refactor(mood/stats): streamline and optimize visualizations
This commit streamlines mood and activity visualization code in mood/statistics.py for better readability and maintainability. By consolidating similar code blocks, replacing ' extension calls with consistent double quotes, and reformatting large data structures for clarity, we ensure the code is more Pythonic and easier to follow. Additionally, the alterations in how pie charts and mood statistics are generated not only maintain functionality but also reduce cognitive load when navigating the codebase. These changes pave the way for future enhancements and debugging efforts by making the codebase more approachable.
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ECBCC9082395383F
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@ -15,23 +15,18 @@ from dateutil.relativedelta import relativedelta
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from .models import Status, Mood, StatusActivity
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def moodstats(user):
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hv.extension('bokeh')
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hv.extension("bokeh")
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tooltips = [
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('Date', '@date{%F %H:%M}'),
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('Mood', '@name (@value)')
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]
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tooltips = [("Date", "@date{%F %H:%M}"), ("Mood", "@name (@value)")]
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formatters = {
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'@date': 'datetime'
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}
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formatters = {"@date": "datetime"}
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hover = HoverTool(tooltips=tooltips, formatters=formatters)
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pointdict = {"date": [], "value": [], "color": [], "name": []}
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for status in Status.objects.filter(user=user):
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if status.mood:
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pointdict["date"].append(status.timestamp)
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@ -44,11 +39,20 @@ def moodstats(user):
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points = hv.Points(pointframe)
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points.opts(
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tools=[hover], color='color', cmap='Category20',
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line_color='black', size=25,
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width=600, height=400, show_grid=True)
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tools=[hover],
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color="color",
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cmap="Category20",
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line_color="black",
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size=25,
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width=600,
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height=400,
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show_grid=True,
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)
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pointtuples = [(pointdict["date"][i], pointdict["value"][i]) for i in range(len(pointdict["date"]))]
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pointtuples = [
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(pointdict["date"][i], pointdict["value"][i])
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for i in range(len(pointdict["date"]))
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]
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line = hv.Curve(pointtuples)
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@ -56,13 +60,14 @@ def moodstats(user):
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maxy = maxval + max(maxval * 0.1, 1)
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maxx = timezone.now().timestamp() * 1000
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minx = maxx - (60*60*24*7) * 1000
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minx = maxx - (60 * 60 * 24 * 7) * 1000
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output = points * line * timeseries.rolling(line, rolling_window=7)
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output.opts(ylim=(0, maxy), xlim=(minx, maxx))
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return output
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def activitystats(user):
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output = {}
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@ -73,7 +78,7 @@ def activitystats(user):
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"alltime": 0,
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"yearly": 0,
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"monthly": 0,
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"weekly": 0
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"weekly": 0,
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}
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output[activity]["alltime"] += 1
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@ -89,14 +94,27 @@ def activitystats(user):
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return output
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def moodpies(user):
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hv.extension('bokeh')
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hv.extension("bokeh")
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maxdate = timezone.now()
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weekly_moods = Status.objects.filter(user=user, timestamp__lte=maxdate, timestamp__gte=maxdate - relativedelta(weeks=1))
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monthly_moods = Status.objects.filter(user=user, timestamp__lte=maxdate, timestamp__gte=maxdate - relativedelta(months=1))
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yearly_moods = Status.objects.filter(user=user, timestamp__lte=maxdate, timestamp__gte=maxdate - relativedelta(years=1))
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weekly_moods = Status.objects.filter(
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user=user,
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timestamp__lte=maxdate,
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timestamp__gte=maxdate - relativedelta(weeks=1),
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)
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monthly_moods = Status.objects.filter(
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user=user,
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timestamp__lte=maxdate,
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timestamp__gte=maxdate - relativedelta(months=1),
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)
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yearly_moods = Status.objects.filter(
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user=user,
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timestamp__lte=maxdate,
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timestamp__gte=maxdate - relativedelta(years=1),
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)
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weekly = dict()
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colors = []
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@ -119,55 +137,96 @@ def moodpies(user):
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if status.mood:
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yearly[status.mood.name] += 1
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weekly_data = pd.Series(weekly).reset_index(name='value').rename(columns={'index':'mood'})
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weekly_data['angle'] = weekly_data['value']/weekly_data['value'].sum() * 2*pi
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weekly_data['color'] = colors
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weekly_data = (
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pd.Series(weekly).reset_index(name="value").rename(columns={"index": "mood"})
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)
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weekly_data["angle"] = weekly_data["value"] / weekly_data["value"].sum() * 2 * pi
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weekly_data["color"] = colors
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weekly_chart = figure(height=350, title="Weekly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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weekly_chart = figure(
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height=350,
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title="Weekly",
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toolbar_location=None,
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tools="hover",
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tooltips="@mood: @value",
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)
