diff --git a/__pycache__/Visualisation_des_donnes_de_mapping.cpython-310.pyc b/__pycache__/Visualisation_des_donnes_de_mapping.cpython-310.pyc
deleted file mode 100644
index 29488e81ca132689de06d97aa4b1dff35665d9c8..0000000000000000000000000000000000000000
Binary files a/__pycache__/Visualisation_des_donnes_de_mapping.cpython-310.pyc and /dev/null differ
diff --git a/Tests_unitaires/Cors_file_for_test/Corres_file_for_CPDB_unittest.csv b/src/processing_mapping_results/Tests_unitaires/Cors_file_for_test/Corres_file_for_CPDB_unittest.csv
similarity index 100%
rename from Tests_unitaires/Cors_file_for_test/Corres_file_for_CPDB_unittest.csv
rename to src/processing_mapping_results/Tests_unitaires/Cors_file_for_test/Corres_file_for_CPDB_unittest.csv
diff --git a/Tests_unitaires/Cors_file_for_test/Corres_file_for_RAMP_unittest.csv b/src/processing_mapping_results/Tests_unitaires/Cors_file_for_test/Corres_file_for_RAMP_unittest.csv
similarity index 100%
rename from Tests_unitaires/Cors_file_for_test/Corres_file_for_RAMP_unittest.csv
rename to src/processing_mapping_results/Tests_unitaires/Cors_file_for_test/Corres_file_for_RAMP_unittest.csv
diff --git a/main.py b/src/processing_mapping_results/Tests_unitaires/main.py
similarity index 100%
rename from main.py
rename to src/processing_mapping_results/Tests_unitaires/main.py
diff --git a/Tests_unitaires/test_Mapping_using_the_API.py b/src/processing_mapping_results/Tests_unitaires/test_Mapping_using_the_API.py
similarity index 100%
rename from Tests_unitaires/test_Mapping_using_the_API.py
rename to src/processing_mapping_results/Tests_unitaires/test_Mapping_using_the_API.py
diff --git a/Tests_unitaires/test_complete_processing_of_mapping_results.py b/src/processing_mapping_results/Tests_unitaires/test_complete_processing_of_mapping_results.py
similarity index 100%
rename from Tests_unitaires/test_complete_processing_of_mapping_results.py
rename to src/processing_mapping_results/Tests_unitaires/test_complete_processing_of_mapping_results.py
diff --git a/src/processing_mapping_results/Tests_unitaires/test_main.py b/src/processing_mapping_results/Tests_unitaires/test_main.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccb3337b9031e4c19369ca8c4612e13d51c34b22
--- /dev/null
+++ b/src/processing_mapping_results/Tests_unitaires/test_main.py
@@ -0,0 +1,54 @@
+import unittest
+import pandas as pd
+import os
+import tempfile
+import sys
+import openpyxl
+sys.path.append('C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\traitement_des_données')
+from main import *
+
+class Test_shapping_data(unittest.TestCase):
+ """
+ return (datas_for_mapping, for_pdf_ch)
+ "C:\\Users\\mumec\\Desktop\\Datas_mis_en_forme_pour_le_mapping.xlsx"
+ """
+ def test_shapping_data(self):
+ with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as tmp_file:
+ wb = openpyxl.Workbook()
+ ws = wb.active
+ ws['A1'] = 'C_N'
+ ws['B1'] = 'ID'
+ ws['C1'] = 'Cas'
+ ws['D1'] = 'Temoin'
+ ws['A2'] = 'choline'
+ ws['B2'] = 'chebi:15354, hmdb:HMDB0000097'
+ ws['C2'] = '0.41'
+ ws['D2'] = '1.59'
+ ws['A3'] = 'L-Kynurenine'
+ ws['B3'] = 'NA'
+ ws['C3'] = '0.24'
+ ws['D3'] = '1.74'
+ ws['A4'] = 'Leucylleucine'
+ ws['B4'] = 'chebi:191208'
+ ws['C4'] = '3.01'
+ ws['D4'] = '5.01'
+ wb.save(tmp_file.name)
+
+ """
+ self.temp_file = tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.xlsx')
+ self.temp_file.write("C_N;ID;Cas;Temoin\n")
+ self.temp_file.write("choline;chebi:15354, hmdb:HMDB0000097;0,41;1,59\n")
+ self.temp_file.write("L-Kynurenine;NA;0,24;1,74\n")
+ self.temp_file.write("Leucylleucine;chebi:191208;3.01;5.01\n")
+ self.temp_file.close()
+ """
+ print(tmp_file.name)
+ tab_to_map, list_fig = shapping_data(tmp_file.name, "C:\\Users\\mumec\\Desktop\\")
+ os.remove(tmp_file.name)
+ self.assertIsInstance(tab_to_map, list)
+ self.assertIsInstance(list_fig, list)
+ self.assertTrue(os.path.exists("C:\\Users\\mumec\\Desktop\\Datas_mis_en_forme_pour_le_mapping.xlsx"))
+ os.remove("C:\\Users\\mumec\\Desktop\\Datas_mis_en_forme_pour_le_mapping.xlsx")
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/src/processing_mapping_results/Tests_unitaires/test_network_visualization.py b/src/processing_mapping_results/Tests_unitaires/test_network_visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..5069f1f40cc76323fd8c75d348db651c135b9b11
--- /dev/null
+++ b/src/processing_mapping_results/Tests_unitaires/test_network_visualization.py
@@ -0,0 +1,60 @@
+import unittest
+import pandas as pd
+import os
+import tempfile
+import sys
+import openpyxl
+from unittest.mock import patch
+sys.path.append('C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\traitement_des_données')
+from network_visualization import *
+
+class Test_paths_link_cpdb(unittest.TestCase):
+ """
+ paths_link_cpdb(map_data, out_file, mod_data, bdd="Reactome", flow=None)
+ """
+
+ def test_paths_link_cpdb_flow(self):
+ mdata = [["1.5","3.5","1.9", "1.9"], ["5.5","7.7","5.2", "5.2"],
+ ["amine_path","SLC_path","Glutamate", "tryptophan"],
+ ["Reactome","Reactome","HumanCyc", "Reactome"],
+ ["R-HA","R-HA","R-HA", "R-HA"],
+ [["17295""15729"],["17295"],["15729"], ["15729"]],
+ ["k:C","k:C","k:C","k:C"],["32","164","34","34"],
+ ["32","164","34","34"]]
+ modata = [["name","meta1","meta2"],
+ ["chebi","17295","15729"],["hmdb","15729","17295"],
+ ["logp","-0.35", "0.35"]]
+ name_out_file = 'test_default_paths_link_cpdb.xlsx'
+ edges, nodes = paths_link_cpdb(mdata, name_out_file, modata,
+ bdd="Reactome", flow=True)
+ self.assertTrue(os.path.exists(name_out_file))
+ self.assertEqual(len(edges), 7)
+ self.assertEqual(len(nodes), 5)
+ os.remove(name_out_file)
+
+
+ def test_paths_link_cpdb_flow(self):
+ self.temp_file = tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.csv')
+ self.temp_file.write("1.5;5.5;amine_path;Reactome;R-HA;17295,15729;k:C;32;32\n")
+ self.temp_file.write("3.5;7.7;SLC_path;Reactome;R-HA;17295;k:C;164;164\n")
+ self.temp_file.write("1.9;5.2;Glutamate;HumanCyc;R-HA;15729;k:C;34;34\n")
+ self.temp_file.write("1.9;5.2;tryptophan;Reactome;R-HA;15729;k:C;34;34\n")
+ self.temp_file.close()
+ self.temp_file1 = tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.csv')
+ self.temp_file1.write("Chebi;intensité_cas;intensité_témoin\n")
+ self.temp_file1.write("17295;25;23\n")
+ self.temp_file1.write("15729;23;25\n")
+ self.temp_file1.close()
+ name_out_file = 'test_default_paths_link_cpdb.xlsx'
+ edges, nodes = paths_link_cpdb(self.temp_file.name, name_out_file,
+ self.temp_file1.name, bdd="Reactome")
+ os.remove(self.temp_file.name)
+ os.remove(self.temp_file1.name)
+ self.assertTrue(os.path.exists(name_out_file))
+ self.assertEqual(len(edges), 7)
+ self.assertEqual(len(nodes), 5)
+ os.remove(name_out_file)
+
+
+if __name__ == "__main__":
+ unittest.main()
\ No newline at end of file
diff --git a/src/processing_mapping_results/Tests_unitaires/test_pdf_generation_toolbox.py b/src/processing_mapping_results/Tests_unitaires/test_pdf_generation_toolbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8efe276c9a499e09b4b88deec8802f1c5019963
--- /dev/null
+++ b/src/processing_mapping_results/Tests_unitaires/test_pdf_generation_toolbox.py
@@ -0,0 +1,126 @@
+import unittest
+import pandas as pd
+import os
+import tempfile
+import sys
+import openpyxl
+from unittest.mock import patch
+from docx import Document
+sys.path.append('C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\traitement_des_données')
+from pdf_generation_toolbox import *
+
+
+
+class Testread_docx(unittest.TestCase):
+
+ def test_read_docx(self):
+ doc = Document()
+ doc.add_heading('Sample Document', 0)
+ doc.add_paragraph('This is a sample paragraph.')
+ temp_file = tempfile.NamedTemporaryFile(suffix='.docx', delete=False)
+ doc.save(temp_file.name)
+ temp_file.close()
+ csv_list = read_docx(temp_file.name)
+ os.remove(temp_file.name)
+ self.assertIsInstance(csv_list, list)
+
+
+"""
+class Testout_pdf_mapping(unittest.TestCase):
+"""
+"""
+def out_pdf_mapping(file_path, data_input, chebi_hori, recap, results_map_ramp,
+ results_map_cpdb, out_folder, l_visu):
+
+ result_pdf = PDF()
+ result_pdf.add_font('DejaVu', 'I', 'DejaVuSansCondensed.ttf')
+ result_pdf.add_font('DejaVu', 'B', 'DejaVuSansCondensed.ttf')
+ result_pdf.add_font('DejaVu', '', 'DejaVuSansCondensed.ttf')
+ result_pdf.set_font('DejaVu', '', 14)
+ content = read_docx(file_path)
+ index_toc = content.index('Table of contents')
+ l_title = []
+ titles = []
+ for i_strr, strr in enumerate(content[index_toc+1:]):
+ if strr != '':
+ if strr[1] == ' ':
+ l_title.append(strr)
+ titles.append(strr[2:])
+ else:
+ index_end_toc = i_strr
+ break
+ begin_pages = [0, index_toc]
+ for i_sbp, search_begin_page in enumerate(content[index_end_toc:]):
+ if search_begin_page in titles:
+ begin_pages.append(index_end_toc+i_sbp)
+ result_pdf.guard_page(content[0:begin_pages[1]])
+ result_pdf.shaped_page()
+ result_pdf.summary(l_title)
+ recap_cpdb = []
+ recap_ramp = []
+ for line in recap:
+ recap_ramp.append(line[:2])
+ recap_cpdb.append([line[0]]+line[2:-1])
+ l_l_tables = [["NA"], ["NA", data_input], ["NA", chebi_hori],
+ ["NA", results_map_ramp, "NA", "NA", "NA", "NA", recap_ramp],
+ ["NA"], ["NA", recap_cpdb, "NA", "NA", results_map_cpdb],
+ ["NA"], ["NA"], ["NA"]]
+ l_l_images = [["NA"], ["NA"], ["NA"], ["NA"],
+ ["NA", l_visu[0], "NA", "NA", l_visu[1], "NA", l_visu[2]],
+ ["NA"], ["NA", "NA", l_visu[3], l_visu[4], "NA", "NA",
+ l_visu[5], "NA", "NA", "NA", "NA", "NA", l_visu[6]],
+ ["NA"], ["NA"]]
+ for p_index, begin in enumerate(begin_pages[2:-1]):
+ result_pdf.chapter_s_t_i(p_index+1, titles[p_index],
+ content[begin+1: begin_pages[p_index+3]],
+ l_l_tables[p_index], l_l_images[p_index])
+ result_pdf.chapter_s(len(l_title), titles[-1], content[begin_pages[-1]+1:])
+ result_pdf.shaped_page()
+ result_pdf.end_doc("C:\\Users\\mumec\\Desktop\\Mini_codes\\pdf_PyPDF2\\end_page_for_pdf.docx")
+ result_pdf.output(out_folder+"test_tab_sortie_pdf_mapping_results.pdf")
+
+
+if __name__ == "__main__":
+ dinput = [["Current_name", "ID", "Cas", "Temoin"],
+ ["choline", "chebi:15554, hmdb:HMDB0000267", "0.41", "1.59"],
+ ["choline", "chebi:15554, hmdb:HMDB0000267", "0.41", "1.59"],
+ ["choline", "chebi:15554, hmdb:HMDB0000267", "0.41", "1.59"]]
+
+ Chebi_optimal = [["Chebi ex", "is conjugate b of", "is conjugate acide of",
+ "is tautomer of", "is enantiomer of"],
+ ["Choline", "15354", "NA", "NA", "NA"],
+ ["Choline", "15354", "NA", "NA", "NA"],
+ ["Choline", "15354", "NA", "NA", "NA"]]
+
+ reca = [["Name", "RAMP", "CPDB chebi", "CPDB hmdb"],
+ ["Choline", "chebi:15554", "NA", "15554"],
+ ["Ornithine", "NA", "15354", "HMDB0000097"]]
+
+ r_m_r = [["path_Name", "path_source", "pathwayid", "inputid", "commoname"],
+ ["Choline", "wiki", "WP5552", "chebi:15554", "cho,choline"],
+ ["Choline", "wiki", "WP5552", "chebi:15554", "cho,choline"],
+ ["Choline", "wiki", "WP5552", "chebi:15554", "cho,choline"]]
+
+ r_m_c = [["p-value", "q-value", "Pathways", "source", "pathwats links",
+ "menber input", "input overlap", "size", "effective_size",
+ "fesetID", "PMIDS", "URLCPDB", "common name mapped"],
+ ["2.2", "1.2", "Pathways", "wiki", "URL", "HMDB00056450", "5",
+ "219", "172", "142906", "NA", "URLCPDB", "[choline, ornithine]"],
+ ["2.2", "1.2", "Pathways", "wiki", "URL", "HMDB00056450", "5",
+ "219", "172", "142906", "NA", "URLCPDB", "[choline, ornithine]"]]
+
+ FOLD_V = "C:\\Users\\mumec\\Desktop\\fichier_mis_en_forme_programme_total\\main\\"
+ list_visu = [FOLD_V+"Recouvrement moyen voies métaboliques 0.png",
+ FOLD_V+"RAMPbar_plot_of_metabolites.png",
+ FOLD_V+"RAMPmetabolites_bo_of_frequency.png",
+ FOLD_V+"CPDBbar_plot_of_recovery.png",
+ FOLD_V+"CPDBbar_plot_of_metabolites.png",
+ FOLD_V+"CPDBmetabolites_bo_of_frequency.png",
+ FOLD_V+"CPDBup_down_path_plot.png"]
+ FP = "Modele_de_pdf_feneratio_en_anglais_rev_19-02-2024.docx"
+
+ out_pdf_mapping(FP, dinput, Chebi_optimal, reca,
+ r_m_r, r_m_c, FOLD_V, list_visu)
+ """
+if __name__ == "__main__":
+ unittest.main()
\ No newline at end of file
diff --git a/src/processing_mapping_results/Tests_unitaires/test_utils.py b/src/processing_mapping_results/Tests_unitaires/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa07b654fe0cf694d4c987c4791e7723e1fcb530
--- /dev/null
+++ b/src/processing_mapping_results/Tests_unitaires/test_utils.py
@@ -0,0 +1,61 @@
+import unittest
+import pandas as pd
+import os
+import tempfile
+import sys
+sys.path.append('C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\traitement_des_données')
+from utils import *
+
+class Test_utils_without_file(unittest.TestCase):
+
+ def test_comma_cleaning(self):
+ result_clean = comma_cleaning("ceci est un test, enfin, je crois")
+ self.assertIsInstance(result_clean, str)
+ self.assertEqual(result_clean, "ceci est un test_ enfin_ je crois")
+
+
+ def test_pre_cut(self):
+ result_cut = pre_cut(["NA", "hmdb:HMDB011011", "NA", "hmdb:HMDB0584"])
+ self.assertListEqual(result_cut, ["NA", "HMDB011011", "NA", "HMDB0584"])
+
+ def test_cor_index(self):
+ result = cor_index(['d ', 'z', 'b'], ['z', 'b', 'd', 'f', 'h'],['a', 'c', 'e', 'g', 'i'])
+ self.assertListEqual(result, ['e', 'a', 'c'])
+
+
+class Test_utils_with_file(unittest.TestCase):
+
+ def test_excel_file_writer(self):
+ dataframe = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
+ name_out_file='test_default_sheetname.xlsx'
+ excel_file_writer(dataframe, name_out_file,sheetname="test_1")
+ self.assertTrue(os.path.exists(name_out_file))
+ os.remove(name_out_file)
+
+
+ def test_column_recovery(self):
+ self.temp_file = tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.csv')
+ self.temp_file.write("1;it;Doe\n")
+ self.temp_file.write("2;is;Smith\n")
+ self.temp_file.write("3;good;Johnson\n")
+ self.temp_file.close()
+ result = column_recovery(self.temp_file.name, 1)
+ expected_result = ['it', 'is', 'good']
+ os.remove(self.temp_file.name)
+ self.assertEqual(result, expected_result)
+
+
+ def test_excel_m_file_writer(self):
+ list_of_dataframe = [pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), pd.DataFrame({'X': ['a', 'b', 'c'], 'Y': ['d', 'e', 'f']})]
+ name_outfile = 'test_output.xlsx'
+ excel_m_file_writer(list_of_dataframe, name_outfile, ['Sheet1', 'Sheet2'])
+ self.assertTrue(os.path.exists(name_outfile))
+ excel_file = pd.ExcelFile(name_outfile)
+ sheet_names = excel_file.sheet_names
+ self.assertEqual(len(sheet_names),2)
+ excel_file.close()
+ os.remove(name_outfile)
+
+
+if __name__ == "__main__":
+ unittest.main()
\ No newline at end of file
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diff --git a/src/processing_mapping_results/additional_ressources/logo_metabohub_metex.PNG b/src/processing_mapping_results/additional_ressources/logo_metabohub_metex.PNG
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diff --git a/src/processing_mapping_results/chebi_id/recovery_of_associated_chebi_id.py b/src/processing_mapping_results/chebi_id/recovery_of_associated_chebi_id.py
new file mode 100644
index 0000000000000000000000000000000000000000..73f759fa14aec366d70c4af20c28c18c7e0ba6fe
--- /dev/null
+++ b/src/processing_mapping_results/chebi_id/recovery_of_associated_chebi_id.py
@@ -0,0 +1,472 @@
+"""
+This module is designed to find the chebi saaociated.
