Beispiel #1
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    def test_add_track(self):
        new_track_name, new_track_file = self.new_track

        # Open new track
        genome = Genome(self.gdfilepath, mode="r+")
        with genome:
            self.assertEqual(genome.num_tracks_continuous, 0)
            genome.add_track_continuous(new_track_name)

        # Load data for new track
        load_data(self.gdfilepath, new_track_name,
                  test_filename(new_track_file), verbose=self.verbose)

        # Close data with new track
        close_data(self.gdfilepath, verbose=self.verbose)

        # Make sure addition was successful
        genome = Genome(self.gdfilepath)
        with genome:
            # Track ordering should now end with dnase
            self.assertEqual(genome.tracknames_continuous, [new_track_name])

            # Given track ordering, check single track data retrieval
            self.assertArraysEqual(genome["chr1"][305:310, new_track_name],
                                   [-2.65300012, 0.37200001, 0.37200001,
                                     0.37200001, 0.37099999])
Beispiel #2
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    def test_replace_track(self):
        # Test ability to delete and replace a track

        old_trackname = "primate"
        old_entry = (290, -2.327)
        new_trackname = "placental"
        new_entry = (290, -2.297)

        # Test value before deleting track
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", GenomedataDirtyWarning)
            with Genome(self.gdfilepath) as genome:
                chromosome = genome["chr1"]
                self.assertArraysEqual(chromosome[old_entry[0], old_trackname],
                                       old_entry[1])

            # Remove track
            erase_data(self.gdfilepath, old_trackname, verbose=self.verbose)

        # Now replace it with the data from a different track
        track_index = self.tracknames.index(new_trackname)
        datafile = self.trackfiles[track_index]
        load_data(self.gdfilepath,
                  new_trackname,
                  datafile,
                  verbose=self.verbose)

        # Re-close data
        close_data(self.gdfilepath, verbose=self.verbose)

        # Now test that the data matches the new track data
        with Genome(self.gdfilepath) as genome:
            chromosome = genome["chr1"]
            self.assertArraysEqual(chromosome[new_entry[0], new_trackname],
                                   new_entry[1])
Beispiel #3
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 def test_no_context(self):
     genome = Genome(self.gdfilepath)
     chr1 = genome["chr1"]
     tracknames = genome.tracknames_continuous
     data = chr1[100:1000]  # Used to segfault
     chr2 = genome["chrY"]
     chr2.close()  # Make sure manual close doesn't break it
     self.assertTrue(chr1.isopen)
     self.assertFalse(chr2.isopen)
     genome.close()
     self.assertFalse(chr1.isopen)
     self.assertRaises(Exception, iter(chr1).next)
Beispiel #4
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 def test_no_context(self):
     genome = Genome(self.gdfilepath)
     chr1 = genome["chr1"]
     genome.tracknames_continuous  # test access
     chr1[100:1000]  # test access: at one point segfaulted
     chr2 = genome["chrY"]
     chr2.close()  # Make sure manual close doesn't break it
     self.assertTrue(chr1.isopen)
     self.assertFalse(chr2.isopen)
     genome.close()
     self.assertFalse(chr1.isopen)
     self.assertRaises(Exception, next, iter(chr1))
Beispiel #5
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    def test_filter_track(self):
        # Add filter track
        open_data(self.gdfilepath, [UNFILTERED_TRACKNAME],
                  verbose=self.verbose)
        load_data(self.gdfilepath,
                  UNFILTERED_TRACKNAME,
                  test_filename(UNFILTERED_TRACK_FILENAME),
                  verbose=self.verbose)
        close_data(self.gdfilepath, verbose=self.verbose)

        # Perform filtering on data
        hardmask_data(self.gdfilepath,
                      test_filename(self.filter), [UNFILTERED_TRACKNAME],
                      lambda x: x < self.filter_threshold,
                      verbose=self.verbose)

