Hi Phil,
Yes, I can confirm that upgrading fixes the issue. Thanks for the pointer
to cartopy.
Best regards,
Jesper
2014-03-24 12:13 GMT+01:00 Phil Elson <pelson....@gmail.com>:
> I fixed an issue related to this (I too was producing map tiles) in
> matplotlib v1.2 I believe.
>
> The original issue can be found at
> https://github.com/matplotlib/matplotlib/pull/1591 and so I suggest this
> might not be an issue with matplotlib >= v1.3.
>
> Incidentally, if you are producing map tiles you might be interested in
> cartopy which will allow you to produce properly referenced geo maps (and
> therefore tiles) with coastlines etc.
> I've put a short-sh example in a gist () with the rendered results also
> available (https://rawgithub.com/pelson/9738051/raw/map.html). I've also
> got a tornado based handler version which generates the tiles upon HTTP
> request rather than storing the tiles on disk (much more efficient if you
> have highly dynamic data and a caching layer).
>
> Let me know if updating your matplotlib version helps,
>
> Cheers,
>
> Phil
>
>
>
>
>
>
>
> On 24 March 2014 09:45, Jesper Larsen <jesper.webm...@gmail.com> wrote:
>
>> Hi matplotlib users,
>>
>> I am using matplotlib to produce plots (tiles) in a Web Map Service.
>> Unfortunately I cannot get Matplotlib to plot on the entire image. There
>> are one transparent (pixel) line at the bottom and one transparent line at
>> the right. This is of course a problem when the tiles are shown in a map.
>> Please see example below. Can anyone see what I am doing wrong?
>>
>> Best regards,
>> Jesper
>>
>> import numpy as np
>> import matplotlib as mpl
>> from matplotlib.figure import Figure
>> from matplotlib.backends.backend_agg import FigureCanvasAgg as
>> FigureCanvas
>>
>> w = 256
>> h = 256
>> dpi = 128
>> figsize = w/dpi, h/dpi
>> fig = Figure(figsize=figsize, dpi=dpi, frameon=False)
>> canvas = FigureCanvas(fig)
>> ax = fig.add_axes([0, 0, 1, 1])
>>
>> x = np.arange(0, 10, 0.1)
>> y = np.arange(10, 20, 0.2)
>> X, Y = np.meshgrid(x, y)
>> D = np.ones((X.shape[0]-1, X.shape[1]-1))
>> V = np.linspace(0.0, 1.0, 10)
>> ax.pcolor(X, Y, D, antialiased=False)
>> ax.axis( [x[0], x[-1], y[0], y[-1]] )
>> ax.axis('off')
>> filename = 'testfile.png'
>> fig.savefig(filename, dpi=128)
>>
>> # Test image
>> from PIL import Image
>> im = Image.open(filename)
>> print im.getcolors()
>>
>>
>>
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"Graph Databases" is the definitive new guide to graph databases and their
applications. Written by three acclaimed leaders in the field,
this first edition is now available. Download your free book today!
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