diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5fdeb03 --- /dev/null +++ b/.gitignore @@ -0,0 +1,15 @@ +__pycache__/ +*.py[cod] +.pytest_cache/ +.ipynb_checkpoints +debug* +log/ +diskcache_* +results_* +*.egg-info +.vscode + +# AI-assisted development notes (local working docs, not for the repo yet) +ai-docs/ + +presentations/ diff --git a/cadetgui/__init__.py b/cadetgui/__init__.py new file mode 100644 index 0000000..76de7cf --- /dev/null +++ b/cadetgui/__init__.py @@ -0,0 +1,5 @@ +"""Widget-based GUI layer on top of CADET-Process.""" + +# Version information +name = "CADET-GUI" +__version__ = "0.0.1" diff --git a/cadetgui/_record_store.py b/cadetgui/_record_store.py new file mode 100644 index 0000000..f3f0c7c --- /dev/null +++ b/cadetgui/_record_store.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +import json +from dataclasses import asdict +from pathlib import Path +from typing import List, Type, TypeVar + +__all__ = ["save_record", "load_record", "list_records", "delete_record"] + +T = TypeVar("T") + + +def _manifest_path(prefix: str, id_: str, store_dir: Path) -> Path: + return store_dir / f"{prefix}_{id_}.json" + + +def save_record(prefix: str, id_: str, state: object, store_dir: Path) -> None: + """Write one dataclass instance as `/_.json`. + + Shared by `cadetgui.run_store` and `cadetgui.parameter_history_store` -- + both are the same shape (one JSON manifest per record, a `_*` + glob to list them, newest-by-mtime first) over a different dataclass. + """ + store_dir.mkdir(parents=True, exist_ok=True) + _manifest_path(prefix, id_, store_dir).write_text(json.dumps(asdict(state), indent=2)) + + +def load_record(prefix: str, id_: str, cls: Type[T], store_dir: Path) -> T: + """Read one record by id, raising `FileNotFoundError` if it isn't there.""" + path = _manifest_path(prefix, id_, store_dir) + if not path.exists(): + raise FileNotFoundError(f"No recorded {prefix} {id_!r} in {store_dir}.") + return cls(**json.loads(path.read_text())) + + +def list_records(prefix: str, cls: Type[T], store_dir: Path) -> List[T]: + """List every recorded `_*` manifest, newest first. Skips unreadable ones.""" + paths = sorted( + store_dir.glob(f"{prefix}_*.json"), key=lambda p: p.stat().st_mtime, reverse=True + ) + records = [] + for path in paths: + try: + records.append(cls(**json.loads(path.read_text()))) + except Exception: # noqa: BLE001 + continue + return records + + +def delete_record(prefix: str, id_: str, store_dir: Path) -> bool: + """Remove one record's manifest; return whether it existed.""" + path = _manifest_path(prefix, id_, store_dir) + if not path.exists(): + return False + path.unlink() + return True diff --git a/cadetgui/assets/pid/Column.drawio.svg b/cadetgui/assets/pid/Column.drawio.svg new file mode 100644 index 0000000..aeea78a --- /dev/null +++ b/cadetgui/assets/pid/Column.drawio.svg @@ -0,0 +1,4 @@ + + + +
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 \ No newline at end of file diff --git a/cadetgui/backend_versions.py b/cadetgui/backend_versions.py new file mode 100644 index 0000000..fc8c83b --- /dev/null +++ b/cadetgui/backend_versions.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +from functools import lru_cache +from importlib import metadata +from typing import Optional + +__all__ = ["cadet_process_version", "cadet_core_version"] + + +@lru_cache(maxsize=1) +def cadet_process_version() -> Optional[str]: + """Installed CADET-Process version, or None if it cannot be determined.""" + try: + import CADETProcess + + version = getattr(CADETProcess, "__version__", None) + if version: + return str(version) + except Exception: # noqa: BLE001 + pass + try: + return metadata.version("CADET-Process") + except Exception: # noqa: BLE001 + return None + + +@lru_cache(maxsize=1) +def cadet_core_version() -> Optional[str]: + """CADET-Core version of the simulator `run_process` uses, or None if unavailable. + + Queried once (it runs `cadet-cli --version`) and cached. + """ + try: + from . import simulation + + if simulation.Cadet is None: + return None + version = simulation.Cadet().version + return str(version) if version else None + except Exception: # noqa: BLE001 + return None diff --git a/cadetgui/cadetprocessadapter.py b/cadetgui/cadetprocessadapter.py new file mode 100644 index 0000000..faec960 --- /dev/null +++ b/cadetgui/cadetprocessadapter.py @@ -0,0 +1,885 @@ +from __future__ import annotations + +import math +import re +from dataclasses import dataclass, field +from typing import Any, Callable, Collection, Dict, Literal, Mapping, Optional, Sequence + +from CADETProcess.instruments import ( + LWE, + Breakthrough, + LCFlowSheet, + PulseInjection, + Step, + StepElution, +) +from CADETProcess.processModel import ( + BindingBaseClass, + ChromatographicColumnBase, + ComponentSystem, + Cstr, + FlowSheet, + GeneralRateModel, + Inlet, + Langmuir, + Linear, + LumpedRateModelWithoutPores, + LumpedRateModelWithPores, + Outlet, + Process, + StericMassAction, + TubularReactorBase, +) + +from .parameters import get_parameters as _param_metadata_for + +Validator = Callable[[Any], None] +Transform = Callable[[Any], Any] + + +@dataclass(frozen=True) +class FieldSpec: + """Describes one form field: its kind, default, bounds, and validation.""" + + name: str + kind: Literal["float", "float_list", "bool", "text", "choice"] + label: str | None = None + default: Any = None + transform: Callable | None = None + validate: Optional[Validator] = None + min: float | None = None + max: float | None = None + units: str | None = None + component_names: tuple[str, ...] | None = None + # "choice" only: (label, value) options for the dropdown. + options: tuple[tuple[str, Any], ...] | None = None + + +def require_positive(x: Any) -> None: + """Raise ValueError unless x is a positive number.""" + if float(x) <= 0: + raise