Add CTF effect to projections of a volume

Hi guys! I’m quite new to cryoEM, and I’m playing with CTF and cryoSPARC Simulate Data (BETA) job, but I find that results of my own CTF code are quite different from the ones cryoSPARC Simulate Data (BETA) job gives. As I can only post one figure, I combine all figures in one and insert it at the end.

Here is the thing. I want to get some clean projections of a volume first (this can be done by cryoSPARC or other softwares like RELION, EMAN2, etc), and then add CTF effect with given parameters to these projections. To my understanding, this can be done by taking the Fourier transform of a projection, multiplying the 2D CTF image in Fourier space, and doing the inverse Fourier transform.

So, I calculate the 2D CTF image according to the CTF formulation. ( [CTF Estimation - CryoSPARC Guide]) It looks like figure 1 with parameters: defocus=15000 angstrom, voltage=300kV, Cs=2.7mm, phase shift=0rad, pixel size=0.6575, image size=440. The first 4 parameters are sufficient to calculate CTF value according to the formula on the website, and the last 2 parameters are consistent with the imported volume.

Clean Projections of the volume are produced by cryoSPARC Simulate Data (BETA) job. I set the SNR to a really really big value, and turn off the CTF option. The overall parameters are shown in figure 2. I think the result particles are very close to clean projections of the imported volume, as shown in figure 3. (It’s viewed with EMAN2

To add CTF, the .mrcs file is read by mrcfile python package firstly, and CTF effect is added to the projections by numpy as:

mrc =, mode=‘r’)
data =
proj = data[k, :, :] # shape (440, 440), k iterates over all projections
ctf_2D = … # ctf_2D is the one shown in figure 1
proj_with_CTF = np.abs(np.fft.ifft2(np.fft.fft2(proj)*np.fft.fftshift(ctf_2D)))
CTF_added_proj_stack[k, :, :] = proj_with_CTF
… # write .mrcs file with CTF_added_proj_stack

When I turn on the CTF option in Simulate Data (BETA) job, set the CTF parameters the same as used in my own code, and remain the noise parameters the same, I also get the projections affected by CTF. (Overall parameters are shown in figure 4, and I think it matches the parameters used in my code, which is described above to produce figure 1) As shown in figure 5, however, comparing the results of my code (figure 5 bottom) and cryoSPARC Simulate Data (BETA) (figure 5 top), there exists significant difference. (viewed in EMAN2

I have tried but can’t figure out what’s wrong. I also tried to use projections produced by EMAN2 and add CTF effect by my code, but the results are similar as described above.

Why is it? Do I make mistakes on data processing or CTF calculation? Or do I miss some steps to add CTF effect? Anyone can help me? Thanks for any suggestions!


The CTF image (figure 1) is not correct after posted, probably because I take a screen shot to make this combined figure. It’s quite different with the original image. I try to upload the original CTF image here.

Yep, this one looks good!

Hi @CirenSangzhu,

In the simulate data job, the CTF is indeed applied by multiplying the Fourier-space projections of the volume by the CTF, then transforming back to real space. However, the particles are also then multiplied by the scalar blob/sign value found in the output dataset. By convention, this -1 in order for particles to visually appear as “dark-on-light” (at least at the lowest frequencies).

Your process looks correct except for the use of np.abs on the projections – this isn’t done in our simulation. Do you see that the discrepancy is eliminated if you remove the call to np.abs, and instead just multiply the projections by -1 before plotting?


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@CirenSangzhu You may enjoy this simple notebook (and the called code in the main pyem repo).

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Thanks for you reply! @mmclean

I get the idea of blob/sign and know how it affects the plots according to your explanation! Thanks! Also, I take your suggestion of removing np.abs. So, my code of applying CTF now looks like:

mrc =, mode=‘r’)
data =
proj = data[k, :, :] # shape (440, 440), k iterates over all projections
ctf_2D = … # ctf_2D is the one shown in figure 1
proj_with_CTF = -np.fft.ifft2(np.fft.fft2(proj)*np.fft.fftshift(ctf_2D))
CTF_added_proj_stack[k, :, :] = proj_with_CTF
… # write .mrcs file with CTF_added_proj_stack

Indeed, the results of my code get better, at least the backgrund looks better. However, they are still quite different from the cryoSPARC simulate data job results. The following figure shows the difference. Top is the results of the modified code, while the bottom is the results of cryoSPARC simulate data job.

