Parallel Function not working in EEMD

I’m trying to run the EEMD function on environmental data. The function works when parallel is set to False and processes is set to None. However, when I turn parallel to True, and set processors to any integer, I get an error. I’d really like be able to get this feature working because I need to speed up the processing .

`

#pandas dataframe
data_temp 

if __name__ == "__main__":
 
 eemd = EEMD(trials= 100, 
                        noise_width = 0.05, 
                        ext_EMD=None, 
                        separate_trends= True,
                        DTYPE = np.float16,
                        spline_kind='akima',
                        parallel = True,
                        processes = 1)

   eemd.noise_seed(42)
   eIMF = eemd(data_temp[var].values).T #Transverse to match with index
eIMF = pd.DataFrame(eIMF, index=data_temp.index)

` When I run this code, it seems like the decomposition gets skipped and tries to put the data into a dataframe, yet the dataframe hasn’t been defined. I’ve attached a screenshot of the error.

I’m running emd-signal version 0.2.15 with Anaconda, on Windows using a Ryzen 7 CPU.

I’d appreciate any help.

[Saul-the-engineer]

It looks like I figured it out. If I’m trying to run the eemd script in parallel within a for loop on windows, all the subsequent actions need to be inside of the if statement.

Thank you so much for the help, this shortened the runtime from 118 seconds to 14 seconds.

for j, var in enumerate(data_temp.columns):
        if __name__ == '__main__':
            eemd = EEMD(trials= 1000, 
                    noise_width = 0.05, 
                    ext_EMD=None, 
                    separate_trends= True,
                    DTYPE = np.float16,
                    spline_kind='akima',
                    parallel = True,
                    processes = 2)
            eemd.noise_seed(42)
            eIMF = eemd(data_temp[var].values).T
            out = pd.DataFrame(eIMF, index=data_temp.index)
            label = [var + '_imf_' + str(k+1) for k in range(len(out.columns))]
            out.columns = label

[laszukdawid]

Great to hear that 😃