diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 14429e13..c876b144 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -36,6 +36,7 @@ jobs: pip install dask[dataframe] --upgrade pip install pystore if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install pandas==1.1.5 pip install codecov pip install pytest-cov pip install pytest-dependency diff --git a/examples/01_groundwater_observations.ipynb b/examples/01_groundwater_observations.ipynb index edb7c663..ab74faaa 100644 --- a/examples/01_groundwater_observations.ipynb +++ b/examples/01_groundwater_observations.ipynb @@ -55,26 +55,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:hydropandas.io.io_dino:reading -> B33F0080001_1.csv\n" - ] - } - ], + "outputs": [], "source": [ "# reading a dino csv file\n", - "fname = '../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'\n", + "fname = 'data/Grondwaterstanden_Put/B33F0080001_1.csv'\n", "gw = hpd.GroundwaterObs.from_dino(fname=fname)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -93,81 +85,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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" - ], "text/plain": [ "-----metadata------\n", - "name : ROTTERDAM \n", + "name : EV24_ROTTERDAM \n", "x : 90598.0 \n", "y : 442813.0 \n", - "meta : {'LON_east': {'344': 4.447}, 'LAT_north': {'344': 51.962}, 'ALT_m': {'344': -4.3}, 'NAME': {'344': 'Rotterdam'}, 'EV24': 'Referentiegewasverdamping (Makkink) (in m) / Potential evapotranspiration (Makkink) (in m)', 'x': 90598.0, 'y': 442813.0, 'station': 344, 'name': 'ROTTERDAM'} \n", + "meta : {'LON_east': {'344': 4.447}, 'LAT_north': {'344': 51.962}, 'ALT_m': {'344': -4.3}, 'NAME': {'344': 'Rotterdam'}, 'EV24': 'Referentiegewasverdamping (Makkink) (in m) / Potential evapotranspiration (Makkink) (in m)', 'x': 90598.0, 'y': 442813.0, 'station': 344, 'name': 'EV24_ROTTERDAM', 'variable': 'EV24'} \n", "filename : \n", "station : 344 \n", "meteo_var : EV24 \n", @@ -200,7 +118,7 @@ "outputs": [ { "data": { - "image/png": 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Ti1gpxw0VEBzXgNwlAHkTxWl89Q5jDgQtvBQmdEWwCoO9c7eJ0WEENgle3PlUKQLB+LHoQsrv80vzDAgSgE87ZyRwGnVmgY4qbQDWCBn1guePgwGQ01vOYgAh2QBKISmcnk0VBiscne/gGUN7r0ICkIzAcgZELkl89r43sHlPp3U+IulNlWMQIoG92sqRwHzy6YIwGmFjQLXT8ydmGYGdmxluh0rlKALINoBcVUlhQs+mCoO9CzH+qxZ/wG0DkF3fRE+ITQIDkPWmKogqIO9x2v2l08yalJoBaISN+mqnIqRalAAEGrUYQI4bd9l7iPMRv7xZhYaeTRUGvvvmfGBfhxyNaO9+OKKKAtmiOCt+lieNCqIRmFyOpM42xliZ1a5a8F7S7qAaYUBOLmhJAORUhfIAxFx37vIcciVg9JgLhYRmABUGvmjy//ukcPTnVhveECItxiIRhQrI/iwSdiYbAGMMXzMTYfmpgESmwmAzFVECEPs97Y6X8PAbmz371dDwgrwDt9xApXoA1VkYgXcc7Mbh1z+J1TvaABjzTVb551pYJkwEYgBEdC4RrSWi9UR0reI6EdHd5vXlRDQji3u/Q0SMiIbn9yoaQZCUbAC9CTU1O7KBRsitAvKQADLZAJJSW0efpG6XSjProhi/IPa7aU8nbnhspXfHGhoekBdm7mgg1wPgkkKQQLDnV+9GPJnGHxZvAaA2+OYaUBYmMjIAIooC+DWAOQCmAphHRFOlZnMATDb/LgdwT5B7iWgcgLMAbM37TTQCwfICMnc9smcCh6MeQFShAhKIV2QOkQw2AJHoSZIWxPmQklRAKgkgUQJGNI3yhzwFqoSdSURhBA6yc4+Zz0ilvOeZ19zrSwSRAGYBWM8Y28gYiwOYD2Cu1GYugIeZgcUABhPR6AD3/gLA9+CfPFIjIBZt2IP/+ONSvO8Rgfv7RZuxw6yuxRjQ0ZvEDx9boWzrkgA80kED7kXdzwYgrtm8fKQKYn+vr99rtasWGECqBERojfKHnIzQdgMlV04sIJj7Zsxsyzcpqipg8rliGIODMIBDAIhFWFvMc0HaeN5LRBcC2M4Ye9evcyK6nIiWENGS1tbWAMOtXPx1SQueXLETj7/7gfL6jQtsFUmaMdz38gaHB48IR0lIRSSwuOiLi7WxqAeTAKIRPxuAc3fPd2IqCUAXidfIB5x6PjVjLADBCAxnuvOqKKEq6g6KVIFHEyd9JADZiaEYAkEQBqCaoq4UMB5tlOeJqB7ADwDckKlzxtj9jLGZjLGZTU1NGQdbyeBVvta3driuqfL5+9UmdRWFl+73lADg7wWUcqmA1O3ksfF2YiAYH0MpiNIa5Qu+gbjspAkA7E1GRPICikYINbEo4snMDIBH2nP1qIpGM3kF9QWCMIAWAOOE47EA5C2mVxuv8xMBTADwLhFtNs+/TUSjshm8hhN7Ow2Png273bt6sQQkYBC9n9OZUwJw2wDEQ7FIeyTi7wWU9jECA8CWvcbY5QlDlgRg38R3V3r91wiKrnjSyvvDwdddvtsXcwGJtFwViaAmFnGVKFWBexLxJIqyE0V1LOJa8IshyQZhAG8BmExEE4ioGsBFABZIbRYAuNT0BjoewEHG2A6vexlj7zHGRjDGmhljzTAYxQzG2M6wXqwSsd9kANv2d7mu7et0unsy5u+t42AAkYhLghDx6ro9wn3eu3rAuVir4gBO+dlLABQSgPm/VqgHbHs0aQ6gEQw/+L8V+OrDS7BWyEHFSY3TYkyRC4inhaiJRazaAX7gEoBdCN55vTamUqtm+zb5I2MuIMZYkoiuAvA0gCiA3zHGVhLRFeb1ewEsBHAegPUAugB8ye/egryJhsUAuuIppNLMkfBNZgCGZ433Si2rgPzURSJksVmG02U0WBwAbwsAQ4Si3ZwpaQagERSbTQmzo9eWiLnx1VrszfNiIBj36qmtigZiAJwmEx40Wlcd9bWr9RUCJYNjjC2EsciL5+4VPjMAVwa9V9GmOcg4NLzRm0yhvTeJQXVVONidQGc8iYFCgWu5/miaeXvgAG4jcHAGkMELSLYBKNrsauvB9gPdyvEMqa+2ziV8DGwaGirYZUjtc5YE4EqEaNMy30xVxyJW8Zh0mmFPZy9GNLoz1HJVp62mdNJobZWCARTBq1lHAvcT7DVTOowfVg/AneJ5X6fCBuArAdgw3ECDUScB/jYA0QvIaOxqM/snz7sKZPBJObDOZmpyYjsNjUzg1CbSDNe9c7oV1T5kSQDGUlkjSAC/fH4dZv34eew46NysALZ6Mml5qjmv18aiLnfSUjUCa5QBNrYaou20MYMAGFWGROyT6o8KwbVKiIt4hLIL1PBP8iapgLJ8ZqOQupe7geqcQBpBoaJNubwpp33DBmB85hKAYQMwJIAX1xiV7Frb3bV9bRWQWkqtitkp1q3aGiXqBqpRBthgun5OH2cwADnL577OhCPtLctkAyDxs1HjN4iXAoO/DUB8RMQnDsA9HqNhQ63NACw3UG0D0MgSIi1zG4C18IuGX/NzTGAAPWb6FOs+xTZG9v+Xd/dRcjMALQFo5IyNrR1oqInhsKYGAMBdz63D4T940rq+vyuOoQ22/jyjDUD4rNKbeoFJz93b0Yvma5/AK+8bQXyOzKHwH4NqPMMG1FjnvAxsGhpe4Iu1SDGWGyg52xjJ4GQJwFYBWZKDgohTLiOw83o0Qnj5/VY0X/uExYyKQcWaAfQTdMVTaKyNocFUkbz8fivigt5+b2ccQ+urcfOF0wAYuxeRcP92xQmO54nX+MQIutCKEsAr64yF/y9LjIBwZyBYoMc5xjB1zEB8fvahxrM8XOw0NDyhoDnZDdRpAzA+WxJAla0C8psOXC0pV+D73rlT8OAXP+zw0It7GIr7ApoB9BNw1UtDjdOxixPe/s44hgyoxomTjKSrhg3AJsIPjR0kPVGwAZjEGkTVLjMWbpuYOHyAYzyqMZx55EjP54rtPn2cEbLv5WGhoZEJIsnYRmDjmG9gxMSGUTM4rDYWtTLo8keoNjK2EdjpqNA8bABOO2KEUk1aqoFgGmUAvgi6GYDxf58pAfCNh+EFZLeLRZykoCLqoAutuFhzBjBiYC1uWrASa3faaSoYc0YDi1G+MpztzKyMOhWERpaw6F9QuMiqHMELVIgD4F5AEUEF5E13fK7wtBH82LIpOGhd7c12oCuObz/6Ltp6nB58YUIzgP4CZoSyi5GygE14XfEk6muiFkHLgWCy777zWnBdjbyo7+kwPCS27e/CQ4s248o/ve0YtCPgTCr3OHJgjXBkt7PyrKTULnYaGl6wbACiBGAFgnEjsO0FxElf5QUkP1OEVw1tEqQL+5r5X+IAjy7Zhr+/3YJfv7g+0LvlAs0A+gl42URpI2/tc1JphlgkYhF0Ou1Uh8q++04jsN1HJsheQHy3pErd7CrEIXEhruoRxwAImRa1F5BGllBLts5rtg2A3DaAgEZgea5wO5UcWSy2lSWA0YPqAACbWtUZe8OAZgD9BMbC61blcDrkqSEsj54Mz3MagYPbACCpliwGoFikX1y7Gx8IQTQxSQUkvov4zGiEq4C0F5BGbnDaAIz/tgRgHHvFAcSTaaPEozmL/r1pH9bvdtbg8ErzwIVc0QiclgzFHFyaf2bVLvxk4WpsVGT5zReaAfQTcIOqrMrhRJpMM8QEv/s0Y747Z1GspSwkAKO9IAEIYfOqMbfsFxmAVJzbozJTzFIBcS8gzQA0goGTkWgDsNQz5rHICOQdO68KFk+lLcZx44KVOPPnrzj64dKpPHdUKiA7DsA5VrEmxv2vbHTMlbCgGUA/ATfqykxADDJxSACM+S6cKgmABXC35JIIB0+dG0RNI6uAolE3EwJsSUH2sNDQyAS1DcC6KP5zZAPlO3bHwu3TT9rFAOC4X5R2bRWQ84ly9hXZvhcGNAMoUxzoimNvhx2CLgZgiaoTTlOyBLCxtdNFYF7IRgIwjMAKG0CAVbrKRwJw1CiOaC8gjdxgSwACLA8dyQgs1AOICUViAGDrvi5fLyBXuUfJC0jFSOT5JdcQqImFv1xrBlCmmH7LszjuR89Zxwy2V49oBmAwdvqMGf78vM19r2zEki3OhGsilBJAwLGJ+3hLAgiwSMsqoKhoAxDOcyNwIqltABq5QaQZOxDMgMUI4E4FwefF2b94Be1SwkXV82X1jmxTULVVjREwXFDDhmYA/QTptOCyJukX+W5ElAAAYP1ub6OSaAPIygtIci+1JYCMt7riAJyeSPZRTcwQheM6FYRGluA7elH9yVwSAG/sXrBFOuyKe1cGs6PUnVKq7FYKCG6gGVRAnO7DhGYA/QSiBCDvLmwPhIiDgH1rApP4mXsBBXMDFe+11TSZOYDsweQ1Hm6I4xGZWgWkERScjESS8XQDFXIB2RKAuLnypjs5UaHMZMRNWtrDliXPGa0CqnCccedLuOR/33Sc293eg+Zrn8DTK+06pyTpF/kiHI04d9V+C6dSBRRgnWVMXQ8gSEEZ2Q1UhFycuypKVkCOXv8rG9/48zs48fYXsrpHpH3+SfbQiZC4Y3faAII+n+/ieXd8c+ZnBD7htudxxR+WKiSA8JfrQBXBNEoDG1o7saG1E92C6Lltn13/N6LYqTNmE6NLAvDRy+SqAhLbi5AjI4kUgWCyCkiUQqTniQE5WgVU2Vjw7gcAjJ10JMMKrXJokHMBWd5ACjfQoF5AfMPDpMVdZQTmzIEPacfBHuw4uBMnThrmeGaN9gLSAIB9jvKOCldJR5CL7e8fE4zAgL8EIM4j2ZXND4YbqEoCcDIbVRt/FZCzvRiSr+MANABgZ1tPxjacihwSgGSgtY3AdmQ9d0lWuVirwBf8lMQAOB1HFYxKVinJ81NLABoAgF89v876zL1sAG8JgC++0QiBBBrK1gYQNFuhKjQ+IUkAUSKkpD2UnxFYfmZNLILnV+/G4o0v+xqzNfovDnYn8J2/vmsdr9/dgTGD63zvUdmz3IFgvK0idYNDAghgA5CKvqjsdBwbWjtw9Z/fsZ8hPT4WVP+UBTQDKEPMf2ub9VlUB6l8nBlEFRAFtgE40kFnZQNQN5IlABWTkN1ARci0X1MVxaY9hcuRolH6+OuSbXh2lW37OtCdOWumbQRW2QB4G9sbyB0IZj/Lbz54VfuynqeYAIs27MWiDXuFZ8hzJnwGoFVAZQKvhbUrITIA90LNGHMwgFy8gGQbQC55yxNJ5z1qFZBsAxDUW5IVoBDisEZ5Qd7A+Nm0ODhJiU3dKiCrNTjLUNkA/GxPsgTA//tJAHId76CBmvlAz6IyQdyDGtqEXY9qd5NmwOPLdwBw2wD8oPLBt/2Vve/zmhMJlw3A3UaOBHYwIYlSNQPQkDcwqg3NP9/Zju0HxBw63iogu4lKAnB7AflJ0LIbqF8gGEenFFcQxHU6XwSaRUR0LhGtJaL1RHSt4joR0d3m9eVENCPTvUR0q9l2GRE9Q0Rjwnml/omeuJoYDgoMwC52YYOB4fYn1wAwVUCKhXfudPdX79h9SxKA387n3KNGKc+7vYAUEoBkA3B2I0sA4XtEaJQXZOO/Kwd/muGbf1mGT/z69Yz3AioJwG0DIIcE4D022f/figNQZAPl6CpFCYCIogB+DWAOgKkA5hHRVKnZHACTzb/LAdwT4N6fMcaOZoxNB/A4gBvyfpt+jK6EOuxcrBZEXkYAEyoGcOjQetx10bGu5zoNsE4jsBcD2Hz7+Rg3tF4pBSRSmW0AsnQiqprcNgAtAVQ65B2/vGPmdLq73c6ZxelITE4oJ24TbV5+kcB+kFU/sgSg2gC5JIA+cG8OMotmAVjPGNvIGIsDmA9grtRmLoCHmYHFAAYT0Wi/exljbcL9AxA81UxFotsj7LxNKQE4VUAcKhWQaicCeNkAzOdn+KVU3hHyZFX1K49NvMXtBaSWANbubFeel9Ha3osX1uyySvZplB9kxwLZ00y1Q7ekWUUgmJgDSG5vSQABxyYu/EyIxrfjANz3HJSM2Kl02nN+hoUgDOAQANuE4xbzXJA2vvcS0Y+JaBuAz8NDAiCiy4loCREtaW1tDTDc/onuhJoBiESjMgKLO285EAzwjmwkHy8glQRwSAb3O9lAJ3c7a8JQ11jSDglAUgF5SADn/PIVtAo7Pi9c94/l+PJDS/DMqp0Z22qUJjp7ZZ25zADcdEqWDcDdznIDtQjRDiyz4gACCp5ynIErDiCAJJFKG+0aa2OBI5CzRZDXUXUtf7NebXzvZYz9gDE2DsAjAK5Sdc4Yu58xNpMxNrOpqSnAcPsnejwYwIEutxFY/HGcDMC94GcnATjFWREvfOcU5XPscXh7AR1/2FA88pXZChWQMB7peX5G4K64d5ZGDh470NXrndBLo7TRIenMZUeDIG6aYjvLDVRB+yovID+IzCfFmNvTKMCKziWAt35wJlbdcm6gfrNFEAbQAmCccDwWwAcB2wS5FwD+BOBTAcZSsej2MAKLNoCIpKsHnN5D0UjEpXsMQtBy8IxqZyWqZILYAMQJ0FBThapoxLW7EvtxRwJ7G4GDvFObmco3SI4ijdICp2+X26RHIXYR/qkguJrHLf3yKPWgvvgiXaXSTHADdY7DD6m0sUGrrYoWpBgMEIwBvAVgMhFNIKJqABcBWCC1WQDgUtMb6HgABxljO/zuJaLJwv0XAliT57v0a3ipgNq67UmgsgGf+8tXrc+qSEIvCUBcoPkkOP/u13DjYysyVgZTMQB5oXW6mTr74Rg1qNazj1ofI3CQycVtJ33haqcRLj784+fw6XsWKSSA4AzgR0+sthIrMqh3/UZiQ+OzLQEEG6MoYaQdNoDgkkRJ2AAYY0kY6pmnAawG8ChjbCURXUFEV5jNFgLYCGA9gN8C+LrfveY9txPRCiJaDuBsANeE91r9D6IO/XMzx+HmC6cBcKo7MkXsqojOUwXkuM/+/Ps3tuSUfM0lAZCbwcjju/CYMTj20MHK5/nZHDJNLsaYxZC0BFBeYIxhT0ccS7bsdzlGuL2A/J/16ro95jOdNKPKhGvnAgq2IIvOBYYh2HxeFoykJ1F4BhAoFQRjbCGMRV48d6/wmQG4Mui95nmt8skC4kI1oWkApoxqBKBOBeEFVbrlIDYA+bm5MADZR9vhax3h55wdERFOnzIC72w94HrepBENnn1lGp+4c9S1BMoLezvtRIhyQRaZxlQR6yoVTpoxZeCjyBiylQB6BAaQSqu8gDI/qCuRCsxwcoV2pi4TiAtVhOyiKN2KVBBeUC32Xt4Ioh5Ufu5jy1RmHBuqJdWdC0glAbjv8zKWjR82wLv/DGv6/k7bbqIlgNLBUyt24uxfvIzX1+/xbLNBSPwnO0Z0xVP4zL2LsGzbAQBq5u7lleKUAEwGAOaKBPZzBN22rwvn3/0q9nT0olcYWzrNAsUByOiOpwqSAE6EZgBlAnGhIhDqqw2jkMNXPsMzVMTktcCqxGCOWx5f5duPauflygaqsDGoxuI1T5qH1eMLJ4xXXsu0qxeZppYASgcL39uB93d14M2Nez3b7DMlgGiEXHaxtTvb8dbm/bj278sBeMUBqCUAcfKILQbVVeG750zBOdNGAvCXAB58fTNWftCGf76zXVIBMcEN1DjnVfvogUtnYnhDNQCDwRXdBqBRGhD1m0RAQ41be5eJVpQ2AE8JIPhzc4Ez3J7/VzAAD7ZGRPjmmYcrr2VSAYmLvqw20CgeNu4xdvftvd5uvHzRVzEAHhvCXaNVGxFlIkOmpkde3e7K0yZh7JB685r3ZOCbss7elFWsCOBuoJIR2GNSnTl1JK6dc6T1rpoBaABwqypUDCCTWJmNDQA+EkAmBFlS1UZgVTvvZ1R5xAKodn4vrd2Nl9buBuBkAEu27MP3/7Ycv1+0OfOgNQqGdJphw24jvXdHT2YGUBUhlxGYOxoc6DakBJEOHnx9E5jgj+/omzGn62eEq4Dc8AsEqzMZwDOrdmL7gW47fibtzgbqN1ct9W5cMwANE5yA6qqi+NgxYzAgBwlApQIS3SmvOcP2zHXaALIcbAAO4Mz06T0pfHdcVVEcdchA13mVBPDFB9/CFx98C4Azx8qr6/bgL0u24dbHV7k8lTT6Dp3xpLW4d/oE8vFFP0Lk2GUDtpqxJ+EuFXrzv1ahZX+3kjaYJAH4kbvfws0lgJUftJnHMWscVk1gn82O9RzT5/9gd6JgEcAcmgGUCbiqYvF1Z2DkwFpURSOKaFh/alEtpkPqq63P3zrLVqk4vIACZ0AJDqcEYPxX7Xb8mE8kQnj86pNd5zPlaVfp/ZNphi17uxR3aPQFxJ+k3UcC4IZfOeoXgCuvk0wHW/Z2Oc7x+ZNmcvZbb6ILogLi4MFbohcQeXi8iRgywJiTezp6fcukhgHNAMoEVlEXQY3TWOuUAjJLAO6fe2hDtaJlfjYAv1J51vMVKiZVP7lUQfKL7frgQLeLARx/2FAA0KUliwgxOZsc4SuCSwl8ly9CZgDyPuDPb211nOMLNoO3G6gMP2qUo3VtRw1FKgifBw0zGUBvMh0oZUQ+0AygTMBtAKIaR7YDZForVZuJofVqBuDYoWdJhJncMOVxkjUpVEbg7OEnAWzb3+ViAKdOGQEA2LJXl5gsFsTfTI7wFSH7/lcLUrBcNEmmgyeW73BIF1xFI0b8AhmkTp+LMtlxBqCKA/Db2HAJAChMHWARmgGUCbgXkEgQKjtAthCJTYQqND4suFNSu/tUjcMLmQLVRM8PlQpoRGMNqqMR7OuKQ6M4EO0ycpZPEbLvf6MwB9wqIMX9Sfv+wfVVAAz6IIVKUiXJ+s0F2YbEJYI0U1QE8yHsgbUxO/BMM4DKA2PMMnYxxtCTSFkGrqiPBJBp561SjQzzYgA+gWCZkEkBFCVyjNWvTJ71TJ+XkycTX+ATqTR6EinHjjLN3IU2qmMRDB1Qjf2dmgEUC/wnqY5F0N7jXdxd9vwR1aCiUVicQyLERbo6FkEylTYDwew2fjYvv7ngFeuiigPwU+0TkbUx84oXCAuaAZQgfvPSBhx5w1No60ngz//ehiN++BS27O00K3p5M4BMqK12/9wjGj0SruXhBpqpNkAk4mQS2STIUmG2qcPn4LutyT94Ekf88CmccNsLwjXmKgdYHY1gyIBqK8hIo+/BmfbA2pirMpYI2fd/YF2V9Tku7O47epM47+5XISORtH/7d7YewJy7XjXcQFW5gBR7Dr8NuRztLlYFS0tuoJlonatmtRG4AvHg65sBABtbO7HwPaOg+/rWDtcOWS6i7rfz/u2lM12L/f2XHKd0owRkL6Ds8NHDm3D9+Udax7IeUyb+QeYkzlXave+SmTjvQ3YtYllaECUAMREcR3UsgmGaARQVfLGsjkaQSjNPia9bMv5OGG6nBBFtAHs61L+lrKZZt7vD5QZqGYEV9/upZLy8kFRuoJmkai7ZFHj91wyglCHmPUmmmGshVQV2eWH6uMGuc2dPG+VJiCqviGzw4WZ7Vy6P01AB2dNriKmLVY0lSM8NNTEcdcgg69gvvYMYlMPBJYD9Xd6qB43CgpNDLGq7ZqrQI0kHhw6ttz6LC7CXJ5HKfdToK1jcix89umsUmwwgDbcKKANhc/uelgAqEHyXcuOClXjNTIyVSjOXBODlIVAddf+s2a7hSpE4C4hMQ94Zyc+z9Z25KzzF/vzS++xu78UVf1zqOFcdi2BofRX2dmQuJalRGHC7TFXU1psDwOvr9+DE21+w9PmiCmjMoFrHAin+7l6eRKIKyAZTLshBs4naz3bS+QDTy+jqP79tGpr9Pd5ENFgSgDYCVxQOdMVxsDuB5mH1DqJMpVUSgPrne/KbJ+P8D422jk+ePBzDG2qyGke+EoB4i7wgy4xsoKUCyp3Y6wQfbD830OdW73Kdq45FUFsddUWWavQd0hYDMGia755vfXwVth/oxqY9hotur6Dnb2qs8dxJewWT8c1VU6M9H9JptRuoUgUU0AtoRGMNLjt5AgBg894upJmTvjOt6w0m82gMwdPPD5oBlBg2tBpqnxs+NtWR8z6RTgspaQ3IDIGvexObGvCp4w6xzn/xI81Zj8OZqiHr230X82iEHJOL2wDySX0uusT6MQBVuofqWARRopzqHGiEA24k5epC/lOkpPOiNNlQG/OkGV7xTUY8lcbsCUNxzNjB1jkjEEyQeH0UPX50LVYk+9ZZh