def test_is_standardized():
df = pd.DataFrame({'filename':'test.wav','label':[1],'start':[0],'end':[2],'freq_min':[None],'freq_max':[None]})
df = st.standardize(df)
assert st.is_standardized(df) == True
df = pd.DataFrame({'filename':'test.wav','label':[1]})
df = st.standardize(df)
assert st.is_standardized(df) == True
df = pd.DataFrame({'filename':'test.wav','label':[1]})
assert st.is_standardized(df) == False
def test_create_rndm_backgr_files_missing_duration(annot_table_std, file_duration_table):
""" Check that the random background selection creation method works even when
some of the files are missing from the file duration list"""
np.random.seed(1)
df = st.standardize(annot_table_std)
dur = file_duration_table.drop(0)
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=dur, length=2.0, num=11)
def test_select_by_segmenting(annot_table_std, file_duration_table):
a = st.standardize(annot_table_std)
f = file_duration_table
sel = st.select_by_segmenting(f, length=5.1, annotations=a, step=4.0, discard_empty=True, pad=True)
# check selection table
d = '''filename sel_id start end
f0.wav 0 0.0 5.1
f0.wav 1 4.0 9.1
f1.wav 0 0.0 5.1
f1.wav 1 4.0 9.1
f2.wav 0 0.0 5.1
f2.wav 1 4.0 9.1
f2.wav 2 8.0 13.1'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(ans, sel[0][ans.columns.values])
# check annotation table
d = '''filename sel_id annot_id label start end
f0.wav 0 0 3 0.0 3.3
f0.wav 0 1 2 3.0 5.1
f0.wav 1 1 2 0.0 2.3
f1.wav 0 0 4 1.0 4.3
f1.wav 0 1 2 4.0 5.1
f1.wav 1 0 4 0.0 0.3
f1.wav 1 1 2 0.0 3.3
f2.wav 0 0 5 2.0 5.1
f2.wav 0 1 1 5.0 5.1
f2.wav 1 0 5 0.0 1.3
f2.wav 1 1 1 1.0 4.3
f2.wav 2 1 1 0.0 0.3'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1,2])
pd.testing.assert_frame_equal(ans, sel[1][ans.columns.values])
def test_create_rndm_backgr_nonzero_offset(annot_table_std, file_duration_table):
""" Check that the random background selection creation method works when file duration
table includes offsets"""
np.random.seed(1)
df = st.standardize(annot_table_std)
file_duration_table['offset'] = [1, 2, 3, 4, 5, 6]
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=file_duration_table, length=2.0, num=11)
def test_standardize(annot_table_std):
res = st.standardize(annot_table_std)
d = '''filename annot_id label start end
f0.wav 0 3 0.0 3.3
f0.wav 1 2 3.0 6.3
f1.wav 0 4 1.0 4.3
f1.wav 1 2 4.0 7.3
f2.wav 0 5 2.0 5.3
f2.wav 1 1 5.0 8.3'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(ans, res[ans.columns.values])
def test_select_step(annot_table_std):
df = annot_table_std
df = st.standardize(df)
N = len(df[df['label']!=-1])
K = len(df[df['label']==0])
df_new = st.select(df, length=1, center=True, min_overlap=0, step=0.5, keep_id=True)
M = len(df_new)
assert M == (N - K) * (2 * int((3.3/2+0.5)/0.5) + 1) + K * (2 * int((3.3/2-0.5)/0.5) + 1)
df_new = st.select(df, length=1, center=True, min_overlap=0.4, step=0.5)
M = len(df_new)
assert M == (N - K) * (2 * int((3.3/2+0.5-0.4)/0.5) + 1) + K * (2 * int((3.3/2-0.5)/0.5) + 1)
def test_create_rndm_backgr_keeps_misc_cols(annot_table_std, file_duration_table):
""" Check that the random background selection creation method keeps
any miscellaneous columns"""
np.random.seed(1)
df = st.standardize(annot_table_std)
dur = file_duration_table
dur['extra'] = 'testing'
