This is the official code for Matplotlib：
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter, FixedLocator
# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))
# plot with various axes scales
fig, axs = plt.subplots(3, 2, figsize=(6, 8),
constrained_layout=True)
# linear
ax = axs[0, 0]
ax.plot(x, y)
ax.set_yscale('linear')
ax.set_title('linear')
ax.grid(True)
# log
ax = axs[0, 1]
ax.plot(x, y)
ax.set_yscale('log')
ax.set_title('log')
ax.grid(True)
# symmetric log
ax = axs[1, 1]
ax.plot(x, y - y.mean())
ax.set_yscale('symlog', linthresh=0.02)
ax.set_title('symlog')
ax.grid(True)
# logit
ax = axs[1, 0]
ax.plot(x, y)
ax.set_yscale('logit')
ax.set_title('logit')
ax.grid(True)
# Function x**(1/2)
def forward(x):
return x**(1/2)
def inverse(x):
return x**2
ax = axs[2, 0]
ax.plot(x, y)
ax.set_yscale('function', functions=(forward, inverse))
ax.set_title('function: $x^{1/2}$')
ax.grid(True)
ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 1, 0.2)**2))
ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 1, 0.2)))
# Function Mercator transform
def forward(a):
a = np.deg2rad(a)
return np.rad2deg(np.log(np.abs(np.tan(a) + 1.0 / np.cos(a))))
def inverse(a):
a = np.deg2rad(a)
return np.rad2deg(np.arctan(np.sinh(a)))
ax = axs[2, 1]
t = np.arange(0, 170.0, 0.1)
s = t / 2.
ax.plot(t, s, '-', lw=2)
ax.set_yscale('function', functions=(forward, inverse))
ax.set_title('function: Mercator')
ax.grid(True)
ax.set_xlim([0, 180])
ax.yaxis.set_minor_formatter(NullFormatter())
ax.yaxis.set_major_locator(FixedLocator(np.arange(0, 90, 10)))
plt.show()
We focus on the 'function: x^(1/2)' picture
What I need is to reverse the axis scale.
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter, FixedLocator
# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(1,len(y)+1)
#x=np.array(x,dtype=np.complex)
# plot with various axes scales
fig, axs = plt.subplots(1, 1, figsize=(6, 8),
constrained_layout=True)
# Function x**(1/2)
def forward(x):
return x**(1/3)
def inverse(x):
return x**3
ax = axs
ax.plot(x, y)
ax.set_yscale('function', functions=(inverse,forward)) #I reverse the order.
ax.set_title('function: $x^{1/3}$')
ax.grid(True)
plt.show()
But the results were not satisfactory
And it triggered a warning:
RuntimeWarning: invalid value encountered in power
return x**(1/3)
I don't know what went wrong.I want the bigger the Y-axis, the bigger the spacing.Please help me!Thanks.
Related
I have the following script that plots me a 2D field of some quantity in the domain:
def field(x, y, z):
fig, (ax) = plt.subplots()
ax.tricontour(x, y, z)
cntr = ax.tricontourf(x, y, z)
fig.colorbar(cntr, ax=ax)
plt.show()
Which gives me something like:
I want to add profiles on top of that figure, i.e.:
How do I do this?
Just plot on ax, setting linewidth (short lw) and color (short c) as needed:
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
x = np.random.uniform(-5, 40, 50)
y = np.random.uniform(-.25, .25, 50)
z = y * 100
fig, ax = plt.subplots(figsize=(10,2))
cntr = ax.tricontourf(x, y, z)
fig.colorbar(cntr, ax=ax)
ax.set(xlim=(0, 35), ylim=(-.2, .2))
for i in range(5, 35, 5):
ax.plot([i-1, i+1], [0.2, -0.2], lw=5, c='k')
The sample data is generated as follows,
import matplotlib as mpl
print(mpl.__version__) # 3.3.3
import matplotlib.pyplot as plt
import numpy as np
def f(x, y=0):
return np.piecewise(x, [x < 1, np.logical_and(1 <= x, x < 10), x >= 10], [lambda x: 0, lambda x: (x - 1) / 9 * 1000, lambda x: 1000])
x = np.logspace(-5, 5, 100)
y = np.logspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
I try to plot using the following code, but some contours disappear after calling clabel.
fig, ax = plt.subplots(figsize=(5, 3), dpi=120)
cr = ax.contour(X, Y, Z, levels=3, colors='black')
ax.clabel(cr, inline=True, fontsize=8, fmt='%d')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
This issue still appears even when contour linewidth and label font size are decreased.
fig, ax = plt.subplots(figsize=(5, 3), dpi=120)
cr = ax.contour(X, Y, Z, levels=3, colors='black', linewidths=0.6)
ax.clabel(cr, inline=True, fontsize=3, fmt='%d')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
I cannot figure out how to fix the weird behaviours of contour and clabel, and I suspect it is due to their incompatibility with log scale.
