I need four different color bars for a single plot. My plot consists of 5 subplots, but only the first one requires four different color bar plots. I would like to position them below a graph. I achieved it, but my color bars are of different sizes:
For your reference, my figure is split into 5 figures using the matplotlib subplot2grid method. A minimal working example (without subplot2grid) is shown below:
import matplotlib.pyplot as plt
import numpy as np
frame = np.zeros([512, 512])
fig = plt.figure(figsize=(16, 8))
ax = plt.imshow(frame)
for i in range(4):
plt.colorbar(fraction=0.046, pad=0.04, location="bottom")
plt.show()
How do I position the color bar plots below the plot and them next to each other and of the same length (e.g. the same length as the first bar plot, or image size)?
Related
I am having an issue trying to superimpose plots with seaborn. I am able to generate the two plots separetly as
fig, (ax1,ax2) = plt.subplots(ncols=2,figsize=(30, 7))
sns.lineplot(data=data1, y='MSE',x='pct_gc',ax=ax1)
sns.boxplot(x="pct_gc", y="MSE", data=data2,ax=ax2,width=0.4)
The output looks like this:
But when i try to put both plots superimposed, but assiging both to the same ax object.
fig, (ax1,ax2) = plt.subplots(ncols=2,figsize=(30, 7))
sns.lineplot(data=data1, y='MSE',x='pct_gc',ax=ax1)
sns.boxplot(x="pct_gc", y="MSE", data=data2,ax=ax2,width=0.4)
I am not able to identify with the X axis in the Lineplot changes when superimposing both plots (both plots X axis go from 0 to 0.069).
My goal is for both plots to be superimposed, while keeping the same X axis range.
Seaborn's boxplot creates categorical x-axis, with all boxes nicely with the same distance. Internally the x-axis is numbered as 0, 1, 2, ... but externally it gets the labels from 0 to 0.069.
To combine a line plot with a boxplot, matplotlib's boxplot can be addressed directly, so that positions and widths can be set explicitly. When patch_artist=True, a rectangle is created (instead of just lines), for which a facecolor can be given. manage_ticks=False prevents that boxplot changes the x ticks and their limits. Optionally notch=True would accentuate the median a bit more, but depending on the data, the confidence interval might be too large and look weird.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data1 = pd.DataFrame({'pct_gc': np.linspace(0, 0.069, 200), 'MSE': np.random.normal(0.02, 0.1, 200).cumsum()})
data1['pct_range'] = pd.cut(data1['pct_gc'], 10)
fig, ax1 = plt.subplots(ncols=1, figsize=(20, 7))
sns.lineplot(data=data1, y='MSE', x='pct_gc', ax=ax1)
for interval, color in zip(np.unique(data1['pct_range']), plt.cm.tab10.colors):
ax1.boxplot(data1[data1['pct_range'] == interval]['MSE'],
positions=[interval.mid], widths=0.4 * interval.length,
patch_artist=True, boxprops={'facecolor': color},
notch=False, medianprops={'color':'yellow', 'linewidth':2},
manage_ticks=False)
plt.show()
I am attempting to overlay two graphs, a bar graph and a scatter plot, that share an x-axis, but have separate y-axis on either side of the graph. I have tried using matplotlib, ggplot, and seaborn, but I am having the same problem with all of them. I can graph them both separately, and they graph correctly, but when I try to graph them together, the bar graph is correct, but, only a couple data points from the scatter plot show up. I have zoomed in and can confirm that almost none of the scatter-plot points are appearing.
Here is my code. I have loaded a pandas dataframe and am trying to graph 'dKO_Log2FC' as a bar graph, and 'TTCAAG' as a scatter plot. They both share 'bin_end' postion on the x-axis. If I comment out sns.barplot, the scatter plot graphs perfectly. If I comment out the sns.scatterplot, the bar plot graphs as well. When I graph them together without commenting out either, the bar graph graphs, but only two datapoints from 'TTCAAG' column show up. I have played with with size of the scatter dots, zoomed in, etc, but nothing working.
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
file = pd.read_csv('path/to/csv_file.csv')
df = pd.DataFrame(file, columns=['bin_end', 'TTCAAG', 'dKO_Log2FC'])
bin_end = df['bin_end']
TTCAAG = df['TTCAAG']
dKO_Log2FC = df['dKO_Log2FC']
fig, ax = plt.subplots()
ax2 = ax.twinx()
sns.barplot(x=bin_end, y=dKO_Log2FC, ax=ax, color="blue", data=df)
sns.scatterplot(x=bin_end, y=TTCAAG, ax=ax2, color="red", data=df)
plt.title('Histone Position in TS559 vs dKO')
plt.xlabel('Genomic Position (Bin = 1000nt)', fontsize=10)
plt.xticks([])
plt.ylabel('Log2 Fold Change', fontsize=10)
plt.show()
I have have no idea why this the scatter plot won't completely graph. The dataset is quite large, but even when I break it up into smaller bits, only a few scatter points show up.
