def test_make_tiles_with_resolution():
sc = cfp.FakeSparkContext()
entries = cmt.load_entries_from_file(sc, 'test/sample_data/smallFullMatrix.tsv',
column_names = ['pos1', 'pos2', 'count'])
dim_names = ['pos1', 'pos2']
max_zoom = 1
# create sparse format tiles (default)
tiles = cmt.make_tiles_by_binning(sc, entries, dim_names, max_zoom,
value_field='count',
bins_per_dimension=2,
resolution=1)
tiles = tiles['tiles'].collect()
# make sure the top-level tile is there
assert((0,0,0) in [t[0] for t in tiles])
assert('dense' in tiles[0][1])
# create dense format tiles
tiles = cmt.make_tiles_by_binning(sc, entries, dim_names, max_zoom,
value_field='count',
bins_per_dimension=2,
resolution=1)
tiles = tiles['tiles'].collect()
def test_make_tiles_by_binning():
sc = cfp.FakeSparkContext()
entries = cmt.load_entries_from_file(sc, 'test/sample_data/simpleMatrix.tsv',
column_names = ['pos1', 'pos2', 'count'])
dim_names = ['pos1', 'pos2']
max_zoom = 2
tiles = cmt.make_tiles_by_binning(sc, entries, dim_names, max_zoom,
value_field='count',
bins_per_dimension=2)
def test_dnase_sample_data():
sc = cfp.FakeSparkContext()
entries = cmt.load_entries_from_file(sc,'test/sample_data/E116-DNase.fc.signal.bigwig.bedGraph.genome.225',
column_names=['pos1', 'pos2', 'val'], delimiter=None)
entries = entries.flatMap(lambda x: cmt.expand_range(x, 'pos1', 'pos2', range_except_0 = 'val'))
tile_sample_data = cmt.make_tiles_by_binning(sc, entries,
['pos1'], max_zoom = 1000,
value_field = 'val', importance_field = 'val',
resolution = 1, bins_per_dimension = 64)
tile = tile_sample_data['tiles'].collect()[0]
def test_data_bounds():
sc = cfp.FakeSparkContext()
entries = cmt.load_entries_from_file(sc, 'test/sample_data/smallBedGraph.tsv',
column_names=['chr1', 'pos1', 'pos2', 'val'],
delimiter=' ')
dim_names = ['pos1']
entries.map(cmt.add_pos(dim_names))
(mins, maxs) = cmt.data_bounds(entries, 1)
assert(mins[0] == 1.0)
assert(maxs[0] == 8.0)
def test_make_tiles_with_importance():
sc = cfp.FakeSparkContext()
entries = cmt.load_entries_from_file(sc, 'test/sample_data/smallRefGeneCounts.tsv',
column_names=['refseqid', 'chr', 'strand', 'txStart', 'txEnd', 'genomeTxStart', 'genomeTxEnd', 'cdsStart', 'cdsEnd', 'exonCount', 'exonStarts', 'exonEnds', 'count'])
#tiles = cmt.make_tiles_by_importance(entries, dim_names, max_zoom, value_field
dim_names = ['txStart']
max_zoom = None
tiles = cmt.make_tiles_by_importance(sc, entries, dim_names = ['txStart'],
max_zoom = None,
mins=[1],
maxs=[3000000000],
importance_field='count',
max_entries_per_tile=1)
for (tile_pos, tile_values) in tiles['tiles'].collect():
assert(len(tile_values) <= 1)
def test_position_ranges():
sc = cfp.FakeSparkContext()
entries = cmt.load_entries_from_file(sc, 'test/sample_data/smallBedGraph.tsv',
column_names=['chr1', 'pos1', 'pos2', 'val'],
delimiter=' ')
entries = entries.map(lambda x: dict(x, pos1=int(x['pos1']), pos2=int(x['pos2'])))
entries = entries.flatMap(lambda x: cmt.expand_range(x, 'pos1', 'pos2'))
froms = entries.map(lambda x: x['pos1']).collect()
assert(1 in froms)
assert(2 in froms)
assert(3 in froms)
assert(4 in froms)
assert(8 in froms)
assert(9 in froms)
for entry in entries.collect():
assert(entry['pos1'] != 5)
assert(entry['pos1'] != 10)
def main():
usage = """
python make_tiles.py input_file
Create tiles for all of the entries in the JSON file.
