##
## Python program will run on selection.
##
from GC_Wrapper import GC_wrapper as GC
import pathlib
from datetime import datetime
import time
import collections
import tempfile
import plotly
import plotly.graph_objs as go
import numpy as np
import statistics
compares = GC.season(compare=True)
activities_list = GC.activities(filter='isRun<>0')
athlete_zones = GC.athleteZones()
hr_max = max(athlete_zones['hrmax'])
temp_file = tempfile.NamedTemporaryFile(mode="w+t",
prefix="GC_",
suffix=".html",
delete=False)
# Define GC background color
gc_bg_color = 'rgb(52,52,52)'
# Define GC Text color
gc_text_color = 'rgb(255,255,255)'
# For an given subset of activities this function will return a data frame that contains all hr with corresponding speed
def main():
start_time = datetime.now()
season = GC.season(compare=True)
all_lat_long_df = pd.DataFrame()
activities_list = GC.activities(filter='Data contains "G"')
start_dt = datetime.combine(season['start'][0], datetime.min.time())
end_dt = datetime.combine(season['end'][0], datetime.min.time())
activities_sub_list = []
for i in np.arange(0, len(activities_list)):
if start_dt <= activities_list[i] <= end_dt:
activities_sub_list.append(activities_list[i])
activity_time_calculation = round(len(activities_sub_list) * 0.5)
map_time_calculation = round(len(activities_sub_list) * 0.05)
html_write_time_calculation = round(len(activities_sub_list) * 0.2)
duration = activity_time_calculation + map_time_calculation + html_write_time_calculation
msg = """You are about to process: """ + str(len(activities_sub_list)) + """ (activities)
Based on your selection an rough estimation on how long the script will run: """ +\
str(timedelta(seconds=duration)) + """
Do you want to continue?
"""
if mb.askyesno("Expected run time", msg):
for activity in activities_sub_list:
current_activity = GC.activity(activity=activity)
current_activity_df = pd.DataFrame(
current_activity, index=current_activity['seconds']).filter(
["longitude", "latitude"])
all_lat_long_df = all_lat_long_df.append(current_activity_df)
heat_data = all_lat_long_df[['latitude', 'longitude']].to_numpy()
print('Gathering data est:{} act:{} '.format(
activity_time_calculation,
round((datetime.now() - start_time).total_seconds(), 2)))
before = datetime.now()
fmap = folium.Map(
tiles='CartoDB positron' if light_map else 'CartoDB dark_matter',
prefer_canvas=True)
HeatMap(heat_data,
radius=radius,
blur=blur,
gradient=heatmap_grad['light' if light_map else 'dark'],
min_opacity=0.3,
max_val=1).add_to(fmap)
fmap.fit_bounds(fmap.get_bounds())
print('Create map est:{} act:{}'.format(
map_time_calculation,
round((datetime.now() - before).total_seconds(), 2)))
before = datetime.now()
html = fmap.get_root().render()
temp_file.writelines(html)
temp_file.close()
print('Write HTML est:{} act:{}'.format(
html_write_time_calculation,
round((datetime.now() - before).total_seconds()), 2))
print('Total time est:{} act:{}'.format(
timedelta(seconds=duration),
str(datetime.now() - start_time).split('.')[0]))
else:
create_cancel_html()
GC.webpage(pathlib.Path(temp_file.name).as_uri())
def main():
compares = GC.season(compare=True)
activities_list = GC.activities(
filter='Data contains "P" and Data contains "H"')
athlete_zones = GC.athleteZones()
hr_max = max(athlete_zones['hrmax'])
start_time = time.time()
data = []
for start, end, color, name in zip(compares['start'], compares['end'],
compares['color'], compares['name']):
# print("start: " + str(start) + ", end: " + str(end) + ", color: " + str(color))
start_dt = datetime.combine(start, datetime.min.time())
end_dt = datetime.combine(end, datetime.min.time())
activities_sub_list = []
for i in np.arange(0, len(activities_list)):
if activities_list[i] >= start_dt and activities_list[i] <= end_dt:
activities_sub_list.append(activities_list[i])
# print(activities_sub_list)
heart_rate_power_dict = get_hr_list_of_activities(activities_sub_list)
mean_power_by_hr = create_mean_power_by_hr(heart_rate_power_dict)
# print(mean_power_by_hr)
trace = go.Scatter(
x=list(mean_power_by_hr.keys()),
y=list(mean_power_by_hr.values()),
mode='lines+markers',
line=dict(color=color, ),
name=name,
showlegend=True,
)
data.append(trace)
# End for
layout = go.Layout(
title="Average power at HR",
paper_bgcolor=gc_bg_color,
plot_bgcolor=gc_bg_color,
font=dict(color=gc_text_color, size=12),
yaxis=dict(
title="Watts",
nticks=50,
rangemode='nonnegative',
showgrid=True,
zeroline=True,
showline=True,
gridcolor="grey",
),
xaxis=dict(
range=[90, hr_max + 5],
nticks=int(hr_max / 5),
ticks='outside',
showgrid=True,
zeroline=True,
showline=True,
gridcolor="grey",
title="HR",
rangemode='nonnegative',
),
margin=go.layout.Margin(l=100, r=0, b=100, t=150, pad=0),
)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, auto_open=False, filename=temp_file.name)
GC.webpage(pathlib.Path(temp_file.name).as_uri())
print("Total graph time: " + str(time.time() - start_time))