Wouter Nieuwerth WouterNieuwerth

## Instant_page_GTM.html
<script>
  (function() {
    // Rewrite of the Instant.page <html> snippet for Google Tag Manager.

    var el = document.createElement('script');

    el.setAttribute('src', '//instant.page/1.2.2');

    el.setAttribute('type', 'module');
    el.setAttribute('integrity', 'sha384-2xV8M5griQmzyiY3CDqh1dn4z3llDVqZDqzjzcY+jCBCk/a5fXJmuZ/40JJAPeoU');

## GPX_analysis_step_1.py
import gpxpy
import matplotlib.pyplot as plt
import datetime
from geopy import distance
from math import sqrt, floor
import numpy as np
import pandas as pd
import haversine

## GPX_analysis_step_3.py
for segment in gpx.tracks[0].segments: # all segments

    data = segment.points

    for point in data:
        df = df.append({'lon': point.longitude, 'lat' : point.latitude, 'alt' : point.elevation, 'time' : point.time}, ignore_index=True)

df = df.sort_values(by=['time'])
df = df.reset_index()

## GPX_analysis_step_4.py
# Create a column with values that are 'shifted' one forwards, so we can create calculations for differences.
df['lon-start'] = df['lon']
df['lon-start'].iloc[-1] = np.nan
df['lon-start'] = np.roll(df['lon-start'], 1)
df['lat-start'] = df['lat']
df['lat-start'].iloc[-1] = np.nan
df['lat-start'] = np.roll(df['lat-start'], 1)
df['alt-start'] = df['alt']
df['alt-start'].iloc[-1] = np.nan
df['alt-start'] = np.roll(df['alt-start'], 1)

## GPX_analysis_step_5.py
df['time'] = pd.to_datetime(df['time'], utc=True)
df['time'] = df['time'].dt.tz_localize(tz=None)
df['time-start'] = pd.to_datetime(df['time-start'], utc=True)
df['time-start'] = df['time-start'].dt.tz_localize(tz=None)

## GPX_analysis_step_6.py
# Calculate distances and time deltas
df['distance_dis_2d'] = df.apply(lambda x: distance.distance((x['lat-start'], x['lon-start']), (x['lat'], x['lon'])).m, axis = 1)
df['alt_dif'] = df.apply(lambda x: x['alt-start'] - x['alt'], axis=1)
df['distance_dis_3d'] = df.apply(lambda x: sqrt(x['distance_dis_2d']**2 + (x['alt_dif'])**2), axis=1)
df['time_delta'] = df.apply(lambda x: (x['time'] - x['time-start']).total_seconds(), axis=1)

## GPX_analysis_step_7.py
df_selected = df.loc[:, ['distance_dis_3d','time_delta']]

df_selected['distance_cumsum'] = df_selected['distance_dis_3d'].cumsum()
df_selected['time_cumsum'] = df_selected['time_delta'].cumsum()

## GPX_analysis_step_8.py
# Here we loop over sections
for section in sections:

    if df['distance_dis_3d'].sum() < section: # If the total distance of the workout is smaller then the section we're looking for we can skip this iteration.
        continue

    df_output = pd.DataFrame(columns=['date', 'section', 'filename', 'time', 'distance', 'minutes_per_kilometer', 'total_distance', 'total_time'])

    for i in range(len(df_selected.index)):

## GPX_analysis_step_2.py
# All the sections you PB's for in meters:
sections = [1000,(1000*1.60934),3000,5000,(5000*1.60934),10000,15000,(10000*1.60934),20000,21097.5,25000,30000,40000,42195]

# The file you want to import:
file = 'my_run_001.gpx'

# This Pandas DataFrame will contain the final output of our analysis:
df_final = pd.DataFrame(columns=['time', 'distance', 'minutes_per_kilometer'])

gpx_file = open(file, 'r')

