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|
#Import needed modules (needed : comb for gmpy, corresponding at combinatory calculations)
import math
import decimal
import gmpy
import os
import time
decimal.getcontext().prec = 6
#Defined variables for the main program and for the calculation of the rarefaction curve
treated_files = list()
Cluster_analyzed = list()
tmp = list()
Matrix = list()
Nb_seq_MOTUs = 0
Result_tmp = decimal.Decimal(0)
Result,Result_fin = float(), float()
Result2 = ""
Nb_MOTUs,verif = 0,0
i,j,n = 0,0,0
N_Ni,x,result = 0,0,0
Rarefact_chose = ""
string_printed = "Sample\t"
tmp_printed = list()
Result_printed = [""]
Children = []
Child_pid = 0
#-------------------------------------------------------------------------------------------------------
#First part of the program, asking for data chosen by the user to analyze files in 'IN' directory
#and also estimators needed to be calculated and chose by the user
#-------------------------------------------------------------------------------------------------------
#Asking for the desired threshold of dissimilarity
print ("- Please give the chosen similarity threshold to select the clustering\n(in percent) [0-100] ?")
cutoff = raw_input()
cutoff = float(cutoff)
cutoff = (100-cutoff)/100
#Asking for the user if he wants the calculation of the rarefaction curve
print ("- Do you want to determine a rarefaction curve based on these data\n[yes, no] ?")
Rarefact_chose = raw_input()
#Asking for the user if he wants the determination of the Chao1 richness estimator
print ("- Do you want to calculate the full bias corrected Chao1 richness estimator\
\non these data [yes, no] ?")
Chao_chose = raw_input()
#Asking for the user if he wants the determination of the ACE richness estimator
print ("- Do you want to calculate the ACE richness estimator on these\ndata [yes, no] ?")
ACE_chose = raw_input()
#Asking for the user if he wants the determination of the ACE richness estimator
print ("- Do you want to calculate the bootstrap estimate and also shannon and simpson\
\nindexes on these data [yes, no] ?")
Index_chose = raw_input()
#Deleting the last character of the string and transforming it in integer
#-------------------------------------------------------------------------------------------------------
#Needed function
#-------------------------------------------------------------------------------------------------------
def read_folder(path): ##read_folder (to acquire all file names in a defined directory)
tab = []
tab = os.listdir(path)
return tab
def combinatory(N,n): ##combinatory (permit the calculation of combinatorial values)
X = math.factorial(N)
Xb = math.factorial(n)
Xc = math.factorial(N-n)
final = X/(Xb*Xc)
return final
#-------------------------------------------------------------------------------------------------------
#Main program : selection and storage of the chosen cluster
#-------------------------------------------------------------------------------------------------------
string_printed +="Nb_seqs\tNb_clusters\t"
if Chao_chose.lower() == "yes":
string_printed += "Chao1\tvar(Chao1)\tLCI95\tUCI95\t"
if ACE_chose.lower() == "yes":
string_printed += "ACE\tRare_ACE\tAbundant_ACE\t"
if Index_chose.lower() == "yes":
string_printed += "Bootstrap\tShannon\tvar(Shannon)\tSimpson\t1/Simpson"
Result_printed[0] = string_printed
#Automatic acquisition of file names in the target directory
treated_files = read_folder("IN")
#For each detected file in the target directory ("IN")
for i in range(len(treated_files)):
Result_printed.append(str(treated_files[i])+"\t")
#Defined variables for chao calculation
C_chao1,S_chao1,var_S_chao1 = 0,0,0
n1,n2 = 0,0
LCI95_chao1,UCI95_chao1 = 0,0
#Defined variables for ACE calculation
N_rare_ACE,C_ACE = 0,0
Gamma_ACE,S_rare_ACE = 0,0
S_ACE,S_abund_ACE,n1_ACE = 0,0,0
#Defined variables for shannon and simpson index calculations
S_bootstrap = 0
H_Shannon,var_H_Shannon = decimal.Decimal(0),decimal.Decimal(0)
D_Simpson = decimal.Decimal(0)
#Needed empty variables
verif = 0
tmp = list()
n = 0
Cluster_analyzed = list()
#open the targeted cluster file
Cluster_file = open("IN/"+treated_files[i], 'r')
#For each read line of the file (as the line is not empty)
Read_line = Cluster_file.readline()
while Read_line != "":
Read_line = Cluster_file.readline()
#We search for the line containg the number of sequences
if 0<=Read_line.find("Sequences", 0):
#The line is splitted based on "\t" and stored in tmp
tmp = Read_line.split("\t")
#The number of sequences in stored in Nb_seqs_tot after conversion in int