        # Make sure filtering was successful
        genome = Genome(self.gdfilepath)
        with genome:
            self.assertArraysEqual(genome["chr1"][0:4, UNFILTERED_TRACKNAME],
                                   [nan, nan, nan, nan])
            self.assertArraysEqual(
                genome["chr1"][128:132, UNFILTERED_TRACKNAME],
                [nan, nan, 0.5, 0.5])
            self.assertArraysEqual(
                genome["chr1"][168:172, UNFILTERED_TRACKNAME],
                [0.9, 0.9, nan, nan])
            self.assertArraysEqual(
                genome["chr1"][206:210, UNFILTERED_TRACKNAME],
                [nan, nan, nan, nan])
Beispiel #6
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    def test_repr_str(self):
        genome = Genome(self.gdfilepath, mode="r")
        self.assertEqual(repr(genome),
                         "Genome('%s', **{'mode': 'r'})" % self.gdfilepath)
        chr = genome["chr1"]
        if self.mode == "dir":
            self.assertEqual(
                repr(chr), "<Chromosome 'chr1', file='%s/chr1.genomedata'>" %
                self.gdfilepath)
            self.assertEqual(str(chr), "chr1")
        elif self.mode == "file":
            self.assertEqual(
                repr(chr), "<Chromosome 'chr1', file='%s'>" % self.gdfilepath)
            self.assertEqual(str(chr), "chr1")

        genome.close()
def genomedata_fill_random(gdfilename):
    with Genome(gdfilename, mode="r+") as genome:
        print >> sys.stderr, "Opening %s..." % gdfilename
        for chromosome in genome:
            print >> sys.stderr, "Overwriting %s with random data" % chromosome
            for supercontig, continuous in chromosome.itercontinuous():
                continuous[...] = rand(*continuous.shape)
Beispiel #8
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def make_continuous_cells(track_indexes, genomedata_names, chromosome_name,
                          start, end):
    """
    returns 2-dimensional numpy.ndarray of continuous observation
    data for specified interval. This data is untransformed

    dim 0: position
    dim 1: track
    """

    continuous_cells = None

    # For every track in each genomedata archive
    zipper = zip(track_indexes, genomedata_names)
    for track_index, genomedata_name in zipper:
        with Genome(genomedata_name) as genome:
            chromosome = genome[chromosome_name]
            # If we haven't started creating the continous cells
            if continuous_cells is None:
                # Copy the first track into our continous cells
                continuous_cells = copy(chromosome[start:end, [track_index]])
            else:
                # Otherwise append the track to our continuous cells
                continuous_cells = append(continuous_cells,
                                          chromosome[start:end,
                                                     [track_index]], DIM_TRACK)

    return continuous_cells
Beispiel #9
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def process_signals(gdarchive, trackname, score_type, verbose=False):
    '''
    Process signals from a genomedata archive.

    Args:
        genome (genomedata.Genome): genomedata archive
        trackname (str): name of track in genomedata archive
        score_type (str): scoring function
        verbose (Optional[bool]): maximum verbosity

    Returns:
        None. Writes signals to stdout or specified file.
    '''

    score_func = _score_func(score_type)

    with Genome(gdarchive) as genome:

        for chrom in genome:
            for pos in range(chrom.start, chrom.end):

                if isnan(chrom[pos, trackname]): continue

                score = score_func(chrom, pos, trackname, verbose)

                if not score: continue

                fields = (chrom, pos, pos + 1, score)
                print(*map(str, fields), sep='\t')
Beispiel #10
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    def test_repr_str(self):
        genome = Genome(self.gdfilepath, mode="r")
        self.assertEqual(repr(genome), "Genome('%s', **{'mode': 'r'})" %
                         self.gdfilepath)
        chr = genome["chr1"]
        if self.mode == "dir":
            self.assertEqual(repr(chr),
                             "<Chromosome 'chr1', file='%s/chr1.genomedata'>" %
                             self.gdfilepath)
            self.assertEqual(str(chr), "chr1")
        elif self.mode == "file":
            self.assertEqual(repr(chr),
                             "<Chromosome 'chr1', file='%s'>" %
                             self.gdfilepath)
            self.assertEqual(str(chr), "chr1")

        genome.close()
Beispiel #11
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    def test_open_chromosomes(self):
        genome = Genome(self.gdfilepath)
        with genome:
            chr1 = genome["chr1"]
            chr2 = genome["chr1"]  # Memoized
            self.assertEqual(chr1, chr2)
            genome["chrY"]
            self.assertEqual(len(genome.open_chromosomes), 2)

        self.assertEqual(genome.open_chromosomes, {})
Beispiel #12
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    def test_add_track(self):
        new_track_name, new_track_file = self.new_track

