Forum posts will always be answered before PM requests. Please don't PM me asking for direct support, please ask a public question instead so that others can see the question and answer. Here's a quick example I think works: from bokeh.layouts import layout from import Tabs, Panel from bokeh.io import curdoc from otting. The Bokeh DataTable widget is one of the great tools giving you all freedom for customization. Customizing the visualization of tabular data is a nice tool in presenting data in a easy accessible and concise way. Interests: Diagnosis of problems in sonar/fishfinders, NMEA2000, ethernet comms, autopilots, thermal cameras from otting import figure, outputfile, show import random count 10 x range ( count ) y random.sample (range ( 0, 101 ), count ) p figure () figure is a type of plot using various glyph methods to create scatter plots of different marker shapes p.circle (x, y, size 30, color 'red', legendlabel 'circle' ) p.line (x, y. Table with a 3x5 result visualization and PCR curve preview using HTML and svg. low supply voltage to the display, starving the sounder ping reservoir and preventing proper sounder power output, which is resolved when you get some surface charge in the batteries. ![]() hull section resonating with a harmonic of engine revs (this really happens, and just like a rattle in your car - or at least my car - is engine-rev-range specific) It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations. These plots still have many interactive tools and features, including linked panning and brushing, and hover inspectors. By standalone we mean that these examples make no use of the Bokeh server. ![]() poor flow of water or aeration across the transducer face when the boat is displacement/ploughing but not when planing Bokeh is an interactive data visualization library for Python, and other languages, that targets modern web browsers for presentation. All of the examples below are located in the examples subdirectory of your Bokeh checkout. ![]() propertieswithrefs Collect the names of all properties on this class that also have references. descriptors List of property descriptors in the order of definition. properties (, withprops) Collect the names of properties on this class. How sure are you that this is a speed-related problem, working at higher speeds, and not for example related to a change in bottom conditions that perhaps coincides with you leaving a no-wash zone or similar? If you're sure it's speed, not location that's the factor that makes it work, there are three things that spring to my mind: Find the PropertyDescriptor for a Bokeh property on a class, given the property name. Have you tried Manual Range, setting the range to something a bit deeper than the expected depth? Change the generation of the data metric to repeat element-wise and it should be correct: 'metric': item for item in list (nba.columns) for i in range (len (nba.index)), So the code that works for me is the following: from bokeh.charts import HeatMap, show, outputfile import pandas as pd, numpy as np from urllib2 import urlopen nba pd. In the meantime then, can you describe the sounder image (in traditional/200kHz, let's not worry about 3D for now as it is much more complex)?
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