7s2JSo7gxe4BDBkQJVvu3yhGUCJ4dbHVwMAJjU1YqjgopnK0gbg8GvOQYxU1UXNBpnc3EQOYBmB8xB3GxwMQDEe89Eq+0BVNIJohHRq6CKCf/VcpXPPS+sB2L/Xga4E/vupNehO2DnyG2uqPFUyP1m4Wnk+kUojQgRReE6z4A4IfnMhKW0uRElXTjeRaU7xILLBdWo37bCgGUAJ4WB3Asu2HcCA6igOGVLnyNOTYiobgPzz2QuY2DIX3Xo2XkC3zJ3mOudnvIqSLQEQAeNNQ57fODMtzaI/uOzmCQCjBxmuqaoCMNXRCCJEZuk+zQSKAb7QcxvA3S+sR3c8ZdkGbnhsBe55aQN2tfVatNlYG/NcSPd6eHQlUoYuXryPscxeORx+zcQ4AM5oOHoSKWUVvEzP8qt9HQY0AyghcPXPXRcdi2iEHHl6kinm2vH7hYmLBJ1LOLlTJ+p//6UnNLvOyYmxRIiM7KEvzbJsGfnYuzKpgPjCotrl18Qi1pi0FFAc8N9M3DgwMCsJoqjT54tjQ20sa5pJpRkiRE4GAKZc2JW5gALaABIpp8qnvSfp2OBkmlNc1VXtkfI8LGgGUELgbp8TTd2/mKcnqfAC4onhVAu8w4ibtwoo69sd+k/Xs8neaUeyYDR+yKQC4kxGKQGIDEBLAEWBxQCETY6YQ0c0/nI01njbAPxgeOPYx8+u2pWFEdjHBiAwgGTKWdC9rSehrIGR6VlyrE/Y0AyghLD9QDcAYNwQQ10xSIhyTKbSroW+ytwtqVIoiAQmXx9QHcUJhw3zHYsqW2c2qMsgAfDJlckzIihTEBmAahcfs3b4biMwtwEA/plENQoH/pOJC146bTNsMfvnRyYatHvW1FG52aeIHHOivSeJbfu6A9/rBecYhzvosK07kTG/1idn2I4b5x5lBDaeOGl4oHHlisL6GGlkhVTaEEX5blWcDL3JtEuvzom4KhpBbzLtEFlFYpMZwMpbzs04Fi830N98fga+/sjbGe931ypwPs8KjBHO51P9qEGwATBFuge+s1SVgOReQICWAIoFy9tHoIFkOm39jqL///GHDcOfvno8AODNTd71g70QoWCbGpU9yI9EeezBuh/PQVU0ghfNCnSAwWRESVzu/9a503CJoEo9cdJwbL79/IxjzBeaAZQAEqk0uhMpI9hL1N1HxcngtgFwvbaKJvM3Aov6Svu8n2rH634ZEQK4tBy0EEcm1AvjSinSPXDmqVIBxSJkTU5tAygO5DgAwPgt+O8l/i6iXjxXCcA32tcnFYQfjXLXUz7fxKjlTCqgmliweRU2tAqoBHDFH5bi6JueQYoxxy5B1v+5bACSRCASLPmogILAy4ZQE4JXgqECctsA/IaZaWMujjHN3As531mq4gCIyMq6qPIg0ig85DgAwGDkKoYsRrrnagPwYxx+j/Sj0TYziyl/9IiBdu6t9p6kL62HMa9yQaBeiehcIlpLROuJ6FrFdSKiu83ry4loRqZ7iehnRLTGbP9/RDQ4lDcqQzy/xhAV07IEkCH1A5cAVBTrpwIKAi830DB2KhGPXb94fun1Z2b93Ie+9GEAPOGbc6HnC4ucT55DG4GLi5RCAkim1AygKk8JwGD4Ae5TegH5SQBJR7qH48YPwb3/z1gKexIp39gcP5VpIZGxVyKKAvg1gDkApgKYR0RTpWZzAEw2/y4HcE+Ae58FcBRj7GgA7wO4Lu+3KXOk0s7FOpMEwImfnxV1lvnq1r0W5qAqID9EyK4HEPFgVMOyTF0BAM3DjMyQqqIv/LtUlRIEoFVARQanhyrJC0jFkGvylAAilClQ0RxTlgVh2noSLsZytBlx3JtMO665JIAiqYCC2ABmAVjPGNsIAEQ0H8BcAKuENnMBPMyMFWgxEQ0motEAmr3uZYw9I9y/GMCn832ZcoccLu72+3dSbRA9JpCfcRVwEms+gSkRMtQz0YjNAPJNOifCLsDh1vXza54SAGkGUAzc8fRa/M+L661jkcY9VUAxkQHkZgPwU/TkmgqiK55yJWIU57BfbE3JSgAADgGwTThuMc8FaRPkXgD4MoAnVZ0T0eVEtISIlrS2tgYYbvkiLvkOe3n9cPgRo2NnnefKKhJrPn7JqoCvfN1NRfDb04qFg3+XYsK3p7/5UTxoqo20BFAciIs/4I4DyMgAAvTxtVMOw/zLj7eO86Ezr1vtEo7O8yJDEOevywhcwjYA1SvLv4pXm4z3EtEPACQBPKLqnDF2P2NsJmNsZlNTU4Dhli96E2lJBeRvA+A0pMyjn6cNQIQzhD33Z6nqnGYKjslmrvL702nmMvbK3yUATBnViNPMgvAxS3rQDKCYkG0AKoiLapDF/GNHj7HUgwCnqdx+Z6/+BnlktBXfJ+age+f9pewF1AJgnHA8FsAHAdv43ktEXwBwAYDPM52EBfFU2pnCIYMNQF74nV9gmCog4Vl57J74OLyCv/JNfS6qgFwSQAbJRU4Fcc9LG7B0y/78BqShRG8yhev/+Z6y/oK4SHoxY6cROHN/RO7dd5DVRtXGiwFY+ft9GIBfHEChc/54IYgN4C0Ak4loAoDtAC4CcLHUZgGAq0wd/2wABxljO4io1eteIjoXwPcBnMIY6wrlbcocCclQJO/4ayQDLL+qoskwJYBcn3X3vGPRsr8LP31qLQB7MjhyouRRfN5rnOo4AP9nW9KDOev/+6k1ANAnwTiVhmdW7sIfF29FhyJnv8ioVTEbgFMC+NgxY/DS2laMGFiDh9/YomwfIXL8/pnIzDIC+2SVlVFr7uDl66LkGfNTAZWqBMAYSwK4CsDTAFYDeJQxtpKIriCiK8xmCwFsBLAewG8BfN3vXvOe/wHQCOBZIlpGRPeG91rlCdkGIOvb5SLwMhGJBOuXCiJb5GpQvvCYMfj6qZOsY1FPygW+oGu+yiNDBmcsTGkD8O/IlgCCjUcjd9gGeUVaDuF3UqXtAJw2gAE1Mdx7yXEYM7jOs78IkZU3ix/7wfeqlw0g6lZvAsbc4UxAjNuRbQXFMgIHigRmjC2EsciL5+4VPjMAVwa91zw/SdG8orDwvR045XDbrhFPph2EIXsBNdRIEoAPpYqX8jUCe7lqZouYoAJS5QLKF/xZB7oSuOelDc6+M6iA+L1y/IBG+OAbm7iC28YckcDq+1WLpR+NRwguCSCQCkjpBqruhz9fNY5YJIJEKuWoQVAqEoBOBVEkrPqgDV9/5G18fPoY61xcUgHJLmUNNc7qQH5Lp8O7JkwbQD4MgKuABDfQMGte83E+sXwH1u5qd/YdUALQ63/hwXfEqqjsKkf6E/WPMUBRJtGPLklK/rZhdwemjGr0aW/8z8YGwGlbpcasihK6E5IEIDWrrS5dLyCNAuCgmTfkg4M91rneVNqxWMsSwAAPCUBFkqJuPZd6ACLCMgKLEgCHn891tuDza3d7j+uaX/U0ANbuTEcCFx58YxNX1GCWcwGpwLPlivBjAIYEYD+3ZX93BgnA/1kq2A4O7mtcZeWUQkpDAtAMoEhQZT+UJQA5DkC2AXAiUiWvKgUjsAxxkqhyAeULzlj2d7nrwWaKX+D3ptJMVwXLAbvbepQ5+1Xg365KApDjAFRQqfP8pFyjAIx97FUtzGucIrwcFWw9v0oCcDs/5KuWDQuaARQJfKcZdTCAlG8cgJjyGAiuAioVBqBSAWXy/Mmmt0yF6H3HZjLbNGPKgjIa3mCMYdZPnsdVf3onUHvu3aOyAVRF/CWARoX6B/CXco3sn/b148YP8XUp8CNJbwmABzn6MAAfL6BiQTOAIoF7ODgYgFRHVN7pyLpP/4yG4e02wrIBWOmrQ0z/IMLPnlCVYdz8XrEKlUYw8AX92VW7ArXnmT8TSUVqbikFuojXrz0dr193uvKZfjQuX3r4y7MCGYHVz/KQACKctt3X+Dt5pYUoJjQDKBL45kfc5bi8gKRFS9792MYqhbeC6HEQog0AyN1ljTMPsSh8IbyA1H1nCAQTooh1Oojs0KvQ5fuBL+xqFZC3BNDUUIOBtVXyLQAyqICkawNqYoHcirOJA7BoW9GgOuqWDvKdk2FBM4A+wvrd7Wi+9gm8u+0AAFsCEIk8kXKmg5b11rIEMLjemAwjGmshI1TjqvmopkYjQ2dtjhlBY8IkUWUD9UOQHZvfLjAa8e9LTAetBYDs0OuRYM8LnPZVjEOU1Dp7nYFifmoefxVQVsPLUA/AywaQWQXklQqCz+NiQLuB9hGeXWXk/F/43g4cM24w4maeE1HdYEgA3uqWIUKReAA4bcoI/Pyzx6ChJobL/7DUmQ46xA0GEeGBS2fi6LGDANgSwC8/N906FwRcz+7Iihgio/KNiyCySlGeNqUJV53uDEPh33vSIwWxhjeylQD4xj+eSmP8sHps399tSQWiBNAhMYBsdvmOayZh/PGy2Rg/rD6LkbrpwIvG/LyAVAZikVE8ec3JWYwpXGgJoI/APSS4S5hq1yR7AcmQdwpEhE/OGOtKEQFkZzwNgjOnjrQqHPHMhcceOhiHNTUEfkZMMRHCZlR+E5RfmzZmEI4bP9R5XVABaRtAdsheBWS0T6TSSKYY5k63EwSLenJVqggvBLEBnDR5OMYNNRmAz09sedVlFQdAntdVRmDeLBYhjB7kHcVcaGgJoI/A9Z2cGFSTJi7FAcjIRvWSb14dP9RXGWST7cR3JoMz3UAzyOe2q2vAPoiQVMzcqJAATBlJao7jna0H8MTyHcE60wCAwO6fHFztGU+mURNLO1QjoheQLAH4wT8OwH3N1wvI51qmSGDVdb7pE8dopS3x6asvoBlAH4EHvfDFhxcmkdeqnAq4q84V0MZ03yXH4aFFmzEpi90/YKuAnIFg/vj0cWOx8oOD+PZZUwL1wZnLJ2ccgn+8vd3uh2zjY7WCAfAxyfnpNTLDq8qaF/jvkDQN7uKuX/zcniMD+Mvlx2Pp1v1WEsJMjgbjh9Xj22e76UsZB+DxDKvWhYIRceagSgZX7JgTrQLqI3AGYKmAPHbP+frscxTSx6B5+ADcdOG0rD0ZbF0oBCOw/zNqq6K47ZNHY8iAat92HPxxY4fUY66QZkOUiFQMIKzvvRKRrRGYu4EadRuYUwLIUQUkPmP2YcMcSQhVP6248H5+9qG48BiRVtxt5Gte/attAG7moOMAKgw86GX97g4s3rjXYgCyvjks97BCqoByhagCst1Aw+3Dqi3r82BV2L1mALlD3Mxs3duFF9fsxsbWDs/2XALgJR9Fw68Y/b7gXbnsiDf85o1qLoizTlatZnImUEEl3XJUqVJBlMjKq1VAfYReU0x+5M2teOTNrfjaKYcBcOdDkVPWTBnZiLW72nHJ8eMz9uFMB53feAsBVUWwsEUVbmCs8olV0BJAuBAZwMUPLEbL/m4A3rUUuA2AMcM25pQAcowxyZANVIY4V3hVOI5jzELul510WOD+vdJBA3YRe2cyOG0DqCj0SkEvnCHI+lN5B/H0tz6a8dkq2g/TvTIsxIScKFy8DlsU5mEV8kIi9pJtOmENf4hGYJ7k0A9i7ItsA1CV7gyCbI3AHHddNN32DDIxrKEm60JAfiog7jXnTAed1eMLhhIRRPo/5J0+3zXJaYvzUQGJ0Y2luJ5VRe1JUohIYBHVPguJ0gjsMROSqTTW725XX9TAhtYOtIu6emFL6/W9iQwgmWYOtU+mug1eyJUBhAU/LyCucoypJIAiiwCaAfQR3AxAbTjLzQuoBFd7BURjmJUMTtFu5MCaUPo6ceJw61j8WrNRAd357Ps48+evYNOezrzH1N/Qk0jhjDtfxnX/eM86J3runPnzV7Bi+0HXfX7lOnNNXe7HN1RTKux1149x1SjcQLURuMIg5z3xyjcTli66VHKNiGiszaxxXHHzOXj5u6fl3VcsGsFnZo7FBUePdl2TC+0A3oyXF4bf1eauMVDpkFM1qDJ1Ltm8z3VOLvUoLp650q1fridlHEAeW+93bzhb0b8ZSKh4rpoB5Nx9qNAMoI8gSwBeBa9D8wIK5Snhgify6kmkPWsCN9TEcs41JD6vKmqkAB6mcB9VGRq9vnd+WieIc6Mr7pRiRw1y56TavLfLdU7OASfu+nO1xWRrBObIxVtukCJ3D1dvqsiER+qnhYtaAqgwyLnPUykPCSAPuhA3HyVCXw40mgygO560RPCw1Vd8MZF3+WIvKkOj1wLCv9NkmmHWj5/Dxb9dHMo4+wN6JP9/lfT60KLNmHL9k2i+9gms3WnYBNwSQP474zAjgXPr36A3lWTBJQBx08eHNGWkd2nKvoD2AuojyDvIMCWAcvEC4iqgTnHnGPIweQFuP3fCbCQA/iul0mnsbu/F7vbeMIbZL9AtMAAib4Mmd3h4ff0eTBnV6GsDyF0F5MMAlH6gxr98ye+fV56I4Q3VeGGNkexRpQLi9JZyMADCn796PA4fmV00fdjQDKCPINOFvAviyMcdsdTjAHhJy55EqmAO0LEoAQk7DkDVjYoBeBofzQckPCS2Ska3wMgJmesp88VRbuewAeSqAspSl8E95vKVlKePG2z2bzxIUeLAknDkIvcnTByWX+chQKuACoQteztx8W8XWwmt5J3Bi2tblfflYgRW3lGCDIDXM+iKp5QZTMMAX8j9IoFVKiCvhYcvFOLu7VP3LMKejmCSwL837cNVf3rbof/tLxAlgOENNRntJD96YjXm3PUqdh10GtRVSdKyRaaCPzKqFTn680GVjwqIX0uW4CYi0LdGROcS0VoiWk9E1yquExHdbV5fTkQzMt1LRJ8hopVElCaimeG8Tungzmfex6INe/H86mBl8jhCiwMoQQ5QX20s+p29Scy//Hh85+zDMagu3GIYfCHwiwRWSQB11VF85+zD8enjxjrOM0sCsHdvS7fsx1ub3N4tKnzpwX/j8eU7nL7y/QTcBjDnqFF4+LJZgQzlq3e04Z/LnCkehjdU47ErT8RNH5vqSJHwwwum4i+XHx9oLOOG1GHerENxwwVTrXMLv3Ey/uu8I5Ttb/jYNFz+0cNw5pEjAz0/EzgTU9Ezv+al9i0mMqqAiCgK4NcAzgLQAuAtIlrAGFslNJsDYLL5NxvAPQBmZ7h3BYBPArgvxPcpGfB1nO/8g+aYD0t1U4oqIM4AuhMpTBrRgKtOnxx6H3x3zxd5VfF5VRwAAGs8f1vaYp3jv5usAlq/2zvXjQqd8aTSe6ScwSWA7597BJqHD8jZU2pSUyMOHVaPY8YNdriWXnbSBGXZSBVi0Qhu++SHHOemjhmIqWMGKtsPHVCN/zrvyJzGq0KX+V00DxugGJtBe0HfpS8RRAKYBWA9Y2wjYywOYD6AuVKbuQAeZgYWAxhMRKP97mWMrWaMrQ3tTfoQ63a1KxNV7enoxe8XbQZjzFIpqGr/+iGslASlmAyuvtpWARUKfLclq3nEryObfDP8V5MD9x5ctBm3LVyNjt4kUmmGnz/7Ph5btt39ABP3v7IxcJ/lgu64Qdx1JmPPlQEcMsQuiCKr4krFXTITtpiBguOHuyuOqYzApYIgM+EQANuE4xbzXJA2Qe71BRFdTkRLiGhJa6tab97XOOsXr+Abf37Hdf7qP72DGxesxIbWTkuVYxm+Av74uaiAjho7CEPqq/DNMw+3zuU6ba4+fRI+enhTjnf7Y3hDNcYOqcMtF04ryPMBe7L5LfKxLHxtuQTRK+Vs2tcZx32vbMRbm/Zh055O3P38Olwzf5nncx5atLnfBZN1xY3dOo/buGVubr+rI0BK+tlKUZIFgKPHDsIPBXXT5z48DqMG1uKTx451tS1rFRDUa4n8Jl5tgtzrC8bY/QDuB4CZM2eW3jcowJ7gzCJc5qFC8EIuEsDA2iq8I0Un5rpxUhXGCAuxaASvff/0gj0fcBvRmYLcVJHAXuC/n1f9hu5EyuENwxjzlL7auhMYOdAdLFWu4DYArto7e9oojB1SZ2UDDYIGKXpY3vGXoiQLAAuuOslxPHlkIxb/1xnKttzQnCxTFVALgHHC8VgAsv7Dq02Qe/sNeLBXmtmEzJl+JgmAE0loBWFKdOIUGvx7lPWtzkCw4AxguZnLxit3U3c85fCGWbxxn2fbJVv2Y39n3DpmjFmpJsoBu9t7sFnIidSdSCEWIcf3ma3KZuwQZz3c/piVlbu5lqIEEGQmvAVgMhFNIKJqABcBWCC1WQDgUtMb6HgABxljOwLeW7aQXb64m1dvIm0twHzhl32AZXA9angFYUJ5TCg44bC+83f+xLGGhnFEo/dOOxsma6mABAlATCfdlUhZqhAAmPfbxbjxsZXKZ133j/cw565XreM/vrkVn7pnUdaeYsXCGXe+jFPveMk67oqnUJdFMRUVLj2hWXn/RR8e525cpjhsuGEYPnvaqCKPxI2MKiDGWJKIrgLwNIAogN8xxlYS0RXm9XsBLARwHoD1ALoAfMnvXgAgok8A+BWAJgBPENEyxtg5Yb9gIZFMM4exkS/yvcmUSwWk4v7nf2g0nnjPKEBeVxVFe08yPCNwKE8JB3+4bFaf7X4u/+hhuOSE8ZbBOSyIap7qWMRiCD3xlCslwhKfXf1OwQ6wzkwFvnWfO19OKYK7svYkUqitiqKzN4kGKcFfNnS34uZzXCogIsKaW8/NuTBMKWLc0HqsvPkcS1VWSgg0SxhjC2Es8uK5e4XPDMCVQe81z/8fgP/LZrClAHHXH08a1YzILHDCi7v0JtMOFdD+zrhrkQCcO9H60CWA0mEBsWgEiiqMBQERORb/sPKt7zho67VFNUd3wqkCApxJv/y6J+l/ueDdbQcw+7Bh6OxNWcF9HNmogOTFnyOfZIClCvl7KhX0HzbbR/j2o+9an6fd+DR+9rThyfrg65utqF9RAljechDH3vqsq/IX4FwceNGIsCSAUvWe6GtMMMXv0YPqMrT0x9MrbTVN83Db19swAkuJ/qRNQn/D5+5fjBfW7EJ7b9K1iB91yKAijUojF2gGkCX+8Y7T1/s3L20AADy+3LZt9ybS1k7+/V3qqkiAOjhsQE04u59SjAQuBr584gT88bLZOHNqOBGfAPDp48biD5fNQmNNzGUEBmy7TzyZRjLNcM40dd+lZxL0RjrNEI2QFen6wYEeQwUkMYD//tTR+NpHM9fS/eeVJxZknBrZQTOALOHlQijWFRVVQDv9fL+FFSBh2g/CEhVLSANUVEQihJMmD8/cMJtnEnDy5CbUVUfRk3DbADgD4Izhw81DPdUd5YL2HiPg7QsnjAdgMLeOnqRrw1JXHQ2U5OzI0cVNg6xhoLypsggY3lCND6RkVi+u3e3w7/7mX5bh87MPBQC0BkwfzN0Wy32hqATUxuyUFvPf2ua6nkwzPL78A/z6RUM6rKuOKlVy4qlfPPs+OnqTjuCiUsLeToOOR5h0nkil0dGbREONO71FkDiLbGIxNAoH/StkiUmKAg7X/PkdV6a/9xS1UGWIQUpcVxwWAyiXEPpywUDT22X0oFrMnT4GABwJ3gYIHh7pNMNVf3oHq3e0ATA8vDIVO7/r+XX439c2hT3s0LDF9FTiLo3xJGcAbpWlVyK+P1w2y/pcSk4KlQzNALJEXBHkM2ZwHXqTTp/oIMnfxCY8Ulh2q8sVen6FC+6W+O2zpygX8zrB80jOd19fHXUwZM7seasSjA9yYYOZ/G7KqEYQGUGPKjdQwDvF8smTC5NiRCN3aAZg4rFl2/Himt34+TNrlSHbb23eh5v/tRKLN7rTAK/Z2Y5H3tzqKHre1p05/a+4TvBFYUBI/ut6/Q8Xdo4h9TdbV21PJTnqu7Yq6ihY8pOFqx2uopmCBPsCu9t6cNdz65BKM7T3JPDzZ9aiozeJO59Zi55EChtaOzCkvgrDGmpQFY3gVy+sRzLNlDar/uTD39+hFc4mxEReR4weiPM+NNpx/TP3vpHxGeKu/0BX3KelAQaGn376aDy/ehdeXGMkumsMSQLQKiA3rj59Us6ZSKti7lQd159/JH70xGoAQH2V/bvJxV/qqqIO996HFm3GJ2fYORFLwVX05n+twhPv7cCsCUPx1Iod+P0bW/Ds6t1YvaMNddVR7DzYY2XtFMc7c/xQ17O80m3niguOHo3ZE9z9aOQPzQAUyKS9aaiJWT7/IsSEb20BCoAwBnx25jh8duY4NF/7hPXsMKDXfzfySXRXZVWQshe3r5x8GNp6krj7+XWoF3ThctRzXXUUUUlyYMymM3FB7U2mrJiQvgSXWna19Vi0y72bOnqS6OhNolEy+N510XTMUizMYUsA/3PxjMyNNHJCRchqS7fsx1+X2N4a72zdj4NdCc/2nH637u3ChtYOh48/YOftkZFtwQcVnwnPDVRzgDDBy/rJ+m1u96mKeKuAZAkAAJ5eudNa+DsFqWTZ1gN4dV1rIFpa9UFb1imm93XG8e62A9ZxW08Cf12yzUpgt3FPpzV+nvPI8PhxR/16Rex6qck0Sg8VIQEsWLYdf1vaYpX7+8RvFmHamIF44hsnK9tzNcFHf/ai8roXeWdb81OUNL74kWY8tGhz6OLzFz/SHOrzKgERchtmuQpIriXAU3iIqiGVBPD/jh9vqYsAI4Bw/DAjdkTMEPq5+xcDMH63mzLUTTjv7lfRUBPDipuDp9D62K9ew/YD3dh8+/kAgL8uacGtj9vF/bbt67KM2HyBT6QYOnoTaKx1esDJieA4/Fw8JzYNwH6fzZdG36IiJIBJIxrQGU9hZ1uPlcRr5Qdt1nU5q6dKvSPCa5mPp9LYdNt5GbNN3nfJca4n3XThNGy67Tzf+7LF5tvPx40fK02/8lKGyn4Si7hVQIC9CPr95nVVUVx20gRs/Ml5eOHbp1jneYzIPoW96NV1/sWPOM1molUZ2w8YOY24hCHHqbT3JC0bBqfORMoI+pLVk17JzfxcXp/7z1Ow9PozsxqzRuFQEQxg4ogGAMCG3Z0uIyBjDPs6nRMw20klgohchlx5beALjGxrKITaRquCsofqK+O7WrliVa1CApBRVx0FESESIQwUioZzSUGVDXTrvi50x1NIptJWuumueNJSz+wOGGBo9ZVKY8teO5c/r727r9P5nM7eJA6YO/Qus00ilVYmfstFBUREmiZLCJXBAJoMBrBpT4crb8sDr27CcT96znGuI4AB1w8yAxhU5zSecfIvA/fvioRqgeIqIFm/z/XkXr7vgB05DDiN/NwGsLG103VPIsVw5A1P4Rvz38HUG54GYwxTb3ga1/9zBQBg9k+eD/o6AICfLFyDU372knXMg9j2dTrVMW9s3Is3Nu4FABzoNq51xlOIp9IuuvayhWk30PJBRdgABtcbC/DB7oQjrzsAZXH3zjwkAAA4dGg9tu2z0wfXV8ccek++vsiqJ43SAF/LH/3aCfjsfYb7L1/UvOw8YhrvR792Auqqokik0yDpWo2PjUflXbbwvZ0AbK+yP/97K3788aOyeyEAG/d0OI47Taliv4+7Mndl5v8HSAu+lw1AZACPXXkihjVU69QPJYqKYAA1sSiqoxF09LoTd6lqxrb3Jh2+3MMbqrGnI7NfP8ekpga8vn6v3Ye00FsMIPATNfoSPJPqxCY77TPX/ccl7xxOJ6IEoHKNtJ7to/44/0Oj8Zcl7txCANCy31YTtfVkb0SVVZ9cyt3f6U3X3K15T7vRpqHWKcl6MQBRHXbMuMFZj1Wj71AxbLmhNoa2ngQu+NVr1rlL/vdNrNje5mq7cnsbpt/yjHU8dYwzx/mwAdXKz3wRmGTaHDiaGmscx1x3OrjOnUhLo/jg65e4kDU1Gr+zrN/mXltDBTrIFWMGe9csOP9um26n3/Ks6/rG1g6cd9er2N3egx0HuzHl+ifRfO0TWLvTSEcuL/R/XdKCc37xClo7bBvAkHo1PfKMtnLeHy8VkEb5oCIkAMDIs//BgW7HuVfX7VG2XSvk8G8eVo85R43CK+/bXhk//+x0nHe3Udv1r1ecgCVb9qO2KoppYwYCsG0OAPBf5x2BS09oxsNvbMbRYwcjkUrjhMOG4YYLpuLTM8eG9n4a4YHv0okI9/6/4zB+WD0OHVqPiU0NOPXwEY62p00ZgevPPxLzZh2KS04Yj817Mpd3fODSmfjKw0sAGOpCbgQeOiD7DUGEDAnz9fV7sGpHG97esh9b93VZ3m5/enMLbp57lEvVo5I0Dh1aj/1d7iSGB01bwKA6J5PzU2dplAcqhgE01FQFqr06eUQD1u229aVfP22SQ9Q9cdIwjBho7+ibhw3AYU3OHb8oAVz+0YmO/xxfPmlCdi+g0WcQtTTnHmUX8v