In JupyterLab locally, everything is interactive¶

(static site generator Pelican disables interactivity once this post is pushed to Github :( )¶

Altair is cool and all, but damn that's a lot of lines. Kind of prefer matplotlib, but I guess interactivity comes at a cost.

In [42]:

import pandas as pd
import numpy as np
from bokeh.models import GMapPlot, GMapOptions, ColumnDataSource, Circle, Range1d, PanTool, WheelZoomTool, ResetTool, SaveTool
from bokeh.plotting import figure, output_file, show
from bokeh.io import show, output_notebook
from bokeh.embed import file_html
output_notebook()
import altair as alt
import seaborn as sns
import matplotlib.pyplot as plt

Loading BokehJS ...

In [43]:

df_lap1 = pd.read_csv('unpublished/zipped_csv/saita_natTT_lap1.csv')
df_lap2 = pd.read_csv('unpublished/zipped_csv/saita_natTT_lap2.csv')
df_lap3 = pd.read_csv('unpublished/zipped_csv/saita_natTT_lap3.csv')

frames = [df_lap1, df_lap2, df_lap3]
df_all = pd.concat(frames, keys=['lap1','lap2','lap3'])

In [44]:

df_lap3.head()

Out[44]:

	elapsed_time	distance	latitude	longitude	altitude	l_r_balance	power	cadence	speed	heartrate	...	leg_range_log_left	leg_range_log_right	pedaling_delay_log_left	pedaling_delay_log_right	waist_angle_log_center	waist_d_rot_y_center	waist_d_rot_z_center	torso_angle_log_center	torso_d_rot_y_center	torso_d_rot_z_center
0	2158	25599.0	36.985397	136.79150	47	NaN	341	87	12.74	NaN	...	NaN	NaN	9.4	7.5	49.3	9.2	4.5	11.8	7.3	8.4
1	2159	25616.0	36.985508	136.79149	47	NaN	331	87	12.72	NaN	...	NaN	NaN	4.8	3.6	47.5	8.1	5.2	11.1	7.7	8.7
2	2160	25624.0	36.985620	136.79144	47	NaN	299	87	12.67	NaN	...	NaN	NaN	30.7	46.0	44.9	6.6	4.3	10.5	7.1	8.7
3	2161	25637.0	36.985725	136.79141	47	NaN	297	87	12.72	NaN	...	NaN	NaN	30.7	18.2	48.2	7.3	5.0	10.0	7.9	6.7
4	2162	25649.0	36.985847	136.79140	47	NaN	376	87	12.79	NaN	...	NaN	NaN	26.1	47.5	49.5	6.2	5.5	12.8	7.2	6.0

5 rows × 24 columns

In [45]:

df_all.loc['lap1'].head()

Out[45]:

	elapsed_time	distance	latitude	longitude	altitude	l_r_balance	power	cadence	speed	heartrate	...	leg_range_log_left	leg_range_log_right	pedaling_delay_log_left	pedaling_delay_log_right	waist_angle_log_center	waist_d_rot_y_center	waist_d_rot_z_center	torso_angle_log_center	torso_d_rot_y_center	torso_d_rot_z_center
0	2	NaN	36.985600	136.79163	41	NaN	NaN	NaN	0.00	NaN	...	NaN	NaN	NaN	NaN	57.9	3.2	0.7	28.0	2.0	2.2
1	3	0.0	36.985603	136.79161	41	NaN	NaN	NaN	0.00	NaN	...	NaN	NaN	NaN	NaN	52.1	6.8	2.0	19.8	1.4	1.7
2	4	NaN	36.985607	136.79160	41	NaN	NaN	NaN	0.01	NaN	...	NaN	NaN	NaN	NaN	49.3	4.6	2.7	18.2	1.0	2.7
3	5	3.0	36.985622	136.79156	41	NaN	NaN	NaN	4.09	NaN	...	NaN	NaN	NaN	NaN	54.7	8.3	4.6	20.9	4.4	10.1
4	6	7.0	36.985695	136.79149	41	NaN	462.0	32.0	5.56	NaN	...	NaN	NaN	7.2	7.5	54.4	24.1	7.6	21.2	4.1	14.4

5 rows × 24 columns

In [36]:

data = df_all

In [37]:

data.loc['lap3'].reset_index().iloc[2:].head()

Out[37]:

	index	elapsed_time	distance	latitude	longitude	altitude	l_r_balance	power	cadence	speed	...	leg_range_log_left	leg_range_log_right	pedaling_delay_log_left	pedaling_delay_log_right	waist_angle_log_center	waist_d_rot_y_center	waist_d_rot_z_center	torso_angle_log_center	torso_d_rot_y_center	torso_d_rot_z_center
2	2	2160	25624.0	36.985620	136.79144	47	NaN	299.0	87.0	12.67	...	NaN	NaN	30.7	46.0	44.9	6.6	4.3	10.5	7.1	8.7
3	3	2161	25637.0	36.985725	136.79141	47	NaN	297.0	87.0	12.72	...	NaN	NaN	30.7	18.2	48.2	7.3	5.0	10.0	7.9	6.7
4	4	2162	25649.0	36.985847	136.79140	47	NaN	376.0	87.0	12.79	...	NaN	NaN	26.1	47.5	49.5	6.2	5.5	12.8	7.2	6.0
5	5	2163	25662.0	36.985970	136.79137	47	NaN	397.0	88.0	12.85	...	NaN	NaN	26.9	18.5	50.1	5.7	5.5	12.1	7.1	7.0
6	6	2164	25674.0	36.986084	136.79135	47	NaN	376.0	88.0	12.88	...	NaN	NaN	41.0	36.8	49.2	8.6	6.7	11.1	6.8	8.7