It is indeed a problem of the log axes, especially around the asymptote zero. However, why not defining the log axes before plotting, so matplotlib can take this into consideration when plotting?
import matplotlib as mpl
print(mpl.__version__) # 3.3.3
import matplotlib.pyplot as plt
import numpy as np
def f(x, y=0):
return np.piecewise(x, [x < 1, np.logical_and(1 <= x, x < 10), x >= 10], [lambda x: 0, lambda x: (x - 1) / 9 * 1000, lambda x: 1000])
x = np.logspace(-5, 5, 100)
y = np.logspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig, ax = plt.subplots(figsize=(5, 3), dpi=120)
ax.set_xscale('log')
ax.set_yscale('log')
cr = ax.contour(X, Y, Z, levels=3, colors='black')
ax.clabel(cr, inline=True, fontsize=8, fmt='%d')
plt.show()
Sample output:
The sample data is generated as follows,
import matplotlib as mpl
print(mpl.__version__) # 3.3.3
import matplotlib.pyplot as plt
import numpy as np
def f(x, y=0):
return np.piecewise(x, [x < 1, np.logical_and(1 <= x, x < 10), x >= 10], [lambda x: 0, lambda x: (x - 1) / 9 * 1000, lambda x: 1000])
x = np.logspace(-5, 5, 100)
y = np.logspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
I try to plot using the following code, but some contours disappear after calling clabel.
fig, ax = plt.subplots(figsize=(5, 3), dpi=120)
cr = ax.contour(X, Y, Z, levels=3, colors='black')
ax.clabel(cr, inline=True, fontsize=8, fmt='%d')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
This issue still appears even when contour linewidth and label font size are decreased.
fig, ax = plt.subplots(figsize=(5, 3), dpi=120)
cr = ax.contour(X, Y, Z, levels=3, colors='black', linewidths=0.6)
ax.clabel(cr, inline=True, fontsize=3, fmt='%d')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
I cannot figure out how to fix the weird behaviours of contour and clabel, and I suspect it is due to their incompatibility with log scale.
It is indeed a problem of the log axes, especially around the asymptote zero. However, why not defining the log axes before plotting, so matplotlib can take this into consideration when plotting?
import matplotlib as mpl
print(mpl.__version__) # 3.3.3
import matplotlib.pyplot as plt
import numpy as np
def f(x, y=0):
return np.piecewise(x, [x < 1, np.logical_and(1 <= x, x < 10), x >= 10], [lambda x: 0, lambda x: (x - 1) / 9 * 1000, lambda x: 1000])
x = np.logspace(-5, 5, 100)
y = np.logspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig, ax = plt.subplots(figsize=(5, 3), dpi=120)
ax.set_xscale('log')
ax.set_yscale('log')
cr = ax.contour(X, Y, Z, levels=3, colors='black')
ax.clabel(cr, inline=True, fontsize=8, fmt='%d')
plt.show()
Sample output:
Is there a plot function available in Python that is same as MATLAB's stackedplot()?
stackedplot() in MATLAB can line plot several variables with the same X axis and are stacked vertically. Additionally, there is a scope in this plot that shows the value of all variables for a given X just by moving the cursor (please see the attached plot). I have been able to generate stacked subplots in Python with no issues, however, not able to add a scope like this that shows the value of all variables by moving the cursor. Is this feature available in Python?