7Kye5CJ5EIWeePGDUQR4wamPH5Z04dacUaXHqCHR9Ql2UeqAuOHo3Hl+9AdyKF9SbNrt/d4YhIH1xfjXSaBfK9P2RIHd5tOYjqaARjBtdi817nfJGlHO3NU/6oIAYQxfrd3tW5OD48YaiDAezrjONQodgL4Iz6VNXwlVU+GuUF2023cFYabmKaKGwWvHTqXjjEVBld8r//xlKzEP07Ww/g+TW7rTZd8SS+9/flLu8lEdz4PGyATbcqF88w1FwapYUKYgAxx85IhZEDa/CljzRjzY42nDBxGNbubMenjxuL+uooTpw0DDsP9uDmC6ehsTaGs6aOdLl3chARrj59kkMVpFE+eOALM/G71zZhYG3hbDRXnz4Jb27ahxnjhuCmj01FmjkzivrhvA+NQjQSwYXTx+C+VzZaiz8Ax+IPGGrONTvbXZItx9lTR+IbZ0zGPS9twImThuMPi7cgnkrjqEMGYc1O54aJe9P9z8XH4u0tB3zH+JWTJuj6wGUAKidXxJkzZ7IlS5bkdO9Vf3objy/fYR0fMarRReALv3Eypo7JLMJraBQCizbswcW/fdN1/uunTrRqTwNGJPk50wzV1KW/+7dln4pFyAouW3XLOfjErxdZ9qwXv3MqTrvjJQCGd9OG1k58fPoY/PKiY63nbt7TiVPNNu/eeDaOudl2hABgpY/QKD8Q0VLG2Ez5fMVYceQglg8pdic1VRXzdWiUILxUQHLRFS/Jku+4iYzYE66yqY5GMG6I7WF05GhjkzNEUumMFdp4Sbca/QuBVjwiOpeI1hLReiK6VnGdiOhu8/pyIpqR6V4iGkpEzxLROvP/kHBeSY3JI5yJrE6dMsLSoQKGe9/wBq271ygeuBsodyI4fKTxf2JTA645Y7LVTrRJ8SLtAPAfpxqOBuebtSz4Qj9j/GDEohGrLc+tf6RksI5FIxjeUG2VvJwystFiCmcc4fR+0ugfyKgCIqIogPcBnAWgBcBbAOYxxlYJbc4DcDWA8wDMBnAXY2y2371E9FMA+xhjt5uMYQhj7Pt+Y8lHBQQYEb41sYgrWVUqzZBKs9AzcWpoZIveZApVkYjlXNCTSHnm3FGhJ5FCTSwCIgJjDJ1xo1SpnKsom+f2JFKojkaUDg8a5YF8VECzAKxnjG1kjMUBzAcwV2ozF8DDzMBiAIOJaHSGe+cC+L35+fcAPp7tS2WLATUxZabCaIT04q9REqiJRR0LbTaLP28vxjE01MSUieqyeW5tVVQv/v0UQVa9QwCIUSMt5rkgbfzuHckY2wEA5n8tY2poaGj0IYIwABXrl/VGXm2C3OvfOdHlRLSEiJa0tvrnSNfQ0NDQCI4gDKAFwDjheCwAOYWmVxu/e3eZaiKY/50OzCYYY/czxmYyxmY2NTUFGK6GhoaGRhAEYQBvAZhMRBOIqBrARQAWSG0WALjU9AY6HsBBU63jd+8CAF8wP38BwGN5vouGhoaGRhbIGAnMGEsS0VUAngYQBfA7xthKIrrCvH4vgIUwPIDWA+gC8CW/e81H3w7gUSK6DMBWAJ8J9c00NDQ0NHxRMZHAGhoaGpUKLzfQsmIARNQKYEsejxgOQJ0DurAoVr+l0L9+9+KhEum9UvvO1P94xpjLiFpWDCBfENESFRfsr/2WQv/63Svv3Sv1Oy/H31tHP2loaGhUKDQD0NDQ0KhQVBoDuL/C+i2F/vW7V17/lfqdl93vXVE2AA0NDQ0NG5UmAWhoaGhomNAMQENDQ6NCoRmARlmDeO5jDY0KQNj03q8YQDEXAyIaWqxxENGpRFS0THlE9G0iOtv83Ne/gVXqrRi/f5Fprih9F5PWzT6LRu9FpnUgZHrvFwyAiOYS0e8BHFOEvs8lolcA/JKI7gQA1keWdaHvzwPo7Ys+pf7PJqKnAXwfwKVAn777WUT0GoA7iOh7fdm32X8xaa4ofReT1qX++5zei0nrZv+FoXfGWFn+wfZgOg3AcgBLAfwHjNKSBe8bRnK7ywEshlHd7FAALwGY0wd9RwDMA9AG4DN9/b0DqAbwIwCvwkgC+HEAPwZQxX+XAo9hLIDXAXwMxo7oCQD/LdJFgfsvCs0Vo+9i0rrQf1HovRRo3RxHwei9T77IQvwwwudmAKMBnA7gIQCn9GHfUwHEzM8jADxqTo5IGD9Ohr5PAfA/AI41jz9jEkpVIfpW9P9haSwb+vB7PxPAvcLxeQDiACYX6t2lsTQDGFUkmuszei8mrSv671N6LyatK/ovGL2XnQrITC/9DyL6FhGNYoxtZoztYIy9AGAXgFOISC5ZWYi+RzPGVjEj5fUMAP+EMTm/D+Dn/JYC9P2fRDQcwGswdoL3ENEaAJ8F8CsAvwm7b6l//u5vmeerGGMvA9hIRHPC7NOj74EA3gdwEhGdYDYZAWAlgOsL1P/XiehT5mcCsI0xtrMINNdn9F5MWpf673N6LyatK/ovLL0XmpOFzBU/AaPIzGkAHoSxI5guXD8awB8BfNKLmxaibwATABxqfh4A4ACAmQV8718DmAJgDIDbYO+KhgJoBXBcH3zvx/Dv1uz3AQBn98Fvfg+AkQAug7EDfh3An8zf4F0AzSH23QjgXgA7AXTA3gFHOE0Vi+YK2Xcxab3Y9F5MWi8GvYf+AoX8g1FE5svm5/EAvg2jyIzY5hoAN8AQkb9f4L4f9Gh7P4DTC9j3dwE8YB7XSm1/i5DVAgG/90cA3GZ+jhSw7+/BFIdh6GFnmZ+j5rsPDfnd55n/5wO4j/dVRJorOL0Xk9Y9+u8zei8mrXv0X1B6L0kVkOzeJBxvBHAxADDGtsAwhgwgoguF5n8G8BUAf4GRHzsrd6ks+66X+gYRXQ9gGoBVQfvMoe9/AWgkogsZYz1C+x+afa/Jtu8s+1d9738EMIuIahlj6QL2vQDAECL6BGMswRj7t9nuVhg70vZs+87QPy9h+k0A84hoMmMsRUQxoU1eNOczloLTexb9hkrrWfZfEHoP2HfotJ5l/wWhd46SZAAwOJ0FZrI8AH8D0EVEc83jHTC8EaaSgQYAdwF4D8DRjLHvSvcXpG8AIKI5ppvW4QA+zRjbmUWf+fZ9MhG9aPb9KcbYrhz6zql/gXDrYOySU33U9xQAIKLJRPQYgKMAfIsxlgizf8ZYJxFFzN/zNzDEfzDGkowxRkQDANyN/GgORBSV+0Yf0Hu2/Zr3hEHr+fQfCr3n8p2b5/Kl9Vz6D5verY5L5g/ACQD+CoOop8IUtWHrXglGveGnYOtgvwvgJt4OwIg+7vtm83MzgKOK1Pc4AFOL8L3fKDwj2sd989+8EcDYArx7FLaHS0Rov9W8ZyRM75A8ae4WxXn+ngWh9zz6zZvWQ+o/Z3rPo++8aT2k3zwvepf/SkYCIKIRMAwuCwHshaHb/DJg7LbMZnUwCszvAHA/EY0BcCyABG/HGNvdx33HzXabGWMritT3NsZYTmJ4nv3z62CMZb0bCuk3b2eMtWTbd4D+U4yxtLnLHiTc9t8wDHGvAqg32+ZCc18A8HsA1xPRZ81zMfN5fDcYOr3n2W9etB5i/znRe55950XrIfSfN70rERYnyfcPwFkA/mx+HgDgHACPAzjCPPcj84s5FoYl/kcwRKPfIA+OXMl9F7v/Mnj3W2HsxE42j+fA0DffAdP/PI++z4Thx342gK3CeS6B3FSIdy9Wv6XQfyW/u+eYCvHQgF/GxwH8F4DzzeMmAOsATDSPhwK4EcaOqx6G69NE6Rn1uu/y6b/c3x2Gimhcnn1fYB5HYQcxvQbgVqHtiLDevVj9lkL/lfzugcdYyId7fClNMAJJXgFwBYDdMAxJgOEC9UvzcwTASZBcnZCH21Wl9l3s/vvBu+ez41b1/QnzWrX5fxqAgwBGKu7P6d2L1W8p9F/J7571WPuqI+HljgfwXeH4EgCLzM/HwBC5zzSPj4ThBjUgpB+mIvsudv/63dV9m8dc/H8Apq89QsixU6x+S6H/Sn73rMfaJ50Y2fNOhSFWV8HkejBEouMA/NY8jgD4IoAVACYBuAqGD/Bg3Xd59a/f3bdvHlBGcHoYpQHsB3AtcmA8xeq3FPqv5HfP5y+GAsH0mR0FQ6+VBrABwFcBXMMY20VEUWYE1BwJ08uCGUEVD5neGdcCOALAVxljB3Tfpd+/fvfAfQ8x+2YAGBGNB/ALGJ5FV7IsPGyK1W8p9F/J7x4aCsFVYIs4hwP4o/k5BiN50z+kNg8D+Kz5eZTwjGrdd/n0r989p76bzP+DYYb4l0O/pdB/Jb97mH+hSgCmT+stAKJEtBDAQJjRcszIJPgNAB8Q0SnMyKoHGEm2NhHRLQA+SUTnMsZaGGNx3Xfp96/fPe++z2OMbQXwb0UXJdVvKfRfye9eEITFSWDkyX4XRva6r8KwgJ8LI3JyltDuPwC8yDkkgB4AWwD8EiZ31H2XR//63fu+b/2dV+a7F+ovvAcBJwO4RDj+jflFfBHAUvNcBIbO7FEYme4mml/KDN13+fWv373v+9bfeWW+e6H+wnuQYf2uga33+jzslKnLAFxtfp4JYH6oL1GhfRe7f/3ufd+3/s4r890L9RdaLiDGWBdjrJfZeTLOglGsATCSGx1JRI/DSF+7FMgvZa7uu/j963fv+771d16Z714wFIBLRmGIQU8CmGSemwTD6n0SgEMKxc0qte9i96/fve/71t95Zb572H+hSQAC0jACIfYAONrkiD8EkGaMvcYY216APiu972L3r9+97/vW33llvnu4KBCHPB7Gl/QagMv6kqNVat/F7l+/e9/3rb/zynz3MP94wYFQQURjYeS/+DljrDf0DnTfJde/fve+71t/55X57mGiIAxAQ0NDQ6P0UQgbgIaGhoZGGUAzAA0NDY0KhWYAGhoaGhUKzQA0NDQ0KhSaAWhoaGhUKDQD0NDQ0KhQaAagoaGhUaH4//qBISREHpCTAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -239,7 +157,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "name: ROTTERDAM\n", + "name: EV24_ROTTERDAM\n", "x,y: (90598.0, 442813.0)\n", "station: 344\n", "metadata:\n", @@ -251,7 +169,8 @@ " x: 90598.0\n", " y: 442813.0\n", " station: 344\n", - " name: ROTTERDAM\n" + " name: EV24_ROTTERDAM\n", + " variable: EV24\n" ] } ], @@ -287,9 +206,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 2019-12-30 00:00:00 and 2021-08-15 11:46:12.695806\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 2019-12-30 00:00:00 and 2021-08-30 17:19:44.435013\n", "INFO:hydropandas.io.io_knmi:station 344 has 0 missing measurements\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 2019-12-30 00:00:00 and 2021-08-15 11:46:12.896029\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 2019-12-30 00:00:00 and 2021-08-30 17:19:44.619482\n", "INFO:hydropandas.io.io_knmi:station 344 has 0 missing measurements\n" ] }, @@ -335,9 +254,19 @@ "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:hydropandas.io.io_knmi:download knmi TG data from station 344-ROTTERDAM between 1959-12-30 00:00:00 and 2021-08-30 17:19:44.838818\n", + "INFO:hydropandas.io.io_knmi:station 344 has 2 missing measurements\n", + "INFO:hydropandas.io.io_knmi:trying to fill 2 measurements with station [210]\n", + "INFO:hydropandas.io.io_knmi:download knmi TG data from station 210-VALKENBURG between 1959-12-30 00:00:00 and 2021-08-30 17:19:44.838818\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -359,9 +288,24 @@ "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:hydropandas.io.io_knmi:download knmi RD data from station 892-GIERSBERGEN between 1971-12-31 00:00:00 and 2021-08-30 17:19:46.333991\n", + "INFO:hydropandas.io.io_knmi:last RD measurement available at the Bilt is from 2021-08-10\n", + "INFO:hydropandas.io.io_knmi:assuming no measurements are available at other stations before this date\n", + "INFO:hydropandas.io.io_knmi:changing end_date to 2021-08-10\n", + "INFO:hydropandas.io.io_knmi:download knmi RD data from station 892-GIERSBERGEN between 1971-12-30 00:00:00 and 2021-08-10 09:00:00\n", + "INFO:hydropandas.io.io_knmi:station 892 has no measurements before 1993-11-01 09:00:00\n", + "INFO:hydropandas.io.io_knmi:station 892 has 7976 missing measurements\n", + "INFO:hydropandas.io.io_knmi:trying to fill 7976 measurements with station [910]\n", + "INFO:hydropandas.io.io_knmi:download knmi RD data from station 910-AMMERZODEN between 1971-12-30 00:00:00 and 2021-08-10 09:00:00\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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FWOr9793Ao97/+3wbKAKmWhTucSGKeXnSlmX3wKLjRDNZkhBCTBTff/lwqoMghBAiTulSTWKmBfVaoFRrXa61HgCeB24N2OZW4Blt2AVMV0rNB1BKLQQ+CjxhYbhtLV0iRzqJZXIkKcsKIYQQQgiRXBFbUIEFQJXf39WMbh0Ntc0CoA64H/gBUBDuJEqp24HbAebOnYvT6Qy6XXd3d8jv7KK4ehCA+vp6nM6JOUthb6+xhmhhYSE1+bEPdS4vHwCgqqoKp7Mh5uMcaXID0NbaFjH+1NX2A1BSUoLTdSrstsmKj3aP83YwHu+R1WEeD+ljMoyHe1DW7gGgq7PTVuG1Kizh4mJRrZEeNzQ02Orax6PSCiO/UVNTjdMpS6P5849biUwb3W4jPm/bvo0p2dJEIcIzExfjiasnWo13S3tH+7hOX80UUIM9bYFtS0G3UUp9DGjUWu9TSjnCnURrvRJYCbBs2TLtcATf3Ol0Euo7u2jcWwVHDzN//jwcjstTHZyUyNvnhJ4err32Gs6bE7ZuIqwiyqCkmEVnLcLhuCjm46iSJti3hxkzZ+BwBNavjLau9QhUV3LBBefjePfZYbdNeHxcuxrA9nE+pcbjPUpQmMdD+phQ4yguTKtsg107KJg6FYfjhlQHx/J7Fy4udh6qhcMHmDNnDg7HVUG3EeaUbzsFxcdZsGAhDsclqQ6OPQSJy4lMG7Oc68Dt5r03vJdpedkJOYdIH2HjogXp8OTyFtizi+nTpuNwXB/zcVLNTNNWNbDI7++FQK3JbW4APqGUqsDoGvwBpdRfYg6tEEIIkQZkBIEQQggRnJkCaiGwVCm1RCmVA9wGrArYZhXwOe9svtcBHVrrOq31D7XWC7XWi737bdJa/6eVFyAmiDhzczI5khDCjiZih0BJj4UQdtHlGqSh05XqYIgAEbv4aq3dSqlvAOuATOBJrfUxpdQd3u8fA9YA/wiUAr3AFxMXZCFiF8tkSUIIkSgTuagm6bEQItU+/Iet1HW4qLjno6kOivBjZgwqWus1GIVQ/88e8/u3Br4e4RhOwBl1CIUAy5sZDla1k52puOTMaSG3kUp+IUSiSNFMiPSiJ3R10/hV1yGtp3ZkqoAqRLr5p4e3AwStMZNKfSGESDzp6ivSgfQEEMJ6sa//IUQyST5GCCFs51RzDwcqo1tOTTL00elyDdLT7051MCxX1tTNoar2VAcjblLRIoT1pAU1gSTNiv8epCIfI7+bEEKY8/57nUDw3ijCGu+6az2TsjI48ctbUh0US33w91uA9Ik7SjruC2EZaUFNAEmi7Cuamk6p5BdiYhka0nT0DaY6GEKM0e8eSnUQhBDjyHgfEy0FVJFQ8RbyrGrNlC5lQohI7l1/gst/tp723oGEn8suWYd73irmtpU7Ux0MIYQQFkiX/K508U0Au2Q80kp6PG9CCBtbc6QOgLbeQabn5STlnKlO2h7bUpbiEAghxPi373QrZY09/Ps1i1IdlLQgLagJlCaVGPaQglK/jEUVwh76Bjy85+6NbDvZnOqgWE6SGTGe7a1o5e8HalIdjITwDGkGPdK1OtFKG7tZvHw1v11bnOqgxOVfHt3JD145nOpgpM2kXVJAFbZmVSF/Io49fXFvFcdrO1MdDCHiVtbUTW2Hi1+vKUp1UCyTJslMyrkGPew7Hd0swsI6//rYTr7zwsFUByMhbr5/K0vvfCvVwUh7z+6sAOARp/TmsNJ4n7RLCqhiQkmXvvlm/ODlw/zjindSHQwhhEiYn/z9KP/y6A4qW3pTHRSRZk42dqc6CEJMWFJAFbaWJj0VhBDjgCQ3488xby+RTpfMvixSazzOmvrc7tP8T5q2gE904zE++pMCqhgfktjwKYViIYQQQpgxnntm3fnaUV5L0zHEE9V4jo/+pIAqEsqywl4KCo1p8owLIUySR14IEa10mZRGCDuRAmoCTeQ0y6oaHCkkCiHS0QR+PQyTeyDSyXiflCZV0qXFT1hLCqgJII9aepjIFQxCiOSYiO+LiXjNQiRaa88Arx2oTnUwhLCEFFBFQti1y4uZcEllnhATUypSLXumlBOH1pqK5p5UB0OIsE639DA0FD61+Ppz+/mfFw5R1RrbjNaf/dNuPvB7Z0z7xsOu+cXxKl3upxRQRULZpbBn9y4k3f1u/veFg7T3DqQ6KEKIJEhWihRrZjWRrMw+xXusP++owHGvk4NV7VYERwjLnWzo4qbfOXl0S/h1Qhs6XQAMeIZiOs87J5spb5LKGmEPpgqoSqmblVInlFKlSqnlQb5XSqkV3u8PK6Wu8n6eq5Tao5Q6pJQ6ppT6mdUXYEfpUXeRXuxeo/TcrtO8eqBGFqoWIoXsXY0VvQ3HG7jxt5tZf6w+1UEBYFd5C4uXr6a2vQ+wx/3eV9kOGC1UqVbf4cIToZVMTDzV3udlz6nWFIfE/k7Ud8Vc2bSjrNnawKSI3RtkzIpYQFVKZQIPA7cAFwOfUkpdHLDZLcBS73+3A496P+8HPqC1vhy4ArhZKXWdNUG3vzSJI2kl2Q/uz944huN3m5N6TiGEADha2+H9f2eKQ2L4255KAAotzGiny2u2rqOP6+7eyP0bSlIdlIh6+t388NXDdMnas8ICVubLPnL/Vv7p4e0x7fvpx3dbFg4RPzMtqNcCpVrrcq31APA8cGvANrcCz2jDLmC6Umq+9+9u7zbZ3v+kelCY9so+Y8B/rF1WYmFlY+tT2yuoaLFfFzur7TvdyuNby1MdDCHEOCKZgRGNnf0AbClpimq/VNzDZ3ae5m97qnhUevyMoiVGx82dxLyesLcsE9ssAKr8/q4G3m1imwVAnbcFdh9wHvCw1jpoFYVS6naM1lfmzp2L0+kMGpju7u6Q39nFiWqjVrG+vh6nsy3FoUmN3l6jULZnTyHV+bEPdT7ZaHS7OllRjdMZ3Yvb3+EmNwCtra2j4k+wuFRXa2QUSkpKcLpOhT2u2fgYaZuyU8bY08rKSpzOhqj3j+fcVvjCWuN3WjpUmfBzhWL3dCEYq8M8HtLHWJzu9ADWPW8APYOayVmQ4Vd739tndKXbvXs3p6ckdoqG0nbjmro6OxP2m1VUGOnK6YoKnM5aU/vEGpbA/YL9Vg0Nxhi5lpYWAJoaG+O+9u5u4zfbt28vzSczo96/0RumouNFTGs/GVdY4lHe4YsPXSHvSbDPSyuM/EZNTXzvyGiUlXnj1emR9N6O7yj/48aTNkbaz+Mx8hfbtm0nP8f6Nv0j3vxLS0D+JdBwvmv3HqryM/jO5l4+uiSbDy3OjniOSPmiRKqu7h/+94OvbOLKOaGLJnvq3WQAy+aFL74k6re24hhm4mI84TjRaqQlHR0d4zo/YKaAGuxpC6wmCrmN1toDXKGUmg68ppS6VGt9dMzGWq8EVgIsW7ZMOxyOoIFxOp2E+s4uGvdWwdHDzJs3D4fj8lQHJyXy9jmhp4drrrmGpXMLYj/Q2tUA8d/LE42wr5CZM2ficFw7fNxgcWl92xGoruT888/Hcd3Zw58fr+3k4w9t450fvJ8zp08GTMTHMOfxV5JRBieKWbRoEQ6HXw96k/vHc25LJPNcdjp3rOIM8xPvlHPdObO4dMG0UZ+Ph/QRoNM1yNTcyJkmn6M1HbBjG/n5+TgcN4be0OR97egd5PKfr+drjnP5wc0XDn8+uXAz9PZy3bvfzeIzppgOXyymVrbBrh1MnToVh+OGhJzjoLsESk9y9uLFOBznB9/Ie898oo4/Ie55sLj4Wv0BqKvlYJORgZo9Zw4Ox1XRnS9A/qF3oKuTrvyzcDiWRr3/K3UHoL6Wiy6+CMcVC+IKSzxmVLXDzu0UTC3A4Xjv6C/DxOvybaeg+DgLFizE4bgk8QEFjulSOHmCs84+C06VhQxbRIlKu4McN6a00WT4srash8FB3vveG5ielxPdOUzQgfmXECbvdUJvD9e++1rOnZ1P+9rVPFc8wK++8KHQB/e/xhS9S52dx6CyAoBLLrkUxyXzQm77heVGGCvu+YfgG8RyDX5pYFzXbvLcYeOiBb9B3qlW2LOTadOm4XC8J+bjpJqZKuJqYJHf3wuBwKrYiNtordsBJ3BztIEU40+6DNL299zu03iGNBuLGy0/ts3ncBI288vVRXzswW2pDkZM9p1u5bK71vP28bE9BSKx6jFp9c6WveZIXULPk2q2T1c0PLTppCUzDd+7voQdpdFPcpJ+b6rksX38SjMSV0U0xvvzaaaAWggsVUotUUrlALcBqwK2WQV8zjub73VAh9a6Tik129tyilJqMvAPQLF1wbe38R45RPKlY8E+mC8+tYeHN5emOhgT1oB7KOKaeolywDtr6s6ylpScP5yJ8fTZR3VbL/euL+Erf94b8zFaevr9/h39Ml3ymo5eur6mPEOa+9afoKMvusmf7LJKQJr+LGKCilhA1Vq7gW8A64Ai4EWt9TGl1B1KqTu8m60ByoFS4HHga97P5wOblVKHMQq6b2ut37T4GmxHEgnrTZR7muwXnfNEIx+6bwsD7uROTLD5RBO/W3ciqecUhkHPEOf/+C1+uboo1UGJWjqmA4l84u1ekPDVkfQNeuI+RrqwSVknrPEQxlisO1bPik2l/PLN4zHtr9IyhUquNI1aKWH39D8SU7NAaK3XaK3P11qfq7X+lfezx7TWj3n/rbXWX/d+/y6t9V7v54e11ldqrS/TWl+qtf554i5F2InVBa10T7SSlZCcau7B8bvNNHUZrQ53vnaUk43dNHa5khMAkXKD3lkSfUt+iOT5zdpifv6Gkfn90atHUhya1POlezL76fg03jPAgXxpoyvGCttHnKV0ytI7KbM1yhmwhb0ldppCMeHZ7QUWbcG5b8DDxx/cxiH/hZ/HcfXxk9tOUdHSy1tHg4+9C6etZ4Cb79/KqebUL2hvpUHPEI84S3HF0Yoj0kOin+xHnWU8ud2YGby4vivBZ5sYbPaKiZtV78w1R+qMycUSyO6vwjcP1/KlpwuTdr4/bi0froASyVfX0ZfqIAgLSQFViDCO1HRwpKaDX65O7EsnWS/6eFoq1h2rp7i+i8fSbO27Fwqr+O3aEzwyAcfEvnm4lm/8dX+qg5F0kSqq0qXQY/cChE884Yy3QJcuv3Wgrz23P2ETqdmt4jmUb/z1AJsSMKlhOL0DbkuO09jpor4jup5N8TzuH13xzpjPjtZ0sHj56pgmH4vVOIlaIgmkgCrGBasSLUsmIYpwjO5+N3/eUWGbiRP8+YIUy13wrRc5FMN17S5v4St/LkzZxDzh9A0YLac9AxOvBfUbfz3Am4ejb01Pd/aLpfEJ9byvP1af1HAEsiJdT1Qyu+dUK1/5cyEeG6ZZIrHsUEi69tcbue7ujTHtu3JLOYuXr468oZ9jtZ1jPvNNZJfMQr48bdaxYRY0KlJATYB444RnSFPaKN2/xqufvn6Mn646xvZS87OUJqtGejhuxnBC3y6x5Nf+6y/72FDUGPXsiFWtvSGXArHKeGkNENZlXkJVVKUiKiTjnKHuW7p11w/0f68fjTqj7vO154w0qzWGmYHF+BbseVl7tJ6KFD8vZtO/F/ZWJTQcwt7SJU8jBdQEijWS/H79Cf7hvq2UNXVbGyBhjQjVUu3eNRbjGdNY095nWVchf1a0oCarZXhrSRM3/nYzX3suOV1QY7msjr7BcTfB1HisVU2XF26y2eG+LV6+mvveLgGSH/ee2Xk64jZ2exz871Ftgt4DInp3/GUfH/i9M+h3dlkezh6hEMIaUkC1UGOny5KXyd7TbQDDM62OZ1ZlSOyQiUjGO8h3v264ZxOf+9OeRJwBiK2bboY3tfCY3Lepq5/u/tifh889mYjrNzR0uvj6X/cPd++N1fV3b+TaX8XWDUvYR017H/1uT9h0prvfPe4qIxLlvrdLomqZXLHxZNjvx0ulSXVbL4uXr8Z5wtouj/dvKGGjXzfKqtZeNhU38J57NvEff9xl6blEZKFe9dLbO7Tqtt7hWZBFao2X9DQSKaBa6Npfb+RfHt2Z6mDYgl1qFK2SigfeV1FhpbYeo4vtXauORb2vb403s/fiml9t4IMhapxT7Tdri1l9uI7VEboPH6hsY/Hy1SG73PeOw3Gr4/HRTOTz5xr0cMM9m/j+S4eHPwt2i255YGtCKiMSmbQk6r5FKnCGYse4F02QDlS2A/DSvmpLw3D/hpPD9/RITQc3/nYzX3p67/DfgWra+2RsrLCNjr5B3vubzfzk70cBaOxyjepB1tPvZvHy1bxs8XNjBSlU25cUUC1WVDcy0DxdajHswA75mmT8nonOwPnWaIsnbxNNGBs6bdoLwOT1v3HIKMA6T4ysr/b28Qae3VmRgECJSBLxeAx4Myib/VqwgkWPqtbgSxjUttt/aYNQ9y3lBUaLA5CIJNpua7RWtfZywz2beGCDt9t0isOTTmJ9xydr2EukpyXRoQiV1vV4e0pt8a5Deu2vNvLlP48s7+Nb/uVRp/1myn9kc3qtSuAv5el7nKSAmgDjPE7YUioeNP/EPprz2znDYOV79GhNBycbxvdkXv4/q9mM6Fef2ctPXo++BVpYo7vfPZwhilWwDKUm+rR7b0Ur77lnE6/YsGXAjFRXolqRrFv9bvjKnwv57J92W3tQCzV0Gt3Mt5eZn4QvUexWeI/WgxtPcqBybE+l8Z6xT4QdZc28555NvHGo1tT20UwSmUoPboqtN4hIPCmgioSwukYx1RkpSO4Y1IQdP0SG4r2/2cy+KLsUf+zBbXzoD1utCFZKpVt39Ejs8CzF49KfruOSn65LdTAAOOGtoElEd3wrjfOfPKk2FDXyzsmRdR+VzauczYauyzXIT/5+NK7J+0bOae97Ytbv3y7hnx/ZMebz8Z5GJsJx7zI0vm7u6cKdxl3lx3s8lgKqSCir8v7xPmex7B9r0GPZL1kv/HAJ1nO7I894GekYkVS29nKsduyYKjuwS2K++nAdF/7kLUsykuGkulz+dlE9bm/32u5+t6mxQFYvM1PR0stld6234Pg2iTwBUv0bJ9umooaY9zXz/NvzVzbnoU2lPLvrNH/ZZS6dD8dsy2lFc8+oYU8AV//ibZ61IAyJMOC293jE8Rz/zLLLe9gq647Vc/6dbyV1Nu50SfelgCrGhTR53kJKVlepVKf9tz68nY+u2JbiUAR03w67XfLv2N1vFeEaHEr4LN6pzghUtfbx4CZjTNKlP10Xdtbq5PReGLkhfQOe4cJzOFZULEV7hC6X+bWEI/3Gyc7IBIbH6ij494Pmuh/6i3QPxns3Vh/fpEpWPveR4r/jXie3PPDOqM9aegaGJ9Oxmx+8YkyUFs9zkS6ty7EIFbeS9a6x472/d90JBjxDVLfZf64Cu5ECqhgX4s1IxZts+SewiUxrx0PN13gIo1npdC1m2emaa/wm3dhZbv2YpW0nm8e0igy4h3j/vc6w+/3jineStv5uNEobu3jXXet5obAyqv1s9JMHlaxJZnx6B9xc9Yu3eft4Q5jM89i7Zvf7mGzpUni3UirvSaLi53j5le0cH1NdITweSQF1gmjq6h/T1UbEzz2kcfxuMxuOx9a1rN89uhtnwhMxSSSjYscaWWHO/so2/vNPu/nduuJRn5tdy3R9FM90sjIfJxu6Adhc3BRhS3sKrByJ9+k6Ut1BW6/5FmWfkw3dtPYM8NVn9g53LQ8MW3O30YNh/+l2U8c8UNkWVeu21XoH3Ny77oTly2Z4hjT3vV1Ch999ngjpYjSVJuk6j0GwywpXCAx1GwI/L67vTHgPIbtI06iRFFJATQCr8ipWZno++PuxXW1CeXDjSb79/AFLzjuea41CzeLrn9509GsqWnr5yesjXZaCXfKPXjvC3W8VjfqsuL6TC368llWHapM3BjWNS6inmntMb5vsFptY/PjvR/n1mqLIG44zu0+1Rr1PLD9Xa/cAAOVN5uOF5CXiU9vex+Nby4f/NvucxfI0droG+fhD2+IeNxhp/4NV7UE/9wxpHthwko6+QXoH3PzzIzv477+krtX9xb3VPLS5lJf2hp9ROtg7YGhI8+DGk7T3Doz5bkNRAys2nuRnb4yduXwoTSeYGY/rW4P19c9WvSYDj3Pz/e/g+N3miPu5PUNxvas9Qzpt42g44yB7Y4oUUBMo1pqTRGSSOl3mB2j//u0SXo9hLI8/q2sUk/nAJeL+/3V3JX/cUj7qs6M1Rov2t/5mTWVAstg18YvUbTMYpUaeU7sVWreUNLFya3nkDaOU6st8O8beBpD4AmQqbk2oc5Y0dAUtMFh5Dqt95c97+dWaIqpae01tH89rYk959BUdsfAv0Pnfx7ePN/CHDSX88s3jDLqNbw5Xt49sq3VK0hSzLaj+4XuntJnfv13Cj4OMDXV7jG1cfr19fPfk8XdOxRtcWwrf9XviSUS62+NXCRAsHWjtGeC8O9/iiTji2CU/XYsjhnyBsAdTBVSl1M1KqRNKqVKl1PIg3yul1Arv94eVUld5P1+klNqslCpSSh1TSn3b6gtIR750UboG2FOk91YsP1vSJkkyeZoB9xD//Mh29vi1dsUaH4vrO2mPoRtesthtkqRYVLb0DndJNCua37Ouo48P/2EL9R3musfaQbS/XM+Ah9Mt5gpWPolMoz/8h6184qHtiTtBAnR716cdSkLu3up7H22QfQXB3hCzbS/54Rpufdj63y/eO+vrrfPe32zm6l9uAGDQ24rcF6blUAps5qRD9+ea9r6Up/W+87+yP3iPgFWHajlRH34ddtfgEJUmK8vsJp6eIcOV7haFJVUiFlCVUpnAw8AtwMXAp5RSFwdsdguw1Pvf7cCj3s/dwHe11hcB1wFfD7Jv2pIE3TrJLKwH+9m0To8XT7iMo//1Vbb2cKCynR++enj4s1jj8833m+taLmL3vt9tZpk3sxmrNUfqQr7w/7q7kpKGbl7cWxXXOeIVGAVPNffQGWLc31+9yyZtLG4c9fl4Gi9mVebKLlds6QyyKbioUKc8HmJ+h8PVHXS5Bilv6h7+bHtpM4uXr+ZoTWqX26pp76O1x2ih/8oze0NuF+w+2/Fd2NPv5p8f2U5JQ/hCi1kTOft2wz2buO7ujVHvF3nW8ODxJpZ04Vt/O8BH7h+9Drsd46VPtJXddwXpUm+Wfe9CdMy0oF4LlGqty7XWA8DzwK0B29wKPKMNu4DpSqn5Wus6rfV+AK11F1AELLAw/LZkVeSQAm5qKYK/nI/XdlredcvOCWugcZS/DynYr/fnnadxDXoobRzJTI6n3yVagVH4a8/tH/PCD/TAxpMJDFFooeLc++918q+P7gj63Um/3zHR4kkODlW1s3j5ausCYwPR3o/x/q771OO7wn73gd9vGf7b18V9TwxjsSF9Mp9W21nWwoHKdn7zVnHkjYMILNhG847333b1kTraeqzpmh8o2b/99rJmwMp5VcwcKZ7VqO2XkMSah/AfLhAruw1bipaZAuoCwL/avJqxhcyI2yilFgNXArujDmWaK2/qHtWcHyw6v7S3ipYou/Cl0nh/MHxGLy9j/PF8YVXEiSgSZc2RuqCTVVjBjol7si1/5TD/cN+WUTNWpptYKhh8z4HHhhNOlDR08+yu0zy0aXThOU2SoISKlHnSWvO3PZV09MX2PERsUYnpqL59ze/9+sGaOM4UQYRr9M01YBd2SefrOuyzLmR1Wy8f/kP4yrlo+C+fZaVEDC8Kx3kiutnCA98tvnxgKmbsDddd3S7qOvpiTlvD2VJi/G72eNJjl2Vim2BxPvC6w26jlMoHXgG+o7UOmlorpW7H6B7M3LlzcTqdQQPT3d0d8ju7KC42avDq6+txOtvCbtveP8R3Nvfx/kVZfP6SScZn3sTt4MGD9Fdl0tQ7xPe39nHBjAx++O7JYY+ntaauR3NmfvC6h2D3bl+Dm/L2If7tgpyI25rV22t0TSssLKQmRFiiUVcX+V6Gc7jJGBvV2to66rqCXWNtrZGYlpSU0FNrhL2zs4PaoZEa1g17jzOnp4yenl5A0d/fP3ys5hZj7MSRo0fGHNv/fMU1IwlTaVkZAFVVVTidjYG7De/3tbXGjKQ3FZh/cfifs8MvU+B0OnG5RsaZNNQ3DG9b221UmPT09g5/drzWuIcNDQ0h72G0ceaOx9Zx24WToroGK7ZraDCuu6iomN7BkeTMWVQHwKat7zAjN4PqaiMulJWV4fSMXnsy8LpPtHqYlAmLp2WaCkOo8Pr/JqG2CfWdmfTR5Z3QxTM08gKP9BuePj0Q9vtYBaaRoY59utMIa3d3d9BtfxIwuUtg3HY6nZS2G8eYMcl8Ni5YePw/O1FlPMN19XU4neZbxCIdN9TnR+uNZ3DtsXrWbdzMpMzw11JRMeD9fwVO59iJ70pPjc0c+Z+vvN3Dz3e5eGX7cb5xZW7YbZ1OJ319RvqyZ89u8oZ6cW4Z/X1j4+i4vb+yHWBU+mnWkabgE/8FO863nz9IbkvJ8P0q7xiJ+80txvq7RUVFTO8Y20OgqrIKp9No9TxWZ5yzqbGR48r4vRubRtJrt9sd8XesqTHSlNLSUpzu06EuL+QxTrYZYe/o6DDidcXo37Dk5EmcAxVjzjuSlpWzyS8t8w9vU0vLmPAf88a5xqam4e/Ky8e2EEaTTgF84v7NYb+P1pFGI5y+3zPw/OHSxg1bd47dZ8tIq7f/fite2sBls0dnnd3u0XFx3769NJ80/x4Ix+l0Dsf1wPxLoJ7e4EMC4r2/1VVV9DYbeaHq6mqcztH5j5Y+I78Q+BxvdjrJUIovePMtfX75Cf9w+Z4JgGPHjpHbfIJKb5rf09NjOm4VV488C599+O2g20RiRVz0P4aRR4TCwr3UFxj30BcXv7C2h6k5ihUfyBtzjK6u2Ms6D24y7ndnR6el7+tkM1NArQYW+f29EAh804XcRimVjVE4fU5r/Wqok2itVwIrAZYtW6YdDkfQ7ZxOJ6G+S7m1RjetCy+8EI4eZt68eTgcl4fdpbSxCzZvpdI1afi6Hj2xE9paufLKK7junFmUNXXD1i0MZE6OeO0v76vmR+sO8cyXruV9588eE7Zg+3/B273s4f/6cMRtzcrb54SeHq699hrOm1MAGIXn0y29LD5jivkDecMyf37kexmOPtEI+wqZNXMmDse1Ya9xfdsRqKpk6fnnc8HcAtizk2nTpnHmvAKoMl7sCxcuwuG4mFfe2gT0MWnSyO/3TEUhNDXyrkvfBftHj+3xP1/Lvmo4cgiA8849F04UsXCRcVxf+MbsF81vE2Tb+49th4724c9zd20Cl5Gp9I+vpY1dsG0reXl5w/t3HKyBwweZM2cuDseVw8efff6VsHbb2HCtjdxtcW2Fm8fu+EjY8I85bqzb+Xm17gDU1bLycD/zp41kvCdNyoH+fq6//j3Mm5bLlq5jcLqCc889F8eN54w6n8PhGPVv33NUcc9HIwcgzO+Yu3sT9I1UJAS9psD9vX/n5+dHvAc9/W7YsI6szEwGPJ6g1xJob/8JKC8NHZ4IWrr72VnewscuO3PU7zUc5yLE62O1HbBjG/n5+dDVOSbMgQLjtv/vs335B2DLJlPhDhaf/T+r31MJx44wf958HI7LIh8wSNwJeq4Q5+s7UgcHjaVM3nX1dZw5PXyF5YHBEig7yeLFi3E4zh/z/f7BEjgxulDmf77c8hbYtYuMydNwOK4Pex0Oh4PJezZDXy/XXvtuKo4W8r733QTr1gx//1q98dwFypmUE3288qbpgUKlQTfeeCN5OUaWZ2Z1O+w0JjA6Y9YsaGrkoosuwnHlgjG/y6KzFuFwXARA56FaOHSASQUzePSQ0f1xzpw5UG9UbGVlZY15JgPD5ew00pTzzjsPx3uXBN02FIfDQX5FK+w23kkOx3so33YKio8Pb7P0vPNw3LBkTPzZ1n0cKk5x7rnncNN7zxn1u/i2PdzkGfM7+OLc7DNm43BcDUARZVAyuiutmXRKaw1rjfN2DCh87RhW5Oncxxtg/97h3zMwbEHzjt7wXX31Mti5bdRXN910E6x/ayR83m3v29dPxT3/MGrbLOc68CukXn31Mi5dMM102Ltcg7x9vIFPXrVwTNgcDsdw/mWmL/8SQt5eJ/SOXVbL7PszlEWLFjFvWi4UF7Fw4UIcjktGfV/b3gdbNo3kg7zHfN/7biIrM2P477wpU3A4bhoTL3zvWYBLLrkUx6XzOF7bCTveYcqUKTgc7wsZXv9rayisBF/DQO5UoG3UNm09AygF0/P8GmMipcPRCJJmTzmwFbq7WLZsGRfNnwr4lWPWrqZzQAdNs4x3+Y1xhaNg6lQcjhtiO4YNmGnaKgSWKqWWKKVygNuAVQHbrAI+553N9zqgQ2tdp4wR0X8CirTW91ka8jRmpnk6FN/EC6VJHH9l1nO7K3Hc62Tf6dhbQpMh1P3271IWdrFq7/+7+qPvupHoMSbRdvnwD0+o7nobi8a2+I4ndUFmKwz8fdt7B+l0Ddq+6++BRjeNXfabafe/nt3HN/56IOawxdJdN9QwAxnDN2JFkDHF/X7LiUR7r5R3h9cO1CR8mEe0k12NGq4RQ9D2nW4dXhLsnZPN0R8gQaJP02N7AuzSNThZrIy+O8taOBlm8qblrxzhf188xJeeLuS1A4kZPrSjtJkHNiR2DoHAexbbLUxcPLvyF29zxc/fjrwh8OnHd/HJR+KfhdtMMnWsNrUTptlVxAKq1toNfANYhzHJ0Yta62NKqTuUUnd4N1sDlAOlwOPA17yf3wB8FviAUuqg979/tPoi0kGomWNH/z3+XhD+Qd5faRRMTzWPreGLxKrJamK9g7Hc+ie3VcR4tgSyIA5FmS8cVwLjWUu30ZXtoc2lXHbXei7/+fpUBMu0B/b3c9vK0BO2xCrezKlvTJZvTcVY2TXqWZV57+53s3j56sSOmYzg9+tLxnxm9vp8ycsDG09S0mZ+mQSrX21aa8vv4d8PRF4bvNPlNnqfjCO7ysd2iU2UUTPD2vVhjkGkypJPPb6LD4UZ41rfaVTcbSpu5H9eOGRp2Hw+/cRu/rBh7LMdrae2V5jeNnDVgMbOxFaeWjmp4Y6yluEhCIlW1Tp2zHKoKDXgHuLvSagAtANTgwO11mu01udrrc/VWv/K+9ljWuvHvP/WWuuve79/l9Z6r/fzbVprpbW+TGt9hfe/NYm7nPQQGC/H07IIPnYLc6yh8b8M/0xaYEJo2czNFh0nnuPXtPdx16rjIb9//WAtR6pHavzSKZ0MzIibXfTeTipNruUZ6+/25LbYF06PVzRBTka0jDWZ2+CdyTVQdZvx2z2yuSzWIAHw2JYyCitimyXWf0KTeNLxPvfYYq2ly8yE+W5baTPffv6gdSeLwj/cZ91kO5H0uz28daQuqn0Cf5VEVGiF0tDpN1lOmLjw9vGGkMtHJUsiWoy7EnRNWmt++eZxKlqir/w3dXzMpQWBmwQ+752u4OPGg59zZOdjtR0sXr7aGO42wT206STfeeEg647VR9x2vGfN4p+9Rowx3iNForX1DDAUZjbQ8qZuFi9fTXH9yHxavsRKa81v1hbH1Aprln/IzGaodIh/m+WraWxIcA2jGf/7wkG2lYbvwvbxh7aF/d7u7PyMhlqg2+ws3hFneoyzNuXnb4auvIgk1uELNqvvilu4dSch/szxPW8V82+PjZ34xWp2rcXv7AufEY4qPtnoEl2DHrr6R67tN2+dYG/AkJlQwY31GQq2X7THau0ZML2UUnVbL199Zi/fSVEFg088UftjD27DHaRy87svJqZ1tKyphye2nUqrymJ/fz9g9IYIVbHnY+du6PEsvfU/LxzkP58wFkDxVfK02Xy4kRWkgJoAZd7xn+YScfMpfaofvcYuF4uXr2bf6ehq5jt6B0dlZH61pogHN5WG3P6to0bN0OsHx3apem53JY86y/i3x4KvfWgl/19GKfNjUP33MWt7mdHN6s3D0dWGRytSIvnZP+1mt9/6fGVNPcMFplDXY0W3IbuIpovQXhMtVP1uDz9741hUU8kPBMnYbC9t5upfboj4gjbDTpmYRAbF/zoT1XJhNavX3XW5xy618N0XD/HVCAXkQGbjTKyFoFCH/+vuSq78+fqgBeFo486x2uDLvST7cYhneZULf7KWLz41MjFUNMcaPQY3+quOa63fYGs6hogrrkEjzp6OsTUwWDC7+8233JkRqhLRX0//2GevsjV475aDVe1hjxX5sbJRou4nnjgzKr/lPU406Utgxc149tqBmuFGA989MLekrD3jhVlSQI3TjtJmXtk3elD7H7eWA0bciCdj5IuIdmk82F1uZMifjGIMQmVrL5f/fD0fvG/LqER4Y3FsGW3fpB7N3eEXwl57tJ7Lf7Z++GUXi2ge7Xh/I89QbF1JB9xDUV1jpIJ1sMk/2nsTs+h4MJ4hzTW/2mDZRBGNnS4ORXj5B2Nm3c9/NdFC9dr+Gp7aXsG9604E/T5Yl6Vg7xTfs7Ov0rqXbipaJZN5Sv/xT++6a2TscLKue9/ptqjXrra6BeCPW4x3kX9vlFf2V/N2HBUd4Sb0GL1utHmh8lE/eu2IZS0Fz+8ZWVolqnybxfFl3bH4K5l8YonL0VeCmNv+r7srI28Uo/oOF0vvXDM8CWS0TjWFL+yWhJnAKJh/tbiC3I7rS/tTmKvUGDtJkjXX5TuK1RV48fj28wf43kvmW8Stuhe+rtb+xztc3c7T28cOvbF3rIpMCqhx+vQTu/luiEh6oLKNd921nvUm+ooHY+fKj363J2gXlkCnvePhypt6KPd7SYRKZr70dCG/C5GZB2g0ueDzL1cfp6Nv0PQC0R/+w5bhF6x/2HwTSETzW/j2D7bPjtLm4Vky+03UwkZy4283ceFP1prePtx1RMrsJCM+9g16aOrq58evHY28sQkf+P0Wbn147Ex8ZlsQ1sfZYunxnscdIgPi67rkz47dJhMVJPtkN+IT7P78y6M7TFVimNU7EHsrUKQur9H46IqR7v3+1x2sgBIYl0OnMdFHMMvG/UeK3AmK+/WdLu7fUBLX826nDPuPXhu77nc4oUJeXG8UFv3Hq24+0cigR/OXXafHbP/09lMU1Y1UwMRyR14orIpq+8PVowvKqU6zU/3KCPVcf+rx3Uk5jxnlQSqD//eFgzEf7/WDtby8LzEzLsPItQZWXgRrQf3EQ9u5643Yh97YlRRQE6jcO05yR5k1s+RFkwg9uqVseObMYA9mfAGBC368lk89HnpyhVgT7E3FwZcsifZFHO105yUN3WNesBXNPdz3dvjuq9Fe5aef2M0vvGP4on2hBzNq0ok0YPWLPtauXda3stmv0OkvWdPc2/suRC9SuhTtWPlQx9tf2cbF/7eOjSHSx9cOVEdcvuup7aeinmRkODNkYtsfvXYkYa3TwZKFqOOSX9jsMKZ55dZy7t9wkqK6OGb9jeE67DJOL1QontlpFEK7+92jljwC2BNkWMVdbxznlgfesTp4o+LcAQt7rpixMyDPmOpfLNL5Q722zfZeipSOmh5iEOY4/xFkMrBXg1QQR8vsLNix5mE3B6T5ww0gIfbzXwov1RUX8ZIC6jgQy8u0qaufG+7ZxGNbyvjA77dYHyigsCJyoh0y7FFeVIZFMTXSWY9Ud/CaN9HqjGLcYNBzhThZsDVqE52QHKhs4/WDNWHPY4fEbLgrT8DNC9Z9JaHhsOhexNLCkYoZsP1bxexEa80jzlLTPSHMSESrk9WPTmD82+8tfO7wm7ysqK5zODPyPy8c4l8eDd3tcEhrfvbGcf4pSG+CcGxQjku4RCR7a6KYWTdwKY5oBPt9Qh1u9Iz00Uv066G+w8Wp5h5yMkde9hf8eO2oISzlTT0pmcn1QJKWG/Fpi2JYTWOna7gxJNWseHUFr4yKP/b1WjwO2ee2lbtMdT2P5RrcniHcAcO/RlpQgx/vZ28ei/o8diUF1CRxe4aG1wENKsaxO5Hc81axhUdLpdhTPq216Zv68Ye20Ttgfkynf6h2lDXjPNEY28s/gW//qtZe/vmRHXz7+YOjwrb6cN1wK7sZiSw37SxrGVVTGHiqZHVfifUaQ43TNftSKq7vHF5WId777BnS3Hz/1rjGX5uhtY66xTvSpT22ZfTyKoerO/jt2hP8j7crln9XvkhsPqwrbl/+817+7Y/RjYXriTGTtu90m6n4FL4CTIf8TZJROfbq/hrqO0LPkm5ly+LXnttvettv/u1AzOdJRmVWok4ReNjr7t7I++91kpU5+pv+wdEZ9M6+QbaXNvPRFe8kdBmwZLU0B4uT0cSfa3+9kf96dp+VQUq6wHuttQ56/yPF91T1DqgKMfkVxPeMnnfnW/zf66MLnL4K1lBpZqLf+8kkBdQk+cOGEj75yI6IXR5q2vvY451FdcyaUhY8fJ9/ck/M+1r5okpWzfyQ1nzr+YN88enCyBtHoaatj7/tqRz1i3z68d184anI50l2S+WNv90c9POv/3X0SzDU7/u5OOKMWZ96fBdffLowaffG6tP88yOxT5qxqbiBm+9/h9v+aHRBsuIeFNd3caI+jq6DAYIF6aMrtnHenW/FfRx/gRVqvrG7PVFUGkU6W18CXuCpamksaUhea9LDm8fOvB7u9wyMx8/uOs0bh8bOzB7pOADP7amMarzY4WCzxgKvWjT52ihxPq/xLJkW1Wo5cYYzWWlzVoTuUhr4f68c5lhtZ9gKh3jFe72fe2oPa4+Gn3+ko3eQWx6IvG7uMzsq4gtMCgUWmILNu+CvZ8DDkh+u4VHnSGVlvL9FPDNnmxFqjol4+PJjgfOu+D5//J3yiBXEdunOHyspoCZJsXecSaiuauXNPWw43jCq++fw1NoWZn+2lDTR2OmKK2GPJtJb1lUywi1YvHz1qMK3r2XwsS1lITNFYMzeF6o7lv85A8+//ngDP3z1CL4GiXhrspOVkMQyxtM3aUVSMigj0/XFbPHy1Xw6xPjo/3xiN4uXr2a1xcv5hMpkmnl2v/S0seTHcW/roFXjcBP9cx2v68QzpDlaYyyivqMs9Nq58aZgVhYAEzGhiRVHvG/9icSN4YxxP//w9JmoJPDf/s3ywVHnjWdCkZ/8/ajp8WJHqjuGZ9IPFLT7oN9ndp9NNVA0XYn9JX04R4TzfeGpkXd3RsAz8OLeKsuf/yMBkxzFK9j7/1BVO3f8JXzL5o9fPxpxlurypm42n2iKK3zxMnv/g8WrwIrScPkxgLYeozdSsCX3AuOGWdffvSm2HUMw23L/4T9sMdnrZ+yNC9lV3/v/6ra+UUsCpiMpoMbgb3sq+dXq+Locuj1D/GnbqVERPdji7RXNPcOTEVn1Urn21xu57u6N1hwshFSMo9tSMjYRD5yAIvAWfvgPW0N2pxm9blzwQrJvE/+uspGu/HRLL//xx9Gze/rni8J1FxnePkhG6u63ioLOcphOfreumD+bqE0ONTHZttLQhahE8FU8dLrcpn5XSM2zEw/fhB6bioJP4AOpmeQjVHpZ1WZhbbqFP9UKv7Wh7VhMijZM5R3mu19aWWnQ0Bm68jVYujlSJ6ZiGw+awsc1WMtNR98gTwUZs2/nZMUZpgD2qzVFYz6LJ7r8ZddpPv7QtqD5hVHniP0UpplZhtD/N94aIcyJ9MvVY38Hn3jjVqQ15n3pQ6TTJGtW618F3ItQccW/h4t1jTUj1xisoOw/ZNUO84rEQwqoMfjhq0d4/J3RL4B3TkaXcDxfWMUv3jzOyhA1vQCrj9TiuNcZ1TjBWNcJCyZUjbkViUC0CVqsZzRT658ooRKH+k7XmJqvM/Jzhv8dqkuuv/YgEzj9cUs5P/57+OVZ4lo4O4kZnC5X8LFyD28u46erEj8JwDf+uj9oZjZWqw/Xmfpdo7WlpClkDW2y86Nm7lYywxQqPPEMcwAobexi8fLVbPNbM9iqjEBzlOummhXrfY+0FFaqWvtj7b4e7JH2vwYbl+FMe2DjSX5mwZj9lVvLuGvVMUuWQ4tGvDEq1P59bmOZmiJv3Kk0WWGYSBl2rjXwE+qevlBYSXvvQPix5wF/bz4xtiJz1AReATucbOy2vLIg2vRjwD00anKuAzGsrR6euXhwqKqdpyNU0K/1W9byWK35ORvsSAqoFvHPrJjhm6wiXA3ai4Whu0T1DXhYvHz1mNakvRGWGojG7c8aLbqLl6/mvvUja5OujrFbkb+olwqPMR0/EeUC3OGYXepguEY+ijC/b+lsAD551YKowwXmB8bbfUyCXcK3v7I9qpkUQwmszNlzqtXS7mWff3JPyCUWknUnfS0chRWtIcf++US/7I894oM/X+VSsHSwodPFV5/ZG/PyRp+2eN1An1jv4refP2hlMEJq9+vm+Mq+6qDjXX3ePFzLR+7fytqj0b+HwqUvwadlsa94uiObudJfrynm6R0VfGt4EifN/3v5MIUVrabe329HWEM61nd6qN5Mkfzl+AA//vvRkWVB/F7owWYGTsbapmYu42QSx5lHo6Shi//3ypFRaYSZ3yVYtPXfrS5g+JlnSHPQWyC0qnfRR+7fGrSgHMx3XzzEPz+ynQ/+fguNXd6wBcQNM3HFiuhkNszpIivVAUgXoca8QPDIG0udrf9hWnqMmvaVW8v5/HsWmz5GOIuXr+aRz1w1/Pc7foXuFZtKefjTVwXbzRJNXf3kZqdXfUksSWl2iAkifvz3I3zo4nkh9ws3/s8q0VbCxMJsIv715/ZTVNfJLL+WZzvaVDw6k/bvAV27zdhf2cbv1o1UEEVbwB1wD/H9lw/xvQ9fwKKZecNpTzSzVZv5XQ5Xd/CJh7ZTcc9Hae0Z4KpfvM0Dt13B9tLm4UzHPyZgvUI7uf2ZvRyq7uD1g2PHS5Y2WldZlixWLu8TyaBniOzMDL770iEAMoMMOGvvHRhu/YhlgqhUDzF9aW+VZceKdn3zeDPI3f1uNhQ18sr+ar7/kQtCbrdyaxnzpk32K9hGFx4z781g+z5fWBVy/28/fwCXy0jvAmcFBmjpGVsZ+dfdlcP/3ns6MWP9egYiV2RZMcFPInqSDXhb1iOlEWZ+T/9yZ7AWQl9rt5UNzmVBlvsL5pX9Iw1F7b2DzCnIjel8Jxu7+PhD23jlv98T5NvYHs5kps+pIAXUJAlXbxvXcS2u5ftjwDIPL1r4Qg3nml9tYHbBpJDfm+1W/Lkn94Rc8mM8CFWz/Zddlfxl18gL887XjvBWhBkCgx4/jujyUhyTnFjN13oVuP6bld1yrbAhzLjMUAKf6X9/bHSh9uMPRbdm6fbSZl4/WEt77yB//tK1UYcnFr7J3n782lG6/FoTA8fMvbSvOkJLtXdK/RjCkIoWkEPeyoO69rHjIL/1t4MxneOtI3XDLYy1CZy11F9hRSvfffFQxO0i3eHAcfzhBI7/DLZEWqix5WbDFHHWywRHme+/fNiyY7mCFLQSaVe5UUgLN2Ppy/uq+fWa+Ja2i+YnqGob6aa7oSh0i22wdUIjneduv/i35kj071ozfPc0nHDjc836zBPBJw2Mh697sv9za7aXWSCzLaORtoqmD0Qs4809Q5q1R+siTmyltR5zTf/rTU9XH67jfQWRz3XSRAH6f188xCevWhj5YONUejVZ2VSwh8+uIw8OBbTO/CDGF+rwoPYQFxrsnoSrDVLKXOFja0kTh8O0MMV635WKvvbucBTjgZe/eiSqY0cqnB4PMfYg3B20Q5yMN38Y6jcyMxkFWHMP3jnZNDwToZ347s3Xo1hjzyfwxd8YZiIaf5NzMiNuE74Qb25yjNB7JpZ/y3ai/Pdz+3koTJfXHSYm/oq2sP5vj+2MaYxePM+PmSB+7bn9YSdBMnuO4W6egd+HiTV2Gy44EOUaoMkI//deilyp4RNPeHz7BusSHykaRTOnh1mJrAwLnNhv8fLVpifc89lf2W5hiAy+zl7F9V1xp4MRC54JuL2hjhmsu7fPLQ+8wx1/2T8mbQw81s4wFWnuoWDP7dg7MJDksd92JAXUJNBaj4l+sXXxTW3rkJWLYkc9BhXwWHD9wbqNmRHq1Hvqx3bR8Z0hlT9Xb4iuQ+HikL3aHmPjf3kPbjw5/O9ourP6xDre5bN/2sOVv3jb3DmiPPYrUbRit/cO8KPXjowZnxxp9kozrv11+FnAfbcuK9Z1ASyQ6OdPaz28Rt2YheaT+DR9+onI41aHlyzzi9OVLdZPEhO2AsyiqNDSbVT+xPI+9LWaPLNzZLbzSIfxfR1yu3GYcHaEaQHq6BuMOKdBtL9lpOVWovFCYSXVVs7EPQ4lY0iPv2A/t3+vtte8S0AFS/c6g0zo6K+9dyBiZUuH7xhBIl6/2+OdqElH1dId6rGttaACo71vkFWh1nzWvv+PDUE8ayJD8HHpqS43xEO6+I4j/nHPisHiHb2DTMvLNr99hIQmkZSyZgLxaGbNM/NYl7albpbgcEKFvawptgQwkYlcS4JmLv392yUJOa6VngmyLFC4l3Xgot3h3Pd2CYerO0y3doYT688fS5fUS/5vLcd+frP3r9ieerMt5rFIxvve6ufNN3mefwbma3+1rtBgRqRLMps064D/jzlOmH19l+9fwPFvoRrHebmohKtguvxn6zl39hRLzxdsJuBQb/Rgv4F/3Hhxb/hKOrMpxtYSawp5a4/W0xli1vlECTXLfaIEeyyCPa8Nnf1j0q7PhZk1/VhtBx9dYX7ISrDf9it/3ss7J5t5+ovXRFX5Gu5Zj3WSO5+VW8uHJ3Yac17v3awf9V42PtsYppu6GYUVY7uMv7i3iv+45qy4jpsqplpQlVI3K6VOKKVKlVLLg3yvlFIrvN8fVkpd5ffdk0qpRqVU+PUvRES+iN074OaGe4yFh2s7XDE/TJf/fL3p2V/B+kxTv9vDxf+3NmRN05jzW3DO+k7ztWOmJmsw+ZkI7epfbhj+d7xxLGQjRwyH/eiKxE/o0x6kJSNwbFmsdVFuj3HRw61nsR0GiL73RDzn6hnw+HXnjy0+vOuu9ZakV7vKW1i8fDWtQbpsh11aIUWJQFvPAEt+uNrUtkF7mhF6eAAwZnx/TOuGhqC12dkwEzt7baCIcdlmXX+tEGtFZjRC/RbhxpJaKd7z+Fqh798QviL03iDdXzcXN0aV9woUbwHKCqGifTRzY0RTOA3FN5lnsHdpOOHSgu4oKwACj1UfpmLW92rbUTq2G3Ai1kA3M87ZriIWUJVSmcDDwC3AxcCnlFIXB2x2C7DU+9/twKN+3z0N3Iww5TdBJobwd7SmY8z4me/EsRTAT1+3fk3JTtfg8Asu5Ex9yuiq1Tvg4e4gC3HD2ATQirxQZohZcoMxc7qKKBajT6ZYkrlw+yQrwx3uNKFqJM0dN/oLCJzufiLzFXaTdj4LJruyIs76Jo07WNU2/JkvD2Fl4cwqh2s64r7uUEtzHahs44qfj+66HniusUNZ/AazmEiUOvsiZwzjubxUrgMdz7Iw8WjrGaCxy8WhKuuWt7KmP9P49pVnCk1t5z9+vLi+i8PV7Xzx6UJ+8Wbsa9Ume/3UP207Nerv7zx/gO+FmJ8k1nlLzLDyssOv3RpvRXmYoVQaegf18GzlBmsuLFjBuqIl8ZVNiWKmi++1QKnWuhxAKfU8cCvg/3TdCjyjjarNXUqp6Uqp+VrrOq31VqXUYqsDPp4E7R4RYtvjdaFrr7WGjz04tsYp2unm/b2QgFl6v29iogQzLzj/+6aUNeO6Ll84zfS2LweM9wsWYpc9e/jGxIpZeq/79Ua2/uD95GTFNrw93Evjnx7ebmL/0Aew2wy/iRbYVXjryWbeMrGGsdszhEdrJmWNTHAUfGKHxHEPDZFDBqlungraQ8LG0SgzgRnXoyYWfQ93a6Kd2CWUeB5jrXXQ1vCR78PtG/t5Ae5aZX1lsBm3rdxFQ5cr6hamREtUITdZj6dvuaNo+Zav2XMq9patVFcP/P1g6F5viWzdjSfOBFZ6hp2PI8pIFM32A+4h3AmKpF95Zu+Yzw4kYIKsZDFTQF0A+JdiqoF3m9hmAWB6JW2l1O0Yra/MnTsXp9MZdLvu7u6Q3yWb2XDU1NTQ3GfEyCNHj5DVWERZufGSbGkJPQ4iMIPZPxD8xdrb15uUe3Ly5NiZJIOd9+CpkYxIaWnw2SfbO9rZudNYPqO/P/i4usrTI8uq1FRXs3Xr6Nk+Y7nmmpoanM7ox550dHRQo829kFpaIi+FEEpdXWzT2R8+PDILsNPp5KTFY2O/+6S5SX/qO12s3uBkRm5sBdQdO3bEtJ+Pc8uWEMfdyRvlkTNooeJiTGGJIX4G20fHmCPv6TFqTn3x0TOk+W8TM/h++LfrKO8Y4umbR8ahVdeYvy+bNm+mrD2+Aq1zyzvkZavheDwUpIAc6f52WZBRamkxMpGvvTNS6VZSYnTpq6sfeVbr6xtGhee0X9rl+7y72/zwgp7e2ApzRw6bn0W1uzvKis0gv8GWrVtH/d3XN/oat/p93xVhDoOtW7fy4+2R71Gr9zdpaw9eYD5yNPSM6JVVVWzbHrxrZ1FRMXmtJ8d8frqyCqezgZra4M9AY6O5paRe2DN2vHkyBLaIl5WFXrPdrNKyssgbReAZMv+OKj5hfpbYeN6/0XC73TidTnp6optUp6jCKNy1dsbesnWq4hRO59i1ls3YvHkzbg3ZKZzALlYlJaHjQVFR8J54PoHjoMtPnQq63aFDh2mcEt29KSoqwtk5ktdt6Az9vnxlfzXLrtX4VzMcPXqU3OZiyirCp5EVFRVjPnv0lfATFkJseRE7MFNADfZLBeaYzGwTltZ6JbASYNmyZdrhcATdzul0Euq7pFlrjPFxOBzD/w4nM38WZ+RpaGrkXZe+C8fFcylWZVBSzKxZZ0CjubEQHf3Bb2leXt7IPTERnlidt/Q8KB7dLSXYb5G31wm9RuJ73nlj9wGYPn06111/BWzZxKRJk8A1tjvl2WefBaeMF+GiRYu48cYLYP3aUecuru80Fk5ea64AtWDBAhyOS6O+T9OmTWPhgmlQWRFx21mzZkFT9OtfAsybNw9qom/JvOyyd8F+o/bM4XCQX9EKu3dG2Mu810rN175ff/17mDctN6a4+J73vAc2b4i8YQg33XQTrH9rzOeLLrwC55bI9yM3Nxdc1swQ+b733QRr10S1T7A0JSMjA2KYQTsvLw96ejjjjOjiY7m327r/s/1m0yHT8fKlmql85cYlccW/V2sLeOLzyyg4bcTjjIyMMQUks+lvPI40G5noN8pG4v8FF5wPx48yZ+5cqDEyifPmzsXhuGI4PIvOGkm7fPcx/9A70BW5FRJGfrtoXXnlFVBobs3D/Px80+EByMzMhIBCRWnGImAk09jYO/oddefOke0jxeO6yUto7I3cyjhj5gxoaaaiM/ixHtgfOnPYrqZy+dWXwSbnmO8uvPBCOoY0MLqL4llnLcLhuIiN7Uehcmwhc87sOVAfuS5eBYnDqXDuuefAifjWKj3v3HPhRPgCQSSZGZngMVdIveiCC+Goua6jh5qS060pKyvLeNdG8VzDSP4gnnfNOUuW4HAsNf6IMg38700uXINDrPrGDVy2cHrC01ArnX/+BXAseAXUxRdfBIcPmj5WYUsWMDZfc/nll3Hu7HzYssn0sS688CIcVy80fS87mQyM5HkvvfQSHJfOp3zbqaD5ZZ/FixdD6ehKtN8URh6KlPIyU4zMNHNUA4v8/l4IBLbvm9lmwlp/vIGNxcEziMmaEMAKZrsxmK17OhbFOqGh3Hz/O3wsCZPZgPnxD/HUS9ptrb1Y7K9si7xRCHGP/Qix+/OFlcG/SKDAcTvjWTTjLaOZJCOUkXTRfg+EmVsR93CEGHcPvFuXL5oeXzj8BGtwibT+of+s05HuyU+T0AV296lWvvLnsd3gfIKNn/Nddsi02X5RNCwruqgn+z2VzGWbxgOlFFpr7nwtuvXTYWQSvk8+El9vpVT4UQzXG0pVq3VLFWngh6+aH3t7957AQqXxQEV6ru7fMLaHRzozU0AtBJYqpZYopXKA24BVAdusAj7nnc33OqBDa226e+9E0hNifUorJHphX9MvJRPb9Q64uf3Z8MscBL6SAl+svrGu0SxlkYz3ajyv0ljHWNhpQp+vPbc/rvE1ifDqfnPdoaxcxL08hjXNOi1cHsXKLF3qxl2OzzHFdhmnumRWnmXHSvbELKHEe29PNgbv2hzu8hq7XKPWTh0doPjCk2x3R5iIMVn64pjFNh3EO8v4S/uqeW537BWvVkxEl66ivTNaa/62J/b5XA5XtwPjrq4r4SIWULXWbuAbwDqgCHhRa31MKXWHUuoO72ZrgHKgFHgc+Jpvf6XU34CdwAVKqWql1JctvoZx5dtxzLgbii9S/+yN1EzCEMjMQ9Y/GF1hWjG2FtWKSX3SxZ2vjV7FKdWvnrqOGAt6cTc8pfrKR8SSn//sE7utC4AFt+L5PZXc+dqRlC/2bZOyETASlnBpWLyF51j3DlymwMoKl3h/A6uiULKfcQ28si+28X6jjmOfpGncSadZgzd5e9PFska0j1JwKoYKUGFPjzjjH9OdjsyMQUVrvQajEOr/2WN+/9bA10Ps+6l4ApiuKixMXHyZkkPeWphEMd3F1y8nEypTYyaz47+JVS2Ef955mg9fMi+mfc2+JOPJzNspIx6PWJfgSKc8XCw/ZVGwmSFjjBNW3Mvlrxpdqj5x+ZkWHC12gWvDAnzvZfMTAlnJF7VX+82IPKa3R5znsGoJm+IwM42GmzE+mM4o1wYMZNWzvT3I+oGJtHJrOefMnhJ6A5PPZ+CkhyI9pOKVbVWB/YUUDH0ZD6LNw1mVtiViHdTxLLapNgUAz+2OfVa+5wutW96lNESXpVTxf8TMPOehCp/+u64+UmdZDfRnYmil2nu6zXTh0Q6FrFQnc7HOBbIqzPT1ZtiplSKe7ldWGCnkxB8bbHRbh5nttp0K8cbDWPc/Vhswrt+OP5xNhbvn5U3SWiXso7vfmqEg/+8V68Z0plo866THq67dPkOs0okUUOPw9PaKVAdh2PHaTo7WRFcjHi0r8zolDZEL1YEZhlTntcxm850nmhIaDjNSfa9+8vrRyBsF8as18c0M+dUg64CNJ3ZaGXDFxpEJGaxq0UsHwSqqAmvc471fsXZj/dkbo2eAlF/NvN6BiT0mUsQm3Lq6ifLwZukSGuipFObH/7ChxJLjSAPqaFJAjUOoyRZSYUtJ4gtFZrs9WPWQPbZldCI8EcbB+c96OZ6lKrP3zsno17i1k2BxLNZod7oltrU0fe73e+mm+tmzk5MhKtd+HWflij+rbredKhbsHofWHYtx9ml7X1ZaqO+0bwtVqsJ2sLI9JeedCGyeVE0YpsagChHOo84yLpxXwPsvnAOk14QG/pIxPmBTiOWIorXh+PhZvkiMCDbWMn7xx9s1R+JfOiZaqw7V2jIT9vSOijGfKaVYubV8+O9ghbFoxnxaNqGQZLRM88ispqYle9b4+962poUqnewsT+5YbJFYn3hoG4er4196MZ1IATVKTRa0cCWiJrmsKXWtub9Za0xbX3HPR4HE1SjeY5Pp8ceDP/pllsXEloq1lldsLI37GN/62wELQpIcY7v4piggAew0q7Vd7kkoMRdQ07M+Niyp+BDpKlUNLFI4HUsKqFEatGAmvkQk7i+naMmVfadH1rs82dDF6ZZeOvqsW8vRX6onnRkv+ZDtpeO7m6sYbTyOS0nGkAM7+XvA5F7xFgyt6porBQnzYi2grj488ZZ8t1PXcSGsZKdKvYlOCqgiLv/y6M7hf791tN6SAryITyyzFAv7Sky338iUUlLCiZF/WaekoYvpedlR7S+3Pfmau9Nj/L9IDlkSRIjEkgJqlKzIN4zXvMcvV4efBMTtGaK7P7618mxN3kdCCBP8u/y29QzwhyjH0FlViz9e3zWpUBHnpGITid0nvEqGjr5BXtxr3XKBwh4USioIbUIKqCnw7M6KVAchIVZsGjvurNOVmO6+qZCukz8JEYxMGhO7v+0Zybj+/WAN7ijvpXWTJMlvKKwnSYPhBy8fTnUQhMUO17SzaObkVAdDIMvMpMRdAWvVpbP7N5yMvJEQQqSpWAr6VhUABj1SkhDW80jFh0hTT22vkBZUm5ACapSkRnrikiEnQohoDelYRgfIe0bY17mz81MdBCESQmtJfe1CCqhCmNTWM5DqIAghxplYZjyVelBhZ2dOy011EIRICK21zFJtE1JAFcKkTcWNqQ6CEGKcGYqhv65kj4SdyRhUka48WksFoU3IJElRkqnFJ67GLlmGQAgRncA1Us1old4awsZkrUiRrtp7B5EqQnuQFtQoyRhUIYQQQkxUMsO3SGcVzbLklB1IAVUIIYQQQpgi9fQinR2ubk91EAQmC6hKqZuVUieUUqVKqeVBvldKqRXe7w8rpa4yu68QQgghhBgfKlulhUmkrxWbSlMdBIGJAqpSKhN4GLgFuBj4lFLq4oDNbgGWev+7HXg0in3HlboOV6qDIIQQQgiREgcq21IdBCFEmjPTgnotUKq1LtdaDwDPA7cGbHMr8Iw27AKmK6Xmm9x3XPny04WpDoIQQgghREqclhZUIUSCmZnFdwFQ5fd3NfBuE9ssMLkvAEqp2zFaX5k7dy5OpzNoYLq7u0N+lxRD7tSdWwghhBAihcqbelIdBCGECZ9cmp3aMlMczBRQg62rEjhEPtQ2ZvY1PtR6JbASYNmyZdrhcAQNjNPpJNR3yfD6pT1sL23GNeihtLGb3OxMLl0wDQVMzsmkpq2P0609aA0z8nL4wEVzOFbTQUaGYsH0yXT0DdLQ6WJ6Xg4XzC3AeaKJnKwMli2eQWFFKzPyclh3rJ7Fs6aQnamYmpvNlElZnFEwidbufj50yTy2n2xm6uQs+t1DHK/rZFJWJrMLJnG8tpNJWRlUt/XxmevOorHTRUffIIMezfsvmENGBszMy8GjNaeae+gfHKK6vY9B9xD5k7Kobu/D7RkiJyuD3gEPBZOyaO8bpLypm0Uz81g0I4/sTEVFSy9nTs+ltWeQc86YQnVbLwMezcwp2WRmZDBtcjZ5OZnsOdXK9LxsGjr7ycpQnJE/ick5GXT2uel0DXLpgmlorXENDtHv9jAjL4fszAxaewbYV9nGBy+cQ2ljNx6tcQ14mF0widzsTM6dk8/2k80sWzyT6rZemrr7ufG82UzPy+ZEfRdtvQP0u4c4e1YeO8paOOeMKbT2DDBzSg6NXf2UNHTx3vPOYEZeDtPzsqlo6WXRjMnk5WRR1dbLohl5tPcNcKiqnSVn5JOXk0lLzwBHazr48MVzedfCaby0t5qPX34mG7ft5vIrrmB7aTNnTp9MZoaiqasfpeCcM6bwgQvn8vDmUhq7+rl84TSuOnsGGUrR0t3PzCk5dPQNMndqLoUVrZyRP4mShi6Wzi2g8FQrN10wm0NV7eTlZFHS0MVF8wsY0vDJqxZwuLqD82bnU1TXSb97iO5+Nxo4VtvBwhl5LJiey9GaTs6fW0Bjl4uatj4Wzsxjam4WZ+RPorHLxRn5xv3MUDBlUhZljT3Mn55LXk4mAItnTeHPOypYOCOPY7Ud9PS7WTQzjy6Xm75BDxfMLaCksYvrz5nFeXPy6egbZGdZCxfPn0pjVz+TczLp7BtkwDOEwrjmuVNzcVw4m1NNPdR1uJiVn0NudiZvH29g2uRsqtt6ufWKBeRPyuJYbQe9Ax6uOmsGp5p7uPjMqXiGNMdqO6lp62NWfg7nzcmnvsPF9Lxs5k7NpaHTRXF9F4tnTaGuo89736dT1drLrPwculxuJmVl0O8e4tIFU2no7Gf65GwunD+VQ9Xt5GZl0t3vxjOk6Xd7OCN/EhlK4RnSzJ+eS3lTD12uQbpcbjIULJqZR017H2dOn8yiGZPJn5RNR98gi2ZO5q+7K+kd8ACQPymLj142n/XH6ofvu2vQw2sHarhwXgHXLJnJtMneZyVTkZ2RQVvvAN39bmZOyaGmrY/srAx6+9309Lu5ZslMTtR3MW9aLv2DQ2RnZbBwxmR2F+5n/jkXcqSmg0HPEGfNzCMrQ3HenAJOt/YwbXI29R0uzpk9he5+D30Dbs6fW0B2ZgZ7TrVy1dkz2FTUwLlz8qls6WX+9MlUt/WycEYe583J581DtQx4hpidP4l3LZxG/qQsPEOa3OxMmrr6qWrrpXfAw0XzC6jv6OeyhdNo6RmgrLGbGVOyOdXUQ0lDNxfNn8pHLp3LU9squGTBVCZnZ5I/KYu23kEuPnMqRXWddLkGKa7r4qL5UxnSmqO1nZw9M4+zZ+UxbXI2FS09XHLmNOo7XDR29VOQm8WZ03Pp7vfQ2OliUlYG2ZkZuAY9TM7JpHfAw+mWXqZOzkZrTd+Ah+vPnUVDZz817b3MyMuhvLmHM6bkMDkni0UzJ3POGfnsPd1KmTetn5SVwfzpkzle28m7Fk6jrWeA/Nws9p9u59IFU5k7NZeTDd1cOL8Az5Cms2/QCIPbg9aQmaHYc6qV8+bkk5OZwaz8HFp7jPRqyRlTmDIpi5bufjKUYkdZMz0DHnK96XteTiZzCibhGvQwf/pk9la0cuG8qZxu7eX8ufloDcX1nczOn8S8ablsKGpkam42V589g2O1HZxq7mHZ4pnMmpJDW+8A86bm0tTdz/xpk8nKVPQPenAPaeo7XEzNzcbl9rBg+mRcg0McqGzDPaQ5Iz+HaXk5tHuXwblgXgGNXf0MeoZYOCOPxi4Xx2s7OWd2Pq3VZfROmc9Mbzo7PS+H3adauHLRDCpaejh71hRKGrrQWjNv2mT63R4KcrOZlJlBVVsv86blcua0yRyqbmdSViaeIeMcAPtOtzErP4c5BblkKCjIzcY9NMShqg5mTslm5pRJ9A64aeh0cfXZM5g2OceIm/1uMjIUu8pb+OCFc3EPDZHlfdbqO1wsnZuPa3CIg1XtXH/uLCpbepkyyYhXTV39ZGdmUNXayyULplLVajz3CsjLyaSorpOMDOOd3dozwOQc4xlfOGMyPf0eGrpc5Odk0dE3SEGukU4snDHZuNapuXS53CycMZmGzn7a+wa4YG4Bx+s6mZydyfxpufQMeOjoG6TLNcjF86cy6NE0dLo4d3Y+7iHNkZoOLpxXwKz8HHr7PTT3GMuitXQP0DtghCM3O5Mz8nN4eV81n7t+Mb0Dbuo7XNS29zFjipEWD7iH0BrOnJ5LVqZiRl4OrsEhjtZ0cMG8Alq6+7n4zGkMeobYc6qVc+fk4zzRyOz8Sew+1cqsKTksmpnHObOnMDU3m5aeAVq6+7lm8Uyq2nqpaeujq9/Nohl59A64OdnQzQXzCpick8nZM/Moa+rmzOmTOVFvPPstPQOUN3Uzc0oOU3Ozaesd4JzZ+TR19aPRzMzLYcAzxIy8HEoaumjodFHgzTctnZPPrPwctpc2U3rqNGcuWMjU3GwaOl3MKZhEdXsfudmZnDc7n53lLVwwt4Dc7AwKcrNp7x2ktr2PjAyYmmvE35ysDDIUuAaHaOnu5/y5BWwoauC8Ofl4tOacM6bQ1NXPtMnZTJ2cTWFFKxfNn8oHLpxDS/cAR2o66B/0sL+ynRuXnkG/e4i6jj76B4eYNy2XOVNz6el30+v9rc+emYcG3J4hzp2Tz4n6Ls6amcfbxxtYNHMyGUpRXN/FhfMKcA16WDQzjzcO1XLZwunGs9npQilFQ6eL/ElZnD+3gBMNxvuxuq2XTpcb54lGPnvd2dS095E/KYvD1R2cOX0ycwomsaOshS7XIAtmTGbpnAKuOms69Z0uMpSi3z1EU1c/UydnoTV0udzk5WQy6BmiINfIn07Py6GqtZeFMybTO+ChvsPFBfMKqOtwkenNd/QNGu/IgtwsFs7IY/2xegBm5U8iK0NxqrmHOQW5XDCvgMKKVs6amUdrzwDtvQM4LpjDttJm9p028os5WRnsLG9BARfOn8rs/EmcPSuPfrcRfz1a855zz2DQPURDl4uczAzqO1wMeoaYMimL6rY+Fs6YzEXzp3Koqp1O1yBzCnIZ8AyhteZdC6fT2+9m96lWcrMzuWh+AZecOZVVh+oomJSFa9DD9LxsBj2a+k4XBZOyGBzSfPa6s9lY1EBLzwCXLZzGC5sPcMn553LDebNQKEqbuhj0GOnr/tPt9Ay4WTgjj6rWXhbNzOM9587iSE0H3S43m080cvOl88iflEVudia7y1u58qzplDZ2M21yNotm5lHR3ENjl5H23Xzp/GQVjyynIi2bopS6HrhLa/0R798/BNBa3+23zR8Bp9b6b96/TwAOYHGkfYNZtmyZ3rt3b9DvUl1AFcKfxEdhJxIfhV1IXBR2IvFR2IXExRFKqX1a62XBvjMzBrUQWKqUWqKUygFuA1YFbLMK+Jx3Nt/rgA6tdZ3JfYUQQgghhBBCiMhdfLXWbqXUN4B1QCbwpNb6mFLqDu/3jwFrgH8ESoFe4Ivh9k3IlQghhBBCCCGEGNfMjEFFa70GoxDq/9ljfv/WwNfN7iuEEEIIIYQQQgQy08VXCCGEEEIIIYRIOCmgCiGEEEIIIYSwBSmgCiGEEEIIIYSwhYjLzKSCUqoJOB3i6zOA5iQGR4hwJD4KO5H4KOxC4qKwE4mPwi4kLo44W2s9O9gXtiyghqOU2htqzRwhkk3io7ATiY/CLiQuCjuR+CjsQuKiOdLFVwghhBBCCCGELUgBVQghhBBCCCGELYzHAurKVAdACD8SH4WdSHwUdiFxUdiJxEdhFxIXTRh3Y1CFEEIIIYQQQqSn8diCKoQQQgghhBAiDUkBVQghhBBCCCGELUgBVQghhBBRU0qpVIdBCB+Jj8IuJC7Gz9YFVPmBhV1IXBR2IvFR2ER2qgMghB+Jj8IuJC7GyXYFVKXUJUopB4CWGZxECklcFHYi8VHYhVLqeqXUS8C9SqmLlVKZqQ6TmLgkPgq7kLhoHdvM4quUygAeAj4AVAK7gde11nuVUhla66GUBlBMGBIXhZ1IfBR2opSaA7yFEScXAQuAvVrrx5VSSipPRDJJfBR2IXHRWnZqQZ0GFAAXAZ8BWoDvKqXyJQMmkmwGEheFfUwH8pH4KOzhcqBEa/0U8HvgVeBWpdT5Wmst3c9Fkl2KxEdhD5I2WiilBVSl1CeVUn/w/jkLuB7I01o3Aa8ArcDXvdvKDysSRil1lVLqfO+f04D3IHFRpIhSaolSKtf750wkPooUUUp9Sin1M6XUJ7wfHQCWKaXO1Vr3AIXAXuC/QLqfi8RSSt2klHq330eHMOLjORIfRTIppf5JKfUjpdRHvR8dRNJGy6SkgOrtl/1X4CfAt5VSZ2qtS4GdwHe8m9Vh1D5c6f1eflhhOW9BYDXwMPCsUupDWutyYAcSF0WSKaUWK6XeAp4AnlNKXexNG7cC/+vdTOKjSDhluAP4AVAB/E4p9RWgG3gG+LZ303ZgA5CnlJqfgqCKCUApVaCUehV4DfgvpdQMAK11C/AC8C3vpu1IfBQJpJSarZT6O8Y7uRV4Sin1r34VyN/0btqOxMWYJa2A6qvlV0q9D3gc2KW1vhJ4APDVhv0JuEEptURr7QYaABcwOVnhFOkvoMXpe8BBrfX1wOvAl7yfP4nERZEEQeLjbq31B4HNwM+UUhcDTwPXeVsJJD6KhPNWfFwP3OPtsvZ1wAF8EFgDnKeU+gdvN/MWjPFWHSkKrkh/A8Am4D+BWuDf/L57BbhQKfVBiY8iCc4Ftmut36e1fgz4LvA/3u/+hsRFSySzBdWXkToOfFhrvUIplQOcB/jGUR0C9gO/BdBaHwXOBvqTGE6R/nJhuGDQAwx6P58KFCmlzgO2Y3TPuBckLoqE8sXHLO/fxwC01g8B1wK3YWTICpG0USSQUupz3i6UM70fFQELlFJZWusNwFHgOqAJ+Ctwvze9/CCggJxUhFukJ7/4OF1r3Y/Rs2QDUILRlfIC76aHgOeR+CgSxBsXHUqpPGAfRi8SvLP0Hvf+B3AEIy4+IHExPgkvoCqlPqSUehv4rVLqNq11s9a6RymVq7UewPgxPwOgtW4Dfg4sVEo9qJQ6CpwGOmSclYiXX1z8nVLq370tBNuApUqpA8DNQCZGxusm4G5gvlLqIYmLwmpB4qMbo7vQlUqpy5VSl2MUCJZgxMtfImmjsJi3K+98pdRm4PMY7+MHlVJTgSpgDkZFMhgZr0uBWVrrvwDPAcsxKlF+oLVuT3b4RXoJER8fVkqdobV2efONO4FG4N8BtNZDWuungWeBHyLxUVggSFz8NEYP0Gla6walVKbW2oMxgeE0GBUXn0HSxrgkdJkZb+3Bc8CvMZZH+C5wXGv9a6VUttZ6UCl1E/BV4H+8/bdRSs0GlgJnaK1XJSyAYsIIEhe/D+zXWt/rrYW9W2v9Se+2PwHmaq2/oZSai9GdQ+KisEyQ+PgD4B2MDNZ3MIY9TPf++3+BPVrr+yU+Civ5MljKmCDu/7TW/+ltyV8BTAG+AvwRo7v5Kq11h1LqaaBGa32n9xg53kKDEHEJEx//AJyptf4Xv23/GfgQcB/GuPw+rfWQxEdhhQhxcYHW+pN+2zwDvKm1flEpNU9rXe89hsTFOGRF3iQ6ylizD2/f63cD+7TWr3u/2wTcp5R6Qmvd6N0lG6Pmoc13DG9BtcnqsImJJUJc3IARF5/FaLWqUkpdpLUuwsiMfUcppbTWDRjj/YSIi4n4+HvgJa31L7xjTcu9321npCtvozdOChEzb0br50CmUmoNxvAGD4DW2q2U+gZGpv9ijB4l/wQsxOhVMoQxiRze7SUDJuJiIj5+C6hVSt2ktd7i/fw1pdRFwFqMZbjeDxRJfBTxiCUuYkwcd0op9XPgk0qpm7XW1RIX42NpF1+l1BeBauAX3o+OAJ9SSi32/p0NlOEd1wfgHdeyDGMZBSEsYTIulnu/78JYyuNbSqlvY7QYbEhqgEVaMxEfszDSRt+yW6e8+90OfBljbL5MUy/i5u21tA9jvedSjDg5CLxfKXUtDFei/Bz4jfcdvRJ4r1Jqt3c/ZwqCLtKQyfioMeLjXX77/RtwJ0aF8mXeymUhYhZLXFTGGNQvAS9jFGbfr7WuTnrg05BlXXyVUvnAXzASi88Dn9ZaFyul7gfmAmdhZLp+A9wDfFlrXa+Uyga+CKzXWldYEhgxoUUZF38L/Kv3s3/AqCx5VGu9KwVBF2kohrTxS97xLd/BGH/1Na11YSrCLtKPUupGYLHW+lnv349gVJj0Ad/UWl/tbe2fAzwIfF9rXaGUmg5M0VrXpCjoIg1FGR9XAP9Pa33Kux9a63dSFHSRZmKIi9/HqFz+JvCM1np/akKeniwdg6qUOktrXamUugdYorX+D2/twjTgYq31NqXUIozahzu8s7IJYbko4uIvga9KVwyRSFHEx18A/6W17ldK5Wmte1MacJF2lDELpQdwe8dPfQa4VGv9Q6XUQeBPWusHlVLLgO9qrT+VyvCK9CbxUdhFlHHxe1rr21IZ3nRnaRdfrXWl95/3A0uUUh/xznDVobXe5v3uDozaCLeV5xbCXxRxsQfv+AIhEiWK+NiLN22UwqlIBK11r9a63xv/wJhoxjfnwxeBi5RSb2Ks5yctAiKhJD4Ku4gyLu6DMeuYCwtZPkkSgLfr7p+AHwHrvDUR12KMF8jG6MImhQKRcBIXhZ1IfBR24W3B1xjdzH0zQndhxM1LgVPSnVcki8RHYRfRxEWZFyJxErLMjFIqwzvd98sYMwH2Y0w6c1JrXWb5CYUIQeKisBOJj8IuvDX/OcATwGsYE320YIy16kxl2MTEI/FR2IXERXtIVAvqkLcv9xzAAfxca702EecSIhyJi8JOJD4Ku9Baa6XUlRgTcS0BntJa/ynFwRITlMRHYRcSF+0hIQVUr69hjBf4kEyGJFJM4qKwE4mPwi6qMbqX3ydxUdiAxEdhFxIXUywhXXxhpCtbQg4uRBQkLgo7kfgohBBCCBFawgqoQgghhBBCCCFENCxdZkYIIYQQQgghhIiVFFCFEEIIIYQQQtiCFFCFEEIIIYQQQtiCFFCFEEIIIYQQQtiCFFCFEEIIIYQQQtiCFFCFEEIIIYQQQtjC/weMXvIyzpmqxwAAAABJRU5ErkJggg==\n", 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" ] @@ -398,113 +342,26 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'pandas.io.formats.format' has no attribute 'DataFrameRenderer'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\anaconda3\\envs\\wburg\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 344\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 346\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\wburg\\lib\\site-packages\\hydropandas\\observation.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[1;34m(self, metadata_out)\u001b[0m\n\u001b[0;32m 148\u001b[0m )\n\u001b[0;32m 149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m \u001b[0mdf_out\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrameRenderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetadata_out\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'pandas.io.formats.format' has no attribute 'DataFrameRenderer'" + ] + }, { "data": { - "text/html": [ - "
hydropandas.KnmiObs

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metadata
nameGIERSBERGEN
x138555.684384
y406719.896061
filename
station892
meteo_varRD
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RD
YYYYMMDD
1993-11-01 09:00:000.0000
1993-11-02 09:00:000.0000
1993-11-03 09:00:000.0005
1993-11-04 09:00:000.0000
1993-11-05 09:00:000.0000
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" - ], "text/plain": [ "-----metadata------\n", - "name : GIERSBERGEN \n", + "name : RD_GIERSBERGEN \n", "x : 138555.6843835866 \n", "y : 406719.8960605697 \n", - "meta : {'RD': '24-uur som van de neerslag in m van 08.00 voorafgaande dag- 08.00 UTC huidige dag', 'x': 138555.6843835866, 'y': 406719.8960605697, 'station': 892, 'name': 'GIERSBERGEN'} \n", + "meta : {'RD': '24-uur som van de neerslag in m van 08.00 voorafgaande dag- 08.00 UTC huidige dag', 'x': 138555.6843835866, 'y': 406719.8960605697, 'station': 892, 'name': 'RD_GIERSBERGEN', 'variable': 'RD'} \n", "filename : \n", "station : 892 \n", "meteo_var : RD \n", @@ -521,115 +378,26 @@ "metadata": {}, "output_type": "display_data" }, + { + "ename": "AttributeError", + "evalue": "module 'pandas.io.formats.format' has no attribute 'DataFrameRenderer'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\anaconda3\\envs\\wburg\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 344\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 346\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\wburg\\lib\\site-packages\\hydropandas\\observation.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[1;34m(self, metadata_out)\u001b[0m\n\u001b[0;32m 148\u001b[0m )\n\u001b[0;32m 149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m \u001b[0mdf_out\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrameRenderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetadata_out\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'pandas.io.formats.format' has no attribute 'DataFrameRenderer'" + ] + }, { "data": { - "text/html": [ - "
hydropandas.KnmiObs

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metadata
nameGIERSBERGEN
x138555.684384
y406719.896061
filename
station892
meteo_varRD
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RDstation_opvulwaarde
1971-12-31 09:00:000.0910
1972-01-01 09:00:000.0910
1972-01-02 09:00:000.0910
1972-01-03 09:00:000.0910
1972-01-04 09:00:000.0910
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" - ], "text/plain": [ "-----metadata------\n", - "name : GIERSBERGEN \n", + "name : RD_GIERSBERGEN \n", "x : 138555.6843835866 \n", "y : 406719.8960605697 \n", - "meta : {'RD': '24-uur som van de neerslag in m van 08.00 voorafgaande dag- 08.00 UTC huidige dag', 'x': 138555.6843835866, 'y': 406719.8960605697, 'station': 892, 'name': 'GIERSBERGEN'} \n", + "meta : {'RD': '24-uur som van de neerslag in m van 08.00 voorafgaande dag- 08.00 UTC huidige dag', 'x': 138555.6843835866, 'y': 406719.8960605697, 'station': 892, 'name': 'RD_GIERSBERGEN', 'variable': 'RD'} \n", "filename : \n", "station : 892 \n", "meteo_var : RD \n", @@ -661,163 +429,43 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n", "INFO:hydropandas.io.io_knmi:station 344 has no measurements before 1987-09-12 01:00:00\n", "INFO:hydropandas.io.io_knmi:station 344 has 5810 missing measurements\n", "INFO:hydropandas.io.io_knmi:trying to fill 5810 measurements with station [210]\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 210-VALKENBURG between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 210-VALKENBURG between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n", "INFO:hydropandas.io.io_knmi:trying to fill 5564 measurements with station [330]\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 330-HOEK-VAN-HOLLAND between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 330-HOEK-VAN-HOLLAND between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n", "INFO:hydropandas.io.io_knmi:trying to fill 5564 measurements with station [348]\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 348-CABAUW between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 348-CABAUW between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n", "INFO:hydropandas.io.io_knmi:trying to fill 5500 measurements with station [240]\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 240-SCHIPHOL between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 240-SCHIPHOL between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n", "INFO:hydropandas.io.io_knmi:trying to fill 5500 measurements with station [356]\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 356-HERWIJNEN between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 356-HERWIJNEN between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n", "INFO:hydropandas.io.io_knmi:trying to fill 5500 measurements with station [260]\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 260-DE-BILT between 1971-12-30 00:00:00 and 2021-08-15 11:34:06.059254\n" + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 260-DE-BILT between 1971-12-30 00:00:00 and 2021-08-30 17:19:48.832342\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "module 'pandas.io.formats.format' has no attribute 'DataFrameRenderer'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\anaconda3\\envs\\wburg\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 344\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 346\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\wburg\\lib\\site-packages\\hydropandas\\observation.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[1;34m(self, metadata_out)\u001b[0m\n\u001b[0;32m 148\u001b[0m )\n\u001b[0;32m 149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m \u001b[0mdf_out\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrameRenderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetadata_out\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'pandas.io.formats.format' has no attribute 'DataFrameRenderer'" ] }, { "data": { - 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hydropandas.KnmiObs

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metadata
nameROTTERDAM
x90598.0
y442813.0
filename
station344
meteo_varEV24
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EV24station_opvulwaarde
1971-12-31 01:00:000.0001260
1972-01-01 01:00:000.0002260
1972-01-02 01:00:000.0002260
1972-01-03 01:00:000.0002260
1972-01-04 01:00:000.0000260
.........
