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bevstat_de.py
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bevstat_de.py
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import numpy as np
from os import path
from bokeh.io import curdoc
from bokeh.layouts import row, column, widgetbox, layout
from bokeh.models import ColumnDataSource, formatters, BoxAnnotation, BoxSelectTool, HoverTool, Span, Label, Button
from bokeh.models.widgets import Slider, PreText, RadioGroup
from bokeh.plotting import figure
from functools import lru_cache
import timeit
#Szenariodaten sind ab und mit 2016 abgebildet, die Daten aus den "ch" Files sind eqvl. zu den "ch"-Statistiken + "au" Statistiken
#Doc:
#"Jahr" "Lebendgeburten" "Todesfaelle" "Einwanderungen" "Auswanderungen" "Erwerb des Schweizer Buergerrechts"
#Source: https://www.bfs.admin.ch/bfsstatic/dam/assets/290789/master
#TODO:
#slider value shoudlnt be visible
#streamline data format in all the files
#maybe add a ticker
#Add source (code abd data)
data_dir = "pop_data/"
additional_stats = dict()
additional_stats_source = dict()
age_data = dict()
# age_data_source = dict()
display_stats = dict()
#load data from csvs, actual population counts 1971-2015 and scenarios 2016-2045
for data_time in ("historical", "low_prediction", "reference_prediction", "high_prediction"):
age_data[data_time] = dict()
for data_demographic in ("m_ch","m_au","f_ch","f_au"):
age_data[data_time][data_demographic] = np.loadtxt(path.join(data_dir,
data_time +
"_" +
data_demographic +
".csv"),
dtype=int)
#invert all data for the female portion (negative barplots for all female values):
if data_demographic.startswith("f"):
age_data[data_time][data_demographic] *= -1
#load migration and birth stats, also with scenarios
for key in ("historical_ch","historical_au","low_ch","low_au","ref_ch","ref_au","high_ch","high_au"):
additional_stats[key] = np.rot90(np.fliplr(np.loadtxt(path.join(data_dir,key + ".stats"), dtype=int)))
additional_stats[key][2] *= -1
additional_stats[key][4] *= -1
age_groups = [k for k in range(101)]
x_scatter = np.zeros(101)
class Bevstat():
def __init__(self, age_data, additional_stats, first_recorded_year):
self.age_data = age_data
self.additional_stats = additional_stats
self.first_recorded_year = first_recorded_year
self.labor_age_min = 18
self.labor_age_max = 67
self.radio_active = 1
self.age_groups = [group for group in range(101)]
self.age_data_source = dict()
self.display_stats = dict()
self.additional_stats_source = dict()
self.underage_box = BoxAnnotation(top=self.labor_age_min, fill_alpha=0.15, fill_color='red')
self.laborage_box = BoxAnnotation(bottom=self.labor_age_min, top=self.labor_age_max, fill_alpha=0.15, fill_color='green')
self.retired_box = BoxAnnotation(bottom=self.labor_age_max, fill_alpha=0.15, fill_color='red')
self.births_box = BoxAnnotation(left=first_recorded_year,
right=first_recorded_year + 1,
line_width = 0.3,
line_color = "black",
line_alpha = 1,
fill_alpha=0.2,
fill_color='yellow')
self.migration_box = BoxAnnotation(left=first_recorded_year,
right=first_recorded_year + 1,
line_width=0.3,
line_color="black",
line_alpha=1,
fill_alpha=0.2,
fill_color='yellow')
self.dependency_ratio_textfield = PreText(text="", width=500)
self.total_population_textfield = PreText(text="", width=500)
self.display_stats["ch"] = np.hstack((self.additional_stats["historical_ch"], self.additional_stats["ref_ch"]))
self.display_stats["au"] = np.hstack((self.additional_stats["historical_au"], self.additional_stats["ref_au"]))
for data_demographic in ("m_ch", "m_au", "f_ch", "f_au"):
