I'm using the awesome matplotlib_venn to plot Venn diagram derived from three binary conditions. Unfortunately, besides the 7 cases covered by the venn3 function, my case includes an all false ('000') group.
Is there any built in way to do so?
I've failed to find it myself, so any instructions to hijack this plot (or correlated hacks) to include it by myself in the output is very welcome.
matplotlib-venn is build on top of matplotlib so you can add a circle with the normal matplotlib commands.
fig, ax = plt.subplots()
venn3(subsets, ax=ax)
c = plt.Circle((x, y), radius)
ax.add_patch(c)
Related
I am creating a series of boxplots in order to compare different cancer types with each other (based on 5 categories). For plotting I use seaborn/matplotlib. It works fine for most of the cancer types (see image right) however in some the x axis collapses slightly (see image left) or strongly (see image middle)
https://i.imgur.com/dxLR4B4.png
Looking into the code how seaborn plots a box/violin plot https://github.com/mwaskom/seaborn/blob/36964d7ffba3683de2117d25f224f8ebef015298/seaborn/categorical.py (line 961)
violin_data = remove_na(group_data[hue_mask])
I realized that this happens when there are too many nans
Is there any possibility to prevent this collapsing by code only
I do not want to modify my dataframe (replace the nans by zero)
Below you find my code:
boxp_df=pd.read_csv(pf_in,sep="\t",skip_blank_lines=False)
fig, ax = plt.subplots(figsize=(10, 10))
sns.violinplot(data=boxp_df, ax=ax)
plt.xticks(rotation=-45)
plt.ylabel("label")
plt.tight_layout()
plt.savefig(pf_out)
The output is a per cancer type differently sized plot
(depending on if there is any category completely nan)
I am expecting each plot to be in the same width.
Update
trying to use the order parameter as suggested leads to the following output:
https://i.imgur.com/uSm13Qw.png
Maybe this toy example helps ?
|Cat1|Cat2|Cat3|Cat4|Cat5
|3.93| |0.52| |6.01
|3.34| |0.89| |2.89
|3.39| |1.96| |4.63
|1.59| |3.66| |3.75
|2.73| |0.39| |2.87
|0.08| |1.25| |-0.27
Update
Apparently, the problem is not the data but the length of the title
https://github.com/matplotlib/matplotlib/issues/4413
Therefore I would close the question
#Diziet should I delete it or does my issue might help other ones?
Sorry for not including the line below in the code example:
ax.set_title("VERY LONG TITLE", fontsize=20)
It's hard to be sure without data to test it with, but I think you can pass the names of your categories/cancers to the order= parameter. This forces seaborn to use/display those, even if they are empty.
for instance:
tips = sns.load_dataset("tips")
ax = sns.violinplot(x="day", y="total_bill", data=tips, order=['Thur','Fri','Sat','Freedom Day','Sun','Durin\'s Day'])
I am a recent migrant from Matlab to Python and have recently worked with Numpy and Matplotlib. I recoded one of my scripts from Matlab, which employs Matlab's contourf-function, into Python using matplotlib's corresponding contourf-function. I managed to replicate the output in Python, apart that the contourf-plots are not exacly the same, for a reason that is unknown to me. As I run the contourf-function in matplotlib, I get this otherwise nice figure but it has these sharp edges on the contour-levels on top and bottom, which should not be there (see Figure 1 below, matplotlib-output). Now, when I export the arrays I used in Python to Matlab (i.e. the exactly same data set that was used to generate the matplotlib-contourf-plot) and use Matlab's contourf-function, I get a slightly different output, without those sharp contour-level edges (see Figure 2 below, Matlab-output). I used the same number of levels in both figures. In figure 3 I have made a scatterplot of the same data, which shows that there are no such sharp edges in the data as shown in the contourf-plot (I added contour-lines just for reference). Example dataset can be downloaded through Dropbox-link given below. The data set contains three txt-files: X, Y, Z. Each of them are an 500x500 arrays, which can be directly used with contourf(), i.e. plt.contourf(X,Y,Z,...). The code that used was
plt.contourf(X,Y,Z,10, cmap=plt.cm.jet)
plt.contour(X,Y,Z,10,colors='black', linewidths=0.5)
plt.axis('equal')
plt.axis('off')
Does anyone have an idea why this happens? I would appreciate any insight on this!
Cheers,
Jussi
Below are the details of my setup:
Python 3.7.0
IPython 6.5.0
matplotlib 2.2.3
Matplotlib output
Matlab output
Matplotlib-scatter
Link to data set
The confusing thing about the matlab plot is that its colorbar shows much more levels than there are actually in the plot. Hence you don't see the actual intervals that are contoured.
