def test_all(app_name: str, app_author: str):
print("-- app dirs (with optional 'version')")
dirs = AppPath(app_name,
app_author,
app_version="1.0",
ensure_existence_on_access=False)
for prop in props:
print(f"{prop}: {getattr(dirs, prop)}")
dirs.clean()
def clean_arg():
""""""
parser = argparse.ArgumentParser(description="Apppath Clean Path")
parser.add_argument("APP_NAME", metavar="Name", type=str, help="App name to clean AppPath for")
parser.add_argument(
"--DIR",
"-d",
type=str,
default="cache",
metavar="DIR",
help="Which AppPath directory to clean (default: 'cache')",
)
args = parser.parse_args()
project_app_path = AppPath(args.APP_NAME)
if args.DIR == "data":
directory = project_app_path.user_data
elif args.DIR == "config":
directory = project_app_path.user_config
elif args.DIR == "cache":
directory = project_app_path.user_cache
elif args.DIR == "logs":
directory = project_app_path.user_log
else:
raise NotADirectoryError(args.DIR)
print(f"Wiping {directory}")
if directory.exists():
rmtree(directory)
else:
directory.mkdir()
def open_arg():
""""""
parser = argparse.ArgumentParser(description="Apppath Open Path")
parser.add_argument("APP_NAME",
metavar="Name",
type=str,
help="App name to open AppPath for")
parser.add_argument(
"--SITE",
"-s",
type=bool,
default=False,
metavar="SITE",
help="Open user or site dirs (default: User)",
)
parser.add_argument(
"--DIR",
"-d",
type=str,
default="data",
metavar="DIR",
help="Which AppPath directory to open (default: 'data')",
)
args = parser.parse_args()
project_app_path = AppPath(args.APP_NAME)
open_app_path(project_app_path, AppPathSubDirEnum(args.DIR), args.SITE)
def main():
"""description"""
apppath = AppPath("AppPath")
print(apppath.user_config)
print(apppath.user_log)
print(apppath.user_data)
print(apppath.user_cache)
def a():
""" """
_path_to_events_file = next(
AppPath("Draugr", "Christian Heider Nielsen").user_log.rglob(
"events.out.tfevents.*"))
print(_path_to_events_file)
tee = TensorboardEventExporter(_path_to_events_file.parent,
save_to_disk=True)
print(tee.tags_available)
# tee.export_csv('train_loss')
# tee.export_line_plot('train_loss')
# pyplot.show()
print(tee.export_histogram())
print(tee.available_scalars)
print(
tee.pr_curve_export_csv(
*tee.tags_available["tensors"],
out_dir=ensure_existence(Path.cwd() / "exclude"),
))
print(list(iter(tee.TagTypeEnum)))
def dist_is_editable(dist: Any) -> bool:
"""
Return True if given Distribution is an editable install.
"""
for path_item in sys.path:
egg_link = Path(path_item) / f"{dist.project_name}.egg-link"
if egg_link.is_file():
return True
return False
PROJECT_NAME = __project__.lower().strip().replace(" ", "_")
PROJECT_VERSION = __version__
PROJECT_AUTHOR = __author__.lower().strip().replace(" ", "_")
PROJECT_APP_PATH = AppPath(app_name=PROJECT_NAME, app_author=PROJECT_AUTHOR)
distributions = {v.key: v for v in pkg_resources.working_set}
if PROJECT_NAME in distributions:
distribution = distributions[PROJECT_NAME]
IS_DEVELOP = dist_is_editable(distribution)
else:
IS_DEVELOP = True
def get_version(append_time: Any = IS_DEVELOP) -> str:
version = __version__
if not version:
version = os.getenv("VERSION", "0.0.0")
if append_time:
def extract_scalars_as_csv(
train_path: Path = EXPORT_RESULTS_PATH / "csv" / "training",
test_path: Path = EXPORT_RESULTS_PATH / "csv" / "testing",
export_train: bool = True,
export_test: bool = True,
verbose: bool = False,
only_extract_from_latest_event_file: bool = False,
) -> None:
"""
:param train_path:
:param test_path:
:param export_train:
:param export_test:
:param verbose:
:param only_extract_from_latest_event_file:
"""
if only_extract_from_latest_event_file:
max_load_time = max(
list(
AppPath(
"Adversarial Speech", "Christian Heider Nielsen"
).user_log.iterdir()
),
key=os.path.getctime,
)
unique_event_files_parents = set(
[ef.parent for ef in max_load_time.rglob("events.out.tfevents.*")]
)
event_files = {max_load_time: unique_event_files_parents}
else:
event_files = {
a: set([ef.parent for ef in a.rglob("events.out.tfevents.*")])
for a in list(
AppPath(
"Adversarial Speech", "Christian Heider Nielsen"
).user_log.iterdir()
)
}
for k, v in progress_bar(event_files.items()):
for e in progress_bar(v):
relative_path = e.relative_to(k)
mapping_id, *rest = relative_path.parts
mappind_id_test = f"{mapping_id}_Test_{relative_path.name}"
# model_id = relative_path.parent.name can be include but is always the same
relative_path = Path(*(mappind_id_test, *rest))
with TensorboardEventExporter(e, save_to_disk=True) as tee:
if export_test:
out_tags = []
for tag in progress_bar(TestingScalars):
if tag.value in tee.available_scalars:
out_tags.append(tag.value)
if len(out_tags):
tee.scalar_export_csv(
*out_tags,
out_dir=ensure_existence(
test_path / k.name / relative_path,
force_overwrite=True,
verbose=verbose,
),
)
print(e)
else:
if verbose:
print(
f"{e}, no requested tags found {TestingScalars.__members__.values()}, {tee.available_scalars}"
)
if export_train:
out_tags = []
for tag in progress_bar(TrainingScalars):
if tag.value in tee.available_scalars:
out_tags.append(tag.value)
if len(out_tags):
tee.scalar_export_csv(
*out_tags,
out_dir=ensure_existence(
train_path / k.name / relative_path,
force_overwrite=True,
verbose=verbose,
),
)
else:
if verbose:
print(
f"{e}, no requested tags found {TrainingScalars.__members__.values()}, {tee.available_scalars}"
)
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %% [markdown]
# Christian Heider Nielsen
# Created on 05/04/2020
# %%
from apppath import AppPath
# %%
apppath = AppPath("AppPath")
print(apppath.user_config)
print(apppath.user_log)
print(apppath.user_data)
print(apppath.user_cache)
def test_no_author(app_name):
print("\n-- app dirs (with disabled '_app_author')")
dirs = AppPath(app_name, ensure_existence_on_access=False)
for prop in props:
print(f"{prop}: {getattr(dirs, prop)}")
dirs.clean()
def test_no_ver(app_name, app_author):
print("\n-- app dirs (without optional 'version')")
dirs = AppPath(app_name, app_author, ensure_existence_on_access=False)
for prop in props:
print(f"{prop}: {getattr(dirs, prop)}")
dirs.clean()