I'm trying to make a colorplot of a function with matplotlob.pyplot.imshow. However, depending on the size of the plot, I get a vertical line as an artifact.
The code to generate the plot is:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib import cm
def double_vortex(X,Y):
return np.angle((X + 25)+1j*Y) - np.angle((X - 25)+1j*Y)
X = np.arange(-50,50)
Y = np.arange(-50,50)
X, Y = np.meshgrid(X, Y)
phi0_vortex = double_vortex(X,Y)
fig = plt.figure(figsize=(16,8))
gs = gridspec.GridSpec(1, 3, width_ratios=[2.5, 1.5,1])
for i in range(3):
ax = plt.subplot(gs[i])
ax.imshow(phi0_vortex % (2*np.pi), cmap=cm.hsv, vmin=0, vmax=2*np.pi)
The resulting plot is this:
You can see that the two smaller plots exhibit a vertical line as an artefact. Is this a bug in matplotlib or somehow actually to be expected?
This is a consequence of matplotlib's downsampling algorithm, which happens in data space, and in your case a pair of pixels that has [359, 1] in them, get averaged to 180, and you get the cyan line. This is https://github.com/matplotlib/matplotlib/issues/18735 for which we are working on a solution to allow RGB-space downsampling (as well).
What can you do about this until that is improved in Matplotlib? Don't downsample in Matplotlib is the simple answer - make a big png, and then resample in post-processing software like imagemagick.
Related
I want to add to my plot a colorbar, which has a nonlinear scale. For example, for such a plot:
I would like to have just 5 different colors on the bar on the right-hand side, instead of the gradient (don't pay attention to the plot itself; it's just an example).
I don't want to use contourf and would like to find some more general solution.
If you want to have discrete values in your colorbar, a quick way to do this would be to use the cmap=plt.cm.get_cmap() function and pass the name of whatever colormap class you are working with, along with the desired number of bins.
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
import numpy as np
# Random Data Visualation
x = np.linspace(0, 10, 1000)
data = np.sin(x) * np.cos(x[:, np.newaxis])
plt.imshow(data, cmap=plt.cm.get_cmap('viridis', 5))
plt.colorbar()
plt.clim(-1, 1);
More documentation on everything color maps in Matplotlib [here]
I frequently find myself working in log units for my plots, for example taking np.log10(x) of data before binning it or creating contour plots. The problem is, when I then want to make the plots presentable, the axes are in ugly log units, and the tick marks are evenly spaced.
If I let matplotlib do all the conversions, i.e. by setting ax.set_xaxis('log') then I get very nice looking axes, however I can't do that to my data since it is e.g. already binned in log units. I could manually change the tick labels, but that wouldn't make the tick spacing logarithmic. I suppose I could also go and manually specify the position of every minor tick such it had log spacing, but is that the only way to achieve this? That is a bit tedious so it would be nice if there is a better way.
For concreteness, here is a plot:
I want to have the tick labels as 10^x and 10^y (so '1' is '10', 2 is '100' etc.), and I want the minor ticks to be drawn as ax.set_xaxis('log') would draw them.
Edit: For further concreteness, suppose the plot is generated from an image, like this:
import matplotlib.pyplot as plt
import scipy.misc
img = scipy.misc.face()
x_range = [-5,3] # log10 units
y_range = [-55, -45] # log10 units
p = plt.imshow(img,extent=x_range+y_range)
plt.show()
and all we want to do is change the axes appearance as I have described.
Edit 2: Ok, ImportanceOfBeingErnest's answer is very clever but it is a bit more specific to images than I wanted. I have another example, of binned data this time. Perhaps their technique still works on this, though it is not clear to me if that is the case.
import numpy as np
import pandas as pd
import datashader as ds
from matplotlib import pyplot as plt
import scipy.stats as sps
v1 = sps.lognorm(loc=0, scale=3, s=0.8)
v2 = sps.lognorm(loc=0, scale=1, s=0.8)
x = np.log10(v1.rvs(100000))
y = np.log10(v2.rvs(100000))
x_range=[np.min(x),np.max(x)]
y_range=[np.min(y),np.max(y)]
df = pd.DataFrame.from_dict({"x": x, "y": y})
#------ Aggregate the data ------
cvs = ds.Canvas(plot_width=30, plot_height=30, x_range=x_range, y_range=y_range)
agg = cvs.points(df, 'x', 'y')
# Create contour plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.contourf(agg, extent=x_range+y_range)
ax.set_xlabel("x")
ax.set_ylabel("y")
plt.show()
The general answer to this question is probably given in this post:
Can I mimic a log scale of an axis in matplotlib without transforming the associated data?
However here an easy option might be to scale the content of the axes and then set the axes to a log scale.
