To point out shape correspondences I've created, I would like to color a 3d mesh in gray, except some points (e.g. 1 point) in red.
Here's my current code, which Unfortunately colors the whole figure in blue, and the last point in red.
And my code
mlab.figure()
part_color = np.full((self.f.shape[0]),0.98)
part_color[point_idx] = 1
part_plot = mlab.triangular_mesh(self.part.vx, self.part.vy, self.part.vz, self.part.triv,
scalars=part_color[:, np.newaxis])
Here is optimally what I'm aiming for (ignore the rest of the figures, I just want a red ball around some points)
you have to modify the Look Up Table (LUT) for that.
I took the "LUT modification" example and adapted it to your needs (highest value is red, everything else is gray):
# Create some data
import numpy as np
x, y = np.mgrid[-10:10:200j, -10:10:200j]
z = 100 * np.sin(x * y) / (x * y)
# Visualize it with mlab.surf
from mayavi import mlab
mlab.figure(bgcolor=(1, 1, 1))
surf = mlab.surf(z, colormap='cool')
# Retrieve the LUT of the surf object.
lut = surf.module_manager.scalar_lut_manager.lut.table.to_array()
# The lut is a 255x4 array, with the columns representing RGBA
# (red, green, blue, alpha) coded with integers going from 0 to 255.
# We modify the alpha channel to add a transparency gradient
lut[:] = 255/2 # all grey
# red
lut[-1,0] = 255
lut[-1,1] = 0
lut[-1,2] = 0
lut[:, -1] = 255 # 100% translucency
# and finally we put this LUT back in the surface object. We could have
# added any 255*4 array rather than modifying an existing LUT.
surf.module_manager.scalar_lut_manager.lut.table = lut
# We need to force update of the figure now that we have changed the LUT.
mlab.draw()
mlab.view(40, 85)
Related
Let's say I have a vector containing integers from the set [1,2,3]. I would like to create a colormap in which 1 always appears as blue, 2 always appears as red, and 3 always appears as purple, regardless of the range of the input data--e.g., even if the input vector only contains 1s and 2s, I would still like those to appear as blue and red, respectively (and purple is not used in this case).
I've tried the code below:
This works as expected (data contains 1, 2 and 3):
cmap = colors.ListedColormap(["blue", "red", "purple"])
bounds = [0.5,1.5,2.5,3.5]
norm = colors.BoundaryNorm(bounds, cmap.N)
data = np.array([1,2,1,2,3])
sns.heatmap(data.reshape(-1,1), cmap=cmap, norm=norm, annot=True)
Does not work as expected (data contains only 1 and 2):
cmap = colors.ListedColormap(["blue", "red", "purple"])
bounds = [0.5,1.5,2.5,3.5]
norm = colors.BoundaryNorm(bounds, cmap.N)
data = np.array([1,2,1,2,2])
sns.heatmap(data.reshape(-1,1), cmap=cmap, norm=norm, annot=True)
In the first example, 1 appears as blue, 2 appears as red and 3 appears as purple, as desired.
In the second example, 1 appears as blue and 2 appears as purple, while red is not used.
Not completely sure, but I think this minimal example solves your problem. Here, I've taken an actual colormap and edited it to produce a smaller version of it. Hope it helps!
#0. Import libraries
#==============================
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors
import seaborn as sns
import numpy as np
#==============================
#1. Create own colormap
#======================================
#1.1. Choose the colormap you want to
#pick up colors from
source_cmap=matplotlib.cm.get_cmap('Set2')
#1.2. Choose number of colors and set a step
cols=4;step=1/float(cols - 1)
#1.3. Declare a vector to store given colors
cmap_vec=[]
#1.4. Run from 0 to 1 (limits of colormap)
#stepwise and pick up equidistant colors
#---------------------------------------
for color in np.arange(0,1.1,step):
#store color in vector
cmap_vec.append( source_cmap(color) )
#---------------------------------------
#1.5. Create colormap with chosen colors
custom_cmap=\
colors.ListedColormap([ color for color in cmap_vec ])
#====================================
#2. Basic example to plot in
#======================================
A = np.matrix('0 3; 1 2')
B=np.asarray(A)
ax=sns.heatmap(B,annot=True,cmap=custom_cmap)
plt.show()
#======================================
I have a 2-variable discrete function represented in the form of a tuple through the following line of code:
hist_values, hist_x, hist_y = np.histogram2d()
Where you can think of a non-smooth 3d surface with hist_values being the height of the surface at grids with edge coordinates of (hist_x, hist_y).
