I am using the matplotlib.pylot module to generate thousands of figures that all deal with a value called "Total Vertical Depth(TVD)". The data that these values come from are all negative numbers but the industry standard is to display them as positive (I.E. distance from zero / absolute value). My y axis is used to display the numbers and of course uses the actual value (negative) to label the axis ticks. I do not want to change the values, but am wondering how to access the text elements and just remove the negative symbols from each value(shown in red circles on the image).
Several iterations of code after diving into the matplotlib documentation has gotten me to the following code, but I am still getting an error.
locs, labels = plt.yticks()
newLabels = []
for lbl in labels:
newLabels.append((lbl[0], lbl[1], str(float(str(lbl[2])) * -1)))
plt.yticks(locs, newLabels)
It appears that some of the strings in the "labels" list are empty and therefore the cast isn't working correctly, but I don't understand how it has any empty values if the yticks() method is retrieving the current tick configuration.
#SiHA points out that if we change the data then the order of labels on the y-axis will be reversed. So we can use a ticker formatter to just change the labels without changing the data as shown in the example below:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
#ticker.FuncFormatter
def major_formatter(x, pos):
label = str(-x) if x < 0 else str(x)
return label
y = np.linspace(-3000,-1000,2001)
fig, ax = plt.subplots()
ax.plot(y)
ax.yaxis.set_major_formatter(major_formatter)
plt.show()
This gives me the following plot, notice the order of y-axis labels.
Edit:
based on the Amit's great answer, here's the solution if you want to edit the data instead of the tick formatter:
import matplotlib.pyplot as plt
import numpy as np
y = np.linspace(-3000,-1000,2001)
fig, ax = plt.subplots()
ax.plot(-y) # invert y-values of the data
ax.invert_yaxis() # invert the axis so that larger values are displayed at the bottom
plt.show()
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)
I wish to produce a series of 2D histograms using pyplot.
I want to be able to specify the size and scale (or aspect ratio) of the generated image. In addition to this, I would like to remove the ticks and axes labels and borders.
This does not seem to be possible in the arguments to the plt.hist2d() method.
Rather than share my (rather complex) code, I post the pyplot demo script. If what I want is possible with this code, then it will be possible with mine.
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000) + 5
# normal distribution center at x=0 and y=5
plt.hist2d(x, y, bins=40)
plt.show()
Thanks for your help in advance.
Specifying the aspect alone will not help, you need the figure size in width or height in addition.
To get rid of the margins you can use subplots_adjust. And in order to turn the axes off you need axis("off").
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000) + 5
width=4 # inch
aspect=0.8 # height/width ratio
height = width*aspect
plt.figure(figsize=(width, height ))
plt.hist2d(x, y, bins=40)
plt.subplots_adjust(bottom=0, top=1, left=0, right=1)
plt.gca().axis("off")
plt.show()
The figsize should do what you want:
plt.figure(figsize=(20,10))
plt.hist2d(x, y, bins=40)
plt.show()
The following snippet shows how to quickly and easily
set the figure size (and implicitly the aspect ratio)
disable the axis bounding box and tick annotations
set the axis to fill the whole figure (removes borders)
save the resulting figure to an image file.
.
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000) + 5
plt.figure(figsize=[7, 2]) # set figure dimensions to weird but illustrative aspect ratio
plt.hist2d(x, y, bins=40)
plt.box(False) # disable axis box
plt.xticks([]) # no x axis ticks
plt.yticks([]) # no y axis ticks
plt.subplots_adjust(left=0, right=1, top=1, bottom=0) # remove borders
plt.savefig('output.png')
plt.show()
I have a fixed point, and I want to draw a line which goes through this fixed point, and also makes 45 degrees angle i.e. slope 1 with the x-axis.
How can i do it with matplotlib ?
This can be done setting the aspect ratio of the plot to 'equal' and using the right coordinates, for instance (you can also use ax.set_aspect):
import matplotlib.pyplot as plt
xs = [1,2]
ys = [0,1]
ax = plt.subplot(aspect='equal')
ax.plot(xs, ys, '-')
ax.set_xlim(0,5)
ax.set_ylim(0,5)
plt.show()
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.