ValueError("Must be > 0.") + + +def require_finite_above(minimum: float = 0.0, *, inclusive: bool = False) -> Validator: + """Return a validator rejecting NaN/inf and values below (or at, unless inclusive) `minimum`.""" + + def _validate(x: Any) -> None: + value = float(x) + if not math.isfinite(value): + raise ValueError("Must be a finite number.") + if value < minimum or (value == minimum and not inclusive): + comparison = ">=" if inclusive else ">" + raise ValueError(f"Must be {comparison} {minimum:g}.") + + return _validate + + +def parse_float_list(v: Any) -> list[float]: + """Parse a comma/semicolon/whitespace-separated string (or sequence) of floats.""" + if isinstance(v, (list, tuple)): + return [float(x) for x in v] + s = str(v) + vals: list[float] = [] + for chunk in s.replace(";", ",").replace("\n", " ").split(","): + for tok in chunk.split(): + if tok: + vals.append(float(tok)) + return vals + + +CONCENTRATION_UNITS = r"\frac{\mathrm{mol}}{\mathrm{m}^{3}_{\mathrm{IV}}}" + +# Units: mol/m^3 interstitial (inlet.rst CONST_COEFF), s (solver.rst SECTION_TIMES), +# flow_rate m^3/s by consistency with CADET-Core's other flow-rate fields. +# +# Concentration fields (c_buffer_a, c_buffer_b, c_sample) aren't here -- +# they're per-component (`Inlet.c`), built by `_concentration_field()` per +# model-spec call instead. +PARAMS: dict[str, FieldSpec] = { + "flow_rate": FieldSpec( + "flow_rate", "float", "Flow rate", 1.0e-6, + validate=require_positive, units=r"\frac{\mathrm{m}^{3}}{\mathrm{s}}", + ), + "flow_rate_wash": FieldSpec( + "flow_rate_wash", "float", "Flow rate", 1.0e-6, + validate=require_positive, units=r"\frac{\mathrm{m}^{3}}{\mathrm{s}}", + ), + "cycle_time": FieldSpec( + "cycle_time", "float", "Cycle time", 6000.0, + validate=require_positive, units=r"\mathrm{s}", + ), + "pulse_duration": FieldSpec( + "pulse_duration", "float", "Pulse duration", 60.0, + validate=require_positive, units=r"\mathrm{s}", + ), + "delta_t_wash": FieldSpec( + "delta_t_wash", "float", "Wash duration", 600.0, + validate=require_positive, units=r"\mathrm{s}", + ), + "delta_t_elute": FieldSpec( + "delta_t_elute", "float", "Elution duration", 1200.0, + validate=require_positive, units=r"\mathrm{s}", + ), + "delta_t_final_wash": FieldSpec( + "delta_t_final_wash", "float", "Final wash duration", 600.0, + validate=require_positive, units=r"\mathrm{s}", + ), +} + + +@dataclass +class ModelSpec: + """A titled group of fields plus the build function that consumes their values.""" + + title: str + fields: list[FieldSpec] = field(default_factory=list) + build: Callable[[Mapping[str, Any]], Any] | None = None + # Standalone (no-Instrument) templates wire their own FlowSheet directly -- + # there's no LCFlowSheet/model-builder class for a bare feed-into-unit + # setup -- so `export_script()` uses this instead of its LCFlowSheet-based + # default pattern when set. None for every INSTRUMENT_TEMPLATES entry. + export: Callable[[Mapping[str, Any]], list[str]] | None = None + # Display names of the process's `PhasedProcess` phases, in order. + phase_names: tuple[str, ...] = () + + +def _pick(keys: Sequence[str]) -> list[FieldSpec]: + return [PARAMS[k] for k in keys] + + +def _concentration_field( + name: str, label: str, default_scalar: float, component_system: ComponentSystem +) -> FieldSpec: + """Per-component concentration field, sized/named from the given ComponentSystem.""" + names = tuple(component_system.names) + n_comp = len(names) or 1 + return FieldSpec( + name, "float_list", label, [default_scalar] * n_comp, + transform=parse_float_list, units=CONCENTRATION_UNITS, component_names=names, + ) + + +def pulse_injection_spec(flow_sheet: LCFlowSheet) -> ModelSpec: + """Build the Pulse Injection template's ModelSpec for the given instrument.""" + cs = flow_sheet.component_system + + def _build(v: Mapping[str, Any]) -> Any: + return PulseInjection( + "pulse_injection", flow_sheet, + c_buffer_a=v["c_buffer_a"], + c_sample=v["c_sample"], + cycle_time=float(v["cycle_time"]), + flow_rate=float(v["flow_rate"]), + ) + + fields = [ + _concentration_field("c_buffer_a", "Buffer A concentration", 0.0, cs), + _concentration_field("c_sample", "Sample concentration", 10.0, cs), + *_pick(["cycle_time", "flow_rate"]), + ] + return ModelSpec( + title="Pulse Injection", fields=fields, build=_build, phase_names=("Pulse injection",) + ) + + +def step_spec(flow_sheet: LCFlowSheet) -> ModelSpec: + """Build the Step template's ModelSpec for the given instrument.""" + cs = flow_sheet.component_system + + def _build(v: Mapping[str, Any]) -> Any: + return Step( + "step", flow_sheet, + c_buffer_a=v["c_buffer_a"], + c_buffer_b=v["c_buffer_b"], + cycle_time=float(v["cycle_time"]), + flow_rate=float(v["flow_rate"]), + ) + + fields = [ + _concentration_field("c_buffer_a", "Buffer A concentration", 0.0, cs), + _concentration_field("c_buffer_b", "Buffer B concentration", 1000.0, cs), + *_pick(["cycle_time", "flow_rate"]), + ] + return ModelSpec(title="Step", fields=fields, build=_build, phase_names=("Step",)) + + +def lwe_spec(flow_sheet: LCFlowSheet) -> ModelSpec: + """Build the Load-Wash-Elute template's ModelSpec for the given instrument. + + `flow_rate_elute`/`flow_rate_final_wash` aren't exposed separately here -- + both default to `flow_rate_wash` (CADET-Process's own default), a + deliberately narrower slice than the full three-flow-rate constructor. + """ + cs = flow_sheet.component_system + + def _build(v: Mapping[str, Any]) -> Any: + return LWE( + "lwe", flow_sheet, + c_buffer_a=v["c_buffer_a"], + c_buffer_b=v["c_buffer_b"], + c_sample=v["c_sample"], + delta_t_wash=float(v["delta_t_wash"]), + delta_t_elute=float(v["delta_t_elute"]), + delta_t_final_wash=float(v["delta_t_final_wash"]), + flow_rate_wash=float(v["flow_rate_wash"]), + ) + + fields = [ + _concentration_field("c_buffer_a", "Buffer