Graphics with Python - Revision history

WikiSysop at 14:12, 12 April 2018

2018-04-12T14:12:52Z

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	Graphics for plotting data is built into standard Python with the matplotlib module, providing tools for interactive 2-D and 3-D graphics to [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_for_Physics_and_Astronomy our very short course in Python for scientific research]. Matplotlib may be used to generate and save plots in file formats you can display on the web or in other programs, print, and incorporate in documents. It is, however, designed to emulate Matlab, a popular commercial computing environment. The graphics it produces are functional, can be of publication quality, but lack many of the features now appearing in web-based graphical tools. For those dynamic options we will turn to [http://bokeh.pydata.org/en/latest/ Bokeh], data visualization code that utilizes the browser, e.g. Chrome or Firefox. Bokeh is a bridge between the power of Python for analysis, and the power of the browser engine and the web for display and acquisition, and it is at the current (2018) cuttiing edge of interactive visualization. Matplotlib is a solid foundation that remains very useful, especially for simple applications. Let's start there.		Graphics for plotting data is built into standard Python with the matplotlib module, providing tools for interactive 2-D and 3-D graphics to [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_for_Physics_and_Astronomy our very short course in Python for scientific research]. Matplotlib may be used to generate and save plots in file formats you can display on the web or in other programs, print, and incorporate in documents. It is, however, designed to emulate Matlab, a popular commercial computing environment. The graphics it produces are functional, can be of publication quality, but lack many of the features now appearing in web-based graphical tools. For those dynamic options we will turn to [http://bokeh.pydata.org/en/latest/ Bokeh], data visualization code that utilizes the browser, e.g. Chrome or Firefox. Bokeh is a web-first bridge between the power of Python for analysis, and the power of the browser engine and the web for display and acquisition, and it is at the current (2018) cuttiing edge of interactive visualization. Matplotlib is a solid foundation that remains very useful, especially for simple applications. Let's start there.

WikiSysop at 14:06, 12 April 2018

2018-04-12T14:06:47Z

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	The [http://matplotlib.org/users/index.html matplotlib on-line user's guide] offers a tutorial with many [http://matplotlib.org/examples/index.html examples], some of which we will look at here. The guide may		The [http://matplotlib.org/users/index.html matplotlib on-line user's guide] offers a tutorial with many [http://matplotlib.org/examples/index.html examples], some of which we will look at here. The guide may
	also be [http://prancer.physics.louisville.edu/classes/650/python/matplotlib/matplotlib-ref.pdf downloaded as a handy readable pdf] for off-line reference. There is also a helpful but unfinished [http://bespokeblog.wordpress.com/2011/07/05/basic-data-plotting-with-matplotlib-part-1-introduction/ quick start guide] written by an astrophysics graduate student.		also be [http://prancer.physics.louisville.edu/classes/650/python/matplotlib/matplotlib-ref.pdf downloaded as a handy readable pdf] for off-line reference. There is also a helpful but unfinished [http://bespokeblog.wordpress.com/2011/07/05/basic-data-plotting-with-matplotlib-part-1-introduction/ quick start guide] written by an astrophysics graduate student. Attention to size, fonts, color and detail in matplotlib will yield plots suitable for scientific publications, and a helpful guide is available from [https://python4astronomers.github.io/plotting/advanced.html Python 4 Astronomers].

	=== Creating one plot ===		=== Creating one plot ===

WikiSysop at 20:44, 5 April 2018

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	== Bokeh ==		== Bokeh ==

	Bokeh is a visualization library that is not part of the standard Python core distribution, is included in Anaconda or Enthought versions. If you find it is missing from your computer, the [http://bokeh.pydata.org/en/latest/docs/installation.html installation website] will tell you how to install it.		Bokeh is a visualization library that is not part of the standard Python core distribution, is included in Anaconda or Enthought versions. If you find it is missing from your computer, the [http://bokeh.pydata.org/en/latest/docs/installation.html installation website] will tell you how to install it. The short instructions are to use pip if you are managing your own Python. The command line would

			pip3 install bokeh

			which on Linux may require root (or su) privileges. If you are using Anaconda or Canopy distributions, it is probably already installed.

