I use matplotlib in python2.7 to get a figure with multiple trajectories. I am getting this figure as the output of my code:
However, as you see the title of each trajectory is not aligned with the corresponding color line. I played with the options of legend, but did not figure out the solution.
Do you have any idea?
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
liste = range(1,13)
labels = ['3-3','3-4','3-5','3-6','4-3','4-4',
'4-5','4-6','5-3','5-4','5-5','5-6',
'6-3','6-4','6-5','6-6']
plt.figure(1)
plt.subplot(2,1,1)
for cnt, j in enumerate(labels):
a1 = range(100) # iter
a2 = np.random.randint(10,size=[100]) # dnn
a3 = np.ones((100)) # optimal
if cnt == 0:
plt.plot(a1,a3,label='BS', color='blue')
plt.plot(a1, np.divide(a3,a3), color='blue')
title_font = {'size':'11', 'color':'black', 'weight':'normal',
'verticalalignment':'bottom'}
plt.plot(a1,a2,label=j, color='red')
plt.xlabel('episode')
plt.ylabel('cost')
plt.grid(True)
lgd=plt.legend(bbox_to_anchor=(0., 1.0, 1.0, 0.102),
loc=3, ncol=len(liste), mode="expand", borderaxespad=0.)
plt.show()
The legend handles and labels do not fit into the legend box. You may see that when removing the mode="expand" label.
To get the legend entries take less space, use the handlelength, handletextpad and the columnspacing and play with the parameters until they fit. If that would still not be the case, using less columns is necessary. Set the ncols to a lower number.
E.g. with the following
lgd=plt.legend(bbox_to_anchor=(0., 1.02, 1.0, 0.102),
handlelength=0.9, handletextpad=0.3,columnspacing=0.7,
loc=3, ncol=9, mode="expand", borderaxespad=0.)
one would get an image with all legend entries fitting
Related
I am trying to create a corner plot for an upcoming paper, but I'm running into difficulty. I am creating an N x N array of subplots (currently, N = 6) and then deleting a bit over half of them. The issue is that the figure doesn't seem to resize itself after I delete the extraneous subplots, so when I later add a legend using a dummy subplot, it exists in the area where a full row and column of deleted subplots were, thus enlarging the figure. I've been working on this for several hours now and haven't found a solution. Here is the MWE:
import matplotlib.pyplot as plt
%matplotlib notebook
n_char = 8
# Set up the main figure.
fig, ax = plt.subplots(n_char, n_char, figsize=(n_char, n_char))
# Get rid of the axis labels unless it's on the left-most column or bottom-most row.
for i in range(0, n_char):
# For each row, loop over each column.
for j in range(0, n_char):
# If the plot isn't in the bottom-most row, get rid of the x-axis tick labels.
if i != n_char - 1:
ax[i, j].set_xticklabels([])
# If the plot isn't in the left-most column, get rid of the y-axis tick labels.
if j != 0:
ax[i, j].set_yticklabels([])
# Remove the plots that are repetitive or boring (plotting against the same characteristic).
for i in range(0, n_char):
# For each row, loop over each column.
for j in range(0, n_char):
# Delete the offending axes.
if j >= i:
ax[i, j].remove()
# Set the spacing between the plots to a much smaller value.
fig.subplots_adjust(hspace=0.00, wspace=0.00)
# Create a big plot for the legend. Have the frame hidden.
fig.add_subplot(111, frameon=False, xticks=[], yticks=[], xticklabels=[], yticklabels=[])
# Create some dummy data to serve as the source of the legend.
plt.scatter([10], [10], color="k", s=5, zorder=2, label="Targets")
# Set the x-axis limits such that the dummy data point is invisible.
fig.gca().set_xlim(-1, 1)
# Add the legend to the plot. Have it located in the upper right.
plt.legend(scatterpoints=1, loc="upper right", fontsize=5)
# Save the final plot.
fig.savefig("./../Code Output/Other Plots/Corner_Plot_Test.png", bbox_inches="tight", dpi=500)
I have looked at many different questions here on Stack Overflow. The two most promising candidates was this one, but I found the solution wasn't quite workable due to the large number of plots (and, to be frank, I didn't fully understand the solution). I thought that the first answer in this one might also work, as I thought it was a sizing issue (i.e. the figure wasn't resizing, so creating a new subplot was creating one the size of the original figure), but all it did was resize the entire figure, so that didn't work either.
To help, I will also include an image. I took the output of the code above and edited it to show what I want:
I should add that if I don't add a subplot, the output is as I expected (i.e. it's the proper size), so the issue comes in when adding the subplot, i.e. the line fig.add_subplot(111, frameon=False, xticks=[], yticks=[], xticklabels=[], yticklabels=[]).
The use of GridSpec may help.
GridSpec is used to specify array of axes to plot. You can set widths for columns and heights for rows as ratios in the option. The unneeded row should have very small height ratio, while unneeded column very small width ratio.
Here is the runnable code and output plot:-
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
#import numpy as np
fig = plt.figure(figsize=(8, 8))
nn = 6
# will create gridspec of 6 rows, 6 columns
# 1st row will occupy v small heights
# last column will occupy v small widths
sm = 0.01 # the v small width/height
wh = (1.-sm)/(nn-1.) # useful width/height
gs = gridspec.GridSpec(nn, nn, width_ratios=[*[wh]*(nn-1), sm], \
height_ratios= [sm, *[wh]*(nn-1)])
cols, rows = nn, nn
ax = [[0 for i in range(cols)] for j in range(rows)]
for ea in range(nn):
for eb in range(nn):
ax[ea][eb] = fig.add_subplot(gs[ea, eb])
ax[ea][eb].set_xticklabels([])
ax[ea][eb].set_yticklabels([])
if eb>=ea:
ax[ea][eb].remove()
# plot data on some axes
# note that axes on the first row (index=0) are gone
ax[2][0].plot([2,5,3,7])
ax[4][2].plot([2,3,7])
# make legend in upper-right axes (GridSpec's first row, last column)
# first index: 0
# second index: nn-1
rx, cx = 0, nn-1
ax[rx][cx] = fig.add_subplot(gs[rx,cx])
hdl = ax[rx][cx].scatter([10], [10], color="k", s=5, zorder=2, label="Targets")
ax[rx][cx].set_axis_off()
#ax[rx][cx].set_visible(True) # already True
ax[rx][cx].set_xticklabels([])
ax[rx][cx].set_yticklabels([])
# plot legend
plt.legend(bbox_to_anchor=(1.0, 1.0), loc='upper right', borderaxespad=0.)
fig.subplots_adjust(hspace=0.00, wspace=0.00)
plt.show
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()
In the following I use scatter and an own ListedColormap to plot some coloured data points. In addition the corresponding colorbar is also plotted.
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, ListedColormap, BoundaryNorm
from numpy import arange
fig, ax = plt.subplots()
my_cm = ListedColormap(['#a71b1b','#94258f','#ea99e6','#ec9510','#ece43b','#a3f8ff','#2586df','#035e0d'])
bounds=range(8)
norm = BoundaryNorm(bounds, my_cm.N)
data = [1,2,1,3,0,5,3,4]
ret = ax.scatter(range(my_cm.N), [1]*my_cm.N, c=data, edgecolors='face', cmap=my_cm, s=50)
cbar = fig.colorbar(ret, ax=ax, boundaries=arange(-0.5,8,1), ticks=bounds, norm=norm)
cbar.ax.tick_params(axis='both', which='both',length=0)
If my data is not covering each value of the boundary interval, the colorbar does not show all colours (like in the added figure). If data would be set to range(8), I get a dot of each colour and the colorbar also shows all colours.
How can I force the colorbar to show all defined colours even if data does not contain all boundary values?
You need to manually set vminand vmax in your call to ax.scatter:
ret = ax.scatter(range(my_cm.N), [1]*my_cm.N, c=data, edgecolors='face', cmap=my_cm, s=50, vmin=0, vmax=7)
resulting in
If my data is not covering each value of the boundary interval, the colorbar does not show all colours (like in the added figure).
If either vminor vmax are `None the color limits are set via the method
autoscale_None, and the minimum and maximum of your data are therefore used.
So using your code it is actually not necessary for showing all colors in the colorbar that every value of the boundary interval is covered, only the minimum and maximum need to be included.
Using e.g. data = [0,0,0,0,0,0,0,7] results in the following:
When looking for something else, I found another solution to that problem: colorbar-for-matplotlib-plot-surface-command.
In that case, I do not need to set vmin and vmax and it is also working in cases if the arrays/lists of points to plot are empty. Instead a ScalarMappable is defined and provided to colorbar instead of the scatterinstance.
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, ListedColormap, BoundaryNorm
import matplotlib.cm as cm
from numpy import arange
fig, ax = plt.subplots()
my_cm = ListedColormap(['#a71b1b','#94258f','#ea99e6','#ec9510','#ece43b','#a3f8ff','#2586df','#035e0d'])
bounds=range(8)
norm = BoundaryNorm(bounds, my_cm.N)
mappable = cm.ScalarMappable(cmap=my_cm)
mappable.set_array(bounds)
data = [] # also x and y can be []
ax.scatter(x=range(my_cm.N), y=[1]*my_cm.N, c=data, edgecolors='face', cmap=my_cm, s=50)
cbar = fig.colorbar(mappable, ax=ax, boundaries=arange(-0.5,8,1), ticks=bounds, norm=norm)
cbar.ax.tick_params(axis='both', which='both',length=0)
I have a correlation matrix hat I am trying to visualize with matplotlib. I can create a heatmap style figure just fine, but I am running into problems with how I want the labels. I'm not even sure if this is possible, but this is what I'm trying to do and can't seem to make it work:
My correlation matrix is 150 X 150. On either the x or y (or both...this doesn't matter) axis, I would like to group the labels and then simply label them with a color, or a white label on a color background.
To clarify, let's say I'd like to have 1-15 as "Group 1" and either simply be a Blue bar, or "Group 1" text on a blue bar. Then 16-20 as "Group 2" on a red bar, or simply a red bar. Etc, through all of the items in the matrix.
I have been failing at both grouping axis labels as well as getting any color on them. Any help would be greatly appreciated. My code is below, though it's quite basic and I don't know if it will help.
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
# COREELATION MATRIX TEST #
corr = np.genfromtxt(csv_path,delimiter=',')
fig = plt.figure()
ax1 = fig.add_subplot(111)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(corr, cmap=cmap)
ax1.grid(True)
plt.title('THIS IS MY TITLE')
fig.colorbar(cax, ticks=[-1,-0.8,-0.6,-0.4,-0.2,0.0,0.2,0.4,0.6,0.8,1.0])
plt.show()
You may create auxilary axes next to the plot and plot colored bar plots to them. Turning the axes spines off lets those bars look like labelboxes.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
# COREELATION MATRIX TEST #
corr = 2*np.random.rand(150,150)-1
# labels [start,end]
labels = np.array([[0,15],[16,36],[37,82],[83,111],[112,149]])
colors = ["crimson", "limegreen","gold","orchid","turquoise"]
fig, ax = plt.subplots()
im = ax.imshow(corr, cmap="Blues")
ax.set_title('THIS IS MY TITLE')
fig.colorbar(im, ticks=[-1,-0.8,-0.6,-0.4,-0.2,0.0,0.2,0.4,0.6,0.8,1.0])
# create axes next to plot
divider = make_axes_locatable(ax)
axb = divider.append_axes("bottom", "10%", pad=0.06, sharex=ax)
axl = divider.append_axes("left", "10%", pad=0.06, sharey=ax)
axb.invert_yaxis()
axl.invert_xaxis()
axb.axis("off")
axl.axis("off")
# plot colored bar plots to the axes
barkw = dict( color=colors, linewidth=0.72, ec="k", clip_on=False, align='edge',)
axb.bar(labels[:,0],np.ones(len(labels)),
width=np.diff(labels, axis=1).flatten(), **barkw)
axl.barh(labels[:,0],np.ones(len(labels)),
height=np.diff(labels, axis=1).flatten(), **barkw)
# set margins to zero again
ax.margins(0)
ax.tick_params(axis="both", bottom=0, left=0, labelbottom=0,labelleft=0)
# Label the boxes
textkw = dict(ha="center", va="center", fontsize="small")
for k,l in labels:
axb.text((k+l)/2.,0.5, "{}-{}".format(k,l), **textkw)
axl.text(0.5,(k+l)/2., "{}-{}".format(k,l), rotation=-90,**textkw)
plt.show()
I'm trying to animate several text objects in matplotlib at the same time. I have a 3-D numpy array (arrow_data) of data which stores the index of a unicode arrow which I need to plot (text for all unicode arrows are stored in a list arrows). The first and second dimension of this np.array indicate the location on a grid where this arrow needs to be plotted and the 3rd dimension is the 'time' dimension over which I need to update the plot (ie the arrows change through time over the 3d dimension of the array).
Below is the code I have for animating these arrows through 'time' using a loop, but I don't see how I can make a collection of text objects, as I can with other matplotlib functions like scatter, and then collectively (and efficiently) update the set_text property of each of them. The loop over the 'time' variable is fine but I'd prefer to not use a loop over the grid of text objects if possible.
Any thoughts on how to do this without the double nested loop?
Thanks.
from matplotlib import pyplot as plt
import numpy as np
import time
import matplotlib
matplotlib.interactive(True)
arrows = [u'\u2190', u'\u2196', u'\u2191', u'\u2197', u'\u2192', u'\u2198', u'\u2193', u'\u2199']
rest_time = 0.25
steps = 20
fig, ax = plt.subplots(1, 1)
ax.plot([0,4],[0,4], c = 'white')
objs = [[[] for i in range(4)] for j in range(4)]
for i in range(4):
for j in range(4):
objs[i][j] = ax.text(i+0.5,j+0.5, arrows[i], ha = 'center', va = 'center')
fig.canvas.draw()
arrow_data = np.random.randint(0,len(arrows), (4,4,steps))
for t in range(arrow_data.shape[2]):
for i in range(4):
for j in range(4):
objs[i][j].set_text(arrows[arrow_data[i,j,t]])
fig.canvas.draw()
time.sleep(rest_time)