In [6]:
import pandas as pd
import numpy as np
import json
In [8]:
with open("test_resources/test_1.json") as f:
payload = json.load(f)
In [45]:
heading_data = pd.DataFrame(payload["compass"])
heading_data["date"] = pd.to_datetime(data["date"])
heading_data["heading"] = np.float64(data["heading"])
heading_data = heading_data.set_index("date")
In [146]:
heading_data["test"] = 0.1
In [ ]:
np.de
In [149]:
heading_data.as_matrix()
Out[149]:
array([[ 9.30527191e+01, 1.00000000e-01],
[ 9.50284576e+01, 1.00000000e-01],
[ 9.63692169e+01, 1.00000000e-01],
[ 9.49494858e+01, 1.00000000e-01],
[ 9.67889328e+01, 1.00000000e-01],
[ 9.97832565e+01, 1.00000000e-01],
[ 1.02152519e+02, 1.00000000e-01],
[ 1.07053802e+02, 1.00000000e-01],
[ 1.09661003e+02, 1.00000000e-01],
[ 1.12783226e+02, 1.00000000e-01],
[ 1.16312981e+02, 1.00000000e-01],
[ 1.20833298e+02, 1.00000000e-01],
[ 1.24290077e+02, 1.00000000e-01],
[ 1.27298378e+02, 1.00000000e-01],
[ 1.29653763e+02, 1.00000000e-01],
[ 1.31027740e+02, 1.00000000e-01],
[ 1.28165192e+02, 1.00000000e-01],
[ 1.26566368e+02, 1.00000000e-01],
[ 1.25094536e+02, 1.00000000e-01],
[ 1.23238525e+02, 1.00000000e-01],
[ 1.24472710e+02, 1.00000000e-01],
[ 1.27384254e+02, 1.00000000e-01],
[ 1.26274521e+02, 1.00000000e-01],
[ 1.23317726e+02, 1.00000000e-01],
[ 1.21156876e+02, 1.00000000e-01],
[ 1.18873184e+02, 1.00000000e-01],
[ 1.17672836e+02, 1.00000000e-01],
[ 1.15870430e+02, 1.00000000e-01],
[ 1.14142326e+02, 1.00000000e-01],
[ 1.12295044e+02, 1.00000000e-01],
[ 1.09266838e+02, 1.00000000e-01],
[ 1.06351952e+02, 1.00000000e-01],
[ 1.03838638e+02, 1.00000000e-01],
[ 1.02456711e+02, 1.00000000e-01],
[ 1.00518478e+02, 1.00000000e-01],
[ 9.73424606e+01, 1.00000000e-01],
[ 9.37923431e+01, 1.00000000e-01],
[ 9.00946884e+01, 1.00000000e-01],
[ 8.78380127e+01, 1.00000000e-01],
[ 8.61731339e+01, 1.00000000e-01],
[ 8.43943176e+01, 1.00000000e-01],
[ 8.24821548e+01, 1.00000000e-01],
[ 8.00828476e+01, 1.00000000e-01],
[ 7.81978989e+01, 1.00000000e-01],
[ 7.66061172e+01, 1.00000000e-01],
[ 7.43758316e+01, 1.00000000e-01],
[ 7.29160156e+01, 1.00000000e-01],
[ 7.01169891e+01, 1.00000000e-01],
[ 6.74694672e+01, 1.00000000e-01],
[ 6.47354813e+01, 1.00000000e-01],
[ 6.36667252e+01, 1.00000000e-01],
[ 6.48019638e+01, 1.00000000e-01],
[ 6.63147812e+01, 1.00000000e-01],
[ 6.74803848e+01, 1.00000000e-01],
[ 6.91362457e+01, 1.00000000e-01],
[ 7.12299042e+01, 1.00000000e-01],
[ 7.30016708e+01, 1.00000000e-01],
[ 7.47283401e+01, 1.00000000e-01],
[ 8.16324387e+01, 1.00000000e-01],
[ 8.26990814e+01, 1.00000000e-01],
[ 8.46050873e+01, 1.00000000e-01],
[ 8.63301315e+01, 1.00000000e-01],