A concentration", 20.0, cs), + _concentration_field("c_buffer_b", "Buffer B concentration", 1000.0, cs), + _concentration_field("c_sample", "Sample concentration", 20.0, cs), + *_pick(["delta_t_wash", "delta_t_elute", "delta_t_final_wash", "flow_rate_wash"]), + ] + return ModelSpec( + title="Load–Wash–Elute (LWE)", fields=fields, build=_build, + phase_names=("Wash", "Elute (gradient)", "Final wash"), + ) + + +def step_elution_spec(flow_sheet: LCFlowSheet) -> ModelSpec: + """Build the Step Elution template's ModelSpec for the given instrument. + + Same scope note as `lwe_spec`: only `flow_rate_wash` is exposed. + """ + cs = flow_sheet.component_system + + def _build(v: Mapping[str, Any]) -> Any: + return StepElution( + "step_elution", flow_sheet, + c_buffer_a=v["c_buffer_a"], + c_buffer_b=v["c_buffer_b"], + c_sample=v["c_sample"], + delta_t_wash=float(v["delta_t_wash"]), + delta_t_elute=float(v["delta_t_elute"]), + delta_t_final_wash=float(v["delta_t_final_wash"]), + flow_rate_wash=float(v["flow_rate_wash"]), + ) + + fields = [ + _concentration_field("c_buffer_a", "Buffer A concentration", 20.0, cs), + _concentration_field("c_buffer_b", "Buffer B concentration", 1000.0, cs), + _concentration_field("c_sample", "Sample concentration", 20.0, cs), + *_pick(["delta_t_wash", "delta_t_elute", "delta_t_final_wash", "flow_rate_wash"]), + ] + return ModelSpec( + title="Step Elution", fields=fields, build=_build, + phase_names=("Wash", "Elute", "Final wash"), + ) + + +def breakthrough_spec(flow_sheet: LCFlowSheet) -> ModelSpec: + """Build the Breakthrough template's ModelSpec for the given instrument.""" + cs = flow_sheet.component_system + + def _build(v: Mapping[str, Any]) -> Any: + return Breakthrough( + "breakthrough", flow_sheet, + c_sample=v["c_sample"], + flow_rate=float(v["flow_rate"]), + cycle_time=float(v["cycle_time"]), + sample_buffer=v["sample_buffer"], + ) + + fields = [ + _concentration_field("c_sample", "Sample concentration", 10.0, cs), + *_pick(["flow_rate", "cycle_time"]), + FieldSpec( + "sample_buffer", "choice", "Sample delivered via", "F", + options=( + ("Feed inlet (F)", "F"), ("Buffer A", "A"), ("Buffer B", "B"), + ("Buffer C", "C"), ("Buffer D", "D"), + ), + ), + ] + return ModelSpec( + title="Breakthrough", fields=fields, build=_build, phase_names=("Breakthrough",) + ) + + +def pulse_feed_spec(unit: Any) -> ModelSpec: + """Build the standalone Pulse Feed template's ModelSpec for a bare column/unit. + + Used only when `ConfigurationWidget` has no InstrumentWidget bound -- there + is no CADET-Process model-builder class for a plain feed-into-unit setup + (unlike LCFlowSheet's own richer templates), so this wires a minimal + `FlowSheet` (feed -> unit -> outlet) directly, with the same primitives + `LCFlowSheet` itself is built from. Works with any unit operation, not + just a `ChromatographicColumnBase` -- e.g. `Cstr`, which can never be an + `LCFlowSheet` column but is a perfectly good standalone simulation target. + """ + names = tuple(unit.component_system.names) + + def _c_feed(v: Mapping[str, Any]) -> list[float]: + c_feed = [0.0] * (len(names) or 1) + if v["component"] in names: + c_feed[names.index(v["component"])] = float(v["concentration"]) + return c_feed + + def _build(v: Mapping[str, Any]) -> Any: + component_system = unit.component_system + feed = Inlet(component_system, name="feed") + feed.flow_rate = float(v["flow_rate"]) + outlet = Outlet(component_system, name="outlet") + + flow_sheet = FlowSheet(component_system) + flow_sheet.add_unit(feed, feed_inlet=True) + flow_sheet.add_unit(unit) + flow_sheet.add_unit(outlet, product_outlet=True) + flow_sheet.add_connection(feed, unit) + flow_sheet.add_connection(unit, outlet) + + process = Process(flow_sheet, "pulse_feed") + process.cycle_time = float(v["cycle_time"]) + process.add_duration("pulse_duration", float(v["pulse_duration"])) + + c_feed = _c_feed(v) + process.add_event("pulse_on", "flow_sheet.feed.c", c_feed) + process.add_event("pulse_off", "flow_sheet.feed.c", [0.0] * len(c_feed)) + process.add_event_dependency("pulse_off", ["pulse_on", "pulse_duration"], [1, 1]) + return process + + def _export(v: Mapping[str, Any]) -> list[str]: + c_feed = _c_feed(v) + c_off = [0.0] * len(c_feed) + return [ + "", + "feed = Inlet(component_system, name='feed')", + f"feed.flow_rate = {float(v['flow_rate'])!r}", + "outlet = Outlet(component_system, name='outlet')", + "", + "flow_sheet = FlowSheet(component_system)", + "flow_sheet.add_unit(feed, feed_inlet=True)", + "flow_sheet.add_unit(column)", + "flow_sheet.add_unit(outlet, product_outlet=True)", + "flow_sheet.add_connection(feed, column)", + "flow_sheet.add_connection(column, outlet)", + "", + "process = Process(flow_sheet, 'pulse_feed')", + f"process.cycle_time = {float(v['cycle_time'])!r}", + f"process.add_duration('pulse_duration', {float(v['pulse_duration'])!r})", + f"process.add_event('pulse_on', 'flow_sheet.feed.c', {c_feed!r})", + f"process.add_event('pulse_off', 'flow_sheet.feed.c', {c_off!r})", + "process.add_event_dependency('pulse_off', ['pulse_on', 'pulse_duration'], [1, 1])", + ] + + fields = [ + FieldSpec( + "component", "choice", "Component", names[0] if names else None, + options=tuple((n, n) for n in names), + ), + FieldSpec( + "concentration", "float", "Pulse concentration", 10.0, units=CONCENTRATION_UNITS, + ), + *_pick(["flow_rate", "pulse_duration", "cycle_time"]), + ] + return ModelSpec( + title="Pulse Feed (Single Component)", fields=fields, build=_build, export=_export + ) + + +_PHASE_EVENT = re.compile(r"^phase_(\d+)_") +_SAMPLE_INJECTION_EVENT = re.compile(r"^valve_sample_loop_inject(_\d+)?