self.age_data_source[data_demographic] = ColumnDataSource(
data=dict(y=self.age_groups, display=self.age_data["historical"][data_demographic][0]))
self.historical_years = len(self.age_data["historical"]["m_ch"])
self.predicted_years = len(self.age_data["reference_prediction"]["m_ch"])
self.offset_slider = Slider(title=None, value=0, start=0, end=self.historical_years + self.predicted_years - 1,
step=1, sizing_mode="scale_height", orientation="horizontal")
#changing icons on the fly diesnt seem to work
#Icons removed in bokeh 0.12.4
#self.icon_arrow = Icon(icon_name="arrow-circle-up")
self.animate_button = Button(label="Animation", width=70)
self.animate_button.on_click(self.animation_button_click)
for origin in ("ch", "au"):
self.additional_stats_source[origin] = ColumnDataSource(data=dict(years=self.display_stats[origin][0] + 0.25,
births=self.display_stats[origin][1],
deaths=self.display_stats[origin][2],
immigration=self.display_stats[origin][3],
migration=self.display_stats[origin][4]))
def update_stat_plots(self, radio_active):
self.radio_active = radio_active
for num, stat in enumerate(['births', 'deaths', 'immigration', 'migration'], 1):
if self.radio_active == 0:
self.additional_stats_source['ch'].data[stat] = np.hstack(
(self.additional_stats["historical_ch"][num], self.additional_stats["low_ch"][num]))
self.additional_stats_source['au'].data[stat] = np.hstack(
(self.additional_stats["historical_au"][num], self.additional_stats["low_au"][num]))
if self.radio_active == 1:
self.additional_stats_source['ch'].data[stat] = np.hstack(
(self.additional_stats["historical_ch"][num], self.additional_stats["ref_ch"][num]))
self.additional_stats_source['au'].data[stat] = np.hstack(
(self.additional_stats["historical_au"][num], self.additional_stats["ref_au"][num]))
if self.radio_active == 2:
self.additional_stats_source['ch'].data[stat] = np.hstack(
(self.additional_stats["historical_ch"][num], self.additional_stats["high_ch"][num]))
self.additional_stats_source['au'].data[stat] = np.hstack(
(self.additional_stats["historical_au"][num], self.additional_stats["high_au"][num]))
self.update_data(self.offset_slider.value) # war vorher mit offset.value
def animation_button_click(self):
if self.animate_button.label == "Animation":
self.animate_button.label = "Stop"
curdoc().add_periodic_callback(self.button_animation, 200)
else:
self.animate_button.label = "Animation"
curdoc().remove_periodic_callback(self.button_animation)
def button_animation(self):
if self.offset_slider.value < self.historical_years + self.predicted_years - 1:
self.animate_button.label = "Stop"
self.offset_slider.value += 1
else:
self.animate_button.label = "Animation"
curdoc().remove_periodic_callback(self.button_animation)
self.offset_slider.value = 0
self.update_data(self.offset_slider.value)
def update_dependency_box(self, labor_age_min, labor_age_max):
self.labor_age_min = labor_age_min
self.labor_age_max = labor_age_max
self.underage_box.update(top=self.labor_age_min)
self.laborage_box.update(bottom=self.labor_age_min, top=self.labor_age_max)
self.retired_box.update(bottom=self.labor_age_max)
dependency_tuple = self.prepare_dependency_text(self.labor_age_min,
self.labor_age_max,
self.offset_slider.value,
self.radio_active)
self.update_dependency_text(*dependency_tuple)
@lru_cache(maxsize=256)
def prepare_dependency_text(self, labor_age_min, labor_age_max, slider_value, radio_active):
display_data_total = self.age_data_source['m_ch'].data['display'] \
+ (-1 * self.age_data_source['f_ch'].data['display'])