5 rows × 25 columns

In [38]:

mid_lat = data['latitude'].min()+((data['latitude'].max()-data['latitude'].min())/2)
mid_lon = data['longitude'].min()+((data['longitude'].max()-data['longitude'].min())/2)
    
map_options = GMapOptions(lat=mid_lat, lng=mid_lon, map_type="roadmap", zoom=13)
activitymap = GMapPlot(x_range=Range1d(), y_range=Range1d(), map_options=map_options)
activitymap.api_key = "AIzaSyBcIU6DrIyKGVxKJHbL8lgm0YMyj7UEAvU"
source = ColumnDataSource(data=data)
circle = Circle(x="longitude", y="latitude", size=3)
activitymap.add_glyph(source, circle)
activitymap.add_tools(PanTool(), WheelZoomTool(), ResetTool(), SaveTool())

output_file("example.html")

In [39]:

show(activitymap)

In [10]:

data.loc['lap1'].describe()

Out[10]:

	elapsed_time	distance	latitude	longitude	altitude	l_r_balance	power	cadence	speed	heartrate	...	leg_range_log_left	leg_range_log_right	pedaling_delay_log_left	pedaling_delay_log_right	waist_angle_log_center	waist_d_rot_y_center	waist_d_rot_z_center	torso_angle_log_center	torso_d_rot_y_center	torso_d_rot_z_center
count	1089.000000	1084.000000	1029.000000	1029.000000	1089.000000	0.0	1085.000000	1085.000000	1089.000000	0.0	...	0.0	0.0	1053.000000	1052.000000	1089.000000	1089.000000	1089.000000	1089.000000	1089.000000	1089.000000
mean	546.000000	6334.341328	36.972665	136.786747	56.335170	NaN	317.294009	90.710599	11.788586	NaN	...	NaN	NaN	26.319943	26.667110	47.528099	7.521579	6.352617	12.239853	5.769054	6.750964
std	314.511526	3645.353290	0.013729	0.004625	9.786539	NaN	78.066683	14.369887	2.288189	NaN	...	NaN	NaN	10.833275	12.244778	2.794049	3.895105	2.023782	4.152572	2.154695	1.788248
min	2.000000	0.000000	36.953403	136.774750	41.000000	NaN	0.000000	0.000000	0.000000	NaN	...	NaN	NaN	0.000000	0.000000	40.800000	0.800000	0.600000	5.000000	1.000000	0.400000
25%	274.000000	3300.750000	36.958946	136.783370	47.000000	NaN	296.000000	89.000000	10.850000	NaN	...	NaN	NaN	19.300000	19.275000	46.100000	5.900000	5.300000	9.900000	4.000000	5.800000
50%	546.000000	6242.500000	36.970646	136.787870	56.000000	NaN	326.000000	94.000000	12.270000	NaN	...	NaN	NaN	28.500000	29.700000	47.000000	7.000000	6.200000	11.200000	5.700000	6.700000
75%	818.000000	9398.000000	36.984734	136.790560	65.000000	NaN	354.000000	97.000000	12.900000	NaN	...	NaN	NaN	33.800000	35.300000	48.600000	8.000000	7.300000	13.000000	7.300000	7.600000
max	1090.000000	12820.000000	36.998150	136.793520	73.000000	NaN	606.000000	110.000000	17.520000	NaN	...	NaN	NaN	48.700000	51.200000	66.100000	30.600000	13.900000	36.800000	11.500000	14.400000

8 rows × 24 columns

In [11]:

data.drop(['l_r_balance','heartrate','leg_range_log_left','leg_range_log_right'], axis=1).corr(method='pearson').style.background_gradient(cmap='bwr').set_precision(2)

Out[11]:

