"""
Manage bumps server state and session files.
"""
import asyncio
import threading
from copy import deepcopy
from datetime import datetime
from typing import (
TYPE_CHECKING,
Awaitable,
Callable,
Optional,
Dict,
Tuple,
List,
NewType,
TypedDict,
Any,
Literal,
Union,
)
from collections import deque
from dataclasses import dataclass, field, fields
import json
import shutil
import os
import tempfile
from pathlib import Path
import pickle
import warnings
import io
import base64
import numpy as np
from numpy.typing import NDArray
import dill
import cloudpickle
try:
import h5py
except ImportError:
warnings.warn("h5py is not available; can't access session files")
from bumps import __version__
from .fitters import FIT_DEFAULT_ID
from .logger import logger
from .options import lookup_fitter
from .serialize import serialize, deserialize
from .serialize import serialize_bytes, deserialize_bytes
from .util import get_libraries
if TYPE_CHECKING:
import bumps
from .fit_thread import FitThread
from h5py import Group
from bumps.mapper import BaseMapper
SESSION_FILE_NAME = "session.h5"
ARRAY_COMPRESSION = 5
COMPRESSION = 9
# MAX_PROBLEM_SIZE = 100 * 1024 * 1024 # 100 MBi problem max size [unused]
SERIALIZERS = Literal["dataclass", "pickle", "cloudpickle", "dill"]
SERIALIZER_EXTENSIONS = {"dataclass": "json", "cloudpickle": "cloudpickle", "pickle": "pickle", "dill": "dill"}
DEFAULT_SERIALIZER: SERIALIZERS = "dataclass"
TopicNameType = Literal[
"log", # log messages
]
[docs]
@dataclass(frozen=True)
class UNDEFINED_TYPE:
"""Type for the UNDEFINED singleton."""
pass
UNDEFINED = UNDEFINED_TYPE()
"""
Singleton value which is undefined. This is different from a value which is present but None."
"""
[docs]
def now_string():
return f"{datetime.now().timestamp():.6f}"
[docs]
def serialize_problem(problem: "bumps.fitproblem.FitProblem", method: SERIALIZERS | None = None) -> Union[str, bytes]:
if method == "dataclass":
return json.dumps(serialize(problem))
elif method == "pickle":
return serialize_bytes(pickle.dumps(problem))
elif method == "dill":
return serialize_bytes(dill.dumps(problem))
elif method == "cloudpickle":
return serialize_bytes(cloudpickle.dumps(problem))
else:
raise ValueError(f"Unknown serialization method: {method}")
[docs]
def deserialize_problem(serialized: str, method: SERIALIZERS) -> "bumps.fitproblem.FitProblem":
if method == "dataclass":
serialized_dict = json.loads(serialized)
return deserialize(serialized_dict, migration=True)
elif method == "pickle":
return pickle.loads(deserialize_bytes(serialized))
elif method == "dill":
return dill.loads(deserialize_bytes(serialized))
elif method == "cloudpickle":
return cloudpickle.loads(deserialize_bytes(serialized))
else:
raise ValueError(f"Unknown serialization method: {method}")
[docs]
def serialize_problem_bytes(problem: "bumps.fitproblem.FitProblem", method: SERIALIZERS) -> bytes:
if method == "dataclass":
return json.dumps(serialize(problem)).encode()
elif method == "pickle":
return pickle.dumps(problem)
elif method == "dill":
return dill.dumps(problem)
elif method == "cloudpickle":
return cloudpickle.dumps(problem)
else:
raise ValueError(f"Unknown serialization method: {method}")
[docs]
def deserialize_problem_bytes(serialized: bytes, method: SERIALIZERS) -> "bumps.fitproblem.FitProblem":
if method == "dataclass":
serialized_dict = json.loads(serialized.decode())
return deserialize(serialized_dict, migration=True)
elif method == "pickle":
return pickle.loads(serialized)
elif method == "dill":
return dill.loads(serialized)
elif method == "cloudpickle":
return cloudpickle.loads(serialized)
else:
raise ValueError(f"Unknown serialization method: {method}")
[docs]
def write_bytes(group: "Group", name: str, data: bytes):
