diff --git a/test/Test_representative_and_match/test_match.py b/test/Test_representative_and_match/test_match.py
new file mode 100644
index 0000000000000000000000000000000000000000..5df3be0046aaac37a1e09df21deb67fd42894cb3
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+++ b/test/Test_representative_and_match/test_match.py
@@ -0,0 +1,203 @@
+import pandas as pd
+import json
+import re
+import match_reprtranscript_expressionlevel as match
+import os
+import pytest
+import test_Functions as tFun
+import numpy as np
+import representative as repr
+from pandas.testing import assert_frame_equal
+
+def test_dict_reprTrans_to_df():
+ """
+ This function test if a dict of {gene: representativeTranscript}
+ is converted in a dataframe in the right format
+ """
+ dict_repr_test = {"ENSMUSG00000079415":"ENSMUST00000112933",
+"ENSMUSG00000024691" : "ENSMUST00000025595",
+"ENSMUSG00000063683": "ENSMUST00000119960"}
+ dict_mixed = {"a":2, "b":3}
+ str_random = "jflkajflkaelfha"
+ dict_int = {12:34, 13:66}
+ df = match.dict_reprTrans_to_df(dict_repr_test)
+ datatype={'Gene': np.dtype('O'), 'reprTrans': np.dtype('O')}
+
+ with pytest.raises(TypeError, match=r"Only dict are allowed"):
+ match.dict_reprTrans_to_df(str_random)
+ with pytest.raises(TypeError, match=r"Key should be strings"):
+ match.dict_reprTrans_to_df(dict_int)
+ with pytest.raises(TypeError, match=r"Values should be strings"):
+ match.dict_reprTrans_to_df(dict_mixed)
+ assert tFun.column_number(df)==2, "number of columns is not equal to 2"
+ assert tFun.column_dType(df)==datatype, "at least one column has the wrong datatype"
+ assert tFun.duplicated_rows(df).empty, "at least one row are duplicated "
+ assert tFun.NA_value(df) == 0, "at least one row contain NA values "
+
+
+def test_txt_to_dict():
+ path = tFun.find_path("test_dict_repr_trans.txt")
+ dico = match.txt_to_dict(path)
+ dict_test = {'ENSMUSG00000079415': 'ENSMUST00000112933',
+"ENSMUSG00000024691" : "ENSMUST00000025595",
+"ENSMUSG00000063683": "ENSMUST00000119960"}
+ assert dico == dict_test
+
+def test_transcripts_by_gene_inDf():
+ """
+ This function test if a dataframe generated from
+ the intermediate file is converted in another
+ dataframe without the support level column.
+ """
+ path = tFun.find_path_intermediateFile()
+ df = repr.import_gtfSelection_to_df(path)
+ df_gene = match.transcripts_by_gene_inDf(df)
+ datatype={'Gene': np.dtype('O'), 'Transcript': np.dtype('O')}
+ assert tFun.column_number(df_gene)==2, "number of columns is not equal to 2"
+ assert tFun.column_dType(df_gene)==datatype, "at least one column has the wrong datatype"
+ assert tFun.duplicated_rows(df_gene).empty, "at least one row are duplicated "
+ assert tFun.NA_value(df_gene) == 0, "at least one row contain NA values "
+
+
+def test_tsv_or_csv_to_df():
+ """
+ This function test if the function tsv_or_csv_to_df() cans take
+ csv and tsv file as input and return a pandas dataframe in the
+ right format
+ """
+ path_tsv = tFun.find_path(r"test_gene_exprL")
+ df_tsv = match.tsv_or_csv_to_df(path_tsv)
+ path_csv = tFun.find_path(r"test_gene_exprL_csv.csv")
+ df_csv = match.tsv_or_csv_to_df(path_csv)
+ datatype ={'Transcript': np.dtype('O'), 'Expression_level': np.dtype('float64')}
+ assert tFun.column_number(df_tsv)==2, "number of columns is not equal to 2"
+ assert tFun.column_dType(df_tsv)==datatype, "at least one column has the wrong datatype"
+ assert tFun.duplicated_rows(df_tsv).empty, "at least one row are duplicated "
+ assert tFun.NA_value(df_tsv) == 0, "at least one row contain NA values "
+ assert_frame_equal(df_tsv, df_csv), "csv and tsv import doesn't match"
+
+
+def test_exprLevel_byGene():
+ """
+ This function test if the function exprLevel_byGene can find the gene of
+ each transcipt given by the expression level csv/tsv file and sum their
+ expression level
+ """
+ path_tsv = tFun.find_path(r"test_gene_exprL")
+ df_tsv_exprL = match.tsv_or_csv_to_df(path_tsv)
+
+ path_intermediate = tFun.find_path_intermediateFile()
+ df_intermediate = repr.import_gtfSelection_to_df(path_intermediate)
+ df_gene_transcript = match.transcripts_by_gene_inDf(df_intermediate)
+
+ df_exprLevel = match.exprLevel_byGene(df_tsv_exprL, df_gene_transcript)
+
+ datatype ={'Expression_level': np.dtype('float64')}
+ assert tFun.column_number(df_exprLevel)==1, "number of columns is not equal to 1"
+ assert tFun.column_dType(df_exprLevel)==datatype, "at least one column has the wrong datatype"
+ assert tFun.duplicated_rows(df_exprLevel).empty, "at least one row are duplicated "
+ assert tFun.NA_value(df_exprLevel) == 0, "at least one row contain NA values "
+ assert tFun.duplicated_index(df_exprLevel).empty, "at least one index element is duplicated"
+
+def test_match_byGene():
+ """
+ This function test if the function "match_byGene()" can
+ create a pandas dataframe matching representative transcript
+ and their expression level based on their gene in the
+ correct pandas dataframe format.
