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

# # Happy 2017
# We have some GPX files which we want to plot on a nice map.
# A lot of the the code below is based on [this python4oceanographers blog post](https://ocefpaf.github.io/python4oceanographers/blog/2014/08/18/gpx/).

# First we import some packages necessary to achieve what we want.

# In[1]:


import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
import numpy
import gpxpy
import mplleaflet
import glob
import os
import pandas

plt.rcParams['figure.figsize'] = (16, 9)  # Size up figures a bit


# To quickly ingest the GPX files, we write (or actually copy from the aforementioned blog post) a parsing function.

# In[2]:


def load_run_data(gpx_path, filter=""):
    gpx_files = glob.glob(os.path.join(gpx_path, filter + "*.gpx"))
    run_data = []
    for file_idx, gpx_file in enumerate(gpx_files): 
        gpx = gpxpy.parse(open(gpx_file, 'r'))
        # Loop through tracks
        for track_idx, track in enumerate(gpx.tracks):
            track_name = track.name
            track_time = track.get_time_bounds().start_time
            track_length = track.length_3d()
            track_duration = track.get_duration()
            track_speed = track.get_moving_data().max_speed
            
            for seg_idx, segment in enumerate(track.segments):
                segment_length = segment.length_3d()
                for point_idx, point in enumerate(segment.points):
                    run_data.append([file_idx, os.path.basename(gpx_file), track_idx, track_name, 
                                     track_time, track_length, track_duration, track_speed, 
                                     seg_idx, segment_length, point.time, point.latitude, 
                                     point.longitude, point.elevation, segment.get_speed(point_idx)])
    return run_data


# For easy handling, we load the GPX tracks in their subfolder to a [Pandas](https://pypi.python.org/pypi/pandas) `dataframe` and display the first few entries of the data frame.

# In[3]:


data = load_run_data(gpx_path='gpxtracks', filter="")
df = pandas.DataFrame(data, columns=['File_Index', 'File_Name', 'Index', 'Name',
                                     'Time', 'Length', 'Duration', 'Max_Speed',
                                     'Segment_Index', 'Segment_Length', 'Point_Time', 'Point_Latitude',
                                     'Point_Longitude', 'Point_Elevation', 'Point_Speed'])
df.head()


# At last we plot the tracks on top of the *awesome* [Stamen watercolor maps](http://maps.stamen.com/watercolor/#12/46.9413/7.4310). 

# In[4]:


for i in range(max(df['File_Index'])+1):
    plt.plot(df['Point_Longitude'][df['File_Index']==i],
             df['Point_Latitude'][df['File_Index']==i],
             color='black', linewidth=7)
mplleaflet.display(tiles='stamen_wc')


# In[5]:


# Save as HTML page, so we can print the image big...
for i in range(max(df['File_Index'])+1):
    plt.plot(df['Point_Longitude'][df['File_Index']==i],
             df['Point_Latitude'][df['File_Index']==i],
             color='black', linewidth=7)
mplleaflet.save_html(tiles='stamen_wc', fileobj='2017.html')