Now, I would like to collect those grids for which hist_values is above some threshold level.
You could simply compare the hist_values with the threshold, this would give you a mask as an array of bool which can be used in slicing, e.g.:
import numpy as np
# prepare random input
arr1 = np.random.randint(0, 100, 1000)
arr2 = np.random.randint(0, 100, 1000)
# compute 2D histogram
hist_values, hist_x, hist_y = np.histogram2d(arr1, arr2)
mask = hist_values > threshold # the array of `bool`
hist_values[mask] # only the values above `threshold`
Of course, the values are then collected in a flattened array.
Alternatively, you could also use mask to instantiate a masked-array object (using numpy.ma, see docs for more info on it).
If you are after the coordinates at which this is happening, you should use numpy.where().
# i0 and i1 contain the indices in the 0 and 1 dimensions respectively
i0, i1 = np.where(hist_values > threshold)
# e.g. this will give you the first value satisfying your condition
hist_values[i0[0], i1[0]]
For the corresponding values of hist_x and hist_y you should note that these are the boundaries of the bins, and not, for example, the mid-values, therefore you could resort to the lower or upper bound of it.
# lower edges of `hist_x` and `hist_y` respectively...
hist_x[i0]
hist_y[i1]
# ... and upper edges
hist_x[i0 + 1]
hist_y[i1 + 1]
The planned annotation box does not appear on my plot, however, I've tried a wide range of values for its coordinates.
What's wrong with that?!
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
def f(s,t):
a = 0.7
b = 0.8
Iext= 0.5
tau = 12.5
v = s[0]
w = s[1]
dndt = v - np.power(v,3)/3 - w + Iext
dwdt = (v + a - b * w)/tau
return [dndt, dwdt]
t = np.linspace(0,200)
s0=[1,1]
s = odeint(f,s0,t)
plt.plot(t,s[:,0],'b-', linewidth=1.0)
plt.xlabel(r"$t(sec.)$")
plt.ylabel(r"$V (volt)$")
plt.legend([r"$V$"])
annotation_string = r"$I_{ext}=0.5$"
plt.text(15, 60, annotation_string, bbox=dict(facecolor='red', alpha=0.5))
plt.show()
The coordinates to plt.text are data coordinates by default. This means in order to be present in the plot they should not exceed the data limits of your plot (here, ~0..200 in x direction, ~-2..2 in y direction).
Something like plt.text(10,1.8) should work.
The problem with that is that once the data limits change (because you plot something different or add another plot) the text item will be at a different position inside the canvas.
If this is undesired, you can specify the text in axes coordinates (ranging from 0 to 1 in both directions). In order to place the text always in the top left corner of the axes, independent on what you plot there, you can use e.g.
plt.text(0.03,0.97, annotation_string, bbox=dict(facecolor='red', alpha=0.5),
transform=plt.gca().transAxes, va = "top", ha="left")
Here the transform keyword tells the text to use Axes coordinates, and va = "top", ha="left" means, that the top left corner of the text should be the anchor point.
The annotation is appearing far above your plot because you have given a 'y' coordinate of 60, whereas your plot ends at '2' (upwards).