	elapsed_time	distance	latitude	longitude	altitude	power	cadence	speed	ankling_log_left	ankling_log_right	foot_range_log_left	foot_range_log_right	pedaling_delay_log_left	pedaling_delay_log_right	waist_angle_log_center	waist_d_rot_y_center	waist_d_rot_z_center	torso_angle_log_center	torso_d_rot_y_center	torso_d_rot_z_center
elapsed_time	1	1	-0.2	0.027	0.14	0.036	-0.083	0.087	0.029	-0.017	-0.28	-0.066	-0.041	-0.016	0.25	-0.029	0.065	0.08	0.12	0.34
distance	1	1	-0.19	0.03	0.13	0.036	-0.082	0.083	0.03	-0.016	-0.28	-0.065	-0.041	-0.017	0.25	-0.03	0.062	0.082	0.12	0.34
latitude	-0.2	-0.19	1	0.5	-0.73	0.085	0.14	0.13	-0.047	-0.049	0.17	-0.042	0.025	0.02	-0.099	-0.1	-0.13	-0.22	-0.0086	0.058
longitude	0.027	0.03	0.5	1	-0.23	0.2	0.19	0.1	-0.069	-0.0054	0.038	-0.033	-0.023	-0.036	-0.094	-0.033	-0.091	-0.25	0.0077	0.18
altitude	0.14	0.13	-0.73	-0.23	1	-0.032	-0.1	-0.24	0.031	0.067	-0.24	-0.025	-0.062	-0.076	0.065	0.11	0.15	0.14	0.099	0.1
power	0.036	0.036	0.085	0.2	-0.032	1	0.47	-0.41	-0.27	-0.21	0.19	-0.015	-0.0045	-0.12	0.17	0.36	0.44	-0.14	0.41	0.62
cadence	-0.083	-0.082	0.14	0.19	-0.1	0.47	1	0.24	-0.00017	-0.023	0.2	0.42	0.18	0.21	-0.27	-0.069	0.18	-0.46	0.13	0.14
speed	0.087	0.083	0.13	0.1	-0.24	-0.41	0.24	1	0.11	0.088	0.043	0.16	0.081	0.19	-0.48	-0.38	-0.46	-0.46	-0.38	-0.42
ankling_log_left	0.029	0.03	-0.047	-0.069	0.031	-0.27	-0.00017	0.11	1	0.13	-0.16	-0.038	-0.025	-0.004	-0.13	-0.018	-0.015	0.061	-0.095	-0.11
ankling_log_right	-0.017	-0.016	-0.049	-0.0054	0.067	-0.21	-0.023	0.088	0.13	1	-0.16	0.061	-0.085	0.025	-0.15	-0.021	-0.039	-0.0097	-0.073	-0.056
foot_range_log_left	-0.28	-0.28	0.17	0.038	-0.24	0.19	0.2	0.043	-0.16	-0.16	1	0.041	0.18	0.075	-0.16	-0.01	0.009	-0.31	-0.027	-0.12
foot_range_log_right	-0.066	-0.065	-0.042	-0.033	-0.025	-0.015	0.42	0.16	-0.038	0.061	0.041	1	0.076	0.14	0.057	-0.28	-0.019	-0.072	0.035	-0.16
pedaling_delay_log_left	-0.041	-0.041	0.025	-0.023	-0.062	-0.0045	0.18	0.081	-0.025	-0.085	0.18	0.076	1	0.11	0.0058	-0.15	-0.046	-0.089	0.02	-0.073
pedaling_delay_log_right	-0.016	-0.017	0.02	-0.036	-0.076	-0.12	0.21	0.19	-0.004	0.025	0.075	0.14	0.11	1	-0.038	-0.19	-0.1	-0.09	-0.046	-0.16
waist_angle_log_center	0.25	0.25	-0.099	-0.094	0.065	0.17	-0.27	-0.48	-0.13	-0.15	-0.16	0.057	0.0058	-0.038	1	-0.2	0.022	0.61	0.33	0.16
waist_d_rot_y_center	-0.029	-0.03	-0.1	-0.033	0.11	0.36	-0.069	-0.38	-0.018	-0.021	-0.01	-0.28	-0.15	-0.19	-0.2	1	0.59	0.068	-0.0062	0.34
waist_d_rot_z_center	0.065	0.062	-0.13	-0.091	0.15	0.44	0.18	-0.46	-0.015	-0.039	0.009	-0.019	-0.046	-0.1	0.022	0.59	1	0.13	0.22	0.35
torso_angle_log_center	0.08	0.082	-0.22	-0.25	0.14	-0.14	-0.46	-0.46	0.061	-0.0097	-0.31	-0.072	-0.089	-0.09	0.61	0.068	0.13	1	0.11	-0.013
torso_d_rot_y_center	0.12	0.12	-0.0086	0.0077	0.099	0.41	0.13	-0.38	-0.095	-0.073	-0.027	0.035	0.02	-0.046	0.33	-0.0062	0.22	0.11	1	0.48
torso_d_rot_z_center	0.34	0.34	0.058	0.18	0.1	0.62	0.14	-0.42	-0.11	-0.056	-0.12	-0.16	-0.073	-0.16	0.16	0.34	0.35	-0.013	0.48	1

In [12]:

yscale = alt.Scale(domain=(35, 65))

points_left = alt.Chart(data.loc['lap1'], width=650, height=100).mark_circle(opacity=0.3, color='red').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title='')
         ),
    alt.Y('foot_range_log_left',
          scale=yscale,
          axis=alt.Axis(title='Lap 1'),
         ),
).properties(title='Foot Angular Range (Left in Red, Right in Blue)')

points_right = alt.Chart(data.loc['lap1'], width=650, height=100).mark_circle(opacity=0.3, color='blue').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250])),
    alt.Y('foot_range_log_right', scale=yscale),
)

hist_left = alt.Chart(data.loc['lap1'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='red').encode(
    alt.Y('foot_range_log_left:Q',
          bin=alt.Bin(maxbins=60, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[40,60])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 100]), axis=alt.Axis(title='')
         ),
).properties(width=150)

hist_right = alt.Chart(data.loc['lap1'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='blue').encode(
    alt.Y('foot_range_log_right:Q',
          bin=alt.Bin(maxbins=60, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[40,60])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 100])),
).properties(width=150)

hist_left_rule = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(foot_range_log_left):Q',
    size=alt.value(2)
)

hist_right_rule = alt.Chart(data.loc['lap1']).mark_rule(color='blue').encode(
    y='mean(foot_range_log_right):Q',
    size=alt.value(2)
)


lap1 = ((points_left + points_right) | (hist_left + hist_right + hist_left_rule + hist_right_rule))

