In the reference, they are described as:
axis('equal')
changes limits of x or y axis so that equal increments of x and y have the same length; a circle is
circular.:
axis('scaled')
achieves the same result by changing the dimensions of the plot box instead of the axis data limits.:
But I did not understand the part 'by changing the dimensions of the plot box'.
So I compared directly
import numpy as np
import matplotlib.pyplot as plt
plt.close('all')
x = np.array(np.linspace(-np.pi, np.pi))
y = np.sin(x)
ax1 = plt.subplot(2, 1, 1)
ax1 = plt.plot(x, y)
plt.axis('scaled')
ax1 = plt.subplot(2, 1, 2)
plt.plot(x, y)
plt.axis('equal')
There is only a slight difference that the width is shorter when plotted with plt.axis('scaled').
How can I know the difference better?
I think the difference becomes more apparent, if you use different data.
import numpy as np
import matplotlib.pyplot as plt
x = np.array(np.linspace(-np.pi, np.pi))
y = np.sin(x)*np.pi
ax1 = plt.subplot(2, 1, 1)
ax1 = plt.plot(x, y)
plt.axis('scaled')
ax1 = plt.subplot(2, 1, 2)
plt.plot(x, y)
plt.axis('equal')
plt.show()
So the difference is if the axes around the plot are changed according to the aspect, or if they stay the same as in a usual subplot and are scaled such, that the aspect of the plot data is equal.
Related
This question already has answers here:
show origin axis (x,y) in matplotlib plot
(3 answers)
Closed 2 years ago.
So I am working on a program that displays the graph of a function over an interval, and the plot size is automatically handled by matplotlib. The only thing is, it resizes without showing x=0 and y=0 cartesian axes. Everything I tried so far, like plt.subplot(), only affects the axes that show at the bottom and left, not the cartesian axes. Is there a way to add the axes in?
Here is some example code:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-2, 1, 100)
f = lambda x: x**2 - 1
plt.plot(x, f(x))
plt.show()
The graph that comes from this looks like this:
which does not show the cartesian axes. Is there a way to add this in, maybe by adding lines at x=0 and y=0?
You can set the spine axis to be in a custom position, like the origin:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-2,1,100)
y = x**2
fig, ax = plt.subplots(1, figsize=(6, 4))
ax.plot(x, y)
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.set(ylim=(-1, 4))
Otherwise, you can add a vertical and a horizontal line:
fig, ax = plt.subplots(1, figsize=(6, 4))
ax.plot(x, y)
ax.axhline(0, color='black')
ax.axvline(0, color='black')
You can do it by drawing arrows:
import matplotlib.pyplot as plt
import numpy as np
from pylab import *
x = np.linspace(-2, 1, 100)
f = lambda x: x**2 - 1
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_aspect('equal')
plt.plot(x, f(x))
l,r = ax.get_xlim()
lo,hi = ax.get_ylim()
arrow( l-1, 0, r-l+2, 0, length_includes_head = False, head_width = 0.2 )
arrow( 0, lo-1, 0, hi-lo+2, length_includes_head = True, head_width = 0.2 )
plt.show()
I have an image in a nonuniform grid, and want to plot it with one of the axes scaled logarithmically. This is possible for imshow, however that requires regularly spaced data. I can plot my irregularly gridded data with NonUniformImage, however setting ax.set_xscale('log') only has an effect on the axis, not the image itself.
Is this possible to achieve with NonUniformImage, or even possible at all? Here is some code that shows what I mean (top row is imshow, bottom is NonUniformImage).
