def rmtree(self, path: str):
if self._is_local(path):
rmtree(path)
def rm_not_exist_ok():
try:
self.client.rm(path, recursive=True)
except FileNotFoundError:
pass
sync_retry_transient_errors(rm_not_exist_ok)
def get_hgdp(output_dir, overwrite: bool = False):
"""Download subset of the `Human Genome Diversity Panel
<https://www.internationalgenome.org/data-portal/data-collection/hgdp/>`__
dataset and sample annotations.
Notes
-----
The download is about 30MB.
Parameters
----------
output_dir
Directory in which to write data.
overwrite
If ``True``, overwrite any existing files/directories at `output_dir`.
"""
fs = Env.fs()
if not _dir_exists(fs, output_dir):
fs.mkdir(output_dir)
matrix_table_path = os.path.join(output_dir, 'HGDP.mt')
vcf_path = os.path.join(output_dir, 'HGDP.vcf.bgz')
sample_annotations_path = os.path.join(output_dir, 'HGDP_annotations.txt')
gene_annotations_path = os.path.join(output_dir, 'ensembl_gene_annotations.txt')
if (overwrite
or not _dir_exists(fs, matrix_table_path)
or not _file_exists(fs, sample_annotations_path)
or not _file_exists(fs, vcf_path)
or not _file_exists(fs, gene_annotations_path)):
init_temp_dir()
tmp_vcf = os.path.join(tmp_dir, 'HGDP.vcf.bgz')
source = resources['HGDP_matrix_table']
info(f'downloading HGDP VCF ...\n'
f' Source: {source}')
sync_retry_transient_errors(urlretrieve, resources['HGDP_matrix_table'], tmp_vcf)
cluster_readable_vcf = _copy_to_tmp(fs, local_path_uri(tmp_vcf), extension='vcf.bgz')
info('importing VCF and writing to matrix table...')
hl.import_vcf(cluster_readable_vcf, min_partitions=16, reference_genome='GRCh38').write(matrix_table_path, overwrite=True)
tmp_sample_annot = os.path.join(tmp_dir, 'HGDP_annotations.txt')
source = resources['HGDP_annotations']
info(f'downloading HGDP annotations ...\n'
f' Source: {source}')
sync_retry_transient_errors(urlretrieve, source, tmp_sample_annot)
tmp_gene_annot = os.path.join(tmp_dir, 'ensembl_gene_annotations.txt')
source = resources['HGDP_ensembl_gene_annotations']
info(f'downloading Ensembl gene annotations ...\n'
f' Source: {source}')
sync_retry_transient_errors(urlretrieve, source, tmp_gene_annot)
hl.hadoop_copy(local_path_uri(tmp_sample_annot), sample_annotations_path)
hl.hadoop_copy(local_path_uri(tmp_gene_annot), gene_annotations_path)
hl.hadoop_copy(local_path_uri(tmp_vcf), vcf_path)
info('Done!')
else:
info('HGDP files found')
def __enter__(self) -> 'ServiceBackendSocketConnection':
self._conn = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) # pylint: disable=attribute-defined-outside-init
sync_retry_transient_errors(self._conn.connect,
ServiceBackendSocketConnection.FNAME)
return self
def get_movie_lens(output_dir, overwrite: bool = False):
"""Download public Movie Lens dataset.
Notes
-----
The download is about 6M.
See the
`MovieLens website <https://grouplens.org/datasets/movielens/100k/>`__
for more information about this dataset.
Parameters
----------
output_dir
Directory in which to write data.
overwrite
If ``True``, overwrite existing files/directories at those locations.
"""
fs = Env.fs()
if not _dir_exists(fs, output_dir):
fs.mkdir(output_dir)
paths = [os.path.join(output_dir, x) for x in ['movies.ht', 'ratings.ht', 'users.ht']]
if overwrite or any(not _dir_exists(fs, f) for f in paths):
init_temp_dir()
source = resources['movie_lens_100k']
tmp_path = os.path.join(tmp_dir, 'ml-100k.zip')
info(f'downloading MovieLens-100k data ...\n'
f' Source: {source}')
sync_retry_transient_errors(urlretrieve, source, tmp_path)
with zipfile.ZipFile(tmp_path, 'r') as z:
z.extractall(tmp_dir)
user_table_path = os.path.join(tmp_dir, 'ml-100k', 'u.user')
movie_table_path = os.path.join(tmp_dir, 'ml-100k', 'u.item')
ratings_table_path = os.path.join(tmp_dir, 'ml-100k', 'u.data')
assert (os.path.exists(user_table_path))
assert (os.path.exists(movie_table_path))
assert (os.path.exists(ratings_table_path))
user_cluster_readable = _copy_to_tmp(fs, local_path_uri(user_table_path), extension='txt')
movie_cluster_readable = _copy_to_tmp(fs, local_path_uri(movie_table_path), 'txt')
ratings_cluster_readable = _copy_to_tmp(fs, local_path_uri(ratings_table_path), 'txt')
[movies_path, ratings_path, users_path] = paths
genres = ['Action', 'Adventure', 'Animation',
"Children's", 'Comedy', 'Crime',
'Documentary', 'Drama', 'Fantasy',
'Film-Noir', 'Horror', 'Musical',
'Mystery', 'Romance', 'Sci-Fi',
'Thriller', 'War', 'Western']
# utility functions for importing movies
def field_to_array(ds, field):
return hl.if_else(ds[field] != 0, hl.array([field]), hl.empty_array(hl.tstr))
def fields_to_array(ds, fields):
return hl.flatten(hl.array([field_to_array(ds, f) for f in fields]))
def rename_columns(ht, new_names):
return ht.rename({k: v for k, v in zip(ht.row, new_names)})
info(f'importing users table and writing to {users_path} ...')
