def get(self, local_path):
bucket, path = parsePath(local_path)
print(bucket, path)
fs_client = fs.LocalFileSystem()
file_info_list = fs_client.get_file_info(
fs.FileSelector(path, recursive=False))
files = []
dirs = []
for info in file_info_list:
if info.type.value == 2:
# File type
files.append({
'name': info.base_name,
'ext': info.extension,
'size': info.size,
'mtime': info.mtime.isoformat()
})
elif info.type.value == 3:
# Directory type
dirs.append({
'name': info.base_name,
'mtime': info.mtime.isoformat()
})
self.finish(json.dumps({'files': files, 'dirs': dirs}))
def update_object_timeseries(self, detect_time, matches, write=False):
if self.odb is None:
self.get_object_timeseries()
odb = self.odb
name = "Observer: " + self.name
if matches is not None:
for match in matches:
logger.debug(
"Observer: Match class: {match['class']}, score: {match['score']}, roi: {match['roi']}"
)
# y1, x1, y2, x2 = boxes[i]
odb.loc[detect_time] = [
match['class'], match['score'], match['roi'][1],
match['roi'][0], match['roi'][3], match['roi'][2]
# match['roi']
]
if write is True:
logger.debug(f"{name} : odb is {odb}")
parquet_file = os.path.join(self.mydir,
self.name + '_events.parquet')
if os.path.isfile(parquet_file):
logger.debug(f"{name}:File exists, will overwrite")
odbtable = pa.Table.from_pandas(odb)
localfs = pfs.LocalFileSystem()
with localfs.open_output_stream(parquet_file) as pfile:
pq.write_table(odbtable, pfile)
self.odb = odb
def test_parquet_writer_filesystem_buffer_raises():
df = _test_dataframe(100)
table = pa.Table.from_pandas(df, preserve_index=False)
filesystem = fs.LocalFileSystem()
# Should raise ValueError when filesystem is passed with file-like object
with pytest.raises(ValueError, match="specified path is file-like"):
pq.ParquetWriter(pa.BufferOutputStream(),
table.schema,
filesystem=filesystem)
def test_open_dataset_filesystem(tempdir):
# single file
table, path = _create_single_file(tempdir)
# filesystem inferred from path
dataset1 = ds.dataset(str(path))
assert dataset1.schema.equals(table.schema)
# filesystem specified
dataset2 = ds.dataset(str(path), filesystem=fs.LocalFileSystem())
assert dataset2.schema.equals(table.schema)
# local filesystem specified with relative path
with change_cwd(tempdir):
dataset3 = ds.dataset("test.parquet", filesystem=fs.LocalFileSystem())
assert dataset3.schema.equals(table.schema)
# passing different filesystem
with pytest.raises(FileNotFoundError):
ds.dataset(str(path), filesystem=fs._MockFileSystem())
def __init__(self, storage_client: Client):
self.period_per_file = timedelta(minutes=1)
self.times = []
self.amps = []
self.phases = []
self.client = storage_client
self.local = fs.LocalFileSystem()
self.log = logging.getLogger('Buffer')
def test_construct_from_single_directory(tempdir):
directory = tempdir / 'single-directory'
directory.mkdir()
tables, paths = _create_directory_of_files(directory)
d1 = ds.dataset(directory)
d2 = ds.dataset(directory, filesystem=fs.LocalFileSystem())
d3 = ds.dataset(directory.name, filesystem=_filesystem_uri(tempdir))
t1 = d1.to_table()
t2 = d2.to_table()
t3 = d3.to_table()
assert t1 == t2 == t3
def test_construct_from_single_file(tempdir):
directory = tempdir / 'single-file'
directory.mkdir()
table, path = _create_single_file(directory)
relative_path = path.relative_to(directory)