"""
num_args = 1
parser = argparse.ArgumentParser()
#parser.add_argument('-o', '--options', dest='some_option', default='yo', help="Place holder for a real option", type='str')
#parser.add_argument('-u', '--useless', dest='uselesss', default=False, action='store_true', help='Another useless option')
parser.add_argument('input_file')
parser.add_argument('-b',
'--bins-per-dimension',
help='The number of bins to divide the data into',
default=1,
type=int)
parser.add_argument('--use-spark',
default=False,
action='store_true',
help='Use spark to distribute the workload')
parser.add_argument(
'-r',
'--resolution',
help='The resolution of the data (applies only to matrix data)',
type=int)
parser.add_argument('--importance',
action='store_true',
help='Create tiles by importance')
parser.add_argument(
'-i',
'--importance-field',
dest='importance_field',
default='importance_field',
help=
'The field in each JSON entry that indicates how important that entry is',
type=str)
parser.add_argument(
'-v',
'--value',
dest='value_field',
default='count',
help=
'The that has the value of each point. Used for aggregation and display'
)
group = parser.add_mutually_exclusive_group()
group.add_argument('-p',
'--position',
dest='position',
default='position',
help='Where this entry would be placed on the x axis',
type=str)
group.add_argument('-s',
'--sort-by',
default=None,
help='Sort by a field and use as the position')
parser.add_argument(
'--end-position',
default=None,
help=
"Use a field to indicate the end of a particular element so that it appears in all tiles that intersect it"
)
parser.add_argument(
'-e',
'--max-entries-per-tile',
dest='max_entries_per_tile',
default=15,
help=
'The maximum number of entries that can be displayed on a single tile',
type=int)
parser.add_argument('-c',
'--column-names',
dest='column_names',
default=None)
parser.add_argument('-m',
'--max-zoom',
dest='max_zoom',
help='The maximum zoom level',
type=int,
required=True)
parser.add_argument('--min-pos',
dest='min_pos',
default=None,
help='The minimum x position',
type=float)
parser.add_argument('--max-pos',
dest='max_pos',
default=None,
help='The maximum x position',
type=float)
parser.add_argument('--assembly', default=None)
parser.add_argument(
'--min-value',
help=
'The field which will be used to determinethe minimum value for any data point',
default='min_y')
parser.add_argument(
'--max-value',
help=
'The field which will be used to determine the maximum value for any data point',
default='max_y')
parser.add_argument(
'--range',
help="Use two columns to create a range (i.e. pos1,pos2",
default=None)
parser.add_argument('--range-except-0',
help="Don't expand rows which have values less than 0",
default=None)
parser.add_argument('--gzip',
help='Compress the output JSON files using gzip',
action='store_true')
parser.add_argument(
'--output-format',
help=
'The format for the output matrix, can be either "dense" or "sparse"',
default='sparse')
parser.add_argument('--add-uuid',
help='Add a uuid to each element',
action='store_true',
default=False)
parser.add_argument('--reverse-importance',
help='Reverse the ordering of the importance',
action='store_true',
default=False)
output_group = parser.add_mutually_exclusive_group(required=True)
output_group.add_argument(
'--elasticsearch-path',
help='Send the output to an elasticsearch instance',
default=None)
output_group.add_argument('-o',
'--output-dir',
help='The directory to place the tiles',
default=None)
parser.add_argument(
'--delimiter',
help=
"The delimiter separating the different columns in the input files",
default=None)
parser.add_argument(
'--elasticsearch-nodes',
help='Specify elasticsearch nodes to push the completions to',
default=None)
parser.add_argument('--elasticsearch-index',
help="The index to place the results in",
default='test')
parser.add_argument('--elasticsearch-doctype',
help="The type of document to index",
default="autocomplete")
parser.add_argument('--print-status',
action="store_true",
help="Print status messages")
