Pretty confusion matrix visualisation with matplotlib - matplotlib

I'm wondering if there are some templates for viewing confusion matrices in matplotlib with a similar rendering, of which I ignore the specific nomenclature.

I have tried doing something similar with your fig 2. Here is my code using hand written digits data.
import numpy as np
from scipy import ndimage
from matplotlib import pyplot as plt
from sklearn import manifold, datasets
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import leaves_list, linkage
def get_small_Xy(X, y, n=8):
X = np.vstack([X[y==e][0:n] for e in np.unique(y)])
y = np.hstack([[e]*n for e in np.unique(y)])
return X, y
# Load digit data
X_, y_ = datasets.load_digits(return_X_y=True)
# get a small set of data
X, y = get_small_Xy(X_, y_)
# Get similarity matrix
D = 1-squareform(pdist(X, metric='cosine'))
Z = linkage(D, method='ward')
ind = leaves_list(Z)
D = D[ind, :]
D = D[:, ind]
# labels and colors related
lbs = np.array([i if i==j else 10 for i in y for j in y])
colors = np.array(['C{}'.format(i) for i in range(10)]+['gray'])
colors[7] = '#413c39'
c = colors[lbs]
font1 = {'family': 'Arial',
'weight': 'normal',
'size': 8,
}
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
n = np.product(X.shape[0])
xx, yy = np.meshgrid(range(n), range(n))
xy = np.stack([xx.ravel(), yy.ravel()]).T
ax.scatter(xy[:, 0], xy[:, 1], s=D**4*30, fc=c, ec=None, alpha=0.8)
ax.set_xlim(-1, n)
ax.set_ylim(n, -1)
ax.tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
# place text
for i, e in enumerate(y):
ax.text(-1.2, i, e, ha='right', va='center', fontdict=font1, c=colors[e])
for i, e in enumerate(y):
ax.text(i, -1, e, ha='center', va='bottom', fontdict=font1, c=colors[e])
# draw lines
for e in np.where(np.diff(y))[0]:
ax.axhline(e+0.5, color='gray', lw=0.5, alpha=0.8)
ax.axvline(e+0.5, color='gray', lw=0.5, alpha=0.8)
One issue is the alpha of all points, which seems not to possible to set with different values with plot scatters in one run.

Related

multi animation whit subplot

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()