$") + + +def process_phase_spans(process: Any, names: Sequence[str] = ()) -> list[dict[str, Any]]: + """`PhasedProcess` phases as `{"name", "start", "end"}` dicts in seconds. + + Phases are recovered from the `phase__` events `PhasedProcess` + emits; a phase ends where the next one starts (the last, at `cycle_time`). + `names` are applied only when there is one per phase, else "Phase ". + Processes without phase events give an empty list. + """ + starts: dict[int, float] = {} + for event in process.events: + match = _PHASE_EVENT.match(event.name) + if match: + i = int(match.group(1)) + starts[i] = min(float(event.time), starts.get(i, math.inf)) + if not starts: + return [] + order = sorted(starts) + named = len(names) == len(order) + spans = [] + for pos, i in enumerate(order): + end = starts[order[pos + 1]] if pos + 1 < len(order) else float(process.cycle_time) + spans.append( + {"name": names[pos] if named else f"Phase {pos + 1}", "start": starts[i], "end": end} + ) + return spans + + +def sample_injection_times(process: Any) -> list[float]: + """Return the times (s) at which the process switches the sample loop into the flow path.""" + return sorted( + float(e.time) for e in process.events if _SAMPLE_INJECTION_EVENT.match(e.name) + ) + + +# PhasedProcess's fully generic phase-list composition is out of scope here -- +# see REQUIREMENTS.md's "generic user-authored event widget" open decision. +INSTRUMENT_TEMPLATES: dict[str, Callable[[LCFlowSheet], ModelSpec]] = { + "Breakthrough": breakthrough_spec, + "Step": step_spec, + "Pulse Injection": pulse_injection_spec, + "Load–Wash–Elute (LWE)": lwe_spec, + "Step Elution": step_elution_spec, +} + +_TEMPLATE_REQUIRED_UNITS: dict[Callable[..., ModelSpec], frozenset[str]] = { + pulse_injection_spec: frozenset({"sample_loop"}), + lwe_spec: frozenset({"sample_loop"}), + step_elution_spec: frozenset({"sample_loop"}), +} + + +def template_required_units(template: Callable[..., ModelSpec] | None) -> frozenset[str]: + """Flow-path units a process template can't be built without (`"sample_loop"`).""" + return _TEMPLATE_REQUIRED_UNITS.get(template, frozenset()) + + +# Used instead of INSTRUMENT_TEMPLATES when ConfigurationWidget has no +# InstrumentWidget bound -- REQUIREMENTS.md item #32 ("instrument attachment +# optional, not required for a simple simulation"). Deliberately one entry for +# now, matching this codebase's narrow-slice-first pattern; extend when a +# second bare-column workflow is actually needed. +STANDALONE_TEMPLATES: dict[str, Callable[[Any], ModelSpec]] = { + "Pulse Feed (Single Component)": pulse_feed_spec, +} + +# Units `LCFlowSheet(bypass_units=...)` accepts (CADET-Process's own +# `_BYPASSABLE` set, not exported -- mirrored here rather than reached into). +BYPASSABLE_UNITS: tuple[str, ...] = ( + "mixer", "tubing_pre_injection", "tubing_pre_column", + "column", "tubing_post_column", "tubing_detectors", +) + +# Plain names for every LCFlowSheet unit -- shared by the System diagram, the +# unit toggles and the signal dropdowns. +UNIT_LABELS: dict[str, str] = { + "buffer_a": "Buffer A", + "buffer_b": "Buffer B", + "buffer_c": "Buffer C", + "buffer_d": "Buffer D", + "feed_inlet": "Feed", + "mixer": "Mixer", + "tubing_pre_injection": "Tubing (pre injection)", + "sample_loop": "Sample loop", + "tubing_pre_column": "Tubing (pre column)", + "column": "Column", + "tubing_post_column": "Tubing (post column)", + "tubing_detectors": "Tubing (detectors)", + "outlet": "Outlet", + "waste": "Waste outlet", +} + +_INLET_UNITS = ("buffer_a", "buffer_b", "buffer_c", "buffer_d", "feed_inlet") + + +def active_inlets(process: Any) -> list[str]: + """Names of the LC inlets a process actually drives (a flow-rate event is set on them).""" + driven = { + event.parameter_path.split(".")[1] + for event in process.events + if event.parameter_path.startswith("flow_sheet.") + and event.parameter_path.endswith(".flow_rate") + } + driven.update(equilibration_inlets(process)) + return [name for name in _INLET_UNITS if name in driven] + + +def equilibration_inlets(process: Any) -> list[str]: + """Inlets that only pre-equilibrate the system before the process starts (Step: buffer A).""" + return ["buffer_a"] if isinstance(process, Step) else [] + + +def inlet_contents(process: Any) -> dict[str, list[str]]: + """Component names each driven inlet carries; `"sample_loop"` is listed when it holds sample.""" + flow_sheet = process.flow_sheet + names = list(flow_sheet.component_system.names) + units = flow_sheet.units_dict + contents: dict[str, list[str]] = {} + for name in [*active_inlets(process), "sample_loop"]: + if name not in units: + continue + found = [n for n, c in zip(names, units[name].c) if any(_as_list(c))] + if found or name != "sample_loop": + contents[name] = found + return contents + + +def _as_list(value: Any) -> list[Any]: + return list(value) if hasattr(value, "__iter__") else [value] + + +def signal_label(unit: str, port: str) -> str: + """Plain-language name of a signal position, e.g. "Column outlet" or "Outlet".""" + name = UNIT_LABELS.get(unit, unit.replace("_", " ").capitalize()) + if unit in ("outlet", "waste"): + return name + if port == "outlet" and unit in _INLET_UNITS: + return f"{name} inlet" + return f"{name} {port}" + + +def friendly_signal_options( + options: Sequence[tuple[str, tuple[str, str]]], +) -> list[tuple[str, tuple[str, str]]]: + """Relabel `(label, (unit, port))` signal options with `signal_label`.""" + return [(signal_label(unit, port), (unit, port)) for _, (unit, port) in options] + + +# `LCFlowSheet(ColumnModel=...)