# instantiate from a single file
d1 = ds.dataset(path)
# instantiate from a single file with a filesystem object
d2 = ds.dataset(path, filesystem=fs.LocalFileSystem())
# instantiate from a single file with prefixed filesystem URI
d3 = ds.dataset(relative_path, filesystem=_filesystem_uri(directory))
assert d1.to_table() == d2.to_table() == d3.to_table()
def test_open_dataset_filesystem(tempdir):
# # single file
table, path = _create_single_file(tempdir)
# filesystem inferred from path
dataset1 = ds.dataset(str(path))
assert dataset1.schema.equals(table.schema, check_metadata=False)
# filesystem specified
dataset2 = ds.dataset(str(path), filesystem=fs.LocalFileSystem())
assert dataset2.schema.equals(table.schema, check_metadata=False)
# passing different filesystem
with pytest.raises(FileNotFoundError):
ds.dataset(str(path), filesystem=fs._MockFileSystem())
def test_filesystem_uri(tmpdir):
from pyarrow import orc
table = pa.table({"a": [1, 2, 3]})
directory = tmpdir / "data_dir"
directory.mkdir()
path = directory / "data.orc"
orc.write_table(table, str(path))
# filesystem object
result = orc.read_table(path, filesystem=fs.LocalFileSystem())
assert result.equals(table)
# filesystem URI
result = orc.read_table("data_dir/data.orc",
filesystem=util._filesystem_uri(tmpdir))
assert result.equals(table)
def __init__(
self,
address=None,
name: str = None,
namespace: str = None,
partitions: int = 1,
storage_type: LocalFSStoreType = LocalFSStoreType.DISK,
options=None,
):
super(StorageTable, self).__init__(
name=name,
namespace=namespace,
address=address,
partitions=partitions,
options=options,
engine=StorageEngine.LOCALFS,
store_type=storage_type,
)
self._local_fs_client = fs.LocalFileSystem()
def test_construct_from_list_of_files(tempdir):
# instantiate from a list of files
directory = tempdir / 'list-of-files'
directory.mkdir()
tables, paths = _create_directory_of_files(directory)
relative_paths = [p.relative_to(tempdir) for p in paths]
with change_cwd(tempdir):
d1 = ds.dataset(relative_paths)
t1 = d1.to_table()
assert len(t1) == sum(map(len, tables))
d2 = ds.dataset(relative_paths, filesystem=_filesystem_uri(tempdir))
t2 = d2.to_table()
d3 = ds.dataset(paths)
t3 = d3.to_table()
d4 = ds.dataset(paths, filesystem=fs.LocalFileSystem())
t4 = d4.to_table()
assert t1 == t2 == t3 == t4
def read_csv_write_to_parquet(local_data_path, s3_path, local_meta_path):
if s3_path.startswith("s3://"):
s3_path = s3_path.replace("s3://", "", 1)
local = fs.LocalFileSystem()
s3 = fs.S3FileSystem(region=REGION)
with local.open_input_stream(local_data_path) as f:
tab = csv.read_csv(f)
metadata = read_table_json(local_meta_path)
arrow_cols = []
for col in metadata.columns:
if col["name"] not in metadata.partitions:
arrow_cols.append(convert_meta_col_to_arrow_tuple(col))
s = pa.schema(arrow_cols)
tab = tab.cast(s)
with s3.open_output_stream(s3_path) as f:
pq.write_table(tab, f)
def update_video_database(self, start_time, vfile_name, end_time, event):
if self.vfdb is None:
self.get_video_database()
name = "Writer: " + self.config.name
vfdb = self.vfdb
deleted = False
try:
space_used = os.path.getsize(vfile_name)
vfdb.loc[start_time] = [
vfile_name, end_time, event, deleted, space_used
]
except FileNotFoundError:
logger.warning(f"{name}: File missing, zero frames maybe?")