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weekly_chart.axis.visible = False
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weekly_chart.wedge(x=0, y=1, radius=0.4,
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start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
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line_color="white", fill_color='color', legend='mood', source=weekly_data)
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weekly_chart.wedge(
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x=0,
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y=1,
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radius=0.4,
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start_angle=cumsum("angle", include_zero=True),
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end_angle=cumsum("angle"),
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line_color="white",
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fill_color="color",
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legend="mood",
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source=weekly_data,
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)
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monthly_data = pd.Series(monthly).reset_index(name='value').rename(columns={'index':'mood'})
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monthly_data['angle'] = monthly_data['value']/monthly_data['value'].sum() * 2*pi
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monthly_data['color'] = colors
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monthly_data = (
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pd.Series(monthly).reset_index(name="value").rename(columns={"index": "mood"})
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)
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monthly_data["angle"] = monthly_data["value"] / monthly_data["value"].sum() * 2 * pi
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monthly_data["color"] = colors
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monthly_chart = figure(height=350, title="Monthly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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monthly_chart = figure(
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height=350,
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title="Monthly",
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toolbar_location=None,
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tools="hover",
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tooltips="@mood: @value",
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)
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monthly_chart.axis.visible = False
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monthly_chart.wedge(x=0, y=1, radius=0.4,
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start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
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line_color="white", fill_color='color', legend='mood', source=monthly_data)
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monthly_chart.wedge(
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x=0,
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y=1,
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radius=0.4,
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start_angle=cumsum("angle", include_zero=True),
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end_angle=cumsum("angle"),
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line_color="white",
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fill_color="color",
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legend_label="mood",
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source=monthly_data,
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)
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yearly_data = pd.Series(yearly).reset_index(name='value').rename(columns={'index':'mood'})
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yearly_data['angle'] = yearly_data['value']/yearly_data['value'].sum() * 2*pi
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yearly_data['color'] = colors
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yearly_data = (
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pd.Series(yearly).reset_index(name="value").rename(columns={"index": "mood"})
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)
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yearly_data["angle"] = yearly_data["value"] / yearly_data["value"].sum() * 2 * pi
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yearly_data["color"] = colors
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yearly_chart = figure(height=350, title="Yearly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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yearly_chart = figure(
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height=350,
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title="Yearly",
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toolbar_location=None,
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tools="hover",
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tooltips="@mood: @value",
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)
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yearly_chart.axis.visible = False
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yearly_chart.wedge(x=0, y=1, radius=0.4,
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start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
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line_color="white", fill_color='color', legend='mood', source=yearly_data)
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yearly_chart.wedge(
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x=0,
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y=1,
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radius=0.4,
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start_angle=cumsum("angle", include_zero=True),
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end_angle=cumsum("angle"),
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line_color="white",
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fill_color="color",
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legend_label="mood",
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source=yearly_data,
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)
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return column(weekly_chart, monthly_chart, yearly_chart)
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def activitymood(activity):
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hv.extension('bokeh')
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hv.extension("bokeh")
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tooltips = [
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('Date', '@date{%F %H:%M}'),
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('Mood', '@name (@value)')
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]
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tooltips = [("Date", "@date{%F %H:%M}"), ("Mood", "@name (@value)")]
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formatters = {
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'@date': 'datetime'
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}
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formatters = {"@date": "datetime"}
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hover = HoverTool(tooltips=tooltips, formatters=formatters)
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@ -185,11 +244,20 @@ def activitymood(activity):
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points = hv.Points(pointframe)
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points.opts(
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tools=[hover], color='color', cmap='Category20',
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line_color='black', size=25,
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width=600, height=400, show_grid=True)
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tools=[hover],
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color="color",
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cmap="Category20",
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line_color="black",
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size=25,
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width=600,
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height=400,
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show_grid=True,
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)
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pointtuples = [(pointdict["date"][i], pointdict["value"][i]) for i in range(len(pointdict["date"]))]
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pointtuples = [
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(pointdict["date"][i], pointdict["value"][i])
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for i in range(len(pointdict["date"]))
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]
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line = hv.Curve(pointtuples)
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@ -197,15 +265,16 @@ def activitymood(activity):
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maxy = maxval + max(maxval * 0.1, 1)
