diff --git a/envirocar/client/api/track_api.py b/envirocar/client/api/track_api.py index 993e33a..8979b26 100644 --- a/envirocar/client/api/track_api.py +++ b/envirocar/client/api/track_api.py @@ -83,7 +83,7 @@ def _parse_tracks_list_df(tracks_jsons): for tracks_json in tracks_jsons: if tracks_json: ec_data = json.loads(tracks_json) - df = pd.json_normalize(ec_data, 'tracks') + df = pd.io.json.json_normalize(ec_data, 'tracks') df.rename(columns=__rename_track_columns, inplace=True) tracks_meta_df = tracks_meta_df.append(df) @@ -96,13 +96,13 @@ def _parse_track_df(track_jsons): tracks_df = gpd.GeoDataFrame() for track_json in track_jsons: # read properties - car_df = pd.json_normalize(json.loads(track_json)['properties']) + car_df = pd.io.json.json_normalize(json.loads(track_json)['properties']) car_df.columns = car_df.columns.str.replace('sensor.properties.', 'sensor.') car_df.rename(columns=__rename_track_columns, inplace=True) # read geojson values track_df = gpd.read_file(track_json) - track_df = track_df.join(pd.json_normalize(track_df['phenomenons'])).drop(['phenomenons'], axis=1) + track_df = track_df.join(pd.io.json.json_normalize(track_df['phenomenons'])).drop(['phenomenons'], axis=1) # combine dataframes car_df = pd.concat([car_df]*len(track_df.index), ignore_index=True) diff --git a/examples/api_request_deckgl.ipynb b/examples/api_request_deckgl.ipynb index 504d207..d1916df 100644 --- a/examples/api_request_deckgl.ipynb +++ b/examples/api_request_deckgl.ipynb @@ -462,22 +462,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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R4CPiN9SVSBIU+Ij4DXUlkgQFXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCkSBFykQBT4ipaGqY3KKJEqBj8jabcNJF0HkAgp8BEpDozifdCFEqlDgI3Df45Of3TXZpCRFgQ9ZT2kEr/mc3jXZpCRFgQ9RaWgUWweOTrpOcxM12aQkRoEPkUtD3SN3XTDEv0hsFPiQLOzt922om9rcpLO7JEqBD8HC3n4cO33Wd72H7pwfQ2lEalPgG9S1aY9T2JfMma6zuyROgW9AT2kEh46f8V1v7oxW9K1aHEOJRCanwDfAr0UeKIe9f+3S6Asj4kCBr5PLZBLt01oUdkkVBb5OfpNJNBHYv74rptKIuFHg69BTGvFdZ9N7O2IoiUgwCnwd/K7d1SIvaaXAR0At8pJWCnzIVmqKKUkxBT4gv+t3TTElaabAB+Ry710krRT4APzO7poiWtJOgQ/A7+yuKaIl7RR4Ry4963T9LmmnwDvoKY349qxbMmd6TKURqZ8C76OnNOLUUKd775IFzUkXIM1WbNnne2YHdO9dskOBr6Fr0x6nZ92bm6hrd8kMVemrcA07oEEpJVsU+AmChF0PyUjWqEpfwXUwSqAcdjXUSdboDO8JEvaVi2Yp7JJJOsOj3BrvGvbNyztUjZfMKvwZvjQ06nTrDVDYJfsKH/h1Tx70XacJCrvkQ+Gr9K/4TPXaPq1Fg1FKbhT6DO/3QIxGnpW8KXTg/a7dNfKs5E2hA+9H1+ySNwp8DXogRvKosIHXYJRSRIUNvAajlCJyCjzJIyRHSA6THPSWPUzyOZIHSW4neWmV7a7xthn/OUVyjffZgyRHKz67LdxDq58Go5S8CnKGv8nMOsys03vfD+DtZjYfwPcBrJu4gZn9s7dNB4BfBfAzANsrVvnM+OdmtrPOYwidBqOUvKq7Sm9mu81szHs7AOBKn03eAeCwmb1Y7++Mi67fJa9cA28AdpM8QHJ1lc8/BOApn338JoCvTlj2Ee+S4IskL3Msi4jUyTXwN5jZdQBuBfBhkjeOf0ByPYAxAH21NibZAuB2AI9XLH4UwBwAHQBeBvBnNbZdTXKQ5OCJEycciysi1TgF3sxGvT+Po3wNfj0AkLwHwDsBrDAzm2QXtwL4jpkdq9jnMTM7Z2bnAWwZ32eV3/2YmXWaWWdbW5tLcUWkBt/Ak2wlOW38NYCbATxD8hYAfwzgdjP7mc9u7saE6jzJyyve3gHgmSAFj1JpaDTpIohEwuUM3w7gWySfBvBtADvMbBeAzwKYBqDfu632OQAgeQXJ11vcvS+JLgBPTtjvn3q3+g4CuAnAHzR+OOFweWRWJIt8H481sxcAXDA0q5ldXWP9HwK4reL9GQBvrrLe+wOVNEZ+j8yKZFVhe9q1tkxJuggisSts4Hvv0L12KZ7CBl6PvkoRFTbwIkWkwIsUiAIvUiAKvEiBKPAiBaLAixSIAi9SIAq8SIEo8CIFosDX4DeMtUgWKfA1aBhryaNCB95vOOr5D+yKpRwicSl04P2Goz716jks7O2PqTQi0St04F2Goz52+iy6Nu2JvjAiMSh04AG3SSMPHT+j0EsuFD7wG7rnoclhbqlDx89gxZZ90RdIJEKFDzwAbHpvh9N6ew+f1Ii2kmkKPMqj3yyZM91p3TXbhiMujUh0FHhP36rFmDuj1WldneUlqxT4Cv1rlzqF/r7HdZaXbFLgJ+hfuxTt01omXUfD1ktWKfBV7F/flXQRRCKhwIsUiAJfhZ6Uk7xS4Kvwe1LOpXeeSBop8HVw6YMvkkYKvEiBKPABqTovWabAB6TqvGSZAj+BWuglzxT4CTSWneSZAi9SIAp8BVXnJe8U+ArqcCN5p8AHoBZ6yToFXqRAFHhHqs5LHijwjlSdlzxQ4B1pHDvJAwXekcaxkzxQ4B29dl5neck+BT4AneUl6xT4Cn4t8TrLS9Yp8BVcWuLXPXkwhpKIREOBn8DvLP+KBqWXDFPgJ9jQPQ/NPtPJahZZySoFvopH7rp20s/3Hj4ZU0lEwqXAV9G9YGbSRRCJhAJfg9+1vFrrJYsU+Br8WuzVWi9ZpMDXSa31kkUK/CRaW6YkXQSRUCnwk+i9Q4/ESr4o8JNQa73kjQIvUiAKvEiBOAWe5BGSIySHSQ56yx4m+RzJgyS3k7zUdVtv+XSS/SQPeX9eFs4hxUf34iVrgpzhbzKzDjPr9N73A3i7mc0H8H0A6wJsCwD3A/hbM5sL4G+995mie/GSNXVX6c1st5mNeW8HAFwZcBfvAvBl7/WXAXTXW5ak6F68ZI1r4A3AbpIHSK6u8vmHADwVcNt2M3vZe/2vANodyxIrv3vxqtZLlrgG/gYzuw7ArQA+TPLG8Q9IrgcwBqAv6LbjzMxQ/mK4AMnVJAdJDp44ccKxuOHxuxevar1kiVPgzWzU+/M4gO0ArgcAkvcAeCeAFV5onbcFcIzk5d5+LgdwvMb2j5lZp5l1trW1OR5WePzuxataL1niG3iSrSSnjb8GcDOAZ0jeAuCPAdxuZj8Lsq338V8B+KD3+oMAvtHIgURpavPkf01dm/bEUxCRBrmc4dsBfIvk0wC+DWCHme0C8FkA0wD0e7fcPgcAJK8gudNnWwDYCKCL5CEAv+G9T6WH7pw/6eeHjp/RKDiSCaxRE0+lzs5OGxwc9F8xArPv3+G7zpI509G3anEMpRG5EMkDE259X0A97Ry5TCa59/BJVe8l1RR4Ry6DWwLl6v3VH9up23WSSgp8AH6DW44bO29Ys21Y1/WSOgp8AN0LZgaaJ15VfEkbBT6gDd3zAoVeVXxJEwW+Dhu652Hz8g7nv7zxKn5PaSTScon4UeDr1L1gJl7YuAxzZ7Q6b7N14KjO9JIoBb5B/WuXYsmc6c7rr9mmKaclOQp8CPpWLQ5UxV/Y2x9peURqUeBDEqSKf+z0WV3PSyIU+JC5VvG3DhyNoTQib6TAR6Bv1WKn0KsBT+KmwEekb9VitE9rmXQdDZ4hcVPgI7R/fdekn2vwDImbAh8xzU8naaLAR+yO6zRdlaSHAh+xJw68lHQRRF6nwEdM1+mSJgp8hPyeh/cfTkMkXAp8REpDo9h7+OSk66wI8JitSBgU+IisdXhIZkP35JNciIRNgY9AT2kEflfuQQbREAmLAh8Bv37yzU3U2V0SocAnwHUwTJGwKfAxm9rc5DtfnUhUFPiY+U1bJRIlBT5mOrtLkhT4mGmcekmSAh+zQ8fPaHgrSYwCnwANbyVJUeAj4NKpRsNbSRIU+Ai4zDR73+Man17ip8BHxK9zjZ6alSQo8BHpXjATU5v11yvpov+REVInG0kbBT5C6mQjaaPAixSIAi9SIAq8SIEo8CIFosCLFIgCL1IgCrxIgSjwIgWiwIsUiAIfIb+ppkTipsBHpKc04jvVlEjcmpMuQB6t2LLPN+yaSFKSoMCHqDQ0ij96/GmMnTffdTWRpCRBgW9QaWgUn/jrZ/GTn70WaDtNNSVJUODr0FMawVf2H4XDibwqTSQpSVHgHZSGRrF++wjOnD3X8L6WzJmus7skRoGfRGloFB994iBeHQtnALqVi2Yp7JIoBb4Gl5Z2V00ANi3v0Ag4kjgFfoIgLe0udFaXNFHgK5SGRrFmWzjjxSvokkYKfIW1DYb90osvwoO3/4qq7pJaCrxnxZZ9CNo0p4BL1ijwCNbvfcmc6ehbtTjiEolEw+nhGZJHSI6QHCY56C17mORzJA+S3E7y0irbXUXy70l+l+SzJH+/4rMHSY56+xwmeVt4h+WuNDTqNJtrE4DNyzsUdsm0IGf4m8zsRxXv+wGsM7Mxkg8BWAfgoxO2GQPwh2b2HZLTABwg2W9m3/U+/4yZPVJ36UPgct0+d0Yr+tcujb4wIhGr+/FYM9ttZmPe2wEAV1ZZ52Uz+473+jSA7wFIzQVv16Y9vtftzU1U2CU3XANvAHaTPEBydZXPPwTgqcl2QHI2gAUA9lcs/oh3SfBFkpc5liUUXZv24NDxM77r+c0CK5IlroG/wcyuA3ArgA+TvHH8A5LrUa6699XamOTPAXgCwBozO+UtfhTAHAAdAF4G8Gc1tl1NcpDk4IkTJxyLOznXsC+ZM10t8JIrToE3s1Hvz+MAtgO4HgBI3gPgnQBWmFnVrmkkL0I57H1m9mTFPo+Z2TkzOw9gy/g+q/zux8ys08w629ranA+smtLQKK7+2E6nsM+d0aoGOskd38CTbPUa3ECyFcDNAJ4heQuAPwZwu5n9rMa2BPDnAL5nZpsmfHZ5xds7ADxT3yG46SmNYM22Yacus2qkk7xyaaVvB7C9nF00A/iKme0i+TyAqQD6vc8GzOxeklcA+IKZ3QZgCYD3AxghOd4c/jEz2wngT0l2oNw+cATAfw/xuN6gpzTidOsNUCOd5Jtv4M3sBQAXtFyZ2dU11v8hgNu8199CjeHbzOz9gUpapyBhB9RIJ/mW61Frg4R9vGONGukkz3LbtTZI2HXNLkWRyzO8a3dZoHzrTWGXoshl4F2fadeDMFI0uQt816Y9Tusp7FJEuQp8aWjUuQedwi5FlKtGO9eq/N7DJzH7/h1VP2ttmYLeO+aptV5yiTV6xKZSZ2enDQ4OVv1sYW8/jp0+G3OJyp0MVmj8OkkBkgfMrHOydXJxhl+xZV8iYQfK3QS3Dhx9/a6AvgAkzXIR+DRNy1z5BaDLA0mbXDXapc2Zs+ewZtuw850Dkagp8DE4dPwMZt+/Az2lkaSLIgWnwMdo68BRBV8SlYtr+JWLZgV6Im7itXVpaBTrnjyIV14LZ9JIP5WNfLU0EXjfQjX+Sbhyc1uupzSCr+7/Ac6ZYQqJuxdeFWlYgj522wi1/IsLl9tyuQl80uL6AlDLv9SiwCcgzAkpg1JNoNgU+ASFOb98PfS8QPG4BF6t9BHpW7UYm5d34KKE/obHnxfQHQGppMBHqHvBTBz6k2XYvLwDFyeU/K0DR7Gwtz+R3y3poyp9ykTV+Nc+rQX713eFvl9JD1XpM2hD9zwc2bgMRzYuw5I500Pb77HTZ1W9FwU+zfpWLcaRjctCawuIq9+ApFcuetrlXfeCmZPed4+zE5Bkm87wOTB+GeByCbBiy74YSiRppcDnSN+qxVi5aNak66Rp7ACJnwKfM+plJ5NR4HMozNZ9yRcFPof8utSWhkZjKomkjQJfQEk93CPJU+ALav4Du5IugiRAgS+oU6+ew+z7d+BXPr5LVfwCUeBzyu/23LjxkXUV/mJQ4HNqQ/c8tE9rCbTNePiv/thOBT+nFPgc27++C6xju7HzhjXbhtUrL4cU+Jz7zPKOurfde/iknqXPGQU+57oXzMTmBkJ/7PRZnelzRIEvgO4FM3Fk4zLnhryJ1P8+PxT4Ahl/qq6eIbc0P14+aIirggsyrHZzE/HIXddqTPyU0hBX4mu8uu/ywM14672GysouBV4AlB+4aW5yu4m3deCo7tNnlAIvr3vkrmud19UDONmkwMvruhfMDPQsvW7XZY8CL2/Qt2qxc+h1uy57FHi5wPg0WS7/OXSWzxYFXqrqXjATL2xchkumTpl0PZ3ls0X34cXX7Pt31LXdkY3LQi6JTEb34SUU9Q6KWe8XhURHgRdfjcwz/1aFPlUUeHEytbm+/yoGaJ76FFHgxclDd85vaHvNU58OCrw46V4ws+7Ha8cdO31WZ/uEKfDibEP3vIYG0xi3deCohslOiG7LSV1WbNkX2j34lYtmaU68ELjcllPgpSFhzk1/8UVN+PS75+t5+zop8BKrMMOvs35wCrwkIsgoOn4uagIevqtDZ30HoQWe5BEApwGcAzBmZp0kHwbw3wCcBXAYwG+Z2U+rbHsLgP8FYAqAL5jZRm/5WwF8DcCbARwA8H4zOztZORT4bAnzOh9Q+P2EHfhOM/tRxbKbAfydmY2RfAgAzOyjE7abAuD7ALoAvATgnwDcbWbfJfmXAJ40s6+R/ByAp83s0cnKocBnT9ihr+Ra7b963Q6MVflvnre+/pH2pTez3WY25r0dAHBlldWuB/C8mb3gnb2/BuBdJAng1wF83VvvywC66y2LpFffqsWYO6M1kn1vHTjqe1+/VtiBYvb1dw28AdhN8gDJ1VU+/xCAp6osnwngBxXvX/KWvRnAT6zjwnIAAAoxSURBVCu+MMaXSw71r10aWeiB/wh+taG0a4V9XNH6+jc7rneDmY2SnAGgn+RzZvYPAEByPYAxAH1RFND7glkNALNmNdbTS5LTv3ZppNV7ADh0/Axm378DS+ZMd37gxwDMf2AXDn7ilsjKVY+uTXtw6PiZC5a3T2vB/vVdde/X6QxvZqPen8cBbEe5qg6S9wB4J4AVVr0xYBTAVRXvr/SW/RjApSSbJyyv9rsfM7NOM+tsa2tzKa6kVN+qxQ13z3Wx9/BJzL5/h/PIuqdePYfZ9+9ITRV/9v07qoYdKHdPbuSZBN9GO5KtAJrM7LT3uh/AJ72PNwH4NTM7UWPbZpQb7d6BcqD/CcD7zOxZko8DeKKi0e6gmf2fycqiRrt8KA2N4r7Hh/Ha+aRLUl3YjXm1ztaNqFZGl0Y7lyp9O4Dt5XY2NAP4ipntIvk8gKkoV/EBYMDM7iV5Bcq3327zWvA/AuCbKN+W+6KZPevt96MAvkZyA4AhAH/ucqCSfd0LZqJ7wczUBn/Fln2BxwAIs9NRlNTxRhJXGhrF2m3DSFnuJz3Tz39gF069ei7G0rxRlGd4kUhVnvHXPXkQr6TklJ+Wa/qJ2qe11L2tAi+pMR78cVmpJsep0VZ6Vekl89J6Jg7T5uX+XYpVpRfJgTDvGijwIjFzOVtHRYEXicCbphDP9d6WdDEuoMCLNKiZwPOfzsaTdwq8SABpPXO7UuAl845sXBZ5S31enp3XMNUiBaLASy74TWvdqLzc61fgJReS7NeeJQq8SIEo8CIFosBLLkR9DZ8XCrzkwsFP3BJp6BnZnuOlwEtuzLvy5xvafsmc6VWXE8C/5OQ+vDreSG64jojbPq0Fx06ffcP7Rp4xzxIFXnLhbet3Oq03d0Yr+tcujbYwKaYqvWTewt5+/Ps5hynTgEKHHVDgJeO6Nu15Q/W8lkumTsnNdXgjFHjJrJ7SiNN473NntKZuZpmkKPCSWS4DXL5pCgtfja+kwEsmuU63lOVn16OgVnrJHNdJIDYv74ihNNmiM7xkyoot+5zCvmTO9MQGikwzBV4yxbVzTdC54YpCVXpJtdLQKNZvH8GZs+7Pu9fqIisKvKRYaWgUa7YNB9rmkqlTdHafhKr0klpBw75kznTdb/ehwEsuNDdRZ3YHCrzkwiN3XZt0ETJBgZdU6tq0x3ndJOdqyxo12kkqufSRX7loFjZ0z4uhNPmhwEsm5WUmmLipSi+p41ednzujNZ6C5JACL6njV53X02/1U+BFCkSBl0xRt9nGKPCSKn6TNqpzTWMUeEmNt+ZkhtY0U+AlFXpKI/Afd1YapcBLKnx1/w+SLkIhKPCSCufM//y+ctGsGEqSbwq8pMIU+k/XqG60jVPgJRXuXnhV0kUoBAVeUmFD9zy0T2tJuhi5p8BLarhMGSWNUeBFCkSBFykQBV4yozQ0mnQRMk+Bl9Twu88edBRbuZACL6mh++zRU+AlU1xnjZXqFHhJFb9qvW7dNUaBl1RRtT5aCrykytvW70y6CLmmYaolcfXMECv1UeAlUfXMECv1c6rSkzxCcoTkMMlBb9ldJJ8leZ5kZ43trvG2Gf85RXKN99mDJEcrPrstvMOSrFgbMOx6Jr4xQc7wN5nZjyrePwPg3QA+X2sDM/tnAB0AQHIKgFEA2ytW+YyZPRKgDJIz5wOsu2TOdDXqNajuKr2ZfQ8A6DBwgecdAA6b2Yv1/k7Jl57SiPO6mjAyHK6BNwC7SRqAz5vZY3X8rt8E8NUJyz5C8gMABgH8oZn9pI79Ssas2LIPew+fdFr3TVOI53p1tRcW19tyN5jZdQBuBfBhkjcG+SUkWwDcDuDxisWPApiDcpX/ZQB/VmPb1SQHSQ6eOHEiyK+VFAoSdgAKe8icAm9mo96fx1G+Br8+4O+5FcB3zOxYxT6Pmdk5MzsPYEutfZrZY2bWaWadbW1tAX+tpE2QsEv4fANPspXktPHXAG5GucEuiLsxoTpP8vKKt3fUsU8RCcjlDN8O4FsknwbwbQA7zGwXyTtIvgRgMYAdJL8JACSvIPl6dynvS6ILwJMT9vun3q2+gwBuAvAHIRyP5IjGuAsfzWE88LTo7Oy0wcHBpIshAZSGRvHRJw7i1bEgN+DKYd+/viuiUuUTyQNmVrVPzDj1tJNIBG2cq3Rk47KQSyPjFHgJhfrDZ4MCL3Xr2rQHh46fCXWfmv89Wno8VuoSRdjbp7Vo/veIKfBSl7DDvnLRLDXSxUBVeknMxRc14dPvnq8+8jFS4CU2CnjyFHiJ3MpFs/RYa0roGl4ip7CnhwIvdXnTFOdxECRFFHipy3O9tzmF/pKpU2IojbjSNbzUbfxZ9VoDUV4ydQoOfuKWuIslk1DgpWHdC2aq5T0jVKUXKRAFXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCkSBFykQBV6kQBR4kQJR4EUKRIEXKRAFXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCoRmlnQZnJE8AeDFhIvxFgA/SrgMUdBxZUu14/oFM2ubbKNMBT4NSA6aWWfS5Qibjitb6j0uVelFCkSBFykQBT64x5IuQER0XNlS13HpGl6kQHSGFykQBX4CkteQHK74OUVyzYR1VpA8SHKE5D+SvDap8rpyOa6Kdf8zyTGS74m7nEG5HhfJpd7nz5L8v0mUNSjH/4s/T/KvST7tHdtvTbpTM9NPjR8AUwD8K8r3NyuX/xcAl3mvbwWwP+myhnFcFZ/9HYCdAN6TdFlD+ve6FMB3Aczy3s9IuqwhHtvHADzkvW4DcBJAS6396Aw/uXcAOGxmb+jsY2b/aGY/8d4OALgy9pI1pupxeX4PwBMAjsdbpFDUOq73AXjSzI4CgJnl6dgMwDSSBPBzKAd+rNZOFPjJ/SaAr/qs89sAnoqhLGGqelwkZwK4A8CjsZcoHLX+vX4JwGUk95A8QPIDMZcrDLWO7bMAfhnADwGMAPh9Mztfcy9JV1XS+gOgBeWui+2TrHMTgO8BeHPS5Q3juAA8DmCR9/pLyFCV3ue4PotyTawV5S6phwD8UtJlDunY3gPgMwAI4GoA/wLgklr7ag7yFVMwtwL4jpkdq/YhyfkAvgDgVjP7cawla8xkx9UJ4Gvl2iHeAuA2kmNmVoqzgHWa7LheAvBjMzsD4AzJfwBwLYDvx1nABkx2bL8FYKOV0/88yX8B8DYA3662I1Xpa7sbNarzJGcBeBLA+80sK/9pxtU8LjN7q5nNNrPZAL4O4HczEnZgkuMC8A0AN5BsJvmfACxEuWaWFZMd21GUr+9Bsh3ANQBeqLUjdbypgmQryn+Rv2hm/+YtuxcAzOxzJL8A4E78x5N7Y5aBBzT8jmvCul8C8Ddm9vW4yxmUy3GRvA/ls+F5AF8ws80JFTcQh/+LV6B8+XU5ytX6jWa2teb+FHiR4lCVXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCkSBFymQ/w8mmGO+lQWCQwAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -501,22 +501,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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" ] @@ -705,9 +705,9 @@ ], "metadata": { "kernelspec": { - "display_name": "envirocar", + "display_name": "Python 3", "language": "python", - "name": "envirocar" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -719,7 +719,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/examples/api_request_deckgl_pablocruz9.ipynb b/examples/api_request_deckgl_pablocruz9.ipynb new file mode 100644 index 0000000..7042aa4 --- /dev/null +++ b/examples/api_request_deckgl_pablocruz9.ipynb @@ -0,0 +1,715 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Package loading and basic configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# load dependencies'\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "from envirocar import TrackAPI, DownloadClient, BboxSelector, ECConfig, TimeSelector\n", + "\n", + "# create an initial but optional config and an api client\n", + "config = ECConfig()\n", + "track_api = TrackAPI(api_client=DownloadClient(config=config))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Querying enviroCar Tracks" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "The following cell queries tracks from the enviroCar API. It defines a bbox for the area of Münster (Germany) and requests 50 tracks. The result is a GeoDataFrame, which is a geo-extended Pandas dataframe from the GeoPandas library. It contains all information of the track in a flat dataframe format including a specific geometry column. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2449 rows × 54 columns

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" + ], + "text/plain": [ + " id time geometry \\\n", + "0 5e10ec059115b85a12318e64 2019-12-30T09:31:20 POINT (7.59969 51.93433) \n", + "1 5e10ec059115b85a12318e66 2019-12-30T09:31:26 POINT (7.59983 51.93449) \n", + "2 5e10ec059115b85a12318e67 2019-12-30T09:31:31 POINT (7.60002 51.93457) \n", + "3 5e10ec059115b85a12318e68 2019-12-30T09:31:36 POINT (7.60003 51.93458) \n", + "4 5e10ec059115b85a12318e69 2019-12-30T09:31:41 POINT (7.60003 51.93458) \n", + ".. ... ... ... \n", + "283 5d222f4644ea855023c1d1de 2019-07-07T17:00:47 POINT (7.59671 51.99927) \n", + "284 5d222f4644ea855023c1d1df 2019-07-07T17:00:52 POINT (7.59628 51.99961) \n", + "285 5d222f4644ea855023c1d1e0 2019-07-07T17:00:58 POINT (7.59577 