saved_data = [data] if data is not None else []
return group.create_dataset(name, data=np.void(saved_data), compression=COMPRESSION)
[docs]
def read_bytes(group: "Group", name: str):
if name not in group:
return UNDEFINED
raw_data = group[name][()]
size = raw_data.size
if size is not None and size > 0:
return raw_data[0].tobytes().rstrip(b"\x00")
else:
return None
[docs]
def write_string(group: "Group", name: str, data: str, encoding="utf-8"):
if data is None:
return group.create_dataset(name, data="")
# saved_data = np.bytes_([data]) if data is not None else []
dtype = h5py.string_dtype(encoding=encoding, length=len(data))
saved_data = np.array([data], dtype=dtype) if data is not None else []
# print(f"write_string {dtype=}")
return group.create_dataset(name, data=saved_data, compression=COMPRESSION, dtype=dtype)
[docs]
def read_string(group: "Group", name: str):
if name not in group:
return UNDEFINED
raw_data = group[name][()]
size = raw_data.size
if size is not None and size > 0:
return np.bytes_(raw_data.flat[0]).decode()
else:
return None
[docs]
def write_fitproblem(group: "Group", name: str, fitProblem: "bumps.fitproblem.FitProblem", method: SERIALIZERS):
encoding = str if method == "dataclass" else bytes
if encoding is bytes:
serialized = serialize_problem_bytes(fitProblem, method) if fitProblem is not None else None
dset = write_bytes(group, name, serialized)
else:
serialized = serialize_problem(fitProblem, method) if fitProblem is not None else None
dset = write_string(group, name, serialized)
return dset
[docs]
def read_fitproblem(group: "Group", name: str, method: SERIALIZERS) -> "bumps.fitproblem.FitProblem":
if name not in group:
return UNDEFINED
if group[name].dtype.kind == "V":
# Old encoding stored bytes directly
serialized = read_bytes(group, name)
fitProblem = deserialize_problem_bytes(serialized, method) if serialized is not None else None
else:
# New encode uses base64 to encode bytes to string
serialized = read_string(group, name)
fitProblem = deserialize_problem(serialized, method) if serialized is not None else None
return fitProblem
[docs]
def write_json(group: "Group", name: str, data):
dset = write_string(group, name, json.dumps(data))
return dset
[docs]
def read_json(group: "Group", name: str):
if name not in group:
return UNDEFINED
serialized = read_string(group, name)
try:
# if JSON fails to load, then just return None
result = json.loads(serialized) if serialized is not None else None
except Exception:
result = None
return result
[docs]
def write_ndarray(group: "Group", name: str, data: Optional[NDArray]):
if data is None:
data = []
dtype = "d"
compression = 0
else:
dtype = data.dtype
compression = ARRAY_COMPRESSION
return group.create_dataset(name, data=data, dtype=dtype, compression=compression)
[docs]
def read_ndarray(group: "Group", name: str):
if name not in group:
return UNDEFINED
raw_data = group[name][()]
size = raw_data.size
if size is not None and size > 0:
return raw_data
else:
return None
[docs]
def read_version(group: "Group"):
version_string = group.attrs.get("version", "0.0")
version = tuple(int(v) for v in version_string.split("."))
return version
[docs]
def write_version(group: "Group", version: Tuple[int]):
version_string = ".".join(str(v) for v in version)
group.attrs["version"] = version_string
[docs]
class StringAttribute:
[docs]
@classmethod
def serialize(value, obj=None):
return json.dumps(value)
[docs]
@classmethod
def deserialize(value, obj=None):
return json.loads(value) if value else None
[docs]
@dataclass
class ProblemState:
"""
Stores the state of a Bumps fit problem.
Attributes:
fitProblem: The Bumps fit problem instance.
serializer: The serialization method to use.
