPJg4s/TsQdJt/NGiAgbvVMQmNJvLzm48vR9Max/PMwwsPSr+mmsKUoOS8PgxhyriY7cnNE74iDj1am+z8TnYaJsVGC38C85X45sL1yTTcBNRRGVMp9SwMzSl8utkyNLwjU2KOri7EMlZmbpQ/IbpY5cRT1imUp6JwV9qbtOb+mrz7FxTtwHQ6dgVXWZDFppOf3oeVa/TKbm0Pqtb+kLo/IOQ+L7zzKlX4ilT3x6x6XSBGhJRWd1IjOL2dTS/9v5h4ECuPzOOYll53IwSW7leXz2+biJowK6q93AMWjsMBSSibH+IKVlZC8ip2xMKYnUaRi4Fbm6aCZd3DmDoPNjv1O+/F9vmAwf4K1DUK0uJT49VXHNc+/ILtNz+gRtJXmiMzYoebl2ZPeayeTJED/GmvTjk8fizbVbsOeOgzRyJpWvfoaW3ikI7CVgHfvMg0bjmmmL8No7YdIvltmRTY3ztD1BlNMXX7KkB5vVmTP1iYX+9Fai9CU9+VDf6WWyElFGYZhCds91Ty4KPrM2P/uQsdh/zFAAZrnjzS1K77/YBIRIoncSrGCddRZx5NaI0zdZkRuN3on/SKnVK34XDnzx6F0walAHPjJJnR68XGJKX25Vi9Zx2tTCDiGY+4OTpe8mVVj6wbuvLFVejwr7jRmCX3x0f+V5k7o62+Opw03vrRdant5hq0b39cP4CCF4/15R58t2g7zVk9UE7tIUXvSOZ32LjrNESz9j3V0VV514SlNk0rJ97/4Myp5E//NYsmZL8LkSDLThhe1l7iaDqim8jab33CFugekGrLiTM152ZJOLZFH8ctJb+uwe3WI1We4dsaZonH68/ZM2AWLyRO4pOfjowWO0gxlz5sqMFrlVb3YskIkQdLaVYjmUAM7Sd+LOeUBlIKiMCbUMJq9BkjEysEO9p6+19AvC2OH9cdeFR2BvLpe92Km3Hxzd1Sk/vYMgTl+kWxItfQOHlQqqHN+6MsXNRYpGUksGEVO80i/J0zlHyhWOq3LdiPROeH98GEi0gg2n5GnpHUJCpa8bhMN9VdVlidE7MdkkN7M7VG1lgo3+/gLKwVWxI1nsOgV0MkWVPmfps3vVt3LXJr/7JjPdpCsGtHsqdVt3XOk3ic5vfUsf8HbTiuaiibYu2wyiUpAj13E8eicLp28y4Kguka3GBfSW53WfPUR5Ll6x+aWm6JG0eRtn6auq1Fm70utFpS85JneCM/rGnNPPQtGF6xjUi9WCMV2i2Bhii7NissUFNpntmWL5uq2xY9Lnk9bS15yMOnIlVaXot7JnJ+4noENSXWx/Cn6T9LDu5tD6LW/pyyB2oP7+6FtUnL6Xe4fGNioH1Er91i8ehrtfWq7d7jAJMqUF6DviuBH9E8vNoxKSpruy2VWbQcgmf1grX0AzJQ++SeGMuhTPkZDNDCYbowElwUwBwlQN6muii7PiPh4pvVOczkenJOWAqdNS996ZGEMipx/Wn3xvqNizzTZM62LG57ae+INuEp3fu5X+hw/cCb/+2IGBpVNE9A67v+pSEMQtfVXZh+82AofvNkJ6zhRpkkBlQZZSku6pSJzn/GIf1f1po0TYf/7lFpslqZ3kfLFfrkEOIx12Gur5lcYNVw/CsgFGV5PM0t9tVHw7RVHn5zH8Zbd68or0juw6NUxmT+JOWYwOTdP9tRRTAVPdfsEmQRJ6p0lcub1S6fP580sOAfxB964X3wKQb8QlhPi5dwDAjfHstRzNlfROA8nCpKplnH4kTFJxv7nik/PUnkKMHkxKrSyldwwcxCY4buJ2uPm8Q/UDvybcL+GWAJPHD8edXz4CZ/7hmeBYFuWogtTvZEi5ZNkohUe5pHDkJt4ZQvuu5Giff3zF20cjtPRljtzs5ReJXsHpi2A0SJCwyT/OPOq5Uys7bJcqGadfuyfbloHeMUGSA1mHJOtl2uteqme+zRMzW8Kc3pFZx8Fx0dKXhmxyskiUbpGP86gJI7WzBHbKeJZDZGtywxQVDIwSYluE5vlNsojTX5y5X+xY2hWpJoaLI1r6/n/ZO8f8eGlkyPOo9x09BPuOHoLt/KAR2cbxdnFWDcFWfLoKC6cIeodtTRjn9HMVrYXK0mdphPP66wgh+MWZ+2H8iAHJFwc36U+v3LANQLRdkvLdpEEQsimWI4nekS7Oklj6ZYdI00fUGia5d3gQqPj06HdGTf35c4fg/rlvY0fJAkZTyJzCZ0wag4WrNuP3jy0IZZMq/ZyWvuDI3XenIf690euW/Pw0ZRm1VryDO9vw6pWnoLMt3cr5eqJXWvrtQirl2IrEnL+acfoyS7+WsbiqzZqB4iiejx8yLljYpUNgYXPVnrLPDurrFZa+yfVaekcoUxfGJ6V3/P/8frv8dfW0zmRtmgVi+zI9PXZ4f1xwbLo9oRmmfupgAOptJGXRU0lypYXDhWxe8v4JnDWfXC4Tux6Kt197KfWgV0/0SqUfZtWUn88bp19y2HaJbizrZS2fK4tCkiHPb8qyslGG0/bfUXlO9bKpOf0QWnpHE/2TlH4gfr/3uWzgaK4FgllLilp1zmeGIiI22YxWRQWKYqTdRKRqICQ/q44OzOE1D15ytLaMRijeNsVeCo1Cr1b6qvjk/PSOt21dljQMeTCwQ630