This is a plot using MATLAB's stackedplot():
import pandas as pd
import numpy as np
from datetime import datetime, date, time
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.transforms as transforms
import mplcursors
from collections import Counter
import collections
def flatten(x):
result = []
for el in x:
if isinstance(x, collections.Iterable) and not isinstance(el, str):
result.extend(flatten(el))
else:
result.append(el)
return result
def shared_scope(sel):
sel.annotation.set_visible(False) # hide the default annotation created by mplcursors
x = sel.target[0]
for ax in axes:
for plot in plotStore:
da = plot.get_ydata()
if type(da[0]) is np.datetime64: #pd.Timestamp
yData = matplotlib.dates.date2num(da) # to numerical values
vals = np.interp(x, plot.get_xdata(), yData)
dates = matplotlib.dates.num2date(vals) # to matplotlib dates
y = datetime.strftime(dates,'%Y-%m-%d %H:%M:%S') # to strings
annot = ax.annotate(f'{y:.30s}', (x, vals), xytext=(15, 10), textcoords='offset points',
bbox=dict(facecolor='tomato', edgecolor='black', boxstyle='round', alpha=0.5))
sel.extras.append(annot)
else:
y = np.interp(x, plot.get_xdata(), plot.get_ydata())
annot = ax.annotate(f'{y:.2f}', (x, y), xytext=(15, 10), textcoords='offset points', arrowprops=dict(arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"),
bbox=dict(facecolor='tomato', edgecolor='black', boxstyle='round', alpha=0.5))
sel.extras.append(annot)
vline = ax.axvline(x, color='k', ls=':')
sel.extras.append(vline)
trans = transforms.blended_transform_factory(axes[0].transData, axes[0].transAxes)
text1 = axes[0].text(x, 1.01, f'{x:.2f}', ha='center', va='bottom', color='blue', clip_on=False, transform=trans)
sel.extras.append(text1)
# Data to plot
data = pd.DataFrame(columns = ['timeOfSample','Var1','Var2'])
data.timeOfSample = ['2020-05-10 09:09:02','2020-05-10 09:09:39','2020-05-10 09:40:07','2020-05-10 09:40:45','2020-05-12 09:50:45']
data['timeOfSample'] = pd.to_datetime(data['timeOfSample'])
data.Var1 = [10,50,100,5,25]
data.Var2 = [20,55,70,60,50]
variables = ['timeOfSample',['Var1','Var2']] # variables to plot - Var1 and Var2 to share a plot
nPlot = len(variables)
dataPts = np.arange(0, len(data[variables[0]]), 1) # x values for plots
plotStore = [0]*len(flatten(variables)) # to store all the plots for annotation purposes later
fig, axes = plt.subplots(nPlot,1,sharex=True)
k=0
for i in range(nPlot):
if np.size(variables[i])==1:
yData = data[variables[i]]
line, = axes[i].plot(dataPts,yData,label = variables[i])
plotStore[k]=line
k = k+1
else:
for j in range(np.size(variables[i])):
yData = data[variables[i][j]]
line, = axes[i].plot(dataPts,yData,label = variables[i][j])
plotStore[k]=line
k = k+1
axes[i].set_ylabel(variables[i])
cursor = mplcursors.cursor(plotStore, hover=True)
cursor.connect('add', shared_scope)
plt.xlabel('Samples')
plt.show()
mplcursors can be used to create annotations while hovering, moving texts and vertical bars. sel.extras.append(...) helps to automatically hide the elements that aren't needed anymore.
