def update_plot(attrname, old, new):
form = form_select.value[-1:]
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(techs) + str(form))
tbar = Bar(
cap_source,
values=blend(*cols, name='medals', labels_name='medal'),
label='n_list',
stack='medal',
legend=None,
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
title="Energy Capacity per Node for Each Form of Energy",
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Storage Capacity(kW)")
hover = HoverTool(tooltips=[
("Node", "@n_list"),
("Value", "@height"),
])
tbar.add_tools(hover)
disp_row_1.children[0] = tbar
fin_column.children[1] = self.create_legend()
def update_plotw(attrname, old, new):
node = node_selectw.value[-1:]
form = form_selectw.value[-1:]
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(node) + str(form) + str(techs))
print('n' + str(node) + str(form) + str(techs))
pbarw = Bar(
prod_dataw,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Energy Production per Node for Each Form of Energy",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Weeks",
ylabel="Energy Production(kW)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
pbarw.add_tools(hover)
disp_row_2.children[0] = pbarw
fin_column.children[1] = self.create_legend()
def generate_users_bar(current_user_source):
users_bar = Bar(current_user_source,values="editions",xlabel = None,ylabel = None,label="users",stack="edited",legend = None,title = "Editions for users",name ="Editions for users",tools = tools, width = 1000,height = 400)
hover = users_bar.select(dict(type=HoverTool))
hover.tooltips = [
('User','$x'),
('Page','@edited'),
('Editions','@height')]
users_bar.background_fill_color = "#2f2f2f"
users_bar.border_fill_color = "#2f2f2f"
return users_bar
def test_bar_chart_below_visibility():
from bkcharts import Bar
# Visible because we have multiple bars
df = dict(types=['foo', 'bar'], counts=[3, 2])
p = Bar(df, values='counts')
p.below[0].visible
# Visible because we excplicitly specify labels
df = dict(types=['foo'], counts=[3])
p = Bar(df, values='counts', label='types')
assert p.below[0].visible
# Not visible because only one item and no labels
df = dict(types=['foo'], counts=[3])
p = Bar(df, values='counts')
assert not p.below[0].visible
def create_bar(self):
#Here, we interact with bokeh to create the bar chart. We use the dataframe we created above, set birth to the values on the y-axis and stack the data by sex.
#The x axis label is represented by the areas. We define hover over the tooltips and define Gender and Births, with the corresponding value of the y axis.
#Last but not least, we colour the data with always coloring female red and male blue.
bar = Bar(
self._dataframe, values='Births', label='Area', stack="Sex", agg="mean",
title="Births in Zurich, 2015 per Area", legend='top_right', height=450, width=1050,
tooltips=[('Gender:', '@Sex'), ('Births:', '@height')], palette=["#cc3737", "#0053d8"]
)
#we return the bar to plot in on the Dashboard class
return bar
def update_plot_st(attrname, old, new):
form = form_select_st.value[-1:]
st_bar = Bar(storage_dict,
values='f' + str(form),
label='n_list',
legend=None,
title="Storage Capacity per Node",
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Storage Capacity(kW)")
hover = HoverTool(tooltips=[
("Node", "@n_list"),
("Value", "@height"),
])
st_bar.add_tools(hover)
disp_row_2.children[0] = st_bar
fin_column.children[1] = self.create_legend()
def goChart(label, stack_or_group, values, ylabel=None, color=None):
convertPDFDate(workingPDF, keyFields[0])
if ylabel is None:
ylabel = values
label = label if isinstance(label, (list, tuple)) else [label]
if stacked:
charts.append(
Bar(workingPDF,
label=CatAttr(columns=label, sort=False),
stack=stack_or_group,
color=color,
values=values,
legend=self.showLegend(),
ylabel=ylabel))
else:
charts.append(
Bar(workingPDF,
label=CatAttr(columns=label, sort=False),
group=stack_or_group,
color=color,
values=values,
legend=self.showLegend(),
