Our primary goal here is to import, interpolate, and extend our bike riding GPS data. It'll be somewhat complicated as it is not small task on code, but using vectorization on all processes will keep execution really fast. We have for this task about 1.7M datapoints. Each datapoint corresponds to a fancy one point in space time, i.e. one point made of 4 dimensions of timestamp, latitude, longitude and elevation. We hope that connecting each one sequentially on time will render a path that a biker rode. To guarantee book keeping and post processing capabilities, we will record the point id, ride id and rider id.
# import needed packages
import gpxpy
import datetime
import time
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import os
import numpy as np
import re
import sys
import pandas as pd
import pickle as pk
from tqdm import tqdm_notebook as tqdm
from geopy.distance import great_circle
# from scipy.signal import gaussian
# from scipy.ndimage import filters
from scipy.ndimage.filters import gaussian_filter
from mpl_toolkits.axes_grid1 import make_axes_locatable
import nvector as nv
from pandas.plotting import andrews_curves# import the gpx data
gpx_filename_pattern = re.compile("(gpx)$")
def build_meta(gpx):
meta = {
"tracks": 0,
"segments": 0,
"points": 0
}
for track in gpx.tracks:
meta["tracks"] += 1
for segment in track.segments:
meta["segments"] += 1
for point in segment.points:
meta["points"] += 1
return meta
def import_gpx_file(path, filename):
build_meta
file = open(path+'/'+filename, 'r')
gpx = gpxpy.parse(file)
meta = build_meta(gpx)
data = np.zeros((meta['points'], 5))
n = 0
for track in gpx.tracks:
for segment in track.segments:
for point in segment.points:
t = time.mktime(point.time.timetuple())
data[n, ] = [point.latitude, point.longitude, point.elevation, t, n]
n += 1
return pd.DataFrame({
'latitude': data[:,0],
'longitude': data[:,1],
'elevation': data[:,2],
'timestamp': data[:,3],
'point': data[:,4],
'ride': 0,
'rider': ""
})
def import_gpx_dir(path):
filenames = [f for f in os.listdir(path) if gpx_filename_pattern.search(f)]
data = pd.DataFrame()
n = 0
for filename in tqdm(filenames, total=len(filenames)):
dt = import_gpx_file(path, filename)
dt['ride'] = n
n += 1
data = data.append(dt, ignore_index=True)
return data
def interpret_gpx(path, name):
data = import_gpx_dir(path)
data["rider"] = name
data.elevation = data.elevation.astype(int)
data.timestamp = data.timestamp.astype(int)
data.point = data.point.astype(int)
#save to cache
cache = open(str(name)+'.pkl', 'wb')
pk.dump(data, cache)
cache.close()
return data
def load_gpx(path):
cache = open(path, 'rb')
data = pk.load(cache)
cache.close()
return data
def save_data(data, name):
cache = open(name+".pkl", 'wb')
pk.dump(data, cache)
cache.close()
def load_data(name):
cache = open(name+".pkl", 'rb')
data = pk.load(cache)
cache.close()
return data
if(os.path.exists("0.pkl")):
data1 = load_gpx("0.pkl")
data2 = load_gpx("1.pkl")
else:
data1 = interpret_gpx("/Users/esse/activ/rodrigo_sztelzer", 0)
data2 = interpret_gpx("/Users/esse/activ/marcos_paulo", 1)
imported = data1.append(data2, ignore_index=True)
print(imported.shape)(1739684, 7)
There will have some steps to have a gooda data shaped to most processes. The idea is to cleanup dirt distances introduced by bad GPS and interpolate the points as to make all rows 1 meter of distance, that will simplify most phisical calculations.
This will transform the list in a origin/destination list of points, so the resulting list is [M-1, N*2]. These new columns are equal to the next row. Finally remove the last element of each ride that has value zero.
def haversine(lon1, lat1, lon2, lat2):
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
a = np.sin((lat2-lat1)/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2
return 6372800 * 2 * np.arcsin(np.sqrt(a))
def destiny(dt):
# create a column with the next points
dt = dt.sort_values(['timestamp'])
dt['duration'] = np.roll(dt.timestamp, -1) - dt['timestamp']
dt.drop(dt.tail(1).index, inplace=True)
#repeat the data, set repeats to nan, interpolate nans.
