def CreateGauge(percentage, scaleInfo):
gaugeFig = Plot(x_range=Range1d(start=-1.25, end=1.25), y_range=Range1d(start=-1.25, end=1.25), plot_width=250, plot_height=250)
gaugeFig.title.text = "Scale " + str(scaleInfo.Num) + ": " + scaleInfo.Name
gaugeFig.title.align = 'center'
gaugeFig.toolbar_location = None
gaugeFig.border_fill_color = "#101010"
gaugeFig.background_fill_color = "#101010"
gaugeFig.title.text_color = "white"
gaugeFig.outline_line_color = None
glyph = AnnularWedge(x=0, y=0, inner_radius=.7, outer_radius=1, start_angle=math.pi / 2 - (2 * math.pi),
end_angle=math.pi / 2, fill_color="#444444", name="back")
glyph2 = AnnularWedge(x=0, y=0, inner_radius=.7, outer_radius=1, start_angle=math.pi / 2 - (2 * math.pi * percentage),
end_angle=math.pi / 2, fill_color=gaugeColors[(scaleInfo.Num - 1) % len(gaugeColors)], name="front")
PercentageText = Label(text_align='center', text=str(round((percentage * scaleInfo.MaxCapacity), 1)),text_color = 'white',
text_font_size="35px")
lowerText = Label(text_align='center', y=-0.25, text=scaleInfo.Units, text_color='white')
lowerText2 = Label(text_align='center', y=-0.48, text="Of " + str(scaleInfo.MaxCapacity), text_color='white')
gaugeFig.add_glyph(glyph)
gaugeFig.add_glyph(glyph2)
gaugeFig.add_layout(PercentageText)
gaugeFig.add_layout(lowerText)
gaugeFig.add_layout(lowerText2)
return gaugeFig
def drawgraph(self, ds, filterMin, filterMax, layout):
G = nx.Graph() # create an empty graph with no nodes and no edges
# nodes = []
# for i in range(len(ds.getNodes())):
# nodes.append([])
# # print(ds.getNodes()[i].getName())
# for j in range(len(ds.getNodes())):
# nodes[i].append(ds.getNodes()[i].getLinks()[j][1])
# # print(" " + str(ds.getNodes()[i].getLinks()[j][1]))
# ds.toMinSpanTree()
nodes = ds.getDoubleList(filterMin, filterMax, False)
x = filterMin
adj = np.array(nodes)
G = nx.from_numpy_matrix(adj)
for i in range(len(G.node)):
G.node[i]['name'] = ds.getNames()[i]
#pos = nx.drawing.layout.circular_layout(G, 1, None, 2)
#nx.draw_networkx(G, pos, with_labels=True)
# pt.show()
#plt.show()
plot = Plot(plot_width=500, plot_height=500,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
node_hover_tool = HoverTool(tooltips=[("Name of this node", "@name")])
# plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool())
plot.add_tools(node_hover_tool, TapTool(), BoxSelectTool(), BoxZoomTool(), UndoTool(), RedoTool(), SaveTool(),
ResetTool())
plot.toolbar_location = 'left'
if layout == 0:
graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))
elif layout == 1:
graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))
else:
graph_renderer = from_networkx(G, nx.random_layout)
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
#graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
output_file("interactive_graphs.html")
return plot
def plotGauge(speedvalue, offset = 0,
name = '', unit = '', color = '', maxValue = 0,
major_step = 2, minor_step = .5):
'''
draw a gauge for show online data
:param speedvalue: data value for a especific channel
:param offset: offset is the minimum value of the channel
:param name: name of the channel
:param unit: units of the data value
:param color: color of the gauge
:param maxValue: max value of the chaneel
:param major_step: step for points inside the gauge
:param minor_step: step for points inside the gauge
:return: figure plot in bokeh engine
'''
maxValue = maxValue - offset
xdr = Range1d(start=-1.25, end=1.25)
ydr = Range1d(start=-1.25, end=1.25)
renderer = 'webgl'
plt = Plot(x_range=xdr, y_range=ydr, plot_width=300, plot_height=300, output_backend=renderer,)