ylabel=ylabel))
def createFigure1(self):
#Plot 1: Births in 2015 per gender
births_2015 = pd.DataFrame(self.data.loc[self.data['Jahr'] == 2015],
copy=True)
# Removing the unnecessary columns
births_2015 = births_2015.drop("SexCd", axis=1)
births_2015 = births_2015.drop("StatZoneSort", axis=1)
births_2015 = births_2015.drop("QuarSort", axis=1)
#X-Axis Values: The names of the different parts of the city of Zurich
quartier_names = []
for name in births_2015['Quartier']:
if name not in quartier_names:
quartier_names.append(name)
quartier_names.sort()
#Y-Axis Values: Nr of Births (male & female) per city part in 2015
births = pd.DataFrame(births_2015, copy=True)
births = births.drop("Jahr", axis=1)
births = births.groupby(['Sex',
'Quartier'])['Births'].sum().reset_index()
colors = ["#e84d60", "#718dbf"]
#Creating the vertical stacked bar chart
self.plot1 = Bar(
births,
label='Quartier',
stack='Sex',
values='Births',
title=
'Aggregated Number of Births per City Part (Quartier) in 2015',
color=colors,
)
# Designing the plot (legend, axis alignment & spacing)
self.plot1.width = 1800
self.plot1.height = 450
self.plot1.legend.location = "top_right"
self.plot1.legend.orientation = "horizontal"
#X-Axis design
self.plot1.xaxis.major_label_orientation = 0.3
self.plot1.x_range.range_padding = 0.05
self.plot1.xgrid.grid_line_color = None
self.plot1.xaxis.axis_label = 'City Parts in Zurich'
#Y-Axis design
self.plot1.y_range.start = 0
self.plot1.yaxis[0].ticker.max_interval = 50
self.plot1.yaxis[0].ticker.num_minor_ticks = 0
self.plot1.yaxis.axis_label = 'Number of Births'
#Creating the hover tooltip
hover = HoverTool(
tooltips=[("Quartier",
"@Quartier"), ("Sex", "@Sex"), ("Births", "@height")])
self.plot1.tools.append(hover)
# transform ColumnDataFrame to ColumnDataSource
tax_increase = ColumnDataSource(
pd.DataFrame(df_dropped["Tax Units with Tax Increase"]))
tax_cut = ColumnDataSource(pd.DataFrame(df_dropped["Tax Units with Tax Cut"]))
cds_df_dropped = ColumnDataSource(df_dropped)
# the plots with the bars
p = Bar(
df_dropped,
label=cat(columns='Intervals', sort=False),
values=blend('Tax Units with Tax Increase',
'Tax Units with Tax Cut',
name='values',
labels_name='vars'),
tooltips=[('Value', '@values')],
ylabel="Tax units with tax cut Tax units with tax increase",
stack=cat(columns='values', sort=False),
color=color(columns='vars', palette=['silver', 'orange'], sort=True),
bar_width=0.4,
tools="pan,box_zoom, reset, save",
plot_width=600,
plot_height=450,
toolbar_sticky=False,
legend=False)
p.xaxis.axis_label = 'Income [$ thousands]'
p.toolbar.logo = None
values = blend('Tax Units with Tax Increase',
'Tax Units with Tax Cut',
name='values',
labels_name='vars')
def carbon_emissions(self):
c_em_tech = {}
c_em_tech['time_step'] = np.linspace(1, self.time_steps,
self.time_steps)
for techs in range(1, self.n_techs + 1):
c_em_tech['t' + str(techs)] = np.zeros((self.time_steps))
for time_step in range(1, self.time_steps):
c_em_tech['t' + str(techs)][time_step] = value(
model.carbonFactors[techs] *
sum(model.P[i, time_step, techs] for i in model.hub_i))
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('t' + str(techs))
bar = Bar(c_em_tech,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Carbon Emissions per form of Technology",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Hours",
ylabel="Carbon Emissions(kg)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
bar.add_tools(hover)
tech_legend1 = self.create_legend()
c_em_techw = {}
if (self.time_weeks == 0):
for k, v in c_em_tech.items():
c_em_techw[k] = np.zeros((1))
c_em_techw['time_step'] = np.zeros((1))
else:
for k, v in c_em_tech.items():
v = v[:self.time_weeks * self.week_h]
c_em_techw[k] = np.sum(v.reshape(-1, self.week_h), axis=1)
c_em_techw['time_step'] = np.linspace(1, self.time_weeks,
self.time_weeks)
bar_w = Bar(c_em_techw,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Carbon Emissions per form of Technology per Week",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Weeks",