dt = dt.loc[dt.index.repeat(dt.duration)] # repeat rows n times based on n duration
dt['subindex'] = dt.groupby(level=0).cumcount() # creat an intern index of the repeats
dt[dt.subindex > 0] = np.nan
dt = dt.interpolate() # interpolate the nans
dt = dt.reset_index()
del(dt['index'])
del(dt['duration'])
del(dt['subindex'])
dt['ilon'] = dt['longitude']
dt['ilat'] = dt['latitude']
dt['ielev'] = dt['elevation']
dt['itime'] = dt['timestamp']
dt['flon'] = np.roll(dt.longitude, -1)
dt['flat'] = np.roll(dt.latitude, -1)
dt['felev'] = np.roll(dt.elevation, -1)
dt['ftime'] = np.roll(dt.timestamp, -1)
del(dt['longitude'])
del(dt['latitude'])
del(dt['timestamp'])
del(dt['elevation'])
dt['dist'] = haversine(dt.ilon, dt.ilat, dt.flon, dt.flat)
dt = dt[dt.dist > 0]
dt['heading'] = np.arctan2(dt.flon-dt.ilon, dt.flat-dt.ilat) * 180 / np.pi
dt['ddist'] = np.roll(dt.dist, -1, 0) - dt.dist
dt.loc[dt.point==0, 'ddist'] = dt.dist
dt['delev'] = dt.felev - dt.ielev
dt['delev'] = np.clip(dt.delev, -1, 1)
dt['climb'] = dt.delev / dt.dist
dt['climb'] = dt['climb'].fillna(0)
dt.drop(dt.tail(1).index, inplace=True)
return dtdata = imported.groupby(['rider', 'ride'], as_index=True, sort=False).apply(destiny)save_data(data, "grouped")ride = 0
head = data.loc[0].loc[ride].head(210)
tail = data.loc[0].loc[ride].tail(220)
plt.figure(figsize=(10,10))
plt.quiver(head.ilon, head.ilat, head.flon-head.ilon, head.flat-head.ilat, color="blue", units='xy', angles='xy', scale=1, pivot='tail')
plt.quiver(tail.ilon, tail.ilat, tail.flon-tail.ilon, tail.flat-tail.ilat, color="red", units='xy', angles='xy', scale=1, pivot='tail')
plt.axis('equal')
plt.show()
data.loc[1].loc[0]
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| point | ride | rider | ilon | ilat | ielev | itime | flon | flat | felev | ftime | dist | heading | ddist | delev | climb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 0.0 | 1.0 | -46.685961 | -23.594525 | 736.000000 | 1.450308e+09 | -46.685954 | -23.594531 | 735.947368 | 1.450308e+09 | 0.989036 | 131.338064 | 9.890356e-01 | -0.052632 | -0.053215 |
| 1 | 0.026316 | 0.0 | 1.0 | -46.685954 | -23.594531 | 735.947368 | 1.450308e+09 | -46.685947 | -23.594537 | 735.894737 | 1.450308e+09 | 0.989036 | 131.338064 | -2.463006e-08 | -0.052632 | -0.053215 |
| 2 | 0.052632 | 0.0 | 1.0 | -46.685947 | -23.594537 | 735.894737 | 1.450308e+09 | -46.685940 | -23.594543 | 735.842105 | 1.450308e+09 | 0.989036 | 131.338064 | -2.298186e-08 | -0.052632 | -0.053215 |
| 3 | 0.078947 | 0.0 | 1.0 | -46.685940 | -23.594543 | 735.842105 | 1.450308e+09 | -46.685933 | -23.594550 | 735.789474 | 1.450308e+09 | 0.989036 | 131.338064 | -2.558778e-08 | -0.052632 | -0.053215 |
| 4 | 0.105263 | 0.0 | 1.0 | -46.685933 | -23.594550 | 735.789474 | 1.450308e+09 | -46.685926 | -23.594556 | 735.736842 | 1.450308e+09 | 0.989035 | 131.338064 | -2.320463e-08 | -0.052632 | -0.053215 |