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import mplcursors
import numpy as np
def shared_scope(sel):
x = sel.target[0]
annotation_text = f'x: {x:.2f}'
for ax, plot in zip(axes, all_plots):
y = np.interp(x, plot.get_xdata(), plot.get_ydata())
annotation_text += f'\n{plot.get_label()}: {y:.2f}'
vline = ax.axvline(x, color='k', ls=':')
sel.extras.append(vline)
sel.annotation.set_text(annotation_text)
trans = transforms.blended_transform_factory(axes[0].transData, axes[0].transAxes)
text1 = axes[0].text(x, 1.01, f'{x:.2f}', ha='center', va='bottom', color='blue', clip_on=False, transform=trans)
sel.extras.append(text1)
fig, axes = plt.subplots(figsize=(15, 10), nrows=3, sharex=True)
y1 = np.random.uniform(-1, 1, 100).cumsum()
y2 = np.random.uniform(-1, 1, 100).cumsum()
y3 = np.random.uniform(-1, 1, 100).cumsum()
all_y = [y1, y2, y3]
all_labels = ['Var1', 'Var2', 'Var3']
all_plots = [ax.plot(y, label=label)[0]
for ax, y, label in zip(axes, all_y, all_labels)]
for ax, label in zip(axes, all_labels):
ax.set_ylabel(label)
cursor = mplcursors.cursor(all_plots, hover=True)
cursor.connect('add', shared_scope)
plt.show()
Here is a version with separate annotations per subplot:
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import mplcursors
import numpy as np
def shared_scope(sel):
sel.annotation.set_visible(False) # hide the default annotation created by mplcursors
x = sel.target[0]
for ax, plot in zip(axes, all_plots):
y = np.interp(x, plot.get_xdata(), plot.get_ydata())
vline = ax.axvline(x, color='k', ls=':')
sel.extras.append(vline)
annot = ax.annotate(f'{y:.2f}', (x, y), xytext=(5, 0), textcoords='offset points',
bbox=dict(facecolor='tomato', edgecolor='black', boxstyle='round', alpha=0.5))
sel.extras.append(annot)
trans = transforms.blended_transform_factory(axes[0].transData, axes[0].transAxes)
text1 = axes[0].text(x, 1.01, f'{x:.2f}', ha='center', va='bottom', color='blue', clip_on=False, transform=trans)
sel.extras.append(text1)
fig, axes = plt.subplots(figsize=(15, 10), nrows=3, sharex=True)
y1 = np.random.uniform(-1, 1, 100).cumsum()
y2 = np.random.uniform(-1, 1, 100).cumsum()
y3 = np.random.uniform(-1, 1, 100).cumsum()
all_y = [y1, y2, y3]
all_labels = ['Var1', 'Var2', 'Var3']
all_plots = [ax.plot(y, label=label)[0]
for ax, y, label in zip(axes, all_y, all_labels)]
for ax, label in zip(axes, all_labels):
ax.set_ylabel(label)
cursor = mplcursors.cursor(all_plots, hover=True)
cursor.connect('add', shared_scope)
plt.show()
I can't find a way to draw errorbars in a 3D scatter plot in matplotlib.
Basically, for the following piece of code
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(1)
ax.scatter(X, Y, zs = Z, zdir = 'z')
I am looking for something like
ax.errorbar(X,Y, zs = Z, dY, dX, zserr = dZ)
Is there a way to do this in mplot3d? If not, are there other libraries with this function?
There is clearly example on forum http://mple.m-artwork.eu/home/posts/simple3dplotwith3derrorbars
Here is the code but is not built-in functionality:
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as axes3d
fig = plt.figure(dpi=100)
ax = fig.add_subplot(111, projection='3d')
#data
fx = [0.673574075,0.727952994,0.6746285]
fy = [0.331657721,0.447817839,0.37733386]
fz = [18.13629648,8.620699842,9.807536512]
#error data
xerror = [0.041504064,0.02402152,0.059383144]
yerror = [0.015649804,0.12643117,0.068676131]
zerror = [3.677693713,1.345712547,0.724095592]
#plot points
ax.plot(fx, fy, fz, linestyle="None", marker="o")
#plot errorbars
for i in np.arange(0, len(fx)):
ax.plot([fx[i]+xerror[i], fx[i]-xerror[i]], [fy[i], fy[i]], [fz[i], fz[i]], marker="_")
ax.plot([fx[i], fx[i]], [fy[i]+yerror[i], fy[i]-yerror[i]], [fz[i], fz[i]], marker="_")
ax.plot([fx[i], fx[i]], [fy[i], fy[i]], [fz[i]+zerror[i], fz[i]-zerror[i]], marker="_")
#configure axes
ax.set_xlim3d(0.55, 0.8)
ax.set_ylim3d(0.2, 0.5)
ax.set_zlim3d(8, 19)
plt.show()
I ended up writing the method for matplotlib: official example for 3D errorbars:
import matplotlib.pyplot as plt
import numpy as np
ax = plt.figure().add_subplot(projection='3d')
# setting up a parametric curve
t = np.arange(0, 2*np.pi+.1, 0.01)
x, y, z = np.sin(t), np.cos(3*t), np.sin(5*t)
estep = 15
i = np.arange(t.size)
zuplims = (i % estep == 0) & (i // estep % 3 == 0)
zlolims = (i % estep == 0) & (i // estep % 3 == 2)
ax.errorbar(x, y, z, 0.2, zuplims=zuplims, zlolims=zlolims, errorevery=estep)
ax.set_xlabel("X label")
ax.set_ylabel("Y label")
ax.set_zlabel("Z label")
plt.show()