wrong line index in datapoint file when 1-hot encoding nts

 #!/usr/bin/python3.8
 import numpy as np
 import pandas as pd
-import concurrent.futures, Bio.PDB.StructureBuilder, gzip, io, itertools, json, multiprocessing, os, psutil, re, requests, sqlalchemy, subprocess, sys, time, warnings
+import concurrent.futures, Bio.PDB.StructureBuilder, gzip, io, itertools, json, os, psutil, re, requests, sqlalchemy, subprocess, sys, time, warnings
 from Bio import AlignIO, SeqIO
 from Bio.PDB import MMCIFParser, PDBIO
 from Bio.PDB.mmcifio import MMCIFIO
@@ -16,9 +16,10 @@ from collections import OrderedDict
 from ftplib import FTP
 from functools import partial
 from os import path, makedirs
-from multiprocessing import Pool, cpu_count, Manager
+from multiprocessing import Pool, cpu_count, Manager, freeze_support, RLock, current_process
 from time import sleep
 from tqdm import tqdm
+from tqdm.contrib.concurrent import process_map
 
 if path.isdir("/home/ubuntu/"): # this is the IFB-core cloud
     path_to_3D_data = "/mnt/Data/RNA/3D/"
@@ -478,7 +479,7 @@ def read_cpu_number():
     # it reads info from /sys wich is not the VM resources but the host resources.
     # Read from /proc/cpuinfo instead.
     p = subprocess.run(['grep', '-Ec', '(Intel|AMD)', '/proc/cpuinfo'], stdout=subprocess.PIPE)
-    return int(p.stdout.decode('utf-8')[:-1])/2
+    return int(int(p.stdout.decode('utf-8')[:-1])/2)
 
 def warn(message, error=False):
     if error:
@@ -510,7 +511,7 @@ def execute_job(j, jobcount):
 
     elif j.func_ is not None:
 
-        #print(f"[{running_stats[0]+running_stats[2]}/{jobcount}]\t{j.func_.__name__}({', '.join([str(a) for a in j.args_ if not ((type(a) == list) and len(a)>3)])})")
+        print(f"[{running_stats[0]+running_stats[2]}/{jobcount}]\t{j.func_.__name__}({', '.join([str(a) for a in j.args_ if not ((type(a) == list) and len(a)>3)])})")
 
         m = -1
         monitor = Monitor(os.getpid())
@@ -771,15 +772,13 @@ def cm_realign(rfam_acc, chains, label):
         subprocess.run(["esl-reformat", "afa", path_to_seq_data + f"realigned/{rfam_acc}++.stk"], stdout=f)
         f.close()
         # subprocess.run(["rm", path_to_seq_data + f"realigned/{rfam_acc}.cm", path_to_seq_data + f"realigned/{rfam_acc}++.fa", path_to_seq_data + f"realigned/{rfam_acc}++.stk"])
-    
-    
     else:
         # Ribosomal subunits deserve a special treatment.
         # They require too much RAM to be aligned with Infernal.
         # Then we will use SINA instead.
 
         # Get the seed alignment from Rfam
-        print(f"\t> Download latest LSU-Ref alignment from SILVA...", end="", flush=True)
+        print(f"\t> Download latest LSU/SSU-Ref alignment from SILVA...", end="", flush=True)
         if rfam_acc in ["RF02540", "RF02541", "RF02543"] and not path.isfile(path_to_seq_data + "realigned/LSU.arb"):
             try:
                 _urlcleanup()
@@ -819,6 +818,7 @@ def cm_realign(rfam_acc, chains, label):
                                "-o", path_to_seq_data + f"realigned/{rfam_acc}++.afa",
                                "-r", path_to_seq_data + arbfile,
                                "--meta-fmt=csv"])
+    return 0
 
 def summarize_position(col):
     # this function counts the number of nucleotides at a given position, given a "column" from a MSA.
@@ -840,22 +840,21 @@ def summarize_position(col):
     else:
         return (0, 0, 0, 0, 0)
 
-def alignment_nt_stats(f, list_of_chains) :
+def alignment_nt_stats(f) :
     global idxQueue
-    #print("\t>",f,"... ", flush=True)
+    list_of_chains =  rfam_acc_to_download[f]
     chains_ids = [ str(c) for c in list_of_chains ]
     thr_idx = idxQueue.get()
 
     # Open the alignment
     align = AlignIO.read(path_to_seq_data + f"realigned/{f}++.afa", "fasta")
     alilen = align.get_alignment_length()
-    #print("\t>",f,"... loaded", flush=True)
 
