I try to generate a sine wave signal with a frequency changing over time. The frequency is randomly defined by generating some random values in the range [0.5, 2] and interpolating the points between them.
The expected output signal is a sine wave with a non-changing amplitude and a changing frequency.
But there are some smaller bumps and the signal is not a 'smooth' sine wave. E.g. the period at x = 200 should be larger than the period at x = 10, but the opposite is the case.
Does anyone know, what happened here?
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
x_samples = np.arange(-100, 3100, 50)
freq_samples = np.random.random(x_samples.shape) * 1.5 + 0.5
x = np.arange(0, 3000, 0.1)
interpolation = interp1d(x_samples, freq_samples, kind='quadratic')
freq = interpolation(x)
y = np.sin(freq * x)
plt.plot(x, y, label="sin(freq(x) * x)")
plt.plot(x, freq, label="freq(x)")
plt.legend()
plt.show()
freq * x is probably not doing what's expected. The frequency needs to multiply the change in x for each point not the cumulative x.
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
x_samples = np.arange(-10, 350, 50)
freq_samples = np.random.random(x_samples.shape) * 1.5 + 0.5
x = np.arange(0, 300, 0.1)
dx = np.full_like(x, 0.1 ) # Change in x
interpolation = interp1d(x_samples, freq_samples, kind='quadratic')
freq = interpolation(x)
x_plot = (freq * dx ).cumsum() # Cumsum freq * change in x
y = np.sin(x_plot)
plt.plot(x, y, label="sin(freq(x) * x)")
plt.plot(x, freq, label="freq(x)")
plt.legend()
plt.show()
Related
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()
I have a random vector (random length and random angle) and would like to plot its approximate PDF (probability density function) via hist2d or hexbin. Unfortunately they seems not to work with polar plots, the following code yields nothing:
import numpy as np
import matplotlib.pyplot as plt
# Generate random data:
N = 1024
r = .5 + np.random.normal(size=N, scale=.1)
theta = np.pi / 2 + np.random.normal(size=N, scale=.1)
# Plot:
ax = plt.subplot(111, polar=True)
ax.hist2d(theta, r)
plt.savefig('foo.png')
plt.close()
I would like it to look like this: pylab_examples example code: hist2d_demo.py only in polar coordinates. The closest result so far is with colored scatter plot as adviced here:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Generate random data:
N = 1024
r = .5 + np.random.normal(size=N, scale=.1)
theta = np.pi / 2 + np.random.normal(size=N, scale=.1)
# Plot:
ax = plt.subplot(111, polar=True)
# Using approach from:
# https://stackoverflow.com/questions/20105364/how-can-i-make-a-scatter-plot-colored-by-density-in-matplotlib
theta_r = np.vstack([theta,r])
z = gaussian_kde(theta_r)(theta_r)
ax.scatter(theta, r, c=z, s=10, edgecolor='')
plt.savefig('foo.png')
plt.close()
Image from the second version of the code
Is there a better way to make it more like real PDF generated with hist2d? This question seems to be relevant (the resulting image is as expected), but it looks messy.
One way to this using pcolormesh:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Generate random data:
N = 10000
r = .5 + np.random.normal(size=N, scale=.1)
theta = np.pi / 2 + np.random.normal(size=N, scale=.1)
# Histogramming
nr = 50
ntheta = 200
r_edges = np.linspace(0, 1, nr + 1)
theta_edges = np.linspace(0, 2*np.pi, ntheta + 1)
H, _, _ = np.histogram2d(r, theta, [r_edges, theta_edges])
# Plot
ax = plt.subplot(111, polar=True)
Theta, R = np.meshgrid(theta_edges, r_edges)
ax.pcolormesh(Theta, R, H)
plt.show()
Result:
Note that the histogram is not yet normalized by the area of the bin, which is not constant in polar coordinates. Close to the origin, the bins are pretty small, so some other kind of meshing might be better.
I have a simple question but have not found any answer..
Let's have a look at this code :
from matplotlib import pyplot
import numpy
x=[0,1,2,3,4]
y=[5,3,40,20,1]
pyplot.plot(x,y)
It is plotted and all the points ared linked.
Let's say I want to get the y value of x=1,3.
How can I get the x values matching with y=30 ? (there are two)
Many thanks for your help
You could use shapely to find the intersections:
import matplotlib.pyplot as plt
import numpy as np
import shapely.geometry as SG
x=[0,1,2,3,4]
y=[5,3,40,20,1]
line = SG.LineString(list(zip(x,y)))
y0 = 30
yline = SG.LineString([(min(x), y0), (max(x), y0)])
coords = np.array(line.intersection(yline))
print(coords[:, 0])
fig, ax = plt.subplots()
ax.axhline(y=y0, color='k', linestyle='--')
ax.plot(x, y, 'b-')
ax.scatter(coords[:, 0], coords[:, 1], s=50, c='red')
plt.show()
finds solutions for x at:
[ 1.72972973 2.5 ]
The following code might do what you want. The interpolation of y(x) is straight forward, as the x-values are monotonically increasing. The problem of finding the x-values for a given y is not so easy anymore, once the function is not monotonically increasing as in this case. So you still need to know roughly where to expect the values to be.
