So I am trying to build a class to create a radialplot, also known as
Galbraith plot (https://en.wikipedia.org/wiki/Galbraith_plot).
It's essentially a cartesian plot of standardised estimates vs associated errors. The estimates values are represented by the slope of the line that goes through the origin.
here is what it looks like.
Now I want reproduce that with matplotlib so I thought I would build a
class that inherits from Axes...
So far so good...
Now I am wondering what to do with the right spine.
I could obviously plot a line and create ticks manually in the plotting area but I would rather do it properly and use the matplotlib machinery...
I saw that there are options to create a circular spine, or even arc spine.
https://matplotlib.org/3.1.0/api/spines_api.html
Looking at the spine documentation a Spine object requires, an axes, a type and a path... I am not sure what to do with that.
If I you give a path to the Spine class, what's the point of having the arc_spine or circular_spine method...
I thought I could do something like this (self refers to an Axis instance):
x, y = self.zscale_coordinates()
verts = list(zip(x,y))
codes = [Path.LINETO for i in range(len(verts))]
codes[0] = Path.MOVETO
spine = Spine(self, "right", Path(verts, codes))
self.spines["right"] = spine
Any help would be really appreciated.
Maybe someone has already done this? I found some R or Java packages but nothing with matplotlib and I would like to include it in some python code.
Thanks!
Related
I have created a grid of hexagons using matplotlib.patches library. The figure that is shown by my program is interactive: It allows for picking a hexagon with the left mouse button, which results in filling the hexagon black. This way, I am marking the circumference of a polygon:
Now, I want to connect the filled hexagons, such that I can see the circumference of the polygon. I want the polygon to be drawn upon pressing a key. Here is my idea:
def draw_circumference(event):
if event.key == 'd':
print(circumference)
plt.Polygon(circumference, fill=False, edgecolor='k')
#fig.canvas.draw()
fig.canvas.mpl_connect("key_press_event", draw_circumference)
The variable circumference contains the (x,y) coordinates of the respective centers of the marked hexagons as a list of tuples: [(x1,y1), x2,y2), ..., (xn,yn)]. I have commented the fig.canvas.draw() because I think it should work without this line. But so far it does not work, neither with, nor without fig.canvas.draw().
The function gets called, however. I know this as the print-statement is executed.
Any ideas what I am doing wrong?
Hard to debug without the full code, but usually you have to add a patch artist such as a polygon explicitly to the axis. Also, you probably do need the redraw call as the contents of the axis have changed.
p = plt.Polygon(circumference, ...)
ax.add_patch(p) # or ax.add_artist(p)
fig.canvas.draw()
I am generating STL files for 3D printing and then using mlab/mayavi to display them. I would like the Z axis to remain vertical as I rotate the image. According to the mayavi documentation, this can be achieved using the following incantation:
fig = mlab.gcf()
from tvtk.api import tvtk
fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain()
Unfortunately, as you can see from this screenshot of my app, it is not the Z axis but the Y axis that gets maintained vertical.
This is an issue because 3D printers always think of Z as the vertical axis, so I really need Z and not Y to be oriented vertically. Is there a way in which this can be achieved?
I have experienced the same problem, vertically aligned along the y-axis.
To get the scene to align vertically along the z-axis (which you want), you can simply add a scene.mlab.view() call before setting the interactor.
The scene.mlab.view() call aligns the camera correctly (with z up), before the interactor is set. I have found this solution by simply testing a bunch of stuff, I could not find this "hack" in the documentation.
New code:
fig = mlab.gcf()
from tvtk.api import tvtk
fig.scene.mlab.view(0, 90)
fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain()
You can rename them using the Mayavi button to access the pipeline, then selecting the appropiate axes object and changing the labels.
With code, you can add the xlabel, ylabel and zlabel keyword arguments to specify them. This is specified in the axes API
I am new to Python and matplotlib, and I recently referenced to THIS to update my tripcolor plot. With following data preparation
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import math
r = np.zeros((100,100))
t = np.zeros((100,100))
for i in range(0,100):
for j in range(0,100):
r[i,j]=i
t[i,j]=2*math.pi*j/100
x=r*np.cos(t)
y=r*np.sin(t)
z=r*r
xf=x.flatten()
yf=y.flatten()
zf=z.flatten()
triang = tri.Triangulation(xf,yf)
If I use tripcolor as it is intended,
# Works well
p = plt.tripcolor(triang, zf)
correct figure appears. But, if I try to update after creating tripcolor,
# Not working well
p = plt.tripcolor(triang, xf)
p.set_array(zf)
then, wrong figure appears. Both xf and zf have identical dimensions.
What am I doing wrong? What is the cause of the problem, and how can I avoid it?
Many thanks in advance.
=========================================================
Update
Thank you all. I actually solved myself.
The key was that I need to assign color for each area, which is controlled by shading argument, and default value for tripcolor is 'flat', which is, color for each vertex. So, when I plot the first figure, I need to make sure shading is 'gouraud', which assigns color for each area.
So,
p = plt.tripcolor(triang, xf, shading='gouraud')
p.set_array(zf)
works as I intended.
The reason
p = plt.tripcolor(triang, xf)
p.set_array(zf)
is not working as (may be) expected, is the following. In your case plt.tripcolor() returns a PolyCollection. The PolyCollection's set_array() will essentially set the colors of that Collection. However, the underlying triangles will not be changed, such that you end up with the triangles from xf but the colors from zf.
Since the generation of the tripcolor PolyCollection is quite involved (as it calls Triangulation itself) and there probably is no helper function to set the data externally (at least I am not aware of any), the solution might be not to update the tripcolor at all and instead generate a new one.