ddU+/LVjcNv5hyWWn0V0/hbt9J1rp7Scvkn9cUtTloFSzjMfu8coXPvZyVJLv55T8jBNdb5yatEHS0FwhJmpz/+GXUYOkMp183mH4syDxuCzh+8sTZQoIuoX4qr2/58zZRz24hZpSsuQxekXGdMqwTlTvBz7srTojYARp08IOQXAfwMoAbiWUvpz4fxxAO4BwHIa30UpvbI4MdOhLaGDFpJamXrbJXa2RcuqraWvXl2rcxBOlKQv4JGn0/OWsq5PK19YA0vfSA4wSzSsQyxblYbhxnOn+GXMBZAtq2SRSBW9Y9BSRSzOCrKEKmbPEpdK7LOocI+aMBJHTRhpLIOjeC7BZioG7aZfFVybZ/39/9gHF79vd63RVk8kSkEIKQH4XwAnAlgOYDoh5F5K6Tzh0icppR+sgYypEdI78s6ed8AtOQTdFRdVl0pCNhtj6bN69x8zBLd8MdmqlyGN5NJ4ba2jTn6d6g7jBXRMoYiWvuR20zh9FXVQcxRE76g4/TxISiMRD5mNz+zyxqmXCJEaN+wdNOkyjaB3Sg4J8n81A0zU3xQACyiliyil3QBuA3B6bcXKhyR6J9fG6BFOv76OXD7VhEwuwMv9kdaiyKMUjOkdpaWvoHeM65dbeURC8CQtqmLXRyx9QzmKhGkXMk0dUQR9IYZBx+qE+j0oiiJzSPhu81ks2cze5N1rlgiaRsJEO4wGsIz7vhzAoZLrDieEzAawAsA3KaWviBcQQs4HcD4AjBs3Lr20hkhKY5tXMRNCUKXewqN6hmzq4umZQsuTQTTv9NaU3jGKzDENW2TWsSR8JG2obkhDqPmnW74o6/py3HPRkUHctglC/4Q5vWQSJaLJ5myM0NI3u55EPif4AwzhOAQdfgRbNxel0e4nFTRJZii9pv7RuQ2FiaUva0mxmV4EsDOl9AAA/wPg77KCKKV/opROppROHjVqVCpB08BJ4B/z0DsEQIkAs5etw+vvbIp15FpywG+s2qQ8xzpz3rTRacH/+ixZFPNy+kT4zx8XjyVb+h60ln4KBTGsf3uqmPhg0OEqTVo4J7X0he9FRu+Y0js8f8/O5ZXDISTYgKeHs/TbU+Sz0r2efWUSYKL+lgMYy30fA8+aD0Ap3UAp3eR/fgBAGyHE3ENTMJgCrAW9A0SVx4atFc2VxeJLmhhr9ptN8qmLyPIyymrRrRVQWWEmCde0cqi0vuRYotJnbWiws1cq2dLel7OO+LH8Wp+1nTJ4JyZDfLaS19IvOSGV0xOx9Bmnb0Lv9BXVroaJ0p8OYAIhZBdCSDuAswHcy19ACNmB+E+WEDLFL3dN0cKagr2zqtjfvHH60Zze9ZkbfvHoXXDsHurZUbCEPw+9k/lOJkNYwuHCAq+0C+JMnX5hmGP8eh3PrAM/eMXk0BTxrVMmBpFAQHaln+YZmuzFWgS9U06IiNNRaUWpWUJIsNq+m1sxbeLI5dMz93UkvoqU0gqAiwE8DOBVALdTSl8hhFxACLnAv+yjAF72Of2rAZxN8yR0yYnA0ldF7+S19Ln7dTshFQnTzVmy/LYsv+ALR+8KAAHH6skQnt9/7BCpfKZIa+mzyyPbGqa09N/yc8UP7he6ulj5LNZazGvD48Ljdo8MzFlnlEWrpSJexWR6Jyo1/40N+Lk5fY7e4R25gaVvoNBrvalQK8AozMOnbB4Qjk3lPv8ewO+LFS07gq3plI7c7GUTRMPGdBtd58WrV56CLd0V/OzB+bjwuN211zKZdKkaisTXT9wDX/c3OmfgXzoxOVzaNjfm9En0P9P5/G5VDKYD4gl7bY/pS94DEMp9+oGjcfqB6j1kpbKlupqPRDJz5Joq82IcuX70jmY/AB6yBXh5x54SIUH/5h25zNI3ebyynDh9Dc2xWqBgsA6nit7Jy+nzt+s2us6Lfu0l9Gsv4VdnHWAsk2pLRSPkNDEjYXp+ciyXU8JZyzKBLMtm1pxLgzrD1aF5Ysszc/oFm/pFLM5iA6YyDUOM3gk/F2WIEAKFI9dX+gbPSrYHb19Drxz22Gbpqql4XlqvXpZ+GjAlGdl03BBFMXH8eEMA7RaWieAu/7V20GOcfvxMWnqHIeIM19zyyg9Pxis/PFkjWTZKq+jNNlQ0ZxqUuD0qZND5Pq4+ZxK+ePQu2G90lPJLLYNDAqNG7shNLqPDWvq909IfO7w//nLeFBw0bpj0fF5nTpTTN5zv1hgBvZPD0s+rbMRVmB1lB1u62R4GaWXxcMJe2+MYjQM7jN6JVyAONKb0TmS5v+a6AQmL4FL/ZuaM5+4rYkAuhN5JGYHDt/3oof1wxWl755bBiThysy3O6pDMOprDbKsfeqXSB4CjJ+gURXHRO7WgdzKFUOagd4r6BaWI0mdT6R7/e1qr1zR6J/qfh1inaVRMYbl3moTeMclgmYTQGSs/n3YhXBaUHODgnYdh15EDcOlJE4PjqSx9Db3TV6I5e63S1yE/vRN+bjZ6py0Df7qTv4BIF5mSRgbAXz3JTaXThpJqDHj59TJ6R2gKU0u/ZGjpJ8qUlt5J6cg1RRH0TrBfgWFq5VooUEIIBnaU8e9vHhc5Hhg6GS39voY+2QJp6Z25PzgJ50wJ16fx99fC0s/ywjCRstA7R00Yidu/dDi+dMyu