` takes the class itself -- it instantiates and +# names the column ("column") internally, so this is a plain class registry, +# not an instance-factory like the old `DEFAULT_COLUMN_FACTORIES`. CSTR is +# back (REQUIREMENTS.md item #32) since it's a real, useful standalone +# simulation target -- it's just never offered once an Instrument is bound +# (see INSTRUMENT_COMPATIBLE_COLUMNS below), since it isn't a +# `ChromatographicColumnBase` and doesn't fit LCFlowSheet's column slot. +COLUMN_MODELS: Dict[str, type] = { + "General Rate Model (GRM)": GeneralRateModel, + "Lumped Rate Model With Pores (LRMP)": LumpedRateModelWithPores, + "Lumped Rate Model Without Pores (LRM)": LumpedRateModelWithoutPores, + "Continuous Stirred Tank Reactor (CSTR)": Cstr, +} + +# Derived, not hand-maintained -- a column model is instrument-compatible iff +# it's a real ChromatographicColumnBase (LCFlowSheet's own constraint). +INSTRUMENT_COMPATIBLE_COLUMNS: frozenset[str] = frozenset( + key for key, cls in COLUMN_MODELS.items() if issubclass(cls, ChromatographicColumnBase) +) + +# "None" first so a ChoiceField over this dict defaults to it -- LCFlowSheet +# leaves the column's own default (NoBinding) in place when BindingModel=None. +BINDING_MODELS: Dict[str, Optional[type]] = { + "None": None, + "Linear": Linear, + "Langmuir": Langmuir, + "Steric Mass Action (SMA)": StericMassAction, +} + + +def _unique_preserve_order(names: Sequence[str]) -> list[str]: + """Return names in original order, dropping duplicates (first occurrence wins).""" + seen: set[str] = set() + out: list[str] = [] + for n in names: + if n not in seen: + seen.add(n) + out.append(n) + return out + + +def _infer_kind(x: Any) -> str: + if isinstance(x, (list, tuple)): + return "float_list" + if isinstance(x, bool): + return "bool" + return "float" + + +# Starting values so a blank form doesn't Apply a degenerate column; +# CADET-Core has no canonical default for these. `None` model slot = shared +# across column models; add a specific model name only to override it. +_GUI_SEED_DEFAULTS: dict[tuple[str, Optional[str], str], float] = { + ("column", None, "diameter"): 0.024, + ("column", None, "length"): 0.5, + ("column", None, "axial_dispersion"): 1e-8, + ("column", None, "bed_porosity"): 0.72, + ("column", None, "total_porosity"): 0.72, + ("column", None, "particle_porosity"): 0.6, + ("column", None, "particle_radius"): 5.0e-6, + ("column", None, "film_diffusion"): 1e-3, + ("column", None, "pore_diffusion"): 1e-10, +} + + +def _seed_default(category: str, model_name: str, name: str) -> float: + key = (category, model_name, name) + if key in _GUI_SEED_DEFAULTS: + return _GUI_SEED_DEFAULTS[key] + return _GUI_SEED_DEFAULTS.get((category, None, name), 0.0) + + +# Per-component in CADET-Process but usually entered as one shared value, +# unlike binding parameters (per-component by physical necessity). Default +# scalar; `ConfigurationWidget`'s multiplex toggle opts a name into +# per-component editing -- CADET-Process broadcasts a scalar assignment to +# every component either way. +MULTIPLEXABLE_COLUMN_PARAMS = frozenset({"axial_dispersion", "film_diffusion", "pore_diffusion"}) + +# `TubularReactor.axial_dispersion` is genuinely component-dependent at the +# CADET-Process level (same SizedUnsignedList(size="n_comp") as a real +# column's, confirmed directly) -- but a plain tubing/mixer dead-volume +# segment gets no multiplex toggle at all, unlike MULTIPLEXABLE_COLUMN_PARAMS +# above, so it must never render as a per-component list regardless of +# `multiplex`. A GUI-layer decision (which fields to render, how), so it +# lives here rather than in cadetgui/parameters. +_FORCE_SCALAR = frozenset({("TubularReactor", "axial_dispersion")}) + + +def _category_and_model(obj: Any) -> tuple[Optional[str], str]: + model_name = type(obj).__name__ + if isinstance(obj, BindingBaseClass): + return "binding", model_name + # TubularReactorBase covers both real columns (ChromatographicColumnBase, + # its subclass) and plain tubing/mixer dead-volume segments (TubularReactor + # itself, which shares the same length/diameter/axial_dispersion + # descriptors, inherited from TubularReactorBase). + if isinstance(obj, (TubularReactorBase, Cstr)): + return "column", model_name + return None, model_name + + +def _resolve_param( + obj: Any, + category: Optional[str], + model_name: str, + name: str, + *, + multiplex: Optional[Dict[str, bool]] = None, +) -> tuple[Any, str, Optional[Transform], dict[str, float], Optional[str]]: + """Resolve one parameter's kind/bounds/units/default. + + Ground truth comes from `parameters.get_parameters()` via + `category`/`model_name` (nested per-model, since e.g. `Langmuir.capacity` + and `StericMassAction.capacity` differ in shape despite the same name) -- + `component_dependent`/`dtype`/bounds/`unit` are all introspected live off + the real CADET-Process descriptor there, not hand-typed. + Falls back to inferring from the object's current value for an + unregistered category/model/parameter, or one live introspection + couldn't resolve a real descriptor for. `multiplex` overrides + `component_dependent` for `MULTIPLEXABLE_COLUMN_PARAMS` only; + `_FORCE_SCALAR` overrides it unconditionally for names that must never + render per-component regardless of `multiplex`. + """ + meta = None + if category is not None: + try: + meta = _param_metadata_for(category, model_name).get(name) + except KeyError: + meta = None + + current = getattr(obj, name, None) + + if meta is None or meta.get("dtype") is None or meta.get("component_dependent") is None: + kind = _infer_kind(current) + transform = parse_float_list if kind == "float_list" else None + return current, kind, transform, {}, None if meta is None else meta.get("unit") + + dtype = meta["dtype"] + component_dependent = meta["component_dependent"] + if multiplex is not None and name in MULTIPLEXABLE_COLUMN_PARAMS: + component_dependent = bool(multiplex.get(name, False)) + if (model_name, name) in _FORCE_SCALAR: + component_dependent = False + kind = "float_list" if (dtype == "float" and component_dependent) else dtype + + if current is not None and isinstance(current, (list, tuple)) and kind == "float": + # CADET-Process stores these as a list even after