pyplot 3d z axis-log plot

In order to create a 3d plot using plot_surface and wireframe I wrote this (looking here around)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.ticker import MultipleLocator
import matplotlib.ticker as mticker
import numpy as np
from matplotlib.ticker import FormatStrFormatter
def log_tick_formatter(val, pos=None):
return f"10$^{{{int(val)}}}$"
data=np.genfromtxt('jpdfomegal2_90.dat')
x_len= len(np.unique(data[:, 0]))
y_len= len(np.unique(data[:, 1]))
X = data[:, 0].reshape(x_len, y_len)
Y = data[:, 1].reshape(x_len, y_len)
Z = data[:, 2].reshape(x_len, y_len)
#identify lowest non-negative Z value Zmin>0
Zmin = np.where(Z > 0, Z, np.inf).min()
Zmax = Z.max()
#and substitute zero with a slightly lower value than Zmin
Z[Z==0] = 0.9 * Zmin
#log transformation because the conversion in 3D
#does not work well in matplotlib
Zlog = np.log10(Z)
rc('font',family='palatino')
rc('font',size=18)
fig = plt.figure(figsize=(12,8))
#ax = fig.add_subplot(projection='3d')
ax = Axes3D(fig)
ax.set_xlim3d(0,15)
ax.set_zlim3d(np.floor(np.log10(Zmin))-1, np.ceil(np.log10(10)))
ax.zaxis.set_major_formatter(mticker.FuncFormatter(log_tick_formatter))
ax.zaxis.set_major_locator(mticker.MaxNLocator(integer=True))
rc('font',family='palatino')
rc('font',size=18)
tmp_planes = ax.zaxis._PLANES
ax.zaxis._PLANES = ( tmp_planes[2], tmp_planes[3],
tmp_planes[0], tmp_planes[1],
tmp_planes[4], tmp_planes[5])
ax.set_xlabel('$\omega^2 /<\omega^2>$')
ax.xaxis.labelpad = 10
ax.yaxis.labelpad = 10
ax.set_ylabel('cos$(\omega,\lambda^2)$')
ax.zaxis.set_rotate_label(False) # disable automatic rotation
ax.zaxis.labelpad = 10
ax.set_zlabel('')
ax.view_init(elev=17, azim=-60)
ax.grid(False)
ax.xaxis.pane.set_edgecolor('black')
ax.yaxis.pane.set_edgecolor('black')
ax.zaxis.pane.set_edgecolor('black')
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.set_major_locator(MultipleLocator(2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
ax.zaxis.set_major_locator(MultipleLocator(1))
#not sure this axis scaling routine is really necessary
scale_x = 1
scale_y = 1
scale_z = 0.8
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([scale_x, scale_y, scale_z, 1]))
ax.contour(X, Y, np.log10(Z), 4, lw=0.1, colors="k", linestyles="--", offset=np.floor(np.log10(Zmin))-1)#-7)
surf = ax.plot_surface(X, Y, np.log10(Z), cmap="binary", lw=0.1,alpha=0.5)
ax.plot_wireframe(X, Y, np.log10(Z),linewidth=1,color='k')
ax.contour(X, Y, np.log10(Z), 4, lw=0.1, colors="k", linestyles="solid")
fig.colorbar(surf, shrink=0.5, aspect=20)
plt.tight_layout()
plt.savefig('jpdf_lambda2_90.png', bbox_inches='tight')
plt.show()
the problem is related to the "minorticks" along zaxis .. I obtain this :
but I would have this format and ticks in the axis
Does somebody clarify how to obtain it and as well I did not find a way to use the log scale in pyplot 3d
There's an open bug on log-scaling in 3D plots, and it looks like there won't be a fix any time soon.
You can use a matplotlib.ticker.FixedLocator to add the z-axis minor ticks, as shown below.
I didn't have your data, so I've plotted an arbitrary surface.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.ticker import MultipleLocator, FixedLocator
import matplotlib.ticker as mticker
import numpy as np
from matplotlib.ticker import FormatStrFormatter
def log_tick_formatter(val, pos=None):
return f"10$^{{{int(val)}}}$"
x = np.linspace(1,15,15)
y = np.linspace(0,1,15)
X, Y = np.meshgrid(x, y)
Z = 1 + X**2 * Y**2
#identify lowest non-negative Z value Zmin>0
Zmin = np.where(Z > 0, Z, np.inf).min()
Zmax = Z.max()
#and substitute zero with a slightly lower value than Zmin
Z[Z==0] = 0.9 * Zmin
rc('font',family='palatino')
rc('font',size=18)
fig = plt.figure(figsize=(12,8))
ax = Axes3D(fig, auto_add_to_figure=False)
fig.add_axes(ax)
ax.set_xlim3d(0,15)
ax.set_zlim3d(np.floor(np.log10(Zmin))-1, np.ceil(np.log10(Zmax)))
ax.zaxis.set_major_formatter(mticker.FuncFormatter(log_tick_formatter))
tmp_planes = ax.zaxis._PLANES
ax.zaxis._PLANES = ( tmp_planes[2], tmp_planes[3],
tmp_planes[0], tmp_planes[1],
tmp_planes[4], tmp_planes[5])
ax.set_xlabel('$\omega^2 /<\omega^2>$')
ax.xaxis.labelpad = 10
ax.yaxis.labelpad = 10
ax.set_ylabel('cos$(\omega,\lambda^2)$')
ax.zaxis.set_rotate_label(False) # disable automatic rotation
ax.zaxis.labelpad = 10
ax.set_zlabel('')
ax.view_init(elev=17, azim=-60)
ax.grid(False)
ax.xaxis.pane.set_edgecolor('black')
ax.yaxis.pane.set_edgecolor('black')
ax.zaxis.pane.set_edgecolor('black')
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.set_major_locator(MultipleLocator(2))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
ax.zaxis.set_major_locator(MultipleLocator(1))
# Z minor ticks
zminorticks = []
zaxmin, zaxmax = ax.get_zlim()
for zorder in np.arange(np.floor(zaxmin),
np.ceil(zaxmax)):
zminorticks.extend(np.log10(np.linspace(2,9,8)) + zorder)
ax.zaxis.set_minor_locator(FixedLocator(zminorticks))
#not sure this axis scaling routine is really necessary
scale_x = 1
scale_y = 1
scale_z = 0.8
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([scale_x, scale_y, scale_z, 1]))
ax.contour(X, Y, np.log10(Z), 4, colors="k", linestyles="--", offset=np.floor(np.log10(Zmin))-1)#-7)
surf = ax.plot_surface(X, Y, np.log10(Z), cmap="binary", lw=0.1,alpha=0.5)
ax.plot_wireframe(X, Y, np.log10(Z),linewidth=1,color='k')
ax.contour(X, Y, np.log10(Z), 4, colors="k", linestyles="solid")
fig.colorbar(surf, shrink=0.5, aspect=20)
# get a warning that Axes3D is incompatible with tight_layout()
# plt.tight_layout()
# for saving
# fig.savefig('log3d.png')
plt.show()

How to set an appropriate point of view?

