Elevations

README.md

@@ -17,6 +17,12 @@ A map of activities viewed in plan.
 
 ![map](https://github.com/marcusvolz/strava_py/blob/main/plots/map001.png "A map of activities viewed in plan.")
 
+### Elevations
+
+A plot of activity elevation profiles as small multiples.
+
+![map](https://github.com/marcusvolz/strava_py/blob/main/plots/elevations001.png "A plot of activity elevation profiles as small multiples.")
+
 ## How to use
 
 ### Bulk export from Strava
@@ -51,3 +57,9 @@ plot_facets(df, output_file = 'plot.png')
 plot_map(df, lon_min=None, lon_max= None, lat_min=None, lat_max=None,
              alpha=0.3, linewidth=0.3, output_file="map.png")
 ```
+
+### Plot elevations
+
+```python
+plot_elevations(df, output_file = 'elevations.png')
+```

src/strava_py/cli.py

@@ -27,6 +27,7 @@ def main():
 
     # Normally imports go at the top, but scientific libraries can be slow to import
     # so let's validate arguments first
+    from strava_py.plot_elevations import plot_elevations
     from strava_py.plot_map import plot_map
     from strava_py.plot_facets import plot_facets
     from strava_py.process_data import process_data
@@ -42,6 +43,10 @@ def main():
     plot_facets(df, output_file=args.output_file)
     print(f"Saved to {args.output_file}")
 
+    print("Plotting elevations...")
+    plot_elevations(df, output_file=args.output_file)
+    print(f"Saved to {args.output_file}")
+
 
 if __name__ == "__main__":
     main()

src/strava_py/plot_elevations.py

@@ -0,0 +1,36 @@
+import math
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def plot_elevations(df, output_file = 'elevations.png'):
+
+    # Compute activity start times (for facet ordering)
+    start_times = df.groupby('name').agg({'time': 'min'}).reset_index().sort_values('time')
+    ncol = math.ceil(math.sqrt(len(start_times)))
+
+    # Create facets
+    p = sns.FacetGrid(
+        data = df,
+        col = 'name',
+        col_wrap = ncol,
+        col_order = start_times['name'],
+        sharex = False,
+        sharey = True,
+        )
+
+    # Add activities
+    p = p.map(
+        plt.plot, "dist", "ele", color = 'black', linewidth = 4
+        )
+
+    # Update plot aesthetics
+    p.set(xlabel = None)
+    p.set(ylabel = None)
+    p.set(xticks = [])
+    p.set(yticks = [])
+    p.set(xticklabels = [])
+    p.set(yticklabels = [])
+    p.set_titles(col_template = '', row_template = '')
+    sns.despine(left = True, bottom = True)
+    plt.subplots_adjust(left = 0.05, bottom = 0.05, right = 0.95, top = 0.95)
+    plt.savefig(output_file)

src/strava_py/process_data.py

@@ -2,6 +2,7 @@
 
 import fit2gpx
 import gpxpy
+import math
 import pandas as pd
 from rich.progress import track
 
@@ -20,9 +21,13 @@ def process_gpx(gpxfile):
         ele = []
         time = []
         name = []
+        dist = []
 
         for track in activity.tracks:
             for segment in track.segments:
+                x0 = activity.tracks[0].segments[0].points[0].longitude
+                y0 = activity.tracks[0].segments[0].points[0].latitude
+                d0 = 0
                 for point in segment.points:
                     x = point.longitude
                     y = point.latitude
@@ -33,10 +38,15 @@ def process_gpx(gpxfile):
                     ele.append(z)
                     time.append(t)
                     name.append(gpxfile)
+                    d = d0 + math.sqrt(math.pow(x - x0, 2) + math.pow(y - y0, 2))
+                    dist.append(d)
+                    x0 = x
+                    y0 = y
+                    d0 = d
 
         df = pd.DataFrame(
-                list(zip(lon, lat, ele, time, name)),
-                columns = ['lon', 'lat', 'ele', 'time', 'name']
+                list(zip(lon, lat, ele, time, name, dist)),
+                columns = ['lon', 'lat', 'ele', 'time', 'name', 'dist']
             )
 
         return df
@@ -48,7 +58,26 @@ def process_fit(fitfile):
         df_lap, df = conv.fit_to_dataframes(fname = fitfile)
 
         df['name'] = fitfile
-        df = df[['longitude', 'latitude', 'altitude', 'timestamp', 'name']]
+
+        dist = []
+
+        for i in range(len(df.index)):
+            if i < 1:
+                x0 = df['longitude'][0]
+                y0 = df['latitude'][0]
+                d0 = 0
+                dist.append(d0)
+            else:
+                x = df['longitude'][i]
+                y = df['latitude'][i]
+                d = d0 + math.sqrt(math.pow(x - x0, 2) + math.pow(y - y0, 2))
+                dist.append(d)
+                x0 = x
+                y0 = y
+                d0 = d
+
+        df = df.join(pd.DataFrame({'dist': dist}))
+        df = df[['longitude', 'latitude', 'altitude', 'timestamp', 'name', 'dist']]
         df = df.rename(columns = {'longitude': 'lon', 'latitude': 'lat', 'altitude': 'ele', 'timestamp': 'time'})
 
         return df