[ 8.75850830e+01, 1.00000000e-01],
[ 8.91252365e+01, 1.00000000e-01],
[ 9.06973953e+01, 1.00000000e-01],
[ 9.20142288e+01, 1.00000000e-01],
[ 9.08472824e+01, 1.00000000e-01],
[ 8.97698593e+01, 1.00000000e-01],
[ 9.08490601e+01, 1.00000000e-01],
[ 8.93929977e+01, 1.00000000e-01],
[ 8.81525497e+01, 1.00000000e-01],
[ 8.93469620e+01, 1.00000000e-01],
[ 8.69266510e+01, 1.00000000e-01],
[ 8.58178635e+01, 1.00000000e-01],
[ 8.27497482e+01, 1.00000000e-01],
[ 7.91101379e+01, 1.00000000e-01],
[ 7.60213470e+01, 1.00000000e-01],
[ 7.31018829e+01, 1.00000000e-01],
[ 7.07477798e+01, 1.00000000e-01],
[ 6.81037064e+01, 1.00000000e-01],
[ 6.61229248e+01, 1.00000000e-01],
[ 6.34148483e+01, 1.00000000e-01],
[ 6.06496811e+01, 1.00000000e-01],
[ 5.89624023e+01, 1.00000000e-01],
[ 5.58650360e+01, 1.00000000e-01],
[ 5.43641739e+01, 1.00000000e-01],
[ 5.28739815e+01, 1.00000000e-01],
[ 5.41381607e+01, 1.00000000e-01],
[ 5.31357307e+01, 1.00000000e-01],
[ 5.19597855e+01, 1.00000000e-01],
[ 5.05041733e+01, 1.00000000e-01],
[ 4.89459839e+01, 1.00000000e-01],
[ 4.70501060e+01, 1.00000000e-01],
[ 4.58224525e+01, 1.00000000e-01],
[ 4.43981133e+01, 1.00000000e-01],
[ 4.27364998e+01, 1.00000000e-01],
[ 4.08832321e+01, 1.00000000e-01],
[ 3.95963974e+01, 1.00000000e-01],
[ 3.78286819e+01, 1.00000000e-01],
[ 3.65587387e+01, 1.00000000e-01],
[ 3.51020126e+01, 1.00000000e-01],
[ 3.38561363e+01, 1.00000000e-01],
[ 3.11103325e+01, 1.00000000e-01],
[ 2.75868454e+01, 1.00000000e-01],
[ 2.50958958e+01, 1.00000000e-01],
[ 2.31226063e+01, 1.00000000e-01],
[ 2.07737408e+01, 1.00000000e-01],
[ 1.87980003e+01, 1.00000000e-01],
[ 1.62273006e+01, 1.00000000e-01],
[ 1.51343689e+01, 1.00000000e-01],
[ 1.39797697e+01, 1.00000000e-01],
[ 1.18148413e+01, 1.00000000e-01],
[ 1.07252026e+01, 1.00000000e-01],
[ 9.62314129e+00, 1.00000000e-01],
[ 7.95971537e+00, 1.00000000e-01],
[ 6.31624937e+00, 1.00000000e-01],
[ 7.52543592e+00, 1.00000000e-01],
[ 4.43431711e+00, 1.00000000e-01],
[ 5.81540823e+00, 1.00000000e-01],
[ 7.50878763e+00, 1.00000000e-01],
[ 8.78547478e+00, 1.00000000e-01],
[ 1.08427753e+01, 1.00000000e-01],
[ 1.26408577e+01, 1.00000000e-01],
[ 1.46058493e+01, 1.00000000e-01],
[ 1.61408195e+01, 1.00000000e-01],
[ 1.89564018e+01, 1.00000000e-01],
[ 2.15417252e+01, 1.00000000e-01],
[ 2.55105362e+01, 1.00000000e-01],
[ 3.04227276e+01, 1.00000000e-01],
[ 3.55485764e+01, 1.00000000e-01],
[ 3.98523254e+01, 1.00000000e-01],
[ 4.48596840e+01, 1.00000000e-01],
[ 4.80683136e+01, 1.00000000e-01],
[ 5.19309311e+01, 1.00000000e-01],
[ 5.64449730e+01, 1.00000000e-01],
[ 6.15212364e+01, 1.00000000e-01],
[ 6.54505768e+01, 1.00000000e-01],