How to set xlim and ylim to see both cureves (omega and y) on a plot? Or how to verify that it is not possible?
import matplotlib.pyplot as plt
import numpy as np
e = 1.602176634e-19
m_e = 9.1093837015e-31
k = np.arange(0.00001, 50000, 0.003)
eps_0 = 8.8541878128e-12
n_0 = 100
c = 299792458
omega_p = np.sqrt(n_0*e**2/(eps_0*m_e))
omega = np.sqrt(omega_p**2+c**2+k**2)
y = k*c
fig, ax = plt.subplots()
plt.rcParams["figure.figsize"] = [5, 5]
# Plot
ax.yaxis.set_label_coords(-0.07, 0.84)
ax.xaxis.set_label_coords(0.95, -0.05)
ax.set_xlabel(r'$k$')
ax.set_ylabel(r'$\omega$', rotation='horizontal')
ax.set_xlim(10000, 40000)
ax.set_ylim(299792454, 299792462.1700816)
ax.plot(k, omega)
ax.plot(k, y)
# Focusing on appropriate part
print(omega[1000000]-omega[999999])
print(omega[-1]-omega[-2])
print(len(omega))
print(k[1000000])
print(k[-1])
print(omega[1000000])
print(omega[-1])
print(y[int(ax.get_xlim()[0])])
print(y[int(ax.get_xlim()[1])])
plt.show()
The output now:
There should be also an assymptote.
An idea is to just let matplotlib choose its default limits. Then you can interactively zoom in to an area of interest. The code below sets a log scale for the y-axis, which might help to fit everything. In order to avoid too many points, the 16 million points of np.arange(0.00001, 50000, 0.003) are replaced by np.linspace(0.00001, 50000, 10000).
import matplotlib.pyplot as plt
import numpy as np
e = 1.602176634e-19
m_e = 9.1093837015e-31
# k = np.arange(0.00001, 50000, 0.003)
k = np.linspace(0.00001, 50000, 10000)
eps_0 = 8.8541878128e-12
n_0 = 100
c = 299792458
omega_p = np.sqrt(n_0 * e ** 2 / (eps_0 * m_e))
omega = np.sqrt(omega_p ** 2 + c ** 2 + k ** 2)
y = k * c
fig, ax = plt.subplots()
plt.rcParams["figure.figsize"] = [5, 5]
ax.set_xlabel(r'$k$')
ax.set_ylabel(r'$\omega$', rotation='horizontal')
ax.plot(k, omega, color='blue')
ax.plot(k, y, color='red')
ax.set_yscale('log')
plt.show()

Matplotlib Interpolate empty pixels

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.

Matplotlib using image for points on plot

I have the following matplotlib script. I want to replace the points on the plot with images. Let's say 'red.png' for the red points and 'blue.png' for the blue points. How can I adjust the following to plot these images instead of the default points?
from scipy import linalg
import numpy as np
import pylab as pl
import matplotlib as mpl
import matplotlib.image as image
from sklearn.qda import QDA
###############################################################################
# load sample dataset
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data[:, 0:2] # Take only 2 dimensions
y = iris.target
X = X[y > 0]
y = y[y > 0]
y -= 1
target_names = iris.target_names[1:]
###############################################################################
# QDA
qda = QDA()
y_pred = qda.fit(X, y, store_covariances=True).predict(X)
###############################################################################
# Plot results
# constants
dpi = 72; imageSize = (32,32)
# read in our png file
im_red = image.imread('red.png')
im_blue = image.imread('blue.png')
def plot_ellipse(splot, mean, cov, color):
v, w = linalg.eigh(cov)
u = w[0] / linalg.norm(w[0])
angle = np.arctan(u[1] / u[0])
angle = 180 * angle / np.pi # convert to degrees
# filled gaussian at 2 standard deviation
ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
180 + angle, color=color)
ell.set_clip_box(splot.bbox)
ell.set_alpha(0.5)
splot.add_artist(ell)
xx, yy = np.meshgrid(np.linspace(4, 8.5, 200), np.linspace(1.5, 4.5, 200))
X_grid = np.c_[xx.ravel(), yy.ravel()]
zz_qda = qda.predict_proba(X_grid)[:, 1].reshape(xx.shape)
pl.figure()
splot = pl.subplot(1, 1, 1)
pl.contourf(xx, yy, zz_qda > 0.5, alpha=0.5)
pl.scatter(X[y == 0, 0], X[y == 0, 1], c='b', label=target_names[0])
pl.scatter(X[y == 1, 0], X[y == 1, 1], c='r', label=target_names[1])
pl.contour(xx, yy, zz_qda, [0.5], linewidths=2., colors='k')
print(xx)
pl.axis('tight')
pl.show()
You can plot images instead of markers in a figure using BboxImage as in this tutorial.
from matplotlib import pyplot as plt
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
# Load images.
redMarker = plt.imread('red.jpg')
blueMarker = plt.imread('blue.jpg')
# Data
blueX = [1, 2, 3, 4]
blueY = [1, 3, 5, 2]
redX = [1, 2, 3, 4]
redY = [3, 2, 3, 4]
# Create figure
fig = plt.figure()
ax = fig.add_subplot(111)
# Plots an image at each x and y location.
def plotImage(xData, yData, im):
for x, y in zip(xData, yData):
bb = Bbox.from_bounds(x,y,1,1)
bb2 = TransformedBbox(bb,ax.transData)
bbox_image = BboxImage(bb2,
norm = None,
origin=None,
clip_on=False)
bbox_image.set_data(im)
ax.add_artist(bbox_image)
plotImage(blueX, blueY, blueMarker)
plotImage(redX, redY, redMarker)
# Set the x and y limits
ax.set_ylim(0,6)
ax.set_xlim(0,6)
plt.show()