I am having a multicolored line plot and I want to add a color bar under it in the same figure like as shown in the image below, Is it possible?
I have attached a color bar image as a reference which I took from another code.
My intention here is to use the color bar like a legend for each segment of the line in the plot.
Edit-1: I want to have the color bar using a mappable object such as an image, So don't want to create a new subplot for the sole purpose of the color bar.
Any suggestion is welcome. Thanks in Advance.
This is the code for multicolored line plot
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
Segments=[[[3,1],[6,1]],[[6,2],[9,2]],[[9,3],[12,3]],[[12,4],[15,4]], [[12,4],[15,4]]]
Points_1 = np.concatenate([Segments[:-1], Segments[1:]], axis=1)
lc = LineCollection(Points_1, colors=['r','g','b','y'], linewidths=2)
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
plt.show()
This is a workaround I'am using:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import matplotlib.colorbar as mcolorbar
import matplotlib.colors as mcolors
Segments=[[[3,1],[6,1]],[[6,2],[9,2]],[[9,3],[12,3]],[[12,4],[15,4]], [[12,4],[15,4]]]
Points_1 = np.concatenate([Segments[:-1], Segments[1:]], axis=1)
lc = LineCollection(Points_1, colors=['r','g','b','y'], linewidths=2)
fig, ax = plt.subplots(2, 1, gridspec_kw={'height_ratios' : [5,1]})
ax[0].add_collection(lc)
bounds = np.linspace(0, 1, 5)[:-1]
labels = ['Action1', 'Action2', 'Action3', 'Action4']
ax[0].set_xlim([0, 15])
ax[0].set_ylim([0, 10])
cb2 = mcolorbar.ColorbarBase(ax = ax[1], cmap = cmap, orientation = 'horizontal', extendfrac='auto')
cb2.set_ticks(bounds)
cb2.set_ticklabels(labels)
plt.tight_layout()
plt.show()
If you specifically want to avoid subplots, you can use a scalar mappable:
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
cmap = mcolors.ListedColormap(['r','g','b','y'])
sm = plt.cm.ScalarMappable(cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
cbar = fig.colorbar(sm, ax=ax, orientation='horizontal',aspect=90)
bounds = np.linspace(0, 1, 5)[:-1]
labels = ['Action1', 'Action2', 'Action3', 'Action4']
ax.set_xlim([0, 15])
ax.set_ylim([0, 10])
cbar.set_ticks(bounds)
cbar.set_ticklabels(labels)
plt.tight_layout()
plt.show()
This helped me to get what I asked.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.collections import LineCollection
Segments=[[[3,1],[6,1]],[[6,2],[9,2]],[[9,3],[12,3]],[[12,4],[15,4]], [[12,4],[15,4]]]
Points_1 = np.concatenate([Segments[:-1], Segments[1:]], axis=1)
lc = LineCollection(Points_1, colors=['r','g','b','y'], linewidths=2)
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
c=[1,2,3,4,5]
labels = ['Action1', 'Action2', 'Action3', 'Action4']
cmap = mcolors.ListedColormap(['r','g','b','y'])
norm = mcolors.BoundaryNorm([1,2,3,4,5],4)
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
cbar=fig.colorbar(sm, ticks=c, orientation='horizontal')
cbar.set_ticklabels(['Action1', 'Action2', 'Action3', 'Action4'])
plt.show()
Related
I am trying to create a figure with 2x10 subplots. I would like them all to be square with a thin white space in between them, but they are coming out as rectangles (longer in height than width). The images I'm putting in each cell of the grid are all square, but the cell itself is not square so the extra space just becomes white space which creates a giant gap between the top row and the bottom row. Here's the code that shows the rectangles:
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from PIL import Image
fig = plt.figure()
gs1 = GridSpec(2, 10)
for a in range(10):
ax = plt.subplot(gs1[0, a])
ax2 = plt.subplot(gs1[1, a])
plt.show()
output from above code
Imagine this but with little to no gaps in between cells and each cell is square instead of rectangular. Thanks in advance for any help!
