Kumi
c7295829fc
Standardized the parameter name for setting chart height across all chart creation functions in statistics.py. Replaced `plot_height` with `height` to align with the latest visualization library conventions. This change enhances code consistency and adheres to the updated library API, ensuring future compatibility and easier maintenance.
268 lines
9.3 KiB
Python
268 lines
9.3 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.io import output_file, show
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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 row, 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 = [
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('Date', '@date{%F %H:%M}'),
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('Mood', '@name (@value)')
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]
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formatters = {
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'@date': 'datetime'
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}
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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], 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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pointtuples = [(pointdict["date"][i], pointdict["value"][i]) for i in range(len(pointdict["date"]))]
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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 not activity 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(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 = 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 = 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_chart = figure(height=350, title="Weekly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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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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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_chart = figure(height=350, title="Monthly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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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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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_chart = figure(height=350, title="Yearly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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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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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 = [
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('Date', '@date{%F %H:%M}'),
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('Mood', '@name (@value)')
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]
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formatters = {
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'@date': 'datetime'
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}
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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], 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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pointtuples = [(pointdict["date"][i], pointdict["value"][i]) for i in range(len(pointdict["date"]))]
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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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maxdate = timezone.now()
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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 = 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_chart = figure(height=350, title="Weekly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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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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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_chart = figure(height=350, title="Monthly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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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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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_chart = figure(height=350, title="Yearly", toolbar_location=None,
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tools="hover", tooltips="@mood: @value")
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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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return column(weekly_chart, monthly_chart, yearly_chart) |