plt.toolbar_location = None
plt.outline_line_color = None
plt.add_glyph(Circle(x=0, y=0, radius=1.00, fill_color="white", line_color="black"))
plt.add_glyph(Circle(x=0, y=0, radius=0.05, fill_color="gray", line_color="black"))
plt.add_glyph(Text(x=0, y=+0.15, text=[unit], text_color=color, text_align="center", text_baseline="bottom",
text_font_style="bold"))
plt.add_glyph(Text(x=0, y=-0.15, text=[name], text_color="black", text_align="center", text_baseline="top",
text_font_style="bold"))
add_gauge(plt, 0.75, maxValue, 0.05, +1, color, major_step, minor_step, offset = offset)
valueGliph = Text(x=0, y=-0.6, text=["0"], text_color=color, text_align="center", text_baseline="top")
plt.add_glyph(valueGliph)
a, b = add_needle(plt, speedvalue, offset = offset, max_value = maxValue)
return plt, a, b, valueGliph
from bokeh.document import Document
from bokeh.embed import file_html
from bokeh.models import (AnnularWedge, ColumnDataSource, ImageURL, Plot,
Range1d, Text, Wedge)
from bokeh.resources import INLINE
from bokeh.sampledata.browsers import browsers_nov_2013, icons
from bokeh.util.browser import view
df = browsers_nov_2013
xdr = Range1d(start=-2, end=2)
ydr = Range1d(start=-2, end=2)
plot = Plot(x_range=xdr, y_range=ydr, width=800, height=800)
plot.title.text = "Web browser market share (November 2013)"
plot.toolbar_location = None
colors = {
"Chrome": seagreen,
"Firefox": tomato,
"Safari": orchid,
"Opera": firebrick,
"IE": skyblue,
"Other": lightgray
}
aggregated = df.groupby("Browser").agg(sum)
selected = aggregated[aggregated.Share >= 1].copy()
selected.loc["Other"] = aggregated[aggregated.Share < 1].sum()
browsers = selected.index.tolist()
source = ColumnDataSource(dict(
url = [url]*N,
x1 = np.linspace( 0, 150, N),
y1 = np.linspace( 0, 150, N),
w1 = np.linspace( 10, 50, N),
h1 = np.linspace( 10, 50, N),
x2 = np.linspace(-50, 150, N),
y2 = np.linspace( 0, 200, N),
))
xdr = Range1d(start=-100, end=200)
ydr = Range1d(start=-100, end=200)
plot = Plot(x_range=xdr, y_range=ydr)
plot.title.text = "ImageURL"
plot.toolbar_location = None
image1 = ImageURL(url="url", x="x1", y="y1", w="w1", h="h1", anchor="center", global_alpha=0.2)
plot.add_glyph(source, image1)
image2 = ImageURL(url="url", x="x2", y="y2", w=20, h=20, anchor="top_left")
plot.add_glyph(source, image2)
image3 = ImageURL(url=dict(value=url), x=200, y=-100, anchor="bottom_right")
plot.add_glyph(image3)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis,'left')
N = 9
x = np.linspace(-2, 2, N)
y = x**2
source1 = ColumnDataSource(dict(x=x, y=y, size=[20] * N))
xdr1 = DataRange1d(sources=[source1.columns("x")])
ydr1 = DataRange1d(sources=[source1.columns("y")])
plot1 = Plot(title="Plot1",
x_range=xdr1,
y_range=ydr1,
plot_width=400,
plot_height=400)
plot1.tools.append(TapTool(plot=plot1))
plot1.add_glyph(source1, Circle(x="x", y="y", size="size", fill_color="red"))
plot1.toolbar_location = None
source2 = ColumnDataSource(dict(x=x, y=y, color=["blue"] * N))
xdr2 = DataRange1d(sources=[source2.columns("x")])
ydr2 = DataRange1d(sources=[source2.columns("y")])
plot2 = Plot(title="Plot2",
x_range=xdr2,
y_range=ydr2,
plot_width=400,
plot_height=400)
plot2.tools.append(TapTool(plot=plot2))
plot2.add_glyph(source2, Circle(x="x", y="y", size=20, fill_color="color"))
plot2.toolbar_location = None
def on_selection_change1(obj, attr, _, inds):
def visualize_graph_html(nx_graph,
output_dir=None,
title_text='',
layout='kamada_kawai',
should_show=False):
"""
This method visualizes a NetworkX graph using Bokeh.
:param nx_graph: NetworkX graph with node attributes containing image filenames.
:param output_dir: Optional output directory for saving html.
:param title_text: String to be displayed above the visualization.
:param layout: Which layout function to use.
:param should_show: Open the browser to look at the graph.
"""
from bokeh import palettes
from bokeh.io import output_file, show
from bokeh.models import Circle, HoverTool, MultiLine, Plot, Range1d, TapTool
# noinspection PyProtectedMember
from bokeh.models.graphs import from_networkx, NodesAndLinkedEdges, NodesOnly
pos = parse_layout(nx_graph, layout)
hover_tool = HoverTool(
tooltips='<img src="@imgs" height="200" alt="@imgs" width="200"></img>',
show_arrow=False)
plot = Plot(plot_width=800,
plot_height=800,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
if title_text != '':
plot.title.text = title_text
plot.title.align = 'center'
plot.min_border = 0
plot.outline_line_color = None
plot.add_tools(hover_tool, TapTool())
plot.toolbar.logo = None
plot.toolbar_location = None
graph_renderer = from_networkx(nx_graph, pos)
graph_renderer.node_renderer.data_source.data['imgs'] = [
n[1]['img'] for n in nx_graph.nodes(data=True)
]
graph_renderer.node_renderer.glyph = Circle(
size=10, fill_color=palettes.Spectral4[0], line_color=None)
graph_renderer.node_renderer.selection_glyph = Circle(
size=10, fill_color=palettes.Spectral4[2], line_color=None)
graph_renderer.node_renderer.hover_glyph = Circle(
size=10, fill_color=palettes.Spectral4[1], line_color=None)
graph_renderer.edge_renderer.glyph = MultiLine(line_color='#CCCCCC',
line_alpha=0.8,
line_width=1.5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=palettes.Spectral4[2], line_width=2)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = NodesOnly()
plot.renderers.append(graph_renderer)
if output_dir:
ensure_dir_exists(output_dir)
output_file(join(output_dir, 'visualize_graph.html'))
if should_show:
show(plot)
def survey():
if request.method == "GET":
return render_template('survey.html')
else: #Request was a POST
user_input = pd.DataFrame(data=[[request.form['Average_Sleep'],
request.form['Sex'],
request.form['Marital'],
request.form['Employment'],
request.form['Physical_QoL'],
request.form['Mental_QoL'],
request.form['Physical_Activity'],
request.form['Race'],
request.form['Age'],
request.form['Weight'],
request.form['Height_feet'],
request.form['Height_inches'],
request.form['Education'],
request.form['Income'],
request.form['Soda_Consumption'],
request.form['Sugary_Drink_Consumption'],
request.form['ALCDAY5'],
request.form['AVEDRNK2'],
request.form['Smoker_Status'],
request.form['Marijuana_Use'],
request.form['Calorie_Informed_Choices'],
request.form['HeartAttackOutcomeDummy'],
request.form['AnginaCoronaryHDOutcomeDummy'],
request.form['StrokeOutcomeDummy'],
request.form['AsthmaLifetimeOutcomeDummy'],
request.form['SkinCancerOutcomeDummy'],
request.form['OtherCancerOutcomeDummy'],
request.form['COPDEmphysemaChronicBroncOutcomeDummy'],
request.form['ArthritisOutcomeDummy'],
request.form['DepressionOutcomeDummy'],
request.form['KidneyDiseaseOutcomeDummy'],
request.form['Life_Satisfaction']]],
columns=['Average Sleep', 'Sex', 'Marital', 'Employment',
'Physical QoL', 'Mental QoL', 'Physical Activity',
'Race', 'Age', 'Weight', 'Height_feet','Height_inches',
'Education', 'Income', 'Soda Consumption',
'Sugary Drink Consumption',
'Alcohol Days', 'Alcohol Drinks','Smoker Status',
'Marijuana Use', 'Calorie Informed Choices',
'HeartAttackOutcomeDummy', 'AnginaCoronaryHDOutcomeDummy',
'StrokeOutcomeDummy', 'AsthmaLifetimeOutcomeDummy',
'SkinCancerOutcomeDummy', 'OtherCancerOutcomeDummy',
'COPDEmphysemaChronicBroncOutcomeDummy',
'ArthritisOutcomeDummy', 'DepressionOutcomeDummy',
'KidneyDiseaseOutcomeDummy', 'Life Satisfaction'], dtype=np.float)#.astype(float)
#return model1.predict(transform(user_input))
# print(user_input.values)
prediction=model1.predict_proba(transform(user_input).values)[-1][-1]
def risk_category(risk_probability):
"""
Takes a risk probability (e.g., "prediction") as a float and returns
the corresponding risk level category as a string.
@risk_probably: float
@reutnr: str
"""
if risk_probability < 0.25:
return "Low Risk"
if risk_probability >= 0.25 and risk_probability < 0.50:
return "Low Moderate Risk"
if risk_probability >=.50 and risk_probability < 0.75:
return "High Moderate Risk"
else:
return "High Risk"
def adjusted_risk(user_input): #Need to test this function
"""
Takes DataFrame of user_input, transforms it, and calculates
adjusted predicted risk based on changes to modifible health
behaviors. Returns a new adjusted probability (float) and
the list of suggested health behavior changes contributing
to the reduced risk.
@user_input: DataFrame
@return: float, list<str>
"""
transformed_df = transform(user_input)
suggested_modifications = [] #Append to suggested modification list
#Sets sleep to average sleep recommended for adults 18 - 64 yrs
#(7-9 hrs), adults 65+ (7-8 hrs) by National Sleep Foundation
if transformed_df['Average Sleep'].values[0] < 7.0 or transformed_df['Average Sleep'].values[0] > 9.0:
orig_sleep = transformed_df['Average Sleep'].values[0]
transformed_df['Average Sleep'].values[0] = 8.0
suggested_modifications.append("Get an average of 8.0 hours of sleep per night instead of "+ str(orig_sleep)+ " hours.")
#Sets Physical Activity to had physical activity in past 30 days
if transformed_df['Physical Activity'].values[0] == 0:
transformed_df['Physical Activity'].values[0] = 1
suggested_modifications.append("Incorporate a regular exercise routine into your schedule – this could be as simple as walking versus driving to the corner store or taking the stairs instead of the elevator.")
#Sets Obese -> Overweight and Overweight -> Normal weight
if transformed_df['BMI'].values[0] == 3.0 or transformed_df['BMI'].values[0] == 4.0:
orig_BMI = transformed_df['BMI'].values[0]
transformed_df['BMI'].values[0] = transformed_df['BMI'].values[0] - 1.0
if orig_BMI == 3.0:
orig_BMI = "Overweight (BMI: 25 – < 30)"
suggested_BMI = "Normal Weight (BMI: 18.5 - < 25)"
suggested_modifications.append("Reduce BMI through gradual, healthy weight loss from "+ orig_BMI+ " to "+ suggested_BMI+ ".")
if orig_BMI == 4.0:
orig_BMI = "Obese (BMI: >= 30)"
suggested_BMI = "Overweight (BMI: 25 – < 30)"
suggested_modifications.append("Reduce BMI through gradual, healthy weight loss from "+ orig_BMI+ " to "+ suggested_BMI+ ".")
#Sets Underweight -> Normal weight
if transformed_df['BMI'].values[0] == 1:
transformed_df['BMI'].values[0] = transformed_df['BMI'].values[0] + 1.0
orig_BMI = "Underweight (BMI: < 18.5)"
suggested_BMI = "Normal Weight (BMI: 18.5 - < 25)"
suggested_modifications.append("Reduce BMI through gradual, healthy weight loss from "+ orig_BMI+ " to "+ suggested_BMI+ ".")
#Sets Daily smokers -> Occasional smokers and Occasional smokers -> Former smokers
if transformed_df['Smoker Status'].values[0] == 1.0 or transformed_df['Smoker Status'].values[0] == 2.0:
orig_smoker = transformed_df['Smoker Status'].values[0]
transformed_df['Smoker Status'].values[0] = transformed_df['Smoker Status'].values[0] + 1.0
if orig_smoker == 1.0:
orig_smoker = "smoking every day"
suggested_smoker = "smoking some days"
suggested_modifications.append("Reduce smoking frequency from "+orig_smoker+" to "+suggested_smoker+ ".")
if orig_smoker == 2.0:
orig_smoker = "smoking some days"
suggested_smoker = "former smoker (quit)"
suggested_modifications.append("Reduce smoking frequency from "+orig_smoker+" to "+suggested_smoker+ ".")
#Reduces weekly alcohol consumption by 25% (cutoff arbitrary)
if transformed_df['Alcohol Consumption'].values[0] >= 1.0:
orig_alcohol = transformed_df['Alcohol Consumption'].values[0]
suggested_alcohol = transformed_df['Alcohol Consumption'].values[0]*0.25
transformed_df['Alcohol Consumption'].values[0] = transformed_df['Alcohol Consumption'].values[0]*0.25
suggested_modifications.append("Reduce average weekly alcohol consumption from " +str(orig_alcohol)+ " drink(s) per week to " +str(suggested_alcohol)+ " drink(s) per week.")
#Sets 1-13 days poor Mental QoL -> 0 days and 14+ poor days -> 1-13 days
if transformed_df['Mental QoL'].values[0] == 2.0 or transformed_df['Mental QoL'].values[0] == 3.0:
#orig_mental = transformed_df['Mental QoL'].values[0]
transformed_df['Mental QoL'].values[0] = transformed_df['Mental QoL'].values[0] - 1.0
suggested_modifications.append("Consider consulting a psychologist/psychiatrist to learn tools for coping with stress and emotional difficulties in order to improve your mental health.")
return (transformed_df, suggested_modifications)
adjusted_prediction=model1.predict_proba(adjusted_risk(user_input)[0].values)[-1][-1]
modification_list=adjusted_risk(user_input)[1]
#Gauge Chart Rendering
xdr = Range1d(start=-1.25, end=1.25)
ydr = Range1d(start=-1.25, end=1.25)
plot = Plot(x_range=xdr, y_range=ydr, plot_width=500, plot_height=500)
plot.title.text = "Predicted Diabetes Risk"
plot.toolbar_location = None
start_angle = pi + pi/4
end_angle = -pi/4
max_kmh = 1.0
max_mph = 1.0
major_step, minor_step = .25, .05
plot.add_glyph(Circle(x=0, y=0, radius=1.00, fill_color="white", line_color="black"))
plot.add_glyph(Circle(x=0, y=0, radius=0.05, fill_color="gray", line_color="black"))
plot.add_glyph(Text(x=0, y=+0.15, text=["Current Risk Probability"], text_color="red", text_align="center", text_baseline="bottom", text_font_style="bold"))
plot.add_glyph(Text(x=0, y=-0.15, text=["Adjusted Risk Probability"], text_color="blue", text_align="center", text_baseline="top", text_font_style="bold"))
def data(value):
"""Shorthand to override default units with "data", for e.g. `Ray.length`. """
return dict(value=value, units="data")
def speed_to_angle(speed, units):
max_speed = max_kmh
speed = min(max(speed, 0), max_speed)
total_angle = start_angle - end_angle
angle = total_angle*float(speed)/max_speed
return start_angle - angle
def add_needle(speed, units, color_choice, line_weight):
angle = speed_to_angle(speed, units)
plot.add_glyph(Ray(x=0, y=0, length=data(0.75), angle=angle, line_color=color_choice, line_width=line_weight))
plot.add_glyph(Ray(x=0, y=0, length=data(0.10), angle=angle-pi, line_color=color_choice, line_width=line_weight))
def polar_to_cartesian(r, alpha):
return r*cos(alpha), r*sin(alpha)
def add_gauge(radius, max_value, length, direction, color, major_step, minor_step):