+"""
+import urllib
+import time
+import sys
+import pandas as pd
+from bs4 import BeautifulSoup
+sys.path.append('C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\traitement_des_données')
+from utils import column_recovery
+
+
+def chebi_horizontal(file, outfile_ful_name, n=0, sep=";", flow=None):
+ """
+ Put the culomn n of the file in list
+
+ Arg:
+ file : A csv file to read
+ outfile_ful_name : xlsx file to write
+ sep : type of separator
+
+ Returns:
+ Type of return: list
+ """
+ t1 = time.time()
+ if flow is None:
+ l_chebi_to_test = column_recovery(file, n, sep=sep)
+ else:
+ l_chebi_to_test = file
+ l_equ_to_search = ["is conjugate base of", "is conjugate acid of",
+ "is tautomer of", "is enantiomer of"]
+ chebi_conj_b = ["is conjugate base of"]
+ same_b = ["Same as input Chebi'"]
+ chebi_conj_a = ["is conjugate acid of"]
+ same_a = ["Same as input Chebi'"]
+ chebi_taut_of = ["is tautomer of"]
+ chebi_enant_of = ["is enantiomer of"]
+ chebi_n = ["Name in database"]
+ all_chebi_asso = []
+ for ac_chebi in l_chebi_to_test:
+ ac_chebi = ac_chebi.strip()
+ ac_chebi = ac_chebi.upper()
+ if ac_chebi == "NA":
+ chebi_n.append("NA")
+ chebi_conj_b.append("NA")
+ chebi_conj_a.append("NA")
+ chebi_taut_of.append("NA")
+ chebi_enant_of.append("NA")
+ same_a.append("NA")
+ same_b.append("NA")
+ all_chebi_asso.append(["NA", "NA", "NA", "NA"])
+ else:
+ rl = ac_chebi + "&treeView=True#visualisation"
+ url = "https://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI:" + rl
+ soup = BeautifulSoup(urllib.request.urlopen(url).read().decode("utf-8"), "html.parser")
+ text = soup.get_text()
+ i_beg_p_name = text.find("ChEBI Name")
+ inter_name = text[i_beg_p_name: i_beg_p_name+300]
+ i_end_p_name = inter_name.find("ChEBI ID")
+ name_chebi = text[i_beg_p_name + 10: i_beg_p_name + i_end_p_name]
+ name_chebi = name_chebi.strip()
+ imp_index = []
+ for info in l_equ_to_search:
+ imp_index.append(text.find(info))
+ chebi_asso = []
+ for indice in imp_index:
+ i_betw_d = text[indice:indice+220]
+ i_cheb = i_betw_d.find("CHEBI")
+ if i_cheb == -1:
+ chebi_asso.append("NA")
+ else:
+ if ac_chebi == i_betw_d[i_cheb:i_cheb+len(ac_chebi)]:
+ print(ac_chebi.strip(")"))
+ chebi_asso.append(ac_chebi.replace(")", ""))
+ else:
+ trans = i_betw_d[i_cheb:i_cheb+13]
+ trans = trans.replace(")", "").strip()
+ chebi_asso.append(trans)
+ chebi_n.append(name_chebi)
+ chebi_conj_b.append(chebi_asso[0])
+ chebi_conj_a.append(chebi_asso[1])
+ chebi_taut_of.append(chebi_asso[2])
+ chebi_enant_of.append(chebi_asso[3])
+ if ac_chebi in chebi_asso[0]:
+ same_b.append("oui")
+ else:
+ same_b.append("non")
+ if ac_chebi in chebi_asso[1]:
+ same_a.append("oui")
+ else:
+ same_a.append("non")
+ all_chebi_asso.append(chebi_asso)
+ l_chebi_to_test.insert(0, "Chebi exacte")
+ resultats = [chebi_n, l_chebi_to_test, chebi_conj_b, same_b, chebi_conj_a,
+ same_a, chebi_taut_of, chebi_enant_of]
+ df_exp = pd.DataFrame(resultats)
+ excel_file = pd.ExcelWriter(outfile_ful_name) # pylint: disable=abstract-class-instantiated
+ df_exp.to_excel(excel_file, index=False, header=False)
+ excel_file.close()
+ t2 = time.time()
+ time_of_running = t2-t1
+ print("le temps de récupération des chebi horizontaux: ", time_of_running)
+ return all_chebi_asso
+
+
+def chebi_in_outgouing(file, n, outfill_ful_name, sep=";"):
+ """
+ Put the culomn n of the file in list
+
+ Arg:
+ file : A csv file to read
+ outfill_ful_name : xlsx file to write
+ sep : type of separator
+
+ Returns:
+ Type of return: list
+ """
+ alllinks = ["is a", "has part", "is conjugate base of",
+ "is conjugate acid of", "is tautomer of", "is enantiomer of",
+ "has functional parent", "has parent hydride",
+ "is substituent group from", "has role"]
+ t1 = time.time()
+ l_chebi_to_test = column_recovery(file, n, sep=sep)
+ l_equ_to_search = ["Outgoing", "Incoming"]
+
+ outgoing_ia = ["Outgoing : is a"]
+ outgoing_hp = ["Outgoing : has part"]
+ outgoing_bo = ["Outgoing : is conjugate base of"]
+ outgoing_ao = ["Outgoing : is conjugate acid of"]
+ outgoing_to = ["Outgoing : is tautomer of"]
+ outgoing_eo = ["Outgoing : is enantiomer of"]
+ outgoing_fp = ["Outgoing : has functional parent"]
+ outgoing_ph = ["Outgoing : has parent hydride"]
+ outgoing_sgf = ["Outgoing : is substituent group from"]
+ outgoing_hr = ["Outgoing : has role"]
+ l_outgoing = [outgoing_ia, outgoing_hp, outgoing_bo, outgoing_ao,
+ outgoing_to, outgoing_eo, outgoing_fp, outgoing_ph,
+ outgoing_sgf, outgoing_hr]
+
+ incoming_ia = ["Incoming : is a"]
+ incoming_hp = ["Incoming : has part"]
+ incoming_bo = ["Incoming : is conjugate base of"]
+ incoming_ao = ["Incoming : is conjugate acid of"]
+ incoming_to = ["Incoming : is tautomer of"]
+ incoming_eo = ["Incoming : is enantiomer of"]
+ incoming_fp = ["Incoming : has functional parent"]
+ incoming_ph = ["Incoming : has parent hydride"]
+ incoming_sgf = ["Incoming : is substituent group from"]
+ incoming_hr = ["Incoming : has role"]
+ l_incoming = [incoming_ia, incoming_hp, incoming_bo, incoming_ao,
+ incoming_to, incoming_eo, incoming_fp, incoming_ph,
+ incoming_sgf, incoming_hr]
+
+ outgoing_ia_c = ["Outgoing : is a (commentaire)"]
+ outgoing_hp_c = ["Outgoing : has part (commentaire)"]
+ outgoing_bo_c = ["Outgoing : is conjugate base of (commentaire)"]
+ outgoing_ao_c = ["Outgoing : is conjugate acid of (commentaire)"]
+ outgoing_to_c = ["Outgoing : is tautomer of (commentaire)"]
+ outgoing_eo_c = ["Outgoing : is enantiomer of (commentaire)"]
+ outgoing_fp_c = ["Outgoing : has functional parent (commentaire)"]
+ outgoing_ph_c = ["Outgoing : has parent hydride (commentaire)"]
+ outgoing_sgf_c = ["Outgoing : is substituent group from (commentaire)"]
