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imshow() pixelization in matplotlib 2.0.2 but not in 2.0.0 #8723
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- There have been some bugfixes, IIRC, mostly related to the handling of NaNs and masked points. Are there any in your data?…On Tue, Jun 6, 2017 at 10:54 AM, diogoff ***@***.***> wrote: I'm plotting the same image (a numpy float32 array) with imshow(..., interpolation='bilinear') on matplotlib 2.0.0 and 2.0.2. See the following figure: [image: matplotlib] <https://cloud.githubusercontent.com/assets/7391155/26835386/20980584-4ad0-11e7-8719-83beec5126d5.png> On 2.0.0 the image is perfectly smooth. However, on 2.0.2 there are noticeable pixelization effects. Has the imshow() routine changed? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#8723>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AARy-PvqiPeroQYvc-abNuKTIJk7LZINks5sBWhDgaJpZM4Nxdr0> .
- Another possible source of issues *might* be colormap handling. It would be helpful to have a minimum working example to investigate with.…On Tue, Jun 6, 2017 at 11:23 AM, Benjamin Root ***@***.***> wrote: There have been some bugfixes, IIRC, mostly related to the handling of NaNs and masked points. Are there any in your data? On Tue, Jun 6, 2017 at 10:54 AM, diogoff ***@***.***> wrote: > I'm plotting the same image (a numpy float32 array) with imshow(..., > interpolation='bilinear') on matplotlib 2.0.0 and 2.0.2. > > See the following figure: > [image: matplotlib] > <https://cloud.githubusercontent.com/assets/7391155/26835386/20980584-4ad0-11e7-8719-83beec5126d5.png> > > On 2.0.0 the image is perfectly smooth. However, on 2.0.2 there are > noticeable pixelization effects. > > Has the imshow() routine changed? > > — > You are receiving this because you are subscribed to this thread. > Reply to this email directly, view it on GitHub > <#8723>, or mute the > thread > <https://github.com/notifications/unsubscribe-auth/AARy-PvqiPeroQYvc-abNuKTIJk7LZINks5sBWhDgaJpZM4Nxdr0> > . >Reacted by Paul Hobson
Here is a MWE:
import matplotlib.pyplot as plt data = [[ 0.001071, 0.032820, 0.105207, 0.127595, 0.147364, 0.124911, 0.073015, 0.000074, -0.003899, -0.001462], [ 0.000787, 0.031720, 0.107091, 0.183722, 0.202479, 0.204296, 0.154234, 0.085757, 0.026941, -0.003271], [ 0.001480, -0.000771, 0.016654, 0.214761, 0.278136, 0.304099, 0.246056, 0.169364, 0.085205, -0.001907], [ 0.000549, -0.000475, 0.001674, 0.247847, 0.345098, 0.378256, 0.332674, 0.229220, 0.133641, 0.053745], [ 0.000000, 0.002675, 0.001689, 0.266309, 0.414319, 0.459767, 0.425990, 0.317280, 0.190170, 0.099265], [ 0.000000, 0.001037, -0.002923, 0.254976, 0.408347, 0.529809, 0.532195, 0.406330, 0.272445, 0.163764], [ 0.000000, 0.000792, -0.013151, 0.216619, 0.397671, 0.567617, 0.616055, 0.508699, 0.379961, 0.263546], [ 0.000000, 0.002062, -0.016293, 0.157240, 0.368229, 0.613702, 0.712611, 0.639131, 0.505910, 0.338813], [ 0.000000, 0.001741, 0.003448, 0.100818, 0.333026, 0.611874, 0.787059, 0.773407, 0.644049, 0.450520], [ 0.000000, -0.001268, -0.008104, 0.061512, 0.294558, 0.604565, 0.814691, 0.865676, 0.776763, 0.557480], [ 0.000000, -0.001105, -0.002768, 0.036252, 0.252868, 0.544436, 0.775150, 0.920004, 0.890915, 0.690601], [ 0.000000, -0.001123, -0.003589, 0.028705, 0.194056, 0.471003, 0.728520, 0.921639, 0.969436, 0.773769], [ 0.000000, -0.000430, -0.004625, 0.017730, 0.149156, 0.423797, 0.677714, 0.912435, 0.984722, 0.846838], [ 0.000000, 0.000000, -0.002619, 0.036928, 0.136544, 0.410567, 0.666038, 0.905706, 0.942078, 0.894590], [ 0.000000, -0.000296, -0.003399, 0.050115, 0.189001, 0.451915, 0.696602, 0.869335, 0.893818, 0.826647], [ 0.000000, 0.000174, -0.003604, 0.083219, 0.248855, 0.517874, 0.739961, 0.843343, 0.803800, 0.718687], [-0.001720, 0.005825, 0.078039, 0.154466, 0.324385, 0.550980, 0.745479, 0.817428, 0.750877, 0.624806], [ 0.000719, -0.004020, 0.118178, 0.218264, 0.395383, 0.585784, 0.748400, 0.744401, 0.620376, 0.464464], [ 0.003257, 0.066374, 0.142013, 0.270861, 0.458675, 0.621705, 0.691592, 0.590108, 0.491103, 0.346859], [ 0.042411, 0.111464, 0.193880, 0.308119, 0.475352, 0.602842, 0.600717, 0.487950, 0.392807, 0.258806]] plt.imshow(data, vmin=0., vmax=1., interpolation='bilinear') plt.axis('off') plt.show()
