I'm trying to get the functionality of fill_betweenx() without having to use the function itself, because it doesn't accept the interpolate parameter. I need the interpolate functionality that is supported by fill_between(), but for the filling to happen relative to the x axis. It sounds like the interpolate parameter will be supported for fill_betweenx() in matplotlib 2.1, but it would be great to have access to the functionality via a workaround in the meantime.
This is the line of code in question:
ax4.fill_betweenx(x,300,p, where=p>=150, interpolate=True, facecolor='White', lw=1, zorder=2)
Unfortunately this gives me AttributeError: Unknown property interpolate.
One lazy way to do it is to use the fill_between() function with inverted coordinates on a figure that you don't show (i.e. close the figure before using plt.show()), and then re-use the vertices of the PolyCollection that fill_between() returns on your actual plot. It's not perfect, but it works as a quick fix. Here an example of what I'm talking about:
from matplotlib import pyplot as plt
from matplotlib.collections import PolyCollection
import numpy as np
fig, axes = plt.subplots(nrows = 2, ncols =2, figsize=(8,8))
#the data
x = np.linspace(0,np.pi/2,3)
y = np.sin(x)
#fill_between without interpolation
ax = axes[0,0]
ax.plot(x,y,'k')
ax.fill_between(x,0.5,y,where=y>0.25)
#fill_between with interpolation, keep the PolyCollection
ax = axes[0,1]
ax.plot(x,y,'k')
poly_col = ax.fill_between(x,0.5,y,where=y>0.25,interpolate=True)
#fill_betweenx -- no interpolation possible
ax = axes[1,0]
ax.plot(y,x,'k')
ax.fill_betweenx(x,0.5,y,where=y>0.25)
#faked fill_betweenx:
ax = axes[1,1]
ax.plot(y,x,'k')
#get the vertices from the saved PolyCollection, swap x- and y-values
v=poly_col.get_paths()[0].vertices
#convert to correct format
v2=list(zip(v[:,1],v[:,0]))
#and add to axes
ax.add_collection(PolyCollection([v2]))
#voila
plt.show()
The result of the code looks like this:
Related
I am trying to add a small line outside of my axis range which I want to use as a highly customized legend at a later stage. However, using axes.hlines changes the xlim of my axis, even though I specify transform = axes.transAxes. The xlim appears to be set such that the coordinates of the hlines are included in the datacoordinate range. Only, that these coordinates are meant to be axes coordinates, not data coordinates.
Here comes a minimal working example:
import numpy as np
import matplotlib.pyplot as plt
x_data = np.random.rand(10)+10
y_data = np.random.rand(10)
fig, ax = plt.subplots()
ax.scatter(x_data,y_data)
ax.hlines(0.5,1.1,1.2, transform = ax.transAxes, clip_on = False)
results in xlims being changed by the ax.hlines command:
while with ax.hlines being commented out one gets:
I am trying to use ax.scatter to plot a 3D scattering plot. I've read the data from a fits file and stored data from three column into x,y,z. And I have made sure x,y,z data are the same size. z has been normolized between 0 and 1.
import numpy as np
import matplotlib
from matplotlib import pylab,mlab,pyplot,cm
plt = pyplot
import pyfits as pf
from mpl_toolkits.mplot3d import Axes3D
import fitsio
data = fitsio.read("xxx.fits")
x=data["x"]
y=data["y"]
z=data["z"]
z = (z-np.nanmin(z)) /(np.nanmax(z) - np.nanmin(z))
Cen3D = plt.figure()
ax = Cen3D.add_subplot(111, projection='3d')
cmap=cm.ScalarMappable(norm=z, cmap=plt.get_cmap('hot'))
ax.scatter(x,y,z,zdir=u'z',cmap=cmap)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
What I am trying to achieve is use color to indicate the of size of z. Like higher value of z will get darker color. But I am keep getting a plot without the colormap I want, they are all the same default blue color. What did I do wrong? Thanks.
You can use the c keyword in the scatter command, to tell it how to color the points.
You don't need to set zdir, as that is for when you are plotting a 2d set
As #Lenford pointed out, you can use cmap='hot' in this case too, since you have already normalized your data.
I've modified your example to use some random data rather than your fits file.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
z = (z-np.nanmin(z)) /(np.nanmax(z) - np.nanmin(z))
Cen3D = plt.figure()
ax = Cen3D.add_subplot(111, projection='3d')
ax.scatter(x,y,z,cmap='hot',c=z)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
As per the pyplot.scatter documentation, the points specified to be plotted must be in the form of an array of floats for cmap to apply, otherwise the default colour (in this case, jet) will continue to apply.
As an aside, simply stating cmap='hot' will work for this code, as the colour map hot is a registered colour map in matplotlib.