In [13]:

yscale = alt.Scale(domain=(35, 65))

points_left = alt.Chart(data.loc['lap2'], width=650, height=100).mark_circle(opacity=0.3, color='red').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title=''),
         ),
    alt.Y('foot_range_log_left',
          scale=yscale,
          axis=alt.Axis(title='Lap 2'),
         ),
)

points_right = alt.Chart(data.loc['lap2'], width=650, height=100).mark_circle(opacity=0.3, color='blue').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title='')
         ),
    alt.Y('foot_range_log_right', scale=yscale),
)

hist_left = alt.Chart(data.loc['lap2'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='red').encode(
    alt.Y('foot_range_log_left:Q',
          bin=alt.Bin(maxbins=60, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[40,60])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 100]), axis=alt.Axis(title='')
         ),
).properties(width=150)

hist_right = alt.Chart(data.loc['lap2'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='blue').encode(
    alt.Y('foot_range_log_right:Q',
          bin=alt.Bin(maxbins=60, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[40,60])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 100])),
).properties(width=150)

hist_left_rule = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(foot_range_log_left):Q',
    size=alt.value(2)
)

hist_right_rule = alt.Chart(data.loc['lap2']).mark_rule(color='blue').encode(
    y='mean(foot_range_log_right):Q',
    size=alt.value(2)
)


lap2 = ((points_left + points_right) | (hist_left + hist_right + hist_left_rule + hist_right_rule))

In [14]:

yscale = alt.Scale(domain=(35, 65))

points_left = alt.Chart(data.loc['lap3'], width=650, height=100).mark_circle(opacity=0.3, color='red').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title='Elapsed Time (s)'),
         ),
    alt.Y('foot_range_log_left',
          scale=yscale,
          axis=alt.Axis(title='Lap 3'),
         ),
)

points_right = alt.Chart(data.loc['lap3'], width=650, height=100).mark_circle(opacity=0.3, color='blue').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250])),
    alt.Y('foot_range_log_right', scale=yscale),
)

hist_left = alt.Chart(data.loc['lap3']).mark_area(clip=True, opacity=.3, interpolate='step', color='red').encode(
    alt.Y('foot_range_log_left:Q',
          bin=alt.Bin(maxbins=60, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[40,60])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 100])),
).properties(width=150)

hist_right = alt.Chart(data.loc['lap3']).mark_area(clip=True, opacity=.3, interpolate='step', color='blue').encode(
    alt.Y('foot_range_log_right:Q',
          bin=alt.Bin(maxbins=60, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[40,60])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 100])),
).properties(width=150)

hist_left_rule = alt.Chart(data.loc['lap3'], height=100).mark_rule(color='red').encode(
    y='mean(foot_range_log_left):Q',
    size=alt.value(2)
)

hist_right_rule = alt.Chart(data.loc['lap3'], height=100).mark_rule(color='blue').encode(
    y='mean(foot_range_log_right):Q',
    size=alt.value(2)
)


lap3 = ((points_left + points_right) | (hist_left + hist_right + hist_left_rule + hist_right_rule))

In [15]:

(lap1 & lap2 & lap3)

Out[15]:

In [16]:

yscale = alt.Scale(domain=(0, 40))

DSS1points_left = alt.Chart(data.loc['lap1'], width=650, height=100).mark_circle(opacity=0.3, color='red').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title='')
         ),
    alt.Y('ankling_log_left',
          scale=yscale,
          axis=alt.Axis(title='Lap 1'),
         ),
).properties(title='DSS')

DSS1points_right = alt.Chart(data.loc['lap1'], width=650, height=100).mark_circle(opacity=0.3, color='blue').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250])),
    alt.Y('ankling_log_right', scale=yscale),
)

DSS1hist_left = alt.Chart(data.loc['lap1'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='red').encode(
    alt.Y('ankling_log_left:Q',
          bin=alt.Bin(maxbins=30, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[0,40])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 30]), axis=alt.Axis(title='')
         ),
).properties(width=150)

DSS1hist_right = alt.Chart(data.loc['lap1'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='blue').encode(
    alt.Y('ankling_log_right:Q',
          bin=alt.Bin(maxbins=30, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[0,40])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 30])),
).properties(width=150)

DSS1hist_left_rule = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(ankling_log_left):Q',
    size=alt.value(2)
)

DSS1hist_right_rule = alt.Chart(data.loc['lap1']).mark_rule(color='blue').encode(
    y='mean(ankling_log_right):Q',
    size=alt.value(2)
)


DSSlap1 = ((DSS1points_left + DSS1points_right) | (DSS1hist_left + DSS1hist_right + DSS1hist_left_rule + DSS1hist_right_rule))

In [17]:

yscale = alt.Scale(domain=(0, 40))

DSS2points_left = alt.Chart(data.loc['lap2'], width=650, height=100).mark_circle(opacity=0.3, color='red').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title='')
         ),
    alt.Y('ankling_log_left',
          scale=yscale,
          axis=alt.Axis(title='Lap 2'),
         ),
)

DSS2points_right = alt.Chart(data.loc['lap2'], width=650, height=100).mark_circle(opacity=0.3, color='blue').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250])),
    alt.Y('ankling_log_right', scale=yscale),
)

DSS2hist_left = alt.Chart(data.loc['lap2'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='red').encode(
    alt.Y('ankling_log_left:Q',
          bin=alt.Bin(maxbins=30, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[0,40])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 30]), axis=alt.Axis(title='')
         ),
).properties(width=150)

DSS2hist_right = alt.Chart(data.loc['lap2'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='blue').encode(
    alt.Y('ankling_log_right:Q',
          bin=alt.Bin(maxbins=30, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[0,40])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 30])),
).properties(width=150)

DSS2hist_left_rule = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(ankling_log_left):Q',
    size=alt.value(2)
)

DSS2hist_right_rule = alt.Chart(data.loc['lap2']).mark_rule(color='blue').encode(
    y='mean(ankling_log_right):Q',
    size=alt.value(2)
)


DSSlap2 = ((DSS2points_left + DSS2points_right) | (DSS2hist_left + DSS2hist_right + DSS2hist_left_rule + DSS2hist_right_rule))

In [18]:

yscale = alt.Scale(domain=(0, 40))

DSS3points_left = alt.Chart(data.loc['lap3'], width=650, height=100).mark_circle(opacity=0.3, color='red').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250]),
          axis=alt.Axis(title='')
         ),
    alt.Y('ankling_log_left',
          scale=yscale,
          axis=alt.Axis(title='Lap 3'),
         ),
)

DSS3points_right = alt.Chart(data.loc['lap3'], width=650, height=100).mark_circle(opacity=0.3, color='blue').encode(
    alt.X('elapsed_time',
          scale=alt.Scale(domain=[0, 3250])),
    alt.Y('ankling_log_right', scale=yscale),
)

DSS3hist_left = alt.Chart(data.loc['lap3'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='red').encode(
    alt.Y('ankling_log_left:Q',
          bin=alt.Bin(maxbins=30, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[0,40])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 30]), axis=alt.Axis(title='')
         ),
).properties(width=150)

DSS3hist_right = alt.Chart(data.loc['lap3'], height=100).mark_area(clip=True, opacity=.3, interpolate='step', color='blue').encode(
    alt.Y('ankling_log_right:Q',
          bin=alt.Bin(maxbins=30, extent=yscale.domain),
          stack=None,
          axis=alt.Axis(title=''),
          scale=alt.Scale(domain=[0,40])
         ),
    alt.X('count()', stack=None, scale=alt.Scale(domain=[0, 30])),
).properties(width=150)

DSS3hist_left_rule = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    y='mean(ankling_log_left):Q',
    size=alt.value(2)
)

DSS3hist_right_rule = alt.Chart(data.loc['lap3']).mark_rule(color='blue').encode(
    y='mean(ankling_log_right):Q',
    size=alt.value(2)
)


DSSlap3 = ((DSS3points_left + DSS3points_right) | (DSS3hist_left + DSS3hist_right + DSS3hist_left_rule + DSS3hist_right_rule))

In [19]:

(DSSlap1 & DSSlap2 & DSSlap3)

Out[19]:

In [20]:

lap = [1, 2, 3]
yleft = [data.loc['lap1']['ankling_log_left'].mean(), data.loc['lap2']['ankling_log_left'].mean(), data.loc['lap3']['ankling_log_left'].mean()]
yright = [data.loc['lap1']['ankling_log_right'].mean(), data.loc['lap2']['ankling_log_right'].mean(), data.loc['lap3']['ankling_log_right'].mean()]

# set up data frame
meandata = pd.DataFrame({'lap':lap, 'yleft':yleft, 'yright':yright})

# generate the points
pointsleft = alt.Chart(meandata).mark_line().encode(
    alt.X('lap:Q',
          scale=alt.Scale(domain=(0,4)),
          axis=alt.Axis(title='lap')
         ),
    alt.Y('yleft',
            scale=alt.Scale(zero=False, domain=(0.5, 1)),
            axis=alt.Axis(title='mean DSS')
           ),
    color=alt.value('red')
)

# generate the point
pointsright = alt.Chart(meandata).mark_line().encode(
    alt.X('lap:Q',
          scale=alt.Scale(domain=(0,4)),
          axis=alt.Axis(title='lap')
         ),
    alt.Y('yright',
            scale=alt.Scale(zero=False, domain=(0.5, 1)),
         ),
    color=alt.value('blue')
).properties(title='mean DSS for left (red) and right (blue)')

(pointsleft + pointsright)

Out[20]:

In [21]:

altitude = alt.Chart(data.loc['lap3'], width=900).mark_area(color='lightgrey').encode(
    alt.X('elapsed_time',
        axis=alt.Axis(title='Elapsed Time (s)'),
    ),
    alt.Y('altitude',
          scale=alt.Scale(domain=[0, 200]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
)

power = alt.Chart(data.loc['lap3'], width=900).mark_line().encode(
    alt.X('elapsed_time',
        axis=alt.Axis(title='Elapsed Time (s)'),
    ),
    alt.Y('power',
          scale=alt.Scale(domain=[0, 500]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
    color=alt.value('red')  
)

cadence = alt.Chart(data.loc['lap3'], width=900).mark_line().encode(
    alt.X('elapsed_time',
        axis=alt.Axis(title='Elapsed Time (s)'),
    ),
    alt.Y('cadence',
          scale=alt.Scale(domain=[60, 120]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
    color=alt.value('lightblue')    
)

speed = alt.Chart(data.loc['lap3'], width=900).mark_line().encode(
    alt.X('elapsed_time',
        axis=alt.Axis(title='Elapsed Time (s)'),
    ),
    alt.Y('speed',
          scale=alt.Scale(domain=[0, 20]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
    color=alt.value('green')    
)

# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection(type='single', nearest=True, on='mouseover',
                        fields=['elapsed_time'], empty='none')

# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors = altitude.mark_point().encode(
    x='elapsed_time',
    opacity=alt.value(0),
).add_selection(
    nearest
)

# Draw points on the line, and highlight based on selection
speedpoints = speed.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)
cadencepoints = cadence.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)
powerpoints = power.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)


