The code is to using PyPlot to scatter and save in julia.
using PyPlot;pygui(true)
fig = figure()
for i = 1:400
scatter([i,i+1], [i+1, i+2], color = "blue", s = 0.1)
end
PyPlot.savefig("1.png", figsize = (16, 9),dpi = 1200, bbox_inches="tight")
But the plot result and saved figure is different:
What I want is some simple dots:
But in saved figure the marker shape is circles :
As you can see, the marker type is changed.
I found this only occurs when scattering highly dense dots. How should I fix this?
I just had the exact same problem. The only way I figured out was to set markeredgecolor="none", that got rid of the circles around the dots.
Might not be the most efficient solution, but at least I could produce the plots I needed.
Related
Hi and thanks for reading.
What I am trying to do is to make a web app that would take an image, run it through the model and return a segmented version. I can not use imshow in the webapp though. So I tried adding colormap through matplolib.cm.viridis however it returns a much darker image.
Here are some code and images for refernce:
pred = new_model.predict(np.expand_dims(img, 0))
pred_mask = np.argmax(pred, axis=-1)
pred_mask = pred_mask[0]
This returns me a 2D grayscale image, which when put into matplolib imshow looks like this.(last picture on the right is the output of the model). Code and image below.
axs[0].imshow(m1)
axs[0].set_title('Image')
axs[1].imshow(test_label1)
axs[1].set_title('Ground Truth')
axs[2].imshow(new_pred)
axs[2].set_title('Prediction')
However, when applying colormap to an image using matplolib.cm (something I have to do for app to function) I get this image. Code and image presented below.
Adding colormap. (Viridis, as far as I know is default one from matplolib 3.5)
from matplotlib import cm
pred_mask = cm.viridis(pred_mask / 255)*255
pred_mask = np.asarray(pred_mask, dtype='uint8')
Plotting Image
fig, axs = plt.subplots(1, 3, figsize=(20, 10))
axs[0].imshow(m1)
axs[0].set_title('Image')
axs[1].imshow(test_label1)
axs[1].set_title('Ground Truth')
axs[2].imshow(pred_mask)
axs[2].set_title('Prediction')
But as you can see image is much darker, without even a hint of lighter blue or yellow, i.e. worse. How can I make it closer to imshow output?
PS. Thank you very much for reading and hope that someone has an answer to that. Any suggestions would be much appreciated though.
This is most likely related to the number range of the image or colormap, respectively.
As the prediction mask can be faintly seen my money would be on either multiplying the prediction data with 255 or to set the vmax of imshow to a smaller value. In any case, it would be useful to know the min/max value of pred_mask and additionally show a colorbar for the right plot.
I hope that gets you on the right track.
this is my first question here and one probably very simple, however I tried to fix any mistake and look more info but with no success, I am new to programming graphs using matplotlib, could anyone help me out? thank you in advance
The goal of the program was to graphic a circle and a label, but label was not appearing:
import matplotlib.pyplot as plt
circle1 = plt.Circle((0, 0), 0.2, color='r',label='Men')
fig, ax = plt.subplots()
ax.add_artist(circle1)
circle1 = plt.Circle((0, 0), 2, color='r',label='Men')
plt.legend(loc='best')
plt.show()
Based on your code, you are only plotting the first circle (with radius 0.2). You never call the second circle, so it does not show up. Not sure what you are going for here. However, BigBen is correct, just use ax.add_patch(circle1) instead and it will show with the labels. With this minor change, your plot will look like this:
You would also want to set x and y axis limits in order to see the entire circle. This code below will allow you to see both circles in full with different labels.
import matplotlib.pyplot as plt
circle1 = plt.Circle((0, 0), 0.2, color='r',label='Small Red',zorder=2)
circle2 = plt.Circle((0, 0), 2, color='b',label='Big Blue',zorder=1)
fig, ax = plt.subplots()
ax.add_patch(circle1)
ax.add_patch(circle2)
plt.legend(loc='best')
ax.set_xlim([-3,3])
ax.set_ylim([-3,3])
plt.show()
And your plot will look like this:
The zorder argument will decide which object shows up in front of the other. They will appear front-to-back in descending order.
