kumify/mood/statistics.py

268 lines
9.4 KiB
Python
Raw Normal View History

import holoviews as hv
import pandas as pd
from django.utils import timezone
2021-03-01 17:05:14 +00:00
from math import pi
from bokeh.models import HoverTool
2021-03-01 17:05:14 +00:00
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.transform import cumsum
from bokeh.layouts import row, column
from holoviews.operation import timeseries
from dateutil.relativedelta import relativedelta
2021-03-03 07:39:26 +00:00
from .models import Status, Mood, StatusActivity
def moodstats(user):
hv.extension('bokeh')
tooltips = [
('Date', '@date{%F %H:%M}'),
2021-03-01 17:05:14 +00:00
('Mood', '@name (@value)')
]
formatters = {
'@date': 'datetime'
}
hover = HoverTool(tooltips=tooltips, formatters=formatters)
2021-03-01 17:05:14 +00:00
pointdict = {"date": [], "value": [], "color": [], "name": []}
2021-02-21 20:03:34 +00:00
for status in Status.objects.filter(user=user):
if status.mood:
pointdict["date"].append(status.timestamp)
pointdict["value"].append(status.mood.value)
pointdict["color"].append(status.mood.color)
2021-03-01 17:05:14 +00:00
pointdict["name"].append(status.mood.name)
pointframe = pd.DataFrame.from_dict(pointdict)
points = hv.Points(pointframe)
points.opts(
tools=[hover], color='color', cmap='Category20',
line_color='black', size=25,
width=600, height=400, show_grid=True)
pointtuples = [(pointdict["date"][i], pointdict["value"][i]) for i in range(len(pointdict["date"]))]
line = hv.Curve(pointtuples)
maxval = Mood.objects.filter(user=user).latest("value").value
maxy = maxval + max(maxval * 0.1, 1)
maxx = timezone.now().timestamp() * 1000
minx = maxx - (60*60*24*7) * 1000
2021-02-21 20:03:34 +00:00
output = points * line * timeseries.rolling(line, rolling_window=7)
output.opts(ylim=(0, maxy), xlim=(minx, maxx))
2021-02-21 19:59:39 +00:00
return output
2021-02-26 06:26:52 +00:00
def activitystats(user):
2021-02-21 19:59:39 +00:00
output = {}
for status in Status.objects.filter(user=user):
2021-02-21 19:59:39 +00:00
for activity in status.activity_set:
if not activity in output.keys():
output[activity] = {
"alltime": 0,
"yearly": 0,
"monthly": 0,
"weekly": 0
}
output[activity]["alltime"] += 1
if status.timestamp > timezone.now() - relativedelta(years=1):
output[activity]["yearly"] += 1
if status.timestamp > timezone.now() - relativedelta(months=1):
output[activity]["monthly"] += 1
if status.timestamp > timezone.now() - relativedelta(weeks=1):
output[activity]["weekly"] += 1
2021-03-01 17:05:14 +00:00
return output
def moodpies(user):
hv.extension('bokeh')
maxdate = timezone.now()
weekly_moods = Status.objects.filter(user=user, timestamp__lte=maxdate, timestamp__gte=maxdate - relativedelta(weeks=1))
monthly_moods = Status.objects.filter(user=user, timestamp__lte=maxdate, timestamp__gte=maxdate - relativedelta(months=1))
yearly_moods = Status.objects.filter(user=user, timestamp__lte=maxdate, timestamp__gte=maxdate - relativedelta(years=1))
weekly = dict()
colors = []
for mood in Mood.objects.filter(user=user):
weekly[mood.name] = 0
colors.append(mood.color)
monthly, yearly = weekly.copy(), weekly.copy()
for status in weekly_moods:
if status.mood:
weekly[status.mood.name] += 1
for status in monthly_moods:
if status.mood:
monthly[status.mood.name] += 1
for status in yearly_moods:
if status.mood:
yearly[status.mood.name] += 1
weekly_data = pd.Series(weekly).reset_index(name='value').rename(columns={'index':'mood'})
weekly_data['angle'] = weekly_data['value']/weekly_data['value'].sum() * 2*pi
weekly_data['color'] = colors
weekly_chart = figure(plot_height=350, title="Weekly", toolbar_location=None,
tools="hover", tooltips="@mood: @value")
weekly_chart.axis.visible = False
weekly_chart.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='mood', source=weekly_data)
monthly_data = pd.Series(monthly).reset_index(name='value').rename(columns={'index':'mood'})
monthly_data['angle'] = monthly_data['value']/monthly_data['value'].sum() * 2*pi
monthly_data['color'] = colors
2021-03-03 07:39:26 +00:00
monthly_chart = figure(plot_height=350, title="Monthly", toolbar_location=None,
tools="hover", tooltips="@mood: @value")
monthly_chart.axis.visible = False
monthly_chart.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='mood', source=monthly_data)
yearly_data = pd.Series(yearly).reset_index(name='value').rename(columns={'index':'mood'})
yearly_data['angle'] = yearly_data['value']/yearly_data['value'].sum() * 2*pi
yearly_data['color'] = colors
yearly_chart = figure(plot_height=350, title="Yearly", toolbar_location=None,
tools="hover", tooltips="@mood: @value")
yearly_chart.axis.visible = False
yearly_chart.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='mood', source=yearly_data)
return column(weekly_chart, monthly_chart, yearly_chart)
def activitymood(activity):
hv.extension('bokeh')
tooltips = [
('Date', '@date{%F %H:%M}'),
('Mood', '@name (@value)')
]
formatters = {
'@date': 'datetime'
}
hover = HoverTool(tooltips=tooltips, formatters=formatters)
pointdict = {"date": [], "value": [], "color": [], "name": []}
for statusactivity in StatusActivity.objects.filter(activity=activity):
if statusactivity.status.mood:
pointdict["date"].append(statusactivity.status.timestamp)
pointdict["value"].append(statusactivity.status.mood.value)
pointdict["color"].append(statusactivity.status.mood.color)
pointdict["name"].append(statusactivity.status.mood.name)
pointframe = pd.DataFrame.from_dict(pointdict)
points = hv.Points(pointframe)
points.opts(
tools=[hover], color='color', cmap='Category20',
line_color='black', size=25,
width=600, height=400, show_grid=True)
pointtuples = [(pointdict["date"][i], pointdict["value"][i]) for i in range(len(pointdict["date"]))]
line = hv.Curve(pointtuples)
maxval = Mood.objects.filter(user=activity.user).latest("value").value
maxy = maxval + max(maxval * 0.1, 1)
maxx = timezone.now().timestamp() * 1000
minx = maxx - (60*60*24*7) * 1000
output = points * line * timeseries.rolling(line, rolling_window=7)
output.opts(ylim=(0, maxy), xlim=(minx, maxx))
return output
def activitypies(activity):
hv.extension('bokeh')
maxdate = timezone.now()
sa = StatusActivity.objects.filter(activity=activity)
weekly = dict()
colors = []
for mood in Mood.objects.filter(user=activity.user):
weekly[mood.name] = 0
colors.append(mood.color)
monthly, yearly = weekly.copy(), weekly.copy()
for single in sa:
if single.status.mood:
if single.status.timestamp > timezone.now() - relativedelta(weeks=1):
weekly[single.status.mood.name] += 1
if single.status.timestamp > timezone.now() - relativedelta(months=1):
monthly[single.status.mood.name] += 1
if single.status.timestamp > timezone.now() - relativedelta(years=1):
yearly[single.status.mood.name] += 1
weekly_data = pd.Series(weekly).reset_index(name='value').rename(columns={'index':'mood'})
weekly_data['angle'] = weekly_data['value']/weekly_data['value'].sum() * 2*pi
weekly_data['color'] = colors
weekly_chart = figure(plot_height=350, title="Weekly", toolbar_location=None,
tools="hover", tooltips="@mood: @value")
weekly_chart.axis.visible = False
weekly_chart.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='mood', source=weekly_data)
monthly_data = pd.Series(monthly).reset_index(name='value').rename(columns={'index':'mood'})
monthly_data['angle'] = monthly_data['value']/monthly_data['value'].sum() * 2*pi
monthly_data['color'] = colors
2021-03-01 17:05:14 +00:00
monthly_chart = figure(plot_height=350, title="Monthly", toolbar_location=None,
tools="hover", tooltips="@mood: @value")
monthly_chart.axis.visible = False
monthly_chart.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='mood', source=monthly_data)
yearly_data = pd.Series(yearly).reset_index(name='value').rename(columns={'index':'mood'})
yearly_data['angle'] = yearly_data['value']/yearly_data['value'].sum() * 2*pi
yearly_data['color'] = colors
yearly_chart = figure(plot_height=350, title="Yearly", toolbar_location=None,
tools="hover", tooltips="@mood: @value")
yearly_chart.axis.visible = False
yearly_chart.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='mood', source=yearly_data)
return column(weekly_chart, monthly_chart, yearly_chart)