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Am 19.10.2012 23:26, schrieb Damon McDougall: > Correct me if I'm wrong, but I don't even think you need them. I think > the default cmap behaviour is to normalise to the min and max of the > data. yes, default cmap behaviour will normalise to the min and max of the data.
On Fri, Oct 19, 2012 at 10:23 PM, Daπid <dav...@gm...> wrote: > On Fri, Oct 19, 2012 at 11:08 PM, elmar werling <el...@ne...> wrote: >> vmin=min(z), vmax=max(z) > > A suggestion, when dealing with arrays, it is generally faster to use > the numpy function to compute the max and min, either np.max(z) or > z.max(), than the standard Python one. Correct me if I'm wrong, but I don't even think you need them. I think the default cmap behaviour is to normalise to the min and max of the data. -- Damon McDougall http://www.damon-is-a-geek.com B2.39 Mathematics Institute University of Warwick Coventry West Midlands CV4 7AL United Kingdom
On Fri, Oct 19, 2012 at 11:08 PM, elmar werling <el...@ne...> wrote: > vmin=min(z), vmax=max(z) A suggestion, when dealing with arrays, it is generally faster to use the numpy function to compute the max and min, either np.max(z) or z.max(), than the standard Python one.
thanks for help, finally I found the following solution elmar import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt N = 200 x = np.linspace(0,1,N) y = np.random.randn(N) z = np.random.randn(N)*2+5 cm = mpl.cm.get_cmap('RdYlBu') sc = plt.scatter(x, y, c=z, vmin=min(z), vmax=max(z), s=35, cmap=cm) plt.colorbar(sc) plt.show() Am 19.10.2012 21:59, schrieb Joe Kington: > plt.scatter(x, y, c=z, marker='s') > plt.colorbar()
That's what ``scatter`` is intended for. Basically, you want something like: plt.scatter(x, y, c=z, marker='s') plt.colorbar() Note that you can also vary the markers by size based on an additional parameter, as well. Have a look at this example: http://matplotlib.org/examples/pylab_examples/scatter_demo.html Hope that helps, -Joe On Fri, Oct 19, 2012 at 2:19 PM, elmar werling <el...@ne...> wrote: > Hi, > > is there a way to adjust the marker color in a xy-plot in relation to > the value of a third parameter. Something as the following - not working > - example 1. > > Example 2 is working but rather slow for large arrays. > > cheers > Elmar > > > > > # example 1 > > import matplotlib.pyplot as plt > > x = [1,2,3,4] > y = x > c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3)) > > plt.plot(x,y, color=c, marker='s') > plt.show() > > > example 2: > > import matplotlib.pyplot as plt > > x = [1,2,3,4] > y = x > c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3)) > > for i in range(len(x)): > plt.plot(x[i], y[i], color=c[i], marker='s') > > plt.show() > > > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_sfd2d_oct > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
Hi, is there a way to adjust the marker color in a xy-plot in relation to the value of a third parameter. Something as the following - not working - example 1. Example 2 is working but rather slow for large arrays. cheers Elmar # example 1 import matplotlib.pyplot as plt x = [1,2,3,4] y = x c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3)) plt.plot(x,y, color=c, marker='s') plt.show() example 2: import matplotlib.pyplot as plt x = [1,2,3,4] y = x c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3)) for i in range(len(x)): plt.plot(x[i], y[i], color=c[i], marker='s') plt.show()
Good idea. If the png version works then the jpg version should also be made to work, Would you be willing to open up an issue for the feature request? : https://github.com/matplotlib/matplotlib/issues/new If your ready and willing to implement such a thing, that would be even better (just open a pull request and we can start reviewing)! All the best, Phil On 19 October 2012 15:59, Rich Signell <rsi...@us...> wrote: > MPL folks, > > Would it be possible to enhance Matplotlib to allow "im=imread(url)" > to work if url returns a JPG? > > Currently (it seems): > > 1. If the URL returns a PNG this works: > > im = imread(urllib2.urlopen(url)) > > 2. If the URL returns a JPG, this DOESN'T work: > > im = imread(urllib2.urlopen(url)) > > .. and neither does this: > im = imread(urllib2.urlopen(url),format='jpg') > > ... but this DOES work: > > im = Image.open(cStringIO.StringIO(urllib.urlopen(url).read())) > > See an example in Ipython Notebook here: > http://nbviewer.ipython.org/3918576/ > > So could just be hidden from the user so that "im = imread(url)" would > just work for JPG (assuming PIL was installed)? > > Thanks, > Rich > -- > Dr. Richard P. Signell > USGS, 384 Woods Hole Rd. > Woods Hole, MA 02543-1598 > > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_sfd2d_oct > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
Hi, Le 19/10/2012 06:48, Jae-Joon Lee a écrit : > Figuring out the dpi of the screen, I have no clue at this moment. > Maybe this is something a gui expert can answer. I'm certainly not a gui expert, but as a PyQt user, I know screen resolution is indeed Python-accessible with PyQt. (I guess other toolkits provide their own methods) I've made a quick script that prints the screen X and Y resolution (requires PyQt). Reference links to PyQt API docs are included. In my case, it's 96 dpi, and that what I use in my matplotlibrc file for the "figure.dpi" property. But I use a higher value (say 150) for "savefig.dpi" so that I get better resolution when saving PNG images. I agree with Nikolaus that the dpi value for displaying figures would be better get by the software, if possible. Maybe a property like figure.dpi='auto' should activate such a behavior. But this would require many code duplicates, one for each gui toolkit. Best, Pierre
MPL folks, Would it be possible to enhance Matplotlib to allow "im=imread(url)" to work if url returns a JPG? Currently (it seems): 1. If the URL returns a PNG this works: im = imread(urllib2.urlopen(url)) 2. If the URL returns a JPG, this DOESN'T work: im = imread(urllib2.urlopen(url)) .. and neither does this: im = imread(urllib2.urlopen(url),format='jpg') ... but this DOES work: im = Image.open(cStringIO.StringIO(urllib.urlopen(url).read())) See an example in Ipython Notebook here: http://nbviewer.ipython.org/3918576/ So could just be hidden from the user so that "im = imread(url)" would just work for JPG (assuming PIL was installed)? Thanks, Rich -- Dr. Richard P. Signell USGS, 384 Woods Hole Rd. Woods Hole, MA 02543-1598
> Yeah, that's what I feared. But in the mean time, are there any best > practices to minimize undesired effects like the one above? For example, > are there any other functions that need special parameters to not raster > their output when writing to a vector format? And is there a way to get > a figure on the screen with the right size when I don't know what dpi > the monitor is running with? As I said, if you use interpolation="none" with your inshow, the original image will be sent to the backends. Figuring out the dpi of the screen, I have no clue at this moment. Maybe this is something a gui expert can answer. Regards, -JJ
On Tue, Oct 16, 2012 at 4:04 PM, T J <tj...@gm...> wrote: > I'm interested in clipping the result of plt.contour (and > plt.contourf) to a patch. However, QuadContourSet does not have a > set_clip_path() method. Is there a way to do this? QuadContourSet does not (I think it should), but LineCollection instances in QuadContourSet.collections does. Below is a quick example. import matplotlib.pyplot as plt import numpy as np x = np.arange(100)-50 arr = (x**2 + x[:,np.newaxis]**2)**.5 cont = plt.contour(arr) col1 = cont.collections[3] # contour line to clip with. clip_path = col1.get_paths()[0] # Note that col1 may have multiple paths. for col in cont.collections: col.set_clip_path(clip_path, col1.get_transform()) # set clip_path for individual LineCollection instances. plt.show()