You would achieve the same result in matplotlib by choosing 12 instead of 11 levels.
import numpy as np
import matplotlib.pyplot as plt
X, Y, Z = [np.loadtxt("data/roundcontourdata/{}.txt".format(i)) for i in list("XYZ")]
levels = np.linspace(Z.min(), Z.max(), 12)
cntr = plt.contourf(X,Y,Z,levels, cmap=plt.cm.jet)
plt.contour(X,Y,Z,levels,colors='black', linewidths=0.5)
plt.colorbar(cntr)
plt.axis('equal')
plt.axis('off')
plt.show()
So in conclusion, both plots are correct and show the same data. Just the levels being automatically chosen are different. This can be circumvented by choosing custom levels depending on the desired visual appearance.
I am making log-log plots for different data sets and need to include the best fit line equation. I know where in the plot I should place the equation, but since the data sets have very different values, I'd like to use relative coordinates in the annotation. (Otherwise, the annotation would move for every data set.)
I am aware of the annotate() function of matplotlib, and I know that I can use textcoords='axes fraction' to enable relative coordinates. When I plot my data on the regular scale, it works. But then I change at least one of the scales to log and the annotation disappears. I get no error message.
Here's my code:
plt.clf()
samplevalues = [100,1000,5000,10^4]
ax = plt.subplot(111)
ax.plot(samplevalues,samplevalues,'o',color='black')
ax.annotate('hi',(0.5,0.5), textcoords='axes fraction')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
If I comment out ax.set_xcale('log') and ax.set_ycale('log'), the annotation appears right in the middle of the plot (where it should be). Otherwise, it doesn't appear.
Thanks in advance for your help!
It may really be a bug as pointed out by #tcaswell in the comment but a workaround is to use text() in axis coords:
plt.clf()
samplevalues = [100,1000,5000,10^4]
ax = plt.subplot(111)
ax.loglog(samplevalues,samplevalues,'o',color='black')
ax.text(0.5, 0.5,'hi',transform=ax.transAxes)
plt.show()
Another approach is to use figtext() but that is more cumbersome to use if there are already several plots (panels).
By the way, in the code above, I plotted the data using log-log scale directly. That is, instead of:
ax.plot(samplevalues,samplevalues,'o',color='black')
ax.set_xscale('log')
ax.set_yscale('log')
I did:
ax.loglog(samplevalues,samplevalues,'o',color='black')
How do you add an axis to the outside of another axis, keeping it within the figure as a whole? legend and colorbar both have this capability, but implemented in rather complicated (and for me, hard to reproduce) ways.
You can use the subplots command to achieve this, this can be as simple as py.subplot(2,2,1) where the first two numbers describe the geometry of the plots (2x2) and the third is the current plot number. In general it is better to be explicit as in the following example
import pylab as py
# Make some data
x = py.linspace(0,10,1000)
cos_x = py.cos(x)
sin_x = py.sin(x)
# Initiate a figure, there are other options in addition to figsize
fig = py.figure(figsize=(6,6))
# Plot the first set of data on ax1
ax1 = fig.add_subplot(2,1,1)
ax1.plot(x,sin_x)
# Plot the second set of data on ax2
ax2 = fig.add_subplot(2,1,2)
ax2.plot(x,cos_x)
# This final line can be used to adjust the subplots, if uncommentted it will remove all white space
#fig.subplots_adjust(left=0.13, right=0.9, top=0.9, bottom=0.12,hspace=0.0,wspace=0.0)
Notice that this means things like py.xlabel may not work as expected since you have two axis. Instead you need to specify ax1.set_xlabel("..") this makes the code easier to read.
More examples can be found here.
I have a problem with Matplotlib's subplots. I do not know the number of subplots I want to plot beforehand, but I know that I want them in two rows. so I cannot use
plt.subplot(212)
because I don't know the number that I should provide.
It should look like this:
Right now, I plot all the plots into a folder and put them together with illustrator, but there has to be a better way with Matplotlib. I can provide my code if I was unclear somewhere.
My understanding is that you only know the number of plots at runtime and hence are struggling with the shorthand syntax, e.g.:
plt.subplot(121)
Thankfully, to save you having to do some awkward math to figure out this number programatically, there is another interface which allows you to use the form:
plt.subplot(n_cols, n_rows, plot_num)
So in your case, given you want n plots, you can do:
n_plots = 5 # (or however many you programatically figure out you need)
n_cols = 2
n_rows = (n_plots + 1) // n_cols
for plot_num in range(n_plots):
ax = plt.subplot(n_cols, n_rows, plot_num)
# ... do some plotting
Alternatively, there is also a slightly more pythonic interface which you may wish to be aware of:
fig, subplots = plt.subplots(n_cols, n_rows)
for ax in subplots:
# ... do some plotting
(Notice that this was subplots() not the plain subplot()). Although I must admit, I have never used this latter interface.
HTH