52.00007) \n", + "286 5d222f4644ea855023c1d1e1 2019-07-07T17:01:03 POINT (7.59525 52.00050) \n", + "287 5d222f4644ea855023c1d1e2 2019-07-07T17:01:08 POINT (7.59496 52.00073) \n", + "\n", + " GPS VDOP.value GPS VDOP.unit GPS Speed.value GPS Speed.unit \\\n", + "0 0.800000 precision 9.459943 km/h \n", + "1 0.851000 precision 18.669282 km/h \n", + "2 0.800000 precision 4.359976 km/h \n", + "3 0.832303 precision 0.000000 km/h \n", + "4 0.827140 precision 0.000000 km/h \n", + ".. ... ... ... ... \n", + "283 2.227546 precision 29.906211 km/h \n", + "284 1.800805 precision 40.760203 km/h \n", + "285 2.500000 precision 42.986743 km/h \n", + "286 2.500000 precision 39.836086 km/h \n", + "287 2.000000 precision 18.675272 km/h \n", + "\n", + " Intake Temperature.value Intake Temperature.unit CO2.value ... \\\n", + "0 28.000000 c 13.898474 ... \n", + "1 28.000000 c 4.336944 ... \n", + "2 28.000001 c 4.170298 ... \n", + "3 28.000000 c 4.100011 ... \n", + "4 27.999999 c 4.074120 ... \n", + ".. ... ... ... ... \n", + "283 51.000000 c 7.016605 ... \n", + "284 50.000001 c 9.879350 ... \n", + "285 51.000001 c 6.076898 ... \n", + "286 50.999999 c 2.060684 ... \n", + "287 50.999999 c 6.717579 ... \n", + "\n", + " sensor.constructionYear sensor.manufacturer track.appVersion \\\n", + "0 2007 Dodge NaN \n", + "1 2007 Dodge NaN \n", + "2 2007 Dodge NaN \n", + "3 2007 Dodge NaN \n", + "4 2007 Dodge NaN \n", + ".. ... ... ... \n", + "283 2003 Volvo NaN \n", + "284 2003 Volvo NaN \n", + "285 2003 Volvo NaN \n", + "286 2003 Volvo NaN \n", + "287 2003 Volvo NaN \n", + "\n", + " track.touVersion O2 Lambda Voltage ER.value O2 Lambda Voltage ER.unit \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "283 NaN NaN NaN \n", + "284 NaN NaN NaN \n", + "285 NaN NaN NaN \n", + "286 NaN NaN NaN \n", + "287 NaN NaN NaN \n", + "\n", + " O2 Lambda Voltage.value O2 Lambda Voltage.unit MAF.value MAF.unit \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + ".. ... ... ... ... \n", + "283 NaN NaN 9.083020 l/s \n", + "284 NaN NaN 12.788853 l/s \n", + "285 NaN NaN 7.866566 l/s \n", + "286 NaN NaN 2.667563 l/s \n", + "287 NaN NaN 8.695930 l/s \n", + "\n", + "[2449 rows x 54 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bbox = BboxSelector([\n", + " 7.615825, # min_x\n", + " 51.954942, # min_y\n", + " 7.632137, # max_x\n", + " 51.968326 # max_y\n", + "])\n", + "\n", + "time_interval = TimeSelector(\n", + " '2019-01-01T00:00:00Z',\n", + " '2019-12-31T00:00:00Z'\n", + ")\n", + "\n", + "# issue a query\n", + "track_df = track_api.get_tracks(bbox=bbox, num_results=10, time_interval=time_interval)# requesting 10 tracks inside the bbox\n", + "track_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "track_df.plot(figsize=(8, 