6SvmIMrNh8dlpTqMo3hYhAh3LKulX1TCtXoszjJBEY7c5AWP4vW5q1TKIKI9TcI1G6ffVy39dA9+UGdbxCnJW18V0xg2A5wzZRyeXrAGnz1ifOp72eCTNVJiyi7DM93HI5qHXrD0axSnr9oRiRAJp19vpZ/2+oDTL9iRW+DiLNXes7HoqVrQO4oyg9TKGYfLBi4pagis0k8JQgjaOKVw2n7xDZCzYsTADtxqsNmJDEzpt6XIQ1I0oknVvP1C+e9poMqeqbzeJHrHsLBo7p18fSXbfZmrrBkGd7bhNx87AEfsJs+uUg+ZVXWwtBl5V8c3Y7vXAn1S6as2GNeBNwY6fKv/c0eMxxWn7VWUWLlQCZR+4xg7vmqHACMGtnPfaxO+GHL/kugdMQOqYdM4BVn6WZkE41lOnQ3Ujxw0pr4VClDN1NggXa/V8a2OPsnpZ7H0997J24Ztt1EDgs43YkB70+TyyEvvFIMovTNyYEfke6qSTMMrNddlDdksKnon6yyhFZOC6dIwFAVVuzAjrkdDtX79hD1w/MTaZfZtJfRNSz+Doj7r4DE4YMxQTNxhEGb4S/Sbya5oBkufByEEB4wdGnyvdRoGWXRJ1sVZRUXv9JlcvXWCaqbGUn5UNZb+JSdMUJ5rpve4HmgODVFnZLHOCSHBXrNJuX0+ctBo/OjD+2YXMAMCS79ZlD6A/9h/x+z3k+h/XT2y62SOXFOrvcxpl3pG7zD5TC39emV4NUE8ZLP4EU/VLknvo0UUfdLSz0vJhJtEyzvZbz52YK7ys4CtDG4WS9/bmJxgaP82rNvSk5l/TlT6hEVueIhu6pKtzmg6iTz0Tsb7WnCGUA+ZVfQcC17IGknXgs2dC82hIeqAF797YvA5r9J3mtCyaA5OP4SojLPGiicp3WDTbdHSh3wTbROUCrL00yeNy3ZfM0Am8VP/eTz+femxhdWh5vS952UduWboM5b+8AHZI0lEMGVSLTBGPy/YUvtGhmzyYPp26qcOxjVPLsaw/u36GwSYjhG6KJ+sz7mojdGLWJzVHE8zGTI6Z8yw/sXWobBn2PPqyaj0+9pQ0WeUPo8sjlwebJrZJLnWADQLpx++PkwJHLrrCBwq7KKVqqSER8UepYzfzqp0xfUGWZE5yyZ3W6sopEbSO+Wc9A5D0dlNmxXNwQXUGXmXYjNLv4jl7UWh0mT0Tr0oijA9QHEyRGmhPIuzUl4f3Nd6yifmyK1BHWpHrk/vNBHd2sxoDg1RZ+S19JllUcsNVNKCjT+NdOSOHzEg+FwvvaXbdLsIpd8I/WtaZRPZHHVpKHXIJluc1URT7yZGn1T6eR25LBvlPv6CrWZCI5V+ueTgY5O9VZt516yxePukYgJ6R6IAs+6FzBsF+VJ2pL0+XchmX0OtHLlNNXjWAVbpZ8ARu43Eo984Bh8/ZGzyxXVGlp2zisQkf+OavGmaGZKoDnaahc/yL7DK8ZeEohZnFaG8W0X910POZE7f5t4xQZ905Ga1AHnsvt2gAiQpHu2lxu4BevYhY3HEbiOwM0f15EGype9dwV539l+2OMsUReXe6SM6BIB8cVyt62AIcu80UTRdM6NPWvq9Oad2W4MtfUJIIQrfOGTT/y/n9LPVHdkiN4/S7yumI+oT+aJqT7aCOmvIZl9Dn7T0ezOaZUVuXrAQTNMVuUVG70RCNuu4Ile2qliHZuKiGzm+HbTzUJyw13b4z1P2zHR/EzVjXdA7NIRFgF6j9P03MUmXBMqmwOgdovySspzUjtzsdYnYc4f60o/xkM36jQId5RKu/ewhmLB9c1KuzYbeoSEsAvS1PUBFTj96LmOhRPoxfTEZtXgROzn9+mMHBJ8f+OrRuctLgvhTh/Zvk19o0XBYeqeXobdY+qZgir3IOH3+vvrul1BcXew3TNhuYLAXRC3BLPtj9hiF/zxlIsYOLzYFQy3RdzwvHvqWhugDaJZNXfIioHcSQzbVnH4RuW/4vZHrhSI45rpz7H597SWCfXYaUufK82GYPyvp14Bn3QhYS9+iqWEassksfZ4ayb5HbXhfPemyIhW1jvaqLVrP6Lj8A3th11EDceLe2zdalLrAKn2LpoSpsmKKUmR3RIV/8j7mLzQ/WWqIpc8vMMs5WynCP5CmvlZEv/YSPnvE+EaLUTdYeseiKUENw3fCNAx65fbHT082rpuPPOks10/ph7WGv0X1sz5x6Dh88AD1zmT1pnf60pqEVoe19HsJbjx3CuYsW9doMQpDsLLWcBOVInPf8ekbOtuak9756Rn71U6QDKilyj9y9xF4esGaGtbQt2CVfi/BsXuMwrF7jGq0GIXDfHGWz+kXUSf3uaPB9E7usoorSgv2nGrhA7nx81Ns2uQCYZW+RXMiZRqGGKefo2qeqjCx9O+44HAM7my2uHTGe9WnNqaTa6H0yyUHdWTZej2s0rdoapiuyC3SYck7ck12Ijtk/PDC6haR2ZFbZ4q9u+IlO2uWTXws1LBPyKIpIdv+UIZYaGIBup/3I9TTQdnK2/V1VaoA+t6K8FZEn3pCR+4+AqOH9mu0GBYpkKRzVSty82XHzH5vEeB/Sd4JTL2Y8C7f0m+ED8QiHfoUvfPXLxzWaBEsDGGcWrkW0TsNUvqqNQeZygIrqz