"""
fitProblem: Optional["bumps.fitproblem.FitProblem"] = None
serializer: Optional[SERIALIZERS] = None
[docs]
def write(self, parent: "Group"):
"""
Write the problem state to an HDF5 group.
Args:
parent: The parent HDF5 group.
"""
group = parent.require_group("problem")
# Try the specified serializer, but fall back to cloudpickle if it fails
serializer = self.serializer
try:
write_fitproblem(group, "fitProblem", self.fitProblem, method=serializer)
except Exception:
if serializer != "cloudpickle":
serializer = "cloudpickle"
write_fitproblem(group, "fitProblem", self.fitProblem, method=serializer)
else:
raise
write_string(group, "serializer", serializer)
write_json(group, "libraries", get_libraries(self.fitProblem))
# write_json(group, 'pathlist', self.pathlist)
# write_string(group, 'filename', self.filename)
[docs]
def read(self, parent: "Group"):
"""
Read the problem state from an HDF5 group.
Args:
parent: The parent HDF5 group.
"""
group = parent["problem"]
self.serializer = read_string(group, "serializer")
self.fitProblem = read_fitproblem(group, "fitProblem", method=self.serializer)
# self.pathlist = read_json(group, 'pathlist')
# self.filename = read_string(group, 'filename')
[docs]
class HistoryItem:
"""
Represents a single item in the fit history.
Attributes:
problem: The problem state at this point in history.
fitting: The fit result associated with this history item.
timestamp: The timestamp when this history item was created.
label: A label for this history item.
chisq_str: A string representation of the chi-squared value.
keep: Whether to permanently keep this history item, or drop it when too many history items are saved.
"""
problem: ProblemState
fitting: Optional["FitResult"]
timestamp: str
label: str
chisq_str: str
keep: bool
[docs]
class History:
"""
Manages the history of fit results.
Attributes:
store: A dictionary mapping history item names to HistoryItem instances.
"""
store: Dict[str, HistoryItem]
def __init__(self):
"""
Initialize the History instance.
"""
self.store = {}
[docs]
def get_item(self, name: Union[str, UNDEFINED_TYPE, None], default=None):
"""
Get a history item by name.
Args:
name: The name of the history item.
default: The default value to return if the item is not found.
Returns:
The history item, or the default value if not found.
"""
return self.store.get(name, default)
# def get_item(self, timestamp: Union[str, UNDEFINED_TYPE, None], default=None):
# for item in self.store:
# if item.timestamp == timestamp:
# return item
# return default
[docs]
def write(self, parent: "Group", include_fit_state=True):
"""
Write the history to an HDF5 group.
Args:
parent: The parent HDF5 group.
include_fit_state: Whether to include the fit state in the history.
"""
group = parent.require_group("problem_history")
for name, item in self.store.items():
problem = item.problem
fitting = item.fitting
item_group = group.require_group(name)
problem.write(item_group)
fitting.write(item_group, include_fit_state=include_fit_state)
item_group.attrs["chisq"] = item.chisq_str
item_group.attrs["label"] = item.label
item_group.attrs["keep"] = item.keep
item_group.attrs["timestamp"] = item.timestamp
return group
[docs]
def read(self, parent: "Group"):
"""
Read the history from an HDF5 group.
Args:
parent: The parent HDF5 group.
"""
group = parent.get("problem_history", {})
self.store = {}
for name in group:
item = HistoryItem()
item_group = group[name]
item.problem = ProblemState()
item.fitting = FitResult()
item.problem.read(item_group)
item.fitting.read(item_group)
item.label = item_group.attrs["label"]
item.chisq_str = item_group.attrs["chisq"]
# keep is a boolean, but h5py returns np.bool_ which is not JSON serializable
item.keep = bool(item_group.attrs["keep"])
# if there is no timestamp attribute, then it was created before we had a separate name
item.timestamp = item_group.attrs.get("timestamp", name)
self.store[name] = item
[docs]
def remove_item(self, name: str):
"""
Remove a history item by name.
