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import functools
import geopy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyproj
import requests
import scipy as sp
import rtree
import seaborn as sb
from scipy import signal
# import shapely
import shapely.geometry
%pylab inline

import data_munging


    

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reload(data_munging)


    

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rides, readings = data_munging.read_raw_data()
readings = data_munging.clean_readings(readings)
readings = data_munging.add_proj_to_readings(readings, data_munging.NAD83)


    

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n, p = readings.shape
print rides.columns
print readings.columns


    

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kimball_readings = data_munging.filter_readings_by_bb(readings, data_munging.kimball_bounding_box)
chi_readings = data_munging.filter_readings_to_chicago(readings)


    

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groups = kimball_readings.groupby('ride_id')
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for ride, group in groups:
    ax.plot(group.start_x, group.start_y, marker='o', linestyle='', ms=12, label=ride)
ax.legend()
fig = plt.gcf()
fig.set_size_inches(30, 30)
plt.show()


    

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chi_readings['std_over_speed'] = chi_readings['std_total'] / chi_readings['gps_speed']
chi_readings['std_over_speed_capped'] = chi_readings['std_over_speed'].apply(lambda x: min(x, 1.0))
for var in ['std_total', 'std_over_speed', 'std_over_speed_capped']:
    plot = chi_readings.plot(x='start_x', y='start_y', kind='scatter', c=var, colormap='Oranges')
    fig = plt.gcf()
    fig.set_size_inches(150, 150)
    plot.set_axis_bgcolor('w')
    plt.show()


    

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kimball_readings['std_over_speed'] = kimball_readings['std_total'] / kimball_readings['gps_speed']
kimball_readings['std_over_speed_capped'] = kimball_readings['std_over_speed'].apply(lambda x: min(x, 1.0))
for var in ['std_total', 'std_over_speed', 'std_over_speed_capped']:
    plot = kimball_readings.plot(x='start_x', y='start_y', kind='scatter', c=var, colormap='Oranges')
    fig = plt.gcf()
    fig.set_size_inches(30, 30)
    plot.set_axis_bgcolor('w')
    plt.show()


    

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kimball_readings['std_over_speed'] = kimball_readings['std_total'] / kimball_readings['gps_speed']
kimball_readings.plot(x='start_x', y='start_y', kind='scatter', c='std_over_speed', colormap='Oranges_r')
fig = plt.gcf()
fig.set_size_inches(30, 30)
plt.show()


    

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readings_idx = data_munging.insert_readings_rtree(kimball_readings)


    

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min(kimball_readings.start_x)


    

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bumpy_idx = [i for i in readings_idx.intersection((351200 + 0, 584800 + 0 , 351200 + 100, 584800 + 650))]


    

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kimball_proper_idx = [i for i in readings_idx.intersection((351200 + 280, 584800 + 500 , 351200 + 400, 584800 + 700))]


    

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kimball_readings.loc[kimball_proper_idx, :].plot(x='start_x', y='start_y', kind='scatter', )
fig = plt.gcf()
fig.set_size_inches(30, 30)
plt.show()


    

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kimball_readings.loc[kimball_proper_idx, :].plot(x='gps_speed', y='std_total', kind='scatter')
plt.show()


    

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kimball_readings.loc[bumpy_idx, :].plot(x='start_x', y='start_y', kind='scatter', )
fig = plt.gcf()
fig.set_size_inches(30, 30)
plt.show()