			Once you have it, read the [https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html#getting-started Getting Started Guide] on line and the other documentation as you gain confidence. This technology is still developing, and the Guide is only an introduction to a very rich set of features that are fully described in a complete [https://bokeh.pydata.org/en/latest/docs/user_guide.html#userguide User Guide]. These pages provide interactive html that is produced with bokeh.

WikiSysop at 20:24, 5 April 2018

2018-04-05T20:24:26Z

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			The html content that is produced is readable and can be edited if you are familiar with web programming and Javascript. A key feature is not only that you can display it later on your own computer, but you can put it on a website for others to use. Thus, the interactive graphics you create can be shared with colleagues or students. The first lines of the html file are

			<!DOCTYPE html>
			<html lang="en">
			<head>
			<meta charset="utf-8">
			<title>Bokeh Plot</title>

			<link rel="stylesheet" href="https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css" type="text/css" />

			<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js"></script>
			<script type="text/javascript">
			Bokeh.set_log_level("info");
			</script>

			which tell your browser to load the bokeh javascript and style from the web. The next lines invoke that code and display the data which are embedded in the html file as Javascript "json" data. The rest is between Firefox, Chrome or the browser of your choice and the web to get the display running.

	== Examples ==		One note of caution of course is that you must enable Javascript in your browser, which most users do by default, but some do not because of concern about security.




			== Other Examples ==


			You will find most of these programs on our [http://prancer.physics.louisville.edu/classes/650/python/examples/ examples page] for download. For recent additions or special cases, see the [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_examples examples] section of the wiki.


	~~For examples of Python illustrating plotting with matplotlib and bokeh, see the [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_examples examples] section.~~

	== Assignments ==		== Assignments ==

	For the assigned homework to use these ideas, see the [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_assignments assignments] section.		For the assigned homework to use these ideas, see the [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_assignments assignments] section.

WikiSysop at 20:13, 5 April 2018

2018-04-05T20:13:33Z

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	=== Plotting a Function with Bokeh ==		=== Plotting a Function with Bokeh ===

	Let's start with a really simple example that quickly illustrates how useful this approach is, and how few lines of code are required to get an interactive plot. Start by importing the bokeh elements you will use		Let's start with a really simple example that quickly illustrates how useful this approach is, and how few lines of code are required to get an interactive plot. Start by importing the bokeh elements you will use
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	in the browser target window. The "file:///" tells the browser to look locally, rather than on the web, and the file contains all the code needed to create an interactive plot that looks like this.		in the browser target window. The "file:///" tells the browser to look locally, rather than on the web, and the file contains all the code needed to create an interactive plot that looks like this.

	[[File:~~bokeh_sines~~.png]]		[[File:Bokeh_sines.png]]

WikiSysop at 20:10, 5 April 2018

2018-04-05T20:10:03Z

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	=== How it ~~works~~ ===		=== How it Works ===

	Rather than use the windowing software of your computer's operating system, say X-windows for Linux as an example, Bokeh prepares your data in a format that can be read by Javascript, the language of your computer's browser. While this may seem a roundabout way of getting a display, it is a rather brilliant technique to leverage the power of the browser and to make graphics universal across all platforms. It is also only one step removed from turning your desktop data processing into a website where you can share your work and collaborate with others. There is an alternative, coming from the world of Javascript (perversely often written JavaScript) and the HTML5 web language, where the work is done largely in the browser and Python is used on the "server side" to do the heavy lifting on the data. This is an entirely functional approach too, and lets us use the full featured web for data processing and visualization. However it is more cumbersome, and adds other languages to the toolchain, and requires running a web server. Before we go into that possibility, we look at what can be done with Python, this library, and a browser like Chrome or Firefox.		Rather than use the windowing software of your computer's operating system, say X-windows for Linux as an example, Bokeh prepares your data in a format that can be read by Javascript, the language of your computer's browser. While this may seem a roundabout way of getting a display, it is a rather brilliant technique to leverage the power of the browser and to make graphics universal across all platforms. It is also only one step removed from turning your desktop data processing into a website where you can share your work and collaborate with others. There is an alternative, coming from the world of Javascript (perversely often written JavaScript) and the HTML5 web language, where the work is done largely in the browser and Python is used on the "server side" to do the heavy lifting on the data. This is an entirely functional approach too, and lets us use the full featured web for data processing and visualization. However it is more cumbersome, and adds other languages to the toolchain, and requires running a web server. Before we go into that possibility, we look at what can be done with Python, this library, and a browser like Chrome or Firefox.