logger.debug(
f"{name} : ===============================================")
logger.debug(
f"{name} : start_time: {start_time}, vfile_name : {vfile_name}, end_time: {end_time}, event: {event}, deleted:{deleted}"
)
logger.debug(
f"{name} : ===============================================")
logger.debug(f"{name} : vfdb entries: {self.vfdb}")
logger.debug(
f"{name} : ===============================================")
parquet_file = os.path.join(self.config.mydir,
self.config.name + '_videodb.parquet')
if os.path.isfile(parquet_file):
logger.debug(f"{name}:File exists, will overwrite")
vfdbtable = pa.Table.from_pandas(vfdb)
localfs = pfs.LocalFileSystem()
with localfs.open_output_stream(parquet_file) as pfile:
pq.write_table(vfdbtable, pfile)
self.vfdb = vfdb
fs_protocol_obj = util.FSProtocolClass(path)
result = _read_table(
fs_protocol_obj, use_legacy_dataset=use_legacy_dataset
)
assert result.equals(table)
# combined with non-local filesystem raises
with pytest.raises(TypeError):
_read_table(fs_protocol_obj, filesystem=FileSystem())
@pytest.mark.dataset
@parametrize_legacy_dataset
@pytest.mark.parametrize("filesystem", [
None, fs.LocalFileSystem(), LocalFileSystem._get_instance()
])
def test_relative_paths(tempdir, use_legacy_dataset, filesystem):
# reading and writing from relative paths
table = pa.table({"a": [1, 2, 3]})
# reading
pq.write_table(table, str(tempdir / "data.parquet"))
with util.change_cwd(tempdir):
result = pq.read_table("data.parquet", filesystem=filesystem,
use_legacy_dataset=use_legacy_dataset)
assert result.equals(table)
# writing
with util.change_cwd(tempdir):
pq.write_table(table, "data2.parquet", filesystem=filesystem)
fs_protocol_obj = util.FSProtocolClass(path)
result = _read_table(fs_protocol_obj,
use_legacy_dataset=use_legacy_dataset)
assert result.equals(table)
# combined with non-local filesystem raises
with pytest.raises(TypeError):
_read_table(fs_protocol_obj, filesystem=FileSystem())
@pytest.mark.dataset
@parametrize_legacy_dataset
@pytest.mark.parametrize(
"filesystem",
[None, fs.LocalFileSystem(),
LocalFileSystem._get_instance()])
def test_relative_paths(tempdir, use_legacy_dataset, filesystem):
# reading and writing from relative paths
table = pa.table({"a": [1, 2, 3]})
# reading
pq.write_table(table, str(tempdir / "data.parquet"))
with util.change_cwd(tempdir):
result = pq.read_table("data.parquet",
filesystem=filesystem,
use_legacy_dataset=use_legacy_dataset)
assert result.equals(table)
# writing
with util.change_cwd(tempdir):
def prune_video_database(self):
if self.vfdb is None:
self.get_video_database()
name = "Writer: " + self.config.name
vfdb = self.vfdb
parquet_file = os.path.join(self.config.mydir,
self.config.name + '_videodb.parquet')
if not os.path.isfile(parquet_file):
return
now = datetime.datetime.now()
total, used, free = shutil.disk_usage(parquet_file)
percentage_free = int((free * 100) / total)
logger.warning(
f"{name}: Disk free = {percentage_free}%, total: {int(total/(1024*1024))} MB, used: {int(used/(1024*1024))} MB, free: {int(free/(1024*1024))} MB"
)
# TODO: Get threshold from configuration file
passnum = 0
#if percentage_free <= self.config.deletethreshold:
while percentage_free <= self.config.deletethreshold:
#prune_older_than_hours = datetime.timedelta(days=self.config.deleteafterdays,
deleteafterdays = self.config.deleteafterdays - passnum
prune_older_than_hours = datetime.timedelta(
days=deleteafterdays,
hours=now.hour,
minutes=now.minute,