5qv6vHV/rW0m962Cdk0ZQwDdmsBaffKJqlyMGm3nHz3VVL77QK7BOyaGok0zs+p98LLH0G3p/RaFlMwSx968htfhg9IULIKYSQ1wghCwghl0vOE0LI1f75OYSQg4oX1aIvwVSJ67JsZkWjNiavxQyj7py+ja1seiQqfUJICcD/AvgAgL0BnEMI2Vu47AMAJvh/5wP4Q8FyWvQxFLFzVlY02rgultPPX5YJbMhm68DkCU0BsIBSuohS2g3gNgCnC9ecDuAm6uE5AEMJIerEIBYWCWjz9z1NipNn59tKnppjSqecI8U0G2jqnaW6w18IxqfHzsqRszLqlUbCf1zo324t/WaHSfTOaADLuO/LARxqcM1oAG/zFxFCzoc3E8C4cePSymrRi3DzeYdizeYu5fkPTxqNhe9uwsXH764t57T9d8T8lRvx5WN3AwB8/z/2xg5DOnHCXl5WzV+ddQDGDouH6d547hRs3NYTOXbTuVOwfmsPCCG44tS9cEzGtBZ/v+hIzFuxIfV93/vg3thuUCdO4lL8XnHq3nh7/TYcP3E77DdmCH7zz9fx1fdPiNz3248fgO0Hd0aOjRnWD5eeuAc+PGl0pt+QFv95yp4Y0q8dp+3X+229qZ86ODAyWhEkKeqBEHIWgJMppV/wv38awBRK6Ve4a+4H8DNK6VP+938B+BaldKaq3MmTJ9MZM2YU8BMsLCws+g4IITMppeZpYwWYzP2WAxjLfR8DYEWGaywsLCwsGgwTpT8dwARCyC6EkHYAZwO4V7jmXgCf8aN4DgOwnlL6tliQhYWFhUVjkcjpU0orhJCLATwMoATgekrpK4SQC/zzUwE8AOBUAAsAbAHw+dqJbGFhYWGRFUZpGCilD8BT7PyxqdxnCuCiYkWzsLCwsCgaNqjWwsLCog/BKn0LCwuLPgSr9C0sLCz6EKzSt7CwsOhDSFycVbOKCVkNYGmNih8J4N0alZ0XzSpbs8oFNK9szSoX0LyyNatcQPPKJsq1M6U023JxNFDp1xKEkBl5VqzVEs0qW7PKBTSvbM0qF9C8sjWrXEDzyla0XJbesbCwsOhDsErfwsLCog+htyr9PzVaAA2aVbZmlQtoXtmaVS6geWVrVrmA5pWtULl6JadvYWFhYSFHb7X0LSwsLCwksErfwsLCog+hJZQ+IeR6QsgqQsjL3LGrCCHz/Y3Y7yaEDOXOfdvfpP01QsjJ3PGDCSFz/XNXE5J/B2yFbD/y5ZpFCPknIWSnZpGNO/dNQgglhIyst2yKNvsBIeQtv81mEUJOrbdcKtn841/x63+FEPLLesumaLO/ce21hBAyq95yaWQ7kBDynC/bDELIlHrLppDrAELIs3499xFCBjdArrGEkMcIIa/6/ekS//hwQsgjhJA3/P/DaiIbpbTp/wAcA+AgAC9zx04CUPY//wLAL/zPewOYDaADwC4AFgIo+edeAHA4vH2jHwTwgRrJNpj7/FUAU5tFNv/4WHipspcCGFlv2RRt9gMA35Rc2/A2A3A8gEcBdPjft2uGNhPO/xrA95qozf7JyoaXdv3xZmgzePuDHOt/PhfAjxog144ADvI/DwLwul//LwFc7h+/HDXSaS1h6VNKpwFYKxz7J6W04n99Dt5uXYC3SfttlNIuSulieDn+pxBvo/bBlNJnqddaNwH4cI1k4zdIHQCAecsbLpuP3wL4FidXXWXTyCVDM7TZlwH8nFLa5V+zqt6y6drMt+4+BuDWesulkY0CYFb0EIQ76TW6zSYCmOZ/fgTAmQ2Q621K6Yv+540AXoW3p/jpAG70L7uRq6dQ2VpC6RvgXHijHKDepH20/1k8XhMQQn5CCFkG4JMAvtcsshFCPgTgLUrpbOFUw2UDcDHxaLHrualtM8i1B4CjCSHPE0KeIIQc0kSyAcDRAN6hlL7RRHJ9DcBV/jvwKwDfbhLZXgbwIf/zWQi3eW2IXISQ8QAmAXgewPbU33HQ/79dLWRreaVPCLkCQAXAX9khyWVUc7wmoJReQSkd68t1cTPIRgjpD+AKhINQ5LRChnq12x8A7AbgQABvw6MrmkEuwNtsaBiAwwBcBuB237puBtkA4ByEVj409ddTri8D+Lr/DnwdwHVNItu5AC4ihMyER610N0ouQshAAHcC+JrADsQuLVK2llb6hJDPAvgggE/60xtAvUn7coQUEH+81rgF4RSy0bLtBo8TnE0IWeLX8yIhZIdGy0YpfYdSWqWUugCuAcAcf41uMybDXdTDCwBceEmwGi4bIaQM4CMA/ibI2+g2+yyAu/zPd6BJnieldD6l9CRK6cHwBsqFjZCLENIGT+H/lVLK2ukdn7KB/5/RiMXKlschUc8/AOMRdcicAmAegFHCdfsg6vRYhNDpMR2etcacHqfWSLYJ3OevAPh/zSKbcG4JQkduXWWTtNmO3Oevw+Mwm6LNAFwA4Er/8x7wptqk0W3GvQdPNOE78CqA4/zP7wcws0n6GXPCO/A48HPrLZdfzk0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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Detrends a series by fitting to spline and calculating residuals.\n", "# !!! Note: spline is the current detrent default method;\n", "# !!! Note: Line graph is defaulted to show residuals.\n", "# \n", "# Detrend funtion can modified to fit Hugershoff, modified negative exponential, linear, horizonal.\n", "# This option will be available in the short term future.\n", "#\n", "# The detrend function accepts a specific series as input in the following format:\n", "\n", "dpl.detrend(data[\"CAM191\"])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/michelecosi/miniconda3/envs/dplpy4/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n", " self._init_dates(dates, freq)\n", "/Users/michelecosi/miniconda3/envs/dplpy4/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n", " self._init_dates(dates, freq)\n" ] }, { "data": { "text/plain": [ "const 0.022210\n", "CAM191.L1 0.503373\n", "CAM191.L2 0.087230\n", "CAM191.L3 0.143716\n", "CAM191.L4 0.020119\n", "CAM191.L5 -0.027769\n", "CAM191.L6 -0.010029\n", "CAM191.L7 0.001373\n", "CAM191.L8 0.025588\n", "CAM191.L9 0.042340\n", "CAM191.L10 0.136916\n", "dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Returns parameters\n", "# As default max lag is set to 5; \n", "# add second parameter (integer) to change max lag.\n", "\n", "dpl.autoreg(data[\"CAM191\"], 10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/michelecosi/miniconda3/envs/dplpy4/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n", " self._init_dates(dates, freq)\n", "/Users/michelecosi/miniconda3/envs/dplpy4/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n", " self._init_dates(dates, freq)\n" ] }, { "data": { "text/plain": [ "array([-0.23485391, 0.61703427, 0.07848781, 0.30139532, 0.31970781,\n", " 0.7709527 , -0.13122202, 0.73735556, 0.79259251, 1.03150292,\n", " 0.19654108, -0.13481225, 0.61573077, 0.65068195, 0.43323283,\n", " 0.95951473, -0.37325966, 0.50079401, -0.10560507, 0.05038 ,\n", " 0.56974055, 0.3394715 , 0.51772866, 0.28269221, 0.42226138,\n", " 0.63341811, 1.4739965 , 0.28602606, 0.2604808 , 0.27934854,\n", " -0.34367123, 0.33895547, 0.19844979, -0.07263543, 0.45164677,\n", " 0.85279497, 1.01238366, 0.74549344, 0.51996229, 0.77010877,\n", " 0.46936605, 0.0785818 , 0.09314398, 0.47463727, 0.83439219,\n", " 0.03987213, 0.81898116, 0.58663827, 0.30382456, 0.43938001,\n", " 0.53303445, 0.23979872, 0.42268213, 0.6094634 , 0.44863191,\n", " 0.52719227, 0.14005905, 0.32252156, 0.14725286, 0.29913277,\n", " 0.61191963, 0.44957831, 0.42429965, 0.5959667 , 0.49178689,\n", " 0.45410314, 0.25085001, 0.42356281, 0.55757744, -0.24367237,\n", " 0.15117969, 0.66712127, 0.69258454, -0.12784539, 0.6789434 ,\n", " 0.60783217, 0.61407111, 0.62246195, 0.41272014, -0.00936233,\n", " -0.0480617 , 0.0694901 , 0.33897886, 0.36166267, 0.30446064,\n", " 0.48141051, 0.56882759, 0.64385542, 0.30465005, 0.51569189,\n", " 0.79791196, 0.39735395, 0.31585581, 0.15895399, 0.58666957,\n", " 0.40978068, 0.15985022, 0.40607871, 0.40388826, 0.3096272 ,\n", " 0.0342205 , 0.55427596, 0.52377703, 0.26738502, 0.53938707,\n", " 0.33790184, 0.5382865 , 0.41514302, 0.35265856, 0.30646022,\n", " 0.50990241, 0.4243464 , 0.34850027, 0.18307126, 0.2732797 ,\n", " 0.51736136, 0.54959262, 0.45413807, 0.29612966, 0.45052545,\n", " 0.46909323, 0.36982143, 0.39153275, 0.64037135, 0.27772417,\n", " 0.31826222, 0.52523135, 0.51617064, 0.45047841, 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0.52612092,\n", " 0.35138125, 0.31463398, 0.54032327, 0.32644925, 0.52935574,\n", " 0.37253887, 0.31671287, 0.25945758, 0.24138538, 0.29070475,\n", " 0.48241831, 0.07597918, 0.5339914 , 0.43070055, 0.38209941,\n", " 0.54220723, 0.41888816, 0.13905795, 0.37808339, 0.5182685 ,\n", " 0.33383431, 0.43731525, 0.49870236, 0.39623926, 0.20947203,\n", " 0.43548597, 0.33406516, 0.61692086, 0.20888847, 0.43691344,\n", " 0.40875671, 0.28687993, 0.33627451, 0.45303114, 0.39374527,\n", " 0.30778941, 0.28913639, 0.52006099, 0.29244499, 0.50501874,\n", " 0.30752383, 0.23357822, 0.51230839, 0.49609901, 0.03676054,\n", " 0.66377245, 0.46695285, 0.28583492, 0.24910179, 0.38565254,\n", " 0.51333396, 0.34451973, 0.30623028, 0.28624853, 0.2484529 ,\n", " 0.30577825, 0.23987747, 0.33826147, 0.21603312, 0.39775025,\n", " 0.24295094, 0.37695061, 0.35095463, 0.42067853, 0.29581275,\n", " 0.43572216, 0.26531939, 0.30630855, 0.30089194, 0.3770091 ,\n", " 0.29272387, 0.4289114 , 0.35323252, 0.37325316, 0.33271626,\n", " 0.35549783, 0.39572473, 0.35150282, 0.37620792, 0.35962842,\n", " 0.37867256, 0.31254057, 0.30823814, 0.33498754, 0.30991824,\n", " 0.22106116, 0.47600479, 0.36901305, 0.35061435, 0.34308667,\n", " 0.40811558, 0.42101334, 0.30019725, 0.38620366, 0.35702432,\n", " 0.38213919, 0.46361132, 0.32010587, 0.35373268, 0.32502378,\n", " 0.44608853, 0.38144384, 0.32546815, 0.47334522, 0.447194 ,\n", " 0.2980056 , 0.23089079, 0.10410034, 0.34967115, 0.45274025,\n", " 0.25222561, 0.49008612, 0.40675568, 0.41047231, 0.29493453,\n", " 0.32821789, 0.28024849, 0.35079935, 0.37324237, 0.3868993 ,\n", " 0.37054072, 0.39523113, 0.44287362, 0.31951994, 0.3604808 ,\n", " 0.25996029, 0.48716987, 0.3405697 , 0.36588728, 0.36170745,\n", " 0.47566618, 0.36956056, 0.12281409, 0.17040604, 0.43080876,\n", " 0.45339988, 0.38657195, 0.51062833, 