Is there any reason for you to update? Couldn't you just directly create p = plt.tripcolor(triang, zf)?
In case there is a real reason to it, like in an animation or so, an option would be to delete the first tripcolor plot before setting up the next.
# create one plot
p = plt.tripcolor(triang, xf)
#delete this plot (you need both lines actually!!)
p.remove()
del p
#create second plot
p = plt.tripcolor(triang, zf)
This is not really efficient, though, and in case someone has a better idea, I'd like to hear about that one as well.
been struggling with python and the matplotlib module. I am trying to draw some circles that are not filled and outlined in black. I am putting the correct arguments in the artist circle but it seems to ignore it and put blue. any ideas? Also the figure shows up automatically without me stating draw or show. How can i block that and control when the graph pops up? Thanks in advance.
my code
def draw_lattice(self,chart):
patches = []
for x in range(1,4):
for y in range (1,4):
circle = Circle((x,y), .25,color='k',fill=False)
# chart.add_patch(circle)
patches.append(circle)
p = PatchCollection(patches)
chart.add_collection(p)
Thanks in advance.
* UPDATE *
if i add each circle individually to the axes it will be formatted properly. If i add the collection it does not work. I have many more shapes to add and i would like to go the collection route. Any reason why one way would work and another wouldn't? I read somewhere that you need to add the artist but i tried that and got an error.
This is a rather late answer but I just came across the same problem and here is how to solve it:
What you need to do is tell the PatchCollection to match the original patches. To do this simply add match_original=True, like so:
p = PatchCollection(my_patches, match_original=True)
You can set the color of the circles when you create the patch collection:
p = PatchCollection(patches,facecolors='w',edgecolor='k')
From the Collection documentation:
"If any of edgecolors, facecolors, linewidths, antialiaseds are None, they default to their matplotlib.rcParams patch setting, in sequence form."
all. I want to update the colorbar of a figure when the imagedata is changed. So something like:
img = misc.lena()
fig = plt.figure()
ax = plt.imshow(im)
plt.colorbar(ax)
newimg = img+10*np.randn(512,512)
def update_colorbar(fig,ax,newimg):
cbar = fig.axes[1]
ax.set_data(newimg)
cbar.update_normal(ax)
plt.draw()
but it seems that returned results from fig.axes() does not have the colorbar instance like I expected. I can probably just pass the colorbar instance as an argument to the update function, but I thought just passing one fig parameter may be good enough. Can anyone explain a little bit on how to retrieve the colorbar from the figure? Or why 'fig.axes()' doesn't return the AxesImage or Colobar instance but just the Axes or AxesSubplot? I think I just need more understanding of the Axes/Figure stuff.Thank you!
Sometimes it can be useful to retrieve a colorbar even if it was not held in a variable.
In this case, it is possible to retrieve the colorbar from the plot with:
# Create an example image and colourbar
img = np.arange(20).reshape(5,4)
plt.imshow(img)
plt.colorbar()
# Get the current axis
ax = plt.gca()
# Get the images on an axis
im = ax.images
# Assume colorbar was plotted last one plotted last
cb = im[-1].colorbar
# Do any actions on the colorbar object (e.g. remove it)
cb.remove()
EDIT:
or, equivalently, the one liner:
plt.gca().images[-1].colorbar.remove()
N.B.: see also comments for the use of ax.collections[-1] instead of ax.images[-1]. For me it always worked only the first way, I don't know what depends on, maybe the type of data or plot.
Now you can operate on cb as if it were stored using commands described in the colorbar API. For instance you could change xlim or call update as explained in other comments. You could remove it with cb.remove() and recreate it with plt.colorbar().
plt.draw() or show should be called after to update plot.
As the image is the mappable associated to the colorbar and can be obtained with cb.mappable.
First off, I think you're getting a bit confused between the axes (basically, the plot), the figure, the scalar mappable (the image, in this case), and the colorbar instance.
The figure is the window that the plot is in. It's the top-level container.
Each figure usually has one or more axes. These are the plots/subplots.
Colorbars are also inside the figure. Adding a colorbar creates a new axes (unless you specify otherwise) for the colorbar to be displayed in. (It can't normally be displayed in the same axes as the image, because the colorbar needs to have its own x and y limits, etc.)
Some of your confusion is due to the fact that you're mixing the state-machine interface and the OO interface. It's fine to do this, but you need to understand the OO interface.
fig.axes[1] isn't the colorbar instance. It's the axes that the colorbar is plotted in. (Also, fig.axes[1] is just the second axes in the figure. It happens to be the axes that the colorbar is in for a figure with one subplot and one colorbar, but that won't generally be the case.)
If you want to update the colorbar, you'll need to hold on to the colorbar instance that colorbar returns.
Here's an example of how you'd normally approach things:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random((10,10)) # Generate some random data to plot
fig, ax = plt.subplots() # Create a figure with a single axes.
im = ax.imshow(data) # Display the image data
cbar = fig.colorbar(im) # Add a colorbar to the figure based on the image
If you're going to use update_normal to update the colorbar, it expects a ScalarMappable (e.g. an image created by imshow, the collection that scatter creates, the ContourSet that contour creates, etc) to be passed in. (There are other ways to do it, as well. Often you just want to update the limits, rather than the whole thing.) In the case of the code above, you'd call cbar.update_normal(im).
However, you haven't created a new AxesImage, you've just changed it's data. Therefore, you probably just want to do:
cbar.set_clim(newimg.min(), newimg.max())