I have the following Python code which I am using to plot a filled contour plot:
def plot_polar_contour(values, azimuths, zeniths):
theta = np.radians(azimuths)
zeniths = np.array(zeniths)
values = np.array(values)
values = values.reshape(len(azimuths), len(zeniths))
r, theta = np.meshgrid(zeniths, np.radians(azimuths))
fig, ax = subplots(subplot_kw=dict(projection='polar'))
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
cax = ax.contourf(theta, r, values, 30)
autumn()
cb = fig.colorbar(cax)
cb.set_label("Pixel reflectance")
show()
This gives me a plot like:
However, when I add the line ax.plot(0, 30, 'p') just before show() I get the following:
It seems that just adding that one point (which is well within the original axis range) screws up the axis range on the radius axis.
Is this by design, or is this a bug? What would you suggest doing to fix it? Do I need to manually adjust the axis ranges, or is there a way to stop the extra plot command doing this?
If the axis auto-scaling mode isn't explicitly specified, plot will use "loose" autoscaling and contourf will use "tight" autoscaling.
The same things happens for non-polar axes. E.g.
import matplotlib.pyplot as plt
import numpy as np
plt.imshow(np.random.random((10,10)))
plt.plot([7], [7], 'ro')
plt.show()
You have a number of options.
Explicitly call ax.axis('image') or ax.axis('tight') at some point in the code.
Pass in scalex=False and scaley=False as keyword arguments to plot.
Manually set the axis limits.
The easiest and most readable is to just explicitly call ax.axis('tight'), i.m.o.
I'm trying to do a heat map over a shape file in python. I need to make quite a few of these so don't want to read in the .shp every time.
Instead, I thought I could create a lineCollection instance of the map boundaries and overlay the two images. Problem is - I can't seem to get the two to line up correctly.
Here is the code, where linecol is the lineCollection object.
fig = plt.figure()
ax = fig.add_subplot(111)
ax.contourf(xi,yi,zi)
ax.add_collection(linecol, autolim = False)
plt.show()
Is there an easy way to fix the limits of linecol to match those of the other plot? I've had a play with set_xlim and transforms.Bbox, but can't seem to manage it.
Thank you very much for your help!
Transforms are tricky because of the various coordinate systems involved. See http://matplotlib.sourceforge.net/users/transforms_tutorial.html.
I managed to scale a LineCollection to the appropriate size like this. The key was to realize that I needed to add + ax.transData to the new transform I set on the LineCollection. (When you don't set any transform on an artist object, ax.transData is the default. It converts data coordinates into display coordinates.)
from matplotlib import cm
import matplotlib.pyplot as plt
import matplotlib.collections as mc
import matplotlib.transforms as tx
import numpy as np
fig = plt.figure()
# Heat map spans 1 x 1.
ax = fig.add_subplot(111)
xs = ys = np.arange(0, 1.01, 0.01)
zs = np.random.random((101,101))
ax.contourf(xs, ys, zs, cmap=cm.autumn)
lines = mc.LineCollection([[(5,1), (9,5), (5,9), (1,5), (5,1)]])
# Shape spans 10 x 10. Resize it to 1 x 1 before applying the transform from
# data coords to display coords.
trans = tx.Affine2D().scale(0.1) + ax.transData
lines.set_transform(trans)
ax.add_collection(lines)
plt.show()
(Output here: http://i.stack.imgur.com/hDNN8.png Not enough reputation to post inline.)
It should be easy to modify this if you need the shape translated or scaled unequally on x and y.
How to add strings to the axes in Axes3D instead of numbers?
I just started using the matplotlib. I have used Axes3dD to plot similar to the example given on their website (http://matplotlib.sourceforge.net/examples/mplot3d/bars3d_demo.html). Note that one must use the last verson (matplotlib 0.99.1), otherwise the axis gets a bit freaky. The example use this code:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = Axes3D(fig)
for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]):
xs = np.arange(20)
ys = np.random.rand(20)
ax.bar(xs, ys, zs=z, zdir='y', color=c, alpha=0.8)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
Now to my problem, I cant rename the axis to what I want. Instead of numbers i need to name the staples to string names. This I only want to do in two dimensions - the x dimension, and the z dimension (depth). I have tried to use this command:
ax.set_xticklabels('First staple',(0,0))
I get no error message but nevertheless no sting. If anyone can answer this question I would be most delighted!
Your actually on the right path there. but instead of a string you will want to pass a list or tuple to set_xticklabels(). You may also wish to adjust the center location for the label with set_xticks().
You may also find this function of use get_xmajorticklabels(). It will return the rendered tick labels. So you may also be able to adjust the value from its results.
Also retrieve the axis data, work on that and set it back to the renderer, turned out to be much simpler for me ;) other than that, what NerdyNick says