import holoviews as hv import pandas as pd from django.utils import timezone from math import pi from bokeh.models import HoverTool 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 from .models import Status, Mood, StatusActivity def moodstats(user): 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 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) 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 output = points * line * timeseries.rolling(line, rolling_window=7) output.opts(ylim=(0, maxy), xlim=(minx, maxx)) return output def activitystats(user): output = {} for status in Status.objects.filter(user=user): 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 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 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 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)