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=dur, length=2.0, num=5)
assert np.all(df_bgr['extra'].values == 'testing')
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=dur, length=2.0, num=5, trim_table=True)
assert 'extra' not in df_bgr.columns.values.tolist()
def test_standardize_with_nested_list(annot_table_file):
res, d = st.standardize(filename=annot_table_file, mapper={'fname': 'filename', 'STOP': 'end'},
signal_labels=[[1,'whale'],'k'], backgr_labels=[-99], return_label_dict=True)
ans = {-99: 0, 2: -1, 'zebra': -1, 1: 1, 'whale':1, 'k':2}
assert d == ans
d = '''filename annot_id label start end
f0.wav 0 1 0 1
f1.wav 0 -1 1 2
f2.wav 0 2 2 3
f3.wav 0 0 3 4
f4.wav 0 1 4 5
f5.wav 0 -1 5 6'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(ans, res[ans.columns.values])
def test_select_enforces_overlap(annot_table_std):
np.random.seed(3)
df = annot_table_std
df = st.standardize(df)
# requested length: 5.0 sec
# all annotations have length: 3.3 sec (3.3/5.0=0.66)
length = 5.0
overlap = 0.5
df_new = st.select(df, length=length, min_overlap=overlap, keep_id=True)
t1 = df_new.start.values
t2 = df_new.end.values
idx = zip(df_new.index.get_level_values(0), df_new.annot_id)
df = df.loc[idx]
t2_orig = df.end.values
t1_orig = df.start.values
assert np.all(t2 >= t1_orig + overlap * length)
assert np.all(t1 <= t2_orig - overlap * length)
def test_create_rndm_backgr_selections(annot_table_std, file_duration_table):
np.random.seed(1)
df = st.standardize(annot_table_std)
dur = file_duration_table
num = 5
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=dur, length=2.0, num=num)
assert len(df_bgr) == num
# assert selections have uniform length
assert np.all(df_bgr.end.values - df_bgr.start.values == 2.0)
# assert all selection have label = 0
assert np.all(df_bgr.label.values == 0)
# assert selections do not overlap with any annotations
for bgr_idx, bgr_sel in df_bgr.iterrows():
start_bgr = bgr_sel.start
end_bgr = bgr_sel.end
fname = bgr_idx[0]
q = st.query(df, start=start_bgr, end=end_bgr, filename=fname)
assert len(q) == 0
def test_select_with_varying_overlap(annot_table_std):
""" Test that the number of selections increases as the
minimum required overlap is reduced"""
df = st.standardize(annot_table_std)
# request length shorter than annotations
num_sel = []
for min_overlap in np.linspace(1.0, 0.0, 11):
res = st.select(df, length=1.0, step=0.5, center=True, min_overlap=min_overlap)
num_sel.append(len(res))
assert np.all(np.diff(num_sel) >= 0)
# request length longer than annotations
num_sel = []
for min_overlap in np.linspace(1.0, 0.0, 11):
res = st.select(df, length=4.0, step=0.5, center=True, min_overlap=min_overlap)
num_sel.append(len(res))
assert np.all(np.diff(num_sel) >= 0)
def test_create_rndm_backgr_selections_no_overlap(annot_table_std, file_duration_table):
""" Check that random selections have no overlap"""
np.random.seed(1)
df = st.standardize(annot_table_std)
dur = file_duration_table
num = 30
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=dur, length=2.0, num=num)
num_overlap = 0
for idx,row in df_bgr.iterrows():
q = st.query(df_bgr, filename=idx[0], start=row['start'], end=row['end'])
num_overlap += len(q) - 1
assert num_overlap > 0
df_bgr = st.create_rndm_backgr_selections(annotations=df, files=dur, length=2.0, num=num, no_overlap=True)