        # Open new track
        genome = Genome(self.gdfilepath, mode="r+")
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", GenomedataDirtyWarning)
            with genome:
                self.assertEqual(genome.num_tracks_continuous, 0)
                genome.add_track_continuous(new_track_name)

        # Load data for new track
        load_data(self.gdfilepath,
                  new_track_name,
                  test_filename(new_track_file),
                  verbose=self.verbose)

        # Close data with new track
        close_data(self.gdfilepath, verbose=self.verbose)

        # Make sure addition was successful
        genome = Genome(self.gdfilepath)
        with genome:
            # Track ordering should now end with dnase
            self.assertEqual(genome.tracknames_continuous, [new_track_name])

            # Given track ordering, check single track data retrieval
            self.assertArraysEqual(
                genome["chr1"][305:310, new_track_name],
                [-2.65300012, 0.37200001, 0.37200001, 0.37200001, 0.37099999])
Beispiel #13
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    def test_delete_tracks(self):
        # Test ability to delete a track
        trackname = "primate"
        old_entry = (290, -2.327)

        # Test value before deleting track
        warnings.simplefilter("ignore")
        with Genome(self.gdfilepath, "r+") as genome:
            chromosome = genome["chr1"]
            self.assertArraysEqual(chromosome[old_entry[0], trackname],
                                   old_entry[1])
            chromosome._erase_data(trackname)

        warnings.resetwarnings()
        # Re-close data
        close_data(self.gdfilepath, verbose=self.verbose)

        # Test value after deleting track
        with Genome(self.gdfilepath) as genome:
            chromosome = genome["chr1"]
            self.assertArraysEqual(chromosome[old_entry[0], trackname],
                                   old_entry[1])
def validate(filename,
             genomedatadir,
             dirpath,
             clobber=False,
             quick=False,
             replot=False,
             noplot=False,
             mnemonic_file=None,
             verbose=True,
             ropts=None):
    setup_directory(dirpath)
    if not replot:
        annotation = Annotation(filename, verbose=verbose)
        labels = annotation.labels

        with Genome(genomedatadir) as genome:
            nuc_counts, dinuc_counts = \
                calc_nucleotide_frequencies(annotation, genome,
                                            quick=quick, verbose=verbose)

        save_tab(labels,
                 nuc_counts,
                 dinuc_counts,
                 dirpath,
                 clobber=clobber,
                 verbose=verbose)

    if not noplot:
        with open_transcript(dirpath, MODULE) as transcriptfile:
            save_plot(dirpath,
                      clobber=clobber,
                      verbose=verbose,
                      mnemonic_file=mnemonic_file,
                      ropts=ropts,
                      transcriptfile=transcriptfile)

    save_html(dirpath,
              clobber=clobber,
              mnemonicfile=mnemonic_file,
              verbose=verbose)
def print_random_coordinates(genomedatadir, n=DEFAULT_N, chrom=None,
                             one_chrom=False, in_supercontigs=False):
    with Genome(genomedatadir) as genome:
        # Collect names and total lengths of chromosomes
        # If in_supercontigs, this is the total length of all supercontigs
        chrom_weights = {}
        for chromosome in genome:
            chrom_weight = 0
            if in_supercontigs:
                for supercontig in chromosome:
                    chrom_weight += supercontig.end - supercontig.start
            else:
                chrom_weight = chromosome.end - chromosome.start

            chrom_weights[chromosome.name] = chrom_weight

        total_weight = sum(chrom_weights.values())

        if chrom:
            assert chrom in chrom_weights
            one_chrom = True
            # Use specified chrom
            print >>sys.stderr, "set one_chrom"
        elif one_chrom:
            chrom = rand_chrom(chrom_weights.keys())
            print >>sys.stderr, "using only %s" % chrom

        for i in range(0, n):
            if not one_chrom:
                chrom = rand_chrom_weighted(chrom_weights, total_weight)

            if in_supercontigs:
                index = rand_supercontig_position(genome[chrom],
                                                  chrom_weights[chrom])
            else:
                index = rand_chromosome_position(genome[chrom])

            print "%s\t%d" % (chrom, index)
from __future__ import with_statement, division

import sys
import warnings
from collections import defaultdict
from genomedata import Genome

indices = defaultdict(list)
for line in sys.stdin:
    tokens = line.strip().split()
    if tokens:
        chrom, index = line.strip().split()
        indices[chrom].append(int(index))
indices = dict(indices)  # remove defaultdict behavior

