#!/usr/bin/env python
# coding: utf-8

# # Analyzing Spatial Trajectories with Traja
# Full documentation is available at [traja.readthedocs.io](http://traja.readthedocs.io).

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import traja


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# Create sample random walk
df = traja.generate()

# Visualize x and y values with built-in pandas methods
df.plot()


# ## Plot Trajectory

# ### Plot trajectory with traja accessor method (`.traja.plot()`)

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fig = df.traja.plot()


# ### Visualize distribution of angles and turn-angles

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df.traja.calc_angle().hist() # w.r.t x-axis


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df.traja.calc_turn_angle().hist() # deviation from strait ahead


# ### Visualize flow between grid units

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for kind in ['stream', 'quiver', 'contourf']:
    fig = df.traja.plot_flow(kind=kind)


# ### Visualize distribution of turn angles over time

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df.traja.calc_turn_angle().plot()


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for log in [True, False]:
    for bins in [8, 32]:
        print(f"Bins: {bins}")
        df.traja.trip_grid(bins=bins, log=log)


# ### Plot polar bar chart showing turn preference

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traja.plotting.polar_bar(df)


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# Show non-overlapping histogram
traja.plotting.polar_bar(df, overlap=False)


# ## Resample Trajectory

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# Resample to arbitrary step length (here, 20 meters)
fig = df.traja.rediscretize(R=20).traja.plot()


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# Resample to arbitrary time (here, 1 second)
fig = df.traja.resample_time(step_time='1s').traja.plot()