import numpy as np
import scipy.interpolate
import scipy.optimize
x=np.array([0,1,2,3,4])
y=np.array([5,3,40,20,1])
#if the independent variable is monotonically increasing
print np.interp(1.3, x, y)
# if not, as in the case of finding x(y) here,
# we need to find the zeros of an interpolating function
y0 = 30.
initial_guess = 1.5 #for the first zero,
#initial_guess = 3.0 # for the secon zero
f = scipy.interpolate.interp1d(x,y,kind="linear")
fmin = lambda x: np.abs(f(x)-y0)
s = scipy.optimize.fmin(fmin, initial_guess, disp=False)
print s
I use python 3.
print(numpy.interp(1.3, x, y))
Y = 30
eps = 1e-6
j = 0
for i, ((x0, x1), (y0, y1)) in enumerate(zip(zip(x[:-1], x[1:]), zip(y[:-1], y[1:]))):
dy = y1 - y0
if abs(dy) < eps:
if y0 == Y:
print('There are infinite number of solutions')
else:
t = (Y - y0)/dy
if 0 < t < 1:
sol = x0 + (x1 - x0)*t
print('solution #{}: {}'.format(j, sol))
j += 1
I would like to group my data and to plot the boxplot for all the groups. There are many questions and answer about that, my problem is that I want to group by a continuos variable, so I want to histogramming my data.
Here what I have done. My data:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.random.chisquare(5, size=100000)
y = np.random.normal(size=100000) / (0.05 * x + 0.1) + 2 * x
f, ax = plt.subplots()
ax.plot(x, y, '.', alpha=0.05)
plt.show()
I want to study the behaviour of y (location, width, ...) as a function of x. I am not interested in the distribution of x so I will normalized it.
f, ax = plt.subplots()
xbins = np.linspace(0, 25, 50)
ybins = np.linspace(-20, 50, 50)
H, xedges, yedges = np.histogram2d(y, x, bins=(ybins, xbins))
norm = np.sum(H, axis = 0)
H /= norm
ax.pcolor(xbins, ybins, np.nan_to_num(H), vmax=.4)
plt.show()
I can plot histogram, but I want boxplot
binning = np.concatenate(([0], np.sort(np.random.random(20) * 25), [25]))
idx = np.digitize(x, binning)
data_to_plot = [y[idx == i] for i in xrange(len(binning))]
f, ax = plt.subplots()
midpoints = 0.5 * (binning[1:] + binning[:-1])
widths = 0.9 * (binning[1:] - binning[:-1])
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
majorLocator = MultipleLocator(2)
ax.boxplot(data_to_plot, positions = midpoints, widths=widths)
ax.set_xlim(0, 25)
ax.xaxis.set_major_locator(majorLocator)
ax.set_xlabel('x')
ax.set_ylabel('median(y)')
plt.show()
Is there an automatic way to do that, like ax.magic(x, y, binning)? Is there a better way to do that? (Have a look to https://root.cern.ch/root/html/TProfile.html for example, which plot the mean and the error of the mean as error bars)
In addition, I want to minize the memory footprint (my real data are much more than 100000), I am worried about data_to_plot, is it a copy?
I am trying to plot some histogrammed data on a polar axis but it wont seem to work properly. An example is below, I use the custom projection found How to make the angles in a matplotlib polar plot go clockwise with 0° at the top? it works for a scatter plot so I think my problem is with the histogram function. This has been driving me nuts all day, does anyone know what I am doing wrong...........
import random
import numpy as np
import matplotlib.pyplot as plt
baz = np.zeros((20))
freq = np.zeros((20))
pwr = np.zeros((20))
for x in range(20):
baz[x] = random.randint(20,25)*10
freq[x] = random.randint(1,10)*10
pwr[x] = random.randint(-10,-1)*10
baz = baz*np.pi/180.
abins = np.linspace(0,2*np.pi,360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
plt.figure(figsize=(14,14))
plt.subplot(1, 1, 1, projection='northpolar')
#plt.scatter(baz, freq)
plt.pcolormesh(H)
plt.show()
Your code works if you explicitly pass a mgrid (with similar characteristics than your a bins and sbins) to the pcolormesh command.
Below is an example inspired by your code:
import matplotlib.pyplot as plt
import numpy as np
#Generate the data
size = 200
baz = 10*np.random.randint(20, 25, size)*np.pi/180.
freq = 10*np.random.randint(1, 10, size)
pwr = 10*np.random.randint(-10, -1, size)
abins = np.linspace(0, 2*np.pi, 360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100, 50)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
#Grid to plot your data on using pcolormesh
theta, r = np.mgrid[0:2*np.pi:360j, 1:100:50j]
fig, ax = plt.subplots(figsize=(14,14), subplot_kw=dict(projection='northpolar'))
ax.pcolormesh(theta, r, H)
ax.set_yticklabels([]) #remove yticklabels
plt.show()