num_overlap = 0
for idx,row in df_bgr.iterrows():
q = st.query(df_bgr, filename=idx[0], start=row['start'], end=row['end'])
num_overlap += len(q) - 1
assert num_overlap == 0
def test_query_labeled(annot_table_std):
df = st.standardize(annot_table_std)
df = st.select(df, length=1, center=True)
# query for file that does not exist
q = st.query_labeled(df, filename='fff.wav')
assert len(q) == 0
# query for 1 file
q = st.query_labeled(df, filename='f1.wav')
d = '''filename sel_id label start end
f1.wav 0 4 2.15 3.15
f1.wav 1 2 5.15 6.15'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(q, ans[q.columns.values])
# query for 1 file, and 1 that does not exist
q = st.query_labeled(df, filename=['f1.wav','fff.wav'])
d = '''filename sel_id label start end
f1.wav 0 4 2.15 3.15
f1.wav 1 2 5.15 6.15'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(q, ans[q.columns.values])
# query for 2 files
q = st.query_labeled(df, filename=['f1.wav','f2.wav'])
d = '''filename sel_id label start end
f1.wav 0 4 2.15 3.15
f1.wav 1 2 5.15 6.15
f2.wav 0 5 3.15 4.15
f2.wav 1 1 6.15 7.15'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(q, ans[q.columns.values])
# query for labels
q = st.query_labeled(df, label=[2,5])
d = '''filename sel_id label start end
f0.wav 1 2 4.15 5.15
f1.wav 1 2 5.15 6.15
f2.wav 0 5 3.15 4.15'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(q, ans[q.columns.values])
# query for label that does not exist
q = st.query_labeled(df, label=99)
assert len(q) == 0
def test_query_annotated(annot_table_std, file_duration_table):
a = st.standardize(annot_table_std)
f = file_duration_table
sel = st.select_by_segmenting(f, length=5.1, annotations=a, step=4.0, discard_empty=True, pad=True)
# query for 1 file
q1, q2 = st.query_annotated(sel[0], sel[1], label=[2,4])
d = '''filename sel_id start end
f0.wav 0 0.0 5.1
f0.wav 1 4.0 9.1
f1.wav 0 0.0 5.1
f1.wav 1 4.0 9.1'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(q1, ans[q1.columns.values])
d = '''filename sel_id annot_id label start end
f0.wav 0 1 2 3.0 5.1
f0.wav 1 1 2 0.0 2.3
f1.wav 0 0 4 1.0 4.3
f1.wav 0 1 2 4.0 5.1
f1.wav 1 0 4 0.0 0.3
f1.wav 1 1 2 0.0 3.3'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1,2])
pd.testing.assert_frame_equal(q2, ans[q2.columns.values])
def test_select_center(annot_table_std):
df = st.standardize(annot_table_std)
# request length shorter than annotations
res = st.select(df, length=1, center=True)
d = '''filename sel_id label start end
f0.wav 0 3 1.15 2.15
f0.wav 1 2 4.15 5.15
f1.wav 0 4 2.15 3.15
f1.wav 1 2 5.15 6.15
f2.wav 0 5 3.15 4.15
f2.wav 1 1 6.15 7.15'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(ans, res[ans.columns.values])
# request length longer than annotations
res = st.select(df, length=5, center=True)
d = '''filename sel_id label start end
f0.wav 0 3 -0.85 4.15
f0.wav 1 2 2.15 7.15
f1.wav 0 4 0.15 5.15
f1.wav 1 2 3.15 8.15
f2.wav 0 5 1.15 6.15
f2.wav 1 1 4.15 9.15'''
ans = pd.read_csv(StringIO(d), delim_whitespace=True, index_col=[0,1])
pd.testing.assert_frame_equal(ans, res[ans.columns.values])
def test_select_removes_discarded_annotations(annot_table_std):
df = annot_table_std
df = st.standardize(df)
res = st.select(df, length=1, center=True)
assert len(res[res.label==-1]) == 0