I am trying to align multiple subplots with a zoomed inset plot. Something like this:
|‾ ‾ ‾ ‾| |‾ ‾| |‾ ‾| |‾ ‾|
| | |_ _| |_ _| |_ _|
| |
|_ _ _ _|
I have tried using subplot2grid but I am not sure how to specify this kind of alignment. I am using a modified version of the Zoom region inset axes example in the matplotlib gallery: https://matplotlib.org/stable/gallery/subplots_axes_and_figures/zoom_inset_axes.html
Here is the reproducible example:
from matplotlib import cbook
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
def get_demo_image():
z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
# z is a numpy array of 15x15
return z, (-3, 4, -4, 3)
ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
# make data
Z, extent = get_demo_image()
Z2 = np.zeros((150, 150))
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax1.imshow(Z2, extent=extent, origin="lower")
# inset axes....
axins = ax1.inset_axes([1.1, 0.5, 0.5, 0.5])
axins.imshow(Z2, extent=extent, origin="lower")
# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
axins.set_axis_off()
ax1.indicate_inset_zoom(axins, edgecolor="black")
ax2 = plt.subplot2grid((2, 2), (0, 1))
plt.tight_layout()
plt.show()
output:
I want ax2 to be aligned with axins - i.e. it should be the same size as the zoomed inset plot and placed side-by-side. How do I achieve this with matplotlib?
I've been able to achieve that, but I fear that it only works as long as you use an equal aspect ratio on the second axis (maybe you are plotting another image with height=width):
from matplotlib import cbook
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
def get_demo_image():
z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
# z is a numpy array of 15x15
return z, (-3, 4, -4, 3)
gs = GridSpec(1, 2)
fig = plt.figure()
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
# make data
Z, extent = get_demo_image()
Z2 = np.zeros((150, 150))
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax1.imshow(Z2, extent=extent, origin="lower")
# inset axes....
axins = ax1.inset_axes([1.1, 0.5, 0.5, 0.5])
axins.imshow(Z2, extent=extent, origin="lower")
# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
axins.set_axis_off()
ax1.indicate_inset_zoom(axins, edgecolor="black")
ax2.set_aspect("equal")
plt.tight_layout()
plt.show()
Inspired by Davide_sd's answer it is possible to specify the following GridSpec, which seems to (almost) give my desired results in a hacky way:
gs = GridSpec(2, 4, hspace=-0.5)
fig = plt.figure()
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[0, 3])
output:
Related
I got some sort of a problem with a pendulum animation, I tried to display my animation (the pendulum's movement) next to a graph in two separate axes, but when I try my code, it barely works displaying two axes that overlap on one another... Here is what I tried:
PS: best would be that the circles I was intended to add at the end of my pendulum appear on the final animation, but I really have no idea how to put them only on a particular ax
from numpy import sin, cos, pi, array
import numpy as np
import scipy.integrate
import matplotlib.pyplot as plt
import matplotlib.animation as animation
g = 10
y0 = np.array([np.pi / 2.0, 0]) # angle, vitesse
j = 0.2
def f(y, t):
return np.array([y[1], -g * np.sin(y[0])-j*y[1]])
t = np.linspace(0, 100, 10000)
y = scipy.integrate.odeint(f, y0, t)
theta, thetadot = y[:, 0], y[:, 1]
fig, axs = plt.subplots(1,2)
axs[0] = fig.add_subplot(xlim=(-1.5, 1.5), ylim=(-1.5, 1.5))
axs[0].grid()
axs[0].set_box_aspect(1)
# anchor = plt.Circle((0, 0), 0.01, color='black')
# mass = plt.Circle((sin(y0[0]),-cos(y0[0])), 0.2, color='black')
pendulums = axs[0].plot((0, sin(y0[0])), (0, -cos(y0[0])), 'o-', color = 'black')
# plt.gca().add_patch(weight) # adding circles
# plt.gca().add_patch(attach)
phase = axs[1].plot(theta,thetadot)
def animate(i):
angle = theta[i]
x = (0, sin(angle))
y = (0, -cos(angle))
#mass.center = (x[1],y[1])
pendulums[0].set_data(x, y)
anim = animation.FuncAnimation(fig, animate, interval=10)
plt.show()
I'm trying to personalize some of my graphs so I'd like to change the font of all axis. I wrote the code below for that. The problem is that I can't change the font of the z axis. Somebody have a recommendation?
Thanks in advance.
import matplotlib.pyplot as plt
from matplotlib import cm
font = {'size' : 12}
cm=1/2.54
size_x = 10*cm
size_y = 8*cm
min_limx = 4
max_limx = 10
min_limy = 0
max_limy = 50
min_limz = 0
max_limz = 8
#%%
fig, ax = plt.subplots(figsize=(size_x, size_y), subplot_kw={"projection": "3d"})
X, Y = np.meshgrid(xnew, ynew, indexing='ij')
Z1 = m_fill
Z2 = m_nofill
surf = ax.plot_surface(X, Y, Z1, cmap=cm.viridis, linewidth=0, antialiased=False)
surf = ax.plot_surface(X, Y, Z2, color='w', linewidth=0, antialiased=False)
ax.set_xlabel('$\lambda$', **font)
ax.set_ylabel('$\chi$', **font)
ax.set_zlabel('$\it{m_{0}, кг}$', **font)
lin_x = np.arange(min_limx, max_limx, step=2)
lin_y = np.arange(min_limy, max_limy, step=15)
plt.xticks(lin_x, **font)
plt.yticks(lin_y, **font)
#plt.zticks(lin_z, **font) # AttributeError: module 'matplotlib.pyplot' has no attribute 'zticks'
ax.set_zlim(0, 8, )
#plt.gca().set_aspect('equal', adjustable='box')
ax.view_init(45, -45)
#plt.tight_layout()
plt.grid()
plt.show()
fig.savefig('p0_V_m0.png', format='png', dpi=300)
Ps: This is how the figure looks like right now.