			=== Plotting a Function with Bokeh ==

			Let's start with a really simple example that quickly illustrates how useful this approach is, and how few lines of code are required to get an interactive plot. Start by importing the bokeh elements you will use

			from bokeh.plotting import figure, output_file, show
			import math as m

			and in this case also Python math. For more advanced work, you might use numpy and scipy or other packages as well, but here we are only going to plot something simple.

			Next, create or import the data you want to plot. You need two lists, one for "x" and one for "y" starting like this

			npoints = 1000
			x = []
			y = []
			f1 = 100.
			f2 = 80.
			a1 = 5.
			a2 = 5.

			which created empty lists of x and y, and defined some variables we will use to fill those lists with data, in this case the sum of two sine functions. Generate the lists point by point

			for i in range(npoints):
			t = float(i)/10000.
			arg1 = 2.m.pif1*t
			arg2 = 2.m.pif2*t
			amplitude = a1m.sin(arg1) + a2 m.sin(arg2)
			x.append(t)
			y.append(amplitude)

			by appending values for each x and y. Lastly, give a file name that you want the program to use to store the output, create the graphics, and show the result.

			output_file("sines.html")
			p = figure()
			p.line(x, y, line_width=2)
			show(p)

			The rest is bokeh magic. The program will create an "html" file, that is a page that can be viewed in a browser. When you also have a default browser selected for your operating system and it is running, the browser will be directed to display this file. However, the file remains saved after you are done, and to review it again you would enter

			file:///full_path_to/sines.html

			in the browser target window. The "file:///" tells the browser to look locally, rather than on the web, and the file contains all the code needed to create an interactive plot that looks like this.

			[[File:bokeh_sines.png]]

WikiSysop at 09:00, 14 March 2018

2018-03-14T09:00:59Z

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	Alternatively, an ~~increasinglyh~~ popular solution is to let the system version stay as is even on Linux or MacOS, and install your own copy of Canopy or Anaconda Python. Canopy can coexist with your operating system without issues, as can Anaconda. If you install them as an unprivileged user, then the entire package is under your control and for your use only.		Alternatively, an increasingly popular solution is to let the system version stay as is, even on Linux or MacOS, and install your own copy of Canopy or Anaconda Python. Canopy can coexist with your operating system without issues, as can Anaconda. If you install them as an unprivileged user, then the entire package is under your control and for your use only.

	Whether with Linux, Windows and MacOS, users [https://www.enthought.com/product/canopy/ Enthought Canopy] and [https://www.anaconda.com/download/ Anaconda] Python will have everything they need working together, "out of the box". For others, those who maintain servers and operating systems on a larger scale, look at the [http://matplotlib.org/users/installing.html matplotlib installation website] for directions on how to install it.		Whether with Linux, Windows and MacOS, users [https://www.enthought.com/product/canopy/ Enthought Canopy] and [https://www.anaconda.com/download/ Anaconda] Python will have everything they need working together, "out of the box". For others, those who maintain servers and operating systems on a larger scale, look at the [http://matplotlib.org/users/installing.html matplotlib installation website] for directions on how to install it.

WikiSysop at 08:59, 14 March 2018

2018-03-14T08:59:18Z

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	== Installation of matplotlib ==		== Installation of matplotlib ==

	~~The current version 2.2 should be included in most~~ Linux ~~distributions. Your~~ operating system~~'s most recent version of~~ matplotlib ~~could be added to your Python core~~ ~~after you also~~ install numpy if it is ~~not there now~~. For example, under Ubuntu you would use		On Linux the operating system may not by default include matplotlib. If it is not present, then install numpy first because it is a requirement for matplotlib. Try the package that comes with your operating system for both. For example, under Ubuntu you would use

	sudo apt-get install python-matplotlib		sudo apt-get install python-matplotlib

	to get the most recent version available for your system and resolve missing components. ~~However matplotlib uses other system libraries~~, and the ~~links between them and~~ the ~~distribution~~ may ~~lag what~~ is ~~best to use, especially if you have~~ not ~~recently updated your operating system, or if it~~ is ~~a more conservative one (such as Opensuse)~~. Therefore if you do get messages indicating something is missing or out of date, remove the system package and install directly as superuser or root user with "pip"~~, but~~ [https://pip.pypa.io/en/stable/reference/pip read the documentation on pip] first if you are not familiar with it. Also check on your system that the "pip" command is actually running it for Python3.		to get the most recent version available for your system and resolve missing components. Under Opensuse you would use yast2, and look for python3-matplotlib packages.

