Format with Black autoformatter

src/stravavis/cli.py

@@ -15,35 +15,57 @@ def main():
     parser.add_argument(
         "--lon_min",
         type=float,
-        help="Minimum longitude for plot_map (values less than this are removed from the data)",
+        help="Minimum longitude for plot_map "
+        "(values less than this are removed from the data)",
     )
     parser.add_argument(
         "--lon_max",
         type=float,
-        help="Maximum longitude for plot_map (values greater than this are removed from the data)",
+        help="Maximum longitude for plot_map "
+        "(values greater than this are removed from the data)",
     )
     parser.add_argument(
         "--lat_min",
         type=float,
-        help="Minimum latitude for plot_map (values less than this are removed from the data)",
+        help="Minimum latitude for plot_map "
+        "(values less than this are removed from the data)",
     )
     parser.add_argument(
         "--lat_max",
         type=float,
-        help="Maximum latitude for plot_map (values greater than this are removed from the data)",
+        help="Maximum latitude for plot_map "
+        "(values greater than this are removed from the data)",
     )
     parser.add_argument(
         "--bbox", help="Shortcut for comma-separated LON_MIN,LAT_MIN,LON_MAX,LAT_MAX"
     )
-    parser.add_argument("--alpha", default=0.4, help="Line transparency. 0 = Fully transparent, 1 = No transparency")
+    parser.add_argument(
+        "--alpha",
+        default=0.4,
+        help="Line transparency. 0 = Fully transparent, 1 = No transparency",
+    )
     parser.add_argument("--linewidth", default=0.4, help="Line width")
-    parser.add_argument("--activities_path", help="Path to activities.csv from Strava bulk export zip")
-    parser.add_argument("--year_min", help="The minimum year to use for the calendar heatmap.")
-    parser.add_argument("--year_max", help="The maximum year to use for the calendar heatmap.")
-    parser.add_argument("--max_dist", help="Maximum daily distance for the calendar heatmap; values above this will be capped.")
+    parser.add_argument(
+        "--activities_path", help="Path to activities.csv from Strava bulk export zip"
+    )
+    parser.add_argument(
+        "--year_min", help="The minimum year to use for the calendar heatmap."
+    )
+    parser.add_argument(
+        "--year_max", help="The maximum year to use for the calendar heatmap."
+    )
+    parser.add_argument(
+        "--max_dist",
+        help="Maximum daily distance for the calendar heatmap; "
+        "values above this will be capped.",
+    )
     parser.add_argument("--fig_height", help="Figure height for the calendar heatmap.")
     parser.add_argument("--fig_width", help="Figure width for the calendar heatmap.")
-    parser.add_argument("--local_timezone", help="Timezone for determining local times for activities. See pytz.all_timezones for a list of all timezones.")
+    parser.add_argument(
+        "--local_timezone",
+        help="Timezone for determining local times for activities. "
+        "See pytz.all_timezones for a list of all timezones.",
+    )
     args = parser.parse_args()
 
     # Expand "~" or "~user"
@@ -114,7 +136,7 @@ def main():
         outfile = f"{args.output_prefix}-calendar.png"
         plot_calendar(activities, output_file=outfile)
         print(f"Saved to {outfile}")
-    
+
         print("Plotting dumbbell...")
         outfile = f"{args.output_prefix}-dumbbell.png"
         plot_dumbbell(activities, output_file=outfile)

