Xtykutl

I'm trying the following code before main

manager = Manager()

general_d = manager.dict()

Then in main
I define the following

  p = Pool(4)  # however many process you want to spawn

  p.map(proc_file, directoary_names)



def proc_file(directoary_names):

    try:

        process_data(directoary_names)

    except KeyboardInterrupt:

      pass

The problem is I get frozen exception is not going to be frozen to produce an executable.''')

The main problem is I'm processing many files and I'm getting results from each file, so the problem how would I get the results for example from sensors ( s1 to s8) with time stamps for each sensor and merge them in order of time stamps...

A pseudo code would be helpfull.

In process data, I process the file, read it's data, then I put the results into global lists

             S1.append(df_conv['C_strain_COY'].median())

             S2.append(df_conv['C_strain_CUY'].median())

             S3.append(df_conv['C_strain_ROX'].median())

             S4.append(df_conv['C_strain_CUX'].median())

             S5.append(df_conv['C_strain_CMX'].median())

             S6.append(df_conv['C_strain_COX'].median())

             S7.append(df_conv['C_strain_LOX'].median())



             T1.append(df_conv['C_temp_CUY'].median())

             T2.append(df_conv['C_temp_COY'].median())

             T3.append(df_conv['C_temp_CUX'].median())

             T4.append(df_conv['C_temp_CMX'].median())

             T5.append(df_conv['C_temp_COX'].median())

Say S1 to S7 are lists of Pandas DataFrames, each containing data of a specific sensor and an according timestamp for each data entry.

import pandas as pd

Create a joint DataFrame for each sensor

df_S1 = pd.concat(S1)

Sort these DataFrame along the timestamp axis

df_S1 = df_S1.sort_values(by='timestamps')

Now, if you want to merge all sensors together in a single DataFrame, checkout Pandas' tutorial to decide which function you need (e.g. pd.merge or pd.merge_asof). If you go with pd.merge, you can loop over df_S1, ..., df_S7, since pd.merge supports only merging two DataFrames.