Args:
name: The name of the history item to remove.
"""
self.store.pop(name)
[docs]
def prune(self, target_length: int):
"""
Prune the history to a target length.
Args:
target_length: The desired length of the history.
"""
# remove oldest items with keep=False until the length is target_length
num_to_remove = len(self.store) - target_length
if num_to_remove <= 0:
return
names_to_remove = []
for name, item in self.store.items():
if not item.keep:
names_to_remove.append(name)
num_to_remove -= 1
if num_to_remove == 0:
break
for name in reversed(names_to_remove):
self.store.pop(name)
def _get_unique_name(self, timestamp: str):
"""
Generate a unique name for a history item based on its timestamp.
Args:
timestamp: The timestamp of the history item.
Returns:
A unique name for the history item.
"""
name = timestamp
counter = 1
while name in self.store:
name = f"{timestamp}-{counter}"
counter += 1
return name
[docs]
def add_item(self, item: HistoryItem, target_length: int):
"""
Add a new history item to the history.
Args:
item: The history item to add.
target_length: The maximum length of the history.
Returns:
The name of the added history item.
"""
self.prune(target_length)
stored_name = self._get_unique_name(item.timestamp)
self.store[stored_name] = item
return stored_name
[docs]
def list(self):
"""
List the history items.
Returns:
A list of dictionaries containing information about each history item.
"""
return [
dict(
timestamp=item.timestamp,
label=item.label,
chisq_str=item.chisq_str,
keep=item.keep,
has_convergence=(item.fitting.convergence is not None),
has_uncertainty=hasattr(item.fitting.fit_state, "draw"),
name=name,
)
for name, item in self.store.items()
]
[docs]
def set_keep(self, name: str, keep: bool):
"""
Set whether to keep a history item.
Args:
name: The name of the history item.
keep: Whether to keep the history item.
"""
self.store[name].keep = keep
[docs]
def update_label(self, name: str, label: str):
"""
Update the label of a history item.
Args:
name: The name of the history item.
label: The new label for the history item.
"""
self.store[name].label = label
# TODO: Where do derived expressions and nuisance parameters live? problem or results?
# TODO: Use uncertainties package with cov for derived parameters from amoeba
# TODO: Showing error table requires parameter labels; get them from fit problem?
[docs]
@dataclass
class FitResult:
"""
Stores the result of a fit operation.
Attributes:
method: The fitting method used.
options: Options used to run the fitters.
convergence: List of quantiles for each fit iteration.
fit_state: Fit state for resume, and for sampling from Monte Carlo fitters.
"""
# TODO: chisq, dof, and {model: (chisq, dof)} separate from fx=nllf+constraints
# TODO: Model specific dof is difficult because of shared parameters. Just use #points?
# TODO: Rename fitter_id to method throughout?
# TODO: Should the best point include all available parameters in the model (fitted and fixed)?
# TODO: Save labels in fit results so we don't need to walk the problem definition?
# TODO: Save the initial value in the problem so users can reset after a fit?
# TODO: Add the following to FitResult:
method: str = FIT_DEFAULT_ID # => shared.selected_fitter
"""Fitting method"""
options: Dict[str, Any] = field(default_factory=dict) # => shared.fitter_settings
"""Options used to run the fitters"""
# x0: NDArray
# """Initial value"""
# x: NDArray # Currently resides in problem definition
# """Best point"""
# TODO: include dx, cov and entropy?
# TODO: these are odd men out: they are only available on completion
# dx: Optional[NDArray]
# """Uncertainty from derivative if fit is complete"""
# fx: float # nllf maybe including constraints and penalties
# """Best value"""
# TODO: Maybe add maxsteps (it could be guessed from options)
# step: int # Should equal the length of the population, so unneeded
# """Number of optimizer steps taken"""
# run_time: float # seconds
# """Number of seconds that the fit was run before completion/abort/timeout"""
# cpu_hours: float
# """Total number of cpu hours for the fit (=num_processors*wall_time/3600)"""
# TODO: display completion status in history tab
# status: str
# """Fit status: active, converged, timeout, maxiter, abort, failed"
# TODO: Include a step number column in convergence?
convergence: Optional[List] = None
"""List of best or (best, min, -1sigma, median, +1sigma, max) for the population at each step of the fit."""
fit_state: Any = None
"""Fit state for resume, and for sampling from Monte Carlo fitters."""
[docs]
def write(self, parent: "Group", include_fit_state=True):
"""
Write the fit result to an HDF5 group.
Args:
parent: The parent HDF5 group.
include_fit_state: Whether to include the fit state in the output.
"""
fitting_group = parent.require_group("fitting")
write_version(fitting_group, (1, 0))
write_string(fitting_group, "method", self.method)
write_json(fitting_group, "options", self.options)
write_ndarray(fitting_group, "convergence", self.convergence)
if self.fit_state is not None and include_fit_state:
fitter = lookup_fitter(self.method)
if hasattr(fitter, "h5dump"):
state_group = fitting_group.require_group("fit_state")
fitter.h5dump(state_group, self.fit_state)
[docs]
def read(self, parent: "Group"):
"""
Read the fit result from an HDF5 group.
Args:
parent: The parent HDF5 group containing the entries.
"""
fitting_group = parent["fitting"]
version = read_version(fitting_group)
# Note: fitter h5load needs to deal with its own versioning
if version == (1, 0):
self.method = read_string(fitting_group, "method")
self.options = read_json(fitting_group, "options")
self.convergence = read_ndarray(fitting_group, "convergence")
if "fit_state" in fitting_group:
state_group = fitting_group["fit_state"]
fitter = lookup_fitter(self.method) # shouldn't raise ValueError
self.fit_state = fitter.h5load(state_group)
else:
self.fit_state = None
else:
# Pre 1.0 fit result
self.convergence = read_ndarray(fitting_group, "population")
self.options = {} # options
if "uncertainty_state" in fitting_group:
self.method = "dream"
state_group = fitting_group["uncertainty_state"]
fitter = lookup_fitter(self.method)
self.fit_state = fitter.h5load(state_group)
else:
self.method = FIT_DEFAULT_ID
self.fit_state = None
[docs]
class State:
"""
Manage the state for a bumps server session.
There is a primary state object for the current webview instance defined
in bumps.api. Temporary state objects are created to save
the temporary fit results as a session file.
"""
# These attributes are ephemeral, not to be serialized/stored:
app_name: str = "bumps"
app_version: str = __version__
client_path: Path = Path(__file__).parent / "webview" / "client"
hostname: str
port: int
parallel: int = 0
fit_thread: Optional["FitThread"] = None
fit_abort_event: threading.Event
"""Cleared before the fit and set on Stop button or Ctrl-C to end the fit."""
fit_complete_event: asyncio.Event
"""Cleared before the fit starts and set when the fit is complete and saved."""
# fit_complete_future: asyncio.Future
shutdown_on_fit_complete: bool = False
"""Used to implement the --exit option to halt server on completion."""
# fit_enabled: Event
calling_loop: Optional[asyncio.AbstractEventLoop] = None
base_path: str = ""
console_update_interval: int = 0 # seconds (float would work too, but unnecessary)
# State to be stored:
problem: ProblemState
fitting: FitResult
history: History
topics: Dict[TopicNameType, deque[dict]]
shared: "SharedState"
mapper: Optional[BaseMapper] = None
def __init__(self):
self.problem = ProblemState()
self.fitting = FitResult()
self.history = History()
self.fit_abort_event = threading.Event() # initially unset
self.fit_complete_event = asyncio.Event()
self.fit_complete_event.set() # The program starts out not waiting for a fit
self.topics = {
"log": deque([]),
}
self.shared = SharedState()
def __enter__(self):
return self
[docs]
def setup_backing(self, session_file_name: str, session_pathlist: List[str], read_only: bool = False):
if not read_only:
self.shared.session_output_file = dict(filename=session_file_name, pathlist=session_pathlist)
if session_file_name is not None:
full_path = Path(*session_pathlist) / session_file_name
if full_path.exists():
self.read_session_file(full_path)
else:
self.save()
[docs]
def save_to_history(self, label: str, keep: bool = False) -> str:
if self.problem.fitProblem is None:
return
item = HistoryItem()
item.problem = deepcopy(self.problem)
