In order to run this notebook with the correct PYTHONPATH, you can use

$ ./e-mission-jupyter.bash notebook

This notebook makes heavy use of the following python libraries.

pandas: from the python computing stack (http://pandas.pydata.org/pandas-docs/stable/)
geojson: standard JSON representation of geographic data (http://geojson.org/)
folium: (https://github.com/python-visualization/folium) python bridge to leaflet (http://leafletjs.com/)

Pick a user to work with



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import emission.core.get_database as edb
import pandas as pd
all_users = pd.DataFrame(list(edb.get_uuid_db().find({}, {"user_email":1, "uuid": 1, "_id": 0})))
all_users



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from uuid import UUID



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test_user_id = all_users.iloc[1].uuid # replace with UUID from above

Preferred access technique

The preferred technique to access wrapper objects from the timeseries is to use the abstract timeseries interface. This makes it easier for us to switch to alternative timeseries implementations later



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import emission.storage.timeseries.abstract_timeseries as esta
import emission.storage.decorations.analysis_timeseries_queries as esda
import emission.core.wrapper.entry as ecwe
import emission.storage.decorations.trip_queries as esdt



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ts = esta.TimeSeries.get_time_series(test_user_id)

Accessing entries directly



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entry_it = ts.find_entries(["analysis/cleaned_trip"], time_query=None)

All keys and their mapping to data model objects can be found in https://github.com/e-mission/e-mission-server/blob/master/emission/core/wrapper/entry.py



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for ct in entry_it:
    cte = ecwe.Entry(ct)
    print("=== Trip:", cte.data.start_loc, "->", cte.data.end_loc)
    section_it = esdt.get_sections_for_trip("analysis/cleaned_section", test_user_id, cte.get_id())
    for sec in section_it:
        print("  --- Section:", sec.data.start_loc, "->", sec.data.end_loc, " on ", sec.data.sensed_mode)

Accessing a dataframe



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ct_df = ts.get_data_df("analysis/cleaned_trip", time_query=None)



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len(ct_df)



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ct_df.columns



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ct_df[["start_loc", "end_loc", "start_ts", "end_ts"]]



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cs_df = ts.get_data_df("analysis/cleaned_section", time_query=None)



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len(cs_df)



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cs_df.columns



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cs_df[["start_loc", "end_loc", "start_ts", "end_ts", "sensed_mode"]]

Direct mongodb queries

You can also use direct mongodb queries during exploratory work. I do ask that you create a storage decorator (emission/storage/decorations) when you submit a pull request for ongoing use



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import emission.core.get_database as edb



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edb.get_timeseries_db().find_one()



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edb.get_timeseries_db().distinct("metadata.key")

Note that in this case, you need to know whether to use the timeseries or the analysis_timeseries collection



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edb.get_analysis_timeseries_db().distinct("metadata.key")



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edb.get_analysis_timeseries_db().find({"user_id": test_user_id, "metadata.key": "analysis/cleaned_trip"}).count()

In particular, you can use this to access entries that are not in the timeseries



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edb.get_uuid_db().distinct("uuid")

Timeline

The trips and places maintain links to each other - e.g. start_place, end_place



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ct_df[["start_place", "end_place"]]

These are primary key links to other entries in the database. It would be useful to have a doubly linked list representing this properly. The Timeline helps with that.



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import emission.storage.decorations.timeline as esdl



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tl = esdl.get_cleaned_timeline(test_user_id, ct_df.iloc[0].start_ts, ct_df.iloc[-1].end_ts)



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for e in tl:
    if 'enter_ts' in e.data:
        # Must be place-like
        print(e.metadata.key, e.data.enter_fmt_time, "->", e.data.exit_fmt_time)
    else:
        print(e.metadata.key, e.data.start_fmt_time, "->", e.data.end_fmt_time)



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# The timeline is an iterator, so after it is consumed, it is empty
for e in tl:
    if 'enter_ts' in e.data:
        # Must be place-like
        print(e.metadata.key, e.data.enter_fmt_time, "->", e.data.exit_fmt_time)
    else:
        print(e.metadata.key, e.data.start_fmt_time, "->", e.data.end_fmt_time)



