Kumi
ff70221553
Removed unnecessary imports across various modules to streamline the application's dependencies and improve loading times. Specific changes include the removal of unused Django model and admin imports in several apps, simplifying view imports by eliminating unutilized components, and cleaning up static CSS for better maintainability. Corrections were made to conditional expressions for clearer logic. The removal of the django.test.TestCase import in test files reflects a shift towards a different testing strategy or the current lack of tests. Exception handling has been made more explicit to avoid catching unintended exceptions, paving the way for more robust error handling and logging in the future. Additionally, a new CSS file was added for frontend enhancements, indicating ongoing UI/UX improvements. These changes collectively aim to make the codebase more maintainable and efficient, reducing clutter and focusing on used functionalities. It's a step towards optimizing the application's performance and ensuring a clean, manageable codebase for future development.
379 lines
10 KiB
Python
379 lines
10 KiB
Python
import holoviews as hv
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import pandas as pd
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from django.utils import timezone
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from math import pi
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from bokeh.models import HoverTool
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from bokeh.plotting import figure
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from bokeh.transform import cumsum
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from bokeh.layouts import column
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from holoviews.operation import timeseries
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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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tooltips = [("Date", "@date{%F %H:%M}"), ("Mood", "@name (@value)")]
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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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pointdict["value"].append(status.mood.value)
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pointdict["color"].append(status.mood.color)
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pointdict["name"].append(status.mood.name)
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pointframe = pd.DataFrame.from_dict(pointdict)
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points = hv.Points(pointframe)
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points.opts(
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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 = [
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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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maxval = Mood.objects.filter(user=user).latest("value").value
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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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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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for status in Status.objects.filter(user=user):
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for activity in status.activity_set:
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if activity not in output.keys():
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output[activity] = {
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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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}
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output[activity]["alltime"] += 1
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if status.timestamp > timezone.now() - relativedelta(years=1):
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output[activity]["yearly"] += 1
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if status.timestamp > timezone.now() - relativedelta(months=1):
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output[activity]["monthly"] += 1
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if status.timestamp > timezone.now() - relativedelta(weeks=1):
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output[activity]["weekly"] += 1
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return output
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def moodpies(user):
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hv.extension("bokeh")
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maxdate = timezone.now()
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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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for mood in Mood.objects.filter(user=user):
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weekly[mood.name] = 0
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colors.append(mood.color)
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monthly, yearly = weekly.copy(), weekly.copy()
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for status in weekly_moods:
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if status.mood:
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weekly[status.mood.name] += 1
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for status in monthly_moods:
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if status.mood:
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monthly[status.mood.name] += 1
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for status in yearly_moods:
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if status.mood:
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yearly[status.mood.name] += 1
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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(
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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(
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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 = (
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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(
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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(
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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 = (
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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(
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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(
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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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tooltips = [("Date", "@date{%F %H:%M}"), ("Mood", "@name (@value)")]
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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 statusactivity in StatusActivity.objects.filter(activity=activity):
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if statusactivity.status.mood:
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pointdict["date"].append(statusactivity.status.timestamp)
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pointdict["value"].append(statusactivity.status.mood.value)
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pointdict["color"].append(statusactivity.status.mood.color)
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pointdict["name"].append(statusactivity.status.mood.name)
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pointframe = pd.DataFrame.from_dict(pointdict)
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points = hv.Points(pointframe)
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points.opts(
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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 = [
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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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maxval = Mood.objects.filter(user=activity.user).latest("value").value
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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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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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sa = StatusActivity.objects.filter(activity=activity)
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weekly = dict()
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colors = []
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for mood in Mood.objects.filter(user=activity.user):
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weekly[mood.name] = 0
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colors.append(mood.color)
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monthly, yearly = weekly.copy(), weekly.copy()
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for single in sa:
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if single.status.mood:
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if single.status.timestamp > timezone.now() - relativedelta(weeks=1):
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weekly[single.status.mood.name] += 1
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if single.status.timestamp > timezone.now() - relativedelta(months=1):
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monthly[single.status.mood.name] += 1
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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 = (
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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(
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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(
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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 = (
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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(
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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(
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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 = (
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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(
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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(
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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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