ylabel="Carbon Emissions(kg)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
c_em_nodes = {}
c_em_nodes['time_step'] = np.linspace(1, self.time_steps,
self.time_steps)
for hub_step in range(1, self.n_hubs + 1):
c_em_nodes['n' + str(hub_step)] = np.zeros((self.time_steps))
for time_step in range(1, self.time_steps):
c_em_nodes['n' + str(hub_step)][time_step] = value(
sum(model.carbonFactors[inp] *
model.P[hub_step, time_step, inp] for inp in model.In))
col_nodes = []
for hubs in range(1, self.n_hubs + 1):
col_nodes.append('n' + str(hubs))
if (self.n_hubs == 1):
hub_colour = ["red"]
elif (self.n_hubs == 2):
hub_colour = ["red", "blue"]
else:
hub_colour = brewer['Set1'][self.n_hubs]
n_bar = Bar(c_em_nodes,
values=blend(*col_nodes,
name='medals',
labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Carbon Emissions per Node",
color=color(columns='medal', palette=hub_colour,
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Carbon Emissions(kg)")
n_hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
n_bar.add_tools(n_hover)
y = [0] * len(self.nodes)
x = self.nodes
#pal = ['SaddleBrown', 'Silver', 'Goldenrod']
node_legend = figure(width=1000,
height=50,
toolbar_location=None,
active_drag=None,
x_range=self.nodes)
node_legend.rect(x, y, color=hub_colour, width=1, height=10)
node_legend.yaxis.major_label_text_color = None
node_legend.yaxis.major_tick_line_color = None
node_legend.yaxis.minor_tick_line_color = None
return column(bar, tech_legend1, bar_w, n_bar, node_legend)
def costs(self):
op_cost = model.OpCost.get_values()[None]
opcost_dict = {}
opcost_dict.update(
(x, y * op_cost) for x, y in model.P.get_values().items())
opcost_data = {}
for techs in range(1, self.n_techs + 1):
opcost_data['t' + str(techs)] = np.zeros((self.time_steps))
for time_step in range(1, self.time_steps):
for hub_step in range(1, self.n_hubs + 1):
opcost_data['t' + str(techs)][time_step] += opcost_dict[(
hub_step, time_step, techs)]
opcost_data['time_step'] = np.linspace(1, self.time_steps,
self.time_steps)
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('t' + str(techs))
opcost_bar = Bar(opcost_data,
values=blend(*cols,
name='medals',
labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Operational Cost per form of Technology",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Hours",
ylabel="CHF")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
opcost_bar.add_tools(hover)
tech_legend1 = self.create_legend()
opcost_dataw = {}
if (self.time_weeks == 0):
for k, v in opcost_data.items():
opcost_dataw[k] = np.zeros((1))
opcost_dataw['time_step'] = np.zeros((1))
else:
for k, v in opcost_data.items():
v = v[:self.time_weeks * self.week_h]
print(v[:self.time_weeks * self.week_h])
opcost_dataw[k] = np.sum(v.reshape(-1, self.week_h), axis=1)
opcost_dataw['time_step'] = np.linspace(1, self.time_weeks,
self.time_weeks)
opcostbar_w = Bar(
opcost_dataw,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Operational Cost per form of Technology per Week",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Hours",
ylabel="CHF")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
mtc_cost = data.VarMaintCost()
P = model.P.get_values()
for hub_step in range(1, self.n_hubs + 1):
for techs in range(1, self.n_techs + 1):
for time_step in range(1, self.time_steps):
P[(hub_step, time_step, techs)] *= mtc_cost[(hub_step,
techs)]
mtccost_data = {}
for techs in range(1, self.n_techs + 1):
mtccost_data['t' + str(techs)] = np.zeros((self.time_steps))
for time_step in range(1, self.time_steps):
for hub_step in range(1, self.n_hubs + 1):
mtccost_data['t' + str(techs)][time_step] += P[(hub_step,
time_step,
techs)]
mtccost_data['time_step'] = np.linspace(1, self.time_steps,
self.time_steps)
mtccost_bar = Bar(mtccost_data,
values=blend(*cols,
name='medals',
labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Maintenance Cost per form of Technology",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Hours",
ylabel="CHF")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
mtccost_bar.add_tools(hover)
opc = model.OpCost.get_values()[None]
mtc = model.MaintCost.get_values()[None]
inc = model.InvCost.get_values()[None]
income = model.IncomeExp.get_values()[None]
sum_pi = opc + mtc + inc + income
values = [opc, mtc, inc, income]
percents = [
0, opc / sum_pi, (opc + mtc) / sum_pi, (opc + mtc + inc) / sum_pi,
(opc + mtc + inc + income) / sum_pi
]
starts = [p * 2 * np.pi for p in percents[:-1]]
ends = [p * 2 * np.pi for p in percents[1:]]
colors = brewer["Set1"][4]
cost_labels = [
"Operational Cost", "Maintenance Cost", "Investment Cost", "Income"
]
pie_chart_source = ColumnDataSource(data=dict(x=[0 for x in percents],
y=[0 for x in percents],
percents=percents,
starts=starts,
colors=colors,
ends=ends,
values=values,
cost_labels=cost_labels))
pie_chart = figure(title="Cost vs Income Breakdown",
plot_width=500,
plot_height=300,
toolbar_location="left")
r = pie_chart.wedge(source=pie_chart_source,
x='x',
y='y',
alpha=0.8,
radius=0.8,
start_angle='starts',
end_angle='ends',
color='colors')
legend = Legend(
items=[LegendItem(label=dict(field="cost_labels"), renderers=[r])],
location=(0, -50))
pie_chart.add_layout(legend, 'right')
hover = HoverTool(tooltips=[
("Type", "@cost_labels"),
("Value", "@values"),
])
pie_chart.add_tools(hover)
pie_chart.xaxis.visible = False
pie_chart.xgrid.visible = False
pie_chart.yaxis.visible = False
pie_chart.ygrid.visible = False
op_column = column(opcost_bar, tech_legend1, opcostbar_w, mtccost_bar,
pie_chart)
return op_column
def capacities(self):
cap_source = {}
for techs in range(1, self.n_techs + 1):
for forms in range(1, self.n_forms + 1):
cap_source['n' + str(techs) + str(forms)] = np.zeros(
(self.n_hubs))
for hub_step in range(1, self.n_hubs + 1):
cap_source['n' + str(techs) +
str(forms)][hub_step -
1] = self.cap_dict[(hub_step, techs,
forms)]
#cap_source['n' + str(techs) + str(forms)] = np.array(cap_source['n' + str(techs) + str(forms)])
# for forms in range(1,self.n_forms+1):
# cap_source['n' + str(self.n_techs+2) + str(forms)] = np.zeros((self.n_hubs))
# for hub_step in range(1,self.n_hubs+1):
# for techs in range(1,self.n_techs+1):
# cap_source['n' + str(self.n_techs+2) + str(forms)][hub_step-1] += cap_source['n' + str(techs) + str(forms)][hub_step-1]
#
# cap_source['n_list'] = []
# for hub_step in range(1,self.n_hubs+1):
# cap_source['n_list'].append( "Node " + str(hub_step))
cap_source['n_list'] = self.nodes
#cap_source['n21'] = np.array([34.8,78,54,12,90])
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(techs) + str(1))
v_bar = Bar(cap_source,
values=blend(*cols, name="medal_v", labels_name="medals"),
label='n_list',
stack='medals',
legend=None,
color=color(columns='medals',
palette=brewer['Set1'][self.n_techs],
sort=True),
title="Energy Capacity per Node for Each Technology",
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Energy Capacity(kW)")
hover = HoverTool(tooltips=[
("Node", "@n_list"),
("Value", "@height"),
])
v_bar.add_tools(hover)
def update_plot(attrname, old, new):
form = form_select.value[-1:]
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(techs) + str(form))
tbar = Bar(
cap_source,
values=blend(*cols, name='medals', labels_name='medal'),
label='n_list',
stack='medal',
legend=None,
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
title="Energy Capacity per Node for Each Form of Energy",
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Storage Capacity(kW)")
hover = HoverTool(tooltips=[
("Node", "@n_list"),
("Value", "@height"),
])
tbar.add_tools(hover)
disp_row_1.children[0] = tbar
fin_column.children[1] = self.create_legend()
form_select = Select(value="Form1",
title='Forms',
options=self.e_forms)
form_select.on_change('value', update_plot)
tech_legend1 = self.create_legend()
storage = model.StorageCap.get_values()