I'm sucessfully generating a 3D plot with Mayavi, but can't find any way to rescale the axes to a semilog representation.
Is this possible?
I also tried taking a screenshot (as suggested by another answer I read before), which I placed after my mayavi code
arr = mayavi.mlab.screenshot()
fig = plt.figure(figsize=(5, 5))
pylab.imshow(arr)
plt.semilogx()
plt.show()
However this just produces a segmentation fault.
Thanks in advance!
I apologise if this has already been asked, I've searched long and hard on this site and couldn't find anything that worked. I'm using Julia, specifically the Juno IDE, and I am trying to use PyPlot to create my graphs. I wanted to set the y axis height when plotting, but leave the x axis variable. Here is the code I have been using to generate my plots
fig = figure()
ax = fig[:add_axes]
BEFE250 = (plot(s1, s2, lw=1.0, "-", color="b"))
ylabel("u(x,t)", size=20)
xlabel("t", size=20)
gcf()
which gives me
However, I need space in the top left corner as I am going to layer another picture on top in latex. So I need to set the y-axis height to between -3 and 3. However, if I set the axes height in PyPlot
fig = figure()
ax = fig[:add_axes]([0.1, 0.1, -3.0, 3.0])
BEFE250 = (plot(s1, s2, lw=1.0, "-", color="b"))
ylabel("u(x,t)", size=20)
xlabel("t", size=20)
gcf()
then it switches the orientation of the x-axis. If I set the axis height after running the plot, PyPlot puts the picture in a box in a legend off to the side of the main picture, and the main picture is empty? If someone could help me out it would be greatly appreciated.
Thanks for your help.
EDIT: Using xlim=(-10.,10.) and ylim=(-2.,12.) doesn't work either. PyPlot still adapts the axes to the data.
Try xlim(-10, 10) and ylim(-2, 12) after the plot command:
plot(s1, s2, lw=1.0, "-", color="b")
ylim(-3, 3)
Just try this, without the add_axes.
You probably also want LaTeX labels -- just add an L before the string, which gives a special LaTeX string from the LaTeXString package. You can either just add the L, or add $ inside too:
ylabel(L"u(x,t)", size=20)
ylabel(L"$u(x,t)$", size=20)
[The $ are necessary in certain circumstances that I forget.]
I'm not sure how good the PyPlot support is in Juno.
You might want to try this in IJulia.
By the way, is there a reason you want to layer on a separate figure in LaTeX? That might not be the best way to do it.
I am using the following example Example to create two polar contour subplots. When I create as the pdf there is a lot of white space which I want to remove by changing figsize.
I know how to change figsize usually but I am having difficulty seeing where to put it in this code example. Any guidance or hint would be greatly appreciated.
Many thanks!
import numpy as np
import matplotlib.pyplot as plt
#-- Generate Data -----------------------------------------
# Using linspace so that the endpoint of 360 is included...
azimuths = np.radians(np.linspace(0, 360, 20))
zeniths = np.arange(0, 70, 10)
r, theta = np.meshgrid(zeniths, azimuths)
values = np.random.random((azimuths.size, zeniths.size))
#-- Plot... ------------------------------------------------
fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))
ax.contourf(theta, r, values)
plt.show()
Another way to do this would be to use the figsize kwarg in your call to plt.subplots.
fig, ax = plt.subplots(figsize=(6,6), subplot_kw=dict(projection='polar')).
Those values are in inches, by the way.
You can easily just put plt.figsize(x,y) at the beginning of the code, and it will work. plt.figsize changes the size of all future plots, not just the current plot.
However, I think your problem is not what you think it is. There tends to be quite a bit of whitespace in generated PDFs unless you change options around. I usually use
plt.savefig( 'name.pdf', bbox_inches='tight', pad_inches=0 )
This gives as little whitespace as possible. bbox_inches='tight' tries to make the bounding box as small as possible, while pad_inches sets how many inches of whitespace there should be padding it. In my case I have no extra padding at all, as I add padding in whatever I'm using the figure for.