# Draw text labels near the points, and highlight based on selection
speedtext = speed.mark_text(align='left', dx=5, dy=-5, fontSize=14, fontWeight='bold').encode(
    text=alt.condition(nearest, 'speed', alt.value(' '))
)
cadencetext = cadence.mark_text(align='left', dx=5, dy=-5, fontSize=14, fontWeight='bold').encode(
    text=alt.condition(nearest,'cadence', alt.value(' '))
)
powertext = power.mark_text(align='left', dx=5, dy=-5, fontSize=14, fontWeight='bold').encode(
    text=alt.condition(nearest,'power', alt.value(' '))
)

# Draw a rule at the location of the selection
rules = altitude.mark_rule(color='grey').encode(
    x='elapsed_time',
).transform_filter(
    nearest
)

alt.layer(
    altitude,
    power,
    cadence,
    speed,
    selectors,
    powerpoints,
    cadencepoints,
    speedpoints,
    powertext,
    cadencetext,
    speedtext,
    rules,
    data=data.loc['lap3']
).resolve_scale(
    y='independent'
).interactive(bind_y=False)

Out[21]:

In [22]:

altitude1 = alt.Chart(data.loc['lap1'], width=900).mark_area(color='lightgrey').encode(
    alt.X('elapsed_time',
          axis=alt.Axis(title='Elapsed time (s)'),
    ),
    alt.Y('altitude',
          scale=alt.Scale(domain=[0, 150]),
          axis=alt.Axis(title='Altitude (m)', ticks=False, labels=False, grid=False)
         ),
)

altitude2 = alt.Chart(data.loc['lap2'], width=900).mark_area(color='lightgrey').encode(
    alt.X('elapsed_time',
#           axis=alt.Axis(title='Elapsed lap time (s)'),
    ),
    alt.Y('altitude',
          scale=alt.Scale(domain=[0, 150]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
)

altitude3 = alt.Chart(data.loc['lap3'], width=900).mark_area(color='lightgrey').encode(
    alt.X('elapsed_time',
#           axis=alt.Axis(title='Elapsed lap time (s)'),
    ),
    alt.Y('altitude',
          scale=alt.Scale(domain=[0, 150]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
)

speed1 = alt.Chart(data.loc['lap1'], width=900).mark_line().encode(
    alt.X('elapsed_time',
    ),
    alt.Y('speed',
          scale=alt.Scale(domain=[0, 18]),
          axis=alt.Axis(title='Speed (m/s)')
         ),
    color=alt.value('green')    
)

speed2 = alt.Chart(data.loc['lap2'], width=900).mark_line().encode(
    alt.X('elapsed_time',
#         axis=alt.Axis(title='Elapsed Time (s)'),
    ),
    alt.Y('speed',
          scale=alt.Scale(domain=[0, 18]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
    color=alt.value('orange')    
)

speed3 = alt.Chart(data.loc['lap3'], width=900).mark_line().encode(
    alt.X('elapsed_time',
#         axis=alt.Axis(title='Elapsed Time (s)'),
    ),
    alt.Y('speed',
          scale=alt.Scale(domain=[0, 18]),
          axis=alt.Axis(title='', ticks=False, labels=False, grid=False)
         ),
    color=alt.value('blue')    
)


# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection(type='single', nearest=True, on='mouseover',
                        fields=['elapsed_time'], empty='none')

# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors = altitude.mark_point().encode(
    x='elapsed_time',
    opacity=alt.value(0),
).add_selection(
    nearest
)

# Draw points on the line, and highlight based on selection
speedpoints1 = speed1.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)
speedpoints2 = speed2.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)
speedpoints3 = speed3.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)



# Draw text labels near the points, and highlight based on selection
speedtext1 = speed1.mark_text(align='left', dx=5, dy=-5, fontSize=12, fontWeight='bold').encode(
    text=alt.condition(nearest, 'speed', alt.value(' '))
)
speedtext2 = speed2.mark_text(align='left', dx=5, dy=-5, fontSize=12, fontWeight='bold').encode(
    text=alt.condition(nearest, 'speed', alt.value(' '))
)
speedtext3 = speed3.mark_text(align='left', dx=5, dy=-5, fontSize=12, fontWeight='bold').encode(
    text=alt.condition(nearest, 'speed', alt.value(' '))
)


# Draw a rule at the location of the selection
rules = altitude.mark_rule(color='grey').encode(
    x='elapsed_time',
).transform_filter(
    nearest
)

alt.layer(
    altitude1,
    altitude2,
    altitude3,
    speed1,
    speed2,
    speed3,
#     selectors,
#     speedpoints1,
#     speedpoints2,
#     speedpoints3,
#     speedtext1,
#     speedtext2,
#     speedtext3,
#     rules,
#     data=data.loc['lap1']
).resolve_scale(
    y='independent'
).interactive(bind_y=False)

Out[22]:

In [23]:

altitude = alt.Chart(data.loc['lap1'].reset_index(), width=900).mark_area(color='lightgrey').encode(
    alt.X('index',
          axis=alt.Axis(title='Elapsed lap time (s)'),
    ),
    alt.Y('altitude',
          scale=alt.Scale(domain=[0, 150]),
          axis=alt.Axis(title='Altitude (m)')
         ),
)

torso1 = alt.Chart(data.loc['lap1'].reset_index(), width=900).mark_line().encode(
    alt.X('index',
    ),
    alt.Y('torso_angle_log_center',
          scale=alt.Scale(domain=[0, 40]),
#           axis=alt.Axis(title='Torso Angle (deg)')
         ),
    color=alt.value('yellow')    
)

torso2 = alt.Chart(data.loc['lap2'].reset_index(), width=900).mark_line().encode(
    alt.X('index',
    ),
    alt.Y('torso_angle_log_center',
          scale=alt.Scale(domain=[0, 40]),
         ),
    color=alt.value('orange')    
)

torso3 = alt.Chart(data.loc['lap3'].reset_index(), width=900).mark_line().encode(
    alt.X('index',
    ),
    alt.Y('torso_angle_log_center',
          scale=alt.Scale(domain=[0, 40]),
         ),
    color=alt.value('red')    
)

alt.layer(
    altitude,
    torso1,
    torso2,
    torso3
).resolve_scale(
    y='independent'
).interactive(bind_y=False)

Out[23]:

In [24]:

xtitle='FAR Left for Q1'
ytitle='FAR Right for Q1'
xscl=[0,55]
yscl=[0,55]

farq11 = alt.Chart(data.loc['lap1'], height=300, width=300).mark_rect().encode(
    alt.X('pedaling_delay_log_left:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('pedaling_delay_log_right:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 1')

farq12 = alt.Chart(data.loc['lap2'], height=300, width=300).mark_rect().encode(
    alt.X('pedaling_delay_log_left:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('pedaling_delay_log_right:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 2')

farq13 = alt.Chart(data.loc['lap3'], height=300, width=300).mark_rect().encode(
    alt.X('pedaling_delay_log_left:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('pedaling_delay_log_right:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 3')

farq11xmean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    x='mean(pedaling_delay_log_left):Q',
    size=alt.value(1)
)

farq11ymean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(pedaling_delay_log_right):Q',
    size=alt.value(1)
)

farq12xmean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    x='mean(pedaling_delay_log_left):Q',
    size=alt.value(1)
)

farq12ymean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(pedaling_delay_log_right):Q',
    size=alt.value(1)
)

farq13xmean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    x='mean(pedaling_delay_log_left):Q',
    size=alt.value(1)
)

farq13ymean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    y='mean(pedaling_delay_log_right):Q',
    size=alt.value(1)
)

(farq11 + farq11xmean + farq11ymean) | (farq12 + farq12xmean + farq12ymean) | (farq13 + farq13xmean + farq13ymean)

Out[24]:

In [25]:

mb=100
xtitle='FAR Left'
ytitle='FAR right'

foot1 = alt.Chart(data.loc['lap1'], height=300, width=300).mark_rect().encode(
    alt.X('foot_range_log_left',
          bin=alt.Bin(maxbins=mb),
          scale=alt.Scale(domain=[30, 70]),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('foot_range_log_right',
          bin=alt.Bin(maxbins=mb),
          scale=alt.Scale(domain=[30, 70]),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 1')

foot2 = alt.Chart(data.loc['lap2'], height=300, width=300).mark_rect().encode(
    alt.X('foot_range_log_left',
          bin=alt.Bin(maxbins=mb),
          scale=alt.Scale(domain=[30, 70]),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('foot_range_log_right',
          bin=alt.Bin(maxbins=mb),
          scale=alt.Scale(domain=[30, 70]),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 2')

foot3 = alt.Chart(data.loc['lap3'], height=300, width=300).mark_rect().encode(
    alt.X('foot_range_log_left',
          bin=alt.Bin(maxbins=mb),
          scale=alt.Scale(domain=[30, 70]),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('foot_range_log_right',
          bin=alt.Bin(maxbins=mb),
          scale=alt.Scale(domain=[30, 70]),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 3')

foot1xmean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    x='mean(foot_range_log_left):Q',
    size=alt.value(1)
)

foot1ymean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(foot_range_log_right):Q',
    size=alt.value(1)
)

foot2xmean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    x='mean(foot_range_log_left):Q',
    size=alt.value(1)
)

foot2ymean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(foot_range_log_right):Q',
    size=alt.value(1)
)

foot3xmean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    x='mean(foot_range_log_left):Q',
    size=alt.value(1)
)

foot3ymean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    y='mean(foot_range_log_right):Q',
    size=alt.value(1)
)

(foot1 + foot1xmean + foot1ymean) | (foot2 + foot2xmean + foot2ymean) | (foot3 + foot3xmean + foot3ymean)

Out[25]:

Angle¶

Average Pelvic angle increases from 47.5 to 49.5 from Lap 1 to Lap 3¶

In [26]:

yscl=[3, 37]
xscl=[38, 67]
xtitle='Pelvic Angle'
ytitle='Torso Angle'

ang1=alt.Chart(data.loc['lap1'], height=300, width=300).mark_rect().encode(
    alt.X('waist_angle_log_center:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_angle_log_center:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 1')

ang2=alt.Chart(data.loc['lap2'], height=300, width=300).mark_rect().encode(
    alt.X('waist_angle_log_center:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_angle_log_center:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 2')

ang3=alt.Chart(data.loc['lap3'], height=300, width=300).mark_rect().encode(
    alt.X('waist_angle_log_center:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_angle_log_center:Q',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 3')

ang1xmean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    x='mean(waist_angle_log_center):Q',
    size=alt.value(1)
)

ang1ymean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(torso_angle_log_center):Q',
    size=alt.value(1)
)

ang2xmean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    x='mean(waist_angle_log_center):Q',
    size=alt.value(1)
)

ang2ymean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(torso_angle_log_center):Q',
    size=alt.value(1)
)

ang3xmean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    x='mean(waist_angle_log_center):Q',
    size=alt.value(1)
)

ang3ymean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    y='mean(torso_angle_log_center):Q',
    size=alt.value(1)
)