You can use plt.tight_layout() to clean up your subplot figure. Also, play around with plt.rcParams for the figure size:
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from PIL import Image
plt.rcParams["figure.figsize"] = (20,10)
fig = plt.figure()
gs1 = GridSpec(2, 10)
for a in range(10):
ax = plt.subplot(gs1[0, a])
ax2 = plt.subplot(gs1[1, a])
plt.tight_layout()
plt.show()
Output
For more control, you can use fig,ax and turn off all the labels and ticks. Then you can remove the white space between the subplots.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from PIL import Image
plt.rcParams["figure.figsize"] = (20,4)
fig, ax = plt.subplots(2,10)
gs1 = GridSpec(2, 10)
for x in range(2):
for y in range(10):
ax[x,y].plot()
ax[x,y].tick_params(axis = 'both', bottom= False, left = False,
labelbottom = False, labelleft = False)
ax[1,0].tick_params(axis = 'both', bottom= True, left = True,
labelbottom = True, labelleft = True)
plt.subplots_adjust(wspace=0.05, hspace=0.05)
plt.show()
Output:
I have the following code:
import matplotlib.pyplot as plt
import numpy as np
img1 = np.zeros([512,512])
img2 = np.zeros([512,512])
plt.figure(figsize=(10,10))
plt.imshow(img1, cmap='inferno')
plt.axis('off')
cba = plt.colorbar(shrink=0.25)
cba.ax.set_ylabel('Events / counts', fontsize=14)
cba.ax.tick_params(labelsize=12)
plt.imshow(img2, cmap='turbo', alpha=0.5)
plt.axis('off')
cba = plt.colorbar(shrink=0.25)
cba.ax.set_ylabel('Lifetime / ns)', fontsize=14)
cba.ax.tick_params(labelsize=12)
plt.tight_layout()
plt.show()
which produces the following output:
My question is, how can I get color bars that are on top of one another as opposed to next to each other? Ideally, I would like to get something like this:
You can grab the position of the ax and use it to create new axes for the colorbars. Here is an example:
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage
data = ndimage.gaussian_filter(np.random.randn(512, 512), sigma=15, mode='nearest') * 20
fig, ax = plt.subplots()
im1 = ax.imshow(data, vmin=-1, vmax=0, cmap='viridis')
data[data < 0] = np.nan
im2 = ax.imshow(data, vmin=0.001, vmax=1, cmap='Reds_r')
ax.axis('off')
pos = ax.get_position()
bar_h = (pos.y1 - pos.y0) * 0.5 # 0.5 joins the two bars, e.g. 0.48 separates them a bit
ax_cbar1 = fig.add_axes([pos.x1 + 0.02, pos.y0, 0.03, bar_h])
cbar1 = fig.colorbar(im1, cax=ax_cbar1, orientation='vertical')
ax_cbar2 = fig.add_axes([pos.x1 + 0.02, pos.y1 - bar_h, 0.03, bar_h])
cbar2 = fig.colorbar(im2, cax=ax_cbar2, orientation='vertical')
plt.show()
How can I modify this plot to show me the value of each bar upon hovering mouse?
sns.barplot(x = "date", y = "no_of_dogs", data = dogs_adopted_per_day, palette="husl")
plt.show()
You could employ mplcursors as follows:
import matplotlib.pyplot as plt
import mplcursors
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.DataFrame({"date": pd.date_range('20210101', periods=10),
"no_of_dogs": np.random.randint(10, 30, 10)})
fig, ax = plt.subplots(figsize=(15, 5))
sns.barplot(x="date", y="no_of_dogs", data=df, palette="husl", ax=ax)
x_dates = df['date'].dt.strftime('%Y-%m-%d')
ax.set_xticklabels(labels=x_dates)
cursor = mplcursors.cursor(hover=True)
#cursor.connect("add")
def on_add(sel):
x, y, width, height = sel.artist[sel.target.index].get_bbox().bounds
sel.annotation.set(text=f"{x_dates[round(x)]}\n{height:.0f}",
position=(0, 20), anncoords="offset points")
sel.annotation.xy = (x + width / 2, y + height)
plt.show()
I can generate an error-bar plot using the code below. The graph produced by the code shows vertical lines that represent the errors in y. I would like to have horizontal lines at the tips of these errors ("error bars") and am not sure how to do so.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting')
ax.grid(alpha=0.5, linestyle=':')
plt.show()
plt.close(fig)
The code generates the figure below. I've played with the solid_capstyle kwarg. Is there a specific kwarg that does what I am trying to do?
And as an example of what I'd like, the figure below:
In case it's relevant, I am using matplotlib 2.2.2
The argument you are looking for is capsize= in ax.errorbar(). The default is None so the length of the cap will default to the value of matplotlib.rcParams["errorbar.capsize"]. The number you give will be the length of the cap in points:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting', capsize=5)
ax.grid(alpha=0.5, linestyle=':')
plt.show()
With the following code I create four histograms:
import numpy as np
import pandas as pd
data = pd.DataFrame(np.random.normal((1, 2, 3 , 4), size=(100, 4)))
data.hist(bins=10)
I want the histograms to look like this:
I know how to make it one graph at the time, see here
But how can I do it for multiple histograms without specifying each single one? Ideally I could use 'pd.scatter_matrix'.
Plot each histogram seperately and do the fit to each histogram as in the example you linked or take a look at the hist api example here. Essentially what should be done is
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
for ax in [ax1, ax2, ax3, ax4]:
n, bins, patches = ax.hist(**your_data_here**, 50, normed=1, facecolor='green', alpha=0.75)
bincenters = 0.5*(bins[1:]+bins[:-1])
y = mlab.normpdf( bincenters, mu, sigma)
l = ax.plot(bincenters, y, 'r--', linewidth=1)
plt.show()