How to set linewidth of axis in 3d plot in python? Is it somehow possible with mpl.rcParams?
Code:
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')
ax.legend()
plt.show()
Try this:
ax.plot_surface(X, Y, Z, linewidth=1)
Related
How to colour space between two lines with a colour transition? For instance, with grey colour - the dark grey from the upper line should become lighter as proceeding to the lower line. Thank you
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
plt.rcParams["figure.figsize"] = [8, 8]
x = np.linspace(0, 1, 100)
y = 0.3*x
ax.set_ylim(-0.2, 0.6)
ax.plot(x, y)
width_l = ax.get_ylim()[1] - ax.get_ylim()[0]
ax.plot(x, y - 0.1*width_l)
plt.show()
Edit
And this, please? How to make the width of the coloured part the same?
import matplotlib.pyplot as plt
import numpy as np
import pywt
fig, ax = plt.subplots()
plt.rcParams["figure.figsize"] = [8, 8]
wavelet = pywt.ContinuousWavelet('morl')
psi, x = wavelet.wavefun(level=2)
cmap = plt.get_cmap('Greys_r')
ax.plot(x, psi)
ax.set_xlim(ax.get_xlim()[0], ax.get_xlim()[1])
y_a = ax.get_ylim()
ax.set_ylim(y_a[0],y_a[1]*1.3)
width_l = ax.get_ylim()[1] - ax.get_ylim()[0]
x_range = ax.get_xlim()[1] - ax.get_xlim()[0]
x_shift = x_range * 0.1
ax.plot([x[0]+x_shift, x[1]+x_shift], [psi[0], psi[1]])
ax.plot([x[2]-x_shift, x[3]-x_shift], [psi[2], psi[3]])
ax.plot([x[1], x[2]], [psi[1]-width_l*0.1, psi[2]-width_l*0.1])
for t in np.linspace(0, 1, 40):
ax.plot(x, psi - t * 0.1 * width_l, color=cmap(t/2 + 0.25))
plt.show()
You could draw a lot of parallel lines (or curves) using a color from a gray-scale colormap. The example code below uses a transformation u = t/2 + 0.25, so when t goes from 0 to 1, u would just go between 0.25 and 0.75 to select of specific range from the colormap, avoiding the very dark and very light parts.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
plt.rcParams["figure.figsize"] = [8, 8]
x = np.linspace(0, 1, 100)
y = 0.3 * x
width_l = ax.get_ylim()[1] - ax.get_ylim()[0]
ax.set_ylim(-0.2, 0.6)
cmap = plt.get_cmap('Greys_r')
for t in np.linspace(0, 1, 40):
u = t/2 + 0.25
ax.plot(x, y - t * 0.1 * width_l, color=cmap(u))
ax.plot(x, y)
ax.plot(x, y - 0.1 * width_l)
plt.show()
Could you help me with the following script please? How to set the width of ticks in this multiplot for plotting 6 subplots?
import numpy as np
import matplotlib.pyplot as plt
from numpy import array
import matplotlib as mpl
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, ax = plt.subplots(sharex=True)
plt.figure(figsize=(12, 9))
fig1 = plt.subplot(231)
plt.plot(x, y**2)
fig1.set_xlim(0e-13,2e-13)
fig1.set_ylim(-1.15e-14,0.01e-14)
fig2=plt.subplot(232)
plt.plot(x, y**2)
fig2.set_xlim(0e-13,2e-13)
fig2.set_ylim(-7.3e-15,7.3e-15)
fig3=plt.subplot(233)
plt.plot(x, y**2)
fig3.set_ylim(0e-13,1.2e-13)
fig3.set_xlim(0e-13,2e-13)
fig4=plt.subplot(234)
plt.plot(x, y**2)
fig4.set_xlim(-1.15e-14,0.01e-14)
fig4.set_ylim(-7.3e-15,7.3e-15)
fig5=plt.subplot(235)
plt.plot(x, y**2)
fig5.set_xlim(-7.3e-15,7.3e-15)
fig5.set_ylim(0e-13,1.2e-13)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig6=plt.subplot(236)
plt.plot(x, y**2)
fig6.set_xlim(-1.5e-14,0e-14)
fig6.set_ylim(0e-13,1.2e-13)
plt.show()
I tried:
ax.xaxis.set_tick_params(width=2)
ax.yaxis.set_tick_params(width=2)
and
for figures in [fig1, fig2, fig3, fig4, fig5, fig6]:
ax.xaxis.set_tick_params(width=2)
ax.yaxis.set_tick_params(width=2)
but nothing has changed and the width of ticks stayed the same.
First of all, the following
fig, ax = plt.subplots(sharex=True)
plt.figure(figsize=(12, 9))
creates two figures, which I guess you do not want.
Second, when you execute fig1 = plt.subplot(231), you do not create a Figure object but rather an Axes one. This call is redundant as it can be handled directly with plt.subplots().
Third, ax.xaxis.set_tick_params(width=2) has no effect in the figure you are interested in because ax refers to the axis created by fig, ax = plt.subplots(sharex=True) and not to any axis in the current figure you are drawing in, which was created by plt.figure(figsize=(12, 9)).
Have a look below for a cleaner version.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, axarr = plt.subplots(nrows=2, ncols=3)
for ax in axarr.flatten():
ax.plot(x, y ** 2)
ax.tick_params(width=2)
fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.show()
How to set ticks to be on the opposite axes please? When I drop it the axes changes but not in this way. Thank you
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')
ax.legend()
plt.show()
I am using the following codes to plot several data points (xi, yi)
import numpy as np
import matplotlib.pyplot as plt
xi = np.array(data_df[['col_A']])
yi = np.array(data_df[['col_B']])
plt.figure()
plt.plot(xi, yi)
x = np.linspace(0, 30, 30)
y= np.exp(x*0.16)
plt.plot(x, y)
plt.show()
I want the plot to look like this:
Thanks!
User subplots to plot more than 1 plots in 1 figure.You need to call plt.show() only once.
import numpy as np
import matplotlib.pyplot as plt
xi = np.array(data_df[['col_A']])
yi = np.array(data_df[['col_B']])
plt.figure()
plt.subplot(2,1,1)
plt.plot(xi, yi)
plt.subplot(2,1,2)
x = np.linspace(0, 30, 30)
y= np.exp(x*0.16)
plt.plot(x, y)
plt.show()
draw a graph of equation in the form of y=mx+b in python3.x
example y = 5x + 9
This is a very general question. Try to be more specific. It depends how you want to draw it.
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0., 5., 0.2)
y = 5 * x + 9
plt.plot(x, y)
plt.show()
or
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(-1., 5., 0.2)
y = 5 * x + 9
fig, ax = plt.subplots()
ax.plot(x,y)
ax.grid(True, which='both')
ax.axhline(y=0, color='k')
ax.axvline(x=0, color='k')
These are very basic drawing. You can create more sophisticated graphs, but you will have to be more specific in your question.
You can define your y(x) function and then plot it as follows:
import matplotlib.pyplot as plt
def y(x):
return [5*i+9 for i in x]
x = range(0,10)
plt.plot(x,y(x))
plt.show()
This produces follwing graph:
With turtle
You can as well get a graph with turtle with following code for example:
from turtle import Turtle, Screen
def y(x):
return 5*x+9
def plotter(turtle, x_range):
turtle.penup()
for x in x_range:
turtle.goto(x, y(x))
turtle.pendown()
screen = Screen()
screen.setworldcoordinates(0, 0, 9, 60)
turtle = Turtle(visible=False)
x = range(0,10)
plotter(turtle, x)
screen.exitonclick()
Which produces: