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190 lines (160 loc) · 6.27 KB
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from __future__ import annotations
import os
import socket
from functools import lru_cache
from pathlib import Path
from typing import Dict, List, Literal, Optional, Union
import yaml
from backports.entry_points_selectable import entry_points
from pydantic import BaseModel, BaseSettings, Extra, validator
class MachineConfig(BaseModel, extra=Extra.allow): # type: ignore
acquisition_software: List[str]
calibrations: Dict[str, Dict[str, Union[dict, float]]]
data_directories: List[Path]
rsync_basepath: Path
default_model: Path
display_name: str = ""
instrument_name: str = ""
image_path: Optional[Path] = None
software_versions: Dict[str, str] = {}
external_executables: Dict[str, str] = {}
external_executables_eer: Dict[str, str] = {}
external_environment: Dict[str, str] = {}
rsync_module: str = ""
create_directories: list[str] = ["atlas"]
analyse_created_directories: List[str] = []
gain_reference_directory: Optional[Path] = None
eer_fractionation_file_template: str = ""
processed_directory_name: str = "processed"
gain_directory_name: str = "processing"
node_creator_queue: str = "node_creator"
superres: bool = False
camera: str = "FALCON"
data_required_substrings: Dict[str, Dict[str, List[str]]] = {}
allow_removal: bool = False
data_transfer_enabled: bool = True
processing_enabled: bool = True
machine_override: str = ""
processed_extra_directory: str = ""
plugin_packages: Dict[str, Path] = {}
software_settings_output_directories: Dict[str, List[str]] = {}
process_by_default: bool = True
recipes: Dict[str, str] = {
"em-spa-bfactor": "em-spa-bfactor",
"em-spa-class2d": "em-spa-class2d",
"em-spa-class3d": "em-spa-class3d",
"em-spa-preprocess": "em-spa-preprocess",
"em-spa-refine": "em-spa-refine",
"em-tomo-preprocess": "em-tomo-preprocess",
"em-tomo-align": "em-tomo-align",
}
# Find and download upstream directories
upstream_data_directories: List[Path] = [] # Previous sessions
upstream_data_download_directory: Optional[Path] = None # Set by microscope config
upstream_data_tiff_locations: List[str] = ["processed"] # Location of CLEM TIFFs
model_search_directory: str = "processing"
initial_model_search_directory: str = "processing/initial_model"
failure_queue: str = ""
instrument_server_url: str = "http://localhost:8001"
frontend_url: str = "http://localhost:3000"
murfey_url: str = "http://localhost:8000"
rsync_url: str = ""
security_configuration_path: Optional[Path] = None
notifications_queue: str = "pato_notification"
def from_file(config_file_path: Path, instrument: str = "") -> Dict[str, MachineConfig]:
with open(config_file_path, "r") as config_stream:
config = yaml.safe_load(config_stream)
return {
i: MachineConfig(**config[i])
for i in config.keys()
if not instrument or i == instrument
}
class Security(BaseModel):
murfey_db_credentials: Path
crypto_key: str
auth_key: str = ""
auth_algorithm: str = ""
auth_url: str = ""
sqlalchemy_pooling: bool = True
allow_origins: List[str] = ["*"]
session_validation: str = ""
session_token_timeout: Optional[int] = None
auth_type: Literal["password", "cookie"] = "password"
cookie_key: str = ""
rabbitmq_credentials: Path
feedback_queue: str = "murfey_feedback"
graylog_host: str = ""
graylog_port: Optional[int] = None
ispyb_credentials: Optional[Path] = None
@validator("graylog_port")
def check_port_present_if_host_is(
cls, v: Optional[int], values: dict, **kwargs
) -> Optional[int]:
if values["graylog_host"] and v is None:
raise ValueError("The Graylog port must be set if the Graylog host is")
return v
def security_from_file(config_file_path: Path) -> Security:
with open(config_file_path, "r") as config_stream:
config = yaml.safe_load(config_stream)
return Security(**config)
class Settings(BaseSettings):
murfey_machine_configuration: str = ""
murfey_security_configuration: str = ""
settings = Settings()
@lru_cache()
def get_hostname():
return socket.gethostname()
def get_microscope(machine_config: MachineConfig | None = None) -> str:
if machine_config:
microscope_name = machine_config.machine_override or os.getenv("BEAMLINE", "")
else:
microscope_name = os.getenv("BEAMLINE", "")
return microscope_name
@lru_cache(maxsize=1)
def get_security_config() -> Security:
if settings.murfey_security_configuration:
return security_from_file(Path(settings.murfey_security_configuration))
if settings.murfey_machine_configuration and os.getenv("BEAMLINE"):
machine_config = get_machine_config(instrument_name=os.getenv("BEAMLINE"))[
os.getenv("BEAMLINE", "")
]
if machine_config.security_configuration_path:
return security_from_file(machine_config.security_configuration_path)
return Security(
rabbitmq_credentials="",
session_validation="",
murfey_db_credentials="",
crypto_key="",
auth_key="",
auth_algorithm="",
sqlalchemy_pooling=True,
)
@lru_cache(maxsize=1)
def get_machine_config(instrument_name: str = "") -> Dict[str, MachineConfig]:
machine_config = {
"": MachineConfig(
acquisition_software=[],
calibrations={},
data_directories=[],
rsync_basepath=Path("dls/tmp"),
murfey_db_credentials="",
default_model="/tmp/weights.h5",
)
}
if settings.murfey_machine_configuration:
microscope = instrument_name
machine_config = from_file(
Path(settings.murfey_machine_configuration), microscope
)
return machine_config
def get_extended_machine_config(
extension_name: str, instrument_name: str = ""
) -> Optional[BaseModel]:
machine_config = get_machine_config(instrument_name=instrument_name).get(
instrument_name or get_microscope()
)
if not machine_config:
return None
model = entry_points().select(group="murfey.config", name=extension_name)[0].load()
data = getattr(machine_config, extension_name, {})
return model(**data)