A. image
You may plot your image on a logarithmic scale but make all pixels the same size in log units. Unfortunately imshow does not allow for such kind of image (any more), but one may use pcolormesh for that purpose.
import numpy as np
import matplotlib.pyplot as plt
import scipy.misc
img = scipy.misc.face()
extx = [-5,3] # log10 units
exty = [-45, -55] # log10 units
x = np.logspace(extx[0],extx[-1],img.shape[1]+1)
y = np.logspace(exty[0],exty[-1],img.shape[0]+1)
X,Y = np.meshgrid(x,y)
c = img.reshape((img.shape[0]*img.shape[1],img.shape[2]))/255.0
m = plt.pcolormesh(X,Y,X[:-1,:-1], color=c, linewidth=0)
m.set_array(None)
plt.gca().set_xscale("log")
plt.gca().set_yscale("log")
plt.show()
B. contour
The same concept can be used for a contour plot.
import numpy as np
from matplotlib import pyplot as plt
x = np.linspace(-1.1,1.9)
y = np.linspace(-1.4,1.55)
X,Y = np.meshgrid(x,y)
agg = np.exp(-(X**2+Y**2)*2)
fig, ax = plt.subplots()
plt.gca().set_xscale("log")
plt.gca().set_yscale("log")
exp = lambda x: 10.**(np.array(x))
cf = ax.contourf(exp(X), exp(Y),agg, extent=exp([x.min(),x.max(),y.min(),y.max()]))
ax.set_xlabel("x")
ax.set_ylabel("y")
plt.show()
I need to generate a stack of 2D polar plots (a 3D cylindrical plot) so that I can view a distorted cylinder. I want to use matplotlib since I already have it installed and want to distribute my code to others who only have matplotlib. For example, say I have a bunch of 2-D arrays. Is there any way I can do this without having to download an external package? Here's my code.
#!usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-180.0,190.0,10)
theta = (np.pi/180.0 )*x # in radians
A0 = 55.0
offset = 60.0
R = [116.225,115.105,114.697,115.008,115.908,117.184,118.61,119.998,121.224,122.216,\
122.93,123.323,123.343,122.948,122.134,120.963,119.575,118.165,116.941,116.074,115.66\
,115.706,116.154,116.913,117.894,119.029,120.261,121.518,122.684,123.594,124.059,\
123.917,123.096,121.661,119.821,117.894,116.225]
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8],polar=True) # Polar plot
ax.plot(theta,R,lw=2.5)
ax.set_rmax(1.5*(A0)+offset)
plt.show()
I have 10 more similar 2D polar plots and I want to stack them up nicely. If there's any better way to visualize a distorted cylinder in 3D, I'm totally open to suggestions. Any help would be appreciated. Thanks!
If you want to stack polar charts using matplotlib, one approach is to use the Axes3D module. You'll notice that I used polar coordinates first and then converted them back to Cartesian when I was ready to plot them.
from numpy import *
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
n = 1000
fig = plt.figure()
ax = fig.gca(projection='3d')
for k in linspace(0, 5, 5):
THETA = linspace(0, 2*pi, n)
R = ones(THETA.shape)*cos(THETA*k)
# Convert to Cartesian coordinates
X = R*cos(THETA)
Y = R*sin(THETA)
ax.plot(X, Y, k-2)
plt.show()
If you play with the last argument of ax.plot, it controls the height of each slice. For example, if you want to project all of your data down to a single axis you would use ax.plot(X, Y, 0). For a more exotic example, you can map the height of the data onto a function, say a saddle ax.plot(X, Y, -X**2+Y**2 ). By playing with the colors as well, you could in theory represent multiple 4 dimensional datasets (though I'm not sure how clear this would be). Examples below:
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.
I would like to be able to produce a stacked line graph (similar to the method used here) with Python (preferably using matplotlib, but another library would be fine too). How can I do this?
This similar to the stacked bar graph example on their website, except I'd like the top of bar to be connected with a line segment and the area underneath to be filled. I might be able to approximate this by decreasing the gaps between bars and using lots of bars (but this seems like a hack, and besides I'm not sure if it is possible).
Newer versions of matplotlib contain the function plt.stackplot, which allow for several different "out-of-the-box" stacked area plots:
import numpy as np
import pylab as plt
X = np.arange(0, 10, 1)
Y = X + 5 * np.random.random((5, X.size))
baseline = ["zero", "sym", "wiggle", "weighted_wiggle"]
for n, v in enumerate(baseline):
plt.subplot(2 ,2, n + 1)
plt.stackplot(X, *Y, baseline=v)
plt.title(v)
plt.axis('tight')
plt.show()
I believe Area Plot is a common term for this type of plot, and in the specific instance recited in the OP, Stacked Area Plot.
Matplotlib does not have an "out-of-the-box" function that combines both the data processing and drawing/rendering steps to create a this type of plot, but it's easy to roll your own from components supplied by Matplotlib and NumPy.
The code below first stacks the data, then draws the plot.
import numpy as NP
from matplotlib import pyplot as PLT
# just create some random data
fnx = lambda : NP.random.randint(3, 10, 10)
y = NP.row_stack((fnx(), fnx(), fnx()))
# this call to 'cumsum' (cumulative sum), passing in your y data,
# is necessary to avoid having to manually order the datasets
x = NP.arange(10)
y_stack = NP.cumsum(y, axis=0) # a 3x10 array
fig = PLT.figure()
ax1 = fig.add_subplot(111)
ax1.fill_between(x, 0, y_stack[0,:], facecolor="#CC6666", alpha=.7)
ax1.fill_between(x, y_stack[0,:], y_stack[1,:], facecolor="#1DACD6", alpha=.7)
ax1.fill_between(x, y_stack[1,:], y_stack[2,:], facecolor="#6E5160")
PLT.show()
If you have a dataframe, it's quite easy:
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot.area();
From: pandas documentation
A slightly less hackish way would be to use a line graph in the first place and matplotlib.pyplot.fill_between. To emulate the stacking you have to shift the points up yourself.
x = np.arange(0,4)
y1 = np.array([1,2,4,3])
y2 = np.array([5,2,1,3])
# y2 should go on top, so shift them up
y2s = y1+y2
plot(x,y1)
plot(x,y2s)
fill_between(x,y1,0,color='blue')
fill_between(x,y1,y2s,color='red')