dependency_ratio_minor = (sum(display_data_total[0:labor_age_min])) / sum(display_data_total[labor_age_min:])
dependency_ratio_major = (sum(display_data_total[labor_age_max:])) / sum(display_data_total[:labor_age_max])
dependency_ratio = dependency_ratio_minor + dependency_ratio_major
return dependency_ratio, dependency_ratio_minor, dependency_ratio_major
def update_dependency_text(self, dep, dep_min, dep_maj):
self.dependency_ratio_textfield.update(
text="Abhaengigenquotient: {dependency_ratio:.2f} \n"
"\t- Jugendquotient: {dependency_ratio_minor:.2f} bei Eintrittsalter: {working_age}\n"
"\t- Altersquotient: {dependency_ratio_major:.2f} bei Rentenalter: {retirement_age}".format(
retirement_age=self.labor_age_max,
working_age=self.labor_age_min,
dependency_ratio=dep,
dependency_ratio_minor = dep_min,
dependency_ratio_major = dep_maj))
@lru_cache(maxsize=256)
def prepare_population_text(self, slider_value, radio_active):
return sum(self.age_data_source['m_ch'].data['display']),\
sum(self.age_data_source['f_ch'].data['display']) * -1,\
sum(self.age_data_source['m_au'].data['display']),\
sum(self.age_data_source['f_au'].data['display']) * -1
def update_population_text(self, m_ch_sum, f_ch_sum, m_au_sum, f_au_sum):
self.total_population_textfield.update(text="Gesamtbevoelkerung: {total_pop:,}\n"
"\t- Maennlich: {total_pop_m:,}\n"
"\t\t- Schweizer: {total_pop_m_ch:,}\n"
"\t\t- Auslaender: {total_pop_m_au:,}\n"
"\t- Weiblich: {total_pop_f:,}\n"
"\t\t- Schweizerinnen: {total_pop_f_ch:,}\n"
"\t\t- Auslaenderinnen: {total_pop_f_au:,}".format(
total_pop=m_ch_sum + f_ch_sum,
total_pop_m=m_ch_sum,
total_pop_m_ch=m_ch_sum - m_au_sum,
total_pop_m_au=m_au_sum,
total_pop_f=f_ch_sum,
total_pop_f_ch=f_ch_sum - f_au_sum,
total_pop_f_au=f_au_sum))
def get_new_display_data(self, slider_value, radio_active):
if slider_value < self.historical_years:
return self.age_data['historical']['m_ch'][slider_value],\
self.age_data['historical']['m_au'][slider_value],\
self.age_data['historical']['f_ch'][slider_value],\
self.age_data['historical']['f_au'][slider_value]
elif slider_value >= self.historical_years and self.radio_active == 0:
return self.age_data['low_prediction']['m_ch'][slider_value - self.historical_years],\
self.age_data['low_prediction']['m_au'][slider_value - self.historical_years],\
self.age_data['low_prediction']['f_ch'][slider_value - self.historical_years],\
self.age_data['low_prediction']['f_au'][slider_value - self.historical_years]
elif slider_value >= self.historical_years and self.radio_active == 1:
return self.age_data['reference_prediction']['m_ch'][slider_value - self.historical_years],\
self.age_data['reference_prediction']['m_au'][slider_value - self.historical_years],\
self.age_data['reference_prediction']['f_ch'][slider_value - self.historical_years],\
self.age_data['reference_prediction']['f_au'][slider_value - self.historical_years]
elif slider_value >= self.historical_years and self.radio_active == 2:
return self.age_data['high_prediction']['m_ch'][slider_value - self.historical_years],\
self.age_data['high_prediction']['m_au'][slider_value - self.historical_years],\
self.age_data['high_prediction']['f_ch'][slider_value - self.historical_years],\
self.age_data['high_prediction']['f_au'][slider_value - self.historical_years]
def update_current_year_box(self, slider_value):
for box in (self.births_box, self.migration_box):
box.update(left=self.first_recorded_year + slider_value,
right=self.first_recorded_year + slider_value + 1)
def update_data(self, slider_value):
#self.slider_value = slider_value