major_angles, minor_angles = [], []
major_labels, minor_labels = [], []
total_angle = start_angle - end_angle
major_angle_step = float(major_step)/max_value*total_angle
minor_angle_step = float(minor_step)/max_value*total_angle
major_angle = 0
while major_angle <= total_angle:
major_angles.append(start_angle - major_angle)
major_angle += major_angle_step
minor_angle = 0
while minor_angle <= total_angle:
minor_angles.append(start_angle - minor_angle)
minor_angle += minor_angle_step
major_labels = [ major_step*i for i, _ in enumerate(major_angles) ]
minor_labels = [ minor_step*i for i, _ in enumerate(minor_angles) ]
n = major_step/minor_step
minor_angles = [ x for i, x in enumerate(minor_angles) if i % n != 0 ]
minor_labels = [ x for i, x in enumerate(minor_labels) if i % n != 0 ]
glyph = Arc(x=0, y=0, radius=radius, start_angle=start_angle, end_angle=end_angle, direction="clock", line_color=color, line_width=2)
plot.add_glyph(glyph)
rotation = 0 if direction == 1 else -pi
x, y = zip(*[ polar_to_cartesian(radius, angle) for angle in major_angles ])
angles = [ angle + rotation for angle in major_angles ]
source = ColumnDataSource(dict(x=x, y=y, angle=angles))
glyph = Ray(x="x", y="y", length=data(length), angle="angle", line_color=color, line_width=2)
plot.add_glyph(source, glyph)
x, y = zip(*[ polar_to_cartesian(radius, angle) for angle in minor_angles ])
angles = [ angle + rotation for angle in minor_angles ]
source = ColumnDataSource(dict(x=x, y=y, angle=angles))
glyph = Ray(x="x", y="y", length=data(length/2), angle="angle", line_color=color, line_width=1)
plot.add_glyph(source, glyph)
x, y = zip(*[ polar_to_cartesian(radius+2*length*direction, angle) for angle in major_angles ])
text_angles = [ angle - pi/2 for angle in major_angles ]
source = ColumnDataSource(dict(x=x, y=y, angle=text_angles, text=major_labels))
glyph = Text(x="x", y="y", angle="angle", text="text", text_align="center", text_baseline="middle")
plot.add_glyph(source, glyph)
add_gauge(0.75, max_kmh, 0.05, +1, "red", major_step, minor_step)
add_gauge(0.70, max_mph, 0.05, -1, "blue", major_step, minor_step)
add_needle(prediction, "Current Risk", "red", 6)
add_needle(adjusted_prediction, "Adjusted Risk", "blue", 3)
script, div = components(plot)
return render_template('orig_output.html', script=script, div=div, prediction=prediction, adjusted_prediction=adjusted_prediction, risk_level=risk_category(prediction), adjusted_risk_level=risk_category(adjusted_prediction), modifications=modification_list)
session = Session()
session.use_doc('linked_tap_server')
session.load_document(document)
N = 9
x = np.linspace(-2, 2, N)
y = x**2
source1 = ColumnDataSource(dict(x = x, y = y, size = [20]*N))
xdr1 = DataRange1d(sources=[source1.columns("x")])
ydr1 = DataRange1d(sources=[source1.columns("y")])
plot1 = Plot(title="Plot1", x_range=xdr1, y_range=ydr1, plot_width=400, plot_height=400)
plot1.tools.append(TapTool(plot=plot1))
plot1.add_glyph(source1, Circle(x="x", y="y", size="size", fill_color="red"))
plot1.toolbar_location=None
source2 = ColumnDataSource(dict(x = x, y = y, color = ["blue"]*N))
xdr2 = DataRange1d(sources=[source2.columns("x")])
ydr2 = DataRange1d(sources=[source2.columns("y")])
plot2 = Plot(title="Plot2", x_range=xdr2, y_range=ydr2, plot_width=400, plot_height=400)
plot2.tools.append(TapTool(plot=plot2))
plot2.add_glyph(source2, Circle(x="x", y="y", size=20, fill_color="color"))
plot2.toolbar_location=None
def on_selection_change1(obj, attr, _, inds):
color = ["blue"]*N
if inds:
[index] = inds
color[index] = "red"
source2.data["color"] = color