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import NonUniformImage
# Linear x array for cell centers:
x = np.linspace(1, 10, 10)
# Highly nonlinear x array:
x2 = x**3
# Linear y-array
y = np.linspace(1, 10, 10)
z = np.sqrt(x[np.newaxis, :]**2 + y[:, np.newaxis]**2)
fig, axs = plt.subplots(nrows=2, ncols=2)
fig.subplots_adjust(bottom=0.07, hspace=0.3)
# Uniform Grid, linear x-axis
ax = axs[0, 0]
im = ax.imshow(z, extent=(1, 10, 1, 10), aspect='auto',origin='lower')
ax.set_title("Uniform Grid, linear x-axis")
# Uniform Grid, log x-axis (image changes)
ax = axs[0, 1]
im = ax.imshow(z, extent=(1, 10, 1, 10),aspect='auto',origin='lower')
ax.set_xscale('log')
ax.set_title('Uniform Grid, log x-axis')
# Correct ticklabel formatting
from matplotlib.ticker import StrMethodFormatter, NullFormatter
ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))
ax.xaxis.set_minor_formatter(NullFormatter())
# NonUniform Grid, linear x-axis
ax = axs[1, 0]
im = NonUniformImage(ax, interpolation='nearest', extent=(1, 1000, 1, 10))
im.set_data(x2, y, z)
ax.images.append(im)
ax.set_xlim(1, 1000)
ax.set_ylim(1, 10)
ax.set_title('NonUniform Grid, lin x-axis')
# NonUniform Grid, logarithmic x-axis (this doesn't work as intended)
ax = axs[1, 1]
im = NonUniformImage(ax, interpolation='nearest', extent=(1, 1000, 1, 10))
im.set_data(x2, y, z)
ax.images.append(im)
ax.set_xlim(1, 1000)
ax.set_ylim(1, 10)
ax.set_xscale('log')
ax.set_title('NonUniform Grid, log x-axis')
plt.show()
I have two subplots that share the x-axes. The first one has data and a fit function, in the second one is the difference between the data and the fit function. In the figure both subplots have the same y axis size (in pixels). Now i want the y axis of the data and the fit to be bigger than the axis of the errors. my code is the following:
import matplotlib.pyplot as plt
f, axarr = plt.subplots(2, sharex=True,figsize=(15, 12))
axarr[0].scatter(x, data , facecolors='none', edgecolors='crimson')
axarr[0].plot(x, fit, color='g',linewidth=1.5)
axarr[0].set_ylim([18,10])
axarr[1].plot(x,data-fit,color='k',linewidth=width)
axarr[1].set_ylim([-0.4,0.4])
yticks[-1].label1.set_visible(False)
plt.subplots_adjust(hspace=0.)
is there any code that sets the size of the second plot?
Take a look at this example, using gridspec. I believe it is exactly what you want. Below is the example adopted for your case. Edited to also share the x-axis
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
# generate some data
x = np.arange(0, 10, 0.2)
y = np.sin(x)
# plot it
fig = plt.figure(figsize=(8, 6))
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1], sharex=ax0) # <---- sharex=ax0 will share ax1 with ax2
ax0.plot(x, y)
ax1.plot(y, x)
plt.show()
Or even simpler by following Hagnes answer in the first link:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.2)
y = np.sin(x)
f, (a0, a1) = plt.subplots(2,1, gridspec_kw = {'height_ratios':[1, 3]}, sharex=True) # <---- sharex=True will share the xaxis between the two axes
a0.plot(x, y)
a1.plot(y, x)
plt.show()
What is wrong with my residual plot that is causing to not be aligned with my main graph? My code is below.
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = np.array([0.030956,0.032956,0.034956,0.036956,0.038956,0.040956])
y = np.array([10.57821088,11.90701212,12.55570876,13.97542486,16.05403248,16.36634177])
yerr = [0.101614114,0.363255259,0.057234211,0.09289917,0.093288198,0.420165796]
xerr = [0.00021]*len(x)
fig1 = plt.figure(1)
frame1=fig1.add_axes((.1,.3,.8,.6))
m, b = np.polyfit(x, y, 1)
print 'gradient',m,'intercept',b
plt.plot(x, m*x + b, '-', color='grey', alpha=0.5)
plt.plot(x,y,'.',color='black',markersize=6)
plt.errorbar(x,y,xerr=0,yerr=yerr,linestyle="None",color='black')
plt.ylabel('$1/\sqrt{F}$ $(N)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=True)
plt.grid(False)
frame2=fig1.add_axes((.1,.1,.8,.2))
s = m*x+b #(np.sqrt(4*np.pi*8.85E-12)/2.23E-8)*x
difference = y-s
plt.plot(x, difference, 'ro')
frame2.set_ylabel('$Residual$',fontsize=20)
plt.xlabel('$2s+d_0$ $(m)$',fontsize=20)
you can specify the axis limits. the problem is that autoscale is moving your two plots differently. if you insert 2 lines of code, each specifying the axis limits, it will fix it.
plt.axis([.030,.0415, 10, 17]) #line 17
plt.axis([.030,.0415, -.6, .8]) #line 26
i believe this is what you're looking for.
Try using GridSpec.
from matplotlib import gridspec
fig = plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax0.plot(x, m*x + b, '-', color='grey', alpha=0.5)
ax0.plot(x,y,'.',color='black',markersize=6)
ax1.plot(x, difference, 'ro')
And use set_ylabel instead of ylabel (which you use for plt for example) for axes.
I have a range of points x and y stored in numpy arrays.
Those represent x(t) and y(t) where t=0...T-1
I am plotting a scatter plot using
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.show()
I would like to have a colormap representing the time (therefore coloring the points depending on the index in the numpy arrays)
What is the easiest way to do so?
Here is an example
import numpy as np
import matplotlib.pyplot as plt
x = np.random.rand(100)
y = np.random.rand(100)
t = np.arange(100)
plt.scatter(x, y, c=t)
plt.show()
Here you are setting the color based on the index, t, which is just an array of [1, 2, ..., 100].
Perhaps an easier-to-understand example is the slightly simpler
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(100)
y = x
t = x
plt.scatter(x, y, c=t)
plt.show()
Note that the array you pass as c doesn't need to have any particular order or type, i.e. it doesn't need to be sorted or integers as in these examples. The plotting routine will scale the colormap such that the minimum/maximum values in c correspond to the bottom/top of the colormap.
Colormaps
You can change the colormap by adding
import matplotlib.cm as cm
plt.scatter(x, y, c=t, cmap=cm.cmap_name)
Importing matplotlib.cm is optional as you can call colormaps as cmap="cmap_name" just as well. There is a reference page of colormaps showing what each looks like. Also know that you can reverse a colormap by simply calling it as cmap_name_r. So either
plt.scatter(x, y, c=t, cmap=cm.cmap_name_r)
# or
plt.scatter(x, y, c=t, cmap="cmap_name_r")
will work. Examples are "jet_r" or cm.plasma_r. Here's an example with the new 1.5 colormap viridis:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(100)
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='viridis')
ax2.scatter(x, y, c=t, cmap='viridis_r')
plt.show()
Colorbars
You can add a colorbar by using
plt.scatter(x, y, c=t, cmap='viridis')
plt.colorbar()
plt.show()
Note that if you are using figures and subplots explicitly (e.g. fig, ax = plt.subplots() or ax = fig.add_subplot(111)), adding a colorbar can be a bit more involved. Good examples can be found here for a single subplot colorbar and here for 2 subplots 1 colorbar.
To add to wflynny's answer above, you can find the available colormaps here
Example:
import matplotlib.cm as cm
plt.scatter(x, y, c=t, cmap=cm.jet)
or alternatively,
plt.scatter(x, y, c=t, cmap='jet')
Subplot Colorbar
For subplots with scatter, you can trick a colorbar onto your axes by building the "mappable" with the help of a secondary figure and then adding it to your original plot.
As a continuation of the above example:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='viridis')
ax2.scatter(x, y, c=t, cmap='viridis_r')
# Build your secondary mirror axes:
fig2, (ax3, ax4) = plt.subplots(1, 2)
# Build maps that parallel the color-coded data
# NOTE 1: imshow requires a 2-D array as input
# NOTE 2: You must use the same cmap tag as above for it match
map1 = ax3.imshow(np.stack([t, t]),cmap='viridis')
map2 = ax4.imshow(np.stack([t, t]),cmap='viridis_r')
# Add your maps onto your original figure/axes
fig.colorbar(map1, ax=ax1)
fig.colorbar(map2, ax=ax2)
plt.show()
Note that you will also output a secondary figure that you can ignore.
Single colorbar for multiple subplots
sometimes it is preferable to have a single colorbar to indicate data values visualised on multiple subplots.
In this case, a Normalize() object needs to be created using the minimum and maximum data values across both plots.
Then a colorbar object can be created from a ScalarMappable() object, which maps between scalar values and colors.
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = x
t1 = x # Colour data for first plot
t2 = 2*x # Color data for second plot
all_data = np.concatenate([t1, t2])
# Create custom Normalise object using the man and max data values across both subplots to ensure colors are consistent on both plots
norm = plt.Normalize(np.min(all_data), np.max(all_data))
fig, axs = plt.subplots(1, 2)
axs[0].scatter(x, y, c=t1, cmap='viridis', norm=norm)
axs[1].scatter(x**2, y, c=t2, cmap='viridis', norm=norm)
# Create the colorbar
smap = plt.cm.ScalarMappable(cmap='viridis', norm=norm)
cbar = fig.colorbar(smap, ax=axs, fraction=0.1, shrink = 0.8)
cbar.ax.tick_params(labelsize=11)
cbar.ax.set_ylabel('T', rotation=0, labelpad = 15, fontdict = {"size":14})
plt.show()
subplots_colorbar