(ang1 + ang1xmean + ang1ymean) | (ang2 + ang2xmean + ang2ymean) | (ang3 + ang3xmean + ang3ymean)

Out[26]:

In [28]:

f, (ax1, ax2, ax3) = plt.subplots(1, 3)
sns.violinplot(y=data.loc['lap1']['waist_angle_log_center'], ax=ax1, scale="count", inner="quartile", color="red").set_title("Lap 1")
sns.violinplot(y=data.loc['lap2']['waist_angle_log_center'], ax=ax2, scale="count", inner="quartile", color="skyblue").set_title("Lap 2")
sns.violinplot(y=data.loc['lap3']['waist_angle_log_center'], ax=ax3, scale="count", inner="quartile", color="lightgreen").set_title("Lap 3")
plt.tight_layout()

In [29]:

f, (ax1, ax2, ax3) = plt.subplots(1, 3)
sns.violinplot(y=data.loc['lap1']['torso_angle_log_center'], ax=ax1, scale="count", inner="quartile", color="red").set_title("Lap 1")
sns.violinplot(y=data.loc['lap2']['torso_angle_log_center'], ax=ax2, scale="count", inner="quartile", color="skyblue").set_title("Lap 2")
sns.violinplot(y=data.loc['lap3']['torso_angle_log_center'], ax=ax3, scale="count", inner="quartile", color="lightgreen").set_title("Lap 3")
plt.tight_layout()

Rotation¶

Red lines indicate the lap-averaged rotation.¶

Generation of high power requires more and more Torso rotation as the ride progresses and fatigue sets in.¶

The trend in Pelvic rotation is less pronounced.¶

In [30]:

xscl=[0, 35]
yscl=[0, 20]
xtitle='Pelvic Rotation'
ytitle='Torso Rotation'

rot1 = alt.Chart(data.loc['lap1'], height=300, width=300).mark_rect().encode(
    alt.X('waist_d_rot_y_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_d_rot_y_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 1')

rot2 = alt.Chart(data.loc['lap2'], height=300, width=300).mark_rect().encode(
    alt.X('waist_d_rot_y_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_d_rot_y_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 2')

rot3 = alt.Chart(data.loc['lap3'], height=300, width=300).mark_rect().encode(
    alt.X('waist_d_rot_y_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_d_rot_y_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 3')

rot1xmean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    x='mean(waist_d_rot_y_center):Q',
    size=alt.value(1)
)

rot1ymean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(torso_d_rot_y_center):Q',
    size=alt.value(1)
)

rot2xmean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    x='mean(waist_d_rot_y_center):Q',
    size=alt.value(1)
)

rot2ymean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(torso_d_rot_y_center):Q',
    size=alt.value(1)
)

rot3xmean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    x='mean(waist_d_rot_y_center):Q',
    size=alt.value(1)
)

rot3ymean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    y='mean(torso_d_rot_y_center):Q',
    size=alt.value(1)
)

(rot1 + rot1xmean + rot1ymean) | (rot2 + rot2xmean + rot2ymean) | (rot3 + rot3xmean + rot3ymean)

Out[30]:

Rock¶

Increased average Torso and Pelvic Rock throughout the ride, as fatigue builds up¶

In [31]:

xscl=[0, 20]
yscl=[0, 20]
xtitle='Pelvic Rock'
ytitle='Torso Rock'

roc1 = alt.Chart(data.loc['lap1'], height=300, width=300).mark_rect().encode(
    alt.X('waist_d_rot_z_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_d_rot_z_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 1')

roc2 = alt.Chart(data.loc['lap2'], height=300, width=300).mark_rect().encode(
    alt.X('waist_d_rot_z_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_d_rot_z_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 2')

roc3 = alt.Chart(data.loc['lap3'], height=300, width=300).mark_rect().encode(
    alt.X('waist_d_rot_z_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=xscl),
          axis=alt.Axis(title=xtitle)
         ),
    alt.Y('torso_d_rot_z_center',
          bin=alt.Bin(maxbins=40),
          scale=alt.Scale(domain=yscl),
          axis=alt.Axis(title=ytitle)
         ),
    alt.Color('power:Q', scale=alt.Scale(domain=[0, 600], scheme='greenblue'))
).properties(title='Lap 3')

roc1xmean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    x='mean(waist_d_rot_z_center):Q',
    size=alt.value(1)
)

roc1ymean = alt.Chart(data.loc['lap1']).mark_rule(color='red').encode(
    y='mean(torso_d_rot_z_center):Q',
    size=alt.value(1)
)

roc2xmean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    x='mean(waist_d_rot_z_center):Q',
    size=alt.value(1)
)

roc2ymean = alt.Chart(data.loc['lap2']).mark_rule(color='red').encode(
    y='mean(torso_d_rot_z_center):Q',
    size=alt.value(1)
)

roc3xmean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    x='mean(waist_d_rot_z_center):Q',
    size=alt.value(1)
)

roc3ymean = alt.Chart(data.loc['lap3']).mark_rule(color='red').encode(
    y='mean(torso_d_rot_z_center):Q',
    size=alt.value(1)
)

(roc1 + roc1xmean + roc1ymean) | (roc2 + roc2xmean + roc2ymean) | (roc3 + roc3xmean + roc3ymean)

Out[31]: