Working with Files

The following highlights some DASCore features for working with IO.

Writing Patches to Disk

Patches can be written to disk using the io namespace. The following shows how to write a Patch to disk in the DASDAE format

from pathlib import Path
import dascore as dc

write_path = Path("output_file.h5")
patch = dc.get_example_patch()

patch.io.write(write_path, "dasdae")

PosixPath('output_file.h5')

DirectorySpool

The DirectorySpool is used to retrieve data from a directory of dascore-readable files. It has the same interface as other spools and is created with the dascore.spool function.

For example:

import dascore
from dascore import examples as ex

# Get a directory with several files
diverse_spool = dascore.get_example_spool('diverse_das')
path = ex.spool_to_directory(diverse_spool)

# Create a spool for interacting with the files in the directory.
spool = (
  dascore.spool(path)
  .select(network='das2')  # sub-select das2 network
  .select(time=(None, '2022-01-01'))  # unselect anything after 2022
  .chunk(time=2, overlap=0.5)  # change the chunking of the patches
)

# Iterate each patch and do something with it
for patch in spool:
  ...

Converting Patches to Other Library Formats

The Patch.io namespace also includes functionality for converting Patch instances to datastructures used by other libraries including Pandas, Xarray, and ObsPy. See the external conversion recipe for examples.

Directory Indexer

The ‘DirectoryIndexer’ is used to track the contents of a directory which contains fiber data. It creates a small, hidden HDF index file at the top of the directory which can be efficiently queried for directory contents (it is used internally by the DirectorySpool).

import dascore
from dascore.io.indexer import DirectoryIndexer
from dascore import examples as ex

# Get a directory with several files
diverse_spool = dascore.get_example_spool('diverse_das')
path = ex.spool_to_directory(diverse_spool)

# Create an indexer and update the index. This will include any new files
# with timestamps newer than the last update, or create a new HDF index file
# if one does not yet exist.
indexer = DirectoryIndexer(path).update()

# get the contents of the directory's files
df = indexer.get_contents()

# This dataframe can be used to ascertain data availability, detect gaps, etc.

	file_format	time_min	time_max	time_step	network	tag	station	dims	file_version	path
0	DASDAE	1989-05-04	1989-05-04 00:00:07.996	0 days 00:00:00.004000		random	wayout	distance,time	1	DAS____wayout__random__1989_05_04T00_00_00_000...
1	DASDAE	2020-01-03	2020-01-03 00:00:07.996	0 days 00:00:00.004000	das2	random		distance,time	1	DAS__das2____random__2020_01_03T00_00_00_00000...
2	DASDAE	2020-01-03	2020-01-03 00:00:07.996	0 days 00:00:00.004000		some_tag		distance,time	1	DAS______some_tag__2020_01_03T00_00_00_0000000...
3	DASDAE	2020-01-03	2020-01-03 00:00:07.996	0 days 00:00:00.004000		random		distance,time	1	DAS______random__2020_01_03T00_00_00_000000000...
4	DASDAE	2020-01-03	2020-01-03 00:00:07.996	0 days 00:00:00.004000		random	big_gaps	distance,time	1	DAS____big_gaps__random__2020_01_03T00_00_00_0...