Does my modified code meet take your suggestion correctly? Or is there any other mistake?

What’s more, I have another question of removing np.abs. To my understanding, np.fft.ifft2 results in a 2D array containing complex number. Then, without np.abs, CTF applied projections are complex matrix. I’m confused, because I think elements in .mrcs file are real numbers. (Though there is no error
arised when I write the complex matrix proj_with_CTF to a .mrcs file using mrcfile python package)

Thanks and Looking forward to your reply!


Thanks for your reply! @DanielAsarnow

I check the provided notebook, and browse the pyem repo. By the way, it’s really a great repo containing many useful functions. Thanks!

Back to the problem. I haven’t clearly understood each called main pyem repo code in detail, but in the notebook, it makes a projection of a volume in Fourier space, then transforms it into the real space. After that, CTF is applied to the projection by multiplying 2D CTF and Fourier slice.

In my case, I start with a clean projection in real space rather than Fourier space, so I apply CTF to the projection using the code in the original post. I haven’t clearly understood the code of getting Fourier slice in pyem, but I noticed that the notebook uses np.fft.rfft2/np.fft.irfft2 rather than np.fft.fft2/np.fft.ifft2. Is this the key to my problem?

I change my code and try this to apply CTF:

proj_with_CTF = -np.fft.fftshift(np.fft.irfft2(np.fft.rfft2(proj)*np.fft.fftshift(ctf_2D)[:, -221:]), axes=1)

The outermost np.fft.fftshift with axes=1 is to make the results look fine, though I don’t know why. The - sign is according to @mmclean blob/sign. The [:, -221:] is to match the shape of np.fft.rfft2(proj). I don’t know whether the code is correct, but it does give a result, as shown in the following figure. Top is the result of cryoSPARC simulate data, while bottom is my result.

It’s difficult for me to judge the result, but I feel there is still something wrong. Do you have any suggestion? Is it possible to realise applying CTF to a projection in real space with just a few lines of code and only numpy package rather than lots of other package? Thanks!


I start with a clean projection in real space rather than Fourier space

Once you understand the projection-slice theorem, I encourage you to use Fourier slices instead. Many things are simpler to understand and implement starting from Fourier space.

I noticed that the notebook uses np.fft.rfft2/np.fft.irfft2

Real signals have Friedel symmetry: f(i,j,k) = f(-i,-j,-k). Thus we can use the specialized FFT functions for real arrays, which save memory and time by computing only one unique half of the FT. Experiment: look at the array shapes and 2D sections of the rfft outputs, it will begin to make sense.

The outermost np.fft.fftshift with axes=1 is to make the results look fine, though I don’t know why.

The FFT algorithm computes the coefficients in a certain order. First, is the 0th coef, then 1, 2, 3, …, until N. Then it does the negative frequencies -N, -(N-1), -(N-2), … , -1. fftshift (and its partner ifftshift which is the same for even-sized arrays) swap the half-spaces along each axis, so that the order is instead -N, …, -1, 0, 1, …, N. Experiment: Transform any real-space image, and then back-transform again. Only one order of using fftshift will let you arrive at an image that is numerically identical to the original. (Hint: you will need to shift twice).

My Fourier-slice code works with the real transforms, in the conventional frequency order, but the CTF code is agnostic. It computes the CTF on the frequency grid that is given, regardless of its shape or order. By using the fftfreq and rfftfreq methods, we can get grids that already match our transforms. This is the most convenient and efficient way - you can see that in my code, only one Fourier shift is required at the end.

I don’t know whether the code is correct, but it does give a result, as shown in the following figure.

