more tests and checks for expections

focus_app/focus.py

@@ -132,16 +132,24 @@ def count_kmers(query_file, kmer_size, threads, kmer_order):
     os.system("rm {}".format(output_count))
 
     if output_dump.exists():
-        counts = defaultdict(int)
-        with open(output_dump) as counts_file:
-            counts_reader = csv.reader(counts_file, delimiter=' ')
-            for kmer, count in counts_reader:
-                counts[kmer] = int(count)
+        # not empty file
+        if output_dump.stat().st_size:
+            counts = defaultdict(int)
+            with open(output_dump) as counts_file:
+                counts_reader = csv.reader(counts_file, delimiter=' ')
+                for kmer, count in counts_reader:
+                    counts[kmer] = int(count)
+            # delete dump file
+            os.system("rm {}".format(output_dump))
+
+            return [counts[kmer_temp] for kmer_temp in kmer_order]
+
+        else:
+            os.system("rm {}".format(output_dump))
+            raise Exception('{} has no k-mers count. Probably not valid file'.format(query_file))
 
-        # delete dump file
-        os.system("rm {}".format(output_dump))
-
-        return [counts[kmer_temp] for kmer_temp in kmer_order]
+    else:
+        raise Exception('Something went wrong when trying to dump the k-mer couting.')
 
 
 def write_results(results, output_directory, query_files, taxonomy_level):
@@ -167,7 +175,7 @@ def aggregate_level(results, position):
     """Aggregate abundance of metagenomes by taxonomic level.
 
     Args:
-        results (dict): Path to database
+        results (dict): Path to results
         position (int): Position of level in the results
 
     Returns:

tests/data/k6_small_sample.txt

@@ -0,0 +1,3 @@
+Kingdom	Phylum	Class	Order	Family	Genus	Species	Strain	GAACGC	GAACGA	CACCCA
+Bacteria	Spirochaetes	Spirochaetia	Spirochaetales	Spirochaetaceae	Sphaerochaeta	Sphaerochaeta_pleomorpha	Sphaerochaeta_pleomorpha_Grapes_uid82365	990	1439	1320
+Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcus_suis	Streptococcus_suis_98HAH33_uid58665	548	753	661

tests/data/k6_small_sample_empty_record.txt

@@ -0,0 +1,3 @@
+Kingdom	Phylum	Class	Order	Family	Genus	Species	Strain	GAACGC	GAACGA	CACCCA
+Bacteria	Spirochaetes	Spirochaetia	Spirochaetales	Spirochaetaceae	Sphaerochaeta	Sphaerochaeta_pleomorpha	Sphaerochaeta_pleomorpha_Grapes_uid82365	0	0	0
+Bacteria	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	Streptococcus_suis	Streptococcus_suis_98HAH33_uid58665	0	0	1

tests/test_basic_functions.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 
-from focus_app.focus import normalise, load_database, is_wanted_file, count_kmers, which, load_database
+import random
+
+from focus_app.focus import normalise, load_database, is_wanted_file, count_kmers, which, run_nnls, aggregate_level
 
 import pytest
 
 from numpy import array
 
 
-
 def test_normalise():
     assert list(normalise(array([1, 1,]))) == [0.5, 0.5]
     assert list(normalise(array([2, 2, 2, 2]))) == [0.25, 0.25, 0.25, 0.25]
@@ -17,20 +18,6 @@ def test_normalise():
         normalise([0, 0, 0])
 
 
-def test_load_database():
-    database_path = "tests/data/k6_subset.txt"
-    database_matrix, organisms, kmers_order = load_database(database_path)
-
-    assert list(organisms) == ['Bacteria\tSpirochaetes\tSpirochaetia\tSpirochaetales\tSpirochaetaceae\tSphaerochaeta\tSphaerochaeta_pleomorpha\tSphaerochaeta_pleomorpha_Grapes_uid82365',
-                               'Bacteria\tFirmicutes\tBacilli\tLactobacillales\tStreptococcaceae\tStreptococcus\tStreptococcus_suis\tStreptococcus_suis_98HAH33_uid58665',
-                               'Bacteria\tProteobacteria\tGammaproteobacteria\tAlteromonadales\tAlteromonadaceae\tAlteromonas\tAlteromonas_macleodii\tAlteromonas_macleodii__Deep_ecotype__uid58251'
-                               ]
-    # look into small subset of the matrix
-    assert list(database_matrix[0])[0] == 0.00027570089293671023
-    assert list(database_matrix[1])[0] == 0.00040074099488477375
-    assert list(database_matrix[2])[0] == 0.00036760119058228027
-
-
 def test_is_wanted_file():
     assert is_wanted_file(["a.fasta", "b.fastq", "x.FASTq", "y.FASTA", "n.fna"]) == ['a.fasta', 'b.fastq', 'n.fna', 'x.FASTq', 'y.FASTA']
     assert is_wanted_file(["f.png", "a.fasta", "b.fastq", "x.FASTq", "y.FASTA", "n.fna"]) == ['a.fasta', 'b.fastq', 'n.fna', 'x.FASTq', 'y.FASTA']
@@ -50,24 +37,94 @@ def test_count_kmers():
         kmer_order = ["AAAAAAA", "AAAGAAT", "ATTTTTT"]
         assert count_kmers(query_file, kmer_size, threads, kmer_order) == [17, 0, 0]
 