| 5 | 0.131579 | 0.0 | 1.0 | -46.685926 | -23.594556 | 735.736842 | 1.450308e+09 | -46.685919 | -23.594562 | 735.684211 | 1.450308e+09 | 0.989035 | 131.338064 | -2.487507e-08 | -0.052632 | -0.053215 |
| 6 | 0.157895 | 0.0 | 1.0 | -46.685919 | -23.594562 | 735.684211 | 1.450308e+09 | -46.685912 | -23.594568 | 735.631579 | 1.450308e+09 | 0.989035 | 131.338064 | -2.369461e-08 | -0.052632 | -0.053215 |
| 7 | 0.184211 | 0.0 | 1.0 | -46.685912 | -23.594568 | 735.631579 | 1.450308e+09 | -46.685905 | -23.594574 | 735.578947 | 1.450308e+09 | 0.989035 | 131.338064 | -2.438511e-08 | -0.052632 | -0.053215 |
| 8 | 0.210526 | 0.0 | 1.0 | -46.685905 | -23.594574 | 735.578947 | 1.450308e+09 | -46.685898 | -23.594580 | 735.526316 | 1.450308e+09 | 0.989035 | 131.338064 | -2.371685e-08 | -0.052632 | -0.053215 |
| 9 | 0.236842 | 0.0 | 1.0 | -46.685898 | -23.594580 | 735.526316 | 1.450308e+09 | -46.685891 | -23.594587 | 735.473684 | 1.450308e+09 | 0.989035 | 131.338064 | -2.460788e-08 | -0.052632 | -0.053215 |
| 10 | 0.263158 | 0.0 | 1.0 | -46.685891 | -23.594587 | 735.473684 | 1.450308e+09 | -46.685884 | -23.594593 | 735.421053 | 1.450308e+09 | 0.989035 | 131.338064 | -2.369463e-08 | -0.052632 | -0.053215 |
| 11 | 0.289474 | 0.0 | 1.0 | -46.685884 | -23.594593 | 735.421053 | 1.450308e+09 | -46.685877 | -23.594599 | 735.368421 | 1.450308e+09 | 0.989035 | 131.338064 | -2.512008e-08 | -0.052632 | -0.053215 |
| 12 | 0.315789 | 0.0 | 1.0 | -46.685877 | -23.594599 | 735.368421 | 1.450308e+09 | -46.685870 | -23.594605 | 735.315789 | 1.450308e+09 | 0.989035 | 131.338064 | -2.320467e-08 | -0.052632 | -0.053215 |
| 13 | 0.342105 | 0.0 | 1.0 | -46.685870 | -23.594605 | 735.315789 | 1.450308e+09 | -46.685863 | -23.594611 | 735.263158 | 1.450308e+09 | 0.989035 | 131.338064 | -2.512009e-08 | -0.052632 | -0.053215 |
| 14 | 0.368421 | 0.0 | 1.0 | -46.685863 | -23.594611 | 735.263158 | 1.450308e+09 | -46.685856 | -23.594617 | 735.210526 | 1.450308e+09 | 0.989035 | 131.338064 | -2.320468e-08 | -0.052632 | -0.053215 |
| 15 | 0.394737 | 0.0 | 1.0 | -46.685856 | -23.594617 | 735.210526 | 1.450308e+09 | -46.685849 | -23.594624 | 735.157895 | 1.450308e+09 | 0.989035 | 131.338064 | -2.416240e-08 | -0.052632 | -0.053215 |
| 16 | 0.421053 | 0.0 | 1.0 | -46.685849 | -23.594624 | 735.157895 | 1.450308e+09 | -46.685842 | -23.594630 | 735.105263 | 1.450308e+09 | 0.989035 | 131.338064 | -2.465237e-08 | -0.052632 | -0.053215 |
| 17 | 0.447368 | 0.0 | 1.0 | -46.685842 | -23.594630 | 735.105263 | 1.450308e+09 | -46.685835 | -23.594636 | 735.052632 | 1.450308e+09 | 0.989035 | 131.338064 | -2.414018e-08 | -0.052632 | -0.053215 |
| 18 | 0.473684 | 0.0 | 1.0 | -46.685835 | -23.594636 | 735.052632 | 1.450308e+09 | -46.685828 | -23.594642 | 735.000000 | 1.450308e+09 | 0.989035 | 131.338064 | -2.440740e-08 | -0.052632 | -0.053215 |