[ 7.05739975e+01, 1.00000000e-01],
[ 7.54276047e+01, 1.00000000e-01],
[ 7.94929199e+01, 1.00000000e-01],
[ 8.33406754e+01, 1.00000000e-01],
[ 8.66361771e+01, 1.00000000e-01],
[ 8.95467529e+01, 1.00000000e-01],
[ 9.22241440e+01, 1.00000000e-01],
[ 9.59534760e+01, 1.00000000e-01],
[ 9.92036972e+01, 1.00000000e-01],
[ 1.03961029e+02, 1.00000000e-01],
[ 1.08010384e+02, 1.00000000e-01],
[ 1.12920311e+02, 1.00000000e-01],
[ 1.17071617e+02, 1.00000000e-01],
[ 1.21942986e+02, 1.00000000e-01],
[ 1.26936668e+02, 1.00000000e-01],
[ 1.32089767e+02, 1.00000000e-01],
[ 1.35897827e+02, 1.00000000e-01],
[ 1.39374908e+02, 1.00000000e-01],
[ 1.42080597e+02, 1.00000000e-01],
[ 1.45244766e+02, 1.00000000e-01],
[ 1.47072083e+02, 1.00000000e-01],
[ 1.49067474e+02, 1.00000000e-01],
[ 1.50972000e+02, 1.00000000e-01],
[ 1.53684891e+02, 1.00000000e-01],
[ 1.56634521e+02, 1.00000000e-01],
[ 1.58802643e+02, 1.00000000e-01],
[ 1.61727692e+02, 1.00000000e-01],
[ 1.63821228e+02, 1.00000000e-01],
[ 1.65906525e+02, 1.00000000e-01],
[ 1.68026154e+02, 1.00000000e-01],
[ 1.70562897e+02, 1.00000000e-01],
[ 1.72808884e+02, 1.00000000e-01],
[ 1.75619843e+02, 1.00000000e-01],
[ 1.78170532e+02, 1.00000000e-01],
[ 1.80323318e+02, 1.00000000e-01],
[ 1.83238037e+02, 1.00000000e-01],
[ 1.85843750e+02, 1.00000000e-01],
[ 1.88503479e+02, 1.00000000e-01],
[ 1.92244705e+02, 1.00000000e-01],
[ 1.95601883e+02, 1.00000000e-01],
[ 2.00608963e+02, 1.00000000e-01],
[ 2.03038712e+02, 1.00000000e-01],
[ 2.05415375e+02, 1.00000000e-01],
[ 2.08199585e+02, 1.00000000e-01],
[ 2.11428391e+02, 1.00000000e-01],
[ 2.13014847e+02, 1.00000000e-01],
[ 2.14841583e+02, 1.00000000e-01],
[ 2.17270218e+02, 1.00000000e-01],
[ 2.19651291e+02, 1.00000000e-01],
[ 2.22381958e+02, 1.00000000e-01],
[ 2.24736786e+02, 1.00000000e-01],
[ 2.27133850e+02, 1.00000000e-01],
[ 2.28511749e+02, 1.00000000e-01],
[ 2.37167389e+02, 1.00000000e-01],
[ 2.39064011e+02, 1.00000000e-01],
[ 2.40320312e+02, 1.00000000e-01],
[ 2.41579926e+02, 1.00000000e-01],
[ 2.43312469e+02, 1.00000000e-01],
[ 2.44586151e+02, 1.00000000e-01],
[ 2.46452301e+02, 1.00000000e-01],
[ 2.48228912e+02, 1.00000000e-01],
[ 2.50025284e+02, 1.00000000e-01],
[ 2.52307709e+02, 1.00000000e-01],
[ 2.54950668e+02, 1.00000000e-01],
[ 2.56836395e+02, 1.00000000e-01],
[ 2.59381592e+02, 1.00000000e-01],
[ 2.61482117e+02, 1.00000000e-01],
[ 2.63617676e+02, 1.00000000e-01],
[ 2.65024536e+02, 1.00000000e-01],
[ 2.67200897e+02, 1.00000000e-01],
[ 2.68694427e+02, 1.00000000e-01],
[ 2.70051971e+02, 1.00000000e-01],
[ 2.66986298e+02, 1.00000000e-01],
[ 2.68678528e+02, 1.00000000e-01],
[ 2.69796295e+02, 1.00000000e-01],