+ """
+
+
+ dict_repr_test = {'ENSMUSG00000079415': 'ENSMUST00000112933',
+"ENSMUSG00000024691" : "ENSMUST00000025595",
+"ENSMUSG00000063683": "ENSMUST00000119960"}
+ df_dict_reprTrans = match.dict_reprTrans_to_df(dict_repr_test)
+
+
+ path_tsv = tFun.find_path(r"test_gene_exprL")
+ df_tsv_exprL = match.tsv_or_csv_to_df(path_tsv)
+ path_intermediate = tFun.find_path_intermediateFile()
+ df_intermediate = repr.import_gtfSelection_to_df(path_intermediate)
+ df_gene_transcript = match.transcripts_by_gene_inDf(df_intermediate)
+ df_exprLevel = match.exprLevel_byGene(df_tsv_exprL, df_gene_transcript)
+
+ df_match = match.match_byGene(df_dict_reprTrans, df_exprLevel)
+ datatype = {'reprTrans': np.dtype('O'), 'Expression_level': np.dtype('float64')}
+
+ assert tFun.column_number(df_match)==2, "number of columns is not equal to 2"
+ assert tFun.column_dType(df_match)==datatype, "at least one column has the wrong datatype"
+ assert tFun.duplicated_rows(df_match).empty, "at least one row are duplicated "
+ assert tFun.NA_value(df_match) == 0, "at least one row contain NA values "
+ assert tFun.duplicated_index(df_match).empty, "at least one index element is duplicated"
+
+def test_output_tsv():
+ """
+ This function test if a tsv file is generated from a pandas
+ dataframe in the right format.
+ """
+
+ dict_repr_test = {'ENSMUSG00000079415': 'ENSMUST00000112933',
+"ENSMUSG00000024691" : "ENSMUST00000025595",
+"ENSMUSG00000063683": "ENSMUST00000119960"}
+ df_dict_reprTrans = match.dict_reprTrans_to_df(dict_repr_test)
+
+
+ path_tsv = tFun.find_path(r"test_gene_exprL")
+ df_tsv_exprL = match.tsv_or_csv_to_df(path_tsv)
+ path_intermediate = tFun.find_path_intermediateFile()
+ df_intermediate = repr.import_gtfSelection_to_df(path_intermediate)
+ df_gene_transcript = match.transcripts_by_gene_inDf(df_intermediate)
+
+ df_exprLevel = match.exprLevel_byGene(df_tsv_exprL, df_gene_transcript)
+
+ df_match = match.match_byGene(df_dict_reprTrans, df_exprLevel)
+
+ match.output_tsv(df_match)
+
+ ref_path=tFun.find_path("test_ref_output.tsv")
+ output_path = tFun.find_output()
+
+ with open(ref_path, 'r') as t1, open(output_path, 'r') as t2:
+ fileRef = t1.readlines()
+ fileOutput = t2.readlines()
+
+
+ assert sorted(fileRef) == sorted(fileOutput), "the output does't match the expected tsv file"
+
+
+def test_match_reprTranscript_expressionLevel():
+ input_path = tFun.find_path("test_gene_exprL")
+ intermediate_path = tFun.find_path_intermediateFile()
+ dict_repr_test = {'ENSMUSG00000079415': 'ENSMUST00000112933',
+"ENSMUSG00000024691" : "ENSMUST00000025595",
+"ENSMUSG00000063683": "ENSMUST00000119960"}
+
+ match.match_reprTranscript_expressionLevel(input_path, dict_repr_test, intermediate_path)
+
+ ref_path=tFun.find_path("test_ref_output.tsv")
+ output_path = tFun.find_output()
+
+
+ with open(ref_path, 'r') as t1,\
+ open(output_path, 'r') as t2,\
+ open(input_path, 'r') as t3 :
+ fileRef = t1.readlines()
+ fileOutput = t2.readlines()
+ fileInput = t3.readlines()
+
+ assert sorted(fileRef) == sorted(fileOutput), "the output does't match the expected tsv file"
+ assert sorted(fileRef) != sorted(fileInput), "the output does't match the expected tsv file"
+
+
+
+
+test_dict_reprTrans_to_df()
+test_txt_to_dict()
+test_transcripts_by_gene_inDf()
+test_tsv_or_csv_to_df()
+test_exprLevel_byGene()
+test_match_byGene()
+test_output_tsv()
+test_match_reprTranscript_expressionLevel()
+
+print("test_match is done ! No error was found")