| 19 | 0.500000 | 0.0 | 1.0 | -46.685828 | -23.594642 | 735.000000 | 1.450308e+09 | -46.685821 | -23.594648 | 734.947368 | 1.450308e+09 | 0.989035 | 131.338064 | -2.369468e-08 | -0.052632 | -0.053215 |
| 20 | 0.526316 | 0.0 | 1.0 | -46.685821 | -23.594648 | 734.947368 | 1.450308e+09 | -46.685814 | -23.594654 | 734.894737 | 1.450308e+09 | 0.989035 | 131.338064 | -2.438518e-08 | -0.052632 | -0.053215 |
| 21 | 0.552632 | 0.0 | 1.0 | -46.685814 | -23.594654 | 734.894737 | 1.450308e+09 | -46.685807 | -23.594660 | 734.842105 | 1.450308e+09 | 0.989035 | 131.338064 | -2.347193e-08 | -0.052632 | -0.053215 |
| 22 | 0.578947 | 0.0 | 1.0 | -46.685807 | -23.594660 | 734.842105 | 1.450308e+09 | -46.685800 | -23.594667 | 734.789474 | 1.450308e+09 | 0.989035 | 131.338064 | -2.509791e-08 | -0.052632 | -0.053215 |
| 23 | 0.605263 | 0.0 | 1.0 | -46.685800 | -23.594667 | 734.789474 | 1.450308e+09 | -46.685793 | -23.594673 | 734.736842 | 1.450308e+09 | 0.989035 | 131.338064 | -2.369470e-08 | -0.052632 | -0.053215 |
| 24 | 0.631579 | 0.0 | 1.0 | -46.685793 | -23.594673 | 734.736842 | 1.450308e+09 | -46.685786 | -23.594679 | 734.684211 | 1.450308e+09 | 0.989035 | 131.338064 | -2.463018e-08 | -0.052632 | -0.053215 |
| 25 | 0.657895 | 0.0 | 1.0 | -46.685786 | -23.594679 | 734.684211 | 1.450308e+09 | -46.685779 | -23.594685 | 734.631579 | 1.450308e+09 | 0.989035 | 131.338064 | -2.344972e-08 | -0.052632 | -0.053215 |
| 26 | 0.684211 | 0.0 | 1.0 | -46.685779 | -23.594685 | 734.631579 | 1.450308e+09 | -46.685772 | -23.594691 | 734.578947 | 1.450308e+09 | 0.989035 | 131.338064 | -2.487518e-08 | -0.052632 | -0.053215 |
| 27 | 0.710526 | 0.0 | 1.0 | -46.685772 | -23.594691 | 734.578947 | 1.450308e+09 | -46.685765 | -23.594697 | 734.526316 | 1.450308e+09 | 0.989035 | 131.338064 | -2.322698e-08 | -0.052632 | -0.053215 |
| 28 | 0.736842 | 0.0 | 1.0 | -46.685765 | -23.594697 | 734.526316 | 1.450308e+09 | -46.685758 | -23.594704 | 734.473684 | 1.450308e+09 | 0.989035 | 131.338064 | -2.509795e-08 | -0.052632 | -0.053215 |
| 29 | 0.763158 | 0.0 | 1.0 | -46.685758 | -23.594704 | 734.473684 | 1.450308e+09 | -46.685751 | -23.594710 | 734.421053 | 1.450308e+09 | 0.989035 | 131.338064 | -2.344975e-08 | -0.052632 | -0.053215 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1426 | 1017.000000 | 0.0 | 1.0 | -46.642827 | -23.587794 | 797.000000 | 1.450309e+09 | -46.642825 | -23.587779 | 797.500000 | 1.450309e+09 | 1.625616 | 7.853313 | 3.069071e-09 | 0.500000 | 0.307576 |
| 1427 | 1017.500000 | 0.0 | 1.0 | -46.642825 | -23.587779 | 797.500000 | 1.450309e+09 | -46.642823 | -23.587765 | 798.000000 | 1.450309e+09 | 1.625616 | 7.853313 | 8.687803e-01 | 0.500000 | 0.307576 |
