def run_rpc(self, *, user_prompt: UserPrompt, method_name: str, arguments: JsonObject) -> Optional[UsageError]:
if(method_name == "set_live_update"):
live_update = ensureJsonBoolean(arguments.get("live_update"))
result = self.set_live_update(user_prompt=user_prompt, live_update=live_update)
if isinstance(result, CascadeError):
return UsageError(result.message)
return None
raise ValueError(f"Invalid method name: '{method_name}'")
def run_rpc(self, *, user_prompt: UserPrompt, method_name: str,
arguments: JsonObject) -> Optional[UsageError]:
raw_feature_array = ensureJsonArray(
arguments.get("feature_extractors"))
feature_extractors = [
IlpFilter.from_json_value(raw_feature)
for raw_feature in raw_feature_array
]
if method_name == "add_feature_extractors":
return UsageError.check(
self.add_feature_extractors(
user_prompt=user_prompt,
feature_extractors=feature_extractors))
if method_name == "remove_feature_extractors":
return UsageError.check(
self.remove_feature_extractors(user_prompt,
feature_extractors))
raise ValueError(f"Invalid method name: '{method_name}'")
def start_simple_segmentation_export_job(
self, *, datasource: DataSource,
datasinks: Sequence[DataSink]) -> "UsageError | None":
classifier = self._in_operator()
if classifier is None:
return UsageError("Upstream not ready yet")
if len(datasinks) != classifier.num_classes:
return UsageError(
f"Wrong number of datasinks. Expected {classifier.num_classes} but got {len(datasinks)}"
)
expected_shape = datasource.shape.updated(c=3)
if any(sink.shape != expected_shape for sink in datasinks):
return UsageError(
"All data sinks should have 3 channels for this kind of export"
)
if any(sink.dtype != np.dtype("uint8") for sink in datasinks):
return UsageError(
"All data sinks should have dtype of uint8 for this kind of export"
)
def launch_export_job(job_id: uuid.UUID,
result: "Exception | Sequence[DataSinkWriter]"):
if isinstance(result, Exception):
raise result
self._remove_job(job_id)
_ = self._create_job(
name=f"Simple Segmentation Export Job",
target=ExportAsSimpleSegmentationTask(operator=classifier,
sink_writers=result),
args=datasource.roi.get_datasource_tiles(
), #FIXME: use sink tile_size
num_args=datasource.roi.get_num_tiles(
tile_shape=datasource.tile_shape),
)
_ = self._create_job(
name=f"Creating datasinks",
target=_create_datasinks,
args=[datasinks],
num_args=1, #FIXME: maybe one per datasink?
on_success=launch_export_job,
)
def start_export_job(self, *, datasource: DataSource,
datasink: DataSink) -> "UsageError | None":
classifier = self._in_operator()
if classifier is None:
return UsageError("Upstream not ready yet")
expected_shape = datasource.shape.updated(c=classifier.num_classes)
if datasink.shape != expected_shape:
return UsageError(
f"Bad sink shape. Expected {expected_shape} but got {datasink.shape}"
)
if datasink.dtype != np.dtype("float32"):
return UsageError(
"Data sink should have dtype of float32 for this kind of export"
)
def launch_export_job(job_id: uuid.UUID,
result: "Exception | DataSinkWriter"):
if isinstance(result, BaseException):
raise result #FIXME?
self._remove_job(job_id)
_ = self._create_job(
name=f"Export Job",
target=ExportTask(operator=classifier, sink_writer=result),
args=datasource.roi.get_datasource_tiles(
), #FIXME: use sink tile_size
num_args=datasource.roi.get_num_tiles(
tile_shape=datasource.tile_shape),
)
_ = self._create_job(
name=f"Creating datasink",
target=_create_datasink,
args=[datasink],
num_args=1,
on_success=launch_export_job,
# on_failure=lambda exception: self._remove_job(sink_creation_job.uuid)
)
def run_rpc(self, *, user_prompt: UserPrompt, method_name: str,
arguments: JsonObject) -> "UsageError | None":
if method_name == "recolor_label":
return UsageError.check(
self.recolor_label(user_prompt,
label_name=ensureJsonString(
arguments.get("label_name")),
new_color=Color.from_json_data(
arguments.get("new_color"))))
if method_name == "rename_label":
return UsageError.check(
self.rename_label(
user_prompt,
old_name=ensureJsonString(arguments.get("old_name")),
new_name=ensureJsonString(arguments.get("new_name"))))
if method_name == "create_label":
return UsageError.check(
self.create_label(
user_prompt=user_prompt,
label_name=ensureJsonString(arguments.get("label_name")),
color=Color.from_json_data(arguments.get("color")),
))
if method_name == "remove_label":
return UsageError.check(
self.remove_label(
user_prompt=user_prompt,
label_name=ensureJsonString(arguments.get("label_name")),
))
if method_name == "add_annotation":
return UsageError.check(
self.add_annotation(
user_prompt,
label_name=ensureJsonString(arguments.get("label_name")),
annotation=Annotation.from_json_value(
arguments.get("annotation")),
))
if method_name == "remove_annotation":
return UsageError.check(
self.remove_annotation(
user_prompt,
label_name=ensureJsonString(arguments.get("label_name")),
annotation=Annotation.from_json_value(
arguments.get("annotation")),
))
raise ValueError(f"Invalid method name: '{method_name}'")
def try_from_url(cls, url: Url) -> "HttpFs | UsageError":
if url.protocol not in (Protocol.HTTP, Protocol.HTTPS):
return UsageError(f"Bad url for HttpFs: {url}")
return HttpFs(read_url=url)
def parse_url(url: str) -> Union[Url, UsageError]:
parsed_url = Url.parse(url)
if parsed_url is None:
return UsageError(f"Bad url: {url}")
return parsed_url
def from_environment(cls) -> "UserToken | UsageError":
access_token = os.environ.get(cls.ENV_VAR_NAME)
if access_token is None:
return UsageError(f"Environment variable '{cls.ENV_VAR_NAME}' is not set")
return UserToken(access_token=access_token)