def update_pkg(
df: pd.DataFrame,
user: str,
package: str,
readme: Optional[str] = None,
hash_key=None,
):
r"""
Parameters
----------
df
user
package
readme
hash_key
Returns
-------
"""
pkg_path = f'{user}/{package}'
quilt.build(pkg_path, quilt.nodes.GroupNode(dict(author='@hudlrd')))
quilt.build(f'{pkg_path}/df', quilt.nodes.DataNode(None, None, df, {}))
# TODO: warn the user if readme if not provided
if readme is not None:
with NamedTemporaryFile() as tmp:
tmp.write(readme.encode('UTF-8'))
tmp.flush()
quilt.build(f'{pkg_path}/README', tmp.model_name)
quilt.login()
quilt.push(pkg_path, is_public=True, hash=hash_key)
# %%
os.makedirs(name="model/train", exist_ok=True)
np.save(file="model/train/W1_data.npy", arr=rema)
np.save(file="model/train/W2_data.npy", arr=measuresvelocity)
np.save(file="model/train/W3_data.npy", arr=accumulation)
np.save(file="model/train/X_data.npy", arr=lores)
np.save(file="model/train/Y_data.npy", arr=hires)
# %% [markdown]
# ### Quilt
#
# Login -> Build -> Push
# %%
quilt.login()
# %%
# Tiled datasets for training neural network
quilt.build(package="weiji14/deepbedmap/model/train/W1_data", path=rema)
quilt.build(package="weiji14/deepbedmap/model/train/W2_data",
path=measuresvelocity)
quilt.build(package="weiji14/deepbedmap/model/train/W3_data",
path=accumulation)
quilt.build(package="weiji14/deepbedmap/model/train/X_data", path=lores)
quilt.build(package="weiji14/deepbedmap/model/train/Y_data", path=hires)
# %%
# Original datasets for neural network predictions on bigger area
quilt.build(package="weiji14/deepbedmap/lowres/bedmap2_bed",
path="lowres/bedmap2_bed.tif")