Change the second argument here:
plt.text(15, 60, annotation_string, bbox=dict(facecolor='red', alpha=0.5))
It needs to be <=2 to show up on the plot itself. You may also want to change the x coorinate (from 15 to something less), so that it doesn't obscure your lines.
e.g.
plt.text(5, 1.5, annotation_string, bbox=dict(facecolor='red', alpha=0.5))
Don't be alarmed by my (5,1.5) suggestion, I would then add the following line to the top of your script (beneath your imports):
rcParams['legend.loc'] = 'best'
This will choose a 'best fit' for your legend; in this case, top left (just above your annotation). Both look quite neat then, your choice though :)
In matplotlib, I would like to change colorbar's color in some particular value interval. For example, I would like to change the seismic colorbar, to let the values between -0.5 and 0.5 turn white, how can I do this?
thank you very much
You basically need to create your own colormap that has the particular features you want. Of course it is possible to make use of existing colormaps when doing so.
Colormaps are always ranged between 0 and 1. This range will then be mapped to the data interval. So in order to create whites between -0.5 and 0.5 we need to know the range of data - let's say data goes from -1 to 1. We can then decide to have the lower (blues) part of the seismic map go from -1 to -0.5, then have white between -0.5 and +0.5 and finally the upper part of the seismic map (reds) from 0.5 to 1. In the language of a colormap this corresponds to the ranges [0,0.25], [0.25, 0.75] and [0.75,1]. We can then create a list, with the first and last 25% percent being the colors of the seismic map and the middle 50% white.
This list can be used to create a colormap, using matplotlib.colors.LinearSegmentedColormap.from_list("colormapname", listofcolors).
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
n=50
x = 0.5
lower = plt.cm.seismic(np.linspace(0, x, n))
white = plt.cm.seismic(np.ones(100)*0.5)
upper = plt.cm.seismic(np.linspace(1-x, 1, n))
colors = np.vstack((lower, white, upper))
tmap = matplotlib.colors.LinearSegmentedColormap.from_list('terrain_map_white', colors)
x = np.linspace(0,10)
X,Y = np.meshgrid(x,x)
z = np.sin(X) * np.cos(Y*0.4)
fig, ax = plt.subplots()
im = ax.imshow(z, cmap=tmap)
plt.colorbar(im)
plt.show()
For more general cases, you may need a color normalization (using matplotlib.colors.Normalize). See e.g. this example, where a certain color in the colormap is always fixed at a data value of 0, independent of the data range.
I am trying to vary the color of a line plotted from data in two arrays (eg. ax.plot(x,y)). The color should vary as the index into x and yincreases. I am essentially trying to capture the natural 'time' parameterization of the data in arrays x and y.
In a perfect world, I want something like:
fig = pyplot.figure()
ax = fig.add_subplot(111)
x = myXdata
y = myYdata
# length of x and y is 100
ax.plot(x,y,color=[i/100,0,0]) # where i is the index into x (and y)
to produce a line with color varying from black to dark red and on into bright red.
I have seen examples that work well for plotting a function explicitly parameterized by some 'time' array, but I can't get it to work with raw data...
The second example is the one you want... I've edited it to fit your example, but more importantly read my comments to understand what is going on:
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
x = myXdata
y = myYdata
t = np.linspace(0,1,x.shape[0]) # your "time" variable
# set up a list of (x,y) points
points = np.array([x,y]).transpose().reshape(-1,1,2)
print points.shape # Out: (len(x),1,2)
# set up a list of segments
segs = np.concatenate([points[:-1],points[1:]],axis=1)
print segs.shape # Out: ( len(x)-1, 2, 2 )
# see what we've done here -- we've mapped our (x,y)
# points to an array of segment start/end coordinates.
# segs[i,0,:] == segs[i-1,1,:]
# make the collection of segments
lc = LineCollection(segs, cmap=plt.get_cmap('jet'))
lc.set_array(t) # color the segments by our parameter
# plot the collection
plt.gca().add_collection(lc) # add the collection to the plot
plt.xlim(x.min(), x.max()) # line collections don't auto-scale the plot
plt.ylim(y.min(), y.max())