from io import StringIO import numpy as np import pandas as pd import pytest from bioframe.core.checks import ( is_bedframe, is_cataloged, is_contained, is_covering, is_overlapping, is_sorted, is_tiling, is_viewframe, ) from bioframe.ops import sort_bedframe def test_is_cataloged(): # chr2q is not in view view_df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr1", 13, 26, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "name"], ) df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr1", 5, 15, "chr1p"], ["chr2", 13, 26, "chr2q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "view_region"], ) assert not is_cataloged(df, view_df) # chr1q is in view, df_view_col and view_name_col have funny labels. view_df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr1", 13, 26, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "funny_name"], ) df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr2", 13, 26, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "funny_view_region"], ) assert is_cataloged( df, view_df, df_view_col="funny_view_region", view_name_col="funny_name" ) def test_is_contained(): view_df = pd.DataFrame( [ ["chr1", 0, 20, "chr1p"], ["chr1", 21, 30, "chr1q"], ["chrX", 1, 10, "chrX_0"], ], columns=["chrom", "start", "end", "name"], ) # not contained because chr2q is not cataloged df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr2", 13, 26, "chr2q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "view_region"], ) assert not is_contained(df, view_df, df_view_col="view_region") # not contained because second interval falls outside the view regions df = pd.DataFrame( [ ["chr1", 14, 15, "chr1p"], ["chr1", -1, 1, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "view_region"], ) assert not is_contained(df, view_df) df = pd.DataFrame( [ ["chr1", 12, 12, "chr1p"], ["chr1", 13, 14, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "view_region"], ) # is contained, because assignments are inferred assert is_contained(df, view_df) # using previous assignments with non-standard column names # in dataframes without argument view_df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr1", 13, 26, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["CHROM", "START", "END", "NAME"], ) df = pd.DataFrame( [ ["chr1", 12, 12, "chr1p"], ["chr1", 13, 14, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom1", "start1", "end1", "VIEW_REGION"], ) assert is_contained( df, view_df, cols=["chrom1", "start1", "end1"], cols_view=["CHROM", "START", "END"], df_view_col="VIEW_REGION", view_name_col="NAME", ) with pytest.raises(TypeError): # cols and view_cols are not passed as an arguments is_contained(df, view_df, raise_errors=True) # is not contained, because assignments are not inferred view_df = pd.DataFrame( [ ["chr1", 0, 12, "chr1p"], ["chr1", 13, 26, "chr1q"], ["chrX", 1, 8, "chrX_0"], ], columns=["chrom", "start", "end", "name"], ) assert not is_contained(df, view_df, df_view_col="view_region") # second interval falls completely out of the view df = pd.DataFrame( [ ["chr1", 12, 12, "chr1p"], ["chr1", 100, 101, "chr1q"], ], columns=["chrom", "start", "end", "view_region"], ) # fails due to NAs after overlap to infer the regions with pytest.raises(AssertionError): is_contained(df, view_df, raise_errors=True) # fails due to some of the intervals being trimmed with pytest.raises(ValueError): is_contained(df, view_df, df_view_col="view_region", raise_errors=True) def test_is_overlapping(): # interval on chr1 overlaps d = """chrom start end 0 chr1 3 6 1 