2021-08-11 01:00:000.0036NaN
2021-08-12 01:00:000.0035NaN
2021-08-13 01:00:000.0035NaN
2021-08-14 01:00:000.0030NaN
2021-08-15 01:00:000.0035NaN
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18126 rows × 2 columns

\n", - "
" - ], "text/plain": [ "-----metadata------\n", - "name : ROTTERDAM \n", + "name : EV24_ROTTERDAM \n", "x : 90598.0 \n", "y : 442813.0 \n", - "meta : {'LON_east': {'344': 4.447}, 'LAT_north': {'344': 51.962}, 'ALT_m': {'344': -4.3}, 'NAME': {'344': 'Rotterdam'}, 'EV24': 'Referentiegewasverdamping (Makkink) (in m) / Potential evapotranspiration (Makkink) (in m)', 'x': 90598.0, 'y': 442813.0, 'station': 344, 'name': 'ROTTERDAM'} \n", + "meta : {'LON_east': {'344': 4.447}, 'LAT_north': {'344': 51.962}, 'ALT_m': {'344': -4.3}, 'NAME': {'344': 'Rotterdam'}, 'EV24': 'Referentiegewasverdamping (Makkink) (in m) / Potential evapotranspiration (Makkink) (in m)', 'x': 90598.0, 'y': 442813.0, 'station': 344, 'name': 'EV24_ROTTERDAM', 'variable': 'EV24'} \n", "filename : \n", "station : 344 \n", "meteo_var : EV24 \n", @@ -829,13 +477,13 @@ "1972-01-03 01:00:00 0.0002 260\n", "1972-01-04 01:00:00 0.0000 260\n", "... ... ...\n", - "2021-08-11 01:00:00 0.0036 NaN\n", - "2021-08-12 01:00:00 0.0035 NaN\n", - "2021-08-13 01:00:00 0.0035 NaN\n", - "2021-08-14 01:00:00 0.0030 NaN\n", - "2021-08-15 01:00:00 0.0035 NaN\n", + "2021-08-26 01:00:00 0.0031 NaN\n", + "2021-08-27 01:00:00 0.0019 NaN\n", + "2021-08-28 01:00:00 0.0028 NaN\n", + "2021-08-29 01:00:00 0.0020 NaN\n", + "2021-08-30 01:00:00 0.0007 NaN\n", "\n", - "[18126 rows x 2 columns]" + "[18141 rows x 2 columns]" ] }, "execution_count": 10, @@ -870,13 +518,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:hydropandas.io.io_knmi:download knmi RH data from station 344-ROTTERDAM between 2019-12-29 00:00:00 and 2021-08-15 11:34:09.602931\n", + "INFO:hydropandas.io.io_knmi:download knmi RH data from station 344-ROTTERDAM between 2019-12-29 00:00:00 and 2021-08-30 17:19:52.773008\n", "INFO:hydropandas.io.io_knmi:station 344 has 0 missing measurements\n", - "INFO:hydropandas.io.io_knmi:download knmi RH data from station 260-DE-BILT between 2019-12-29 00:00:00 and 2021-08-15 11:34:09.602931\n", + "INFO:hydropandas.io.io_knmi:download knmi RH data from station 260-DE-BILT between 2019-12-29 00:00:00 and 2021-08-30 17:19:52.773008\n", "INFO:hydropandas.io.io_knmi:station 260 has 0 missing measurements\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 2019-12-29 00:00:00 and 2021-08-15 11:34:10.025671\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 344-ROTTERDAM between 2019-12-29 00:00:00 and 2021-08-30 17:19:53.133098\n", "INFO:hydropandas.io.io_knmi:station 344 has 0 missing measurements\n", - "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 260-DE-BILT between 2019-12-29 00:00:00 and 2021-08-15 11:34:10.025671\n", + "INFO:hydropandas.io.io_knmi:download knmi EV24 data from station 260-DE-BILT between 2019-12-29 00:00:00 and 2021-08-30 17:19:53.133098\n", "INFO:hydropandas.io.io_knmi:station 260 has 0 missing measurements\n" ] }, @@ -920,75 +568,75 @@ " \n", " \n", " \n", - " ROTTERDAM\n", + " RH_ROTTERDAM\n", " 90598.000000\n", " 442813.000000\n", " \n", " 344\n", " RH\n", " -----metadata------\n", - "name : ROTTERDAM \n", - "x : 9059...\n", + "name : RH_ROTTERDAM \n", + "x : 9...\n", " \n", " \n", - " DE-BILT\n", + " RH_DE-BILT\n", " 141002.996557\n", " 456772.017696\n", " \n", " 260\n", " RH\n", " -----metadata------\n", - "name : DE-BILT \n", - "x : 141002...\n", + "name : RH_DE-BILT \n", + "x : 141...\n", " \n", " \n", - " ROTTERDAM\n", + " EV24_ROTTERDAM\n", " 90598.000000\n", " 442813.000000\n", " \n", " 344\n", " EV24\n", " -----metadata------\n", - "name : ROTTERDAM \n", - "x : 9059...\n", + "name : EV24_ROTTERDAM \n", + "x :...\n", " \n", " \n", - " DE-BILT\n", + " EV24_DE-BILT\n", " 141002.996557\n", " 456772.017696\n", " \n", " 260\n", " EV24\n", " -----metadata------\n", - "name : DE-BILT \n", - "x : 141002...\n", + "name : EV24_DE-BILT \n", + "x : 1...\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " x y filename station meteo_var \\\n", - "name \n", - "ROTTERDAM 90598.000000 442813.000000 344 RH \n", - "DE-BILT 141002.996557 456772.017696 260 RH \n", - "ROTTERDAM 90598.000000 442813.000000 344 EV24 \n", - "DE-BILT 141002.996557 456772.017696 260 EV24 \n", + " x y filename station meteo_var \\\n", + "name \n", + "RH_ROTTERDAM 90598.000000 442813.000000 344 RH \n", + "RH_DE-BILT 141002.996557 456772.017696 260 RH \n", + "EV24_ROTTERDAM 90598.000000 442813.000000 344 EV24 \n", + "EV24_DE-BILT 141002.996557 456772.017696 260 EV24 \n", "\n", - " obs \n", - "name \n", - "ROTTERDAM -----metadata------\n", - "name : ROTTERDAM \n", - "x : 9059... \n", - "DE-BILT -----metadata------\n", - "name : DE-BILT \n", - "x : 141002... \n", - "ROTTERDAM -----metadata------\n", - "name : ROTTERDAM \n", - "x : 9059... \n", - "DE-BILT -----metadata------\n", - "name : DE-BILT \n", - "x : 141002... " + " obs \n", + "name \n", + "RH_ROTTERDAM -----metadata------\n", + "name : RH_ROTTERDAM \n", + "x : 9... \n", + "RH_DE-BILT -----metadata------\n", + "name : RH_DE-BILT \n", + "x : 141... \n", + "EV24_ROTTERDAM -----metadata------\n", + "name : EV24_ROTTERDAM \n", + "x :... \n", + "EV24_DE-BILT -----metadata------\n", + "name : EV24_DE-BILT \n", + "x : 1... " ] }, "execution_count": 11, @@ -1019,7 +667,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:hydropandas.io.io_knmi:download knmi RH data from station 340-WOENSDRECHT between 2019-12-28 00:00:00 and 2021-08-15 11:34:09.602931\n", + "INFO:hydropandas.io.io_knmi:download knmi RH data from station 340-WOENSDRECHT between 2019-12-28 00:00:00 and 2021-08-30 17:19:52.773008\n", "INFO:hydropandas.io.io_knmi:station 340 has 0 missing measurements\n" ] }, @@ -1063,30 +711,30 @@ " \n", " \n", " \n", - " WOENSDRECHT\n", + " RH_WOENSDRECHT\n", " 82881.403813\n", " 384958.510811\n", " \n", " 340\n", " RH\n", " -----metadata------\n", - "name : WOENSDRECHT \n", - "x : 82...\n", + "name : RH_WOENSDRECHT \n", + "x :...\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " x y filename station meteo_var \\\n", - "name \n", - "WOENSDRECHT 82881.403813 384958.510811 340 RH \n", + " x y filename station meteo_var \\\n", + "name \n", + "RH_WOENSDRECHT 82881.403813 384958.510811 340 RH \n", "\n", - " obs \n", - "name \n", - "WOENSDRECHT -----metadata------\n", - "name : WOENSDRECHT \n", - "x : 82... " + " obs \n", + "name \n", + "RH_WOENSDRECHT -----metadata------\n", + "name : RH_WOENSDRECHT \n", + "x :... " ] }, "execution_count": 12, @@ -1108,7 +756,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:hydropandas.io.io_knmi:download knmi RH data from station 340-WOENSDRECHT between 2019-12-27 00:00:00 and 2021-08-15 11:34:09.602931\n", + "INFO:hydropandas.io.io_knmi:download knmi RH data from station 340-WOENSDRECHT between 2019-12-27 00:00:00 and 2021-08-30 17:19:52.773008\n", "INFO:hydropandas.io.io_knmi:station 340 has 0 missing measurements\n" ] }, @@ -1152,30 +800,30 @@ " \n", " \n", " \n", - " WOENSDRECHT\n", + " RH_WOENSDRECHT\n", " 82881.403813\n", " 384958.510811\n", " \n", " 340\n", " RH\n", " -----metadata------\n", - "name : WOENSDRECHT \n", - "x : 82...\n", + "name : RH_WOENSDRECHT \n", + "x :...\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " x y filename station meteo_var \\\n", - "name \n", - "WOENSDRECHT 82881.403813 384958.510811 340 RH \n", + " x y filename station meteo_var \\\n", + "name \n", + "RH_WOENSDRECHT 82881.403813 384958.510811 340 RH \n", "\n", - " obs \n", - "name \n", - "WOENSDRECHT -----metadata------\n", - "name : WOENSDRECHT \n", - "x : 82... " + " obs \n", + "name \n", + "RH_WOENSDRECHT -----metadata------\n", + "name : RH_WOENSDRECHT \n", + "x :... " ] }, "execution_count": 13, @@ -1187,13 +835,20 @@ "ObsCollection.from_knmi(xmid=np.array([77500]), ymid=np.array([399500]), \n", " meteo_vars=['RH'])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "dev", + "display_name": "wburg", "language": "python", - "name": "dev" + "name": "wburg" }, "language_info": { "codemirror_mode": { diff --git a/examples/03_pastastore_from_observations.ipynb b/examples/03_pastastore_from_observations.ipynb index 63f0a9d1..d034fec4 100644 --- a/examples/03_pastastore_from_observations.ipynb +++ b/examples/03_pastastore_from_observations.ipynb @@ -62,10 +62,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING:hydropandas.io.io_dino:could not read metadata -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\Grondwaterstanden_Put\\B27D0260001_1.csv\n", - "WARNING:hydropandas.io.io_dino:no NAP measurements available -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\Grondwaterstanden_Put\\B27D0260001_1.csv\n", - "WARNING:hydropandas.io.io_dino:could not read metadata -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\Grondwaterstanden_Put\\B37A0112001_1.csv\n", - "WARNING:hydropandas.io.io_dino:could not read measurements -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\Grondwaterstanden_Put\\B37A0112001_1.csv\n" + "WARNING:hydropandas.io.io_dino:could not read metadata -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\Grondwaterstanden_Put\\B27D0260001_1.csv\n", + "WARNING:hydropandas.io.io_dino:no NAP measurements available -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\Grondwaterstanden_Put\\B27D0260001_1.csv\n", + "WARNING:hydropandas.io.io_dino:could not read metadata -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\Grondwaterstanden_Put\\B37A0112001_1.csv\n", + "WARNING:hydropandas.io.io_dino:could not read measurements -> C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\Grondwaterstanden_Put\\B37A0112001_1.csv\n" ] }, { @@ -121,7 +121,7 @@ " B58A0092-004\n", " 186924.0\n", " 372026.0\n", - " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\...\n", + " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\...\n", " B58A0092\n", " 4.0\n", " -115.23\n", @@ -137,7 +137,7 @@ " B58A0092-005\n", " 186924.0\n", " 372026.0\n", - " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\...\n", + " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\...\n", " B58A0092\n", " 5.0\n", " -134.23\n", @@ -153,7 +153,7 @@ " B58A0102-001\n", " 187900.0\n", " 373025.0\n", - " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\...\n", + " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\...\n", " B58A0102\n", " 1.0\n", " -3.35\n", @@ -169,7 +169,7 @@ " B58A0167-001\n", " 185745.0\n", " 371095.0\n", - " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\...\n", + " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\...\n", " B58A0167\n", " 1.0\n", " 23.33\n", @@ -185,7 +185,7 @@ " B58A0212-001\n", " 183600.0\n", " 373020.0\n", - " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\...\n", + " C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\...\n", " B58A0212\n", " 1.0\n", " 26.03\n", @@ -212,11 +212,11 @@ "\n", " filename locatie \\\n", "name \n", - "B58A0092-004 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\... B58A0092 \n", - "B58A0092-005 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\... B58A0092 \n", - "B58A0102-001 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\... B58A0102 \n", - "B58A0167-001 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\... B58A0167 \n", - "B58A0212-001 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpsdkqa83i\\... B58A0212 \n", + "B58A0092-004 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\... B58A0092 \n", + "B58A0092-005 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\... B58A0092 \n", + "B58A0102-001 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\... B58A0102 \n", + "B58A0167-001 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\... B58A0167 \n", + "B58A0212-001 C:\\Users\\oebbe\\AppData\\Local\\Temp\\tmpyqay_031\\... B58A0212 \n", "\n", " filternr bovenkant_filter onderkant_filter maaiveld \\\n", "name \n", @@ -260,7 +260,7 @@ ], "source": [ "extent = [117850, 117980, 439550, 439700] # Schoonhoven zuid-west\n", - "dinozip = '../tests/data/2019-Dino-test/dino.zip'\n", + "dinozip = 'data/dino.zip'\n", "oc_dino = ObsCollection.from_dino(dirname=dinozip, keep_all_obs=False)\n", "oc_dino = oc_dino.loc[['B58A0092-004', 'B58A0092-005', 'B58A0102-001', 'B58A0167-001', 'B58A0212-001']]\n", "oc_dino" @@ -541,17 +541,17 @@ "INFO:pastas.timeseries:Time Series B58A0092-005: 3 nan-value(s) was/were found and filled with: drop.\n", "INFO:pastas.timeseries:Inferred frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:Inferred frequency for time series EV24_ELL: freq=D\n", - "Bulk creation models: 40%|████████████████████████▊ | 2/5 [00:00<00:00, 10.89it/s]INFO:pastas.timeseries:Cannot determine frequency of series B58A0102-001: freq=None. The time series is irregular.\n", + "Bulk creation models: 40%|████████████████████████▊ | 2/5 [00:00<00:00, 10.90it/s]INFO:pastas.timeseries:Cannot determine frequency of series B58A0102-001: freq=None. The time series is irregular.\n", "INFO:pastas.timeseries:Inferred frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:Inferred frequency for time series EV24_ELL: freq=D\n", "INFO:pastas.timeseries:Cannot determine frequency of series B58A0167-001: freq=None. The time series is irregular.\n", "INFO:pastas.timeseries:Inferred frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:Inferred frequency for time series EV24_ELL: freq=D\n", - "Bulk creation models: 80%|█████████████████████████████████████████████████▌ | 4/5 [00:00<00:00, 11.53it/s]INFO:pastas.timeseries:Cannot determine frequency of series B58A0212-001: freq=None. The time series is irregular.\n", + "Bulk creation models: 80%|█████████████████████████████████████████████████▌ | 4/5 [00:00<00:00, 11.47it/s]INFO:pastas.timeseries:Cannot determine frequency of series B58A0212-001: freq=None. The time series is irregular.\n", "INFO:pastas.timeseries:Time Series B58A0212-001: 16 nan-value(s) was/were found and filled with: drop.\n", "INFO:pastas.timeseries:Inferred frequency for time series RD_SOMEREN: freq=D\n", "INFO:pastas.timeseries:Inferred frequency for time series EV24_ELL: freq=D\n", - "Bulk creation models: 100%|██████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 11.48it/s]\n" + "Bulk creation models: 100%|██████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 11.46it/s]\n" ] } ], @@ -573,14 +573,14 @@ "INFO:pastas.timeseries:Time Series B58A0092-004: 13 nan-value(s) was/were found and filled with: drop.\n", "INFO:pastas.timeseries:User provided frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:User provided frequency for time series EV24_ELL: freq=D\n", - "Solving models: 20%|█████████████▌ | 1/5 [00:01<00:04, 1.08s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0092-005: freq=None. The time series is irregular.\n", + "Solving models: 20%|█████████████▌ | 1/5 [00:01<00:04, 1.14s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0092-005: freq=None. The time series is irregular.\n", "INFO:pastas.timeseries:Time Series B58A0092-005: 3 nan-value(s) was/were found and filled with: drop.\n", "INFO:pastas.timeseries:User provided frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:User provided frequency for time series EV24_ELL: freq=D\n", - "Solving models: 40%|███████████████████████████▏ | 2/5 [00:02<00:03, 1.28s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0102-001: freq=None. The time series is irregular.\n", + "Solving models: 40%|███████████████████████████▏ | 2/5 [00:02<00:04, 1.34s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0102-001: freq=None. The time series is irregular.\n", "INFO:pastas.timeseries:User provided frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:User provided frequency for time series EV24_ELL: freq=D\n", - "Solving models: 60%|████████████████████████████████████████▊ | 3/5 [00:03<00:02, 1.26s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0167-001: freq=None. The time series is irregular.\n", + "Solving models: 60%|████████████████████████████████████████▊ | 3/5 [00:03<00:02, 1.30s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0167-001: freq=None. The time series is irregular.\n", "INFO:pastas.timeseries:User provided frequency for time series RD_HEIBLOEM-L: freq=D\n", "INFO:pastas.timeseries:User provided frequency for time series EV24_ELL: freq=D\n", "Solving models: 80%|██████████████████████████████████████████████████████▍ | 4/5 [00:05<00:01, 1.26s/it]INFO:pastas.timeseries:Cannot determine frequency of series B58A0212-001: freq=None. The time series is irregular.\n", @@ -589,7 +589,7 @@ "INFO:pastas.timeseries:User provided frequency for time series EV24_ELL: freq=D\n", "INFO:pastas.timeseries:Time Series RD_SOMEREN was extended to 1953-05-30 00:00:00 with the mean value of the time series.\n", "INFO:pastas.timeseries:Time Series EV24_ELL was extended to 1953-05-30 00:00:00 with the mean value of the time series.\n", - "Solving models: 100%|████████████████████████████████████████████████████████████████████| 5/5 [00:06<00:00, 1.27s/it]\n" + "Solving models: 100%|████████████████████████████████████████████████████████████████████| 5/5 [00:06<00:00, 1.29s/it]\n" ] } ], @@ -688,6 +688,13 @@ "ml1 = pstore.get_models('B58A0167-001')\n", "ml1.plots.results();" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/data/Grondwaterstanden_Put/B33F0080001_0.csv b/examples/data/Grondwaterstanden_Put/B33F0080001_0.csv new file mode 100644 index 00000000..77c7cd33 --- /dev/null +++ b/examples/data/Grondwaterstanden_Put/B33F0080001_0.csv @@ -0,0 +1,4002 @@ +Titel: +Gebruikersnaam: +Periode aangevraagd: 01/01/1800-05/10/2015 +Datum: 05/10/2015 +Referentie: Maaiveld (MV) + + +LOCATIE,FILTERNUMMER,EXTERNE AANDUIDING,X-COORDINAAT,Y-COORDINAAT,MAAIVELD NAP,GESCHAT,MEETPUNT NAP,BOVENKANT FILTER,ONDERKANT FILTER,START DATUM,EIND DATUM +B33F0080,001,33FP0080,213260,473900,685,,717,385,285,1972/11/28,1986/11/28 +B33F0080,001,33FP0080,213260,473900,685,,709,385,285,1986/11/28,1997/09/10 +B33F0080,001,33FP0080,213260,473900,692,,716,385,285,1997/09/10,2004/07/06 +B33F0080,001,33FP0080,213260,473900,692,,718,385,285,2004/07/06,2015/06/17 + +LOCATIE,FILTERNUMMER,PEIL DATUM TIJD,STAND (MV),BIJZONDERHEID +B33F0080,001,1972/11/28 00:00:00,109, +B33F0080,001,1972/12/07 00:00:00,108, +B33F0080,001,1972/12/14 00:00:00,115, +B33F0080,001,1972/12/21 00:00:00,121, +B33F0080,001,1972/12/28 00:00:00,128, +B33F0080,001,1973/03/15 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+B33F0080,001,2015/06/14 00:00:00,150, +B33F0080,001,2015/06/15 00:00:00,152, +B33F0080,001,2015/06/16 00:00:00,152, +B33F0080,001,2015/06/17 00:00:00,152, diff --git a/examples/data/Grondwaterstanden_Put/B33F0080001_1.csv b/examples/data/Grondwaterstanden_Put/B33F0080001_1.csv new file mode 100644 index 00000000..14230311 --- /dev/null +++ b/examples/data/Grondwaterstanden_Put/B33F0080001_1.csv @@ -0,0 +1,4007 @@ +Titel:,,,,,,,,,,, +Gebruikersnaam:,,,,,,,,,,, +Periode aangevraagd:,01-01-1800,tot:,05-10-2015,,,,,,,, +Gegevens beschikbaar:,28-11-1972,tot:,17-06-2015,,,,,,,, +Datum: ,05-10-2015,,,,,,,,,, +Referentie:,NAP,,,,,,,,,, + +NAP:,Normaal Amsterdams Peil,,,,,,,,,, +MV:,Maaiveld,,,,,,,,,, +MP:,Meetpunt,,,,,,,,,, + +Locatie,Filternummer,Externe aanduiding,X-coordinaat,Y-coordinaat,Maaiveld (cm t.o.v. NAP),Datum maaiveld gemeten,Startdatum,Einddatum,Meetpunt (cm t.o.v. NAP),Meetpunt (cm t.o.v. MV),Bovenkant filter (cm t.o.v. NAP),Onderkant filter (cm t.o.v. NAP) +B33F0080,001,33FP0080,213260,473900,685,05-10-1972,28-11-1972,28-11-1986,717,32,385,285 +B33F0080,001,33FP0080,213260,473900,685,05-10-1972,28-11-1986,10-09-1997,709,24,385,285 +B33F0080,001,33FP0080,213260,473900,692,10-09-1997,10-09-1997,06-07-2004,716,24,385,285 +B33F0080,001,33FP0080,213260,473900,692,10-09-1997,06-07-2004,17-06-2015,718,26,385,285 + + +Locatie,Filternummer,Peildatum,Stand (cm t.o.v. MP),Stand (cm t.o.v. MV),Stand (cm t.o.v. NAP),Bijzonderheid,Opmerking,,, +B33F0080,001,28-11-1972,141,109,576,,,,,, +B33F0080,001,07-12-1972,140,108,577,,,,,, +B33F0080,001,14-12-1972,147,115,570,,,,,, +B33F0080,001,21-12-1972,153,121,564,,,,,, +B33F0080,001,28-12-1972,160,128,557,,,,,, +B33F0080,001,15-03-1973,145,113,572,,,,,, +B33F0080,001,29-03-1973,154,122,563,,,,,, +B33F0080,001,12-04-1973,138,106,579,,,,,, +B33F0080,001,26-04-1973,150,118,567,,,,,, +B33F0080,001,15-05-1973,153,121,564,,,,,, +B33F0080,001,29-05-1973,163,131,554,,,,,, +B33F0080,001,15-06-1973,168,136,549,,,,,, +B33F0080,001,29-06-1973,183,151,534,,,,,, +B33F0080,001,16-07-1973,189,157,528,,,,,, +B33F0080,001,15-08-1973,189,157,528,,,,,, +B33F0080,001,29-08-1973,201,169,516,,,,,, +B33F0080,001,13-09-1973,201,169,516,,,,,, +B33F0080,001,27-09-1973,172,140,545,,,,,, +B33F0080,001,16-10-1973,166,134,551,,,,,, +B33F0080,001,30-10-1973,156,124,561,,,,,, +B33F0080,001,15-11-1973,134,102,583,,,,,, +B33F0080,001,29-11-1973,146,114,571,,,,,, 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DATUM,EIND DATUM +B33F0133,001,33FP0133,210400,473366,650,,702,-6750,-7000,1989/12/14,1995/10/23 +B33F0133,001,33FP0133,210400,473366,650,,714,-6750,-7000,1995/10/23,2011/01/18 + +LOCATIE,FILTERNUMMER,PEIL DATUM TIJD,STAND (MV),BIJZONDERHEID +B33F0133,001,1989/12/14 00:00:00,530, +B33F0133,001,1990/01/15 00:00:00,493, +B33F0133,001,1990/01/29 00:00:00,480, +B33F0133,001,1990/02/14 00:00:00,497, +B33F0133,001,1990/03/01 00:00:00,494, +B33F0133,001,1990/03/13 00:00:00,496, +B33F0133,001,1990/03/28 00:00:00,511, +B33F0133,001,1990/04/13 00:00:00,521, +B33F0133,001,1990/04/27 00:00:00,522, +B33F0133,001,1990/05/14 00:00:00,513, +B33F0133,001,1990/05/28 00:00:00,511, +B33F0133,001,1990/06/14 00:00:00,532, +B33F0133,001,1990/06/28 00:00:00,536, +B33F0133,001,1990/07/13 00:00:00,533, +B33F0133,001,1990/07/27 00:00:00,521, +B33F0133,001,1990/08/14 00:00:00,543, +B33F0133,001,1990/08/28 00:00:00,548, +B33F0133,001,1990/09/14 00:00:00,551, +B33F0133,001,1990/09/28 00:00:00,546, 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a/examples/data/Grondwaterstanden_Put/B33F0133001_1.csv b/examples/data/Grondwaterstanden_Put/B33F0133001_1.csv new file mode 100644 index 00000000..eb6ffda7 --- /dev/null +++ b/examples/data/Grondwaterstanden_Put/B33F0133001_1.csv @@ -0,0 +1,2229 @@ +Titel:,,,,,,,,,,, +Gebruikersnaam:,,,,,,,,,,, +Periode aangevraagd:,01-01-1800,tot:,05-10-2015,,,,,,,, +Gegevens beschikbaar:,14-12-1989,tot:,18-01-2011,,,,,,,, +Datum: ,05-10-2015,,,,,,,,,, +Referentie:,NAP,,,,,,,,,, + +NAP:,Normaal Amsterdams Peil,,,,,,,,,, +MV:,Maaiveld,,,,,,,,,, +MP:,Meetpunt,,,,,,,,,, + +Locatie,Filternummer,Externe aanduiding,X-coordinaat,Y-coordinaat,Maaiveld (cm t.o.v. NAP),Datum maaiveld gemeten,Startdatum,Einddatum,Meetpunt (cm t.o.v. NAP),Meetpunt (cm t.o.v. MV),Bovenkant filter (cm t.o.v. NAP),Onderkant filter (cm t.o.v. NAP) +B33F0133,001,33FP0133,210400,473366,650,01-11-1989,14-12-1989,23-10-1995,702,52,-6750,-7000 +B33F0133,001,33FP0133,210400,473366,650,01-11-1989,23-10-1995,18-01-2011,714,64,-6750,-7000 + + +Locatie,Filternummer,Peildatum,Stand (cm t.o.v. MP),Stand (cm t.o.v. MV),Stand (cm