with Genome(sys.argv[1]) as genome:
    tracknames = sys.argv[2:]
    for trackname in tracknames:
        assert trackname in genome.tracknames_continuous

    warnings.simplefilter("ignore")
    count = 0
    for chrom in indices:
        indices[chrom].sort()  # Sort by index ascending
        index_iter = iter(indices[chrom])
        index = index_iter.next()
        chromosome = genome[chrom]
        chrom_done = False
        for supercontig, continuous in chromosome.itercontinuous():
            start = supercontig.start
            end = supercontig.end
Beispiel #17
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def close_data(gdfilename, verbose=False):
    with Genome(gdfilename, mode="r+") as genome:
        write_genome_metadata(genome, verbose)
Beispiel #18
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### DenseCPT ###
input_master.append(
    InlineSection(
        ("start_seg", DenseCPT([1 / 3, 1 / 3, 1 / 3])),
        ("seg_subseg", DenseCPT([[1.0], [1.0], [1.0]])),
        ("seg_seg", DenseCPT([[0, 0.5, 0.5], [0.5, 0, 0.5], [0.5, 0.5, 0]])),
        ("seg_subseg_subseg", DenseCPT([[[1.0]], [[1.0]], [[1.0]]])),
        ("segCountDown_seg_segTransition",
         DenseCPT([[[0.99, 0.00999, 0.00001], [0.99, 0.00999, 0.00001],
                    [0.99, 0.00999, 0.00001]],
                   [[0.99, 0.01, 0.0], [0.99, 0.01, 0.0], [0.99, 0.01, 0.0]]
                   ]))))

### Mean and Covar Sections ###
# These sections are taken from the genomedata archive, and what we aim to change
with Genome(genomedata) as genome:
    # Load info from GD archive to get mean and variance
    sums = genome.sums
    sums_squares = genome.sums_squares
    num_datapoints = genome.num_datapoints

mean = sums / num_datapoints
var = (sums_squares / num_datapoints) - mean**2

sd = sqrt(var)
# Set group means to be 2 SD from the actual mean
means = [mean - 2 * sd, mean, mean + 2 * sd]
# Transform for arcsinh dist
var_transformed = arcsinh(var)
means_transformed = arcsinh(means)
#data is a numpy array
def get_nonzero_min(data, zero):
    #test1 = numpy.zeros(10)
    #test1[5] = 5
    #print("test1:", test1)
    #test1_result = get_nonzero_min(test1, 0)
    #print("Should be 5:", test1_result)
    if type(data) == list:
        temp = numpy.concatenate(([data[0]], [data[1]]))
        ma = numpy.ma.masked_equal(temp, 0.0, copy=False)
    else:
        ma = numpy.ma.masked_equal(data, 0.0, copy=False)
    return ma.min()


with Genome(str(sys.argv[1])) as genome:
    with open(str(sys.argv[2])) as bedfile:
        for line in bedfile:
            print(line)
            bed_items = line.strip().split()
            chr_name = bed_items[0]
            start = int(bed_items[1])
            end = int(bed_items[2])

            # HERE IS YOUR GENOMEDATA DATA, a numpy array
            print("data grabbing")
            data = genome[chr_name][start:end]
            print("min&max")
            min = get_nonzero_min(data, 0)
            max = data.max()
            print("concatenation")
(options, args) = parser.parse_args()

if options.inVCF is None:
    parser.error('input VCF not given')
if options.outVCF is None:
    parser.error('output VCF not given')
if options.genomedata is None:
    parser.error('genomedata archive not given')

###############################################################################


# try to open up the genome data archieve
try:
    genome = Genome(options.genomedata)
except:
    print "ERROR!! Couldn't open the genomedata archive:  " + options.genomedata + "\n"
    sys.exit(1)

#setup file open/close with or without gzip
# if options.isGzip is True:
#     try:
#         gc = 'gunzip -c ' + options.inVCF
#         inFile = os.popen(gc, 'r')
#     except:
#         print "ERROR!! Couldn't open the file" + options.inVCF + " (with gzip)\n"
#         sys.exit(1)
#
#     try:
#         gc = 'gzip > ' + options.outVCF
    stageFileMatches = stageFileMatches.set_index("Stage")
    # Get the list of features
    featureList = list(stageFileMatches.columns.values)
    # Since "Stage" is the index, don't need to drop it specifically.