seconds=now.second,
microseconds=now.microsecond)
prune_from = now - datetime.timedelta(days=1 * 365,
hours=now.hour,
minutes=now.minute,
seconds=now.second,
microseconds=now.microsecond)
prune_to = now - prune_older_than_hours
logger.warning(
f"{name}: Pruning from {prune_from} to {prune_to}, pass:{passnum}"
)
passnum = passnum + 1
#logger.warning(f"Starting vfdb is {vfdb}")
vfdb_prune = vfdb[prune_from:prune_to]
# Do not delete files with events if freespace greater than events threshold
if percentage_free > self.config.deleteeventsthreshold:
vfdb_prune = vfdb_prune[vfdb_prune['event'] ==
False].sort_index()
files_to_prune = vfdb_prune[['vfile_name']]
total_freed = 0
for file_tobe_deleted in files_to_prune['vfile_name']:
try:
space_freed = os.path.getsize(file_tobe_deleted)
total_freed += space_freed
logger.warning(
f"{name}: Deleting file: {file_tobe_deleted}, freed: {int(space_freed/(1024*1024))} MB"
)
os.remove(file_tobe_deleted)
except FileNotFoundError:
logger.debug(
f"{name}: File {file_tobe_deleted} not found, might have been manually deleted?"
)
logger.warning(
f"{name}: Total Freed : {int(total_freed/(1024*1024))} MB")
#vfdb_prune['deleted'].loc[vfdb_prune['deleted'] == False] = True
vfdb.update(vfdb_prune)
#logger.warning(f"vfdb: index is {vfdb.loc[vfdb_prune.index]['deleted']}")
logger.debug(f"{name} : vfdb entries after update: {vfdb}")
total, used, free = shutil.disk_usage(parquet_file)
percentage_free = int((free * 100) / total)
logger.warning(
f"{name}: After pass: {passnum}, Disk free = {percentage_free}%, total: {int(total/(1024*1024))} MB, used: {int(used/(1024*1024))} MB, free: {int(free/(1024*1024))} MB"
)
if os.path.isfile(parquet_file):
logger.warning(f"{name}:File exists, will overwrite")
vfdbtable = pa.Table.from_pandas(vfdb)
localfs = pfs.LocalFileSystem()
with localfs.open_output_stream(parquet_file) as pfile:
pq.write_table(vfdbtable, pfile)
#logger.warning(f"Updated vfdb is {vfdb}")
self.vfdb = vfdb
# Don't spin if we cannot even clear up enough after going to 1
# day retention
if passnum >= self.config.deleteafterdays:
break
except Exception as e:
assert str(e) == error_text
buf = out.getvalue()
result = _read_table(pa.BufferReader(buf),
use_legacy_dataset=use_legacy_dataset)
expected = pd.concat(frames, ignore_index=True)
tm.assert_frame_equal(result.to_pandas(), expected)
@pytest.mark.pandas
@pytest.mark.parametrize("filesystem", [
None,
LocalFileSystem._get_instance(),
fs.LocalFileSystem(),
])
def test_parquet_writer_filesystem_local(tempdir, filesystem):
df = _test_dataframe(100)
table = pa.Table.from_pandas(df, preserve_index=False)
path = str(tempdir / 'data.parquet')
with pq.ParquetWriter(path,
table.schema,
filesystem=filesystem,
version='2.0') as writer:
writer.write_table(table)
result = _read_table(path).to_pandas()
tm.assert_frame_equal(result, df)
def parse_single_path(path: str):
if Path(path).exists():
return fs.LocalFileSystem(), path
else:
return fs.FileSystem.from_uri(path)
table = table.select(["ix", "x", "y", "title", "first_author_name", "date", "language"])
# truncate the title after 101 characters (matching display logic)
truncated_title = pc.utf8_replace_slice(table.column("title"), start=101, stop=1000, replacement="")
table = table.set_column(table.schema.get_field_index("title"), "title", truncated_title)
# ensure all dictionaries in the file use the same key/value mappings
table = table.unify_dictionaries()