Here are the graphs
I`m not sure what is the problem, I think is something related to the amount of data or some other data related problem, however as you can plot the data separately, you can generate an image for each plot and then blend the two images to get the required plot.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from PIL import Image
npoints=200
xRange=np.arange(0,npoints,1)
randomdata0=np.abs(np.random.normal(0,1,npoints))
randomdata1=np.random.normal(10,1,npoints)
axtick=[7,10,14]
ax2tick=[0,1.5,3]
fig0=plt.figure(0)
ax=fig0.gca()
ax2=ax.twinx()
sns.scatterplot(x=xRange,y=randomdata1,ax=ax)
ax.set_yticks(axtick)
ax.set_ylim([6,15])
ax2.set_yticks(ax2tick)
ax2.set_ylim([0,3.5])
plt.xticks([])
canvas0 = FigureCanvas(fig0)
s, (width, height) = canvas0.print_to_buffer()
X0 = Image.frombytes("RGBA", (width, height), s) #Contains the data of the first plot
fig1=plt.figure(1)
ax=fig1.gca()
ax2=ax.twinx()
sns.barplot(x=xRange,y=randomdata0,ax=ax2)
ax.set_yticks(axtick)
ax.set_ylim([6,15])
ax2.set_yticks(ax2tick)
ax2.set_ylim([0,3.5])
plt.xticks([])
canvas1 = FigureCanvas(fig1)
s, (width, height) = canvas1.print_to_buffer()
X1 = Image.frombytes("RGBA", (width, height), s) #Contains the data of the second plot
plt.figure(13,figsize=(10,10))
plt.imshow(Image.blend(X0,X1,0.5),interpolation='gaussian')
Axes=plt.gca()
Axes.spines['top'].set_visible(False)
Axes.spines['right'].set_visible(False)
Axes.spines['bottom'].set_visible(False)
Axes.spines['left'].set_visible(False)
Axes.set_xticks([])
Axes.set_yticks([])
Just remember to set the twin axes with the same range and ticks in both plots, otherwise, there will be some shift in the images and the numbers will not align.
Hope it helps
I'm doing some analysis on MCMC samples and I'm using the GetDist python package to create my contour plots. However the contour plots are only a part of the whole analysis, and I would like to show some other plots along with the contour plot in the same figure.
I'm using matplotlib to generate all my other plots, so my question is: is there any way to have a GetDist plot in a matplotlib subplot, so that I have a matplotlib figure with multiple plots and a GetDist plot in it?
I'm using GridSpec to split the figure in subplots (and also to split subplots in subsubplots).
I tried to set a particular subplot as the current axis before creating the triangle plot, and I also tried to look at the GetDist source code to find a way to pass the wanted subplot as an argument to GetDist, but with no luck.
Right now, my code looks something like this
import matplotlib.pyplot as plt
import getdist.plots
from matplotlib import gridspec
import numpy as np
import numpy.random
N = 4
# Generate mock random data
chain = np.array([numpy.random.normal(loc=(i+0.5), scale=(i+0.5), size=100000) for i in xrange(N)])
Names = [str(unichr(i+97)) for i in xrange(N)]
Labels = [str(unichr(i+97)) for i in xrange(N)]
Sample = getdist.MCSamples(samples=chain.T, names=Names, labels=Labels)
#Set up plot layout
fig = plt.figure(figsize=(10.5,12.5))
gs = gridspec.GridSpec(3, 2, width_ratios=[2,3], height_ratios=[10,0.5,4], wspace=0.2, hspace=.05)
Lplot = gridspec.GridSpecFromSubplotSpec(N, 1, subplot_spec=gs[0,0], hspace=0.)
Rplot = gridspec.GridSpecFromSubplotSpec(2,1,subplot_spec=gs[0,1], height_ratios=[4,1.265])
Dplot = plt.subplot(gs[2,0:2])
axL = [plt.subplot(Lplot[i]) for i in xrange(N)]
axR = plt.subplot(Rplot[1])
GD = plt.subplot(Rplot[0])
for i in axL+[axR]+[GD]+[Dplot]: i.set_xticks([]); i.set_yticks([])
plt.sca(GD)
# Generate triangle plot
g = getdist.plots.getSubplotPlotter()
g.triangle_plot(Sample, filled=True)
plt.savefig("outfile.pdf", bbox_inches='tight')
I would like to have my contour plot in the "GD" subplot.