chr1 5 10 2 chr2 5 10""" df = pd.read_csv(StringIO(d), sep=r"\s+") assert is_overlapping(df) # adjacent intervals do not overlap d = """chrom start end 0 chr1 3 6 1 chr1 6 10 2 chr2 5 10""" df = pd.read_csv(StringIO(d), sep=r"\s+") assert not is_overlapping(df) def test_is_covering(): # test is_covering where an interval from df completely overlaps # two different regions from view df1 = pd.DataFrame( [ ["chr1", -5, 25], ], columns=["chrom", "start", "end"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert is_covering(df1, chromsizes) # test is_covering where two intervals from df overlap # two different regions from view df1 = pd.DataFrame( [ ["chr1", -5, 10], ["chr1", 11, 12], ["chr1", 12, 20], ], columns=["chrom", "start", "end"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert is_covering(df1, chromsizes) # test is_covering with non-standard columns names df1 = pd.DataFrame( [ ["chr1", -5, 10], ["chr1", 11, 12], ["chr1", 12, 20], ], columns=["chrom1", "start1", "end1"], ) chromsizes = pd.DataFrame( [["chr1", 0, 9, "chr1p"], ["chr1", 11, 20, "chr1q"]], columns=["CHROM", "START", "END", "NAME"], ) assert is_covering( df1, chromsizes, cols=["chrom1", "start1", "end1"], cols_view=["CHROM", "START", "END"], view_name_col="NAME", ) with pytest.raises(ValueError): # cols and view_cols are not passed as an arguments is_covering(df1, chromsizes) # test is_covering where two intervals from df overlap # two different regions from view df1 = pd.DataFrame( [ ["chr1", -5, 10, "chr1q"], ["chr1", 11, 12, "chr1q"], ["chr1", 12, 20, "chr1q"], ], columns=["chrom", "start", "end", "view_region"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert is_covering(df1, chromsizes) def test_is_tiling(): # view region chr1p is tiled by one interval, chr1q is tiled by two df1 = pd.DataFrame( [ ["chr1", 0, 9, "chr1p"], ["chr1", 11, 12, "chr1q"], ["chr1", 12, 20, "chr1q"], ], columns=["chrom", "start", "end", "view_region"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert is_tiling(df1, chromsizes) # testing for non-standard column names df1 = pd.DataFrame( [ ["chr1", 0, 9, "chr1p"], ["chr1", 11, 12, "chr1q"], ["chr1", 12, 20, "chr1q"], ], columns=["chrom1", "start1", "end1", "view_region"], ) chromsizes = pd.DataFrame( [["chr1", 0, 9, "chr1p"], ["chr1", 11, 20, "chr1q"]], columns=["CHROM", "START", "END", "NAME"], ) assert is_tiling( df1, chromsizes, cols=["chrom1", "start1", "end1"], cols_view=["CHROM", "START", "END"], view_name_col="NAME", ) with pytest.raises(KeyError): # cols and view_cols are not passed as an arguments is_tiling(df1, chromsizes) # not tiling, since (chr1,0,9) is associated with chr1q df1 = pd.DataFrame( [ ["chr1", 0, 9, "chr1q"], ["chr1", 11, 12, "chr1q"], ["chr1", 12, 20, "chr1q"], ], columns=["chrom", "start", "end", "view_region"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert not is_tiling(df1, chromsizes) # not tiling, contains overlaps df1 = pd.DataFrame( [ ["chr1", 0, 9, "chr1p"], ["chr1", 11, 13, "chr1q"], ["chr1", 12, 20, "chr1q"], ], columns=["chrom", "start", "end", "view_region"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert not is_tiling(df1, chromsizes) # not tiling, since it doesn't cover df1 = pd.DataFrame( [ ["chr1", 11, 12, "chr1q"], ["chr1", 12, 20, "chr1q"], ], columns=["chrom", "start", "end", "view_region"], ) chromsizes = [("chr1", 0, 9, "chr1p"), ("chr1", 11, 20, "chr1q")] assert not is_tiling(df1, chromsizes) def test_is_bedframe(): # missing a column df1 = pd.DataFrame( [ ["chr1", 11], ["chr1", 