The fontsize of the tick labels is set by the labelsize parameter:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
font = {'size': 20}
ax.tick_params('z', labelsize=font['size'])
(To set the label size for all three axes you can use ax.tick_params(labelsize=20).)
I am working with the following image:
from matplotlib import cbook
import matplotlib.patches as mpatches
from matplotlib.axes._base import _TransformedBoundsLocator
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
# a numpy array of 15x15
Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
gs = GridSpec(2, 3)
fig = plt.figure(figsize=(3*3,2*3))
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[1, 2])
Z2 = np.zeros((150, 150))
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax1.imshow(Z2)
ax1.set_aspect("equal")
ax2.set_aspect("equal")
plt.tight_layout()
plt.show()
output:
As shown in the image, the x-axis of both plots are aligned. However, when I am adding a patch to the first plot the alignment becomes distorted:
Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
gs = GridSpec(2, 3)
fig = plt.figure(figsize=(3*3,2*3))
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[1, 2])
Z2 = np.zeros((150, 150))
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax1.imshow(Z2)
x, y, width, height = 30, 30, 15, 15
ex, ey = (0,1)
xy_data = x + ex * width, y + ey * height
p = mpatches.ConnectionPatch(
xyA=(0,1), coordsA=ax2.transAxes,
xyB=xy_data, coordsB=ax1.transData)
ax1.add_patch(p)
ax1.set_aspect("equal")
ax2.set_aspect("equal")
plt.tight_layout()
plt.show()
output:
Why is this? How can I add a patch whilst retaining the original layout?
I have a file 'mydata.tmp' which contains 3 colums like this:
3.81107 0.624698 0.000331622
3.86505 0.624698 0.000131237
3.91903 0.624698 5.15136e-05
3.97301 0.624698 1.93627e-05
1.32802 0.874721 1.59245
1.382 0.874721 1.542
1.43598 0.874721 1.572
1.48996 0.874721 4.27933
etc.
Then I want to make a heatmap color plot where the first two columns are coordinates, and the third column are the values of that coordinates.
Also, I would like to set the third column in log scale.
I have done this
import pandas as pd
import matplotlib.pyplot as plt
import scipy.interpolate
import numpy as np
import matplotlib.colors as colors
# import data
df = pd.read_csv('mydata.tmp', delim_whitespace=True,
comment='#',header=None,
names=['1','2','3'])
x = df['1']
y = df['2']
z = df['3']
spacing = 500
xi, yi = np.linspace(x.min(), x.max(), spacing), np.linspace(y.min(),
y.max(), spacing)
XI, YI = np.meshgrid(xi, yi)
rbf = scipy.interpolate.Rbf(x, y, z, function='linear')
ZI = rbf(XI, YI)
fig, ax = plt.subplots()
sc = ax.imshow(ZI, vmin=z.min(), vmax=z.max(), origin='lower',
extent=[x.min(), x.max(), y.min(),
y.max()], cmap="GnBu", norm=colors.LogNorm(vmin=ZI.min(),
vmax=ZI.max()))
fig.colorbar(sc, ax=ax, fraction=0.05, pad=0.01)
plt.show()
And I get this Image
which has all these empty pixels.
I am looking for something like this instead (I have done this other picture with GNUplot):
How can I do it?
You could use cmap.set_bad to define a color for the NaN values:
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.colors as colors
from matplotlib import cm
import copy
# Some data
x = np.array([0, 1, 3, 0, 2, 4])
y = np.array([0, 0, 0, 1, 1, 1])
z = np.array([2, 2, 3, 2, 3, 4])
# Interpolation on a grid:
nrb_points = 101
xi = np.linspace(-.5, 4.5, nrb_points)
yi = np.linspace(-.5, 1.5, nrb_points)
XI, YI = np.meshgrid(xi, yi)
xy = np.vstack((x, y)).T
XY = (XI.ravel(), YI.ravel())
ZI = griddata(points, z, XY,
method='linear',
fill_value=np.nan) # Value used [for] points
# outside of the convex hull
# of the input points.
ZI = ZI.reshape(XI.shape)
# Color map:
cmap = copy.copy(cm.jet)
cmap.set_bad('grey', 1.)
# Graph:
plt.pcolormesh(xi, yi, ZI,
#norm=colors.LogNorm(),
cmap=cmap);
plt.colorbar(label='z');
plt.plot(x, y, 'ko');
plt.xlabel('x'); plt.ylabel('y');
the result is:
I would also use griddata instead of RBF method for the interpolation. Then, point outside the input data area (i.e. the convex hull) can be set to NaN.
I would like to define colors sections (blue: [0-15000], green: [15000-23000], red[23000,]) that should be used for y-values. Is it somehow possible in matplotlib?
You can color regions on a matplotlib plot using collections.BrokenBarHCollection:
import matplotlib.pyplot as plt
import matplotlib.collections as collections
fig = plt.figure()
ax = fig.add_subplot(111)
# Plot your own data here
x = range(0, 30000)
y = range(0, 30000)
ax.plot(x, y)
xrange = [(0, 30000)]
yrange1 = (0, 15000)
yrange2 = (15000, 23000)
yrange3 = (23000, 30000)
c1 = collections.BrokenBarHCollection(xrange, yrange1, facecolor='blue', alpha=0.5)
c2 = collections.BrokenBarHCollection(xrange, yrange2, facecolor='green', alpha=0.5)
c3 = collections.BrokenBarHCollection(xrange, yrange3, facecolor='red', alpha=0.5)
ax.add_collection(c1)
ax.add_collection(c2)
ax.add_collection(c3)
plt.show()