+ outgoing_hr_c = ["Outgoing : has role (commentaire)"]
+ l_outgoing_c = [outgoing_ia_c, outgoing_hp_c, outgoing_bo_c, outgoing_ao_c,
+ outgoing_to_c, outgoing_eo_c, outgoing_fp_c, outgoing_ph_c,
+ outgoing_sgf_c, outgoing_hr_c]
+
+ incoming_ia_c = ["Incoming : is a (commentaire)"]
+ incoming_hp_c = ["Incoming : has part (commentaire)"]
+ incoming_bo_c = ["Incoming : is conjugate base of (commentaire)"]
+ incoming_ao_c = ["Incoming : is conjugate acid of (commentaire)"]
+ incoming_to_c = ["Incoming : is tautomer of (commentaire)"]
+ incoming_eo_c = ["Incoming : is enantiomer of (commentaire)"]
+ incoming_fp_c = ["Incoming : has functional parent (commentaire)"]
+ incoming_ph_c = ["Incoming : has parent hydride (commentaire)"]
+ incoming_sgf_c = ["Incoming : is substituent group from (commentaire)"]
+ incoming_hr_c = ["Incoming : has role (commentaire)"]
+ l_incoming_c = [incoming_ia_c, incoming_hp_c, incoming_bo_c, incoming_ao_c,
+ incoming_to_c, incoming_eo_c, incoming_fp_c, incoming_ph_c,
+ incoming_sgf_c, incoming_hr_c]
+
+ outgoing = ["Outgoing"]
+ incoming = ["Incoming"]
+ chebi_n = ["Chebi exacte Name"]
+ l_oic = [outgoing, incoming, chebi_n]
+
+ for ac_chebi in l_chebi_to_test:
+ ac_chebi = ac_chebi.strip()
+ ac_chebi = ac_chebi.upper()
+ if ac_chebi == "NA":
+ for out_now in l_outgoing:
+ out_now.append("NA")
+ for in_now in l_incoming:
+ in_now.append("NA")
+ for oic in l_oic:
+ oic.append("NA")
+ else:
+ url = "https://www.ebi.ac.uk/chebi/searchId.do?chebiId="+ac_chebi
+ soup = BeautifulSoup(urllib.request.urlopen(url).read().decode("utf-8"), "html.parser")
+ text = soup.get_text()
+ i_beg_p_name = text.find("ChEBI Name")
+ inter_name = text[i_beg_p_name: i_beg_p_name+300]
+ i_end_p_name = inter_name.find("ChEBI ID")
+ name_chebi = text[i_beg_p_name+10: i_beg_p_name + i_end_p_name]
+ name_chebi = name_chebi.strip()
+ imp_index = []
+ for info in l_equ_to_search:
+ imp_index.append(text.find(info))
+ imp_index.append(text.find("IUPAC Name"))
+
+ all_cor_out_ia = []
+ all_cor_out_hp = []
+ all_cor_out_bo = []
+ all_cor_out_ao = []
+ all_cor_out_to = []
+ all_cor_out_eo = []
+ all_cor_out_fp = []
+ all_cor_out_ph = []
+ all_cor_out_sgf = []
+ all_cor_out_hr = []
+
+ l_all_cor_out = [all_cor_out_ia, all_cor_out_hp,
+ all_cor_out_bo, all_cor_out_ao,
+ all_cor_out_to, all_cor_out_eo,
+ all_cor_out_fp, all_cor_out_ph,
+ all_cor_out_sgf, all_cor_out_hr]
+
+ all_cor_in_ia = []
+ all_cor_in_hp = []
+ all_cor_in_bo = []
+ all_cor_in_ao = []
+ all_cor_in_to = []
+ all_cor_in_eo = []
+ all_cor_in_fp = []
+ all_cor_in_ph = []
+ all_cor_in_sgf = []
+ all_cor_in_hr = []
+
+ l_all_c_i = [all_cor_in_ia, all_cor_in_hp,
+ all_cor_in_bo, all_cor_in_ao,
+ all_cor_in_to, all_cor_in_eo,
+ all_cor_in_fp, all_cor_in_ph,
+ all_cor_in_sgf, all_cor_in_hr]
+
+ all_cor_out_ia_c = []
+ all_cor_out_hp_c = []
+ all_cor_out_bo_c = []
+ all_cor_out_ao_c = []
+ all_cor_out_to_c = []
+ all_cor_out_eo_c = []
+ all_cor_out_fp_c = []
+ all_cor_out_ph_c = []
+ all_cor_out_sgf_c = []
+ all_cor_out_hr_c = []
+
+ l_all_cor_out_c = [all_cor_out_ia_c, all_cor_out_hp_c,
+ all_cor_out_bo_c, all_cor_out_ao_c,
+ all_cor_out_to_c, all_cor_out_eo_c,
+ all_cor_out_fp_c, all_cor_out_ph_c,
+ all_cor_out_sgf_c, all_cor_out_hr_c]
+
+ all_cor_in_ia_c = []
+ all_cor_in_hp_c = []
+ all_cor_in_bo_c = []
+ all_cor_in_ao_c = []
+ all_cor_in_to_c = []
+ all_cor_in_eo_c = []
+ all_cor_in_fp_c = []
+ all_cor_in_ph_c = []
+ all_cor_in_sgf_c = []
+ all_cor_in_hr_c = []
+ all_cor_out = []
+ all_cor_in = []
+
+ l_all_c_i_c = [all_cor_in_ia_c, all_cor_in_hp_c,
+ all_cor_in_bo_c, all_cor_in_ao_c,
+ all_cor_in_to_c, all_cor_in_eo_c,
+ all_cor_in_fp_c, all_cor_in_ph_c,
+ all_cor_in_sgf_c, all_cor_in_hr_c]
+
+ for posi_index in range(2):
+ indice = imp_index[posi_index]
+ i_betw_d = text[indice:imp_index[posi_index+1]]
+ for link in alllinks:
+ all_posi_of_this_link = []
+ len_link = len(link)
+ for posi in range(len(i_betw_d)-len_link):
+ if i_betw_d[posi: posi+len_link] == link:
+ all_posi_of_this_link.append(posi)
+ if len(all_posi_of_this_link) != 0:
+ if posi_index == 0:
+ for posi in all_posi_of_this_link:
+ for s_pos_fin in range(len(i_betw_d[posi:])-len(name_chebi)):
+ if i_betw_d[posi+s_pos_fin] == "(":
+ ind_beg = s_pos_fin + 1
+ if i_betw_d[posi+s_pos_fin] == ")" and ind_beg+7 < s_pos_fin < ind_beg+15:
+ ind_end = s_pos_fin
+ break
+ results = i_betw_d[posi+ind_beg:posi+ind_end]
+ l_all_cor_out[alllinks.index(link)].append(results)
+ for po_c in all_posi_of_this_link:
+ for s_pf_c in range(len(i_betw_d[po_c:])-len(name_chebi)):
+ if i_betw_d[po_c+s_pf_c: po_c+s_pf_c+5] == "CHEBI":
+ ind_end_c = s_pf_c
+ break
+ rtcc = i_betw_d[po_c:po_c+ind_end_c+13]
+ results_nc = (rtcc.replace("\n", "")).replace(" ", "")
+ results_c = results_nc.replace(",", "_")
+ l_all_cor_out_c[alllinks.index(link)].append(results_c)
+ for posi2 in all_posi_of_this_link:
+ for s_pos_fin in range(len(i_betw_d[posi2:])-len(name_chebi)):
+ if i_betw_d[posi2+s_pos_fin: posi2+s_pos_fin+5] == "CHEBI":
+ ind_end = s_pos_fin
+ break
+ result_tc = i_betw_d[posi2:posi2+ind_end+13]
+ results = ((result_tc.replace("\n", "")).replace(" ", "")).replace(",", "_")
+ all_cor_out.append(results)
+ if posi_index == 1:
+ for posi in all_posi_of_this_link:
+ s_pos_beg = 0
+ for iterator in range(len(i_betw_d[posi:])-len(name_chebi)):
+ if i_betw_d[posi - 1 - iterator] == ")":
+ ind_beg = - 1 - iterator
+ if i_betw_d[posi - 1 - iterator] == "(":
+ ind_end = - iterator
+ break
+ results = i_betw_d[posi+ind_end:posi+ind_beg]
+ l_all_c_i[alllinks.index(link)].append(results)
+
+ for po_c1 in all_posi_of_this_link:
+ for s_pf_c1 in range(len(i_betw_d[po_c1:])-len(name_chebi)):
+ if i_betw_d[po_c1+s_pf_c1: po_c1+s_pf_c1+5] == "CHEBI":