src/stravavis/plot_calendar.py

@@ -2,30 +2,36 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-def plot_calendar(activities, year_min=None, year_max=None, max_dist=None,
-                  fig_height = 15, fig_width = 9, output_file='calendar.png'):
+
+def plot_calendar(
+    activities,
+    year_min=None,
+    year_max=None,
+    max_dist=None,
+    fig_height=15,
+    fig_width=9,
+    output_file="calendar.png",
+):
     # Create a new figure
     plt.figure()
-    
+
     # Process data
-    activities['Activity Date'] = pd.to_datetime(activities['Activity Date'])
-    activities['date'] = activities['Activity Date'].dt.date
-    activities = activities.groupby(['date'])['Distance'].sum()
+    activities["Activity Date"] = pd.to_datetime(activities["Activity Date"])
+    activities["date"] = activities["Activity Date"].dt.date
+    activities = activities.groupby(["date"])["Distance"].sum()
     activities.index = pd.to_datetime(activities.index)
     activities.clip(0, max_dist, inplace=True)
-    
+
     if year_min:
-        activities = activities[activities.index.year>=year_min]
-    
+        activities = activities[activities.index.year >= year_min]
+
     if year_max:
-        activities = activities[activities.index.year<=year_max]
-    
+        activities = activities[activities.index.year <= year_max]
+
     # Create heatmap
-    fig, ax = calmap.calendarplot(
-        data = activities
-        )
-
+    fig, ax = calmap.calendarplot(data=activities)
+
     # Save plot
     fig.set_figheight(fig_height)
     fig.set_figwidth(fig_width)
-    fig.savefig(output_file, dpi = 600)
+    fig.savefig(output_file, dpi=600)

src/stravavis/plot_dumbbell.py

@@ -12,61 +12,84 @@
     theme,
     theme_bw,
     xlab,
-    ylab
-) 
+    ylab,
+)
 