Any help?
I would like to minimize white space in my figure. I have a row of sub plots where four plots share their y-axis and the last plot has a separate axis.
There are no ylabels or ticklabels for the shared axis middle panels.
tight_layout creates a lot of white space between the the middle plots as if leaving space for tick labels and ylabels but I would rather stretch the sub plots. Is this possible?
import matplotlib.gridspec as gridspec
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure()
gs = gridspec.GridSpec(1, 5, width_ratios=[4,1,4,1,2])
ax = fig.add_subplot(gs[0])
axes = [ax] + [fig.add_subplot(gs[i], sharey=ax) for i in range(1, 4)]
axes[0].plot(np.random.randint(0,100,100))
barlist=axes[1].bar([1,2],[1,20])
axes[2].plot(np.random.randint(0,100,100))
barlist=axes[3].bar([1,2],[1,20])
axes[0].set_ylabel('data')
axes.append(fig.add_subplot(gs[4]))
axes[4].plot(np.random.randint(0,5,100))
axes[4].set_ylabel('other data')
for ax in axes[1:4]:
plt.setp(ax.get_yticklabels(), visible=False)
sns.despine();
plt.tight_layout(pad=0, w_pad=0, h_pad=0);
Setting w_pad = 0 is not changing the default settings of tight_layout. You need to set something like w_pad = -2. Which produces the following figure:
You could go further, to say -3 but then you would start to get some overlap with your last plot.
Another way could be to remove plt.tight_layout() and set the boundaries yourself using
plt.subplots_adjust(left=0.065, right=0.97, top=0.96, bottom=0.065, wspace=0.14)
Though this can be a bit of a trial and error process.
Edit
A nice looking graph can be achieved by moving the ticks and the labels of the last plot to the right hand side. This answer shows you can do this by using:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
So for your example:
axes[4].yaxis.tick_right()
axes[4].yaxis.set_label_position("right")
In addition, you need to remove sns.despine(). Finally, there is now no need to set w_pad = -2, just use plt.tight_layout(pad=0, w_pad=0, h_pad=0)
Using this creates the following figure:
I am having trouble using the pyplot.hist function to plot 2 histograms on the same figure. For each binning interval, I want the 2 bars to be centered between the bins (Python 3.6 user). To illustrate, here is an example:
import numpy as np
from matplotlib import pyplot as plt
bin_width=1
A=10*np.random.random(100)
B=10*np.random.random(100)
bins=np.arange(0,np.round(max(A.max(),B.max())/bin_width)*bin_width+2*bin_width,bin_width)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(A,bins,color='Orange',alpha=0.8,rwidth=0.4,align='mid',label='A')
ax.hist(B,bins,color='Orange',alpha=0.8,rwidth=0.4,align='mid',label='B')
ax.legend()
ax.set_ylabel('Count')
I get this:
Histogram_1
A and B series are overlapping, which is not good. Knowing there are only 3 option for 'align', (centered on left bin, middle of 2 bins, centered on right bin), i see no other options than modifying the bins, by adding:
bins-=0.25*bin_width
Before plotting A, and adding:
bins+=0.5*bin_width
Before plotting B. That gives me: Histogram
That's better! However, I had to modify the binning, so it is not the same for A and B.
I searched for a simple way to use the same bins, and then shift the 1st and 2nd plot so they are correctly displayed in the binning intervals, but I didn't find it. Any advice?
I hope I explained my problem clearly.
As previously was mentioned in the above comment you do not need a hist plot function. Use numpy histogram function and plot it results with bar function of matplotlib.
According to bins count and count of data types you can calculate bin width. Ticks you may adjust with xticks method:
import numpy as np
import matplotlib.pylab as plt
A=10*np.random.random(100)
B=10*np.random.random(100)
bins=20
# calculate heights and bins for both lists
ahist, abins = np.histogram(A, bins)
bhist, bbins = np.histogram(B, abins)
fig = plt.figure()
ax = fig.add_subplot(111)
# calc bin width for two lists
w = (bbins[1] - bbins[0])/3.
# plot bars
ax.bar(abins[:-1]-w/2.,ahist,width=w,color='r')
ax.bar(bbins[:-1]+w/2.,bhist,width=w,color='orange')
# adjsut xticks
plt.xticks(abins[:-1], np.arange(bins))
plt.show()