print(storage)
storage_dict = {}
storage_dict['n_list'] = self.nodes
for forms in range(1, self.n_forms + 1):
storage_dict['f' + str(forms)] = np.zeros((self.n_hubs))
for hub_step in range(1, self.n_hubs + 1):
storage_dict['f' + str(forms)][hub_step -
1] = storage[(hub_step, forms)]
# storage_dict['f1'] = [23,67,98,41,11]
storage_bar = Bar(storage_dict,
values='f1',
label='n_list',
legend=None,
title="Storage Capacity per Node",
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Storage Capacity(kW)")
hover = HoverTool(tooltips=[
("Node", "@n_list"),
("Value", "@height"),
])
def update_plot_st(attrname, old, new):
form = form_select_st.value[-1:]
st_bar = Bar(storage_dict,
values='f' + str(form),
label='n_list',
legend=None,
title="Storage Capacity per Node",
plot_width=1000,
plot_height=300,
xlabel="Nodes",
ylabel="Storage Capacity(kW)")
hover = HoverTool(tooltips=[
("Node", "@n_list"),
("Value", "@height"),
])
st_bar.add_tools(hover)
disp_row_2.children[0] = st_bar
fin_column.children[1] = self.create_legend()
form_select_st = Select(value="Form1",
title='Forms',
options=self.e_forms)
form_select_st.on_change('value', update_plot_st)
gis_dict = {}
gis_dict['x'] = 14 * np.random.rand(self.n_hubs, )
gis_dict['y'] = 6 * np.random.rand(self.n_hubs, )
if (self.n_hubs == 1):
x_range = (0, gis_dict['x'].max())
y_range = (0, gis_dict['y'].max())
else:
x_range = (gis_dict['x'].min(), gis_dict['x'].max())
y_range = (gis_dict['y'].min(), gis_dict['y'].max())
gis_plot = figure(title="Node Locations with Capacities",
plot_width=600,
plot_height=600,
x_range=x_range,
y_range=y_range)
gis_plot.xaxis.axis_label = "X Coordinates"
gis_plot.yaxis.axis_label = "Y Coordinates"
for k, v in cap_source.items():
gis_dict[k] = v
if (k != 'n_list' and v.max(axis=0) != 0):
gis_dict[k + 'n'] = v / v.max(axis=0) * abs(max(y_range) * 3)
else:
gis_dict[k + 'n'] = gis_dict[k]
gis_dict['temp'] = gis_dict['n11']
gis_dict['tempn'] = gis_dict['n11n']
gis_source = ColumnDataSource(data=gis_dict)
gis_plot.circle(
x='x',
y='y',
radius='tempn',
source=gis_source,
radius_dimension='y',
radius_units='screen',
)
gis_hover = HoverTool(tooltips=[("X", "@x"), ("Y",
"@y"), ("Value",
"@temp")])
gis_plot.add_tools(gis_hover)
def update_plot_gis(attrname, old, new):
form = form_select_gis.value[-1:]
tech = tech_select_gis.value[-1:]
gis_source.data['temp'] = gis_source.data['n' + str(tech) +
str(form)]
gis_source.data['tempn'] = gis_source.data['n' + str(tech) +
str(form) + 'n']
form_select_gis = Select(value="Form1",
title='Forms',
options=self.e_forms)
tech_select_gis = Select(value="Tech1",
title='Technologies',
options=self.e_techs)
form_select_gis.on_change('value', update_plot_gis)
tech_select_gis.on_change('value', update_plot_gis)
controls = column(form_select)
controls_2 = column(form_select_st)
controls_3 = column(form_select_gis, tech_select_gis)
disp_row_1 = row(v_bar, controls)
disp_row_2 = row(storage_bar, controls_2)
disp_row_3 = row(gis_plot, controls_3)
fin_column = column(disp_row_1, tech_legend1, disp_row_2, disp_row_3)
return fin_column
def production(self):
prodmat = {}
for hub_step in range(1, self.n_hubs + 1):
for time_step in range(1, self.time_steps + 1):
for forms in range(1, self.n_forms + 1):
for techs in range(1, self.n_techs + 1):
prodmat[(hub_step, time_step, forms,
techs)] = self.demand[(hub_step, time_step,
forms)] * self.cmatrix[
(techs, forms)]
prod_data = {}
for hub_step in range(1, self.n_hubs + 1):
for forms in range(1, self.n_forms + 1):
for techs in range(1, self.n_techs + 1):
prod_data['n' + str(hub_step) + str(forms) +
str(techs)] = np.zeros((self.time_steps + 1))
for time_step in range(1, self.time_steps + 1):
prod_data['n' + str(hub_step) + str(forms) +
str(techs)][time_step] = prodmat[(hub_step,
time_step,
forms,
techs)]
prod_data['time_step'] = np.linspace(1, self.time_steps + 1,
self.time_steps + 1)
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(11) + str(techs))