+ ind_end_c1 = s_pf_c1
+ break
+ for iterator_c1 in range(len(i_betw_d[po_c1:])-len(name_chebi)):
+ if i_betw_d[po_c1 - iterator_c1 - 1: po_c1 - iterator_c1 - 1 + len(ac_chebi)] == ac_chebi:
+ if po_c1 - iterator_c1 - 1 + len(ac_chebi) + 5 < 0:
+ ind_beg_c1 = -po_c1
+ else:
+ ind_beg_c1 = 5 - 1 - iterator_c1 + len(ac_chebi)
+ break
+ result_tc_c1 = i_betw_d[po_c1+ind_beg_c1:po_c1+ind_end_c1+13]
+ results_c1 = ((result_tc_c1.replace("\n", "")).replace(" ", "")).replace(",", "_")
+ l_all_c_i_c[alllinks.index(link)].append(results_c1)
+
+ for posi3 in all_posi_of_this_link:
+ for s_pos_fin in range(len(i_betw_d[posi3:])-len(name_chebi)):
+ if i_betw_d[posi3+s_pos_fin: posi3+s_pos_fin+5] == "CHEBI":
+ ind_end = s_pos_fin
+ break
+ s_pos_beg = 0
+ for iterator in range(len(i_betw_d[posi3:])-len(name_chebi)):
+ s_pos_beg -= 1
+ if i_betw_d[posi3+s_pos_beg: posi3+s_pos_beg+len(ac_chebi)] == ac_chebi:
+ if posi3+s_pos_beg+len(ac_chebi)+5 < 0:
+ ind_beg = -posi3
+ else:
+ ind_beg = s_pos_beg+len(ac_chebi)+5
+ break
+ result_tc = i_betw_d[posi+ind_beg:posi+ind_end+13]
+ results = ((result_tc.replace("\n", "")).replace(" ", "")).replace(",", "_")
+ all_cor_in.append(results)
+
+ all_l_temp = [all_cor_out_ia, all_cor_out_hp, all_cor_out_bo,
+ all_cor_out_ao, all_cor_out_to, all_cor_out_eo,
+ all_cor_out_fp, all_cor_out_ph, all_cor_out_sgf,
+ all_cor_out_hr, all_cor_in_ia, all_cor_in_hp,
+ all_cor_in_bo, all_cor_in_ao, all_cor_in_to,
+ all_cor_in_eo, all_cor_in_fp, all_cor_in_ph,
+ all_cor_in_sgf, all_cor_in_hr, all_cor_out_ia_c,
+ all_cor_out_hp_c, all_cor_out_bo_c, all_cor_out_ao_c,
+ all_cor_out_to_c, all_cor_out_eo_c, all_cor_out_fp_c,
+ all_cor_out_ph_c, all_cor_out_sgf_c,
+ all_cor_out_hr_c, all_cor_in_ia_c, all_cor_in_hp_c,
+ all_cor_in_bo_c, all_cor_in_ao_c, all_cor_in_to_c,
+ all_cor_in_eo_c, all_cor_in_fp_c, all_cor_in_ph_c,
+ all_cor_in_sgf_c, all_cor_in_hr_c]
+ for i_verif, verif in enumerate(all_l_temp):
+ if verif == []:
+ all_l_temp[i_verif].append("NA")
+ outgoing.append(all_cor_out)
+ incoming.append(all_cor_in)
+ chebi_n.append(ac_chebi)
+
+ tot_try = l_outgoing + l_incoming + l_outgoing_c + l_incoming_c
+ for i_alt, a_l_t in enumerate(all_l_temp):
+ tot_try[i_alt].append(a_l_t)
+
+ all_l_temp.insert(0, all_cor_out)
+ all_l_temp.insert(0, all_cor_in)
+
+ total = [chebi_n, outgoing, outgoing_ia, outgoing_hp, outgoing_bo,
+ outgoing_ao, outgoing_to, outgoing_eo, outgoing_fp, outgoing_ph,
+ outgoing_sgf, outgoing_hr, incoming, incoming_ia, incoming_hp,
+ incoming_bo, incoming_ao, incoming_to, incoming_eo, incoming_fp,
+ incoming_ph, incoming_sgf, incoming_hr]
+
+ r_out_ia = [chebi_n, outgoing_ia, outgoing_ia_c]
+ r_out_hp = [chebi_n, outgoing_hp, outgoing_hp_c]
+ r_out_bo = [chebi_n, outgoing_bo, outgoing_bo_c]
+ r_out_ao = [chebi_n, outgoing_ao, outgoing_ao_c]
+ r_out_to = [chebi_n, outgoing_to, outgoing_to_c]
+ r_out_eo = [chebi_n, outgoing_eo, outgoing_eo_c]
+ r_out_fp = [chebi_n, outgoing_fp, outgoing_fp_c]
+ r_out_ph = [chebi_n, outgoing_fp, outgoing_fp_c]
+ r_out_sgf = [chebi_n, outgoing_sgf, outgoing_sgf_c]
+ r_out_hr = [chebi_n, outgoing_hr, outgoing_hr_c]
+
+ r_in_ia = [chebi_n, incoming_ia, incoming_ia_c]
+ r_in_hp = [chebi_n, incoming_hp, incoming_hp_c]
+ r_in_bo = [chebi_n, incoming_bo, incoming_bo_c]
+ r_in_ao = [chebi_n, incoming_ao, incoming_ao_c]
+ r_in_to = [chebi_n, incoming_to, incoming_to_c]
+ r_in_eo = [chebi_n, incoming_eo, incoming_eo_c]
+ r_in_fp = [chebi_n, incoming_fp, incoming_fp_c]
+ r_in_ph = [chebi_n, incoming_fp, incoming_fp_c]
+ r_in_sgf = [chebi_n, incoming_sgf, incoming_sgf_c]
+ r_in_hr = [chebi_n, incoming_hr, incoming_hr_c]
+
+ df_out_ia = pd.DataFrame(r_out_ia)
+ df_out_hp = pd.DataFrame(r_out_hp)
+ df_out_bo = pd.DataFrame(r_out_bo)
+ df_out_ao = pd.DataFrame(r_out_ao)
+ df_out_to = pd.DataFrame(r_out_to)
+ df_out_eo = pd.DataFrame(r_out_eo)
+ df_out_fp = pd.DataFrame(r_out_fp)
+ df_out_ph = pd.DataFrame(r_out_ph)
+ df_out_substituent_group = pd.DataFrame(r_out_sgf)
+ df_out_hr = pd.DataFrame(r_out_hr)
+
+ df_in_ia = pd.DataFrame(r_in_ia)
+ df_in_hp = pd.DataFrame(r_in_hp)
+ df_in_bo = pd.DataFrame(r_in_bo)
+ df_in_ao = pd.DataFrame(r_in_ao)
+ df_in_to = pd.DataFrame(r_in_to)
+ df_in_eo = pd.DataFrame(r_in_eo)
+ df_in_fp = pd.DataFrame(r_in_fp)
+ df_in_ph = pd.DataFrame(r_in_ph)
+ df_isg = pd.DataFrame(r_in_sgf)
+ df_ihr = pd.DataFrame(r_in_hr)
+ df_total = pd.DataFrame(total)
+
+ all_df = [df_total, df_out_ia, df_out_hp, df_out_bo, df_out_ao, df_out_to,
+ df_out_eo, df_out_fp, df_out_ph, df_out_substituent_group,
+ df_out_hr, df_in_ia, df_in_hp, df_in_bo, df_in_ao, df_in_to,
+ df_in_eo, df_in_fp, df_in_ph, df_isg, df_ihr]
+
+ sheets_name = ['Total', 'Out is a', 'Out has part', 'Out base of',
+ 'Out acid of', 'Out tautomer of', 'Out enantiomer of',
+ 'Out functional parent', 'Out parent hybride',
+ 'Out subtituent group', 'Out has role', 'In is a',
+ 'In has part', 'In base of', 'In acid of', 'In tautomer of',
+ 'In enantiomer of', 'In functional parent',
+ 'In parent hybride', 'In subtituent group', 'In has role']
+
+ excel_file = pd.ExcelWriter(outfill_ful_name) # pylint: disable=abstract-class-instantiated
+ for i_l_s, l_sheet in enumerate(all_df):
+ l_sheet.to_excel(excel_file, sheet_name=sheets_name[i_l_s], index=False, header=False)
+ excel_file.close()
+ t2 = time.time()
+ time_of_running = t2-t1
+ print(time_of_running)
+
+
+if __name__ == "__main__":
+ LOCAL = "C:\\Users\\mumec\\Desktop\\fichier_mis_en_forme_programme_total\\recovery_asso_chebi"
+ OUTF1 = LOCAL + "outgoing_test.xlsx"
+ OUTF2 = LOCAL + "horizontal_test.xlsx"
+ INF = LOCAL + "l_chebi_to_change_oeil_for_test.csv"
+ COL = [1, 3]
+ chebi_in_outgouing(INF, 0, OUTF1)