12], ], columns=["chrom", "start"], ) assert not is_bedframe(df1) # end column has invalid dtype df1 = pd.DataFrame( [ ["chr1", 10, "20"], ["chr1", 10, "12"], ], columns=["chrom", "start", "end"], ) assert not is_bedframe(df1) # second interval start > ends. df1 = pd.DataFrame( [ ["chr1", 10, 20], ["chr1", 15, 10], ], columns=["chrom", "start", "end"], ) assert not is_bedframe(df1) # third interval has a null in one column df1 = pd.DataFrame( [ ["chr1", 10, 20, "first"], ["chr1", 10, 15, "second"], ["chr1", pd.NA, 15, "third"], ], columns=["chrom", "start", "end", "name"], ) # should raise a TypeError if the second column is an object with pytest.raises(TypeError): is_bedframe(df1, raise_errors=True) # should raise a ValueError after recasting to pd.Int64Dtype df1 = df1.astype({"start": pd.Int64Dtype(), "end": pd.Int64Dtype()}) with pytest.raises(ValueError): is_bedframe(df1, raise_errors=True) # first interval is completely NA df1 = pd.DataFrame( [ [pd.NA, pd.NA, pd.NA, "first"], ["chr1", 10, 15, "second"], ["chr1", 10, 15, "third"], ], columns=["chrom", "start", "end", "name"], ) df1 = df1.astype({"start": pd.Int64Dtype(), "end": pd.Int64Dtype()}) assert is_bedframe(df1) def test_is_viewframe(): # not a bedframe df1 = pd.DataFrame( [ ["chr1", 10, 20, "chr1p"], ["chr1", 15, 10, "chr1q"], ], columns=["chrom", "start", "end", "name"], ) assert not is_viewframe(df1) # no column for region name df1 = pd.DataFrame( [ ["chr1", 10, 20], ["chr1", 30, 40], ], columns=["chrom", "start", "end"], ) assert not is_viewframe(df1) # contains null values df1 = pd.DataFrame( [ ["chr1", 10, 20, "chr1p"], ["chr1", pd.NA, np.nan, "chr1q"], ], columns=["chrom", "start", "end", "name"], ) assert not is_viewframe(df1) # overlapping intervals df1 = pd.DataFrame( [ ["chr1", 10, 20, "chr1p"], ["chr1", 15, 25, "chr1q"], ], columns=["chrom", "start", "end", "name"], ) assert not is_viewframe(df1) # valid view df1 = pd.DataFrame( [ ["chr1", 10, 20, "chr1p"], ["chr1", 20, 25, "chr1q"], ["chr2", 20, 25, "chrTEST_2p"], ], columns=["chrom", "start", "end", "name"], ) assert is_viewframe(df1) def test_is_sorted(): view_df = pd.DataFrame( [ ["chrX", 1, 8, "oranges"], ["chrX", 8, 20, "grapefruit"], ["chr1", 0, 10, "apples"], ], columns=["chrom", "start", "end", "fruit"], ) df_view_cat = pd.CategoricalDtype( categories=["oranges", "grapefruit", "apples"], ordered=True ) view_df = view_df.astype({"fruit": df_view_cat}) assert is_sorted( view_df, view_df=view_df, view_name_col="fruit", df_view_col="fruit" ) # testing for non-standard column names view_df = pd.DataFrame( [ ["chrX", 1, 8, "oranges"], ["chrX", 8, 20, "grapefruit"], ["chr1", 0, 10, "apples"], ], columns=["CHROM", "START", "END", "FRUIT"], ) assert is_sorted( view_df, view_df=view_df, view_name_col="FRUIT", df_view_col="FRUIT", cols=["CHROM", "START", "END"], cols_view=["CHROM", "START", "END"], ) with pytest.raises(ValueError): # cols and view_cols are not passed as an arguments is_sorted(view_df, view_df=view_df) df = pd.DataFrame( [ ["chr1", 0, 10, "+"], ["chrX", 5, 10, "+"], ["chrX", 0, 5, "+"], ["chr2", 5, 10, "+"], ], columns=["chrom", "start", "end", "strand"], ) assert not is_sorted(df) view_df = pd.DataFrame( [ ["chrX", 1, 8, "oranges"], ["chrX", 8, 20, "grapefruit"], ["chr1", 0, 10, "apples"], ], columns=["chrom", "start", "end", "fruit"], ) bfs = sort_bedframe(df, view_df=view_df, view_name_col="fruit") assert is_sorted(bfs, view_df=view_df, view_name_col="fruit") # view_df specifies a different ordering, so should not be sorted assert not is_sorted(bfs)