args = parser.parse_args()
if not args.importance:
if args.output_format not in ['sparse', 'dense']:
print(
'ERROR: The output format must be one of "dense" or "sparse"',
file=sys.stderr)
dim_names = args.position.split(',')
position_cols = dim_names
sc = None
if args.use_spark:
from pyspark import SparkContext
sc = SparkContext()
else:
sys.stderr.write("setting sc:")
sc = cfp.FakeSparkContext
if args.column_names is not None:
args.column_names = args.column_names.split(',')
if args.assembly is not None:
mins = [1 for p in position_cols]
maxs = [
nc.get_chrominfo(args.assembly).total_length for p in position_cols
]
else:
mins = [float(p) for p in args.min_pos.split(',')]
maxs = [float(p) for p in args.max_pos.split(',')]
max_width = max([b - a for (a, b) in zip(mins, maxs)])
print("start time:", strftime("%Y-%m-%d %H:%M:%S", gmtime()))
entries = cti.load_entries_from_file(
sc,
args.input_file,
args.column_names,
delimiter=args.delimiter,
elasticsearch_path=args.elasticsearch_path)
print("load entries time:", strftime("%Y-%m-%d %H:%M:%S", gmtime()))
if args.range is not None:
# if a pair of columns specifies a range of values, then create multiple
# entries for each value within that range (e.g. bed files)
range_cols = args.range.split(',')
entries = entries.flatMap(lambda x: cti.expand_range(
x, *range_cols, range_except_0=args.range_except_0))
if args.importance:
# Data will be aggregated by importance. Only more "important" pieces of information will
# be passed onto the lower resolution tiles if they are too crowded
tileset = cti.make_tiles_by_importance(
sc,
entries,
dim_names=args.position.split(','),
end_dim_names=args.end_position.split(','),
max_zoom=args.max_zoom,
importance_field=args.importance_field,
output_dir=args.output_dir,
max_entries_per_tile=args.max_entries_per_tile,
gzip_output=args.gzip,
add_uuid=args.add_uuid,
reverse_importance=args.reverse_importance,
adapt_zoom=False,
mins=mins,
maxs=maxs)
else:
# Data will be aggregated by binning. This means that it two adjacent bins should be able
# to be reduced into one using some function (i.e. 'sum', 'min', 'max')
tileset = cti.make_tiles_by_binning(
sc,
entries,
args.position.split(','),
args.max_zoom,
args.value_field,
args.importance_field,
bins_per_dimension=args.bins_per_dimension,
resolution=args.resolution)
all_tiles = tileset['tiles']
if args.elasticsearch_nodes is not None:
# save the tiles to an elasticsearch database
save_tile_to_elasticsearch = ft.partial(
cst.save_tile_to_elasticsearch,
elasticsearch_nodes=args.elasticsearch_nodes,
elasticsearch_path=args.elasticsearch_path,
print_status=args.print_status)
(all_tiles.map(lambda x: {
"tile_id": ".".join(map(str, x[0])),
"tile_value": x[1]
}).foreachPartition(save_tile_to_elasticsearch))
dataset_info = cdd.describe_dataset(sys.argv, args)
print("saving tileset_info to:", args.elasticsearch_path)
(sc.parallelize([{
"tile_value": tileset['tileset_info'],
"tile_id": "tileset_info"
}]).foreachPartition(save_tile_to_elasticsearch))
(sc.parallelize([{
"tile_value": dataset_info,
"tile_id": "dataset_info"
}]).foreachPartition(save_tile_to_elasticsearch))
if 'histogram' in tileset:
histogram_rdd = sc.parallelize([{
"tile_value": tileset['histogram'],
"tile_id": "histogram"
}])
histogram_rdd.foreachPartition(save_tile_to_elasticsearch)
else:
# dump tiles to a directory structure
all_tiles.foreach(
ft.partial(cst.save_tile,
output_dir=args.output_dir,
gzip_output=args.gzip))
dataset_info = cdd.describe_dataset(sys.argv, args)
with open(op.join(args.output_dir, 'dataset_info'), 'w') as f:
json.dump(
{
"_source": {
"tile_id": "dataset_info",
"tile_value": dataset_info
}
},
f,
indent=2)
with open(op.join(args.output_dir, 'tileset_info'), 'w') as f:
json.dump(
{
"_source": {
"tile_id": "tileset_info",
"tile_value": tileset['tileset_info']
}
},
f,
indent=2)
if 'histogram' in tileset:
with open(op.join(args.output_dir, 'value_histogram'), 'w') as f:
json.dump(
{
"_source": {
"tile_id": "histogram",
"tile_value": tileset['histogram']
}
},
f,
indent=2)