    stageFileDict = {}
    for eachStage in stageList:
        stageFileDict[eachStage] = {}
        # Now each stage is a key to an inner dictionary. Fill this inner dictionary
        for eachFeature in featureList:
            stageFileDict[eachStage][eachFeature] = stageFileMatches.get_value(
                eachStage, eachFeature)

    # Next, I need to read in a genomedata file to acccess tracks for defining our features
    genomeDataDir = "/net/noble/vol2/home/katecook/proj/2016predictExpression/data/pfal3D7.genomedata"
    with Genome(genomeDataDir) as myGenome:
        # Add a functor for applying operations to each row
        for eachFeature in featureList:
            print("Assinging " + str(eachFeature))
            # fileLookupFunctor = featureAssinger(stageFileMatches, eachFeature, windowBack, windowFor)
            # # Add the row
            # groSeqData[eachFeature] = groSeqData.apply(fileLookupFunctor, axis = 1)
            # Loop for each section where averages should be taken
            for eachSection in windowDict:
                fileLookupFunctorAverage = featureAssinger(
                    stageFileMatches,
                    eachFeature,
                    windowDict[eachSection][0],
                    windowDict[eachSection][1],
                    valueToUse="Average")
                fileLookupFunctorMax = featureAssinger(stageFileMatches,
def validate(bedfilename,
             genomedatadir,
             dirpath,
             clobber=False,
             quick=False,
             replot=False,
             noplot=False,
             verbose=True,
             mnemonic_file=None,
             create_mnemonics=False,
             inputdirs=None,
             chroms=None,
             ropts=None,
             label_order_file=None,
             track_order_file=None,
             transformation=None):

    if not replot:
        setup_directory(dirpath)
        genome = Genome(genomedatadir)
        segmentation = Segmentation(bedfilename, verbose=verbose)

    if inputdirs:
        # Merge stats from many input directories
        stats = SignalStats()
        for inputdir in inputdirs:
            try:
                sub_stats = SignalStats.from_file(inputdir, verbose=verbose)
            except IOError as e:
                log("Problem reading data from %s: %s" % (inputdir, e))
            else:
                stats.add(sub_stats)
    elif replot:
        stats = SignalStats.from_file(dirpath, verbose=verbose)
    else:
        # Calculate stats over segmentation
        stats = SignalStats.from_segmentation(genome,
                                              segmentation,
                                              transformation=transformation,
                                              quick=quick,
                                              chroms=chroms,
                                              verbose=verbose)

    if not replot:
        stats.save_tab(dirpath, clobber=clobber, verbose=verbose)

        if mnemonic_file is None and create_mnemonics:
            statsfilename = make_tabfilename(dirpath, NAMEBASE)
            mnemonic_file = create_mnemonic_file(statsfilename,
                                                 dirpath,
                                                 clobber=clobber,
                                                 verbose=verbose)

    if not noplot:
        if label_order_file is not None:
            log("Reading label ordering from: %s" % label_order_file)
        label_order = read_order_file(label_order_file)

        if track_order_file is not None:
            log("Reading track ordering from: %s" % track_order_file)
        track_order = read_order_file(track_order_file)

        with open_transcript(dirpath, MODULE) as transcriptfile:
            stats.save_plot(dirpath,
                            namebase=NAMEBASE,
                            clobber=clobber,
                            mnemonic_file=mnemonic_file,
                            verbose=verbose,
                            label_order=label_order,
                            track_order=track_order,
                            ropts=ropts,
                            transcriptfile=transcriptfile)

    save_html(dirpath, genomedatadir, clobber=clobber, verbose=verbose)
Beispiel #23
0
    def test_interface(self):
        original_num_datapoints = 0
        if self.write:
            mode = "r+"
        else:
            mode = "r"
        # catch_warnings acts as a context manager storing the original warning
        # filter and resetting it at the end. All non user warnings should
        # still be displayed
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", GenomedataDirtyWarning)
            warnings.simplefilter("ignore", OverlapWarning)
            with Genome(self.gdfilepath, mode=mode) as genome:
                original_num_datapoints = genome.num_datapoints

                self.assertTrue("chr1" in genome)
                self.assertFalse("chrZ" in genome)

                chromosome = genome["chr1"]

                # Test tracknames are as expected
                self.assertEqual(sorted(chromosome.tracknames_continuous),
                                 sorted(self.tracknames))