# Creates a reference to the current fit_state, not a copy
# When a new fit is started, self.fitting is reset to a new FitResult
# but the handle to the current fit_state is kept in the history item.
item.fitting = self.fitting
item.timestamp = str(datetime.now())
item.label = label
item.chisq_str = item.problem.fitProblem.chisq_str()
item.keep = keep
stored_name = self.history.add_item(item, self.shared.autosave_history_length - 1)
self.shared.updated_history = now_string()
return stored_name
[docs]
def get_history(self):
return dict(problem_history=self.history.list())
[docs]
def remove_history_item(self, name: str):
if self.shared.active_history == name:
self.shared.active_history = None
self.history.remove_item(name)
[docs]
def reload_history_item(self, name: str):
item = self.history.get_item(name, None)
if item is not None:
self.problem = deepcopy(item.problem)
self.fitting = item.fitting
self.shared.active_history = name
self.shared.updated_model = now_string()
self.shared.updated_parameters = now_string()
self.shared.custom_plots_available = get_custom_plots_available(self.problem.fitProblem)
# These are called only to trigger the update signals...
# the convergence and fit_state will be unchanged by the calls below.
self.set_convergence(item.fitting.convergence)
self.set_fit_state(item.fitting.fit_state, item.fitting.method)
[docs]
def set_active(self, problem, fitting: FitResult | None = None):
self.problem = ProblemState(problem) # default serializer
self.fitting = fitting
self.set_convergence(fitting.convergence)
self.set_fit_state(fitting.fit_state, fitting.method)
[docs]
def reset_fitstate(self, copy: bool = False):
"""
Unlink the fitting state from a history item.
This action occurs when fitProblem object is modified so that
it is no longer compatible with fit results.
"""
if copy:
self.fitting = deepcopy(self.fitting)
# print(f"reset_fitstate {copy}: keeping {self.fitting.method} with {self.fitting.fit_state} and convergence={self.fitting.convergence is not None}")
else:
# print(f"reset_fitstate {copy}: keeping {self.fitting.method}")
self.fitting = FitResult(
method=self.shared.selected_fitter,
options=self.shared.fitter_settings[self.shared.selected_fitter]["settings"],
)
# These are called only to trigger the update signals...
self.set_convergence(None)
self.set_fit_state(None)
self.shared.active_history = None
[docs]
def set_convergence(self, convergence):
# print("setting convergence", convergence is not None)
self.fitting.convergence = convergence
self.shared.updated_convergence = now_string()
self.shared.convergence_available = convergence is not None
[docs]
def set_fit_state(self, fit_state, method=None):
self.fitting.fit_state = fit_state
self.shared.updated_uncertainty = now_string()
self.shared.uncertainty_available = dict(
available=hasattr(fit_state, "draw"),
num_points=getattr(fit_state, "Nsamples", 0),
)
self.shared.resumable = method if fit_state is not None else None
[docs]
def autosave(self):
if self.shared.autosave_session:
self.save()
[docs]
def save(self):
if self.shared.session_output_file not in (None, UNDEFINED):
