I am trying to color the errorbar points based on the color from an array. But getting an error. My code is shown below:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable, coolwarm as cmap
from matplotlib.colors import Normalize
fig = plt.figure(1)
sp = fig.add_subplot(1, 1, 1)
sp.set_xlabel(r'$x$')
sp.set_ylabel(r'$y$')
x = np.random.rand(10)
y = np.random.rand(10)
M = np.logspace(9, 10, 10)
norm = Normalize(vmin=8, vmax=11,clip=False) # controls the min and max of the colorbar
smap = ScalarMappable(cmap=cmap, norm=norm)
for xi, yi, Mi in zip(x, y, M):
c = cmap(norm(np.log10(Mi))) # make sure to color by log of mass, not mass
sp.errorbar(
xi,
yi,
yerr=[[.1], [.1]],
xerr=[[.1], [.1]],
ecolor=c,
marker='o',
mec=c,
mfc=c
)
cb = plt.colorbar(smap)
cb.set_label(r'$\log_{10}M$')
I am getting the following error:
TypeError: You must first set_array for mappable
For matplotlib < 3.1, you need to set an array - which can be empty
sm = ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
fig.colorbar(sm)
For matplotlib >= 3.1, this is not necessary any more.
sm = ScalarMappable(cmap=cmap, norm=norm)
fig.colorbar(sm)
Related
I'm trying to change a colorbar attached to a scatter plot so that the minimum and maximum of the colorbar are the minimum and maximum of the data, but I want the data to be centred at zero as I'm using a colormap with white at zero. Here is my example
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 61)
y = np.linspace(0, 1, 61)
C = np.linspace(-10, 50, 61)
M = np.abs(C).max() # used for vmin and vmax
fig, ax = plt.subplots(1, 1, figsize=(5,3), dpi=150)
sc=ax.scatter(x, y, c=C, marker='o', edgecolor='k', vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
cbar=fig.colorbar(sc, ax=ax, label='$R - R_0$ (mm)')
ax.set_xlabel('x')
ax.set_ylabel('y')
As you can see from the attached figure, the colorbar goes down to -M, where as I want the bar to just go down to -10, but if I let vmin=-10 then the colorbar won't be zerod at white. Normally, setting vmin to +/- M when using contourf the colorbar automatically sorts to how I want. This sort of behaviour is what I expect when contourf uses levels=np.linspace(-M,M,61) rather than setting it with vmin and vmax with levels=62. An example showing the default contourf colorbar behaviour I want in my scatter example is shown below
plt.figure(figsize=(6,5), dpi=150)
plt.contourf(x, x, np.reshape(np.linspace(-10, 50, 61*61), (61,61)),
levels=62, vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
plt.colorbar(label='$R - R_0$ (mm)')
Does anyone have any thoughts? I found this link which I thought might solve the problem, but when executing the cbar.outline.set_ydata line I get this error AttributeError: 'Polygon' object has no attribute 'set_ydata' .
EDIT a little annoyed that someone has closed this question without allowing me to clarify any questions they might have, as none of the proposed solutions are what I'm asking for.
As for Normalize.TwoSlopeNorm, I do not want to rescale the smaller negative side to use the entire colormap range, I just want the colorbar attached to the side of my graph to stop at -10.
This link also does not solve my issue, as it's the TwoSlopeNorm solution again.
After changing the ylim of the colorbar, the rectangle formed by the surrounding spines is too large. You can make this outline invisible. And then add a new rectangular border:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 61)
y = np.linspace(0, 1, 61)
C = np.linspace(-10, 50, 61)
M = np.abs(C).max() # used for vmin and vmax
fig, ax = plt.subplots(1, 1, figsize=(5, 3), dpi=150)
sc = ax.scatter(x, y, c=C, marker='o', edgecolor='k', vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
cbar = fig.colorbar(sc, ax=ax, label='$R - R_0$ (mm)')
cb_ymin = C.min()
cb_ymax = C.max()
cb_xmin, cb_xmax = cbar.ax.get_xlim()
cbar.ax.set_ylim(cb_ymin, cb_ymax)
cbar.outline.set_visible(False) # hide the surrounding spines, which are too large after set_ylim
cbar.ax.add_patch(plt.Rectangle((cb_xmin, cb_ymin), cb_xmax - cb_xmin, cb_ymax - cb_ymin,
fc='none', ec='black', clip_on=False))
plt.show()
Another approach until v3.5 is released is to make a custom colormap that does what you want (see also https://matplotlib.org/stable/tutorials/colors/colormap-manipulation.html#sphx-glr-tutorials-colors-colormap-manipulation-py)
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
fig, axs = plt.subplots(2, 1)
X = np.random.randn(32, 32) + 2
pc = axs[0].pcolormesh(X, vmin=-6, vmax=6, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0])
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
fig, axs = plt.subplots(2, 1)
X = np.random.randn(32, 32) + 2
pc = axs[0].pcolormesh(X, vmin=-6, vmax=6, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0])
def keep_center_colormap(vmin, vmax, center=0):
vmin = vmin - center
vmax = vmax - center
dv = max(-vmin, vmax) * 2
N = int(256 * dv / (vmax-vmin))
RdBu_r = cm.get_cmap('RdBu_r', N)
newcolors = RdBu_r(np.linspace(0, 1, N))
beg = int((dv / 2 + vmin)*N / dv)
end = N - int((dv / 2 - vmax)*N / dv)
newmap = ListedColormap(newcolors[beg:end])
return newmap
newmap = keep_center_colormap(-2, 6, center=0)
pc = axs[1].pcolormesh(X, vmin=-2, vmax=6, cmap=newmap)
fig.colorbar(pc, ax=axs[1])
plt.show()
"get_ylim()" is changing the result of the transformation from data to display coordinates in matplotlib (I'm using version 3.2.1). Is it supposed to change axis properties? It's the same effect using "get_xlim()".
