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test/Test_representative_and_match/test_match.py

+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")