| 1428 | 1018.000000 | 0.0 | 1.0 | -46.642823 | -23.587765 | 798.000000 | 1.450309e+09 | -46.642836 | -23.587746 | 798.000000 | 1.450309e+09 | 2.494396 | -34.380345 | 1.012410e-07 | 0.000000 | 0.000000 |
| 1429 | 1018.500000 | 0.0 | 1.0 | -46.642836 | -23.587746 | 798.000000 | 1.450309e+09 | -46.642849 | -23.587727 | 798.000000 | 1.450309e+09 | 2.494396 | -34.380345 | 8.256844e-01 | 0.000000 | 0.000000 |
| 1430 | 1019.000000 | 0.0 | 1.0 | -46.642849 | -23.587727 | 798.000000 | 1.450309e+09 | -46.642865 | -23.587701 | 798.000000 | 1.450309e+09 | 3.320081 | -31.607502 | 1.590601e-07 | 0.000000 | 0.000000 |
| 1431 | 1019.500000 | 0.0 | 1.0 | -46.642865 | -23.587701 | 798.000000 | 1.450309e+09 | -46.642881 | -23.587675 | 798.000000 | 1.450309e+09 | 3.320081 | -31.607502 | 2.099193e+00 | 0.000000 | 0.000000 |
| 1432 | 1020.000000 | 0.0 | 1.0 | -46.642881 | -23.587675 | 798.000000 | 1.450309e+09 | -46.642906 | -23.587632 | 799.000000 | 1.450309e+09 | 5.419274 | -30.173520 | 3.936607e-01 | 1.000000 | 0.184527 |
| 1433 | 1021.000000 | 0.0 | 1.0 | -46.642906 | -23.587632 | 799.000000 | 1.450309e+09 | -46.642935 | -23.587587 | 799.000000 | 1.450309e+09 | 5.812934 | -32.799531 | -3.449872e-01 | 0.000000 | 0.000000 |
| 1434 | 1022.000000 | 0.0 | 1.0 | -46.642935 | -23.587587 | 799.000000 | 1.450309e+09 | -46.642961 | -23.587544 | 800.000000 | 1.450309e+09 | 5.467947 | -31.159305 | 2.088392e-01 | 1.000000 | 0.182884 |
| 1435 | 1023.000000 | 0.0 | 1.0 | -46.642961 | -23.587544 | 800.000000 | 1.450309e+09 | -46.642991 | -23.587501 | 800.000000 | 1.450309e+09 | 5.676786 | -34.902496 | 2.424259e-01 | 0.000000 | 0.000000 |
| 1436 | 1024.000000 | 0.0 | 1.0 | -46.642991 | -23.587501 | 800.000000 | 1.450309e+09 | -46.643022 | -23.587456 | 801.000000 | 1.450309e+09 | 5.919212 | -34.562525 | -5.978414e-01 | 1.000000 | 0.168941 |
| 1437 | 1025.000000 | 0.0 | 1.0 | -46.643022 | -23.587456 | 801.000000 | 1.450309e+09 | -46.643047 | -23.587414 | 801.000000 | 1.450309e+09 | 5.321371 | -30.762720 | -3.412469e-01 | 0.000000 | 0.000000 |
| 1438 | 1026.000000 | 0.0 | 1.0 | -46.643047 | -23.587414 | 801.000000 | 1.450309e+09 | -46.643071 | -23.587375 | 802.000000 | 1.450309e+09 | 4.980124 | -31.607502 | 8.672684e-03 | 1.000000 | 0.200798 |
| 1439 | 1027.000000 | 0.0 | 1.0 | -46.643071 | -23.587375 | 802.000000 | 1.450309e+09 | -46.643097 | -23.587337 | 802.000000 | 1.450309e+09 | 4.988797 | -34.380345 | 5.489145e-02 | 0.000000 | 0.000000 |
| 1440 | 1028.000000 | 0.0 | 1.0 | -46.643097 | -23.587337 | 802.000000 | 1.450309e+09 | -46.643124 | -23.587299 | 802.000000 | 1.450309e+09 | 5.043688 | -35.394796 | 6.737592e-01 | 0.000000 | 0.000000 |
| 1441 | 1029.000000 | 0.0 | 1.0 | -46.643124 | -23.587299 | 802.000000 | 1.450309e+09 | -46.643153 | -23.587255 | 803.000000 | 1.450309e+09 | 5.717447 | -33.388514 | 2.017680e-01 | 1.000000 | 0.174903 |
