1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791
|
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 h5py
import numpy as np
from numpy.typing import NDArray
import dill
import cloudpickle
from bumps import __version__
from bumps.serialize import serialize, deserialize
from bumps.serialize import serialize_bytes, deserialize_bytes
from bumps.util import get_libraries
from .logger import logger
from .fit_options import lookup_fitter, DEFAULT_FITTER_ID
if TYPE_CHECKING:
import bumps
import bumps.fitproblem
import bumps.dream.state
from .webserver import TopicNameType
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"
@dataclass(frozen=True)
class UNDEFINED_TYPE:
pass
UNDEFINED = UNDEFINED_TYPE()
def now_string():
return f"{datetime.now().timestamp():.6f}"
def serialize_problem(problem: "bumps.fitproblem.FitProblem", method: SERIALIZERS) -> 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}")
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}")
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}")
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}")
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)
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
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)
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
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
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
def write_json(group: "Group", name: str, data):
dset = write_string(group, name, json.dumps(data))
return dset
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
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)
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
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
def write_version(group: "Group", version: Tuple[int]):
version_string = ".".join(str(v) for v in version)
group.attrs["version"] = version_string
class StringAttribute:
@classmethod
def serialize(value, obj=None):
return json.dumps(value)
@classmethod
def deserialize(value, obj=None):
return json.loads(value) if value else None
@dataclass
class ProblemState:
fitProblem: Optional["bumps.fitproblem.FitProblem"] = None
serializer: Optional[SERIALIZERS] = None
def write(self, parent: "Group"):
group = parent.require_group("problem")
write_fitproblem(group, "fitProblem", self.fitProblem, method=self.serializer)
write_string(group, "serializer", self.serializer)
write_json(group, "libraries", get_libraries(self.fitProblem))
# write_json(group, 'pathlist', self.pathlist)
# write_string(group, 'filename', self.filename)
def read(self, parent: "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')
class HistoryItem:
problem: ProblemState
fitting: Optional["FitResult"]
timestamp: str
label: str
chisq_str: str
keep: bool
class History:
store: Dict[str, HistoryItem]
def __init__(self):
self.store = {}
def get_item(self, name: Union[str, UNDEFINED_TYPE, None], default=None):
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
def write(self, parent: "Group", include_fit_state=True):
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
def read(self, parent: "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
def remove_item(self, name: str):
self.store.pop(name)
def prune(self, target_length: int):
# 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):
name = timestamp
counter = 1
while name in self.store:
name = f"{timestamp}-{counter}"
counter += 1
return name
def add_item(self, item: HistoryItem, target_length: int):
self.prune(target_length)
stored_name = self._get_unique_name(item.timestamp)
self.store[stored_name] = item
return stored_name
def list(self):
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()
]
def set_keep(self, name: str, keep: bool):
self.store[name].keep = keep
def update_label(self, name: str, label: str):
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?
@dataclass
class FitResult:
# 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 = DEFAULT_FITTER_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"
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."""
def write(self, parent: "Group", include_fit_state=True):
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)
def read(self, parent: "Group"):
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 = DEFAULT_FITTER_ID
self.fit_state = None
class State:
# These attributes are ephemeral, not to be serialized/stored:
app_name: str = "bumps"
app_version: str = __version__
client_path: Path = Path(__file__).parent.parent / "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
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()
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
def get_history(self):
return dict(problem_history=self.history.list())
def remove_history_item(self, name: str):
if self.shared.active_history == name:
self.shared.active_history = None
self.history.remove_item(name)
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)
def reset_fitstate(self, copy: bool = False):
"""
Unlink the fitting state from a history item:
(this action to be taken 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
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
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
def autosave(self):
if self.shared.autosave_session:
self.save()
def save(self):
if self.shared.session_output_file not in [None, UNDEFINED]:
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_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.problem.write(root_group)
history_group = self.history.write(root_group)
if self.shared.active_history is not None:
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)
shutil.move(tmp_name, session_fullpath)
os.chmod(session_fullpath, 0o644)
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}")
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}")
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}")
def write_topics(self, parent: "Group"):
group = parent.require_group("topics")
for topic, messages in self.topics.items():
write_json(group, topic, list(messages))
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)
def get_last_message(self, topic: "TopicNameType"):
return self.topics[topic][-1] if len(self.topics[topic]) > 0 else None
def cleanup(self):
pass
def __del__(self):
self.cleanup()
async def async_cleanup(self):
self.cleanup()
def __exit__(self, exc_type, exc_value, exc_traceback):
self.cleanup()
class ActiveFit(TypedDict):
fitter_id: str
options: Dict[str, Any]
num_steps: int
step: int
chisq: str
value: float
class FileInfo(TypedDict):
filename: str
pathlist: List[str]
class UncertaintyAvailable(TypedDict):
available: bool
num_points: int
class CustomPlotsAvailable(TypedDict):
parameter_based: bool
uncertainty_based: bool
Timestamp = NewType("Timestamp", str)
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
@dataclass
class SharedState:
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))
async def set(self, name, value):
super().__setattr__(name, value)
for callback in self._notification_callbacks.values():
await callback(name, value)
async def notify(self, name, value=None):
value = await self.get(name)
for callback in self._notification_callbacks.values():
await callback(name, value)
async def get(self, name):
return getattr(self, name, UNDEFINED)
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)
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)
|