diff --git a/read_matrix.py b/read_matrix.py
index 00ff35b24c430bbf98184f7f7d30ddb64a7cbbb5..0c43e26a064916bd9b2065d0720cf1c760c73859 100755
--- a/read_matrix.py
+++ b/read_matrix.py
@@ -7,8 +7,6 @@ import time
import random
-import reads_consensus_class as rcc
-
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.insert(0, os.path.dirname(currentdir)+"/synthesis_modules")
import dna_file_reader as dfr
@@ -63,6 +61,7 @@ def generate_random_reads(references_file_path, coverage_list, reads_path):
def get_minimisers(reads_path):
+ #TODO minimizers that can also be found in primers should not be used
kmer_size = 10
minimiser_size = 6
@@ -108,7 +107,7 @@ def generate_matrix(reads_path, minimisers_dict, solutions_path, matrix_path):
soluce_dict[read_cluster] = soluce_dict.get(read_cluster, []) + ["r"+str(i)]
soluce_dict = dict(sorted(soluce_dict.items()))
- print(soluce_dict)
+ #print(soluce_dict)
with open(solutions_path, 'w') as output_file:
for cluster_name in soluce_dict.keys():
@@ -136,15 +135,37 @@ def generate_matrix(reads_path, minimisers_dict, solutions_path, matrix_path):
print(str(ones_counter)+ "/"+ str(cases_counter) +" = "+ str(round(100*ones_counter/cases_counter, 1))+"%")
-def matrix_generation():
+def get_coverage_list(total_sum, min_coverage=30) -> list:
+ """
+ return a list of number for the coverage fo the generated reads
+ """
+ coverage_list = []
+ current_sum = 0
+ while current_sum <= total_sum:
+ coverage_i = random.randint(min_coverage, 2*min_coverage)
+ current_sum += coverage_i
+ coverage_list.append(coverage_i)
+
+ # adjust the last cluster to remove the excedent
+ coverage_list[-1] = coverage_list[-1]-(current_sum-total_sum)
+ # sorted by descending
+ coverage_list = sorted(coverage_list, reverse=True)
+
+ print(coverage_list)
+ print(len(coverage_list),"clusters with a sum of",sum(coverage_list))
+
+ return coverage_list
- coverage_list = [13,11,11,10,9,9,8,8,8,7,5,1]
- ref_path = "matrix_tests/references.fasta"
- reads_path = "matrix_tests/reads.fastq"
- solutions_path = "matrix_tests/soluce.txt"
- matrix_path = "matrix_tests/matrix.csv"
- generate_references_sequences(12, ref_path)
+def matrix_generation(column_number, matrix_name, dir_path):
+
+ coverage_list = get_coverage_list(column_number) # list of coverage for each reference
+ ref_path = dir_path +"references.fasta"
+ reads_path = dir_path +"reads.fastq"
+ solutions_path = dir_path +"soluce.txt"
+ matrix_path = dir_path + matrix_name+".csv"
+
+ generate_references_sequences(len(coverage_list), ref_path)
generate_random_reads(ref_path, coverage_list, reads_path)
minimisers_dict = get_minimisers(reads_path)
@@ -152,19 +173,111 @@ def matrix_generation():
generate_matrix(reads_path, minimisers_dict, solutions_path, matrix_path)
+def matrix_to_COO(input_matrix_csv, output_COO):
+
+ matrix_lines = []
+ with open(input_matrix_csv, 'r') as input_matrix:
+ input_matrix.readline() # skip columns names
+ line = input_matrix.readline()
+ while line != "":
+ content = line.split(",")
+ matrix_lines.append(content[1:])
+ line = input_matrix.readline()
+
+ with open(output_COO, 'w') as output_matrix:
+ output_matrix.write(str(len(matrix_lines))+" "+str(len(matrix_lines[0]))+"\n")
+ for i, line in enumerate(matrix_lines):
+ for j, value in enumerate(line):
+ if value == "1":
+ output_matrix.write(str(i)+" "+str(j)+"\n")
+
+
+def matrix_to_CSVBM(input_matrix_csv, output_CSVBM):
+ """convert a csv matrix to the csvbm format
+ row_br column_nbr ones_nbr\n
+ distance_to_first_one\n
+ ...
+ distance_to_next_one_from_last_one\n
+ ...
+ """
+ matrix_rows = []
+ number_of_ones = 0
+ with open(input_matrix_csv, 'r') as input_matrix:
+ input_matrix.readline() # skip columns names
+ line = input_matrix.readline()
+ while line != "":
+ content = line.replace("\n","").split(",")[1:] # first one is row name
+ matrix_rows.append(content)
+ number_of_ones += sum(int(k) for k in content)
+ line = input_matrix.readline()
+
+ number_rows = len(matrix_rows)
+ number_columns = len(matrix_rows[0])
+
+ with open(output_CSVBM, 'w', encoding='utf-8') as csvbm_file:
+ csvbm_file.write(
+ f'{number_rows} {number_columns} {number_of_ones}\n',
+ )
+ distance = 0
+ for row in matrix_rows:
+ for column in row:
+ if column == "1":
+ csvbm_file.write(f'{distance}\n')
+ distance = 1
+ else:
+ distance += 1
+
+
+
+def display_results(input_matrix_csv, results_dir, result_output):
+
+ matrix_lines = []
+ with open(input_matrix_csv, 'r') as input_matrix:
+ input_matrix.readline() # skip columns names
+ line = input_matrix.readline()
+ while line != "":
+ content = line.replace("\n","").split(",")
+ matrix_lines.append(content[1:])
+ line = input_matrix.readline()
+
+ # get list of ordered lines and columns from results files
+ ordered_lines = []
+ ordered_columns = []
+
+ for filename in sorted(os.listdir(results_dir)):
+ file_path = os.path.join(results_dir, filename)
+ with open(file_path, 'r') as input_matrix:
+ ordered_columns += input_matrix.readline().replace("\n","").split(",")[1:]
+ line = input_matrix.readline()
+ while line != "":
+ line_nbr = line.split(",")[0]
+ ordered_lines.append(line_nbr)
+ line = input_matrix.readline()
+
+ print(ordered_columns)
+ with open(result_output, 'w') as output_file:
+ output_file.write(","+",".join(ordered_columns)+"\n")
+ for line_name in ordered_lines:
+ line_number = int(line_name[1:])
+ matrix_line = line_name
+ for column_name in ordered_columns:
+ column_number = int(column_name[1:])
+ matrix_line += ","+matrix_lines[line_number][column_number]
+ #print(matrix_line)
+ output_file.write(matrix_line + "\n")
+
+ print(sorted(ordered_lines))
if __name__ == "__main__":
print("generate matrix...")
- #for i in [1000*k for k in range(1,21)]:
- # min_occ_testing(i)
- #for i in range(1):
- # consensus_testing(3000)
-
- matrix_generation()
- #consensus_testing(50000)
- #s = verify_check_sum("test_consensus/consensus_test.fasta")
- #print(s)
+ column_number = 10000
+ matrix_name = "matrix_10k_bis"
+
+ dir_path = "matrix_tests/"+matrix_name+"/"
+ matrix_generation(column_number, matrix_name, dir_path)
+ matrix_to_CSVBM(dir_path+ matrix_name+".csv", dir_path + matrix_name+".csvbm")
+ #display_results(dir_path+"/matrix_100/matrix.csv", dir_path+"/matrix_100_results", dir_path+"/matrix_100/matrix_100_ordered_results.csv")
print("\tcompleted !")