Using GridSpec, I have a regular lattice of plots. Assume 3 x 3. All plot axis are turned off as I am interested in the shape of the plot, not the individual axis values.
What I would like to do, is label the x and y axis of the larger box. For example, in the 3 x 3 case above, the x-axis could be ['A', 'B', 'C'] and the y-axis could be [1,2,3].
Is it possible to get this labeling? How can I access the grid spec axis?
Not much in the GridSpec documentation unless I am missing an obvious method name.
Code example. Data is in a pandas dataframe - ignore the brute force extraction with nested loops...
fig = plt.figure(figsize=(12,12))
gs = gridspec.GridSpec(40, 19, wspace=0.0, hspace=0.0)
for j in nseasons:
t = tt[j]
nlats = t.columns.levels[0]
for idx, k in enumerate(nlats):
diurnal = t[k].iloc[0]
ax = plt.subplot(gs[j, idx])
ax.plot(y, diurnal.values, 'b-')
ax.set_xticks([])
ax.set_yticks([])
fig.add_subplot(ax)
sys.stdout.write("Processed plot {}/{}\r".format(cplots, nplots))
sys.stdout.flush()
cplots += 1
#Here the figures axis labels need to be set.
As mentioned in the comments, you can do this with xlabel and ylabel by only labeling axes on the left and bottom of the figure. Example below.
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec
fig = plt.figure(figsize=(12,12))
rows = 40
cols = 19
gs = gridspec.GridSpec(rows, cols, wspace=0.0, hspace=0.0)
for i in range(rows):
for j in range(cols):
ax = plt.subplot(gs[i, j])
ax.set_xticks([])
ax.set_yticks([])
# label y
if ax.is_first_col():
ax.set_ylabel(i, fontsize = 9)
# label x
if ax.is_last_row():
ax.set_xlabel(j, fontsize = 9)
plt.show()
Related
Im trying to plot a scatter matrix. I'm building on the example given in this thread Is there a function to make scatterplot matrices in matplotlib?. Here I have just modified the code slightly to make the axis visible for all the subplots. The modified code is given below
import itertools
import numpy as np
import matplotlib.pyplot as plt
def main():
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
linestyle='none', marker='o', color='black', mfc='none')
fig.suptitle('Simple Scatterplot Matrix')
plt.show()
def scatterplot_matrix(data, names, **kwargs):
"""Plots a scatterplot matrix of subplots. Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names". Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid."""
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ax in axes.flat:
# Hide all ticks and labels
ax.xaxis.set_visible(True)
ax.yaxis.set_visible(True)
# # Set up ticks only on one side for the "edge" subplots...
# if ax.is_first_col():
# ax.yaxis.set_ticks_position('left')
# if ax.is_last_col():
# ax.yaxis.set_ticks_position('right')
# if ax.is_first_row():
# ax.xaxis.set_ticks_position('top')
# if ax.is_last_row():
# ax.xaxis.set_ticks_position('bottom')
# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
axes[x,y].plot(data[x], data[y], **kwargs)
# Label the diagonal subplots...
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center')
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
fig.tight_layout()
plt.xticks(rotation=45)
fig.show()
return fig
main()
I cant seem to be able to rotate the x-axis text of all the subplots. As it can be seen, i have tried the plt.xticks(rotation=45) trick. But this seems to perform the rotation for the last subplot alone.
Just iterate through the axes tied to the figure, set the active axes to the iterated object, and modify:
for ax in fig.axes:
matplotlib.pyplot.sca(ax)
plt.xticks(rotation=90)
plt only acts on the current active axes. You should bring it inside your last loop where you set some of the labels visibility to True:
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
for tick in axes[i,j].get_xticklabels():
tick.set_rotation(45)
for tick in axes[j,i].get_xticklabels():
tick.set_rotation(45)
for ax in fig.axes:
ax.tick_params(labelrotation=90)
I wonder how to set the size of the subplot when figure contains multiple subplots (5 × 2 in my case). No matter how big I allow the whole figure to be, the subplots always seem to be small. I would like to have direct control of the size of the subplot in this figure. The simplified version of the code is pasted below.
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(20)
y = np.random.randn(20)
fig = plt.figure(figsize=(20, 8))
for i in range(0,10):
ax = fig.add_subplot(5, 2, i+1)
plt.plot(x, y, 'o')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# x and y axis should be equal length
x0,x1 = ax.get_xlim()
y0,y1 = ax.get_ylim()
ax.set_aspect(abs(x1-x0)/abs(y1-y0))
plt.show()
fig.savefig('plot.pdf', bbox_inches='tight')
Just switch figure size width and height from:
fig = plt.figure(figsize=(20, 8))
to:
fig = plt.figure(figsize=(8, 20))
to use the whole page for your plots.
This will change your plot from:
to:
I am trying to generate a plot with x-axis being a geometric sequence while the y axis is a number between 0.0 and 1.0. My code looks like this:
form matplotlib import pyplot as plt
plt.xticks(X)
plt.plot(X,Y)
plt.show()
which generates a plot like this:
As you can see, I am explicitly setting the x-axis ticks to the ones belonging to the geometric sequence.
My question:Is it possible to make x-ticks evenly spaced despite their value, as the initial terms of the sequence are small, and crowded together. Kind of like logarithmic scale, which would be ideal if dealing with powers of a base, but not for a geometric sequence, I think, as is the case here.
You can do it by plotting your variable as a function of the "natural" variable that parametrizes your curve. For example:
n = 12
a = np.arange(n)
x = 2**a
y = np.random.rand(n)
fig = plt.figure(1, figsize=(7,7))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(x,y)
ax1.xaxis.set_ticks(x)
ax2.plot(a, y) #we plot y as a function of a, which parametrizes x
ax2.xaxis.set_ticks(a) #set the ticks to be a
ax2.xaxis.set_ticklabels(x) # change the ticks' names to x
which produces:
I had the same problem and spent several hours trying to find something appropriate. But it appears to be really easy and you do not need to make any parameterization or play with some x-ticks positions, etc.
The only thing you need to do is just to plot your x-values as str, not int: plot(x.astype('str'), y)
By modifying the code from the previous answer you will get:
n = 12
a = np.arange(n)
x = 2**a
y = np.random.rand(n)
fig = plt.figure(1, figsize=(7,7))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(x,y)
ax1.xaxis.set_ticks(x)
ax2.plot(x.astype('str'), y)
Seaborn has a bunch of categorical plot handling natively this kind of task.
Such as pointplot:
sns.pointplot(x="x", y="y", data=df, ax=ax)
Exemple
fig, [ax1, ax2] = plt.subplots(2, figsize=(7,7))
sns.lineplot(data=df, x="x", y="y", ax=ax1) #relational plot
sns.pointplot(data=df, x="x", y="y", ax=ax2) #categorical plot
In case of using Pandas Dataframe:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
n = 12
df = pd.DataFrame(dict(
X=2**np.arange(n),
Y=np.random.randint(1, 9, size=n),
)).set_index('X')
# index is reset in order to use as xticks
df.reset_index(inplace=True)
fig = plt.figure()
ax1 = plt.subplot(111)
df['Y'].plot(kind='bar', ax=ax1, figsize=(7, 7), use_index=True)
# set_ticklabels used to place original indexes
ax1.xaxis.set_ticklabels(df['X'])
convert int to str:
X = list(map(str, X))
plt.xticks(X)
plt.plot(X,Y)
plt.show()
I have some code to plot a grid, with the data in each cell being distinct and having a very specific position. The easiest way I found to do this was to create the grid with gridspec and use it to precisely position my subplots, however I'm having a problem where the overall grid is labelled from 0 to 1 along each axis. This happens every time, even when the dimensions of the grid are changed. Obviously these numbers have no relevance to my data, and as what I am aiming to display is qualitative rather than quantitative I would like to remove all labels from this plot entirely.
Here is a link to an image with an example of my problem
And here is the MWE that I used to create that image:
import numpy as np
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
# mock-up of data being used
x = 6
y = 7
table = np.zeros((x, y))
# plotting
fig = plt.figure(1)
gs = gridspec.GridSpec(x, y, wspace=0, hspace=0)
plt.title('Example Plot')
for (j, k), img in np.ndenumerate(table):
ax = fig.add_subplot(gs[x - j - 1, k])
ax.set_xticklabels('')
ax.set_yticklabels('')
plt.show()
I have not been able to find note of anything like this problem, so any help would be greatly appreciated.
If you just want to draw a grid over the plot, use this code:
import numpy as np
import matplotlib.pyplot as plt
# mock-up of data being used
x = 6
y = 7
table = np.zeros((x, y))
# plotting
fig = plt.figure(1)
plt.title('Example Plot')
plt.gca().xaxis.grid(True, color='darkgrey', linestyle='-')
plt.gca().yaxis.grid(True, color='darkgrey', linestyle='-')
plt.show()
Another variant is used gridspec:
...
# hide ticks of main axes
ax0 = plt.gca()
ax0.get_xaxis().set_ticks([])
ax0.get_yaxis().set_ticks([])
gs = gridspec.GridSpec(x, y, wspace=0, hspace=0)
plt.title('Example Plot')
for (j, k), img in np.ndenumerate(table):
ax = fig.add_subplot(gs[x - j - 1, k])
# hide ticks of gribspec axes
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
In my plot, a secondary x axis is used to display the value of another variable for some data. Now, the original axis is log scaled. Unfortunaltely, the twinned axis puts the ticks (and the labels) referring to the linear scale of the original axis and not as intended to the log scale. How can this be overcome?
Here the code example that should put the ticks of the twinned axis in the same (absolute axes) position as the ones for the original axis:
def conv(x):
"""some conversion function"""
# ...
return x2
ax = plt.subplot(1,1,1)
ax.set_xscale('log')
# get the location of the ticks of ax
axlocs,axlabels = plt.xticks()
# twin axis and set limits as in ax
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
#Set the ticks, should be set referring to the log scale of ax, but are set referring to the linear scale
ax2.set_xticks(axlocs)
# put the converted labels
ax2.set_xticklabels(map(conv,axlocs))
An alternative way would be (the ticks are then not set in the same position, but that doesn't matter):
from matplotlib.ticker import FuncFormatter
ax = plt.subplot(1,1,1)
ax.set_xscale('log')
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
ax2.xaxis.set_major_formatter(FuncFormatter(lambda x,pos:conv(x)))
Both approaches work well as long as no log scale is used.
Perhaps there exists an easy fix. Is there something I missed in the documentation?
As a workaround, I tried to obtain the ax.transAxes coordinates of the ticks of ax and put the ticks at the very same position in ax2. But there does not exist something like
ax2.set_xticks(axlocs,transform=ax2.transAxes)
TypeError: set_xticks() got an unexpected keyword argument 'transform'
This has been asked a while ago, but I stumbled over it with the same question.
I eventually managed to solve the problem by introducing a logscaled (semilogx) transparent (alpha=0) dummy plot.
Example:
import numpy as np
import matplotlib.pyplot as plt
def conversion_func(x): # some arbitrary transformation function
return 2 * x**0.5 # from x to z
x = np.logspace(0, 5, 100)
y = np.sin(np.log(x))
fig = plt.figure()
ax = plt.gca()
ax.semilogx(x, y, 'k')
ax.set_xlim(x[0], x[-1]) # this is important in order that limits of both axes match
ax.set_ylabel("$y$")
ax.set_xlabel("$x$", color='C0')
ax.tick_params(axis='x', which='both', colors='C0')
ax.axvline(100, c='C0', lw=3)
ticks_x = np.logspace(0, 5, 5 + 1) # must span limits of first axis with clever spacing
ticks_z = conversion_func(ticks_x)
ax2 = ax.twiny() # get the twin axis
ax2.semilogx(ticks_z, np.ones_like(ticks_z), alpha=0) # transparent dummy plot
ax2.set_xlim(ticks_z[0], ticks_z[-1])
ax2.set_xlabel("$z \equiv f(x)$", color='C1')
ax2.xaxis.label.set_color('C1')
ax2.tick_params(axis='x', which='both', colors='C1')
ax2.axvline(20, ls='--', c='C1', lw=3) # z=20 indeed matches x=100 as desired
fig.show()
In the above example the vertical lines demonstrate that first and second axis are indeed shifted to one another as wanted. x = 100 gets shifted to z = 2*x**0.5 = 20. The colours are just to clarify which vertical line goes with which axis.
Don't need to cover them, just Eliminate the ticks!
d= [7,9,14,17,35,70];
j= [100,80,50,40,20,10];
plt.figure()
plt.xscale('log')
plt.plot(freq, freq*spec) #plot some spectrum
ax1 = plt.gca() #define my first axis
ax1.yaxis.set_ticks_position('both')
ax1.tick_params(axis='y',which='both',direction='in');
ax1.tick_params(axis='x',which='both',direction='in');
ax2 = ax1.twiny() #generates second axis (top)
ax2.set_xlim(ax1.get_xlim()); #same limits
plt.xscale('log') #make it log
ax2.set_xticks(freq[d]); #my own 'major' ticks OVERLAPS!!!
ax2.set_xticklabels(j); #change labels
ax2.tick_params(axis='x',which='major',direction='in');
ax2.tick_params(axis='x',which='minor',top=False); #REMOVE 'MINOR' TICKS
ax2.grid()
I think you can fix your issue by calling ax2.set_xscale('log').
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.semilogx(np.logspace(1.0, 5.0, 20), np.random.random([20]))
new_tick_locations = np.array([10., 100., 1000., 1.0e4])
def tick_function(X):
V = X / 1000.
return ["%.3f" % z for z in V]
ax2 = ax.twiny()
ax2.set_xscale('log')
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(tick_function(new_tick_locations))
ax2.set_xlabel(r"Modified x-axis: $X/1000$")