| 1442 | 1030.000000 | 0.0 | 1.0 | -46.643153 | -23.587255 | 803.000000 | 1.450309e+09 | -46.643184 | -23.587210 | 803.000000 | 1.450309e+09 | 5.919215 | -34.562525 | 3.169620e-01 | 0.000000 | 0.000000 |
| 1443 | 1031.000000 | 0.0 | 1.0 | -46.643184 | -23.587210 | 803.000000 | 1.450309e+09 | -46.643187 | -23.587154 | 804.000000 | 1.450309e+09 | 6.236177 | -3.066486 | -2.801442e-01 | 1.000000 | 0.160355 |
| 1444 | 1032.000000 | 0.0 | 1.0 | -46.643187 | -23.587154 | 804.000000 | 1.450309e+09 | -46.643215 | -23.587107 | 804.000000 | 1.450309e+09 | 5.956033 | -30.784147 | -5.367544e-01 | 0.000000 | 0.000000 |
| 1445 | 1033.000000 | 0.0 | 1.0 | -46.643215 | -23.587107 | 804.000000 | 1.450309e+09 | -46.643240 | -23.587064 | 804.000000 | 1.450309e+09 | 5.419279 | -30.173520 | -8.299216e-01 | 0.000000 | 0.000000 |
| 1446 | 1034.000000 | 0.0 | 1.0 | -46.643240 | -23.587064 | 804.000000 | 1.450309e+09 | -46.643262 | -23.587028 | 804.000000 | 1.450309e+09 | 4.589357 | -31.429566 | -7.813802e-01 | 0.000000 | 0.000000 |
| 1447 | 1035.000000 | 0.0 | 1.0 | -46.643262 | -23.587028 | 804.000000 | 1.450309e+09 | -46.643280 | -23.586998 | 804.000000 | 1.450309e+09 | 3.807977 | -30.963757 | 2.268681e+00 | 0.000000 | 0.000000 |
| 1448 | 1036.000000 | 0.0 | 1.0 | -46.643280 | -23.586998 | 804.000000 | 1.450309e+09 | -46.643337 | -23.586982 | 804.500000 | 1.450309e+09 | 6.076658 | -74.320476 | 6.772585e-07 | 0.500000 | 0.082282 |
| 1449 | 1036.500000 | 0.0 | 1.0 | -46.643337 | -23.586982 | 804.500000 | 1.450309e+09 | -46.643394 | -23.586966 | 805.000000 | 1.450309e+09 | 6.076659 | -74.320476 | -3.148050e+00 | 0.500000 | 0.082282 |
| 1450 | 1037.000000 | 0.0 | 1.0 | -46.643394 | -23.586966 | 805.000000 | 1.450309e+09 | -46.643421 | -23.586975 | 805.000000 | 1.450309e+09 | 2.928609 | -108.434949 | -1.776300e-07 | 0.000000 | 0.000000 |
| 1451 | 1037.500000 | 0.0 | 1.0 | -46.643421 | -23.586975 | 805.000000 | 1.450309e+09 | -46.643448 | -23.586984 | 805.000000 | 1.450309e+09 | 2.928609 | -108.434949 | -4.881016e-01 | 0.000000 | 0.000000 |
| 1452 | 1038.000000 | 0.0 | 1.0 | -46.643448 | -23.586984 | 805.000000 | 1.450309e+09 | -46.643470 | -23.586991 | 805.000000 | 1.450309e+09 | 2.440507 | -108.434949 | -1.233072e-07 | 0.000000 | 0.000000 |
| 1453 | 1038.500000 | 0.0 | 1.0 | -46.643470 | -23.586991 | 805.000000 | 1.450309e+09 | -46.643493 | -23.586999 | 805.000000 | 1.450309e+09 | 2.440507 | -108.434949 | -5.835686e-01 | 0.000000 | 0.000000 |
| 1454 | 1039.000000 | 0.0 | 1.0 | -46.643493 | -23.586999 | 805.000000 | 1.450309e+09 | -46.643510 | -23.587005 | 805.500000 | 1.450309e+09 | 1.856938 | -109.440035 | -7.320583e-08 | 0.500000 | 0.269260 |
| 1455 | 1039.500000 | 0.0 | 1.0 | -46.643510 | -23.587005 | 805.500000 | 1.450309e+09 | -46.643527 | -23.587011 | 806.000000 | 1.450309e+09 | 1.856938 | -109.440035 | 4.403480e+03 | 0.500000 | 0.269260 |
1456 rows × 16 columns