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stl = esdt.get_cleaned_timeline_for_trip(test_user_id, tl.first_trip().get_id())



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for e in stl:
    print(e.metadata.key)



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stl = esdt.get_cleaned_timeline_for_trip(test_user_id, tl.last_trip().get_id())



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for e in stl:
    print(e.metadata.key)

Getting trip and section details

Once we have trip and section objects, we can retrieve the sensed data associated with them by querying for data in various streams that falls within the time ranges associated with the trip/section. Here again, our architecture of storing the analysis results as a separate datastream makes it easy to retrieve data at various levels of processing.

Plot a processed trip or set of trips



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import emission.analysis.plotting.geojson.geojson_feature_converter as gfc
import emission.analysis.plotting.leaflet_osm.our_plotter as lo



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first_trip_for_user = ct_df.iloc[0]
first_trip_start_ts = first_trip_for_user.start_ts
first_trip_end_ts = first_trip_for_user.end_ts
trip_start_end_fuzz = 10 # seconds
trips_geojson_list = gfc.get_geojson_for_ts(test_user_id, first_trip_start_ts-trip_start_end_fuzz, ct_df.iloc[-1].end_ts+trip_start_end_fuzz)



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len(trips_geojson_list)



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map_list = lo.get_maps_for_geojson_trip_list(trips_geojson_list)



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len(map_list)



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map_list[0]



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map_list[-1]



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import branca.element as bre



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nrows = 2
ncols = 3
fig = bre.Figure()
for i, m in enumerate(map_list[:6]):
    fig.add_subplot(nrows,ncols,i+1).add_child(m)
fig



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nrows = 2
ncols = 3
fig = bre.Figure()
for i, map in enumerate(map_list[-6:]):
    fig.add_subplot(nrows,ncols,i+1).add_child(map)
fig

Can you do better?

Get locations with no processing, basic filtering and resampling for the first trip



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all_locs = ts.get_data_df("background/location",
                               time_query = esda.get_time_query_for_trip_like(
                                   "analysis/cleaned_section", ct_df.iloc[0]._id))

filtered_locs = ts.get_data_df("background/filtered_location",
                               time_query = esda.get_time_query_for_trip_like(
                                   "analysis/cleaned_section", ct_df.iloc[0]._id))

resampled_locs = ts.get_data_df("analysis/recreated_location",
                                 time_query = esda.get_time_query_for_trip_like(
                                   "analysis/cleaned_section", ct_df.iloc[0]._id))
print("Locations go from all = %d -> filtered = %d -> resampled = %d" % (len(all_locs),
                                                                         len(filtered_locs),
                                                                         len(resampled_locs)))



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all_locs[["_id", "latitude", "longitude", "fmt_time"]]

Get the raw motion activity, in case you want to do different segmentation



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all_activity = ts.get_data_df("background/motion_activity",
                               esda.get_time_query_for_trip_like(
                                   "analysis/cleaned_section", ct_df.iloc[0]._id))



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all_activity.columns



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import emission.core.wrapper.motionactivity as ecwm

print("Found %d walking entries, %d on_foot entries" % (len(all_activity[all_activity.type == ecwm.MotionTypes.WALKING.value]), 
                                    len(all_activity[all_activity.type == ecwm.MotionTypes.ON_FOOT.value])))
print("Found %d motorized entries" % (len(all_activity[all_activity.type == ecwm.MotionTypes.IN_VEHICLE.value])))

Plot the location points



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map_list = lo.get_maps_for_geojson_unsectioned([gfc.get_feature_list_from_df(all_locs),
                                                gfc.get_feature_list_from_df(filtered_locs),
                                                gfc.get_feature_list_from_df(resampled_locs)])



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fig = bre.Figure()
for i, map in enumerate(map_list):
    fig.add_subplot(1,3,i+1).add_child(map)
fig



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