I used below code to generate the colorbar plot of an image:
plt.imshow(distance)
cb = plt.colorbar()
plt.savefig(generate_filename("test_images.png"))
cb.remove()
The image looks likes this:
I want to draw a single contour line on this image where the signed distance value is equal to 0. I checked the doc of pyplot.contour but it needs a X and Y vector that represents the coordinates and a Z that represents heights. Is there a method to generate X, Y, and Z? Or is there a better function to achieve this? Thanks!
If you leave out X and Y, by default, plt.contour uses the array indices (in this case the range 0-1023 in both x and y).
To only draw a contour line at a given level, you can use levels=[0]. The colors= parameter can fix one or more colors. Optionally, you can draw a line on the colorbar to indicate the value of the level.
import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage # to smooth a test image
# create a test image with similar properties as the given one
np.random.seed(20221230)
distance = np.pad(np.random.randn(1001, 1001), (11, 11), constant_values=-0.02)
distance = ndimage.filters.gaussian_filter(distance, 100)
distance -= distance.min()
distance = distance / distance.max() * 0.78 - 0.73
plt.imshow(distance)
cbar = plt.colorbar()
level = 0
color = 'red'
plt.contour(distance, levels=[level], colors=color)
cbar.ax.axhline(level, color=color) # show the level on the colorbar
plt.show()
Reference: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html
You can accomplish this by setting the [levels] parameter in contour([X, Y,] Z, [levels], **kwargs).
You can draw contour lines at the specified levels by giving an array that is in increasing order.
import matplotlib.pyplot as plt
import numpy as np
x = y = np.arange(-3.0, 3.0, 0.02)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X ** 2 - Y ** 2)
Z2 = np.exp(-(X - 1) ** 2 - (Y - 1) ** 2)
Z3 = np.exp(-(X + 1) ** 2 - (Y + 1) ** 2)
Z = (Z1 - Z2 - Z3) * 2
fig, ax = plt.subplots()
im = ax.imshow(Z, interpolation='gaussian',
origin='lower', extent=[-4, 4, -4, 4],
vmax=abs(Z).max(), vmin=-abs(Z).max())
plt.colorbar(im)
CS = ax.contour(X, Y, Z, levels=[0.9], colors='black')
ax.clabel(CS, fmt='%1.1f', fontsize=12)
plt.show()
Result (levels=[0.9]):
I am plotting using the contourf function from matplotlib and would like to add a colorbar, I've noticed that sometimes the ticks don't go the max/min values.
Is there a clean way to force it to set ticks at these values?
Note: Checking the max and min of z shows that the colorbar represents values from approx -1 to 1, therefor I would expect this ot be reflected such that one can see the range from the colobar, in addition to some ticks in between.
Plot and code demonstrating what I am talking about:
import matplotlib.pyplot as plt
import numpy as np
# Data to plot.
x, y = np.meshgrid(np.arange(7), np.arange(10))
z = np.sin(0.5 * x) * np.cos(0.52 * y)
fig, ax = plt.subplots()
cs = ax.contourf(x, y, z, levels=25)
ax.grid(c="k", ls="-", alpha=0.3)
fig.colorbar(cs, ax=ax)
fig.savefig("example.png", bbox_inches="tight")
The cleanest way seems to be to give explicit levels to contourf. If no explicit levels are given, contourf seems to choose its own, depending on the minimum and maximum value in the data, and also tries to find "nice looking" numbers. After that, ticks get set to a subset of these numbers, such that a tick always coincides with a real level. (If you use colorbar(..., ticks=...) those ticks will not necessarily coincide with the levels.)
As the sine and cosine don't reach -1 and 1 exact in the given example, they are not part of the range.
The following code shows how the ticks depend on the chosen levels. With np.linspace(-1, 1, 24) the levels aren't nice round numbers, but matplotlib still chooses a subset to show.
import matplotlib.pyplot as plt
import numpy as np
x, y = np.meshgrid(np.arange(7), np.arange(10))
z = np.sin(0.5 * x) * np.cos(0.52 * y)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 3))
for ax in (ax1, ax2):
numcontours = 25 if ax == ax1 else 24
cs = ax.contourf(x, y, z, levels=np.linspace(-1, 1, numcontours))
ax.grid(c="k", ls="-", alpha=0.3)
fig.colorbar(cs, ax=ax)
ax.set_title(f'{numcontours} levels from -1 to 1')
plt.show()
I'm trying to draw with matplotlib two average vertical line for every overlapping histograms using a loop. I have managed to draw the first one, but I don't know how to draw the second one. I'm using two variables from a dataset to draw the histograms. One variable (feat) is categorical (0 - 1), and the other one (objective) is numerical. The code is the following:
for chas in df[feat].unique():
plt.hist(df.loc[df[feat] == chas, objective], bins = 15, alpha = 0.5, density = True, label = chas)
plt.axvline(df[objective].mean(), linestyle = 'dashed', linewidth = 2)
plt.title(objective)
plt.legend(loc = 'upper right')
I also have to add to the legend the mean and standard deviation values for each histogram.