+ print(chebi_horizontal(INF, OUTF2))
diff --git a/src/processing_mapping_results/chebi_id/recovery_one_type_id.py b/src/processing_mapping_results/chebi_id/recovery_one_type_id.py
new file mode 100644
index 0000000000000000000000000000000000000000..18212d725f20514cf5eb5a42fde72af1c56d9373
--- /dev/null
+++ b/src/processing_mapping_results/chebi_id/recovery_one_type_id.py
@@ -0,0 +1,38 @@
+"""
+Find all same ID
+"""
+import pandas as pd
+
+
+def recovery_same_id(input_file, outfill_ful_name, id_search, nb_col_d):
+ """
+ Put the culomn n of the file in list
+
+ Arg:
+ input_file : A csv file to read
+ id_search : shape of id to search
+ outfill_ful_name : file to write
+ nb_col_d : numbers of columns of the file
+
+ Returns:
+ Type of return: xlsx file
+ """
+ tot_file = pd.read_excel(input_file, header=None)
+ enter_tab = tot_file.to_numpy(dtype='str')
+ all_out_data = []
+ for ite_lines in range(len(enter_tab)):
+ id_lines = []
+ for ite_col in range(len(enter_tab[0])):
+ if ite_col < nb_col_d:
+ id_lines.append(enter_tab[ite_lines, ite_col])
+ elif id_search in enter_tab[ite_lines, ite_col]:
+ if id_search == "hmdb:":
+ if len(enter_tab[ite_lines, ite_col].strip()) == 16:
+ id_lines.append(enter_tab[ite_lines, ite_col].strip())
+ else:
+ id_lines.append(enter_tab[ite_lines, ite_col].strip())
+ all_out_data.append(id_lines)
+ df_sortie = pd.DataFrame(all_out_data)
+ excel_file = pd.ExcelWriter(outfill_ful_name) # pylint: disable=abstract-class-instantiated
+ df_sortie.to_excel(excel_file)
+ excel_file.close()
diff --git a/src/processing_mapping_results/chebi_id/shapping_id.py b/src/processing_mapping_results/chebi_id/shapping_id.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b91fe582da9994fe34946d632f099ba01bbf728
--- /dev/null
+++ b/src/processing_mapping_results/chebi_id/shapping_id.py
@@ -0,0 +1,119 @@
+"""
+This module is designed to change ID for use others treatment
+"""
+import csv
+import pandas as pd
+
+
+def recup_col(file, n, sep=";", enc=None):
+ """
+ Put the culomn n of the file in list
+
+ Arg:
+ file : A csv file to read
+ n : the number of column to read
+ sep : type of separator
+
+ Returns:
+ Type of return: list
+ """
+ with open(file, "r", encoding=enc) as f:
+ r = csv.reader(f, delimiter=sep)
+ lines = list(r)
+ res = []
+ if abs(n) < len(lines[0]):
+ for line in lines:
+ if line[n].strip() != '':
+ res.append(line[n].strip())
+ return res
+
+
+def prefix_cut(file, fout, num_colonnes, prefix):
+ """
+ Give the list of ID without prefix
+
+ Arg:
+ file = csv file of metabolites
+ fout = excel file to write
+ num_colonnes = list of numbers colonnes to change
+ prefix = list of prefix to remove for each column
+
+ Returns:
+ Type of return: excel file
+ """
+ total = []
+ for i_pos, pos in range(num_colonnes):
+ clean_list = []
+ actu_list = recup_col(file, pos)
+ if prefix[i_pos] != '':
+ pref = prefix[i_pos]
+ for item in actu_list:
+ clean_list.append(item.strip(pref))
+ total.append(clean_list)
+ else:
+ total.append(actu_list)
+ l_out = pd.DataFrame(total, dtype=object)
+ e_f_o = pd.ExcelWriter(fout) # pylint: disable=abstract-class-instantiated
+ l_out.to_excel(e_f_o)
+ e_f_o.close()
+
+
+def ajout_prefix(file, fout, numbers_col, prefix):
+ """
+ Give a list of pathways with the correspondent metabolites names
+
+ Arg:
+ file = csv file of metabolites
+ fout = excel file to write
+ numbers_col = list of numbers colonnes to change
+ prefix = prefix to add for each column
+
+ Returns:
+ Type of return: excel file
+ """
+ all_lists = []
+ for i_numbers, numbers in range(numbers_col):
+ l_out = []
+ liste_entree = recup_col(file, numbers)
+ pref = prefix[i_numbers]
+ if pref == '':
+ all_lists.append(liste_entree)
+ else:
+ for l_inp in liste_entree:
+ if l_inp in ("", "NA"):
+ l_out.append("NA")
+ else:
+ l_out.append(pref + l_inp)
+ all_lists.append(l_out)
+
+ liste_final = pd.DataFrame(all_lists, dtype=object)
+ e_f_o = pd.ExcelWriter(fout) # pylint: disable=abstract-class-instantiated
+ liste_final.to_excel(e_f_o)
+ e_f_o.close()
+
+
+def cut_hmdb(file, fout, n_col_tot, n_col_treat, n_numbers_keep):
+ """
+ Give a list of pathways with the correspondent metabolites names
+
+ Arg:
+ ma_mapping_result : MetaboAnalyst results of id mapping file .csv
+ number_of_columns : Number of colums in the file
+
+ Returns:
+ Type of return: list
+ """
+ total = []
+ for pos in range(n_col_tot):
+ actu_list = recup_col(file, pos)
+ if pos == n_col_treat:
+ clean_list = []
+ for item in actu_list:
+ clean_list.append(item[0:4] + item[len(item)-n_numbers_keep:])
+ total.append(clean_list)
+ else:
+ total.append(actu_list)
+ l_out = pd.DataFrame(total, dtype=object)
+ e_f_o = pd.ExcelWriter(fout) # pylint: disable=abstract-class-instantiated
+ l_out.to_excel(e_f_o)
+ e_f_o.close()
diff --git a/complete_processing_of_mapping_results.py b/src/processing_mapping_results/complete_processing_of_mapping_results.py
similarity index 100%
rename from complete_processing_of_mapping_results.py
rename to src/processing_mapping_results/complete_processing_of_mapping_results.py
diff --git a/src/processing_mapping_results/conf.py b/src/processing_mapping_results/conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7348efaef4995d2a90f5258f754771172e04dac
--- /dev/null
+++ b/src/processing_mapping_results/conf.py
@@ -0,0 +1,27 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'Metabolite mapping'
+copyright = '2024, UMEC Mathieu'
+author = 'UMEC Mathieu'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = []
+
+templates_path = ['_templates']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']
diff --git a/Id_matcher_correspondence_of_biosource_and_dataset.py b/src/processing_mapping_results/id_matcher_correspondence_of_biosource_and_dataset.py
