In [3]:

%config Completer.use_jedi = False
%matplotlib inline

import os
import sys
import pytz
from datetime import datetime
import geopandas as gpd
from copy import copy, deepcopy
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from glob import glob
from shapely.geometry import Point, Polygon
from collections import Counter
from dask.distributed import Client
from mpl_toolkits.axes_grid1 import make_axes_locatable

import skmob
import mobilkit

sns.set_context("notebook", font_scale=1.5)
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

Notebook to show how to communicate between skmob and mobilkit¶

So far, given the implementation of skmob the communication happens via the TrajDataFrame.

We load a sample file from csv in skmob, filter and analyze it, port it to dask and then back it to skmob.

In [7]:

accColName = "acc"

In [19]:

# Load a sample file...
df = pd.read_csv("/data/datiCuebiqTiled/part-00000-c02e38a8-f763-4f0f-a3f0-3ebbf4738748-c000.csv.gz",
                 sep=",", compression="gzip", names=["UTC","uid","OS","lat","lng","acc","tz","tile_ID"])
# Add the datetime col
mobilkit.loader.compute_datetime_col(df, "America/Mexico_City")

# Port it to skmob...
traj = skmob.TrajDataFrame(df)

Filter it and do stop detection¶

In [21]:

from skmob.preprocessing import filtering
# filter out all points with a speed (in km/h) from the previous point higher than 500 km/h
ftdf = filtering.filter(traj, max_speed_kmh=500.)

In [22]:

from skmob.preprocessing import detection
# compute the stops for each individual in the TrajDataFrame
stdf = detection.stops(ftdf, stop_radius_factor=0.5, minutes_for_a_stop=20.0, spatial_radius_km=0.2, leaving_time=True)

In [ ]:

Port it to `mobilkit` for later analysis¶

In [23]:

import dask.dataframe as dd
from importlib import reload
reload(mobilkit)
reload(mobilkit.loader)

Out[23]:

<module 'mobilkit.loader' from '/home/ubi/Sandbox/mobilkit_dask/mobilkit/loader.py'>

In [1]:

df_dask = mobilkit.loader.load_from_skmob(stdf)

And back to `skmob`¶

In [25]:

# Back to skmob
back_df = mobilkit.loader.dask_to_skmob(df_dask)

In [ ]:

Notebook to show how to communicate between skmob and mobilkit¶

Filter it and do stop detection¶

Port it to mobilkit for later analysis¶

And back to skmob¶

Port it to `mobilkit` for later analysis¶

And back to `skmob`¶