Matplotlib - Revision history

Admin at 07:18, 16 April 2017

2017-04-16T07:18:05Z

← Older revision		Revision as of 07:18, 16 April 2017
Line 76:		Line 76:
	Let's define a function that will turn that figure into a StringIO object:		Let's define a function that will turn that figure into a StringIO object:

	<~~source lang="python"~~>		<pre>
	def stringify_dummy_figure():		def stringify_dummy_figure():
	figcanvas = make_dummy_figure()		figcanvas = make_dummy_figure()
Line 83:		Line 83:
	img_data_str.seek(0) # After writing, rewind data for further use.		img_data_str.seek(0) # After writing, rewind data for further use.
	return img_data_str.read()		return img_data_str.read()
	</~~source~~>		</pre>

	==Step 4: Make an HTTP Response==		==Step 4: Make an HTTP Response==
Line 89:		Line 89:
	The last step is to pass that string to an HTTP response		The last step is to pass that string to an HTTP response

	<~~source lang="python"~~>		<pre>
	from django.http import HttpResponse, HttpResponseRedirect		from django.http import HttpResponse, HttpResponseRedirect

	img_str = stringify_dummy_figure()		img_str = stringify_dummy_figure()
	response = HttpResponse(img_str, mimetype='image/png')		response = HttpResponse(img_str, mimetype='image/png')
	</~~source~~>		</pre>

	and that can be embedded into your web app, wherever it lays out the logic for parsing URLs and crafting HTTP responses.		and that can be embedded into your web app, wherever it lays out the logic for parsing URLs and crafting HTTP responses.

	~~[[Category:Python]]~~


			{{ScientificComputingFlag}}
			{{PythonFlag}}

2014-05-22T23:57:36Z

Step 3: Should've Put a String on it

2014-05-22T23:37:42Z

Step 3: Should've Put a String on it

← Older revision		Revision as of 23:37, 22 May 2014
Line 74:		Line 74:
	==Step 3: Should've Put a String on it==		==Step 3: Should've Put a String on it==

	Let's turn that figure into a StringIO object:		Let's define a function that will turn that figure into a StringIO object:

	<source lang="python">		<source lang="python">

2014-05-22T23:36:34Z

Part 1: Create Your Figure

@@ Line 9: / Line 9: @@
 Let's walk through how to do this.
-==Part 1: Create Your Figure==
+==Step 1: Create Your Figure==
 Some quick code to make a dummy figure:
 <source lang="python">
@@ Line 17: / Line 47: @@
 from numpy.random import *
-fig = plt.figure()
+matplotlib.use('Agg')
-ax1 = fig.add_subplot(1,2,1)
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
 ax2 = fig.add_subplot(1,2,2)
-x = range(10)
+==Step 4: Make an HTTP Response==
-y1 = rand(10,)
-y2 = 100*rand(10,)
+The last step is to pass that string to an HTTP response
-ax1.plot(x,y1,'b-')
+img_str = stringify_dummy_figure()
-ax2.plot(x,y2,'r-')
+response = HttpResponse(img_str, mimetype='image/png')
 </source>
 [[Category:Python]]

2014-05-22T23:14:22Z

← Older revision		Revision as of 23:14, 22 May 2014
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	~~#REDIRECT [~~[~~Matplotlib~~:~~Main_Page]~~]		=Using Matplotlib in Web Apps=

			I wanted to write a Python web app that would call Matplotlib to visualize some data on the back end, and serve it up to a browser window on the front end.

			Initially I saw [webplotlib https://pypi.python.org/pypi/webplotlib/0.1], which looked promising, but wrapped all of matplotlib into two dinky kinds of plots: time series, and bar charts. I needed something that, like webplotlib, could communicate a figure to a browser, but something that, unlike webplotlib, still kept the full functionality of matplotlib.

			The fix was easy. The core functionality of webplotlib is passing a figure as a string to the browser; this is about 4 lines. The rest is entirely case-dependent.

			Let's walk through how to do this.

			==Part 1: Create Your Figure==

			Some quick code to make a dummy figure:

			<source lang="python">
			import matplotlib.pylab as plt
			from numpy.random import *

			fig = plt.figure()
			ax1 = fig.add_subplot(1,2,1)
			ax2 = fig.add_subplot(1,2,2)

			x = range(10)
			y1 = rand(10,)
			y2 = 100*rand(10,)

			ax1.plot(x,y1,'b-')
			ax2.plot(x,y2,'r-')
			</source>


	[[Category:Python]]		[[Category:Python]]

2012-11-26T22:58:06Z

← Older revision		Revision as of 22:58, 26 November 2012
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	#REDIRECT [[Matplotlib:Main_Page]]		#REDIRECT [[Matplotlib:Main_Page]]



	~~{{Stub}}~~

	~~=Types of Plots=~~

	~~==Histogram==~~

	~~<source lang="python">~~
	~~from matplotlib import rcParams~~
	from pylab import *

	~~# construct the distribution with a given mean and deviation:~~
	~~# -----------------------------------------------------------~~

	~~# mu = mean~~
	~~# sigma = standard deviation~~
	~~# randn(10000) creates an array of size 10,000 x 1 that contains random numbers~~
	~~mu, sigma = 0, 0.5~~
	~~x = mu + sigma*randn(10000)~~

	~~# plot the histogram of the data:~~
	~~# --------------------------~~

	~~figure(1)~~

	~~# this will plot the data with the y-axis as the total number of samples (i.e. 350+)~~
	~~#n, bins, patches = hist(x, 100,)~~
	~~# this will plot the histogram with a normalized y-axis~~
	~~n, bins, patches = hist(x, 100, normed=1)~~
	~~</source>~~







	~~{{Programs}}~~
	~~{{Math Programs}}~~

	[[Category:Python]]		[[Category:Python]]

2012-11-26T22:54:30Z

← Older revision		Revision as of 22:54, 26 November 2012
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	[[~~#REDIRECT~~ Matplotlib:Main_Page]]		#REDIRECT [[Matplotlib:Main_Page]]

2012-11-26T22:54:06Z

← Older revision		Revision as of 22:54, 26 November 2012
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			[[#REDIRECT Matplotlib:Main_Page]]



	{{Stub}}		{{Stub}}

2011-05-28T16:30:53Z

2011-05-28T16:26:51Z