I tried pip install DEERPREdict on my Mac terminal and the install failed.

I have the Apple M1 chip and I'm on OS Monterey 12.0.1.
I believe it's failing because the install seems to be for an older operating system and/or for the Intel chip (I saw "build/lib.macosx-10.9-x86_64-3.8" in the error text).

I was able to install successfully on a Windows.
Please let me know if this is fixed because I'd love to have this package on my Mac!

Thanks,
-Sam

Here's a list of the install warnings I get:

• warning "Using deprecated NumPy API
• warning: comparison of integers of different signs: 'unsigned int' and 'int'
• warning: '/*' within block comment
• warning: 'tp_print' is deprecated
• warning: unused variable 'cur_time'
• warning: unused variable 'tmbuf'
• warning: unused function 'fio_read_int32'
• warning: unused function 'fio_write_str'
• warning: unused function 'swap2_aligned'
• warning: unused function 'swap2_unaligned'
• warning: unused function 'PyConverter_DoubleArray'
• warning: unused function 'PyConverter_DoubleArrayOrNone'
• warning: unused function 'PyConverter_DoubleVector4Copy'
• warning: 'PyUnicode_GetSize' is deprecated
• error: implicit declaration of function 'getpid' is invalid

I'm using Anaconda Powershell Prompt on Windows 10 with Python 3.8.2. After running the installation instructions I get the message that I have successfully installed DEERPREdict and all dependencies. However, when trying to use the commands on the "Running Calculations" instructions I get the error

The 'from' keyword is not supported in this version of the language

after using

from DEERPREdict.DEER import DEERpredict

I'm not sure if this is a windows or python issue and if there are work arounds for running the DEERpredict without using the 'from' keyword or if I should take additional steps to properly set up the environment so that it will work with the 'from' usage. Thank you.

Hi,

I have a question about the code, particularly related to the following lines in PRE.py:

gamma_2 = np.ma.MaskedArray(gamma_2, mask=np.isnan(gamma_2))
gamma_2_av[self.measured_resnums] = np.ma.average(gamma_2, weights=self.weights, axis=0).data
i_ratio = self.r_2 * np.exp(-gamma_2_av * self.delay) / ( self.r_2 + gamma_2_av )
np.savetxt(self.output_prefix+'-{}.dat'.format(self.residue),np.c_[self.resnums,i_ratio,gamma_2_av],header='residue i_ratio gamma_2')

By calculating the average of gamma_2, the i_ratio of a trajectory does not equal the average i_ratio of all individual frames (or the average of i_ratio of multiple trajectories), as exp(average(X)) != average(exp(X)).

As an illustration, here are the PRE results of a (from two individual trajectories) combined trajectory:

vs. the results of the two individual trajectories (same amount of frames):

Does this part of the code work as intended? It is consistent with the description in the paper, but I am not sure if this is correct from a theoretical standpoint.

Best regards

Alexander Jussupow