[ 2.71149170e+02, 1.00000000e-01],
[ 2.70031006e+02, 1.00000000e-01],
[ 2.71470123e+02, 1.00000000e-01],
[ 2.73375732e+02, 1.00000000e-01],
[ 2.74725494e+02, 1.00000000e-01],
[ 2.76768341e+02, 1.00000000e-01],
[ 2.78504028e+02, 1.00000000e-01],
[ 2.76505615e+02, 1.00000000e-01],
[ 2.78097260e+02, 1.00000000e-01],
[ 2.80848389e+02, 1.00000000e-01],
[ 2.83164337e+02, 1.00000000e-01],
[ 2.85077393e+02, 1.00000000e-01],
[ 2.87481995e+02, 1.00000000e-01],
[ 2.89431000e+02, 1.00000000e-01],
[ 2.90808624e+02, 1.00000000e-01],
[ 2.92192474e+02, 1.00000000e-01],
[ 2.93749939e+02, 1.00000000e-01],
[ 2.95863678e+02, 1.00000000e-01],
[ 2.97743103e+02, 1.00000000e-01],
[ 2.95251160e+02, 1.00000000e-01],
[ 2.96296570e+02, 1.00000000e-01],
[ 2.98527893e+02, 1.00000000e-01],
[ 2.99575165e+02, 1.00000000e-01],
[ 2.98459381e+02, 1.00000000e-01],
[ 2.97069244e+02, 1.00000000e-01],
[ 2.94590271e+02, 1.00000000e-01],
[ 2.92030243e+02, 1.00000000e-01],
[ 2.90317719e+02, 1.00000000e-01],
[ 2.88779419e+02, 1.00000000e-01],
[ 2.90889008e+02, 1.00000000e-01],
[ 2.93445038e+02, 1.00000000e-01],
[ 2.94984894e+02, 1.00000000e-01],
[ 2.96739716e+02, 1.00000000e-01],
[ 2.98177155e+02, 1.00000000e-01],
[ 2.99489777e+02, 1.00000000e-01],
[ 3.01305023e+02, 1.00000000e-01],
[ 3.04961334e+02, 1.00000000e-01],
[ 3.08325256e+02, 1.00000000e-01],
[ 3.09878754e+02, 1.00000000e-01],
[ 3.11058655e+02, 1.00000000e-01],
[ 3.12703766e+02, 1.00000000e-01],
[ 3.15730255e+02, 1.00000000e-01],
[ 3.18179810e+02, 1.00000000e-01],
[ 3.19665466e+02, 1.00000000e-01],
[ 3.23544098e+02, 1.00000000e-01],
[ 3.26667145e+02, 1.00000000e-01],
[ 3.30572357e+02, 1.00000000e-01],
[ 3.33513092e+02, 1.00000000e-01],
[ 3.38272888e+02, 1.00000000e-01],
[ 3.40754181e+02, 1.00000000e-01],
[ 3.43679932e+02, 1.00000000e-01],
[ 3.46615753e+02, 1.00000000e-01],
[ 3.49351898e+02, 1.00000000e-01],
[ 3.51913483e+02, 1.00000000e-01],
[ 3.54673859e+02, 1.00000000e-01],
[ 3.56659088e+02, 1.00000000e-01],
[ 3.55511139e+02, 1.00000000e-01],
[ 3.57269440e+02, 1.00000000e-01],
[ 3.59492828e+02, 1.00000000e-01],
[ 2.69677758e+00, 1.00000000e-01],
[ 4.75897121e+00, 1.00000000e-01],
[ 7.68369913e+00, 1.00000000e-01],
[ 1.04439936e+01, 1.00000000e-01],
[ 1.34013977e+01, 1.00000000e-01],
[ 1.57560358e+01, 1.00000000e-01],
[ 1.68571644e+01, 1.00000000e-01],
[ 1.93065090e+01, 1.00000000e-01],
[ 2.12393494e+01, 1.00000000e-01],
[ 2.22917557e+01, 1.00000000e-01],
[ 2.34533920e+01, 1.00000000e-01],
[ 2.59999752e+01, 1.00000000e-01],
[ 2.76910381e+01, 1.00000000e-01],
[ 2.87497196e+01, 1.00000000e-01],
[ 2.67061672e+01, 1.00000000e-01],
[ 2.42487488e+01, 1.00000000e-01],