- It is almost like it is treating those zeros as masked out, isn't it? attn: @tacaswell…On Tue, Jun 6, 2017 at 5:08 PM, diogoff ***@***.***> wrote: Here is a MWE: import matplotlib.pyplot as plt data = [[ 0.001071, 0.032820, 0.105207, 0.127595, 0.147364, 0.124911, 0.073015, 0.000074, -0.003899, -0.001462], [ 0.000787, 0.031720, 0.107091, 0.183722, 0.202479, 0.204296, 0.154234, 0.085757, 0.026941, -0.003271], [ 0.001480, -0.000771, 0.016654, 0.214761, 0.278136, 0.304099, 0.246056, 0.169364, 0.085205, -0.001907], [ 0.000549, -0.000475, 0.001674, 0.247847, 0.345098, 0.378256, 0.332674, 0.229220, 0.133641, 0.053745], [ 0.000000, 0.002675, 0.001689, 0.266309, 0.414319, 0.459767, 0.425990, 0.317280, 0.190170, 0.099265], [ 0.000000, 0.001037, -0.002923, 0.254976, 0.408347, 0.529809, 0.532195, 0.406330, 0.272445, 0.163764], [ 0.000000, 0.000792, -0.013151, 0.216619, 0.397671, 0.567617, 0.616055, 0.508699, 0.379961, 0.263546], [ 0.000000, 0.002062, -0.016293, 0.157240, 0.368229, 0.613702, 0.712611, 0.639131, 0.505910, 0.338813], [ 0.000000, 0.001741, 0.003448, 0.100818, 0.333026, 0.611874, 0.787059, 0.773407, 0.644049, 0.450520], [ 0.000000, -0.001268, -0.008104, 0.061512, 0.294558, 0.604565, 0.814691, 0.865676, 0.776763, 0.557480], [ 0.000000, -0.001105, -0.002768, 0.036252, 0.252868, 0.544436, 0.775150, 0.920004, 0.890915, 0.690601], [ 0.000000, -0.001123, -0.003589, 0.028705, 0.194056, 0.471003, 0.728520, 0.921639, 0.969436, 0.773769], [ 0.000000, -0.000430, -0.004625, 0.017730, 0.149156, 0.423797, 0.677714, 0.912435, 0.984722, 0.846838], [ 0.000000, 0.000000, -0.002619, 0.036928, 0.136544, 0.410567, 0.666038, 0.905706, 0.942078, 0.894590], [ 0.000000, -0.000296, -0.003399, 0.050115, 0.189001, 0.451915, 0.696602, 0.869335, 0.893818, 0.826647], [ 0.000000, 0.000174, -0.003604, 0.083219, 0.248855, 0.517874, 0.739961, 0.843343, 0.803800, 0.718687], [-0.001720, 0.005825, 0.078039, 0.154466, 0.324385, 0.550980, 0.745479, 0.817428, 0.750877, 0.624806], [ 0.000719, -0.004020, 0.118178, 0.218264, 0.395383, 0.585784, 0.748400, 0.744401, 0.620376, 0.464464], [ 0.003257, 0.066374, 0.142013, 0.270861, 0.458675, 0.621705, 0.691592, 0.590108, 0.491103, 0.346859], [ 0.042411, 0.111464, 0.193880, 0.308119, 0.475352, 0.602842, 0.600717, 0.487950, 0.392807, 0.258806]] plt.imshow(data, vmin=0., vmax=1., interpolation='bilinear') plt.axis('off') plt.show() [image: matplotlib] <https://user-images.githubusercontent.com/7391155/26851969-802a9fb4-4b04-11e7-8d29-4d49e3df3c30.png> — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#8723 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AARy-Btlf7KfBsDWGii3gi5L-MELxm0Yks5sBb-zgaJpZM4Nxdr0> .
The pixelization appears around the negative values which are being clipped by vmin=0.
- you are right. It is the pixels that fall below vmin. There was some recent work around vmin/vmax. maybe that had some unintended consequences?…On Tue, Jun 6, 2017 at 5:14 PM, diogoff ***@***.***> wrote: The pixelization appears around the negative values which are being clipped by vmin=0. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#8723 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AARy-D53Gn1CAvCdzYvawoSBkw6yQ4YQks5sBcElgaJpZM4Nxdr0> .
It seems that when those (negative) pixels are being clipped, the interpolation no longer works as it used to.
Thanks for your help.- Possible culprit: #8024…On Tue, Jun 6, 2017 at 5:22 PM, diogoff ***@***.***> wrote: It seems that when those (negative) pixels are being clipped, the interpolation no longer works as it used to. Thanks for your help. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#8723 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AARy-NkZqs3thlZAIm4n-tE0ITMSxu_Qks5sBcL-gaJpZM4Nxdr0> .
- added this to the milestone on Jun 17, 2017
I'm plotting the same image (a numpy float32 array) with imshow(..., interpolation='bilinear') on matplotlib 2.0.0 and 2.0.2.
Click to see in full:
On 2.0.0 the image is perfectly smooth. However, on 2.0.2 there are noticeable pixelization effects.
Has imshow() changed between these two versions?