# print(f"Saving session file (pid={os.getpid()})...")
pathlist = self.shared.session_output_file["pathlist"]
filename = self.shared.session_output_file["filename"]
full_path = Path(*pathlist) / filename
self.write_session_file(full_path)
def _write_session(self, root_group: h5py.File):
self.problem.write(root_group)
history_group = self.history.write(root_group)
if self.shared.active_history not in (None, UNDEFINED):
active_history_group = history_group.get(self.shared.active_history)
# make a hard link instead of writing the fitting state
root_group["fitting"] = active_history_group["fitting"]
else:
# no active history item, so write the fitting state
self.fitting.write(root_group)
self.write_topics(root_group)
self.shared.write(root_group)
[docs]
def write_session_file(self, session_fullpath: str):
# Session filename is assumed to be a full path
tmp_fd, tmp_name = tempfile.mkstemp(
dir=Path(session_fullpath).parent, prefix=Path(session_fullpath).name, suffix=".tmp"
)
with os.fdopen(tmp_fd, "w+b") as output_file:
with h5py.File(output_file, "w") as root_group:
self._write_session(root_group)
shutil.move(tmp_name, session_fullpath)
os.chmod(session_fullpath, 0o644)
[docs]
def get_session_bytes(self) -> bytes:
# Get session as bytes
with h5py.File("in_memory.h5", mode="w", driver="core", backing_store=False) as root_group:
self._write_session(root_group)
root_group.flush()
return root_group.id.get_file_image()
[docs]
def get_session_bytestring(self) -> str:
# Get session as byte string
return base64.b64encode(self.get_session_bytes()).decode("utf-8")
[docs]
def read_session_bytestring(self, session_bytestring: str, read_problem: bool = True, read_fitstate: bool = True):
# load session from byte string
bio = io.BytesIO(base64.b64decode(session_bytestring))
self.read_session_file(bio, read_problem, read_fitstate)
[docs]
def read_session_file(self, session_fullpath: str, read_problem: bool = True, read_fitstate: bool = True):
try:
with h5py.File(session_fullpath, "r") as root_group:
if read_problem:
self.problem.read(root_group)
self.history.read(root_group)
self.shared.read(root_group)
if read_fitstate:
active_item = self.history.get_item(self.shared.active_history, None)
if active_item is not None:
self.fitting = active_item.fitting
else:
self.fitting.read(root_group)
self.read_topics(root_group)
except Exception as e:
# logger.exception(e)
logger.warning(f"could not load session file {session_fullpath} because of {e}")
[docs]
def read_problem_from_session(self, session_fullpath: str):
try:
with h5py.File(session_fullpath, "r") as root_group:
self.problem.read(root_group)
except Exception as e:
# logger.exception(e)
logger.warning(f"could not load fitProblem from {session_fullpath} because of {e}")
[docs]
def read_fitstate_from_session(self, session_fullpath: str):
try:
with h5py.File(session_fullpath, "r") as root_group:
self.fitting.read(root_group)
except Exception as e:
# logger.exception(e)
logger.warning(f"could not load fit state from {session_fullpath} because of {e}")
[docs]
def write_topics(self, parent: "Group"):
group = parent.require_group("topics")
for topic, messages in self.topics.items():
write_json(group, topic, list(messages))
[docs]
def read_topics(self, parent: "Group"):
group = parent.require_group("topics")
for topic in group:
topic_data = read_json(group, topic)
topic_data = np.array([topic_data]).flatten()
if topic_data is not None and topic in self.topics:
self.topics[topic].extend(topic_data)
[docs]
def get_last_message(self, topic: TopicNameType):
return self.topics[topic][-1] if len(self.topics[topic]) > 0 else None
[docs]
def cleanup(self):
pass
def __del__(self):
self.cleanup()
[docs]
async def async_cleanup(self):
self.cleanup()
def __exit__(self, exc_type, exc_value, exc_traceback):
self.cleanup()
[docs]
class ActiveFit(TypedDict):
"""
Information about the currently active fit. This is used
to share information between the webview client and the server.
"""
fitter_id: str
options: Dict[str, Any]
num_steps: int
step: int
chisq: str
value: float
[docs]
class FileInfo(TypedDict):
"""
Webview client treats a path as a filename plus a list of leading
folder names.
"""
filename: str
pathlist: List[str]
[docs]
class UncertaintyAvailable(TypedDict):
"""
Whether or not the fit results contain MCMC samples.