## Google_Cloud_Function_NodeJS_example.js
/**
 * Responds to a HTTP request.
 *
 * @param {!express:Request} req HTTP request context.
 * @param {!express:Response} res HTTP response context.
 */
exports.helloWorld = (req, res) => {
  let example_content = req.body.example_content || 'Error, no example_content found in json';
  let another_example_content = req.body.another_example_content || 'Error, no another_example_content found in json';
	<script>
	(function() {
	// Rewrite of the Instant.page <html> snippet for Google Tag Manager.

	var el = document.createElement('script');

	el.setAttribute('src', '//instant.page/1.2.2');

	el.setAttribute('type', 'module');
	el.setAttribute('integrity', 'sha384-2xV8M5griQmzyiY3CDqh1dn4z3llDVqZDqzjzcY+jCBCk/a5fXJmuZ/40JJAPeoU');
	import gpxpy
	import matplotlib.pyplot as plt
	import datetime
	from geopy import distance
	from math import sqrt, floor
	import numpy as np
	import pandas as pd
	import haversine
	for segment in gpx.tracks[0].segments: # all segments

	data = segment.points

	for point in data:
	df = df.append({'lon': point.longitude, 'lat' : point.latitude, 'alt' : point.elevation, 'time' : point.time}, ignore_index=True)

	df = df.sort_values(by=['time'])
	df = df.reset_index()
	# Create a column with values that are 'shifted' one forwards, so we can create calculations for differences.
	df['lon-start'] = df['lon']
	df['lon-start'].iloc[-1] = np.nan
	df['lon-start'] = np.roll(df['lon-start'], 1)
	df['lat-start'] = df['lat']
	df['lat-start'].iloc[-1] = np.nan
	df['lat-start'] = np.roll(df['lat-start'], 1)
	df['alt-start'] = df['alt']
	df['alt-start'].iloc[-1] = np.nan
	df['alt-start'] = np.roll(df['alt-start'], 1)
	df['time'] = pd.to_datetime(df['time'], utc=True)
	df['time'] = df['time'].dt.tz_localize(tz=None)
	df['time-start'] = pd.to_datetime(df['time-start'], utc=True)
	df['time-start'] = df['time-start'].dt.tz_localize(tz=None)
	# Calculate distances and time deltas
	df['distance_dis_2d'] = df.apply(lambda x: distance.distance((x['lat-start'], x['lon-start']), (x['lat'], x['lon'])).m, axis = 1)
	df['alt_dif'] = df.apply(lambda x: x['alt-start'] - x['alt'], axis=1)
	df['distance_dis_3d'] = df.apply(lambda x: sqrt(x['distance_dis_2d']2 + (x['alt_dif'])2), axis=1)
	df['time_delta'] = df.apply(lambda x: (x['time'] - x['time-start']).total_seconds(), axis=1)
	df_selected = df.loc[:, ['distance_dis_3d','time_delta']]

	df_selected['distance_cumsum'] = df_selected['distance_dis_3d'].cumsum()
	df_selected['time_cumsum'] = df_selected['time_delta'].cumsum()
	# Here we loop over sections
	for section in sections:

	if df['distance_dis_3d'].sum() < section: # If the total distance of the workout is smaller then the section we're looking for we can skip this iteration.
	continue

	df_output = pd.DataFrame(columns=['date', 'section', 'filename', 'time', 'distance', 'minutes_per_kilometer', 'total_distance', 'total_time'])

	for i in range(len(df_selected.index)):
	# All the sections you PB's for in meters:
	sections = [1000,(10001.60934),3000,5000,(50001.60934),10000,15000,(10000*1.60934),20000,21097.5,25000,30000,40000,42195]

	# The file you want to import:
	file = 'my_run_001.gpx'

	# This Pandas DataFrame will contain the final output of our analysis:
	df_final = pd.DataFrame(columns=['time', 'distance', 'minutes_per_kilometer'])

	gpx_file = open(file, 'r')
	/**
	* Responds to a HTTP request.
	*
	* @param {!express:Request} req HTTP request context.
	* @param {!express:Response} res HTTP response context.
	*/
	exports.helloWorld = (req, res) => {
	let example_content = req.body.example_content \|\| 'Error, no example_content found in json';
	let another_example_content = req.body.another_example_content \|\| 'Error, no another_example_content found in json';