0.24663067, 0.2944111 ,\n", " 0.42325223, 0.30723836, 0.39412815, 0.11526089, 0.45728477,\n", " 0.49026851, 0.44408903, 0.52517481, 0.30415652, 0.44375 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0.28026539, 0.47917932, 0.36702031, 0.12126338, 0.41128376,\n", " 0.36910108, 0.32816472, 0.39654297, 0.2728162 , 0.39017996,\n", " 0.41235812, 0.32996245, 0.3658953 , 0.39350694, 0.41380087,\n", " 0.33389311, 0.28319668, 0.28699192, 0.38468972, 0.45064504,\n", " 0.43507548, 0.34339383, 0.38741558, 0.29479123, 0.38417023,\n", " 0.42920096, 0.21992131, 0.35415206, 0.41429713, 0.28045376,\n", " 0.34746292, 0.3414929 , 0.30219699, 0.39595103, 0.36762076,\n", " 0.35817275, 0.43388609, 0.34128867, 0.29889916, 0.25351852,\n", " 0.20326752, 0.42197456, 0.47608529, 0.42410472, 0.38622311,\n", " 0.43329866, 0.38954634, 0.26966697, 0.31011112, 0.41435408,\n", " 0.3312214 , 0.30561928, 0.34896042, 0.14830434, 0.47081585,\n", " 0.16646207, 0.41601987, 0.37207901, 0.42193349, 0.25398928,\n", " 0.24741244, 0.33292854, 0.27909425, 0.44545975, 0.36996414,\n", " 0.40320527, 0.23548363, 0.46307916, 0.35668605, 0.37330783,\n", " 0.37192971, 0.29535696, 0.41388927, 0.34920842, 0.35240151,\n", " 0.17638227, 0.46377493, 0.3939198 , 0.46448029, 0.34974532,\n", " 0.4091069 , 0.2996696 , 0.31868948, 0.27776093, 0.27969009,\n", " 0.31001004, 0.34651993, 0.36511498, 0.25641987, 0.36787472,\n", " 0.25061601, 0.43874015, 0.30907333, 0.30742187, 0.43356778,\n", " 0.37184625, 0.37781472, 0.38811771, 0.36018255, 0.32513911,\n", " 0.41765669, 0.32689887, 0.28030158, 0.39163757, 0.33158315,\n", " 0.3595052 , 0.46889063, 0.32822815, 0.32566163, 0.33101464,\n", " 0.37216688, 0.39733423, 0.44800362, 0.40265194, 0.36828679,\n", " 0.3945686 , 0.25542368, 0.35761989, 0.23298255, 0.35897608,\n", " 0.38835324, 0.35940801, 0.33920676, 0.38774871, 0.35665835,\n", " 0.40969949, 0.37855848, 0.26505381, 0.3750314 , 0.3614608 ,\n", " 0.29583819, 0.387177 , 0.45308406, 0.35631867, 0.3471497 ,\n", " 0.31944481, 0.40949886, 0.32453882, 0.27349772, 0.31247103,\n", " 0.28736743, 0.33053127, 0.28445865, 0.42163137, 0.39165334,\n", " 0.48125848, 0.39958453, 0.36121433, 0.25596448, 0.35028976,\n", " 0.37800471, 0.29054553, 0.39901677, 0.28566321, 0.40228394,\n", " 0.28094665, 0.38282782, 0.32080927, 0.36732028, 0.28021801,\n", " 0.43386441, 0.36566424, 0.17309174, 0.2864465 , 0.48588328,\n", " 0.4379513 , 0.31807606, 0.22276896, 0.3837975 , 0.35437269,\n", " 0.41095687, 0.35536757, 0.36256703, 0.41053693, 0.19875665,\n", " 0.35931273, 0.34938392, 0.35938082, 0.29932014, 0.36026023,\n", " 0.29420832, 0.41583381, 0.41836997, 0.35236038, 