How can I do it? Thank you in advance.
I recommend you using axes to plot your figure. Pls see code below and the artist tutorial here.
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
mu1, sigma1 = 100, 8
mu2, sigma2 = 150, 15
x1 = mu1 + sigma1 * np.random.randn(10000)
x2 = mu2 + sigma2 * np.random.randn(10000)
fig, ax = plt.subplots(1, 1, figsize=(7.2, 7.2))
# the histogram of the data
lbs = ['a', 'b']
colors = ['r', 'g']
for i, x in enumerate([x1, x2]):
n, bins, patches = ax.hist(x, 50, density=True, facecolor=colors[i], alpha=0.75, label=lbs[i])
ax.axvline(bins.mean())
ax.legend()
Before I start I want to say that I've tried follow this and this post on the same problem however they are doing it with imshow heatmaps unlike 2d histogram like I'm doing.
Here is my code(the actual data has been replaced by randomly generated data but the gist is the same):
import matplotlib.pyplot as plt
import numpy as np
def subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles):
fig, a = plt.subplots(2, 2)
a = a.ravel()
for idx, ax in enumerate(a):
image = ax.hist2d(x_data[idx], y_data[idx], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[idx], fontsize=12)
ax.set_xlabel(x_labels[idx])
ax.set_ylabel(y_labels[idx])
ax.set_aspect("equal")
cb = fig.colorbar(image[idx])
cb.set_label("Intensity", rotation=270)
# pad = how big overall pic is
# w_pad = how separate they're left to right
# h_pad = how separate they're top to bottom
plt.tight_layout(pad=-1, w_pad=-10, h_pad=0.5)
x1, y1 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x2, y2 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x3, y3 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x4, y4 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x_data = [x1, x2, x3, x4]
y_data = [y1, y2, y3, y4]
x_labels = ["x1", "x2", "x3", "x4"]
y_labels = ["y1", "y2", "y3", "y4"]
titles = ["1", "2", "3", "4"]
subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles)
And this is what it's generating:
So now my problem is that I could not for the life of me make the colorbar apply for all 4 of the histograms. Also for some reason the bottom right histogram seems to behave weirdly compared with the others. In the links that I've posted their methods don't seem to use a = a.ravel() and I'm only using it here because it's the only way that allows me to plot my 4 histograms as subplots. Help?
EDIT:
Thomas Kuhn your new method actually solved all of my problem until I put my labels down and tried to use plt.tight_layout() to sort out the overlaps. It seems that if I put down the specific parameters in plt.tight_layout(pad=i, w_pad=0, h_pad=0) then the colorbar starts to misbehave. I'll now explain my problem.
