![]() Starting a server interactively and open it in a new browser window.Ĭombining HoloViews and Bokeh models to create a more customized app In this guide we will cover how we can deploy a Bokeh app from a HoloViews plot in a number of different ways: Using periodic and timeout events to drive plot updatesĬombining HoloViews plots with custom Bokeh plots to quickly write highly customized apps. Generating and interacting with plots via the usual widgets that HoloViews supports for HoloMap and DynamicMap objects. Responding to plot events and tool interactions via Linked Streams The Bokeh server allows all the usual interactions that HoloViews lets you define and more including: Luckily, when you need a live Python process during the visualization, the Bokeh server provides a very convenient way of deploying HoloViews plots and interactive dashboards in a scalable and flexible manner. Anything with DynamicMap requires a live, running Python server to dynamically select and provide the data for the various parameters that can be selected by the user. Unfortunately, a static approach like this cannot support any HoloViews object that uses DynamicMap (either directly or via operations that return DynamicMaps like decimate, datashade, and rasterize). it is fully self-contained and does not require any Python server to be installed or running. ![]() This command will generate a file out.html that you can put on any web server, email directly to colleagues, etc. text ( x = dict ( field = "symx", units = "data" ), y = dict ( field = "namey", units = "data" ), text = dict ( field = "name", units = "data" ), text_font_size = "6pt", ** text_props ) p. text ( x = dict ( field = "symx", units = "data" ), y = dict ( field = "numbery", units = "data" ), text = dict ( field = "atomic_number", units = "data" ), text_font_size = "9pt", ** text_props ) p. text ( x = dict ( field = "symx", units = "data" ), y = dict ( field = "period", units = "data" ), text = dict ( field = "sym", units = "data" ), text_font_style = "bold", text_font_size = "15pt", ** text_props ) p. to_bokeh ( name = "sinerror" ))įrom collections import OrderedDict from math import log, sqrt import numpy as np import pandas as pd from six.moves import cStringIO as StringIO from otting import * antibiotics = """ bacteria, penicillin, streptomycin, neomycin, gram Mycobacterium tuberculosis, 800, 5, 2, negative Salmonella schottmuelleri, 10, 0.8, 0.09, negative Proteus vulgaris, 3, 0.1, 0.1, negative Klebsiella pneumoniae, 850, 1.2, 1, negative Brucella abortus, 1, 2, 0.02, negative Pseudomonas aeruginosa, 850, 2, 0.4, negative Escherichia coli, 100, 0.4, 0.1, negative Salmonella (Eberthella) typhosa, 1, 0.4, 0.008, negative Aerobacter aerogenes, 870, 1, 1.6, negative Brucella antracis, 0.001, 0.01, 0.007, positive Streptococcus fecalis, 1, 1, 0.1, positive Staphylococcus aureus, 0.03, 0.03, 0.001, positive Staphylococcus albus, 0.007, 0.1, 0.001, positive Streptococcus hemolyticus, 0.001, 14, 10, positive Streptococcus viridans, 0.005, 10, 40, positive Diplococcus pneumoniae, 0.005, 11, 10, positive """ drug_color = OrderedDict () gram_color = p. title ( "Seaborn tsplot with CI in bokeh." ) show ( mpl. sin ( x / p ) + p ), ( 0, 2 )) a, b = out xx = np. tsplot ( sines, err_style = "ci_bars", interpolate = False ) xmin, xmax = ax. ![]() array () # Generate the Seaborn plot with "ci" bars. normal ( 0, tp_err_sd, n_x ) return y sines = np. set ( palette = "Set2" ) # Build the sin wave def sine_wave ( n_x, obs_err_sd = 1.5, tp_err_sd =. Import numpy as np import seaborn as sns import matplotlib.pyplot as plt from scipy import optimize from bokeh import mpl from otting import show # Set the palette colors. select ( dict ( type = HoverTool )) hover. ![]() ![]() rect ( 'xname', 'yname', 0.9, 0.9, source = source, color = 'colors', alpha = 'alphas', line_color = None ) p. flatten (), ) ) p = figure ( title = "Les Mis Occurrences", x_axis_location = "above", tools = "resize,hover,save", x_range = list ( reversed ( names )), y_range = names ) p. append ( 'lightgrey' ) source = ColumnDataSource ( data = dict ( xname = xname, yname = yname, colors = color, alphas = alpha, count = counts. zeros (( N, N )) for link in data : counts, link ] = link counts, link ] = link colormap = xname = yname = color = alpha = for i, n1 in enumerate ( nodes ): for j, n2 in enumerate ( nodes ): xname. From collections import OrderedDict import numpy as np from otting import * from bokeh.models import HoverTool, ColumnDataSource from _mis import data nodes = data names = for node in sorted ( data, key = lambda x : x )] N = len ( nodes ) counts = np. ![]()
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