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Presenting Summary Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Using .describe() fuction to generate descriptive statistics about the dataframe\n", + "\n", + "track_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inspecting a single Track" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "some_track_id = track_df['track.id'].unique()[5]\n", + "some_track = track_df[track_df['track.id'] == some_track_id]\n", + "some_track.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = some_track['Speed.value'].plot()\n", + "ax.set_title(\"Speed\")\n", + "ax.set_ylabel(some_track['Speed.unit'][0])\n", + "ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interactive Map\n", + "The following map-based visualization makes use of folium. It allows to visualizate geospatial data based on an interactive leaflet map. Since the data in the GeoDataframe is modelled as a set of Point instead of a LineString, we have to manually create a polyline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import folium\n", + "\n", + "lats = list(some_track['geometry'].apply(lambda coord: coord.y))\n", + "lngs = list(some_track['geometry'].apply(lambda coord: coord.x))\n", + "\n", + "avg_lat = sum(lats) / len(lats)\n", + "avg_lngs = sum(lngs) / len(lngs)\n", + "\n", + "m = folium.Map(location=[avg_lat, avg_lngs], zoom_start=13)\n", + "folium.PolyLine([coords for coords in zip(lats, lngs)], color='blue').add_to(m)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: Visualization with pydeck (deck.gl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pydeck library makes use of the basemap tiles from Mapbox. In case you want to visualize the map with basemap tiles, you need to register with MapBox, and configure a specific access token. The service is free until a certain level of traffic is esceeded.\n", + "\n", + "You can either configure it via your terminal (i.e. `export MAPBOX_API_KEY=`), which pydeck will automatically read, or you can pass it as a variable to the generation of pydeck (i.e. `pdk.Deck(mapbox_key=, ...)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pydeck as pdk\n", + "\n", + "# for pydeck the attributes have to be flat\n", + "track_df['lat'] = track_df['geometry'].apply(lambda coord: coord.y)\n", + "track_df['lng'] = track_df['geometry'].apply(lambda coord: coord.x)\n", + "vis_df = pd.DataFrame(track_df)\n", + "vis_df['speed'] = vis_df['Speed.value']\n", + "\n", + "# omit unit columns\n", + "vis_df_cols = [col for col in vis_df.columns if col.lower()[len(col)-4:len(col)] != 'unit']\n", + "vis_df = vis_df[vis_df_cols]\n", + "\n", + "layer = pdk.Layer(\n", + " 'ScatterplotLayer',\n", + " data=vis_df,\n", + " get_position='[lng, lat]',\n", + " auto_highlight=True,\n", + " get_radius=10, # Radius is given in meters\n", + " get_fill_color='[speed < 20 ? 