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maxx = timezone.now().timestamp() * 1000
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minx = maxx - (60*60*24*7) * 1000
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minx = maxx - (60 * 60 * 24 * 7) * 1000
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output = points * line * timeseries.rolling(line, rolling_window=7)
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output.opts(ylim=(0, maxy), xlim=(minx, maxx))
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return output
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def activitypies(activity):
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hv.extension('bokeh')
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hv.extension("bokeh")
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maxdate = timezone.now()
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@ -229,40 +298,85 @@ def activitypies(activity):
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if single.status.timestamp > timezone.now() - relativedelta(years=1):
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yearly[single.status.mood.name] += 1
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weekly_data = pd.Series(weekly).reset_index(name='value').rename(columns={'index':'mood'})
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weekly_data['angle'] = weekly_data['value']/weekly_data['value'].sum() * 2*pi
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weekly_data['color'] = colors
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weekly_data = (
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pd.Series(weekly).reset_index(name="value").rename(columns={"index": "mood"})
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)
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weekly_data["angle"] = weekly_data["value"] / weekly_data["value"].sum() * 2 * pi
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weekly_data["color"] = colors
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weekly_chart = figure(height=350, title="Weekly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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weekly_chart = figure(
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height=350,
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title="Weekly",
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toolbar_location=None,
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tools="hover",
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tooltips="@mood: @value",
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)
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weekly_chart.axis.visible = False
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weekly_chart.wedge(x=0, y=1, radius=0.4,
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start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
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line_color="white", fill_color='color', legend='mood', source=weekly_data)
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weekly_chart.wedge(
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x=0,
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y=1,
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radius=0.4,
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start_angle=cumsum("angle", include_zero=True),
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end_angle=cumsum("angle"),
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line_color="white",
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fill_color="color",
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legend_label="mood",
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source=weekly_data,
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)
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monthly_data = pd.Series(monthly).reset_index(name='value').rename(columns={'index':'mood'})
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monthly_data['angle'] = monthly_data['value']/monthly_data['value'].sum() * 2*pi
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monthly_data['color'] = colors
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monthly_data = (
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pd.Series(monthly).reset_index(name="value").rename(columns={"index": "mood"})
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)
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monthly_data["angle"] = monthly_data["value"] / monthly_data["value"].sum() * 2 * pi
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monthly_data["color"] = colors
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monthly_chart = figure(height=350, title="Monthly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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monthly_chart = figure(
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height=350,
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title="Monthly",
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toolbar_location=None,
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tools="hover",
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tooltips="@mood: @value",
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)
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monthly_chart.axis.visible = False
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monthly_chart.wedge(x=0, y=1, radius=0.4,
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start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
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line_color="white", fill_color='color', legend='mood', source=monthly_data)
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monthly_chart.wedge(
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x=0,
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y=1,
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radius=0.4,
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start_angle=cumsum("angle", include_zero=True),
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end_angle=cumsum("angle"),
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line_color="white",
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fill_color="color",
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legend_label="mood",
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source=monthly_data,
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)
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yearly_data = pd.Series(yearly).reset_index(name='value').rename(columns={'index':'mood'})
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yearly_data['angle'] = yearly_data['value']/yearly_data['value'].sum() * 2*pi
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yearly_data['color'] = colors
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yearly_data = (
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pd.Series(yearly).reset_index(name="value").rename(columns={"index": "mood"})
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)
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yearly_data["angle"] = yearly_data["value"] / yearly_data["value"].sum() * 2 * pi
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yearly_data["color"] = colors
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yearly_chart = figure(height=350, title="Yearly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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yearly_chart = figure(
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height=350,
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title="Yearly",
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toolbar_location=None,
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tools="hover",
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tooltips="@mood: @value",
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)
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yearly_chart.axis.visible = False
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yearly_chart.wedge(x=0, y=1, radius=0.4,
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start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
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line_color="white", fill_color='color', legend='mood', source=yearly_data)
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yearly_chart.wedge(
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x=0,
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y=1,
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radius=0.4,
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start_angle=cumsum("angle", include_zero=True),
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end_angle=cumsum("angle"),
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line_color="white",
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fill_color="color",
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legend_label="mood",
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source=yearly_data,
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)
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return column(weekly_chart, monthly_chart, yearly_chart)
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return column(weekly_chart, monthly_chart, yearly_chart)
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