def make_choropleth(data_source):
# converts data to right format
geo_source = GeoJSONDataSource(geojson=data_source)
# plots countries with colors
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Grain prices 2015 in USD",
x_axis_location=None,
y_axis_location=None,
width=1200,
height=700,
tools=TOOLS,
)
p.grid.grid_line_color = None
p.title.align = "center"
p.title.text_color = "#084594"
p.title.text_font_size = "25px"
p.background_fill_color = '#f7fbff'
p.background_fill_alpha = 0.5
p.patches(xs='xs',
ys='ys',
fill_color="fill",
line_color='black',
line_width=0.5,
source=geo_source)
palette = Blues8
palette.reverse()
mapper = LinearColorMapper(palette=palette, low=0, high=8 / 6)
ticker = FixedTicker()
ticker.ticks = [x / 6 for x in range(1, 8)]
color_bar = ColorBar(color_mapper=mapper,
ticker=ticker,
label_standoff=12,
border_line_color=None,
location=(0, 0),
orientation="horizontal")
p.add_layout(color_bar, 'below')
# sets properties of hovertool
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [("Country:", "@name"), ("Grain price:", "@price"),
("Population:", "@pop"), ("GDP (USD):", "@bbp")]
output_file("plots/choropleth_grain2015.html", title="World map Bokeh")
show(p)
def timeline_days_hours(interval_frequency, all_co2_dataframe,
sensors_with_anomalies, data, upper_bound,
destination_path):
times = pd.date_range(start='00:00:00',
end='23:55:00',
freq=str(interval_frequency) +
'Min').strftime('%H:%M:%S')
days = all_co2_dataframe['timestamp'].dt.strftime('%Y-%m-%d').to_list()
days = list(dict.fromkeys(days))
timestamp = list(times)
for sensor_name in sensors_with_anomalies:
all_durations = []
for k in data['anomalies']['anomaly_co2_values'][sensor_name][0]:
k = data['anomalies']['anomaly_co2_values'][sensor_name][0].index(
k)
all_durations.append(
int(data['anomalies']['anomaly_co2_values'][sensor_name][0][k]
['duration']))
data['anomalies']['anomaly_co2_values'][sensor_name][0]
p = figure(plot_height=500,
plot_width=2000,
x_range=timestamp,
y_range=days,
title='Timeline of periods with CO2 levels higher than ' +
str(upper_bound) + ' ppm in ' + sensor_name +
'\n Based on data for last ' + str(len(days)) + ' days',
active_drag=None,
toolbar_location=None)
p.x_range.range_padding = 0
p.y_range.range_padding = 0
p.title.text_font_size = '15pt'
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
p.yaxis.major_label_text_font_size = '9pt'
p.xaxis.major_label_text_font_size = '5pt'
# set x axis to invisible
p.xaxis.visible = False
# Add custom axis with tickers labels only every 1 hour
labels = np.arange(0, 288, 12).tolist()
ticker = FixedTicker()
ticker.ticks = labels
xaxis = LinearAxis(ticker=ticker)
xaxis.major_label_orientation = math.pi / 3
p.add_layout(xaxis, 'below')
xaxis.major_label_overrides = {
0: '00:00',
12: '01:00',
24: '02:00',
36: '3:00',
48: '04:00',
60: '05:00',
72: '06:00',
84: '07:00',
96: '08:00',
108: '09:00',
120: '10:00',
132: '11:00',
144: '12:00',
156: '13:00',
168: '14:00',
180: '15:00',
192: '16:00',
204: '17:00',
216: '18:00',
228: '19:00',
240: '20:00',
252: '21:00',
264: '22:00',
276: '23:00'
}
#add anomalies recorded for each day in considered period
for i in data['anomalies']['anomaly_co2_values'][sensor_name][0]:
i = data['anomalies']['anomaly_co2_values'][sensor_name][0].index(
i)
x = []
y = []
x.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][0][0][11:])
x.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][-1][0][11:])
y.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][0][0][0:10])
y.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][-1][0][0:10])
if data['anomalies']['anomaly_co2_values'][sensor_name][0][i][
'anomalies_details'][0][0][0:10] == data['anomalies'][
'anomaly_co2_values'][sensor_name][0][i][
'anomalies_details'][-1][0][0:10]:
# print('yes')
p.line(x,
y,
line_width=2,
color='blue',
legend_label='CO2 above critical value')
p.circle(x, y, fill_color="blue", line_color='blue', size=5)
else:
x1 = x.copy()
y1 = y.copy()
x2 = x.copy()
y2 = y.copy()
x1[-1] = times[-1]
y1[-1] = y1[0]
p.line(x1, y1, line_width=2, color='blue')
x2[0] = times[0]
y2[0] = y2[-1]
p.line(x2, y2, line_width=2, color='blue')
x3 = [x1[0], x2[-1]]
y3 = [y1[0], y2[-1]]
p.circle(x3, y3, fill_color="blue", line_color='blue', size=5)
#save graph in output location
output_file(destination_path + '/anomalies_timeline_' + sensor_name +
'.html')
save(p)
return p