     # Compute statistics per column
-    pbar = tqdm(total=alilen, position=thr_idx, desc=f"Worker { thr_idx}: {f}", leave=False, )
+    pbar = tqdm(iterable=range(alilen), position=thr_idx+1, desc=f"Worker {thr_idx+1}: {f}", leave=False)
     results = [ summarize_position(align[:,i]) for i in pbar ]
+    pbar.close()
     frequencies = np.array(results).T
-    #print("\t>",f,"... loaded, computed", flush=True)
 
     for s in align:
         if not '[' in s.id: # this is a Rfamseq entry, not PDB
@@ -868,10 +867,10 @@ def alignment_nt_stats(f, list_of_chains) :
         # Save colums in the appropriate positions
         i = 0
         j = 0
-        warn_gaps = False
+        #warn_gaps = False
         while i<c.full_length and j<alilen:
             # here we try to map c.seq (the sequence of the 3D chain, including gaps when residues are missing), 
-            # with s.seq, the sequence aligned in the MSA, containing any of ACGUacguP and two types of gaps, - and .
+            # with s.seq, the sequence aligned in the MSA, containing any of ACGUacgu and two types of gaps, - and .
 
             if c.seq[i] == s.seq[j].upper(): # alignment and sequence correspond (incl. gaps)
                 list_of_chains[idx].frequencies = np.concatenate((list_of_chains[idx].frequencies, frequencies[:,j].reshape(-1,1)), axis=1)
@@ -899,7 +898,7 @@ def alignment_nt_stats(f, list_of_chains) :
                     continue
 
                 # else, just ignore the gap.
-                warn_gaps = True
+                #warn_gaps = True
                 list_of_chains[idx].frequencies = np.concatenate((list_of_chains[idx].frequencies, np.array([0.0,0.0,0.0,0.0,1.0]).reshape(-1,1)), axis=1)
                 i += 1
             elif s.seq[j] in ['.', '-']: # gap in the alignment, but not in the real chain
@@ -927,7 +926,7 @@ def alignment_nt_stats(f, list_of_chains) :
 
             # one-hot encoding of the actual sequence
             if c.seq[i] in letters[:4]:
-                point[ letters[:4].index(c.seq[i]), i ] = 1
+                point[ 1 + letters[:4].index(c.seq[i]), i ] = 1
             else:
                 point[5,i] = 1
 
@@ -944,9 +943,8 @@ def alignment_nt_stats(f, list_of_chains) :
             line = [str(x) for x in list(point[i,:]) ]
             file.write(','.join(line)+'\n')
         file.close()
-        #print("\t\tWritten", c.chain_label, f"to file\t{validsymb}", flush=True)
 
-    #print("\t>", f, f"... loaded, computed, saved\t{validsymb}", flush=True)
+    idxQueue.put(thr_idx) # replace the thread index
     return 0
 
 if __name__ == "__main__":
@@ -1054,15 +1052,25 @@ if __name__ == "__main__":
     families =  sorted([f for f in rfam_acc_to_download.keys() ])
 
     # Build job list
-    thr_idx_mgr = multiprocessing.Manager()
+    thr_idx_mgr = Manager()
     idxQueue = thr_idx_mgr.Queue()
-    for i in range(10):
+    for i in range(read_cpu_number()):
         idxQueue.put(i)
-    fulljoblist = []
-    for f in families:
-        label = f"Save {f} PSSMs"
-        list_of_chains = rfam_acc_to_download[f]
-        fulljoblist.append(Job(function=alignment_nt_stats, args=[f, list_of_chains], how_many_in_parallel=10, priority=1, label=label))
-    execute_joblist(fulljoblist, printstats=False)
+
+    # freeze_support()
+    # r = process_map(alignment_nt_stats, families, max_workers=read_cpu_number())
+    p = Pool(initializer=tqdm.set_lock, initargs=(tqdm.get_lock(),), processes=read_cpu_number())
+    fam_pbar = tqdm(total=len(families), desc="RNA families", position=0, leave=True)
+    for i, _ in enumerate(p.imap_unordered(alignment_nt_stats, families)):
+        fam_pbar.update(1)
+    fam_pbar.close()
+    p.close()
+    p.join()
+
+    # ==========================================================================================
+    # Do a brief statistics summary of the produced dataset
+    # ==========================================================================================
+
+    #TODO: compute nt frequencies, chain lengths, angle clusters
 
     print("Completed.")