-def plot_dumbbell(activities, year_min=None, year_max=None, local_timezone=None,
-                  fig_height = 34, fig_width = 34, output_file='dumbbell.png'):
-
+
+def plot_dumbbell(
+    activities,
+    year_min=None,
+    year_max=None,
+    local_timezone=None,
+    fig_height=34,
+    fig_width=34,
+    output_file="dumbbell.png",
+):
     # Convert activity start date to datetime
-    activities['Activity Date'] = pd.to_datetime(activities['Activity Date'])
-    
+    activities["Activity Date"] = pd.to_datetime(activities["Activity Date"])
+
     # Convert to local timezone (if given)
     if local_timezone:
-        activities['Activity Date'] = (pd.to_datetime(activities['Activity Date'])
-                                       .dt.tz_localize(tz='UTC', nonexistent='NaT', ambiguous='NaT')
-                                       .dt.tz_convert(local_timezone))
-
+        activities["Activity Date"] = (
+            pd.to_datetime(activities["Activity Date"])
+            .dt.tz_localize(tz="UTC", nonexistent="NaT", ambiguous="NaT")
+            .dt.tz_convert(local_timezone)
+        )
+
     # Get activity start and end times
-    activities['start'] = activities['Activity Date']
-    activities['duration'] = 0
+    activities["start"] = activities["Activity Date"]
+    activities["duration"] = 0
     for i in range(len(activities)):
-        activities['duration'][i] = pd.Timedelta(activities['Elapsed Time'][i], unit= "s")
-    activities['end'] = activities['start'] + activities['duration']
-
+        activities["duration"][i] = pd.Timedelta(
+            activities["Elapsed Time"][i], unit="s"
+        )
+    activities["end"] = activities["start"] + activities["duration"]
+
     # Remove activities outside the year_min -> year_max window
-    activities['year'] = activities['Activity Date'].dt.year
-    
+    activities["year"] = activities["Activity Date"].dt.year
+
     if year_min:
-        activities = activities[activities['year']>=year_min]
-    
+        activities = activities[activities["year"] >= year_min]
+
     if year_max:
-        activities = activities[activities['year']<=year_max]
-    
+        activities = activities[activities["year"] <= year_max]
+
     # Get day of year and time of day data
-    activities['dayofyear'] = activities['Activity Date'].dt.dayofyear
-    activities['start_time'] = activities['start'].dt.time
-    activities['end_time'] = activities['end'].dt.time
-    activities['x'] = (activities['start'].dt.hour 
-                       + activities['start'].dt.minute/60 
-                       + activities['start'].dt.second/60/60)
-    activities['xend'] = (activities['end'].dt.hour 
-                          + activities['end'].dt.minute/60 
-                          + activities['end'].dt.second/60/60)
-
+    activities["dayofyear"] = activities["Activity Date"].dt.dayofyear
+    activities["start_time"] = activities["start"].dt.time
+    activities["end_time"] = activities["end"].dt.time
+    activities["x"] = (
+        activities["start"].dt.hour
+        + activities["start"].dt.minute / 60
+        + activities["start"].dt.second / 60 / 60
+    )
+    activities["xend"] = (
+        activities["end"].dt.hour
+        + activities["end"].dt.minute / 60
+        + activities["end"].dt.second / 60 / 60
+    )
+
     # Create plotnine / ggplot
-    p = (ggplot(activities) +
-     geom_segment(aes(x="x", y="dayofyear", xend="xend", yend="dayofyear"), size = 0.1) +
-     geom_point(aes("x", "dayofyear"), size = 0.05) +
-     geom_point(aes("xend", "dayofyear"), size = 0.05) +
-     facet_grid('.~year') +
-     scale_x_continuous(breaks = [0, 6, 12, 18, 24], labels = ["12am", "6am", "12pm", "6pm", ""]) +
-     scale_y_continuous(breaks = [1, 100, 200, 300, 365]) +
-     xlab("Time of Day") +
-     ylab("Day of Year") +
-     theme_bw() +
-     theme(plot_background = element_rect(fill = "white"), panel_grid_major_y = element_blank())
+    p = (
+        ggplot(activities)
+        + geom_segment(
+            aes(x="x", y="dayofyear", xend="xend", yend="dayofyear"), size=0.1
+        )
+        + geom_point(aes("x", "dayofyear"), size=0.05)
+        + geom_point(aes("xend", "dayofyear"), size=0.05)
+        + facet_grid(".~year")
+        + scale_x_continuous(
+            breaks=[0, 6, 12, 18, 24], labels=["12am", "6am", "12pm", "6pm", ""]
+        )
+        + scale_y_continuous(breaks=[1, 100, 200, 300, 365])
+        + xlab("Time of Day")
+        + ylab("Day of Year")
+        + theme_bw()
+        + theme(
+            plot_background=element_rect(fill="white"),
+            panel_grid_major_y=element_blank(),
+        )
     )
-    
+
     # Save plot
-    p.save(output_file, width=fig_width, height=fig_height, units = "cm", dpi=600)
+    p.save(output_file, width=fig_width, height=fig_height, units="cm", dpi=600)

src/stravavis/plot_elevations.py

@@ -1,39 +1,40 @@
 import math
+
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-def plot_elevations(df, output_file = 'elevations.png'):
 
+def plot_elevations(df, output_file="elevations.png"):
     # Create a new figure
     plt.figure()
 
     # Compute activity start times (for facet ordering)
-    start_times = df.groupby('name').agg({'time': 'min'}).reset_index().sort_values('time')
+    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,
-        )
+        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
-        )
+    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)
+    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/stravavis/plot_facets.py

@@ -1,39 +1,40 @@
 import math
+
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-def plot_facets(df, output_file = 'plot.png'):
 
+def plot_facets(df, output_file="plot.png"):
     # Create a new figure
     plt.figure()
 
     # Compute activity start times (for facet ordering)
-    start_times = df.groupby('name').agg({'time': 'min'}).reset_index().sort_values('time')
+    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 = False,
-        )
+        data=df,
+        col="name",
+        col_wrap=ncol,
+        col_order=start_times["name"],
+        sharex=False,
+        sharey=False,
+    )
 
     # Add activities
-    p = p.map(
-        plt.plot, "lon", "lat", color = 'black', linewidth = 4
-        )
+    p = p.map(plt.plot, "lon", "lat", 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)
+    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)