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I have created a fit via the Gamma_3P distribution with alpha, beta and gamma.
The Problem is, that the output y values(red) does not match the distribution(blue).
Thank you in advance for Help.
Code Following:
#test numpy plot
from reliability.Distributions import Gamma_Distribution
from reliability.Fitters import Fit_Gamma_3P
import matplotlib.pyplot as plt
x_min = 0
x_max = 10000
alpha_G = 1827.9715783463666
beta_G = 0.5175119001035541
gamma_G = 0.9999
Gamma_fit_created = Gamma_Distribution(alpha=alpha_G,beta=beta_G, gamma=gamma_G)
y_values = Gamma_fit_created.SF(xmin = x_min, xmax = x_max, label='fit function',color='green', show_plot = False)
#since the output array is 200 values within the limits, I adapted the x values
x_steps = (x_max - x_min)/len(y_values)
x_values = np.arange(x_min, x_max,x_steps)
x_values_plot = x_values[1:]
y_values_plot = y_values[1:]
plt.plot(x_values_plot, y_values_plot, label='fit function created sf 2',color='red')
Gamma_fit_created.SF(xmin = x_min, xmax = x_max, label='fit function correct',color='blue')
plt.xlim(0,10000)
plt.ylim(0,1)
#plt.plot(Gamma_fit_created.SF(xmin = 0, xmax = 10000, label='fit function created sf',color='blue'))
plt.legend()
plt.show()
Code
This is my ouput. The functions should be the same, but they are differenasdas
Any ideas what's supposed to go where the triple '?'s are?
import pandas as pd
from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider, Select
import bokeh.plotting as bp
from bokeh.plotting import Figure, output_file, show
from bokeh.models import HoverTool, DatetimeTickFormatter
# Create an output file
bp.output_file('columnDataSource.html')
# Create your plot as a bokeh.figure object
myPlot = bp.figure(height = 600,
width = 800,
y_range=(0,3))
x_values = [1, 2, 3, 4, 5]
y_values = [1, 2, 3, 4, 5]
myPlot.line(x = x_values, y= y_values, line_width=2)
callback = CustomJS(args={
'source1': {'x': [1,2,3,4], 'y':[1,1,1,1]},
'source2': {'x': [0,0,0,0], 'y':[2,2,2,2]},
'source3': {'x': [1,2,3,4], 'y':[1,1,1,1]}},
code="""
var data1 = source1;
var data2 = source2;
var data3 = source3;
var f = cb_obj.value;
if(f == 'A'){
console.log("A selected from dropdown.");
data1.x = data1.x;
data1.y = data1.y;
}
else if(f == 'B'){
// Substitute all old data1 values in with data2 values
console.log("B selected from dropdown.");
data1.x = data2.x;
data1.y = data2.y;
}
else{
console.log("C selected.");
// Substitute all old data1 values in with data3 values
data1.x = data3.x;
data1.y = data3.y;
}
// Problematic line!
???.change.emit();
""")
select = Select(title='Choose', value='A', options=['A','B','C'])
select.js_on_change('value', callback)
layout = column(select, myPlot)
show(layout) # et voilà.
I expect my x and y values to change and plot accordingly to my Bokeh graph.
Nothing is changing at the moment as I don't know what object's "trigger" function I'm supposed to be calling. Please help, I'm new to Bokeh.
You do ColumnDataSource.change.emit() if you updated the data source fields by reference e.g. when you update only x or only y:
ColumnDataSource.data['x'] = [4, 3, 2, 1]
ColumnDataSource.change.emit()
When you update them both you do:
ColumnDataSource.data = new_data
Where new_data is a new json object like {'x': [1], 'y':[2]}.
The reason for this is that JS can automatically detect a change when existing object is replaced with a new one but it cannot detect changes by reference so in those cases you need explicitly to call: ColumnDataSource.change.emit() to update the BokehJS model.