Nb_seqs_tot = int(tmp[1])
#We search also for the distance cut-off to clusterize sequences to found the corresponding
#cluster based on the choice of the user
if 0<=Read_line.find("distance", 0):
tmp = Read_line.split("\t")
#We verify that the defined threshold by the user is same or below the analyzed cluster
if float(tmp[1]) >= cutoff and verif == 0:
#Needed variable to 1 to stop the data acquisition
verif = 1
Read_line = Cluster_file.readline()
#Loop to copy needed data
while Read_line != "":
#Stop the analysis if we found the end of the cluster
if Read_line == "\n":
break
#Copy and storage of cluster number needed to calculate rarefact curve
if 0<=Read_line.find("Clusters", 0):
tmp = Read_line.split("\t")
Nb_clusters = int(tmp[1])
#Copy only the needed data (number of sequences in the cluster)
elif Read_line != "":
tmp = Read_line.split("\t")
Cluster_analyzed.append(int(tmp[2]))
Read_line = Cluster_file.readline()
#Close the treated file as it's not needed now
Cluster_file.close()
#-------------------------------------------------------------------------------------------------------
#After recuperation in Cluster_analyzed of needed data (MOTUs and sequences in each MOTUs), the next
#step is developed to create the needed matrix to calculate the rarefaction curve
#-------------------------------------------------------------------------------------------------------
#Rearrange the list by values (ascending 1 to n)
Cluster_analyzed.sort()
#For each line (correponding to each MOTU), creation of the matrix
for element in Cluster_analyzed:
#We count the number of MOTUs with the same number of sequences
if Nb_seq_MOTUs == element:
Nb_MOTUs += 1
#If we found a new number of sequences in a MOTU, we create a new line and store the
#data obtained for the previous MOTU type
else:
Matrix.append([Nb_seq_MOTUs, Nb_MOTUs])
Nb_seq_MOTUs = element
Nb_MOTUs = 1
#As the loop didn't verify the last data, a new step is dedicated to the analysis
tmp = Matrix[-1]
#Addition of the last if needed
if Nb_seq_MOTUs == tmp[0]:
tmp[1] +=1
Matrix[-1] = tmp
#Or creation of a new line
else:
Matrix.append([Nb_seq_MOTUs, 1])
#Close the file
Nb_seq_MOTUs = 0
Nb_MOTUs = 0
#Deletion of the first line not needed for the rarefaction curve
del Matrix[0]
#-------------------------------------------------------------------------------------------------------
#Calculation of the rarefaction curve based on the formula found in various publications if wanted
#for details : see Appendix.A
#-------------------------------------------------------------------------------------------------------
#Verification for the choice of the user for calculation of the rarefaction curve
if Rarefact_chose.lower() == "yes":
#Create a new file to store the rarefaction curve calculation data
Rarefact_curve = open("OUT/"+"rc_"+str(cutoff)+"_"+treated_files[i], 'w')
Rarefact_curve.close()
#A maximum of 1000 points are determined, verification of the number of sequences
#If the number of sequences if less than 1000, we treat each point
if Nb_seqs_tot < 1000:
Nb_steps_calc = 1
#Else, we calculate the needed step to do 1000 points
else:
Nb_steps_calc = math.floor(Nb_seqs_tot/1000)
#Loop to calculate the rarefaction curve
while n < Nb_seqs_tot:
Child_pid = os.fork()
if Child_pid == 0:
#Calculation of the rarefaction curve points (details in the formula)
for element in Matrix:
#For each element in the matrix, we calculate N_Ni
N_Ni = Nb_seqs_tot - element[0]
#Sum of combinatory values in the upper formula using N_Ni, n and Nb_seqs_tot
Result = Result + element[1]*gmpy.comb(gmpy.mpz(N_Ni),gmpy.mpz(n))
#Determination of the value of the rarefaction curve for n sequences in Nb_seqs_tot data
Result_fin = float(Nb_clusters) - Result/(gmpy.comb(gmpy.mpz(Nb_seqs_tot),gmpy.mpz(n)))
#Needed empty variable
Result = 0
#In the result file, storage of results for this step
Result2 = str(n) + "\t" + str(Result_fin) + "\n"
#Append the file to store results
Rarefact_curve = open("OUT/"+"rc_"+str(cutoff)+"_"+treated_files[i], 'a')
Rarefact_curve.write(Result2)
Rarefact_curve.close()
os._exit(0)
else:
Children.append(Child_pid)
#Increment n using the step calculated before
n += Nb_steps_calc
#Wait for all child processes to continue the program
for Child_pid in Children:
status = os.waitpid(Child_pid,0)
Result_printed[i+1]+= str(Nb_seqs_tot)+"\t"+str(Nb_clusters)+"\t" |
Bug [Errno 10] No child processes
). Il fonctionne parfaitement sur un fichier, mais dès qu'il doit traiter plus d'un fichier, jai une erreur système qui apparaît :
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