                # Test tracknames are consistent
                self.assertEqual(sorted(genome.tracknames_continuous),
                                 sorted(chromosome.tracknames_continuous))

                # Test chromosome attributes
                self.assertEqual(chromosome.start, 0)
                self.assertEqual(chromosome.end, 24950)

                # Test sequence inside of data range
                self.assertEqual(seq2str(chromosome.seq[0:20]),
                                 "taaccctaaccctaacccta")

                # Test sequence outside of data range
                self.assertEqual(seq2str(chromosome.seq[30000]), "n")

                # Track ordering should be: placental, primate, vertebrate
                self.assertEqual(chromosome.tracknames_continuous,
                                 self.tracknames)

                # Given track ordering, check multi-track data retrieval
                self.assertArraysEqual(chromosome[290, 0:3],
                                       [-2.297, -2.327, -2.320])

                # test multi-track data retrieval by list
                self.assertArraysEqual(
                    chromosome[290, ["placental", "primate", "vertebrate"]],
                    chromosome[290, 0:3])
                self.assertArraysEqual(
                    chromosome[290, ["placental", "vertebrate"]],
                    [-2.297, -2.320])
                self.assertArraysEqual(chromosome[290, [0, 2]],
                                       [-2.297, -2.320])
                self.assertArraysEqual(chromosome[290, [2, 0]],
                                       [-2.320, -2.297])

                self.assertArraysEqual(chromosome[290, array([1, 0])],
                                       [-2.327, -2.297])

                # Test filling of unassigned continuous segments
                chromosome = genome["chrY"]
                # Get first supercontig
                for supercontig in chromosome:
                    break
                self.assertArraysEqual(supercontig.continuous[0, 2], nan)

                # If we are testing writing to archives
                if self.write:
                    # Test writing scalars to various indexing methods
                    chromosome = genome["chr1"]
                    # Test writing scalar to multiple tracks
                    chromosome[290] = 100.0
                    # Test writing scalar to tracks by named list
                    chromosome[291,
                               ["placental", "primate", "vertebrate"]] = 101.0
                    # Test writing scalar to select tracks by named list
                    chromosome[292, ["placental", "vertebrate"]] = 102.0
                    # Test writing scalar to tracks by index
                    chromosome[293, [0, 2]] = 103.0
                    chromosome[294, [2, 0]] = 104.0

                    # Test writing arrays to various indexing methods
                    # Test writing an array to a single index
                    chromosome[295] = [105.0, 106.0, 107.0]
                    # Test writing a subarray to a index subset
                    chromosome[296,
                               ["placental", "vertebrate"]] = [108.0, 109.0]

                    # Test removing datapoints by writing NaN
                    chromosome[297, ["primate"]] = nan

                    # Test writing around supercontig boundaries
                    # <Supercontig 'supercontig_0', [0:24950]>
                    # Test writing outside a supercontig
                    try:
                        chromosome[300000] = 110.0
                    except ValueError:
                        pass  # we expect a value error here

                    # Test writing overlap across supercontig to no supercontig
                    try:
                        chromosome[24900:30000] = 111.0
                    except ValueError:
                        pass  # we expect a value error here

        # Check write output after closing if testing writes
        if self.write:
            # Close with newly written data
            close_data(self.gdfilepath, verbose=self.verbose)
            # Read data and verify new data and parameters
            with Genome(self.gdfilepath) as genome:
                chromosome = genome["chr1"]

                self.assertArraysEqual(chromosome[290], [100.0, 100.0, 100.0])
                self.assertArraysEqual(chromosome[291], [101.0, 101.0, 101.0])
                # L14 in primate wigFix
                self.assertArraysEqual(chromosome[292], [102.0, 0.371, 102.0])
                self.assertArraysEqual(chromosome[293], [103.0, 0.372, 103.0])
                self.assertArraysEqual(chromosome[294], [104.0, 0.372, 104.0])
                self.assertArraysEqual(chromosome[295], [105.0, 106.0, 107.0])
                self.assertArraysEqual(chromosome[296], [108.0, -2.327, 109.0])
                # Check if one datapoint was successfully removed
                self.assertArraysEqual(original_num_datapoints, [
                    genome.num_datapoints[0], genome.num_datapoints[1] + 1,
                    genome.num_datapoints[2]
                ])