Here is your modified code:
from bokeh.models import CustomJS, ColumnDataSource, Select, Column
from bokeh.plotting import figure, show
myPlot = figure(y_range = (0, 4))
data = {'A': {'x': [1, 2, 3, 4], 'y':[1, 1, 1, 1]},
'B': {'x': [1, 2, 3, 4], 'y':[2, 2, 2, 2]},
'C': {'x': [1, 2, 3, 4], 'y':[3, 3, 3, 3]} }
source = ColumnDataSource(data['A'])
myPlot.line('x', 'y', line_width = 2, source = source)
callback = CustomJS(args = {'source': source, 'data': data},
code = """source.data = data[cb_obj.value]; """)
select = Select(title = 'Choose', value = 'A', options = ['A', 'B', 'C'])
select.js_on_change('value', callback)
layout = Column(select, myPlot)
show(layout)
I plot data in a bar chart. The data is grouped in two levels where one level is year. I add a range slider in order to alter the x_range of the plot with respect to which years to show. This I have tried to implement through a CustomJS callback (first time I try CustomJS).
Using the slider the factors on the x-axis gets updated as expected. However if I then use the zoom tool and afterwards use the reset tool I get an error message in the web console:
Error: duplicate factor or subfactor: 2016
Not sure what I'm doing wrong with the setup of the data. Is the update of the factor range wrong?
I'm using version 1.1.0 of Bokeh on MacOS. Same error observed in Safari and Firefox.
The code below will reproduce the error.
from bokeh.models import ColumnDataSource, FactorRange, RangeSlider, CustomJS
from bokeh.plotting import figure
from bokeh.layouts import column
import pandas as pd
output_file("grouped_customJS.html")
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
data = {'fruits' : fruits,
'2015' : [2, 1, 4, 3, 2, 4],
'2016' : [5, 3, 3, 2, 4, 6],
'2017' : [3, 2, 4, 4, 5, 3]}
df = pd.DataFrame.from_dict(data)
df=df.set_index('fruits').stack().reset_index()
df=df.rename(columns={'level_1':'year', 0:'value'})
# add year as int column for slider
df['year_int'] = df['year'].astype(int)
df=df.set_index(['fruits','year'])
cats = df.index.values
source = ColumnDataSource(
data = {
'categories': cats,
'values': df['value'],
'year': df['year_int']
}
)
p = figure(
x_range=FactorRange(*cats),
plot_height=250,
title="Fruit Counts by Year",
)
p.vbar(x='categories', top='values', width=0.9, source=source)
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
slider = RangeSlider(
start=df['year_int'].min(),
end=df['year_int'].max(),
step = 1,
value = (df['year_int'].min(), df['year_int'].max()),
)
callback = CustomJS(args=dict(slider=slider, source=source, plt = p), code="""
plt.x_range.factors = [];
for (var i = 0; i < source.get_length(); i++){
if (source.data['year'][i] >= slider.value[0] && source.data['year'][i] <= slider.value[1]){
plt.x_range.factors.push(source.data['categories'][i]);
}
}
""")
slider.js_on_change('value', callback)
p.x_range.js_on_change('factors', callback)
show(column(p, slider))
Try this (works fine with Bokeh v1.1.0):
callback = CustomJS(args = dict(slider = slider, source = source, plt = p), code = """
var factors = []
for (var i = 0; i < source.get_length(); i++){
if (source.data['year'][i] >= slider.value[0] && source.data['year'][i] <= slider.value[1]){
factors.push(source.data['categories'][i]);
}
}
plt.x_range.factors = factors; """)
I am attempting to develop a slider which will limit the number of nodes visible in a network graph based on the value of a node's attribute. The below Pandas DataFrame (df) represents the nodes, and the node's associated attributes (count information).
source target source_count target_count
A C 15 10
A D 15 20
A E 15 30
B F 25 10
B G 25 20
B H 25 30
I have used the following code to generate a network graph for the nodes and their associated attributes.
