def FromQuilt(
package: str = DEFAULT_QUILT_PKG,
hash: str = None,
version: str = DEFAULT_QUILT_VERSION,
tag: str = None,
force: bool = True,
) -> object:
"""Create a GroupsData object from quilt."""
quilt.install(
package=package,
version=version,
force=force,
tag=tag,
hash=hash,
)
cc_pkg = quilt.load(DEFAULT_QUILT_PKG)
return GroupsData.FromDataFrame(cc_pkg.data.group_definitions())
def get_pkg(user: str,
package: str,
hash_key=None,
force=True) -> pd.DataFrame:
r"""
Parameters
----------
user
package
hash_key
force
Returns
-------
"""
pkg_path = f'{user}/{package}'
quilt.install(pkg_path, hash=hash_key, force=force)
return quilt.load(pkg_path)
def get_deepbedmap_model_inputs(
window_bound: rasterio.coords.BoundingBox,
padding: int = 1000,
use_whole_rema: bool = False,
) -> (np.ndarray, np.ndarray, np.ndarray, np.ndarray):
"""
Outputs one large tile for each of:
BEDMAP2, REMA, MEASURES Ice Flow Velocity and Antarctic Snow Accumulation
according to a given window_bound in the form of (xmin, ymin, xmax, ymax).
"""
data_prep = _load_ipynb_modules("data_prep.ipynb")
if window_bound == rasterio.coords.BoundingBox(
left=-1_594_000.0, bottom=-166_500.0, right=-1_575_000.0, top=-95_500.0
):
# Quickly pull from cached quilt storage if using (hardcoded) test region
quilt.install(package="weiji14/deepbedmap/model/test", force=True)
pkg = quilt.load(pkginfo="weiji14/deepbedmap/model/test")
X_tile = pkg.X_tile()
W1_tile = pkg.W1_tile()
W2_tile = pkg.W2_tile()
W3_tile = pkg.W3_tile()
#!/usr/bin/env python3.6 import quilt import const import util node1 = quilt.load(const.PKG, hash=const.HASH1) node2 = quilt.load(const.PKG) # latest # Read jsonl jsonl1 = node1.dump_json() jsonl2 = node2.dump_json() print(util.read_jsonl(jsonl1)) print(util.read_jsonl(jsonl2)) # Read text text1 = node1.dump_txt() text2 = node2.dump_txt() print(util.read_text(text1)) print(util.read_text(text2))
copyTime = time.time() - t
print('########Copy Completed#########')
FilterAndSavePandasTable(folds, allImageIDs, annList, packagePath, logPath)
print('######## Panda Table Generated#########')
GenerateREADME(packagePath + 'README.md', classDescription)
if (os.path.exists(packagePath + 'build.yml')):
os.remove(packagePath + 'build.yml')
t = time.time()
quilt.generate(packagePath)
quilt.build(quiltUser + '/' + classDescription, packagePath + 'build.yml')
pkgNode = quilt.load(quiltUser + '/' + classDescription)
pkgNode._meta['trainable'] = classID in oi.classes_trainable().values
pkgNode._meta['labelName'] = classDescription
numImages = GetNumImages(folds, allImageIDs)
pkgNode._meta['image_count'] = numImages
GenerateImageMetadata(folds, allImageIDs, pkgNode, annList, logPath,
imagePrefix)
print('######## Image Metadata Generated#########')
quilt.build(quiltUser + '/' + classDescription, pkgNode)
print('######## New Package Generated#########')
buildPkgTime = time.time() - t