similarity index 100%
rename from Id_matcher_correspondence_of_biosource_and_dataset.py
rename to src/processing_mapping_results/id_matcher_correspondence_of_biosource_and_dataset.py
diff --git a/Id_matcher_frequency_and_pathways_recovery.py b/src/processing_mapping_results/id_matcher_frequency_and_pathways_recovery.py
similarity index 100%
rename from Id_matcher_frequency_and_pathways_recovery.py
rename to src/processing_mapping_results/id_matcher_frequency_and_pathways_recovery.py
diff --git a/Mapping_using_the_API.py b/src/processing_mapping_results/mapping_using_api.py
similarity index 100%
rename from Mapping_using_the_API.py
rename to src/processing_mapping_results/mapping_using_api.py
diff --git a/network_visualization.py b/src/processing_mapping_results/network_visualization.py
similarity index 100%
rename from network_visualization.py
rename to src/processing_mapping_results/network_visualization.py
diff --git a/src/processing_mapping_results/pdf_generation_toolbox.py b/src/processing_mapping_results/pdf_generation_toolbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..d18f30e330e2f9ba8dbedafc959644f2eddf314d
--- /dev/null
+++ b/src/processing_mapping_results/pdf_generation_toolbox.py
@@ -0,0 +1,319 @@
+"""
+this module is design to create pdf of results mapping
+"""
+from fpdf import FPDF
+from docx import Document
+
+
+class PDF(FPDF):
+ """
+ Tools for creating a PDFs from scratch with the FPDF library
+ """
+
+ def footer(self):
+ """
+ Set footer.
+ """
+ self.set_y(-15)
+ self.set_font('DejaVu', 'I', 14)
+ self.cell(0, 10, f"{self.page_no()}/{{nb}}", align="C")
+
+ def chapter_title(self, num, label):
+ """
+ Write the chapter title.
+
+ Parameters:
+ num = chapter number.
+ label = chapter name.
+ """
+ self.set_font('DejaVu', 'B', 18)
+ self.cell(0, 6, f"{num} - {label}", new_x="LMARGIN", new_y="NEXT")
+ self.ln(4)
+
+ def chapter_body(self, txt):
+ """
+ Write the chapter body.
+
+ Parameters:
+ txt = the character string of the entire section body.
+ """
+ with self.text_columns(
+ ncols=1, gutter=5, text_align="J", line_height=1.19
+ ) as cols:
+ self.set_font('DejaVu', '', 12)
+ cols.write(txt)
+ cols.ln()
+ self.set_font(style="I")
+
+ def summary(self, l_title_section):
+ """
+ Write the summary.
+
+ Parameters:
+ l_title_section = list of section titles.
+ """
+ self.set_font('DejaVu', 'B', 18)
+ self.cell(0, 6, "Table of contents", new_x="LMARGIN", new_y="NEXT")
+ self.set_font('DejaVu', '', 12)
+ self.set_fill_color(255, 255, 255)
+ for title in l_title_section:
+ self.cell(0, 6, f"{title}", new_x="LMARGIN", new_y="NEXT")
+ self.ln(4)
+
+ def tab(self, tabbleble):
+ """
+ Write a basic table.
+
+ Parameters:
+ tabbleble = table in list form.
+ """
+ if len(tabbleble[0]) == 2:
+ self.set_font('DejaVu', 'I', 16)
+ else:
+ self.set_font('DejaVu', 'I', 56/len(tabbleble[0]))
+ with self.table(line_height=8, text_align="CENTER") as table:
+ for data_row in tabbleble:
+ row = table.row()
+ for datum in data_row:
+ row.cell(datum)
+
+ def chapter_s(self, num, title, l_paraph):
+ """
+ Write a chapter with more than 1 paragraph.
+
+ Parameters:
+ num = chapter number.
+ title = chapter name.
+ l_paraph = list of string, 1 for each paragraph.
+
+ """
+ self.shaped_page()
+ self.chapter_title(num, title)
+ for str_l_paraph in l_paraph:
+ self.chapter_body(str_l_paraph)
+
+ def chapter_s_t(self, num, title, l_str, l_tab):
+ """
+ Write a chapter with more than 1 paragraph and at least 1 table.
+
+ Parameters:
+ num = chapter number.
+ title = chapter name.
+ l_str = list of string, 1 for each paragraph.
+ l_tab = list of tab, 1 value for each paragraph. "NA" if not table.
+ """
+ self.shaped_page()
+ self.chapter_title(num, title)
+ for i_paraf, paraf in enumerate(l_str):
+ self.chapter_body(paraf)
+ if i_paraf < len(l_tab) and l_tab[i_paraf] != "NA":
+ self.tab(l_tab[i_paraf])
+
+ def chapter_s_t_i(self, num, title, l_str, l_tab, l_image):
+ """
+ Write a chapter with more than 1 paragraph and at least 1 table.
+
+ Parameters:
+ num = chapter number.
+ title = chapter name.
+ l_str = list of string, 1 for each paragraph.
+ l_tab = list of table, 1 value for each paragraph. "NA" if not table.
+ l_image = list of image, 1 value for each paragraph. "NA" if no image.
+ """
+ self.shaped_page()
+ self.chapter_title(num, title)
+ for i_paraf, paraf in enumerate(l_str):
+ if i_paraf < len(l_image) and l_image[i_paraf] != "NA":
+ taille = 200
+ x = 105 - taille/2
+ y = 70
+ self.image(l_image[i_paraf], x, y, taille)
+ self.shaped_page()
+ with self.text_columns(
+ ncols=1, gutter=5, text_align="C", line_height=1.19,
+ ) as cols:
+ self.set_font('DejaVu', '', 12)
+ cols.write(paraf)
+ cols.ln()
+ if i_paraf < len(l_tab) and l_tab[i_paraf] != "NA":
+ self.tab(l_tab[i_paraf])
+ else:
+ self.chapter_body(paraf)
+ self.set_font(style="I")
+ if i_paraf < len(l_tab) and l_tab[i_paraf] != "NA":
+ self.tab(l_tab[i_paraf])
+
+ def shaped_page(self):
+ """
+ displays a page with the correct format.
+ """
+ self.add_page()
+ self.image("logo_metabohub_metex.png", 175, 4, 33) # X, Y, taille
+ self.image("Ensemble_des_logos_pdp.png", 20, 275, 170)
+ self.ln(15)
+
+ def guard_page(self, str_tw):
+ """
+ displays the guard page.
+
+ Parameters:
+ str_tw = list of string, 1 for each paragraph.
+ """
+ self.shaped_page()
+ self.set_font('DejaVu', 'B', 15)
+ self.cell(30, 10, str_tw[0], align="C")
+ self.ln(20)
+ for n_str_tw in str_tw[1:]:
+ with self.text_columns(
+ ncols=1, gutter=5, text_align="C", line_height=1.19
+ ) as cols:
+ self.set_font('DejaVu', '', 12)
+ cols.write(n_str_tw)
+ cols.ln()
+
+ def end_doc(self, path_end_doc):
+ """
+ displays the end of doc.