0.36478763,\n", " 0.37422349, 0.42237656, 0.35491203, 0.38401865, 0.32478055,\n", " 0.39639871, 0.30754248, 0.28298515, 0.35355333, 0.38764711,\n", " 0.24149446, 0.44736186, 0.26846159, 0.30305692, 0.37937295,\n", " 0.35399136, 0.37436065, 0.48975852, 0.32763145, 0.35771297,\n", " 0.24457454, 0.43817467, 0.32318189, 0.40939673, 0.30904956,\n", " 0.4058264 , 0.31548055, 0.34778588, 0.27216749, 0.35959523,\n", " 0.35308367, 0.39804884, 0.31123224, 0.37074699, 0.31098905,\n", " 0.32787846, 0.22225314, 0.36189398, 0.38569857, 0.40244394,\n", " 0.37431475, 0.37646296, 0.30050867, 0.38853535, 0.26827023,\n", " 0.30120785, 0.3924199 , 0.31654751, 0.39963708, 0.35394093,\n", " 0.37008765, 0.34325678, 0.46986591, 0.37089893, 0.37767187,\n", " 0.38052207, 0.21070825, 0.26081478, 0.26066993, 0.43560227,\n", " 0.47730778, 0.26494492, 0.44390498, 0.31396022, 0.46117816,\n", " 0.22760724, 0.45842222, 0.33492516, 0.46903585, 0.32518185,\n", " 0.41382035, 0.33071743, 0.32781117, 0.42697473, 0.29922905,\n", " 0.39353705, 0.34316083, 0.45248865, 0.31556241, 0.29688595,\n", " 0.19042043, 0.30801811, 0.43209191, 0.38171683, 0.36874542,\n", " 0.33110284, 0.43408485, 0.35350848, 0.41717183, 0.3795025 ,\n", " 0.33800292, 0.40788355, 0.3487189 , 0.36334129, 0.36773897,\n", " 0.35716652, 0.31958933, 0.35229354, 0.38192844, 0.131064 ,\n", " 0.46945613, 0.50452416, 0.40170409, 0.33910162, 0.2446623 ,\n", " 0.47178829, 0.35272988, 0.47105281, 0.32434132, 0.29563633,\n", " 0.3489984 , 0.4313384 , 0.26458517, 0.28972957, 0.37328946,\n", " 0.3513375 , 0.4182225 , 0.40488257, 0.35984623, 0.35157567,\n", " 0.36490789, 0.36145163, 0.36219304, 0.31249589, 0.31993087,\n", " 0.34506224, 0.33977483, 0.43941594, 0.34300712, 0.11407017,\n", " 0.55013731, 0.36031895, 0.35651344, 0.3536409 , 0.39214983,\n", " 0.35685251, 0.32191647, 0.42147809, 0.28167082, 0.39399796,\n", " 0.38954763, 0.29320292, 0.28894349, 0.367458 , 0.33458904,\n", " 0.38906359, 0.41711617, 0.46963755, 0.23272096, 0.29462233,\n", " 0.39546486, 0.4524549 , 0.31592476, 0.45280139, 0.33135251,\n", " 0.36175169, 0.36998016, 0.35009763, 0.34600311, 0.40323174,\n", " 0.22948486, 0.29787165, 0.47074459, 0.44792308, 0.43539402,\n", " 0.40254804, 0.2987047 , 0.374543 , 0.37842883, 0.40753657,\n", " 0.34595611])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Returns residuals+mean after \n", "# choosing best AR model fit with chosen max lag (default=5).\n", "\n", "dpl.ar_func(data[\"CAM191\"])" ] } ], "metadata": { "interpreter": { "hash": "3c8fd20ffe14543096afa73893b045d375f02c4f075990b9d1a182776a971eb2" }, "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 2 }