users = rename_columns(
hl.import_table(user_cluster_readable, key=['f0'], no_header=True, impute=True, delimiter='|'),
['id', 'age', 'sex', 'occupation', 'zipcode'])
users.write(users_path, overwrite=True)
info(f'importing movies table and writing to {movies_path} ...')
movies = hl.import_table(movie_cluster_readable, key=['f0'], no_header=True, impute=True, delimiter='|')
movies = rename_columns(movies,
['id', 'title', 'release date', 'video release date', 'IMDb URL', 'unknown'] + genres)
movies = movies.drop('release date', 'video release date', 'unknown', 'IMDb URL')
movies = movies.transmute(genres=fields_to_array(movies, genres))
movies.write(movies_path, overwrite=True)
info(f'importing ratings table and writing to {ratings_path} ...')
ratings = hl.import_table(ratings_cluster_readable, no_header=True, impute=True)
ratings = rename_columns(ratings,
['user_id', 'movie_id', 'rating', 'timestamp'])
ratings = ratings.drop('timestamp')
ratings.write(ratings_path, overwrite=True)
else:
info('Movie Lens files found!')
def get_1kg(output_dir, overwrite: bool = False):
"""Download subset of the `1000 Genomes <http://www.internationalgenome.org/>`__
dataset and sample annotations.
Notes
-----
The download is about 15M.
Parameters
----------
output_dir
Directory in which to write data.
overwrite
If ``True``, overwrite any existing files/directories at `output_dir`.
"""
jhc = Env.backend()._jhc
_mkdir(jhc, output_dir)
matrix_table_path = os.path.join(output_dir, '1kg.mt')
vcf_path = os.path.join(output_dir, '1kg.vcf.bgz')
sample_annotations_path = os.path.join(output_dir, '1kg_annotations.txt')
gene_annotations_path = os.path.join(output_dir,
'ensembl_gene_annotations.txt')
if (overwrite or not Env.jutils().dirExists(jhc, matrix_table_path)
or not Env.jutils().fileExists(jhc, sample_annotations_path)
or not Env.jutils().fileExists(jhc, vcf_path)
or not Env.jutils().fileExists(jhc, gene_annotations_path)):
init_temp_dir()
tmp_vcf = os.path.join(tmp_dir, '1kg.vcf.bgz')
source = resources['1kg_matrix_table']
info(f'downloading 1KG VCF ...\n' f' Source: {source}')
sync_retry_transient_errors(urlretrieve, resources['1kg_matrix_table'],
tmp_vcf)
cluster_readable_vcf = Env.jutils().copyToTmp(jhc,
local_path_uri(tmp_vcf),
'vcf')
info('importing VCF and writing to matrix table...')
hl.import_vcf(cluster_readable_vcf,
min_partitions=16).write(matrix_table_path,
overwrite=True)
tmp_sample_annot = os.path.join(tmp_dir, '1kg_annotations.txt')
source = resources['1kg_annotations']
info(f'downloading 1KG annotations ...\n' f' Source: {source}')
sync_retry_transient_errors(urlretrieve, source, tmp_sample_annot)
tmp_gene_annot = os.path.join(tmp_dir, 'ensembl_gene_annotations.txt')
source = resources['ensembl_gene_annotations']
info(f'downloading Ensembl gene annotations ...\n'
f' Source: {source}')
sync_retry_transient_errors(urlretrieve, source, tmp_gene_annot)
hl.hadoop_copy(local_path_uri(tmp_sample_annot),
sample_annotations_path)
hl.hadoop_copy(local_path_uri(tmp_gene_annot), gene_annotations_path)
hl.hadoop_copy(local_path_uri(tmp_vcf), vcf_path)
info('Done!')
else:
info('1KG files found')