If the code is correct, your output will look just that from cryoSPARC, or pyem, or Relion. I can’t quite tell if you still have a contrast inversion or not because there are also some artifacts that look like some kind of interpolation error or from computing the CTF incorrectly.


Thanks for your reply and sorry for the late response!
I find that if I use np.real, the result looks like right. That is, after getting the cleanprojection in the real space, I use np.real(np.fft.ifft2(np.fft.fft2(cleanprojection)*np.fft.fftshift(ctf_image))), and the CTF applied particle image looks similar to the result of cryosparc Simulate Data job. With this results, it seems to me that it is the imaginary part leads to the wrong result.
What’s more, as @mmclean suggested, removing the np.abs probably is the same as using np.real, because I found that when dealing with an imaginary number, without np.abs it will automatically ignore the imaginary part, just doing the same thing with np.real.
So, it seems that when applying CTF to a particle image, the imaginary part is ignored. However, I think the imaginary part is not neccesarily equal to zero, so I’m confused. Why should we ignore the imaginary part, is this a common operation in cryoEM image processing? And, can the imaginary part really influence the result so much, from a normal image to the image that is so wrong in the previous post? (It’s kind of unbelievable to me)

abs() and real() aren’t the same, but here the non-zero imaginary part is solely from numerical error in the full FFT making the Friedel mates not quite the same. Look at the sizes of the imaginary parts with imag() and you’ll see they are tiny. In general though, the magnitude of the whole complex number, and the magnitude of only the real part are not the same. This is another good reason, aside from performance, to use the real FFTs (rfftn etc) when working with real data - the return value will automatically have the right (real) data type.

(The situation is more complex if we consider beam tilt, because coma makes the Friedel mates have opposite sign phase errors. In that case we must use the full FFTs)

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Thanks for your quick response!
I kind of get it, that non-zero imaginary part is only because numerical error, and I will try rfftn later. However, there are some points I would like to get more clear:
(1) From descriptions above, there shouldn’t have been non-zero imaginary part; the apperance of non-zero imaginary part is only because numerical error. I wonder is this guaranteed by mathmatic theory? I can understand that for a cleanprojection, if I do Fourier transformation and then do the inverse Fourier transformation, the result should have exact zero imaginary part theoretically; but here, after Fourier transformation, the image is multiplied by CTF function, then is the inverse Fourier transformation result still should have zero imaginary part theoretically?
(2) I’ll try rfftn later, but now I use np.real(np.fft.ifft2(np.fft.fft2(cleanprojection)*np.fft.fftshift(ctf_image))), which seems to get right CTF corrupted effect from visualization, and I’m wondering is this row of code also right theoretically? As the non-zero imaginary part is solely from numerical error and its size is small, I think it’s okay to just ignore it by np.real. Am I right about this?
(3) I’m curious that, since the size of non-zero imaginary part is tiny, there should also be little difference between the result of np.abs and ‘np.real’. However, from previous posts, we see huge difference between visualization images of np.real and ‘np.abs’. How should I understand this?
(4) In more complex situation you mentioned above (considering beam tilt), according to your opinion, we must use the full FFTs. I’m wondering, in this case, after inverse Fourier transformation, how should we deal with the non-zero imaginary part? Should we use np.real or something else?
So many thing I don’t know and some questions might be too basic :hot_face:
Thanks for any suggestion!

I’m sorry. Question (3) is silly. np.abs results in all positive value, while np.real keeps the sign, So the difference is huge. Just ignore question (3). :crazy_face:

I think with the full FTs you have to take the real part, the Friedel mates have equal and opposite phase differences that will cancel out so the result should be real, but numerical error will add a small imaginary component.

BTW, it’s common in x-ray crystallography classes to prove that the electron density is real. You should be able to do it if you think about the density equation:

Hint - expand the exponent using Euler’s formula.

Here are two slightly different approaches:

PS the Coulomb potential is real for the same reason, this time there’s no difference :slight_smile:

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WOW! Thanks for your patient and detailed explanation!

I kind of get it. To be honest, I’m new to cryoEM or X-ray crystallography, so the links you provide are really helpful to me. I will learn!

Thanks a lot!