# filter out non-numeric dates (e.g. null, "1850-1853")
# matches the hack in index.js:37
mask = pc.invert(pc.is_null(table.column("date")))
table = table.filter(mask)
# sorting by the date improves the loading aesthetics
# comment this out to exactly match the original appearance
indices = pc.sort_indices(table, sort_keys=[("date", "ascending")])
table = pc.take(table, indices)
# after sorting replace ix with an accurate row index
indices = pc.sort_indices(table, sort_keys=[("date", "ascending")])
table = table.set_column(table.schema.get_field_index("ix"), "ix", pc.cast(indices, pa.uint32()))
temp_path.unlink()
local = fs.LocalFileSystem()
with local.open_output_stream(str(target_path)) as file:
with pa.RecordBatchStreamWriter(file, table.schema) as writer:
writer.write_table(table, 10000)
def _parse_data_to_pandas(filepath: str, table_params: dict, metadata: dict):
"""
Reads in the data from the given filepath and returns
a dataframe
"""
meta_col_names = [
c["name"] for c in metadata["columns"]
if c["name"] not in metadata.get("partitions", [])
]
# For string based file types convert make arrow readers read them in as strings
# validators will still treat these as dates but will run validation against strings
# cols expecting values to match a timestamp format
if "json" in metadata["file_format"] or "csv" in metadata["file_format"]:
md_obj = Metadata.from_dict(metadata)
cols = md_obj.columns
cols_to_force_str_read_in = []
for c in cols:
if c["type"].startswith("time") or c["type"].startswith("date"):
c["type"] = "string"
c["type_category"] = "string"
cols_to_force_str_read_in.append(c["name"])
md_obj.columns = cols
ac = ArrowConverter()
arrow_schema = ac.generate_from_meta(md_obj)
ts_as_str_schema = pa.schema([])
for cname in cols_to_force_str_read_in:
ts_as_str_schema = ts_as_str_schema.append(
arrow_schema.field(cname))
# Set the reader type
if filepath.startswith("s3://"):
reader_fs = fs.S3FileSystem(region="eu-west-1")
fp_for_file_reader = filepath.replace("s3://", "", 1)
else:
reader_fs = fs.LocalFileSystem()
fp_for_file_reader = filepath
with reader_fs.open_input_stream(fp_for_file_reader) as f:
if "csv" in metadata["file_format"]:
# Safer CSV load for newlines_in_values set to True
if table_params.get("expect-header", True):
po = csv.ParseOptions(newlines_in_values=True)
else:
po = csv.ParseOptions(newlines_in_values=True,
column_names=meta_col_names)
if ts_as_str_schema:
co = csv.ConvertOptions(column_types=ts_as_str_schema)
else:
co = None
df = pa_read_csv_to_pandas(
input_file=f,
schema=arrow_schema,
expect_full_schema=False,
parse_options=po,
convert_options=co,
)
# dates/datetimes == string
elif "json" in metadata["file_format"]:
po = json.ParseOptions(
newlines_in_values=True,
explicit_schema=ts_as_str_schema if ts_as_str_schema else None,
)
df = pa_read_json_to_pandas(
input_file=f,
schema=arrow_schema,
expect_full_schema=False,
parse_options=po,
)
# dates/datetimes == string
elif "parquet" in metadata["file_format"]:
df = arrow_to_pandas(pq.read_table(f))
# dates/datetimes == datetime / date
else:
raise ValueError(
f"Unknown file_format in metadata: {metadata['file_format']}.")
if table_params.get("row-limit"):
df = df.sample(table_params.get("row-limit"))
if table_params.get("headers-ignore-case"):
df_cols = [c.lower() for c in df.columns]
df.columns = df_cols
if table_params.get("only-test-cols-in-metadata", False):
keep_cols = [c for c in df.columns if c in meta_col_names]
df = df[keep_cols]
return df