Here is my code:
import matplotlib.pyplot as plt
import numpy as np
dpi = 80
plt.rcParams.update({'font.size': 12})
fig, ax = plt.subplots(figsize=(1280/dpi, 720/dpi), dpi=dpi)
x = np.arange(200)
y = - 0.1 * x
ax.plot(x, y)
points = ax.transData.transform(np.vstack((x, y)).T).astype(int)
print(points[:5])
ax.get_ylim()
points = ax.transData.transform(np.vstack((x, y)).T).astype(int)
print(points[:5])
Both prints output different results only with the ax.get_ylim() in place.
Whenever I try to plot something with matplotlib, I get the following error:
File "C:\Users\username\AppData\Local\Programs\Python\Python37-32\Lib\tkinter\__init__.py", line 2018, in __init__
baseName = os.path.basename(sys.argv[0])
builtins.IndexError: list index out of range
For example, i've tried the following code:
import matplotlib.pyplot as plt
import numpy as np
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()
I have the latest version of matplotlib, please help.
Thank you
You need to import numpy.
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()
I am trying to create a 3D plot but I am having trouble with the z-axis label. It simply doesn't appear in the graph. How do I amend this? The code is as follows
# Gamma vs Current step 2
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
h = np.arange(0.1,5.1,0.1)
gamma = np.arange(0.1,5.1,0.1)
sigmaz_hgam = np.array([.009998,.03988,.08878,.15403
,.230769,.312854,.394358,.4708311,.539697879,.6,.6518698
,.696033486,.73345752165,.7651390123,.792,.814845635
,.8343567,.851098499,.865535727,.8780487,.8889486,.89848986
,.906881,.914295,.9208731,.9267338,.93197569,.93668129
,.9409202379,.94475138,.951383,.9542629,.956895,.959309
,.961526,.9635675,.96545144,.9671934,.968807,.97030539
,.9716983,.972995,.974206,.975337,.97639567,.977387,.978318
,.97919266,.98,.9807902])
mu = 1
sigmaz_hgam = mu*sigmaz_hgam
# creates an empty list for current values to be stored in
J1 = []
for i in range(sigmaz_hgam.size):
expec_sz = sigmaz_hgam[i]
J = 4*gamma[i]*(mu-expec_sz)
J1.append(J.real)
#print(J)
This part of the code is what is used to graph out which is where the problem lies
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
x = h
y = gamma
z = J1
ax.plot(x, y, z, label='Dephasing Model')
ax.legend()
ax.set_xlabel('h', fontsize=10)
ax.set_ylabel('$\gamma$')
ax.yaxis._axinfo['label']['space_factor'] = 3.0
for t in ax.zaxis.get_major_ticks(): t.label.set_fontsize(10)
# disable auto rotation
ax.zaxis.set_rotate_label(False)
ax.set_zlabel('J', fontsize=10, rotation = 0)
plt.show()
On my version of Matplotlib (2.0.2), on a Mac, I see the label (which is there – most of it is just being cropped out of the image in your case).
You could try to reduce the padding between the ticks and the label:
ax.zaxis.labelpad = 0
I have the following colormap in Python which maps each value to a color. But my question: How can I quantize the values for getting a same color for a specified range?
For example: from 0 until 10 (Green) ,
from 10 until 50 (yellow),
from 50 until 55 (red) , ...
import matplotlib as mpl
import matplotlib
from matplotlib import cm
.
.
.
norm = matplotlib.colors.Normalize(vmin = min,vmax = max, clip = True)
.
.
for i in range(numberMaterials):
step = (max-min)/numberMaterials
value = min + step*i
mat = bpy.data.materials.new("mat" +str(i))
color = cm.jet(norm(value),bytes=True)
It seems you are asking for a BoundaryNorm.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
cmap = matplotlib.colors.ListedColormap(["limegreen", "gold", "crimson"])
norm = matplotlib.colors.BoundaryNorm([0,10,50,55], 3)
x = np.linspace(0,55)
fig, (ax, ax2) = plt.subplots(ncols=2)
sc = ax.scatter(x,x, c=x, cmap=cmap, norm=norm)
fig.colorbar(sc, ax=ax, spacing="uniform")
sc2 = ax2.scatter(x,x, c=x, cmap=cmap, norm=norm)
fig.colorbar(sc2, ax=ax2, spacing="proportional")
plt.show()