self.age_data_source['m_ch'].data['display'], \
self.age_data_source['m_au'].data['display'], \
self.age_data_source['f_ch'].data['display'], \
self.age_data_source['f_au'].data['display'] = self.get_new_display_data(self.offset_slider.value, self.radio_active)
poptext_tuple = self.prepare_population_text(self.offset_slider.value, self.radio_active)
self.update_population_text(*poptext_tuple)
deptext_tuple = self.prepare_dependency_text(self.labor_age_min,
self.labor_age_max,
self.offset_slider.value,
self.radio_active)
self.update_dependency_text(*deptext_tuple)
self.update_current_year_box(self.offset_slider.value)
plot.title.text = "Staendige Wohnbevoelkerung: {}".format(str(self.first_recorded_year + self.offset_slider.value))
bevstat = Bevstat(age_data, additional_stats, 1971)
###############################################################################################
###############################################################################################
#TODO: years als 2er Schritt (Jahr 0 = 1,2, Jahr 1 = 3,4 , damit floats entfernt werden koennen
hovertool_births = HoverTool(tooltips=[("Geburtenueberschuss", "@y"),("Jahr","@x")])
hovertool_migration = HoverTool(tooltips=[("Wanderungssaldo", "@y"),("Jahr","@x")])
plot = figure(plot_height=400, plot_width=600, title="Staendige Wohnbevoelkerung Schweiz: 2010",
tools=["save", "box_select"],
x_range=[-85000, 85000],
y_range=[0, 101])
plot_birth = figure(plot_height=400, plot_width=600, title="Geburtenueberschuss",
tools=[hovertool_births],
x_range=[bevstat.display_stats["ch"][0][0],bevstat.display_stats["ch"][0][-1]],
y_range=[-100000, 100000])
plot_migration = figure(plot_height=400, plot_width=600, title="Wanderungssaldo",
tools=[hovertool_migration],
x_range=[bevstat.display_stats["ch"][0][0],bevstat.display_stats["ch"][0][-1]],
y_range=[-180000, 180000])
#Doesn't work atm, no tooltip being displayed
# for glyph_population in [m_plot, ma_plot, f_plot, fa_plot]:
# hovertool_population.renderers.append(glyph_population)
for stat_type in ("births","deaths"):
for key in bevstat.additional_stats_source:
vbar_color = "blue" if "ch" in key else "red"
plot_birth.vbar(x='years',
top=stat_type,
source=bevstat.additional_stats_source[key],
bottom=0,
width=0.5,
alpha=0.1,
color=vbar_color)
hovertool_births.renderers.append(plot_birth.line(x=np.array(bevstat.display_stats["ch"][0]),
y=bevstat.display_stats["ch"][1]+bevstat.display_stats["ch"][2],
line_width=4,
color="blue",
legend="Geburtenueberschuss (Schweizer)"))
hovertool_births.renderers.append(plot_birth.line(x=np.array(bevstat.display_stats["au"][0]),
y=bevstat.display_stats["au"][1]+bevstat.display_stats["au"][2],
line_width=4,
color="red",
legend="Geburtenueberschuss (Auslaender)"))
for stat_type in ("migration","immigration"):
for key in bevstat.additional_stats_source:
vbar_color = "blue" if "ch" in key else "red"
plot_migration.vbar(x='years',
top=stat_type,
source=bevstat.additional_stats_source[key],
bottom=0,
width=0.5,
alpha=0.1,
color=vbar_color)
hovertool_migration.renderers.append(plot_migration.line(x=np.array(bevstat.display_stats["ch"][0]),
y=bevstat.display_stats["ch"][3]+bevstat.display_stats["ch"][4],
line_width=4,
color="blue",
legend="Migrationssaldo (Schweizer)"))
hovertool_migration.renderers.append(plot_migration.line(x=np.array(bevstat.display_stats["ch"][0]),
y=bevstat.display_stats["au"][3]+bevstat.display_stats["au"][4],
line_width=4,
color="red",
legend="Migrationssaldo (Auslaender)"))
#only scatterplots can be selectec with boxselect
#invisible scatter along x=0