[ 2.71205807e+01, 1.00000000e-01],
[ 2.88769016e+01, 1.00000000e-01],
[ 2.66718349e+01, 1.00000000e-01],
[ 2.55596066e+01, 1.00000000e-01],
[ 2.72039890e+01, 1.00000000e-01],
[ 2.95887127e+01, 1.00000000e-01],
[ 2.85221577e+01, 1.00000000e-01],
[ 2.59490604e+01, 1.00000000e-01],
[ 2.41546745e+01, 1.00000000e-01],
[ 2.19943180e+01, 1.00000000e-01],
[ 2.04306870e+01, 1.00000000e-01],
[ 1.92450027e+01, 1.00000000e-01],
[ 2.10771694e+01, 1.00000000e-01]])
In [113]:
distance_data = pd.DataFrame(payload["pedometer"])
distance_data["endDate"] = pd.to_datetime(distance_data["endDate"])
distance_data["startDate"] = pd.to_datetime(distance_data["startDate"])
In [135]:
dist_data = distance_data[0:2]
In [140]:
distance_data
Out[140]:
distance
endDate
floorsAscended
floorsDescended
startDate
steps
0
Optional(10.50349068106152)
2015-07-25 16:01:36
0
0
2015-07-25 16:01:27
13
1
Optional(13.10452706646174)
2015-07-25 16:01:39
0
0
2015-07-25 16:01:27
17
2
Optional(0)
2015-07-25 16:01:41
0
0
2015-07-25 16:01:39
0
In [ ]:
In [128]:
distance_data["endDate"]
Out[128]:
0 2015-07-25 16:01:36
1 2015-07-25 16:01:39
2 2015-07-25 16:01:41
Name: endDate, dtype: datetime64[ns]
In [127]:
distance_data
Out[127]:
distance
endDate
floorsAscended
floorsDescended
startDate
steps
0
Optional(10.50349068106152)
2015-07-25 16:01:36
0
0
2015-07-25 16:01:27
13
1
Optional(13.10452706646174)
2015-07-25 16:01:39
0
0
2015-07-25 16:01:27
17
2
Optional(0)
2015-07-25 16:01:41
0
0
2015-07-25 16:01:39
0
In [ ]:
distance_data["steps"].shift
In [ ]:
distance_data["steps"].shift
In [74]:
distance_data["steps"].astype(int).resample("1s")
Out[74]:
startDate
2015-07-25 16:01:27 15
2015-07-25 16:01:28 NaN
2015-07-25 16:01:29 NaN
2015-07-25 16:01:30 NaN
2015-07-25 16:01:31 NaN
2015-07-25 16:01:32 NaN
2015-07-25 16:01:33 NaN
2015-07-25 16:01:34 NaN
2015-07-25 16:01:35 NaN
2015-07-25 16:01:36 NaN
2015-07-25 16:01:37 NaN
2015-07-25 16:01:38 NaN
2015-07-25 16:01:39 0
Freq: S, Name: steps, dtype: float64
In [69]:
angle_data = heading_data["heading"].resample("1s")
Out[69]:
date
2015-07-25 16:01:28 102.175964
2015-07-25 16:01:29 118.296115
2015-07-25 16:01:30 75.471761
2015-07-25 16:01:31 78.956342
2015-07-25 16:01:32 33.549365
2015-07-25 16:01:33 14.659729
2015-07-25 16:01:34 106.000578
2015-07-25 16:01:35 202.501206
2015-07-25 16:01:36 259.939720
2015-07-25 16:01:37 285.123961
2015-07-25 16:01:38 299.858951
2015-07-25 16:01:39 200.597217
2015-07-25 16:01:40 25.381208
Freq: S, Name: heading, dtype: float64
In [51]:
%matplotlib inline
Content source: ElliotJH/clew-visualiser
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