bar = Bar(prod_data,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Energy Production per Node for Each Form of Energy",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Hours",
ylabel="Energy Production(kW)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
bar.add_tools(hover)
tech_legend1 = self.create_legend()
prod_dataw = {}
if (self.time_weeks == 0):
for k, v in prod_data.items():
prod_dataw[k] = np.zeros((1))
prod_dataw['time_step'] = np.zeros((1))
else:
for k, v in prod_data.items():
v = v[:self.time_weeks * self.week_h]
prod_dataw[k] = np.sum(v.reshape(-1, self.week_h), axis=1)
prod_dataw['time_step'] = np.linspace(1, self.time_weeks,
self.time_weeks)
bar_w = Bar(
prod_dataw,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Energy Production per Node for Each Form of Energy per Week",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Weeks",
ylabel="Energy Production(kW)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
bar_w.add_tools(hover)
def update_plot(attrname, old, new):
node = node_select.value[-1:]
form = form_select.value[-1:]
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(node) + str(form) + str(techs))
pbar = Bar(
prod_data,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Energy Production per Node for Each Form of Energy",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Hours",
ylabel="Energy Production(kW)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
pbar.add_tools(hover)
disp_row.children[0] = pbar
def update_plotw(attrname, old, new):
node = node_selectw.value[-1:]
form = form_selectw.value[-1:]
cols = []
for techs in range(1, self.n_techs + 1):
cols.append('n' + str(node) + str(form) + str(techs))
print('n' + str(node) + str(form) + str(techs))
pbarw = Bar(
prod_dataw,
values=blend(*cols, name='medals', labels_name='medal'),
label='time_step',
stack='medal',
legend=None,
title="Energy Production per Node for Each Form of Energy",
color=color(columns='medal',
palette=brewer['Set1'][self.n_techs],
sort=True),
plot_width=1000,
plot_height=300,
xlabel="Weeks",
ylabel="Energy Production(kW)")
hover = HoverTool(tooltips=[
("Hour", "@time_step"),
("Value", "@height"),
])
pbarw.add_tools(hover)
disp_row_2.children[0] = pbarw
fin_column.children[1] = self.create_legend()
node_select = Select(value="Node1", title='Nodes', options=self.nodes)
form_select = Select(value="Form1",
title='Forms',
options=self.e_forms)
node_select.on_change('value', update_plot)
form_select.on_change('value', update_plot)
controls = column(node_select, form_select)
disp_row = row(bar, controls)
node_selectw = Select(value="Node1", title='Nodes', options=self.nodes)
form_selectw = Select(value="Form1",
title='Forms',
options=self.e_forms)
node_selectw.on_change('value', update_plotw)
form_selectw.on_change('value', update_plotw)
controlsw = column(node_selectw, form_selectw)
disp_row_2 = row(bar_w, controlsw)
fin_column = column(disp_row, tech_legend1, disp_row_2)
return fin_column
def createBokehChart(self):
keyFields = self.getKeyFields()
valueFields = self.getValueFields()
clusterby = self.options.get("clusterby")
stacked = self.options.get("charttype", "grouped") == "stacked"
subplots = self.isSubplot()
workingPDF = self.getWorkingPandasDataFrame().copy()
def convertPDFDate(df, col):
#Bokeh doesn't support datetime as index in Bar chart. Convert to String
if len(keyFields) == 1:
dtype = df[col].dtype.type if col in df else None
if numpy.issubdtype(dtype, numpy.datetime64):
dateFormat = self.options.get("dateFormat", None)
try:
df[col] = df[col].apply(
lambda x: str(x).replace(':', '-')
if dateFormat is None else x.strftime(dateFormat))
except:
self.exception("Error converting dateFormat {}".format(
dateFormat))
df[col] = df[col].apply(
lambda x: str(x).replace(':', '-'))
for index, row in workingPDF.iterrows():
for k in keyFields:
if isinstance(row[k],
str if sys.version >= '3' else basestring):
row[k] = row[k].replace(':', '.')