+        # testing for empty input
         query_file = "tests/data/mock_sample_empty.fasta"
         kmer_size = "6"
         kmer_order = ["AAAAAA", "AAAAAT", "TTTTTT"]
-        assert count_kmers (query_file, kmer_size, threads, kmer_order) == [0, 0, 0]
+        with pytest.raises(Exception):
+            count_kmers(query_file, kmer_size, threads, kmer_order)
 
 
 def test_load_database():
-    pass
+    # simple database
+    database_matrix, organisms, kmer_order = load_database("tests/data/k6_small_sample.txt")
+    assert kmer_order == ['GAACGC', 'GAACGA', 'CACCCA']
+    assert organisms == ['Bacteria\tSpirochaetes\tSpirochaetia\tSpirochaetales\tSpirochaetaceae\tSphaerochaeta\tSphaerochaeta_pleomorpha\tSphaerochaeta_pleomorpha_Grapes_uid82365',
+                  'Bacteria\tFirmicutes\tBacilli\tLactobacillales\tStreptococcaceae\tStreptococcus\tStreptococcus_suis\tStreptococcus_suis_98HAH33_uid58665']
+    assert [list(xx) for xx in database_matrix] == [[0.26407041877834087, 0.27930682976554538],
+                                                    [0.38383568951720459,0.38379204892966362],
+                                                    [0.35209389170445454, 0.33690112130479105]]
 
     # bad database with all keys 0 in row = raise error
+    with pytest.raises (RuntimeWarning):
+        load_database("tests/data/k6_small_sample_empty_record.txt")
 
-def test_write_results():
-    pass
+
+def test_run_nnls():
+    database_matrix, organisms, kmer_order = load_database("tests/data/k6_small_sample.txt")
+
+    # fake metagenomic count
+    # example 1
+    random.seed(1128)
+    fake_query_count = normalise([random.randint(10000, 200000) for _ in range (3)])
+    assert list(run_nnls(database_matrix, fake_query_count)) == [0.11743935706399153, 0.88256064293600844]
+
+    # example 2
+    random.seed(2)
+    fake_query_count = normalise([random.randint(10000, 200000) for _ in range (3)])
+    assert list(run_nnls(database_matrix, fake_query_count)) == [0.79079139795692821, 0.20920860204307179]
+
+    # example 3
+    random.seed(500)
+    fake_query_count = normalise([random.randint(10000, 200000) for _ in range (3)])
+    assert list(run_nnls(database_matrix, fake_query_count)) == [0.40650798820847034, 0.59349201179152955]
 
 
 def test_aggregate_level():
-    pass
 
+    true_answer = [
+        {'Bacteria': [0.54337724854388625, 0.76762773844686816,
+                      0.68899501300924559]},
+        {'Spirochaetes': [0.26407041877834087, 0.38383568951720459,
+                          0.35209389170445454], 'Firmicutes': [0.27930682976554538,
+                                                               0.38379204892966362, 0.33690112130479105]},
+        {'Spirochaetia': [0.26407041877834087, 0.38383568951720459,
+                          0.35209389170445454], 'Bacilli': [0.27930682976554538,
+                                                            0.38379204892966362, 0.33690112130479105]},
+        {'Spirochaetales': [0.26407041877834087, 0.38383568951720459,
+                            0.35209389170445454], 'Lactobacillales': [0.27930682976554538,
+                                                                      0.38379204892966362, 0.33690112130479105]},
+        {'Spirochaetaceae': [0.26407041877834087, 0.38383568951720459,
+                             0.35209389170445454], 'Streptococcaceae': [0.27930682976554538,
+                                                                        0.38379204892966362, 0.33690112130479105]},
+        {'Sphaerochaeta': [0.26407041877834087, 0.38383568951720459,
+                           0.35209389170445454], 'Streptococcus': [0.27930682976554538,
+                                                                   0.38379204892966362, 0.33690112130479105]},
+        {'Sphaerochaeta_pleomorpha': [0.26407041877834087,
+                                      0.38383568951720459, 0.35209389170445454],
+         'Streptococcus_suis': [0.27930682976554538, 0.38379204892966362,
+                                0.33690112130479105]},
+        {'Sphaerochaeta_pleomorpha_Grapes_uid82365': [0.26407041877834087,
+                                                      0.38383568951720459, 0.35209389170445454],
+         'Streptococcus_suis_98HAH33_uid58665': [0.27930682976554538,
+                                                 0.38379204892966362, 0.33690112130479105]},
+    ]
+    database_matrix, organisms, kmer_order = load_database ("tests/data/k6_small_sample.txt")
+    results = {organisms[pos]: profile for pos, profile in enumerate(database_matrix.T)}
+
+    producted_result = []
+
+    # runs aggregate function in all the levels
+    for pos in range(8):
+        aggregate_results = aggregate_level(results, pos)
+
+        # convert results to list rather the numpy.array
+        aggregate_results_list = {level:list(aggregate_results[level]) for level in aggregate_results}
+
+        producted_result.append(aggregate_results_list)
+
+    assert producted_result == true_answer
 
-def test_run_nnls():
+
+def test_write_results():
     pass