I have made some changes to your new method so that it suits what I want, like this
def test_hist_2d(x_data, y_data, x_labels, y_labels, titles):
nrows, ncols = 2, 2
fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True)
##produce the actual data and compute the histograms
mappables=[]
for (i, j), ax in np.ndenumerate(axes):
H, xedges, yedges = np.histogram2d(x_data[i][j], y_data[i][j], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[i][j], fontsize=12)
ax.set_xlabel(x_labels[i][j])
ax.set_ylabel(y_labels[i][j])
ax.set_aspect("equal")
mappables.append(H)
##the min and max values of all histograms
vmin = np.min(mappables)
vmax = np.max(mappables)
##second loop for visualisation
for ax, H in zip(axes.ravel(), mappables):
im = ax.imshow(H,vmin=vmin, vmax=vmax, extent=[-2,2,-2,2])
##colorbar using solution from linked question
fig.colorbar(im,ax=axes.ravel())
plt.show()
# plt.tight_layout
# plt.tight_layout(pad=i, w_pad=0, h_pad=0)
Now if I try to generate my data, in this case:
phi, cos_theta = get_angles(runs)
detector_x1, detector_y1, smeared_x1, smeared_y1 = detection_vectorised(1.5, cos_theta, phi)
detector_x2, detector_y2, smeared_x2, smeared_y2 = detection_vectorised(1, cos_theta, phi)
detector_x3, detector_y3, smeared_x3, smeared_y3 = detection_vectorised(0.5, cos_theta, phi)
detector_x4, detector_y4, smeared_x4, smeared_y4 = detection_vectorised(0, cos_theta, phi)
Here detector_x, detector_y, smeared_x, smeared_y are all lists of data point
So now I put them into 2x2 lists so that they can be unpacked suitably by my plotting function, as such:
data_x = [[detector_x1, detector_x2], [detector_x3, detector_x4]]
data_y = [[detector_y1, detector_y2], [detector_y3, detector_y4]]
x_labels = [["x positions(m)", "x positions(m)"], ["x positions(m)", "x positions(m)"]]
y_labels = [["y positions(m)", "y positions(m)"], ["y positions(m)", "y positions(m)"]]
titles = [["0.5m from detector", "1.0m from detector"], ["1.5m from detector", "2.0m from detector"]]
I now run my code with
test_hist_2d(data_x, data_y, x_labels, y_labels, titles)
with just plt.show() turned on, it gives this:
which is great because data and visual wise, it is exactly what I want i.e. the colormap corresponds to all 4 histograms. However, since the labels are overlapping with the titles, I thought I would just run the same thing but this time with plt.tight_layout(pad=a, w_pad=b, h_pad=c) hoping that I would be able to adjust the overlapping labels problem. However this time it doesn't matter how I change the numbers a, b and c, I always get my colorbar lying on the second column of graphs, like this:
Now changing a only makes the overall subplots bigger or smaller, and the best I could do was to adjust it with plt.tight_layout(pad=-10, w_pad=-15, h_pad=0), which looks like this
So it seems that whatever your new method is doing, it made the whole plot lost its adjustability. Your solution, as wonderful as it is at solving one problem, in return, created another. So what would be the best thing to do here?
Edit 2:
Using fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True, constrained_layout=True) along with plt.show() gives
As you can see there's still a vertical gap between the columns of subplots for which not even using plt.subplots_adjust() can get rid of.
Edit:
As has been noted in the comments, the biggest problem here is actually to make the colorbar for many histograms meaningful, as ax.hist2d will always scale the histogram data it receives from numpy. It may therefore be best to first calculated the 2d histogram data using numpy and then use again imshow to visualise it. This way, also the solutions of the linked question can be applied. To make the problem with the normalisation more visible, I put some effort into producing some qualitatively different 2d histograms using scipy.stats.multivariate_normal, which shows how the height of the histogram can change quite dramatically even though the number of samples is the same in each figure.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec as gs
from scipy.stats import multivariate_normal
##opening figure and axes
nrows=3
ncols=3
fig, axes = plt.subplots(nrows,ncols)
##generate some random data for the distributions
means = np.random.rand(nrows,ncols,2)
sigmas = np.random.rand(nrows,ncols,2)
thetas = np.random.rand(nrows,ncols)*np.pi*2
##produce the actual data and compute the histograms
mappables=[]
for mean,sigma,theta in zip( means.reshape(-1,2), sigmas.reshape(-1,2), thetas.reshape(-1)):
##the data (only cosmetics):
c, s = np.cos(theta), np.sin(theta)
rot = np.array(((c,-s), (s, c)))
cov = rot#np.diag(sigma)#rot.T
rv = multivariate_normal(mean,cov)
data = rv.rvs(size = 10000)
##the 2d histogram from numpy
H,xedges,yedges = np.histogram2d(data[:,0], data[:,1], bins=50, range=[[-2, 2],[-2, 2]])
mappables.append(H)
##the min and max values of all histograms
vmin = np.min(mappables)
vmax = np.max(mappables)
##second loop for visualisation
for ax,H in zip(axes.ravel(),mappables):
im = ax.imshow(H,vmin=vmin, vmax=vmax, extent=[-2,2,-2,2])
##colorbar using solution from linked question
fig.colorbar(im,ax=axes.ravel())
plt.show()
This code produces a figure like this:
Old Answer:
One way to solve your problem is to generate the space for your colorbar explicitly. You can use a GridSpec instance to define how wide your colorbar should be. Below your subplots_hist_2d() function with a few modifications. Note that your use of tight_layout() shifted the colorbar into a funny place, hence the replacement. If you want the plots closer to each other, I'd rather recommend to play with the aspect ratio of the figure.
def subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles):
## fig, a = plt.subplots(2, 2)
fig = plt.figure()
g = gs.GridSpec(nrows=2, ncols=3, width_ratios=[1,1,0.05])
a = [fig.add_subplot(g[n,m]) for n in range(2) for m in range(2)]
cax = fig.add_subplot(g[:,2])
## a = a.ravel()
for idx, ax in enumerate(a):
image = ax.hist2d(x_data[idx], y_data[idx], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[idx], fontsize=12)
ax.set_xlabel(x_labels[idx])
ax.set_ylabel(y_labels[idx])
ax.set_aspect("equal")
## cb = fig.colorbar(image[-1],ax=a)
cb = fig.colorbar(image[-1], cax=cax)
cb.set_label("Intensity", rotation=270)
# pad = how big overall pic is
# w_pad = how separate they're left to right
# h_pad = how separate they're top to bottom
## plt.tight_layout(pad=-1, w_pad=-10, h_pad=0.5)
fig.tight_layout()
Using this modified function, I get the following output:
I have reviewed the response to this question: How would I iterate over a list of files and plot them as subplots on a single figure?
But am none the wiser on how to achieve my goal. I would like to plot multiple data sets, with differing x axes, onto a single figure in Python. I have included a snippet of my code below, which performs an FFT on a dataset, then calculates 3 Butterworth filter outputs. Ideally I would like to have all plotted on a single figure, which I have attempted to achieve in the code below.
The for loop calculates the 3 Butterworth filter outputs, the code above - the FFT and the code directly below attempts to append the FFT curve and sqrt(0.5) line to the previously generated plots for display.
Any Direction or advice would be appreciated.
"""Performs a Fast Fourier Transform on the data specified at the base of the code"""
def FFT(col):
x = io2.loc[1:,'Time']
y = io2.loc[1:,col]
# Number of samplepoints
#N = 600
N = pd.Series.count(x)
N2 = int(N/2)
# sample spacing
#T = 1.0 / 800.0
T = 1/(io2.loc[2,'Time'] - io2.loc[1,'Time'])
#x = np.linspace(0.0, N*T, N)
#y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)
yf = scipy.fftpack.fft(y)
xf = np.linspace(0.0, 1.0/(2.0*T), N2)
fig=plt.figure()
plt.clf()
i=1
for order in [3, 6, 9]:
ax=fig.add_subplot(111, label="order = %d" % order)
b, a = butter_lowpass(cutoff, fs, order=order)
w, h = freqz(b, a, worN=2000)
ax.plot((fs * 0.5 / np.pi) * w, abs(h))
i=i+1
ax4=fig.add_subplot(111, label='sqrt(0.5)', frame_on=False)
ax5=fig.add_subplot(111, label="FFT of "+col, frame_on=False)
ax4.plot([0, 0.5 * fs], [np.sqrt(0.5), np.sqrt(0.5)], '--')
ax5.plot(xf, 2.0/N * np.abs(yf[:N2]))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain')
plt.grid(True)
plt.legend(loc='best')
#fig, ax = plt.subplots()
#ax.plot(xf, 2.0/N * np.abs(yf[:N2]), label="FFT of "+col)
plt.axis([0,5000,0,0.1])
#plt.xlabel('Frequency (Hz)')
#plt.ylabel('Amplitude (mm)')
#plt.legend(loc=0)
plt.show()
return
Kind Regards,
Here you can find a minimal example of how to plot multiple lines with different x and y datasets. You are recreating the plot every time you type add_subplot(111). Instead, you should call plot multiple times. I have added an example for a single plot with multiple lines, as well as an example for one subplot per line.
import numpy as np
import matplotlib.pyplot as plt
x1 = np.arange(0, 10, 1)
x2 = np.arange(3, 12, 0.1)
x3 = np.arange(2, 8, 0.01)
y1 = np.sin(x1)
y2 = np.cos(x2**0.8)
y3 = np.sin(4.*x3)**3
data = []
data.append((x1, y1, 'label1'))
data.append((x2, y2, 'label2'))
data.append((x3, y3, 'label3'))
# All lines in one plot.
plt.figure()
for n in data:
plt.plot(n[0], n[1], label=n[2])
plt.legend(loc=0, frameon=False)
# One subplot per data set.
cols = 2
rows = len(data)//2 + len(data)%2
plt.figure()
gs = plt.GridSpec(rows, cols)
for n in range(len(data)):
i = n%2
j = n//2
plt.subplot(gs[j,i])
plt.plot(data[n][0], data[n][1])
plt.title(data[n][2])
plt.tight_layout()
plt.show()