0 : (speed - 20)*8.5, speed < 50 ? 255 : 255 - (speed-50)*8.5, 0, 140]', # Set an RGBA value for fill\n", + " pickable=True\n", + ")\n", + "\n", + "# Set the viewport location\n", + "view_state = pdk.ViewState(\n", + " longitude=7.5963592529296875,\n", + " latitude=51.96246168188569,\n", + " zoom=10,\n", + " min_zoom=5,\n", + " max_zoom=15,\n", + " pitch=40.5,\n", + " bearing=-27.36)\n", + "\n", + "r = pdk.Deck(\n", + " width=200, \n", + " layers=[layer], \n", + " initial_view_state=view_state,\n", + " mapbox_key='pk.eyJ1IjoienVyY3BoIiwiYSI6ImNrOHgxYWI1cDB0Ym0zbXBrc2VxdGVlYWkifQ.yAnuEOOC0WTKMeIkiPwQKQ'\n", + ")\n", + "r.to_html('tracks_muenster.html', iframe_width=900)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization of Single Track" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changed the attributes (latitude and longitude) to refer to the variable \"some_track\" created in cell 7, instead of showing the whole track data frame.\n", + "\n", + "Redirected the viewport location to the coordinates of this track and enhanced the zoom to focus on the single track instead \n", + "of the whole city of Muenster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pydeck as pdk\n", + "\n", + "# for pydeck the attributes have to be flat\n", + "some_track['lat'] = some_track['geometry'].apply(lambda coord: coord.y)\n", + "some_track['lng'] = some_track['geometry'].apply(lambda coord: coord.x)\n", + "vis_df = pd.DataFrame(some_track)\n", + "vis_df['speed'] = vis_df['Speed.value']\n", + "\n", + "# omit unit columns\n", + "vis_df_cols = [col for col in vis_df.columns if col.lower()[len(col)-4:len(col)] != 'unit']\n", + "vis_df = vis_df[vis_df_cols]\n", + "\n", + "layer = pdk.Layer(\n", + " 'ScatterplotLayer',\n", + " data=vis_df,\n", + " get_position='[lng, lat]',\n", + " auto_highlight=True,\n", + " get_radius=10, # Radius is given in meters\n", + " get_fill_color='[speed < 20 ? 0 : (speed - 20)*8.5, speed < 50 ? 255 : 255 - (speed-50)*8.5, 0, 140]', # Set an RGBA value for fill\n", + " pickable=True\n", + ")\n", + "\n", + "# Set the viewport location\n", + "view_state = pdk.ViewState(\n", + " longitude=7.614217519274591,\n", + " latitude=51.94663734506707,\n", + " zoom=13,\n", + " min_zoom=5,\n", + " max_zoom=15,\n", + " pitch=40.5,\n", + " bearing=-27.36)\n", + "\n", + "r = pdk.Deck(\n", + " width=200, \n", + " layers=[layer], \n", + " initial_view_state=view_state,\n", + " mapbox_key='pk.eyJ1IjoienVyY3BoIiwiYSI6ImNrOHgxY2k2ZTEybTUzbW1oNXNsZ3hjdzYifQ.ykwDPZC1gvrOmMz6YhovAw'\n", + ")\n", + "r.to_html('tracks_muenster_single.html', iframe_width=900)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}