t.o.v. NAP),Bijzonderheid,Opmerking,,, +B33F0133,001,14-12-1989,582,530,120,,,,,, +B33F0133,001,15-01-1990,545,493,157,,,,,, +B33F0133,001,29-01-1990,532,480,170,,,,,, +B33F0133,001,14-02-1990,549,497,153,,,,,, +B33F0133,001,01-03-1990,546,494,156,,,,,, +B33F0133,001,13-03-1990,548,496,154,,,,,, +B33F0133,001,28-03-1990,563,511,139,,,,,, +B33F0133,001,13-04-1990,573,521,129,,,,,, +B33F0133,001,27-04-1990,574,522,128,,,,,, +B33F0133,001,14-05-1990,565,513,137,,,,,, +B33F0133,001,28-05-1990,563,511,139,,,,,, +B33F0133,001,14-06-1990,584,532,118,,,,,, +B33F0133,001,28-06-1990,588,536,114,,,,,, +B33F0133,001,13-07-1990,585,533,117,,,,,, +B33F0133,001,27-07-1990,573,521,129,,,,,, +B33F0133,001,14-08-1990,595,543,107,,,,,, +B33F0133,001,28-08-1990,600,548,102,,,,,, +B33F0133,001,14-09-1990,603,551,99,,,,,, +B33F0133,001,28-09-1990,598,546,104,,,,,, +B33F0133,001,14-10-1990,586,534,116,,,,,, +B33F0133,001,14-11-1990,578,526,124,,,,,, +B33F0133,001,14-12-1990,567,515,135,,,,,, +B33F0133,001,14-01-1991,521,469,181,,,,,, +B33F0133,001,28-01-1991,533,481,169,,,,,, +B33F0133,001,15-02-1991,551,499,151,,,,,, +B33F0133,001,28-02-1991,538,486,164,,,,,, +B33F0133,001,29-03-1991,560,508,142,,,,,, +B33F0133,001,16-04-1991,567,515,135,,,,,, +B33F0133,001,29-04-1991,575,523,127,,,,,, +B33F0133,001,14-05-1991,550,498,152,,,,,, +B33F0133,001,28-05-1991,545,493,157,,,,,, +B33F0133,001,14-06-1991,583,531,119,,,,,, +B33F0133,001,28-07-1991,533,481,169,,,,,, +B33F0133,001,14-08-1991,571,519,131,,,,,, +B33F0133,001,28-08-1991,602,550,100,,,,,, +B33F0133,001,13-09-1991,630,578,72,,,,,, +B33F0133,001,27-09-1991,619,567,83,,,,,, +B33F0133,001,14-10-1991,606,554,96,,,,,, +B33F0133,001,29-10-1991,586,534,116,,,,,, +B33F0133,001,14-11-1991,580,528,122,,,,,, +B33F0133,001,27-11-1991,579,527,123,,,,,, +B33F0133,001,13-12-1991,588,536,114,,,,,, +B33F0133,001,16-01-1992,556,504,146,,,,,, +B33F0133,001,28-01-1992,556,504,146,,,,,, +B33F0133,001,14-02-1992,564,512,138,,,,,, +B33F0133,001,28-02-1992,575,523,127,,,,,, +B33F0133,001,13-03-1992,571,519,131,,,,,, +B33F0133,001,27-03-1992,551,499,151,,,,,, +B33F0133,001,14-04-1992,560,508,142,,,,,, +B33F0133,001,28-04-1992,551,499,151,,,,,, +B33F0133,001,14-05-1992,565,513,137,,,,,, +B33F0133,001,29-05-1992,585,533,117,,,,,, +B33F0133,001,15-06-1992,567,515,135,,,,,, +B33F0133,001,30-06-1992,598,546,104,,,,,, +B33F0133,001,14-07-1992,602,550,100,,,,,, +B33F0133,001,28-07-1992,599,547,103,,,,,, +B33F0133,001,14-08-1992,588,536,114,,,,,, +B33F0133,001,28-08-1992,588,536,114,,,,,, +B33F0133,001,14-09-1992,575,523,127,,,,,, +B33F0133,001,28-09-1992,578,526,124,,,,,, +B33F0133,001,14-10-1992,581,529,121,,,,,, +B33F0133,001,29-10-1992,581,529,121,,,,,, +B33F0133,001,13-11-1992,575,523,127,,,,,, +B33F0133,001,27-11-1992,557,505,145,,,,,, +B33F0133,001,14-12-1992,551,499,151,,,,,, +B33F0133,001,14-01-1993,572,520,130,,,,,, +B33F0133,001,29-01-1993,566,514,136,,,,,, +B33F0133,001,15-02-1993,577,525,125,,,,,, +B33F0133,001,01-03-1993,566,514,136,,,,,, +B33F0133,001,15-03-1993,540,488,162,,,,,, +B33F0133,001,29-03-1993,551,499,151,,,,,, +B33F0133,001,14-04-1993,556,504,146,,,,,, +B33F0133,001,28-04-1993,566,514,136,,,,,, +B33F0133,001,14-05-1993,569,517,133,,,,,, +B33F0133,001,28-05-1993,562,510,140,,,,,, +B33F0133,001,14-06-1993,570,518,132,,,,,, +B33F0133,001,28-06-1993,555,503,147,,,,,, +B33F0133,001,14-07-1993,595,543,107,,,,,, +B33F0133,001,28-07-1993,567,515,135,,,,,, +B33F0133,001,16-08-1993,555,503,147,,,,,, +B33F0133,001,27-08-1993,605,553,97,,,,,, +B33F0133,001,14-09-1993,555,503,147,,,,,, +B33F0133,001,28-09-1993,602,550,100,,,,,, +B33F0133,001,15-10-1993,621,569,81,,,,,, +B33F0133,001,28-10-1993,612,560,90,,,,,, +B33F0133,001,15-11-1993,580,528,122,,,,,, +B33F0133,001,30-11-1993,610,558,92,,,,,, +B33F0133,001,14-12-1993,591,539,111,,,,,, +B33F0133,001,28-12-1993,531,479,171,,,,,, +B33F0133,001,14-01-1994,534,482,168,,,,,, +B33F0133,001,14-02-1994,552,500,150,,,,,, +B33F0133,001,28-02-1994,537,485,165,,,,,, +B33F0133,001,14-03-1994,528,476,174,,,,,, +B33F0133,001,28-03-1994,509,457,193,,,,,, +B33F0133,001,14-04-1994,519,467,183,,,,,, +B33F0133,001,28-04-1994,543,491,159,,,,,, +B33F0133,001,16-05-1994,506,454,196,,,,,, +B33F0133,001,27-05-1994,523,471,179,,,,,, +B33F0133,001,14-06-1994,523,471,179,,,,,, +B33F0133,001,28-06-1994,536,484,166,,,,,, +B33F0133,001,14-07-1994,564,512,138,,,,,, +B33F0133,001,30-08-1994,565,513,137,,,,,, +B33F0133,001,14-09-1994,568,516,134,,,,,, +B33F0133,001,28-09-1994,561,509,141,,,,,, +B33F0133,001,14-10-1994,567,515,135,,,,,, +B33F0133,001,28-10-1994,557,505,145,,,,,, +B33F0133,001,14-11-1994,533,481,169,,,,,, +B33F0133,001,29-11-1994,541,489,161,,,,,, +B33F0133,001,13-12-1994,532,480,170,,,,,, +B33F0133,001,28-12-1994,513,461,189,,,,,, +B33F0133,001,13-01-1995,522,470,180,,,,,, +B33F0133,001,27-01-1995,506,454,196,,,,,, +B33F0133,001,14-02-1995,499,447,203,,,,,, +B33F0133,001,28-02-1995,503,451,199,,,,,, +B33F0133,001,14-03-1995,506,454,196,,,,,, +B33F0133,001,28-03-1995,497,445,205,,,,,, +B33F0133,001,14-04-1995,541,489,161,,,,,, +B33F0133,001,28-04-1995,524,472,178,,,,,, +B33F0133,001,15-05-1995,510,458,192,,,,,, +B33F0133,001,30-05-1995,507,455,195,,,,,, +B33F0133,001,14-06-1995,516,464,186,,,,,, +B33F0133,001,28-06-1995,565,513,137,,,,,, +B33F0133,001,14-07-1995,581,529,121,,,,,, +B33F0133,001,28-07-1995,556,504,146,,,,,, +B33F0133,001,14-08-1995,578,526,124,,,,,, +B33F0133,001,28-08-1995,569,517,133,,,,,, +B33F0133,001,13-09-1995,593,541,109,,,,,, +B33F0133,001,27-09-1995,566,514,136,,,,,, +B33F0133,001,13-10-1995,560,508,142,,,,,, +B33F0133,001,23-10-1995,572,508,142,,CONTROLE,,,, +B33F0133,001,14-11-1995,586,522,128,,,,,, +B33F0133,001,28-11-1995,597,533,117,,,,,, +B33F0133,001,15-12-1995,610,546,104,,,,,, +B33F0133,001,28-12-1995,574,510,140,,,,,, +B33F0133,001,12-01-1996,571,507,143,,,,,, +B33F0133,001,26-01-1996,569,505,145,,,,,, +B33F0133,001,14-02-1996,560,496,154,,,,,, +B33F0133,001,28-02-1996,549,485,165,,,,,, +B33F0133,001,14-03-1996,543,479,171,,,,,, +B33F0133,001,28-03-1996,541,477,173,,,,,, +B33F0133,001,12-04-1996,542,478,172,,,,,, +B33F0133,001,26-04-1996,565,501,149,,,,,, +B33F0133,001,15-05-1996,562,498,152,,,,,, +B33F0133,001,30-05-1996,549,485,165,,,,,, +B33F0133,001,12-06-1996,570,506,144,,,,,, +B33F0133,001,28-06-1996,563,499,151,,,,,, +B33F0133,001,17-07-1996,535,471,179,,,,,, +B33F0133,001,14-08-1996,535,471,179,,,,,, +B33F0133,001,28-08-1996,533,469,181,,,,,, +B33F0133,001,13-09-1996,536,472,178,,,,,, +B33F0133,001,27-09-1996,560,496,154,,,,,, +B33F0133,001,16-10-1996,558,494,156,,,,,, +B33F0133,001,30-10-1996,565,501,149,,,,,, +B33F0133,001,14-11-1996,546,482,168,,,,,, +B33F0133,001,28-11-1996,529,465,185,,,,,, +B33F0133,001,13-12-1996,532,468,182,,,,,, +B33F0133,001,15-01-1997,523,459,191,,,,,, +B33F0133,001,29-01-1997,527,463,187,,,,,, +B33F0133,001,14-02-1997,523,459,191,,,,,, +B33F0133,001,28-02-1997,513,449,201,,,,,, +B33F0133,001,13-03-1997,523,459,191,,,,,, +B33F0133,001,28-03-1997,524,460,190,,,,,, +B33F0133,001,15-04-1997,523,459,191,,,,,, +B33F0133,001,29-04-1997,519,455,195,,,,,, +B33F0133,001,14-05-1997,521,457,193,,,,,, +B33F0133,001,13-06-1997,542,478,172,,,,,, +B33F0133,001,27-06-1997,531,467,183,,,,,, +B33F0133,001,15-07-1997,509,445,205,,,,,, +B33F0133,001,29-07-1997,488,424,226,,,,,, +B33F0133,001,14-08-1997,518,454,196,,,,,, +B33F0133,001,28-08-1997,535,471,179,,,,,, +B33F0133,001,10-09-1997,531,467,183,,CONTROLE,,,, +B33F0133,001,16-09-1997,529,465,185,,,,,, +B33F0133,001,30-09-1997,536,472,178,,,,,, +B33F0133,001,14-10-1997,521,457,193,,,,,, +B33F0133,001,28-10-1997,521,457,193,,,,,, +B33F0133,001,14-11-1997,521,457,193,,,,,, +B33F0133,001,28-11-1997,523,459,191,,,,,, +B33F0133,001,16-12-1997,520,456,194,,,,,, +B33F0133,001,28-01-1998,518,454,196,,,,,, +B33F0133,001,15-02-1998,523,459,191,,,,,, +B33F0133,001,26-02-1998,530,466,184,,,,,, +B33F0133,001,13-03-1998,492,428,222,,,,,, +B33F0133,001,27-03-1998,496,432,218,,,,,, +B33F0133,001,15-04-1998,475,411,239,,,,,, +B33F0133,001,28-04-1998,474,410,240,,,,,, +B33F0133,001,14-05-1998,493,429,221,,,,,, +B33F0133,001,28-05-1998,500,436,214,,,,,, +B33F0133,001,15-06-1998,485,421,229,,,,,, +B33F0133,001,30-06-1998,498,434,216,,,,,, +B33F0133,001,14-07-1998,482,418,232,,,,,, +B33F0133,001,28-07-1998,473,409,241,,,,,, +B33F0133,001,14-08-1998,483,419,231,,,,,, +B33F0133,001,28-08-1998,492,428,222,,,,,, +B33F0133,001,15-09-1998,475,411,239,,,,,, +B33F0133,001,14-10-1998,479,415,235,,,,,, +B33F0133,001,28-10-1998,466,402,248,,,,,, +B33F0133,001,13-11-1998,461,397,253,,,,,, +B33F0133,001,27-11-1998,476,412,238,,,,,, +B33F0133,001,14-12-1998,461,397,253,,,,,, +B33F0133,001,15-01-1999,442,378,272,,,,,, +B33F0133,001,29-01-1999,456,392,258,,,,,, +B33F0133,001,15-02-1999,438,374,276,,,,,, +B33F0133,001,02-03-1999,422,358,292,,,,,, +B33F0133,001,15-03-1999,431,367,283,,,,,, +B33F0133,001,31-03-1999,455,391,259,,,,,, +B33F0133,001,14-04-1999,443,379,271,,,,,, +B33F0133,001,28-05-1999,463,399,251,,,,,, +B33F0133,001,15-06-1999,478,414,236,,,,,, +B33F0133,001,29-06-1999,479,415,235,,,,,, +B33F0133,001,14-07-1999,487,423,227,,,,,, +B33F0133,001,28-07-1999,475,411,239,,,,,, +B33F0133,001,13-08-1999,485,421,229,,,,,, +B33F0133,001,26-08-1999,474,410,240,,,,,, +B33F0133,001,15-09-1999,475,411,239,,,,,, +B33F0133,001,29-09-1999,476,412,238,,,,,, +B33F0133,001,14-10-1999,478,414,236,,,,,, +B33F0133,001,28-10-1999,480,416,234,,,,,, +B33F0133,001,16-11-1999,473,409,241,,,,,, +B33F0133,001,15-12-1999,481,417,233,,,,,, +B33F0133,001,29-12-1999,462,398,252,,,,,, +B33F0133,001,27-01-2000,478,414,236,,,,,, +B33F0133,001,14-02-2000,473,409,241,,,,,, +B33F0133,001,28-02-2000,453,389,261,,,,,, +B33F0133,001,14-03-2000,480,416,234,,,,,, +B33F0133,001,28-03-2000,468,404,246,,,,,, +B33F0133,001,14-04-2000,480,416,234,,,,,, +B33F0133,001,28-04-2000,482,418,232,,,,,, +B33F0133,001,15-05-2000,500,436,214,,,,,, +B33F0133,001,29-05-2000,490,426,224,,,,,, +B33F0133,001,14-06-2000,497,433,217,,,,,, +B33F0133,001,28-06-2000,500,436,214,,,,,, +B33F0133,001,14-07-2000,480,416,234,,,,,, +B33F0133,001,14-08-2000,430,366,284,,,,,, +B33F0133,001,28-08-2000,420,356,294,,,,,, +B33F0133,001,14-09-2000,437,373,277,,,,,, +B33F0133,001,13-10-2000,426,362,288,,,,,, +B33F0133,001,27-10-2000,427,363,287,,,,,, +B33F0133,001,14-11-2000,410,346,304,,,,,, +B33F0133,001,28-11-2000,415,351,299,,,,,, +B33F0133,001,14-12-2000,414,350,300,,,,,, +B33F0133,001,28-12-2000,397,333,317,,,,,, +B33F0133,001,15-01-2001,396,332,318,,,,,, +B33F0133,001,29-01-2001,397,333,317,,,,,, +B33F0133,001,14-02-2001,391,327,323,,,,,, +B33F0133,001,28-02-2001,397,333,317,,,,,, +B33F0133,001,13-04-2001,413,349,301,,,,,, +B33F0133,001,27-04-2001,401,337,313,,,,,, +B33F0133,001,14-05-2001,410,346,304,,,,,, +B33F0133,001,28-05-2001,413,349,301,,,,,, +B33F0133,001,14-06-2001,421,357,293,,,,,, +B33F0133,001,28-06-2001,425,361,289,,,,,, +B33F0133,001,14-07-2001,425,361,289,,,,,, +B33F0133,001,28-07-2001,408,344,306,,,,,, +B33F0133,001,14-08-2001,412,348,302,,,,,, +B33F0133,001,28-08-2001,433,369,281,,,,,, +B33F0133,001,14-09-2001,430,366,284,,,,,, +B33F0133,001,28-09-2001,432,368,282,,,,,, +B33F0133,001,15-10-2001,433,369,281,,,,,, +B33F0133,001,29-10-2001,439,375,275,,,,,, +B33F0133,001,14-11-2001,440,376,274,,,,,, +B33F0133,001,28-11-2001,430,366,284,,,,,, +B33F0133,001,13-12-2001,435,371,279,,,,,, +B33F0133,001,27-12-2001,410,346,304,,,,,, +B33F0133,001,15-01-2002,424,360,290,,,,,, +B33F0133,001,29-01-2002,413,349,301,,,,,, +B33F0133,001,15-02-2002,420,356,294,,,,,, +B33F0133,001,01-03-2002,395,331,319,,,,,, +B33F0133,001,15-03-2002,401,337,313,,,,,, +B33F0133,001,29-03-2002,424,360,290,,,,,, +B33F0133,001,15-04-2002,461,397,253,,,,,, +B33F0133,001,27-04-2002,475,411,239,,,,,, +B33F0133,001,14-05-2002,448,384,266,,,,,, +B33F0133,001,28-05-2002,448,384,266,,,,,, +B33F0133,001,14-06-2002,461,397,253,,,,,, 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+B33F0133,001,14-01-2011,357,293,357,,,,,, +B33F0133,001,15-01-2011,354,290,360,,,,,, +B33F0133,001,16-01-2011,353,289,361,,,,,, +B33F0133,001,17-01-2011,353,289,361,,,,,, +B33F0133,001,18-01-2011,351,287,363,,,,,, diff --git a/examples/data/dino.zip b/examples/data/dino.zip new file mode 100644 index 00000000..fa9cc167 Binary files /dev/null and b/examples/data/dino.zip differ diff --git a/hydropandas/extensions/gwobs.py b/hydropandas/extensions/gwobs.py index a4be8423..2d88bbfc 100644 --- a/hydropandas/extensions/gwobs.py +++ b/hydropandas/extensions/gwobs.py @@ -23,10 +23,10 @@ def get_model_layer_z(z, zvec, left=-999, right=999): zvec : list, np.array elevations of model layers. shape is nlay + 1 left : int, optional - if z is below the lowest value in zvec, this value is returned. + if z is below the lowest value in zvec, this value is returned. The default is -999. right : TYPE, optional - if z is above the highest value in zvec, this value is returned. + if z is above the highest value in zvec, this value is returned. The default is 999. Returns @@ -85,10 +85,10 @@ def get_modellayer_from_filter(ftop, fbot, zvec, left=-999, right=999): zvec : list or np.array elevations of the modellayers at the location of the filter. left : int, optional - value to return if filter is below the modellayers. + value to return if filter is below the modellayers. The default is -999. right : int, optional - value to return if filter is above the modellayers. + value to return if filter is above the modellayers. The default is-999. Raises @@ -213,7 +213,7 @@ def get_modellayer_from_filter(ftop, fbot, zvec, left=-999, right=999): return np.nan -def get_zvec(x, y, gwf=None): +def get_zvec(x, y, gwf=None, ds=None): """get a list with the vertical layer boundaries at a point in the model. Parameters @@ -224,6 +224,9 @@ def get_zvec(x, y, gwf=None): y coordinate. gwf : flopy.mf6.modflow.mfgwf.ModflowGwf modflow model with top and bottoms + ds : xarray.Dataset + xarray Dataset with with top and bottoms, must have + dimensions 'x' and 'y' and variables 'top' and 'bot' Raises ------ @@ -236,12 +239,12 @@ def get_zvec(x, y, gwf=None): list of vertical layer boundaries. length is nlay + 1. """ - if gwf is not None: + if gwf and not ds: if gwf.modelgrid.grid_type == 'structured': r, c = gwf.modelgrid.intersect(x, y, forgive=True) if np.isfinite(r) & np.isfinite(c): - zvec = [gwf.modelgrid.top[r, c]] + [gwf.modelgrid.botm[i, r, c] - for i in range(gwf.modelgrid.nlay)] + zvec = np.array([gwf.modelgrid.top[r, c]] + + [gwf.modelgrid.botm[i, r, c] for i in range(gwf.modelgrid.nlay)]) else: print("Point is not within model extent! Returning NaN.") zvec = np.nan @@ -256,18 +259,34 @@ def get_zvec(x, y, gwf=None): else: raise NotImplementedError( f'gridtype {gwf.modelgrid.grid_type} not (yet) implemented') + elif ds and not gwf: + # assuming modflow type definition with 1 top and N botms + # check extent + xmin, xmax, ymin, ymax = ds.attrs["extent"] + if (x < xmin) or (x > xmax) or (y < ymin) or (y > ymax): + zvec = np.nan + else: + sel = ds.sel(x=x, y=y, method="nearest") + first_notna = np.nonzero(np.isfinite(sel["top"].data))[0][0] + zvec = np.concatenate([sel["top"].data[[first_notna]], sel["bot"].data]) + mask = np.isnan(zvec) + idx = np.where(~mask,np.arange(mask.size),0) + np.maximum.accumulate(idx,axis=0, out=idx) + zvec[mask] = zvec[idx[mask]] + else: + raise ValueError("Pass one of 'gwf' or 'ds'!") return zvec -@accessor.register_obscollection_accessor("gwobs") +@ accessor.register_obscollection_accessor("gwobs") class GwObsAccessor: def __init__(self, oc_obj): - self._obj = oc_obj + self._obj=oc_obj - def set_filter_num(self, radius=1, xcol='x', ycol='y', if_exists='error', - add_to_meta=False): + def set_filter_num(self, radius = 1, xcol = 'x', ycol = 'y', if_exists = 'error', + add_to_meta = False): """This method computes the filternumbers based on the location of the observations. @@ -311,7 +330,7 @@ def set_filter_num(self, radius=1, xcol='x', ycol='y', if_exists='error', if 'filternr' in self._obj.columns: # set type to numeric if self._obj['filternr'].dtype != np.number: - self._obj['filternr'] = pd.to_numeric( + self._obj['filternr']=pd.to_numeric( self._obj['filternr'], errors='coerce') # check if name should be replaced @@ -426,7 +445,7 @@ def set_filter_num_location(self, loc_col, radius=1, xcol='x', ycol='y', self._obj._set_metadata_value(pb_dub, 'locatie', locatie, add_to_meta=add_to_meta) - def get_modellayers(self, gwf): + def get_modellayers(self, gwf=None, ds=None): """Get the modellayer per observation. The layers can be obtained from the modflow model or can be defined in zgr. @@ -434,6 +453,9 @@ def get_modellayers(self, gwf): ---------- gwf : flopy.mf6.modflow.mfgwf.ModflowGwf modflow model + ds : xarray.Dataset + xarray Dataset with with top and bottoms, must have + dimensions 'x' and 'y' and variables 'top' and 'bot' Returns ------- @@ -443,7 +465,7 @@ def get_modellayers(self, gwf): for o in self._obj.obs.values: logger.info("-" * 10 + f"\n {o.name}:") - modellayers.append(o.gwobs.get_modellayer_modflow(gwf)) + modellayers.append(o.gwobs.get_modellayer_modflow(gwf=gwf, ds=ds)) modellayers = pd.Series(index=self._obj.index, data=modellayers, name='modellayer') @@ -470,13 +492,16 @@ class GeoAccessorObs: def __init__(self, obs): self._obj = obs - def get_modellayer_modflow(self, gwf, left=-999, right=999): + def get_modellayer_modflow(self, gwf=None, ds=None, left=-999, right=999): """Add modellayer to meta dictionary. Parameters ---------- gwf : flopy.mf6.modflow.mfgwf.ModflowGwf modflow model + ds : xarray.Dataset + xarray Dataset with with top and bottoms, must have + dimensions 'x' and 'y' and variables 'top' and 'bot' Returns ------- @@ -484,7 +509,7 @@ def get_modellayer_modflow(self, gwf, left=-999, right=999): modellayer """ - zvec = get_zvec(self._obj.x, self._obj.y, gwf) + zvec = get_zvec(self._obj.x, self._obj.y, gwf=gwf, ds=ds) if np.all(np.isnan(zvec)): return np.nan diff --git a/hydropandas/extensions/stats.py b/hydropandas/extensions/stats.py index e3f48134..406f8e85 100644 --- a/hydropandas/extensions/stats.py +++ b/hydropandas/extensions/stats.py @@ -13,7 +13,7 @@ def __init__(self, oc_obj): @property def n_observations(self): - return self._obj.obs.apply(lambda o: o.shape[0]) + return self._obj.obs.apply(lambda o: o.index.size) @property def dates_first_obs(self): diff --git a/hydropandas/io/io_waterinfo.py b/hydropandas/io/io_waterinfo.py index 173dd291..2fa0f787 100644 --- a/hydropandas/io/io_waterinfo.py +++ b/hydropandas/io/io_waterinfo.py @@ -4,7 +4,7 @@ import numpy as np import pandas as pd -from pyproj import Proj, transform +from pyproj import Transformer from tqdm import tqdm @@ -75,11 +75,11 @@ def read_waterinfo_file(path_to_file, index_cols=None, return_metadata=False, " Use ObsCollection.from_waterinfo()!") metadata = {} + if transform_coords: - x, y = transform(Proj('epsg:25831'), - Proj('epsg:28992'), - df[xcol].iloc[-1], - df[ycol].iloc[-1]) + transformer = Transformer.from_crs('epsg:25831', 'epsg:28992') + x, y = transformer.transform(df[xcol].iloc[-1], + df[ycol].iloc[-1]) else: x = df[xcol].iloc[-1] / 100. y = df[ycol].iloc[-1] / 100. @@ -125,6 +125,9 @@ def read_waterinfo_obs(file_or_dir, ObsClass, progressbar=False, **kwargs): # loop over files metadata = {} obs_collection = [] + + transformer = Transformer.from_crs("epsg:25831", "epsg:28992") + for filenm in (tqdm(files) if progressbar else files): # read file or zip df = read_waterinfo_file(filenm, location_col=location_col, **kwargs) @@ -132,10 +135,8 @@ def read_waterinfo_obs(file_or_dir, ObsClass, progressbar=False, **kwargs): # get location and convert to m RD for stn in df[location_col].unique(): mask = df[location_col] == stn - x, y = transform(Proj('epsg:25831'), - Proj('epsg:28992'), - df.loc[mask, 'X'][-1], - df.loc[mask, 'Y'][-1]) + x, y = transformer.transform(df.loc[mask, 'X'][-1], + df.loc[mask, 'Y'][-1]) metadata = {"name": stn, "x": x, "y": y} # add to list diff --git a/hydropandas/obs_collection.py b/hydropandas/obs_collection.py index 6055461b..0409da26 100644 --- a/hydropandas/obs_collection.py +++ b/hydropandas/obs_collection.py @@ -124,7 +124,8 @@ def _set_metadata_value(self, iname, att_name, value, add_to_meta=False): if add_to_meta: o.meta.update({att_name: value}) - logger.info(f'add {att_name} of {iname} with value {value} to meta') + logger.info( + f'add {att_name} of {iname} with value {value} to meta') @classmethod def from_arctic(cls, connstr, libname, ObsClass=obs.GroundwaterObs, @@ -777,7 +778,8 @@ def from_menyanthes(cls, fname, name='', ObsClass=obs.GroundwaterObs): @classmethod def from_modflow(cls, obs_collection, ml, hds_arr, mtime, - modelname='', nlay=None, exclude_layers=None): + modelname='', nlay=None, exclude_layers=None, + method="linear"): """Read modflow groundwater heads at points in obs_collection. Parameters @@ -796,11 +798,14 @@ def from_modflow(cls, obs_collection, ml, hds_arr, mtime, number of layers if None the number of layers from ml is used. exclude_layers : list of int, optional exclude the observations in these model layers + method : str, optional + interpolation method, either 'linear' or 'nearest', + default is linear """ from .io.io_modflow import read_modflow_results mo_list = read_modflow_results(obs_collection, ml, hds_arr, mtime, modelname=modelname, - nlay=nlay, + nlay=nlay, method=method, exclude_layers=exclude_layers) obs_df = util._obslist_to_frame(mo_list) diff --git a/hydropandas/util.py b/hydropandas/util.py index 4d010eff..d8f1a1a1 100644 --- a/hydropandas/util.py +++ b/hydropandas/util.py @@ -224,7 +224,7 @@ def interp_weights(xy, uv, d=2): return vertices, np.hstack((bary, 1 - bary.sum(axis=1, keepdims=True))) -def interpolate(values, vtx, wts): +def interpolate(values, vtx, wts, fill_value=np.nan): """Interpolate values at locations defined by vertices and points [2]_, as calculated by interp_weights function. @@ -236,6 +236,9 @@ def interpolate(values, vtx, wts): array containing interpolation vertices, see interp_weights() wts : np.array array containing interpolation weights, see interp_weights() + fill_value : float + fill value for points that have to be extrapolated (e.g. at or + beyond edges of the known points) Returns ------- @@ -247,8 +250,9 @@ def interpolate(values, vtx, wts): ---------- .. [2] https://stackoverflow.com/questions/20915502/speedup-scipy-griddata-for-multiple-interpolations-between-two-irregular-grids """ - - return np.einsum('nj,nj->n', np.take(values, vtx), wts) + ret = np.einsum('nj,nj->n', np.take(values, vtx), wts) + ret[np.any(wts < 0, axis=1)] = fill_value + return ret def df2gdf(df, xcol='x', ycol='y'): diff --git a/hydropandas/version.py b/hydropandas/version.py index f0ede3d3..a9873473 100644 --- a/hydropandas/version.py +++ b/hydropandas/version.py @@ -1 +1 @@ -__version__ = '0.4.1' +__version__ = '0.4.2' diff --git a/setup.py b/setup.py index 2eb919df..ac188c09 100644 --- a/setup.py +++ b/setup.py @@ -29,7 +29,7 @@ ], platforms='Windows, Mac OS-X', install_requires=['scipy', - "pandas>=1.0,<1.2.0", # see PR #64 + "pandas>=1.0", 'tqdm', 'zeep', 'requests',