workingPDF.loc[index] = row
charts = []
def goChart(label, stack_or_group, values, ylabel=None, color=None):
convertPDFDate(workingPDF, keyFields[0])
if ylabel is None:
ylabel = values
label = label if isinstance(label, (list, tuple)) else [label]
if stacked:
charts.append(
Bar(workingPDF,
label=CatAttr(columns=label, sort=False),
stack=stack_or_group,
color=color,
values=values,
legend=self.showLegend(),
ylabel=ylabel))
else:
charts.append(
Bar(workingPDF,
label=CatAttr(columns=label, sort=False),
group=stack_or_group,
color=color,
values=values,
legend=self.showLegend(),
ylabel=ylabel))
if clusterby is not None and (subplots or len(valueFields) <= 1):
subplots = subplots if len(valueFields) == 1 or subplots else False
if subplots:
for j, valueField in enumerate(valueFields):
pivot = workingPDF.pivot(index=keyFields[0],
columns=clusterby,
values=valueField)
for i, col in enumerate(pivot.columns[:10]): #max 10
data = pd.DataFrame({
'values': pivot[col].values,
'names': pivot.index.values
})
convertPDFDate(data, 'names')
if subplots:
charts.append(
Bar(data,
label=CatAttr(columns=['names'],
sort=False),
color=Colors.hexRGB(1. * i / 2),
values='values',
ylabel=valueField,
legend=False,
title="{0} = {1}".format(
clusterby, pivot.columns[i])))
else:
goChart(keyFields[0], clusterby, valueFields[0])
else:
if subplots:
for i, valueField in enumerate(valueFields):
goChart(keyFields[0],
None,
valueField,
color=Colors.hexRGB(1. * i / 2))
else:
if len(valueFields) > 1:
series = '_'.join(valueFields)
values = blend(*valueFields,
name=series.replace('_', ','),
labels_name=series)
else:
series = False
values = valueFields[0]
goChart(keyFields,
series,
values,
ylabel=','.join(valueFields))
if clusterby is not None:
self.addMessage(
"Warning: 'Cluster By' ignored when grouped option with multiple Value Fields is selected"
)
return charts
from bkcharts import Bar, output_file, show
from bkcharts.attributes import cat, color
from bkcharts.operations import blend
from bkcharts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data
# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)
# filter by countries with at least one medal and sort by total medals
df = df[df['total'] > 0]
df = df.sort_values(by="total", ascending=False)
bar = Bar(df,
values=blend('bronze', 'silver', 'gold', name='medals', labels_name='medal'),
label=cat(columns='abbr', sort=False),
stack=cat(columns='medal', sort=False),
color=color(columns='medal', palette=['SaddleBrown', 'Silver', 'Goldenrod'],
sort=False),
legend='top_right',
title="Medals per Country, Sorted by Total Medals",
tooltips=[('medal', '@medal'), ('country', '@abbr')])
output_file("stacked_bar.html", title="stacked_bar.py example")
show(bar)
y=600,
text='year',
text_font_size='20pt',
text_color='#0569CE',
text_alpha=1)
#add the Bar graph
p = Bar(
renderer_source.data,
label=cat(columns='Intervals', sort=False),
values=blend('Tax Units with Tax Increase',
'Tax Units with Tax Cut',
name='values',
labels_name='vars'),
tooltips=[('Value', '@values')],
ylabel="Tax units with tax cut Tax units with tax increase",
stack=cat(columns='values', sort=False),
color=color(columns='vars', palette=['silver', 'orange'], sort=True),
bar_width=0.4,
tools="pan,box_zoom, reset, save",
plot_width=600,
plot_height=500,
logo=None,
toolbar_sticky=False,
legend=False)
p.xaxis.axis_label = 'Income [$ thousands]'
#add the text as a glyph
p.add_glyph(text_source, text)
# Create the "Average tax" figure
s1 = figure(plot_width=600,
plot_height=200,
from bkcharts import Bar, output_file, show, defaults
from bokeh.layouts import gridplot
from bokeh.sampledata.autompg import autompg as df
df['neg_mpg'] = 0 - df['mpg']
defaults.plot_width = 400
defaults.plot_height = 400
bar_plot = Bar(df, label='cyl', title="label='cyl'")
bar_plot.title.text_font_size = '10pt'
bar_plot2 = Bar(df,
label='cyl',
bar_width=0.4,
title="label='cyl' bar_width=0.4")
bar_plot2.title.text_font_size = '10pt'
bar_plot3 = Bar(df,
label='cyl',
values='mpg',
agg='mean',
title="label='cyl' values='mpg' agg='mean'")
bar_plot3.title.text_font_size = '10pt'
bar_plot4 = Bar(df,
label='cyl',
title="label='cyl' color='DimGray'",
color='dimgray')
bar_plot4.title.text_font_size = '10pt'