Metadata-Version: 1.0
Name: canvas
Version: 0.1
Summary: canvas is a simple interface to most common matplotlib functions
Home-page: https://github.com/mdipierro/canvas
Author: Massimo DiPierro
Author-email: massimo.dipierro@gmail.com
License: BSD
Description: ## Canvas - a quick and dirty interface to matplotlib
        
        I do not know about you but I find matplotlib fanatstic but overwhelming. I can never remember its syntax, yet I find myself often repeating the same boilerplate code.
        
        This simple library is not meant to be general but it allows me to produce the quick and dirty plots I often need.
        
        `canvas.py` exposes a single object, `Canvas`, which has methods `plot`,`hist`,`errorbar`,`ellipses`,`imshow`, and `save`. These methods can be chained to overlap diffent types of plots. For example:
        
            >>> from random import gauss
            >>> from math import sin, cos
            >>> from canvas import Canvas
        
            >>> gaussian = [gauss(0,1) for i in range(1000)]
            >>> Canvas('My First Image').hist(gaussian).save('img1.png')
        
        hist data is an array of numbers.
        [output](https://github.com/mdipierro/canvas/blob/master/screenshots/img1.png)
        
            >>> spiral = [(x*cos(0.1*x),x*sin(0.1*x)) for x in range(0,300)]
            >>> Canvas('My Second Image').plot(spiral).save('img2.png')
        
        plot data is an array of 2-tuples, (x,y).
        [output](https://github.com/mdipierro/canvas/blob/master/screenshots/img2.png)
        
            >>> points = [(x,x+gauss(0,1),0.5) for x in range(20)]
            >>> Canvas('My Third Image').errorbar(points).plot(points).save('img3.png')
        
        errorbar data is an array of 3-tuples, (x,y,dy). In the example above the plot is superimposed to errorbars. [output](https://github.com/mdipierro/canvas/blob/master/screenshots/img3.png)
        
            >>> blobs = [(gauss(0,1),gauss(0,1),0.05,0.05) for i in range(100)]
            >>> Canvas('My Fourth Image').ellipses(blobs).save('img4.png')
        
        ellipses data is an array of 4-tuples, (x,y,dx,dy).
        [output](https://github.com/mdipierro/canvas/blob/master/screenshots/img4.png)
        
            >>> waves = [[sin(0.1*x)*cos(0.1*x*y) for x in range(20)] for y in range(20)]
            >>> Canvas('My Fifth Image').imshow(waves).save('img5.png')
        
        imshow data is a square  2D array of numbers.
        [output](https://github.com/mdipierro/canvas/blob/master/screenshots/img5.png)
        
        The names of the methods of the canvas objects are the same as the methods of the corresponding matplotlib axis object.
        
        ### Django example
        
            def my_image(request):
                data = [gauss(0,1) for i in range(1000)]
                image_data = Canvas('title').hist(data).binary()
                return HttpResponse(image_data, mimetype="image/png")
        
        ### web2py example
        
            def my_image():
                data = [gauss(0,1) for i in range(1000)]
                response.headers['Content-type'] = 'image/png'
                return Canvas('title').hist(data).binary()
        
        ### Flask example
        
            @app.route('/my_image')
            def my_image():
                data = [gauss(0,1) for i in range(1000)]
                image_data = Canvas('title').hist(data).binary()
                response.headers['Content-type'] = 'image/png'
                return Response(image_data)
        
        ## Arguments
        
        Here is the full signature:
        
            class Canvas(object):
                 def __init__(self,title='title',xlab='x',ylab='y',xrange=None,yrange=None): ...
                 def save(self,filename='plot.png'): ...
                 def hist(self,data,bins=20,color='blue',legend=None): ...
                 def plot(self,data,color='blue',style='-',width=2,legend=None): ...
                 def errorbar(self,data,color='black',marker='o',width=2,legend=None): ...
                 def ellipses(self,data,color='blue',width=0.01,height=0.01): ...
                 def imshow(self,data,interpolation='bilinear'): ...
        
        ## Installation
        
        You'll need numpy and matplotlib.
        
        From source:
        
            python setup.py install
        
        If you want to install the dependancies using pip you need to process this way:
        
            pip install numpy
            # THEN
            pip install matplotlib
        
        Be **sure** to have numpy installed before installing matplotlib otherwise the installation will fail.
        
        ## License
        
        3-clause BSD license
        
        
Keywords: matplotlib
Platform: UNKNOWN