import pandas as pd
from bokeh.layouts import column, widgetbox,layout,
from bokeh.plotting import figure, show, output_file,
from bokeh.models import HoverTool, value,PanTool, LabelSet, Legend, ColumnDataSource,Circle,Plot, Range1d, MultiLineBoxSelectTool,ResetTool,LassoSelectTool,Slider
from bokeh.models.callbacks import CustomJS
from bokeh.models.graphs import from_networkx, NodesAndLinkedEdges, EdgesAndLinkedNodes
df = pd.DataFrame({
"source":["A", "A", "A", "B", "B","B"],
"target":["C", "D", "E", "F", "G","H"],
"source_count":["15", "15", "15", "25","25","25"]
"target_count":["10", "20", "30", "10","20","30"]
})
net_graph = from_pandas_edgelist(df, 'source', 'target')
#assign attributes
for index, row in df.iterrows():
net_graph.nodes[row['source']]['yearly_count'] = row['source_count']
net_graph.nodes[row['target']]['yearly_count'] = row['target_count']
graph_plot= Plot(plot_width=800, plot_height=600,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
node_hover_tool = HoverTool(tooltips=[("Name", "#index"),("Yearly Count", "#yearly_count")])
graph_plot.add_tools(node_hover_tool)
graph_setup = from_networkx(net_graph, nx.spring_layout, scale=1, center=(0, 0))
graph_setup.node_renderer.glyph = Circle(size=20,fill_color = 'blue')
graph_setup.edge_renderer.glyph = MultiLine(line_color="red", line_alpha=0.8, line_width=1)
graph_plot.renderers.append(graph_setup)
output_file("test_1.html")
show(graph_plot)
The slider I am trying to would use the yearly_count attribute to limit the number of nodes on display. I know that Bokeh allows the embedding of a JavaScript Callback, however, I have not seen a use-case integrated with NetworkX.
Any assistance that anyone could provide would be greatly appreciated.
If you can run your app with bokeh serve then I would try:
from bokeh.models import Slider
graph_plot= Plot()
graph_setup.node_renderer.glyph = Circle()
graph_setup.edge_renderer.glyph = MultiLine()
def callback(attr, old, new):
//filter your data here to show less nodes and edges based
graph_setup.node_renderer.data_source.data = data
graph_setup.edge_renderer.data_source.data = data
slider = Slider()
slider.on_change('value', callback)
If you want to run a Bokeh standalone app then replace slider callback with:
code = """
//filter your data here to show less nodes and edges
graph_setup.node_renderer.data_source.data = data;
graph_setup.edge_renderer.data_source.data = data; """
callback = CustomJS(args = dict(graph_setup = graph_setup, data = data), code = code)
slider = Slider()
slider.js_on_change('value', callback)
See complete JS callback example below:
import networkx as nx
from bokeh.io import show, output_file
from bokeh.models import Plot, Range1d, MultiLine, Circle, TapTool, OpenURL, HoverTool, CustomJS, Slider, Column
from bokeh.models.graphs import from_networkx, EdgesAndLinkedNodes
from bokeh.palettes import Spectral4
from dask.dataframe.core import DataFrame
import pandas as pd
data = {'source': ['A', 'A', 'A', 'A', 'A', 'A'], 'target': ['C', 'D', 'E', 'F', 'G', 'H'], 'source_count': [15, 15, 15, 25, 25, 25], 'target_count': [10, 20, 30, 10, 20, 30]}
df = pd.DataFrame(data)
net_graph = nx.from_pandas_edgelist(df, 'source', 'target')
for index, row in df.iterrows():
net_graph.nodes[row['source']]['yearly_count'] = row['source_count']
net_graph.nodes[row['target']]['yearly_count'] = row['target_count']
graph_plot = Plot(plot_width = 800, plot_height = 600, x_range = Range1d(-1.1, 1.1), y_range = Range1d(-1.1, 1.1))
node_hover_tool = HoverTool(tooltips = [("Name", "#index"), ("Yearly Count", "#yearly_count")])
graph_plot.add_tools(node_hover_tool)
graph_setup = from_networkx(net_graph, nx.spring_layout, scale = 1, center = (0, 0))
graph_setup.node_renderer.glyph = Circle(size = 20, fill_color = 'blue')
graph_setup.edge_renderer.glyph = MultiLine(line_color = "red", line_alpha = 0.8, line_width = 1)
graph_plot.renderers.append(graph_setup)
code = """
var new_start = start.slice();
var new_end = end.slice();
new_index = end.slice();
new_start = new_start.splice(0, cb_obj.value)
new_end = new_end.splice(0, cb_obj.value)
new_index = ['A'].concat(new_end)
new_data_edge = {'start': new_start, 'end': new_end};
new_data_nodes = {'index': new_index};
graph_setup.edge_renderer.data_source.data = new_data_edge;
graph_setup.node_renderer.data_source.data = new_data_nodes;
"""
callback = CustomJS(args = dict(graph_setup = graph_setup,
start = df['source'].values,
end = df['target'].values), code = code)
slider = Slider(title = 'Slider', start = 1, end = 6, value = 6)
slider.js_on_change('value', callback)
layout = Column(graph_plot, slider)
show(layout)
Result:
Newer versions of Bokeh uses strict mode for JavaScript (see release log), which implies that code from Tony's accepted answer does not work for Bokeh version 2.0.0 and upwards. Only a few small explicit declarations of variables are needed for the code to work for newer Bokeh versions:
code = '''
var new_start = start.slice();
var new_end = end.slice();
var new_index = end.slice();
new_start = new_start.splice(0, cb_obj.value)
new_end = new_end.splice(0, cb_obj.value)
new_index = ['A'].concat(new_end)
var new_data_edge = {'start': new_start, 'end': new_end};
var new_data_nodes = {'index': new_index};
graph_setup.edge_renderer.data_source.data = new_data_edge;
graph_setup.node_renderer.data_source.data = new_data_nodes;
'''
Plot generated out of below code doesn't change when different rows of Datatable is Selected.Would like to know where I am going wrong.Ideally I would like plot to show depending on the selected datatable row. Thanks
from datetime import date
from random import randint
from bokeh.io import output_file, show, curdoc
from bokeh.plotting import figure
from bokeh.layouts import widgetbox, row
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import DataTable, DateFormatter, TableColumn,Button
output_file("data_table.html")
data = dict(
dates=[date(2014, 3, i+1) for i in range(10)],
downloads=[randint(0, 100) for i in range(10)],
)
def update(attr,old,new):
#set inds to be selected
inds = [1]
source.selected = {'0d': {'glyph': None, 'indices': []},
'1d': {'indices': inds}, '2d': {}}
# set plot data
plot_dates = [data['dates'][i] for i in inds]
plot_downloads = [data['downloads'][i] for i in inds]
plot_source.data['dates'] = plot_dates
plot_source.data['downloads'] = plot_downloads
source = ColumnDataSource(data)
plot_source = ColumnDataSource({'dates':[],'downloads':[]})
#table_button = Button(label="Press to set", button_type="success")
columns = [
TableColumn(field="dates", title="Date", formatter=DateFormatter()),
TableColumn(field="downloads", title="Downloads"),
]
data_table = DataTable(source=source, columns=columns, width=400, height=280)
p = figure(plot_width=400, plot_height=400)
# add a circle renderer with a size, color, and alpha
p.circle('dates','downloads',source=plot_source, size=20, color="navy", alpha=0.5)
source.on_change('selected',update)
curdoc().add_root(row([data_table,p]))
Only change needed in above code-:
def update(attr,old,new):
data = Hits_File
selected = source.selected['1d']['indices']
if selected:
data = data.iloc[selected, :]
data.columns = data.columns.str.strip()
print("<{}>".format(data.columns[5]))
testvalue = data.iloc[0,5]
BarrierLine.location = data.iloc[0,7]
update_plot(data,testvalue)
def update_plot(data,testvalue):
src_data_table = ColumnDataSource(data)
plot_source.data.update(src_data_table.data)
data1 = get_all_price_dataset(Combined_AllDates,testvalue)
print(testvalue)
price_src_data_table = ColumnDataSource(data1)
price_plot_source.data.update(price_src_data_table.data)