"""
available: bool
num_points: int
[docs]
class CustomPlotsAvailable(TypedDict):
"""
Whether or not the current model has custom plots.
If *parameter_based* is True then new plots need to be generated
whenever the parameter values are updated.
If *uncertainty_based* is True then new plots need to be generated
whenever the MCMC sample is updated.
"""
parameter_based: bool
uncertainty_based: bool
Timestamp = NewType("Timestamp", str)
"""ISO8601 time string"""
[docs]
def get_custom_plots_available(problem: "bumps.fitproblem.FitProblem"):
output = {"parameter_based": False, "uncertainty_based": False}
for model in problem.models:
if hasattr(model, "webview_plots"):
for plot_title, plot_info in model.webview_plots.items():
if plot_info.get("change_with", None) == "uncertainty":
output["uncertainty_based"] = True
else:
output["parameter_based"] = True
return output
[docs]
@dataclass
class SharedState:
"""
Server state that automatically synchronizes with the attached clients.
"""
updated_convergence: Union[UNDEFINED_TYPE, Timestamp] = UNDEFINED
updated_uncertainty: Union[UNDEFINED_TYPE, Timestamp] = UNDEFINED
updated_parameters: Union[UNDEFINED_TYPE, Timestamp] = UNDEFINED
updated_model: Union[UNDEFINED_TYPE, Timestamp] = UNDEFINED
updated_history: Union[UNDEFINED_TYPE, Timestamp] = UNDEFINED
selected_fitter: Union[UNDEFINED_TYPE, str] = "amoeba"
fitter_settings: Union[UNDEFINED_TYPE, Dict[str, Dict]] = UNDEFINED
active_fit: Union[UNDEFINED_TYPE, ActiveFit] = UNDEFINED
model_file: Union[UNDEFINED_TYPE, FileInfo] = UNDEFINED
model_loaded: Union[UNDEFINED_TYPE, bool] = UNDEFINED
session_output_file: Union[UNDEFINED_TYPE, FileInfo] = UNDEFINED
autosave_session: bool = False
autosave_session_interval: int = 300 # seconds
autosave_history: bool = True
autosave_history_length: int = 10
uncertainty_available: Union[UNDEFINED_TYPE, UncertaintyAvailable] = UNDEFINED
convergence_available: Union[UNDEFINED_TYPE, bool] = UNDEFINED
resumable: Union[UNDEFINED_TYPE, str, None] = None
custom_plots_available: Union[UNDEFINED_TYPE, CustomPlotsAvailable] = UNDEFINED
active_history: Union[UNDEFINED_TYPE, str, None] = UNDEFINED # name of the active history item
_not_reloaded = ["active_fit", "autosave_session", "session_output_file", "_notification_callbacks"]
_notification_callbacks: Dict[str, Callable[[str, Any], Awaitable[None]]] = field(default_factory=dict)
def __setattr__(self, name: str, value):
super().__setattr__(name, value)
try:
loop = asyncio.get_running_loop()
except RuntimeError:
# no event loop running, so no need to notify
return
if hasattr(self, "_notification_callbacks"):
for callback in self._notification_callbacks.values():
loop.create_task(callback(name, value))
[docs]
async def set(self, name, value):
super().__setattr__(name, value)
for callback in self._notification_callbacks.values():
await callback(name, value)
[docs]
async def notify(self, name, value=None):
value = await self.get(name)
for callback in self._notification_callbacks.values():
await callback(name, value)
[docs]
async def get(self, name):
return getattr(self, name, UNDEFINED)
[docs]
def write(self, parent: "Group"):
group = parent.require_group("shared")
for f in fields(self):
if f.name not in self._not_reloaded:
value = getattr(self, f.name)
if value is not UNDEFINED:
write_json(group, f.name, value)
[docs]
def read(self, parent: "Group"):
group = parent.get("shared")
if group is None:
return
for f in fields(self):
if f.name not in self._not_reloaded:
value = read_json(group, f.name)
if value is not UNDEFINED:
setattr(self, f.name, value)