+
+ Parameter:
+ path_end_doc = the path of the docx. file of end pages
+ """
+ cont_end = read_docx(path_end_doc)
+ mh = "METABOHUB, the National infrastructure in metabolomics & fluxomics"
+ begin_2pages = cont_end.index(mh)
+ self.set_font('DejaVu', 'B', 18)
+ self.cell(0, 6, cont_end[0], new_x="LMARGIN", new_y="NEXT")
+ self.ln(4)
+ for str_end1 in cont_end[1:begin_2pages]:
+ self.chapter_body(str_end1)
+ self.shaped_page()
+ self.set_font('DejaVu', 'B', 16)
+ self.cell(0, 6, cont_end[begin_2pages], new_x="LMARGIN", new_y="NEXT")
+ self.ln(4)
+ for str_end1 in cont_end[begin_2pages+1:]:
+ self.chapter_body(str_end1)
+ pypdf_f = "C:\\Users\\mumec\\Desktop\\Mini_codes\\pdf_PyPDF2\\"
+ self.image(pypdf_f + "carte_france_fin_doc.png", 90, 100, 100)
+ self.image(pypdf_f + "bandeau_fin_doc.png", 15, 100, 60)
+
+
+def read_docx(file_path):
+ """
+ Recover text from a docx file.
+
+ file_path = Path of the file to read.
+ """
+ doc = Document(file_path)
+ full_text = []
+ for para in doc.paragraphs:
+ full_text.append(para.text)
+ return full_text
+
+
+def out_pdf_mapping(file_path, data_input, chebi_hori, recap, results_map_ramp,
+ results_map_cpdb, out_folder, l_visu):
+ """
+ Recover text from a docx file.
+
+ Parameters:
+ file_path = Path of the file of example.
+ data_input = tab of data
+ chebi_hori = tab of chebi aso to the id
+ recap = tab of recap id of mapping
+ results_map_ramp = tab of ramp results
+ results_map_cpdb = tab of cpdb results
+ out_folder = folder path where write pdf
+ l_visu = list of path of image to placed
+
+ return:
+ one PDF of global mapping results.
+ """
+ result_pdf = PDF()
+ result_pdf.add_font('DejaVu', 'I', 'DejaVuSansCondensed.ttf')
+ result_pdf.add_font('DejaVu', 'B', 'DejaVuSansCondensed.ttf')
+ result_pdf.add_font('DejaVu', '', 'DejaVuSansCondensed.ttf')
+ result_pdf.set_font('DejaVu', '', 14)
+ content = read_docx(file_path)
+ index_toc = content.index('Table of contents')
+ l_title = []
+ titles = []
+ for i_strr, strr in enumerate(content[index_toc+1:]):
+ if strr != '':
+ if strr[1] == ' ':
+ l_title.append(strr)
+ titles.append(strr[2:])
+ else:
+ index_end_toc = i_strr
+ break
+ begin_pages = [0, index_toc]
+ for i_sbp, search_begin_page in enumerate(content[index_end_toc:]):
+ if search_begin_page in titles:
+ begin_pages.append(index_end_toc+i_sbp)
+ result_pdf.guard_page(content[0:begin_pages[1]])
+ result_pdf.shaped_page()
+ result_pdf.summary(l_title)
+ recap_cpdb = []
+ recap_ramp = []
+ for line in recap:
+ recap_ramp.append(line[:2])
+ recap_cpdb.append([line[0]]+line[2:-1])
+ l_l_tables = [["NA"], ["NA", data_input], ["NA", chebi_hori],
+ ["NA", results_map_ramp, "NA", "NA", "NA", "NA", recap_ramp],
+ ["NA"], ["NA", recap_cpdb, "NA", "NA", results_map_cpdb],
+ ["NA"], ["NA"], ["NA"]]
+ l_l_images = [["NA"], ["NA"], ["NA"], ["NA"],
+ ["NA", l_visu[0], "NA", "NA", l_visu[1], "NA", l_visu[2]],
+ ["NA"], ["NA", "NA", l_visu[3], l_visu[4], "NA", "NA",
+ l_visu[5], "NA", "NA", "NA", "NA", "NA", l_visu[6]],
+ ["NA"], ["NA"]]
+ for p_index, begin in enumerate(begin_pages[2:-1]):
+ result_pdf.chapter_s_t_i(p_index+1, titles[p_index],
+ content[begin+1: begin_pages[p_index+3]],
+ l_l_tables[p_index], l_l_images[p_index])
+ result_pdf.chapter_s(len(l_title), titles[-1], content[begin_pages[-1]+1:])
+ result_pdf.shaped_page()
+ result_pdf.end_doc("C:\\Users\\mumec\\Desktop\\Mini_codes\\pdf_PyPDF2\\end_page_for_pdf.docx")
+ result_pdf.output(out_folder+"test_tab_sortie_pdf_mapping_results.pdf")
+
+
+if __name__ == "__main__":
+ dinput = [["Current_name", "ID", "Cas", "Temoin"],
+ ["choline", "chebi:15554, hmdb:HMDB0000267", "0.41", "1.59"],
+ ["choline", "chebi:15554, hmdb:HMDB0000267", "0.41", "1.59"],
+ ["choline", "chebi:15554, hmdb:HMDB0000267", "0.41", "1.59"]]
+
+ Chebi_optimal = [["Chebi ex", "is conjugate b of", "is conjugate acide of",
+ "is tautomer of", "is enantiomer of"],
+ ["Choline", "15354", "NA", "NA", "NA"],
+ ["Choline", "15354", "NA", "NA", "NA"],
+ ["Choline", "15354", "NA", "NA", "NA"]]
+
+ reca = [["Name", "RAMP", "CPDB chebi", "CPDB hmdb"],
+ ["Choline", "chebi:15554", "NA", "15554"],
+ ["Ornithine", "NA", "15354", "HMDB0000097"]]
+
+ r_m_r = [["path_Name", "path_source", "pathwayid", "inputid", "commoname"],
+ ["Choline", "wiki", "WP5552", "chebi:15554", "cho,choline"],
+ ["Choline", "wiki", "WP5552", "chebi:15554", "cho,choline"],
+ ["Choline", "wiki", "WP5552", "chebi:15554", "cho,choline"]]
+
+ r_m_c = [["p-value", "q-value", "Pathways", "source", "pathwats links",
+ "menber input", "input overlap", "size", "effective_size",
+ "fesetID", "PMIDS", "URLCPDB", "common name mapped"],
+ ["2.2", "1.2", "Pathways", "wiki", "URL", "HMDB00056450", "5",
+ "219", "172", "142906", "NA", "URLCPDB", "[choline, ornithine]"],
+ ["2.2", "1.2", "Pathways", "wiki", "URL", "HMDB00056450", "5",
+ "219", "172", "142906", "NA", "URLCPDB", "[choline, ornithine]"]]
+
+ FOLD_V = "C:\\Users\\mumec\\Desktop\\fichier_mis_en_forme_programme_total\\main\\"
+ list_visu = [FOLD_V+"Recouvrement moyen voies métaboliques 0.png",
+ FOLD_V+"RAMPbar_plot_of_metabolites.png",
+ FOLD_V+"RAMPmetabolites_bo_of_frequency.png",
+ FOLD_V+"CPDBbar_plot_of_recovery.png",
+ FOLD_V+"CPDBbar_plot_of_metabolites.png",
+ FOLD_V+"CPDBmetabolites_bo_of_frequency.png",
+ FOLD_V+"CPDBup_down_path_plot.png"]
+ FP = "Modele_de_pdf_feneratio_en_anglais_rev_19-02-2024.docx"
+
+ out_pdf_mapping(FP, dinput, Chebi_optimal, reca,
+ r_m_r, r_m_c, FOLD_V, list_visu)
diff --git a/Tesselation_de_voronoi_modif.ipynb b/src/processing_mapping_results/tesselation_de_voronoi_modif.ipynb
similarity index 100%
rename from Tesselation_de_voronoi_modif.ipynb
rename to src/processing_mapping_results/tesselation_de_voronoi_modif.ipynb
diff --git a/utilisation_metaboanalystr.R b/src/processing_mapping_results/utilisation_metaboanalystr.R
similarity index 100%
rename from utilisation_metaboanalystr.R
rename to src/processing_mapping_results/utilisation_metaboanalystr.R
diff --git a/utils.py b/src/processing_mapping_results/utils.py
similarity index 100%
rename from utils.py
rename to src/processing_mapping_results/utils.py
diff --git a/Visualisation_des_donnes_de_mapping.py b/src/processing_mapping_results/visu_datas_mapping.py
similarity index 100%
rename from Visualisation_des_donnes_de_mapping.py
rename to src/processing_mapping_results/visu_datas_mapping.py