I have this image shown below
And, here I am trying to define the threshold to distinguish bimodal class by using the Otsu technique based on intensity and then visualise those in the histogram. So far I have written following codes:
import matplotlib.pyplot as plt
import numpy as np
from skimage import data, io, img_as_ubyte
from skimage.filters import threshold_multiotsu
# Read an image
image = io.imread("Fig_1.png")
# Apply multi-Otsu threshold
thresholds = threshold_multiotsu(image,classes=5)
# Digitize (segment) original image into multiple classes.
#np.digitize assign values 0, 1, 2, 3, ... to pixels in each class.
regions = np.digitize(image, bins=thresholds)
output = img_as_ubyte(regions) #Convert 64 bit integer values to uint8
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10, 3.5))
# Plotting the original image.
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original')
ax[0].axis('off')
# Plotting the histogram and the two thresholds obtained from
# multi-Otsu.
ax[1].hist(image.ravel(), bins=255)
ax[1].set_title('Histogram')
for thresh in thresholds:
ax[1].axvline(thresh, color='r')
# Plotting the Multi Otsu result.
ax[2].imshow(regions, cmap='gray')
ax[2].set_title('Multi-Otsu result')
ax[2].axis('off')
plt.subplots_adjust()
plt.show()
This gives me the following result. Here As you can see Multi-Otsu result is totally black and does not show the two class of object present in the figure.
I choose classes=5 but this is bimodal hence putting classes=3 also giving me the same result.
Any advice on how to correct this? Thanks in advance.
I am using matplotlib to create the plots. I have to identify each plot with a different color which should be automatically generated by Python.
Can you please give me a method to put different colors for different plots in the same figure?
Matplotlib does this by default.
E.g.:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)
plt.show()
And, as you may already know, you can easily add a legend:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)
plt.legend(['y = x', 'y = 2x', 'y = 3x', 'y = 4x'], loc='upper left')
plt.show()
If you want to control the colors that will be cycled through:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.gca().set_color_cycle(['red', 'green', 'blue', 'yellow'])
plt.plot(x, x)
plt.plot(x, 2 * x)
plt.plot(x, 3 * x)
plt.plot(x, 4 * x)
plt.legend(['y = x', 'y = 2x', 'y = 3x', 'y = 4x'], loc='upper left')
plt.show()
If you're unfamiliar with matplotlib, the tutorial is a good place to start.
Edit:
First off, if you have a lot (>5) of things you want to plot on one figure, either:
Put them on different plots (consider using a few subplots on one figure), or
Use something other than color (i.e. marker styles or line thickness) to distinguish between them.
Otherwise, you're going to wind up with a very messy plot! Be nice to who ever is going to read whatever you're doing and don't try to cram 15 different things onto one figure!!
Beyond that, many people are colorblind to varying degrees, and distinguishing between numerous subtly different colors is difficult for more people than you may realize.
That having been said, if you really want to put 20 lines on one axis with 20 relatively distinct colors, here's one way to do it:
import matplotlib.pyplot as plt
import numpy as np
num_plots = 20
# Have a look at the colormaps here and decide which one you'd like:
# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
colormap = plt.cm.gist_ncar
plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.jet(np.linspace(0, 1, num_plots))))
# Plot several different functions...
x = np.arange(10)
labels = []
for i in range(1, num_plots + 1):
plt.plot(x, i * x + 5 * i)
labels.append(r'$y = %ix + %i$' % (i, 5*i))
# I'm basically just demonstrating several different legend options here...
plt.legend(labels, ncol=4, loc='upper center',
bbox_to_anchor=[0.5, 1.1],
columnspacing=1.0, labelspacing=0.0,
handletextpad=0.0, handlelength=1.5,
fancybox=True, shadow=True)
plt.show()
Setting them later
If you don't know the number of the plots you are going to plot you can change the colours once you have plotted them retrieving the number directly from the plot using .lines, I use this solution:
Some random data
import matplotlib.pyplot as plt
import numpy as np
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
for i in range(1,15):
ax1.plot(np.array([1,5])*i,label=i)
The piece of code that you need:
colormap = plt.cm.gist_ncar #nipy_spectral, Set1,Paired
colors = [colormap(i) for i in np.linspace(0, 1,len(ax1.lines))]
for i,j in enumerate(ax1.lines):
j.set_color(colors[i])
ax1.legend(loc=2)
The result is the following:
TL;DR No, it can't be done automatically. Yes, it is possible.
import matplotlib.pyplot as plt
my_colors = plt.rcParams['axes.prop_cycle']() # <<< note that we CALL the prop_cycle
fig, axes = plt.subplots(2,3)
for ax in axes.flatten(): ax.plot((0,1), (0,1), **next(my_colors))
Each plot (axes) in a figure (figure) has its own cycle of colors — if you don't force a different color for each plot, all the plots share the same order of colors but, if we stretch a bit what "automatically" means, it can be done.
The OP wrote
[...] I have to identify each plot with a different color which should be automatically generated by [Matplotlib].
But... Matplotlib automatically generates different colors for each different curve
In [10]: import numpy as np
...: import matplotlib.pyplot as plt
In [11]: plt.plot((0,1), (0,1), (1,2), (1,0));
Out[11]:
So why the OP request? If we continue to read, we have
Can you please give me a method to put different colors for different plots in the same figure?
and it make sense, because each plot (each axes in Matplotlib's parlance) has its own color_cycle (or rather, in 2018, its prop_cycle) and each plot (axes) reuses the same colors in the same order.
In [12]: fig, axes = plt.subplots(2,3)
In [13]: for ax in axes.flatten():
...: ax.plot((0,1), (0,1))
If this is the meaning of the original question, one possibility is to explicitly name a different color for each plot.
If the plots (as it often happens) are generated in a loop we must have an additional loop variable to override the color automatically chosen by Matplotlib.
In [14]: fig, axes = plt.subplots(2,3)
In [15]: for ax, short_color_name in zip(axes.flatten(), 'brgkyc'):
...: ax.plot((0,1), (0,1), short_color_name)
Another possibility is to instantiate a cycler object
from cycler import cycler
my_cycler = cycler('color', ['k', 'r']) * cycler('linewidth', [1., 1.5, 2.])
actual_cycler = my_cycler()
fig, axes = plt.subplots(2,3)
for ax in axes.flat:
ax.plot((0,1), (0,1), **next(actual_cycler))
Note that type(my_cycler) is cycler.Cycler but type(actual_cycler) is itertools.cycle.
I would like to offer a minor improvement on the last loop answer given in the previous post (that post is correct and should still be accepted). The implicit assumption made when labeling the last example is that plt.label(LIST) puts label number X in LIST with the line corresponding to the Xth time plot was called. I have run into problems with this approach before. The recommended way to build legends and customize their labels per matplotlibs documentation ( http://matplotlib.org/users/legend_guide.html#adjusting-the-order-of-legend-item) is to have a warm feeling that the labels go along with the exact plots you think they do:
...
# Plot several different functions...
labels = []
plotHandles = []
for i in range(1, num_plots + 1):
x, = plt.plot(some x vector, some y vector) #need the ',' per ** below
plotHandles.append(x)
labels.append(some label)
plt.legend(plotHandles, labels, 'upper left',ncol=1)
**: Matplotlib Legends not working
Matplot colors your plot with different colors , but incase you wanna put specific colors
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
plt.plot(x, x)
plt.plot(x, 2 * x,color='blue')
plt.plot(x, 3 * x,color='red')
plt.plot(x, 4 * x,color='green')
plt.show()
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from skspatial.objects import Line, Vector
for count in range(0,len(LineList),1):
Line_Color = np.random.rand(3,)
Line(StartPoint,EndPoint)).plot_3d(ax,c="Line"+str(count),label="Line"+str(count))
plt.legend(loc='lower left')
plt.show(block=True)
The above code might help you to add 3D lines with different colours in a randomized fashion. Your colored lines can also be referenced with a help of a legend as mentioned in the label="... " parameter.
Honestly, my favourite way to do this is pretty simple: Now this won't work for an arbitrarily large number of plots, but it will do you up to 1163. This is by using the map of all matplotlib's named colours and then selecting them at random.
from random import choice
import matplotlib.pyplot as plt
from matplotlib.colors import mcolors
# Get full named colour map from matplotlib
colours = mcolors._colors_full_map # This is a dictionary of all named colours
# Turn the dictionary into a list
color_lst = list(colours.values())
# Plot using these random colours
for n, plot in enumerate(plots):
plt.scatter(plot[x], plot[y], color=choice(color_lst), label=n)
In python matplotlib, there are two convention used to draw plots:
1.
plt.figure(1,figsize=(400,8))
2.
fig,ax = plt.subplots()
fig.set_size_inches(400,8)
Both have different ways of doing the same thing. eg defining axis label.
Which one is better to use? What is the advantage of one over the other?
Or what is the "Good Practice" for plotting a graph using matplotlib?