			Unfortunately this is a weak link in using the distribution versions because the compiled packages may not include the features you need. At this time (March 2018), Opensuse's matplotlib is outdated and does not support Tk which is needed for graphical user interfaces.

			Therefore if you do get messages indicating something is missing or out of date, remove the system package using apt or yast or zypper as appropriate, and install directly as superuser or root user with "pip" after you [https://pip.pypa.io/en/stable/reference/pip read the documentation on pip] first if you are not familiar with it. Also check on your system that the "pip" command is actually running it for Python3.

	pip --version # will return a line which well tell you what it is for		pip --version # will return a line which well tell you what it is for
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	Alternatively, let the system version stay as is and install your own copy of Canopy or Anaconda Python. Canopy can coexist with your operating system without issues, as can Anaconda.		Alternatively, an increasinglyh popular solution is to let the system version stay as is even on Linux or MacOS, and install your own copy of Canopy or Anaconda Python. Canopy can coexist with your operating system without issues, as can Anaconda. If you install them as an unprivileged user, then the entire package is under your control and for your use only.

	Windows and MacOS users of [https://www.enthought.com/product/canopy/ Enthought Canopy] and [https://www.anaconda.com/download/ Anaconda] Python will ~~also~~ have it "out of the box". For others, look at the [http://matplotlib.org/users/installing.html matplotlib installation website] for directions on how to install it.		Whether with Linux, Windows and MacOS, users [https://www.enthought.com/product/canopy/ Enthought Canopy] and [https://www.anaconda.com/download/ Anaconda] Python will have everything they need working together, "out of the box". For others, those who maintain servers and operating systems on a larger scale, look at the [http://matplotlib.org/users/installing.html matplotlib installation website] for directions on how to install it.

	Once ~~you have it~~ installed, programs that use this library will have to import it with lines such as		Once installed, programs that use this library will have to import it with lines such as

	import numpy as np		import numpy as np

WikiSysop at 22:36, 12 March 2018

2018-03-12T22:36:56Z

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	backend : TkAgg		backend : TkAgg

	in which choices depend on what libraries are installed, and may include Tkagg, GTK3Agg, qt5Agg and others. You can try them out with your own programs below and pick one you like. However "Tkagg" will tie ~~matplot lib~~ to the preferred GUI interface and allow you to build applications that have a graphical user interface surrounding the plotting. Because Tk has improved recently and is the easiest of the GUI systems to use, this is the recommended setting. You can override your personal and the system defaults by specifying the backend in your program with a line such as this one:		in which choices depend on what libraries are installed, and may include Tkagg, GTK3Agg, qt5Agg and others. You can try them out with your own programs below and pick one you like. However "Tkagg" will tie matplotlib to the preferred GUI interface and allow you to build applications that have a graphical user interface surrounding the plotting. Because Tk has improved recently and is the easiest of the GUI systems to use, this is the recommended setting. You can override your personal and the system defaults by specifying the backend in your program with a line such as this one:

	matplotlib.use("TkAgg")		matplotlib.use("TkAgg")

WikiSysop at 22:36, 12 March 2018

2018-03-12T22:36:03Z

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	to make the functions available. With these, numpy functions will start with np. and mathplotlib functions will have plt. in front of the function name, which shortens the code you would write. You can check that your computer has numpy and matplotlib by trying these commands in interactive Python or Idle. The version numbers will be available too with		to make the functions available. With these, numpy functions will start with np. and mathplotlib functions will have plt. in front of the function name, which shortens the code you would write. You can check that your computer has numpy and matplotlib by trying these commands in interactive Python or Idle. The version numbers will be available too with

	print np.__version__		print (np.__version__)
	print plt.__version__		print (plt.__version__)

	Matplotlib is configured by a file matplotlibrc that is kept on your computer to set its defaults. On Linux there will be a version saved with your Python installation, typically in a location such as		Matplotlib is configured by a file matplotlibrc that is kept on your computer to set its defaults. On Linux there will be a version saved with your Python installation, typically in a location such as