scatter = plot.scatter(x=x_scatter, y=age_groups, size=0)
plot.legend.location = "top_right"
plot.legend.background_fill_color = "white"
plot.legend.background_fill_alpha = 0.8
plot.legend.border_line_color = "black"
plot.legend.border_line_width = 2
plot_birth.legend.location = "bottom_left"
plot_migration.legend.location = "bottom_left"
#dotted line separating male / female
m_f_separator = Span(location=0, dimension='height', line_dash='dashed', line_color='black', line_width=1)
#TODO: position Labels relative to main plot
annotation_female = Label(x=85, y=35, x_units='screen', y_units='screen',
text='Weiblich', render_mode='css',
border_line_color='black', border_line_alpha=0.4,
background_fill_color='white', background_fill_alpha=0.7)
annotation_male = Label(x=495, y=35, x_units='screen', y_units='screen',
text='Maennlich', render_mode='css',
border_line_color='black', border_line_alpha=0.4,
background_fill_color='white', background_fill_alpha=0.7)
plot.yaxis.axis_label = "Alter"
plot.xaxis.formatter = formatters.NumeralTickFormatter(format="(0,0)")
plot_birth.yaxis.axis_label = "Todesfaelle (-) Geburten (+)"
plot_birth.yaxis.formatter = formatters.PrintfTickFormatter(format="%d")
plot_birth.xgrid.minor_grid_line_alpha = 0.5
plot_migration.yaxis.axis_label = "Auswanderung (-) Einwanderung (+)"
plot_migration.yaxis.formatter = formatters.PrintfTickFormatter(format="%d")
plot_migration.xgrid.minor_grid_line_alpha = 0.5
#throttling doesnt' work with bokeh server, massive CPU-spike when
#toying with slider
prediction_radio_group = RadioGroup(
labels=["Szenario: \"Tief\"", "Referenzszenario", "Szenario: \"Hoch\""], active=1)
plot.hbar(right='display', y='y', source=bevstat.age_data_source["m_ch"], height=0.5, line_width=3, line_alpha=0.4, color="blue", legend="Schweizer")
plot.hbar(right='display', y='y', source=bevstat.age_data_source["m_au"], height=0.5, line_width=3, line_alpha=0.4, color="red", legend="Auslaender")
plot.hbar(right='display', y='y', source=bevstat.age_data_source["f_ch"], height=0.5, line_width=3, line_alpha=0.4, color="blue")
plot.hbar(right='display', y='y', source=bevstat.age_data_source["f_au"], height=0.5, line_width=3, line_alpha=0.4, color="red")
offset_changed = lambda attr,old,new: bevstat.update_data(new)
bevstat.offset_slider.on_change('value', offset_changed)
radio_group_changed = lambda attr: bevstat.update_stat_plots(prediction_radio_group.active)
prediction_radio_group.on_click(radio_group_changed)
scatter_changed = lambda attr,old,new: bevstat.update_dependency_box(min(new['1d']['indices']),
max(new['1d']['indices']))
scatter.data_source.on_change('selected', scatter_changed)
bevstat.update_dependency_text(*bevstat.prepare_dependency_text(bevstat.labor_age_min,
bevstat.labor_age_max,
0,
1))
bevstat.update_population_text(*bevstat.prepare_population_text(0, 1))
inputs = widgetbox(bevstat.dependency_ratio_textfield, bevstat.total_population_textfield, prediction_radio_group, width=600)
curdoc().add_root(column(row(plot, column(inputs,bevstat.offset_slider, bevstat.animate_button)),
row(plot_birth, plot_migration)))
curdoc().title = "Wohnbevoelkerung der Schweiz, Aufteilung nach Alter und Geschlecht"
#disable bokeh logo on the plots, provide reference on page
for my_plot in [plot_birth, plot_migration]:
my_plot.toolbar.logo = None
for my_layout in [bevstat.underage_box, bevstat.laborage_box, bevstat.retired_box, m_f_separator, annotation_male, annotation_female]:
plot.add_layout(my_layout)
plot_birth.add_layout(bevstat.births_box)
plot_migration.add_layout(bevstat.migration_box)