Although #tacaswell already gave a brief comment on the key difference. I will add more to this question only from my own experience with matplotlib.
plt.figure just creates a Figure (but with no Axes in it), this means you have to specify the ax to place your data (lines, scatter points, images). Minimum code should look like this:
import numpy as np
import matplotlib.pyplot as plt
# create a figure
fig = plt.figure(figsize=(7.2, 7.2))
# generate ax1
ax1 = fig.add_axes([0.1, 0.1, 0.5, 0.5])
# generate ax2, make it red to distinguish
ax2 = fig.add_axes([0.6, 0.6, 0.3, 0.3], fc='red')
# add data
x = np.linspace(0, 2*np.pi, 20)
y = np.sin(x)
ax1.plot(x, y)
ax2.scatter(x, y)
In the case of plt.subplots(nrows=, ncols=), you will get Figure and an array of Axes(AxesSubplot). It is mostly used for generating many subplots at the same time. Some example code:
def display_axes(axes):
for i, ax in enumerate(axes.ravel()):
ax.text(0.5, 0.5, s='ax{}'.format(i+1), transform=ax.transAxes)
# create figures and (2x2) axes array
fig, axes = plt.subplots(2, 2, figsize=(7.2, 7.2))
# four (2*2=4) axes
ax1, ax2, ax3, ax4 = axes.ravel()
# for illustration purpose
display_axes(axes)
Summary:
plt.figure() is usually used when you want more customization to you axes, such as positions, sizes, colors and etc. You can see artist tutorial for more details. (I personally prefer this for individual plot).
plt.subplots() is recommended for generating multiple subplots in grids. You can also achieve higher flexibility using 'gridspec' and 'subplots', see details here.
I'm having a difficulty controlling the zorder of the elements of a polar plot superimposed on a cartesian plot.
Consider this example:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(1, 1, marker='*', s=2000, c='r', zorder=2)
ax2 = fig.add_axes(ax.get_position(), frameon=False, polar=True)
ax2.scatter(1., 0.1, marker='*', s=1000, c='b', zorder=1)
plt.xlim(0, 2)
plt.ylim(0, 2)
plt.show()
The result is:
It looks like matplotlib ignored the zorder of scatter plots. I would expect the red star to be on top of the blue one.
Could you please explain what I'm doing wrong here?
I found one question, which is kind of similar to mine, but concerns ticklines and grids instead. Maybe it's the same bug?
P.S. I'm running Linux x86_64 with Python 2.7.6 and matplotlib 1.3.1.
The problem is that you are setting the z-order of the marks on different axes ax and ax2 but since ax2 has a greater z-order all the plots in it will be on top of ax. One solution could be to set a higher z-order to ax but then you need to make the background transparent or set frameon=False (and that's maybe not desirable for your case), this is a demonstration of what I'm saying:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(1, 1, marker='*', s=2000, c='r', zorder=2)
ax2 = fig.add_axes(ax.get_position(), frameon=False, polar=True)
ax2.scatter(1., 0.1, marker='*', s=1000, c='b', zorder=1)
ax.set_zorder(3)
ax.patch.set_facecolor('none')
#ax.patch.set_visible(False)
plt.xlim(0, 2)
plt.ylim(0, 2)
plt.show()
Plot:
This might sound trivial, but I am unable find a solution in PYTHON. No problem in ROOT or MATLAB.
So, I have a 3x3 array, and I would like each element in the array to represent the height (frequency) of a bin. I should have a histogram with 9 bins. Here's an example of what I have been trying.
import numpy as np
import matplotlib.pyplot as plt
H = np.array([[21,33,6],[25,20,2],[80,40,0]])
hist, bin = np.histogramdd(H, bins=3)
center = 0.5*(bin[:-1] + bin[1:])
plt.bar(center, hist)
plt.show()
I've tried histogram2D, I just can't find any to get this to work with PYTHON. Thanks in advance for any help on this.
If im not mistaken shouldnt this just be:
H=H.reshape(-1)
plt.bar(np.arange(H.shape[0]),H)
You can also do a 3D histogram:
extent = [0,2,0,2]
plt.imshow(H, extent=extent, interpolation='nearest')
plt.colorbar()
plt.show()
3D Bar histogram:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for z,height in enumerate(H):
cs = [c] * len(xs)
cs[0] = 'c'
ax.bar(np.arange(3), height, zs=z, zdir='y', color=cs, alpha=0.8)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
The above should work, I dont have my laptop with me at the moment. More example can be found here. A great example for 3D bars can be found here.