scalar broadcast. + default = current[0] if len(current) else _seed_default(category, model_name, name) + elif current is not None: + default = current + elif kind == "float_list": + n_comp = getattr(obj, "n_comp", None) or 1 + default = [_seed_default(category, model_name, name)] * n_comp + else: + default = _seed_default(category, model_name, name) + + bounds = {k: meta[k] for k in ("min", "max") if meta.get(k) is not None} + transform = parse_float_list if kind == "float_list" else None + return default, kind, transform, bounds, meta.get("unit") + + +def _coerce_to_kind(kind: str, val: Any) -> Any: + if kind == "float_list": + return parse_float_list(val) + if kind == "bool": + return bool(val) + return float(val) + + +def build_parameter_config_spec( + obj: Any, *, multiplex: Optional[Dict[str, bool]] = None, include_optional: bool = False +) -> ModelSpec: + """Build a ModelSpec from any object exposing `required_parameters`. + + Generic over `obj` (column, binding model, ...) so the same function + drives every form. `multiplex` only matters for + `MULTIPLEXABLE_COLUMN_PARAMS`. `include_optional` also renders every + scalar/list parameter `obj` currently holds a value for, beyond + `required_parameters`. + """ + req = getattr(obj, "required_parameters", None) or [] + names = _unique_preserve_order(list(req)) + category, model_name = _category_and_model(obj) + + # `obj.required_parameters` is a CADET-Process class-level property whose + # *order* is not stable across process runs: its metaclass builds it via + # `list(set(parameters))` (CADETProcess/dataStructure/dataStructure.py), + # so which parameter lands first depends on Python's per-process string + # hash seed -- confirmed by observing `total_porosity` and + # `axial_dispersion` swap which one is index 0 between separate `python` + # invocations of the identical code. The *set* of names is correct, only + # the order isn't. Sorting alphabetically sidesteps the hash-seed + # randomness entirely, giving a stable rendered field/checklist order + # without needing a curated ordering anywhere. + names.sort() + + # is_kinetic isn't in CADET-Process's own required_parameters, but every + # binding model with a real isotherm needs it settable. `names` is only + # non-empty for those (NoBinding's required_parameters is []). + if category == "binding" and names: + names.append("is_kinetic") + + if include_optional: + # obj.parameters' values are frozen at construction time, not live -- + # read each one fresh via getattr. Structured (dict-valued) + # parameters aren't renderable by this generic per-parameter form. + optional_names = [ + p for p in getattr(obj, "parameters", {}) + if p not in names and isinstance(getattr(obj, p, None), (int, float, bool, list, tuple)) + ] + names.extend(optional_names) + + component_names = tuple(obj.component_system.names) if hasattr(obj, "component_system") else () + fields: list[FieldSpec] = [] + kinds: dict[str, str] = {} + + for name in names: + default, kind, transform, bounds, units = _resolve_param( + obj, category, model_name, name, multiplex=multiplex + ) + kinds[name] = kind + label = name.replace("_", " ").capitalize() + fs_kwargs = dict( + name=name, kind=kind, label=label, default=default, transform=transform, **bounds + ) + if units is not None: + fs_kwargs["units"] = units + if kind == "float_list" and component_names: + fs_kwargs["component_names"] = component_names + fields.append(FieldSpec(**fs_kwargs)) + + def _apply(values: Mapping[str, Any]) -> Any: + for name in names: + if name not in values: + continue + coerced = _coerce_to_kind(kinds[name], values[name]) + setattr(obj, name, coerced) + return obj + + return ModelSpec( + title=f"Configure {obj.__class__.__name__}", + fields=fields, + build=_apply, + ) + + +def classify_signal_ports(result: Any) -> list[tuple[str, tuple[str, str]]]: + """List (label, (unit, port)) solution signals, collapsed for Inlet/Outlet units. + + An `Inlet`'s "inlet" port and an `Outlet`'s "outlet" port are CADET-Process + bookkeeping, not real signals -- identical to that same unit's other port + for every current template. Only the real port is offered, labeled + "Source"/"Sink" rather than the (there, meaningless) port name. + + Any "Sink" entry is sorted first -- comparing/fitting against the process + outlet is the common case (`ChoiceField.set_options(..., keep_value=True)` + defaults to index 0 when nothing was previously selected, so this is what + determines the signal picker's default in both `SolutionWidget` and + `ParameterEstimationWidget`). An `LCFlowSheet` always has two Outlets + (`outlet`, the product; `waste`) -- the unit literally named `"outlet"` is + sorted first among sinks, not just "any Outlet", so `waste` stays a valid + but secondary choice rather than competing for the default. + """ + return _order_signal_ports( + result.process.flow_sheet.units_dict, + {name: list(ports) for name, ports in result.solution.items()}, + ) + + +def list_signal_ports(process: Any) -> list[tuple[str, tuple[str, str]]]: + """Derive the `classify_signal_ports` list from a process without simulating it.""" + units = process.flow_sheet.units_dict + ports_by_unit = { + name: [ + port + for port in ("inlet", "outlet", "volume") + if getattr(unit.solution_recorder, f"write_solution_{port}", port != "volume") + ] + for name, unit in units.items() + } + return _order_signal_ports(units, ports_by_unit) + + +# Hardware that only moves fluid along (the junction `LCFlowSheet` always +# keeps, the injection loop) -- its ports are never a place anything is +# measured. +_INFRASTRUCTURE_UNITS = ("mixer", "sample_loop") + + +def measurable_signal_ports( + units: Mapping[str, Any], + options: Sequence[tuple[str, tuple[str, str]]], + inlets: Collection[str] = (), +) -> list[tuple[str, tuple[str, str]]]: + """Keep the signal positions worth looking at or comparing against. + + `classify_signal_ports`/`list_signal_ports` list every port of every unit + in the flow sheet -- buffer and feed sources, the mixer junction, the + sample loop, each unit's inlet and `volume` port -- which is far more + than there is anything to compare or look at. Keep the process outlets + (sinks), the outlet port of each remaining unit (column, tubing + segments), the column inlet, and the sources of the inlets named in + `inlets` (the ones the process drives), in the order given. + """ + return [ + (label, (unit, port)) + for label, (unit, port) in options + if isinstance(units[unit], Outlet) + or (isinstance(units[unit], Inlet) and unit in inlets) + or (unit == "column" and port == "inlet") + or ( + port == "outlet" + and not isinstance(units[unit], Inlet) + and unit not in _INFRASTRUCTURE_UNITS + ) + ] + + +def measurable_signal_options( + process: Any, options: Optional[Sequence[tuple[str, tuple[str, str]]]] = None +) -> list[tuple[str, tuple[str, str]]]: + """Plainly-labelled, measurable `(label, (unit, port))` signal options of `process`. + + `options` defaults to `list_signal_ports(process)`; pass a simulated result's + `classify_signal_ports` list to restrict to the ports it actually recorded. + """ + return friendly_signal_options( + measurable_signal_ports( + process.flow_sheet.units_dict, + list_signal_ports(process) if options is None else options, + active_inlets(process), + ) + ) + + +def _order_signal_ports( + units: Mapping[str, Any], ports_by_unit: Mapping[str, Sequence[str]] +) -> list[tuple[str, tuple[str, str]]]: + sinks: list[tuple[str, str, tuple[str, str]]] = [] + others: list[tuple[str, tuple[str, str]]] = [] + for unit_name, ports in ports_by_unit.items(): + unit = units.get(unit_name) + if isinstance(unit, Inlet): + if "outlet" in ports: + others.append((f"{unit_name}: Source", (unit_name, "outlet"))) + elif isinstance(unit, Outlet): + if "inlet" in ports: + sinks.append((unit_name, f"{unit_name}: Sink", (unit_name, "inlet"))) + else: + for port in ports: + others.append((f"{unit_name}: {port}", (unit_name, port))) + sinks.sort(key=lambda entry: entry[0] != "outlet") + return [(label, value) for _, label, value in sinks] + others diff --git a/cadetgui/configuration_store.py b/cadetgui/configuration_store.py new file mode 100644 index 0000000..38f5454 --- /dev/null +++ b/cadetgui/configuration_store.py @@ -0,0 +1,293 @@ +from __future__ import annotations + +import datetime as dt +import hashlib +import json +import re +from dataclasses import asdict, dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +from cadet import H5 + +from . import __version__ as _cadetgui_version + +__all__ = [ + "InstrumentState", + "ConfigurationState", + "compute_hash", + "default_store_dir", + "safe_config_dirname", + "config_dir", + "runs_dir", + "save_h5", + "load_h5", + "save_to_store", + "load_from_store", + "list_store", +] + + +@dataclass(frozen=True) +class InstrumentState: + """The hashed, JSON-safe payload needed to reconstruct an InstrumentWidget. + + Topology only -- component names and column/binding *type* now live + directly on `ConfigurationState` (its own pickers own that choice, see + ARCHITECTURE.md's "instrument attachment optional" section), not nested + in here. + """ + + include_sample_loop: bool = False + sample_loop_volume: float = 50e-9 + sample_loop_diameter_auto: bool = True + sample_loop_diameter: float = 0.75e-3 + bypass_units: List[str] = field(default_factory=list) + # Per-unit parameter values for the mixer/tubing dead-volume units + # (`{"mixer": {"init_liquid_volume": ...}, "tubing_pre_column": {...}, ...}`), + # keyed by unit name -- see ARCHITECTURE.md's "Units In Flow Path" section. + unit_values: Dict[str, Dict[str, float]] = field(default_factory=dict) + + +@dataclass(frozen=True) +class ConfigurationState: + """The hashed, JSON-safe payload needed to reconstruct a ConfigurationWidget. + + `instrument` is `None` when no InstrumentWidget was bound -- the + configuration was a standalone bare-column simulation. + """ + + components: List[str] + column_key: str + binding_key: str + template_key: str + instrument: Optional[InstrumentState] = None + multiplex_state: Dict[str, bool] = field(default_factory=dict) + show_optional_column: bool = False + show_optional_binding: bool = False + column_values: Dict[str, Any] = field(default_factory=dict) + binding_values: Dict[str, Any] = field(default_factory=dict) + model_values: Dict[str, Any] = field(default_factory=dict) + + +def compute_hash(state: ConfigurationState) -> str: + """Content-addressed fingerprint of a configuration (the name is not hashed).""" + payload = json.dumps(asdict(state), sort_keys=True) + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + +def default_store_dir() -> Path: + """Local directory saved/imported-by-hash configurations live in.""" + store_dir = Path.home() / ".cadetgui" / "configurations" + store_dir.mkdir(parents=True, exist_ok=True) + return store_dir + + +_UNSAFE_DIRNAME_CHARS = re.compile(r"[^\w\-.]") + + +def safe_config_dirname(name: str) -> str: + """Filesystem-safe subfolder name for a configuration -- whitespace becomes + underscores, anything else unsafe in a folder name is dropped. + """ + cleaned = _UNSAFE_DIRNAME_CHARS.sub("", name.strip().replace(" ", "_")) + return cleaned or "unnamed" + + +def config_dir(name: str, *, store_dir: Optional[Path] = None) -> Path: + """The per-configuration subfolder one configuration's own save file, and + any run results saved alongside it, live under. Created on demand. + """ + store_dir = store_dir or default_store_dir() + path = store_dir / safe_config_dirname(name) + path.mkdir(parents=True, exist_ok=True) + return path + + +def runs_dir(name: str, *, store_dir: Optional[Path] = None) -> Path: + """Return the `runs` subfolder of a configuration's folder, created on demand. + + Run manifests and raw outputs that older versions left directly in the + configuration's folder are moved in here once, so existing runs stay listed. + """ + folder = config_dir(name, store_dir=store_dir) + runs = folder / "runs" + runs.mkdir(exist_ok=True) + for legacy in [*folder.glob("run_*.json"), *folder.glob("run_*.h5")]: + target = runs / legacy.name + if not target.exists(): + legacy.rename(target) + return runs + + +def _decode_h5_leaf(value: Any) -> Any: + """Coerce one h5py/numpy/bytes leaf back to a plain JSON-safe Python type.""" + if hasattr(value, "to_dict"): + value = value.to_dict() + if isinstance(value, dict): + return {k: _decode_h5_leaf(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [_decode_h5_leaf(v) for v in value] + if isinstance(value, bytes): + return value.decode() + if hasattr(value, "tolist"): # numpy scalar or ndarray + return _decode_h5_leaf(value.tolist()) + return value + + +def save_h5( + state: ConfigurationState, + name: str, + path: "Path | str", + *, + process: Any = None, +) -> None: + """Write a configuration to `path` as h5. + + Always writes a `cadetgui` group holding the full GUI-reconstruction + state (the only thing `load_h5` reads back). When `process` is given, + also embeds the real CADET-Core solver config under the same file + (best-effort -- a missing/unlocatable CADET-Core install must not block + saving the GUI state, which never depends on it). + """ + h5 = H5() + if process is not None: + try: + from CADETProcess.simulator import Cadet + + h5.root.update(Cadet().get_process_config(process)) + except Exception: # noqa: BLE001 + pass + + # HDF5 has no NoneType -- `instrument=None` (standalone config, no + # InstrumentWidget bound) is stored as an empty dict instead; load_h5 + # treats an empty/missing "instrument" the same way. + payload = asdict(state) + if payload.get("instrument") is None: + payload["instrument"] = {} + + h5.root.cadetgui = { + "name": name, + "hash": compute_hash(state), + "created": dt.datetime.now().isoformat(), + "cadetgui_version": _cadetgui_version, + "state": payload, + } + h5.filename = str(path) + h5.save() + + +def load_h5(path: "Path | str") -> Tuple[str, ConfigurationState]: + """Read a configuration previously written by `save_h5`. + + Returns (name, state). Raises ValueError if `path` has no `cadetgui` + group (e.g. a plain CADET-Core h5 file). + """ + h5 = H5() + h5.filename = str(path) + h5.load_from_file() + + root = _decode_h5_leaf(h5.root) + gui = root.get("cadetgui") + if not gui: + raise ValueError(f"{path} has no 'cadetgui' group -- not a saved configuration.") + + payload = gui["state"] + instrument_payload = payload.get("instrument") or {} + instrument = ( + InstrumentState( + include_sample_loop=instrument_payload.get("include_sample_loop", True), + sample_loop_volume=instrument_payload.get("sample_loop_volume", 50e-9), + sample_loop_diameter_auto=instrument_payload.get("sample_loop_diameter_auto", True), + sample_loop_diameter=instrument_payload.get("sample_loop_diameter", 0.75e-3), + bypass_units=list(instrument_payload.get("bypass_units", [])), + unit_values={ + k: dict(v) for k, v in instrument_payload.get("unit_values", {}).items() + }, + ) + if instrument_payload + else None + ) + state = ConfigurationState( + components=payload["components"], + column_key=payload["column_key"], + binding_key=payload["binding_key"], + template_key=payload["template_key"], + instrument=instrument, + multiplex_state=payload.get("multiplex_state", {}), + show_optional_column=payload.get("show_optional_column", False), + show_optional_binding=payload.get("show_optional_binding", False), + column_values=payload.get("column_values", {}), + binding_values=payload.get("binding_values", {}), + model_values=payload.get("model_values", {}), + ) + return gui["name"], state + + +def save_to_store( + state: ConfigurationState, + name: str, + *, + process: Any = None, + store_dir: Optional[Path] = None, +) -> Path: + """Save to the local configuration store, under a subfolder for `name`. + + Content hash stays the true identity, not the folder: renaming a saved + configuration must not change it, and two identical configurations under + different names still collide to one file (ARCHITECTURE.md). So if this + exact hash was already saved under some other name, that existing + subfolder/file is reused (its metadata updated) instead of creating a + duplicate copy under the new name -- the folder simply keeps whichever + name first saved that content. Returns the path. + + The `config_` filename prefix distinguishes it at a glance from a run's + own `run_.h5` raw solver output, which can live in the same + per-configuration folder (see `run_store.run_output_path`). + """ + store_dir = store_dir or default_store_dir() + store_dir.mkdir(parents=True, exist_ok=True) + hash_ = compute_hash(state) + existing = next(store_dir.glob(f"*/config_{hash_}.h5"), None) + path = ( + existing if existing is not None + else config_dir(name, store_dir=store_dir) / f"config_{hash_}.h5" + ) + save_h5(state, name, path, process=process) + return path + + +def load_from_store( + hash_: str, *, store_dir: Optional[Path] = None +) -> Tuple[str, ConfigurationState]: + """Load a configuration from the local store by its hash. + + Only the hash is known to the caller (a run's `config_hash`, or a hash + pasted by hand) -- not which name's subfolder it lives under -- so every + configuration subfolder is searched. + """ + store_dir = store_dir or default_store_dir() + path = next(store_dir.glob(f"*/config_{hash_}.h5"), None) + if path is None: + raise FileNotFoundError(f"No saved configuration with hash {hash_!r} in {store_dir}.") + return load_h5(path) + + +def list_store(*, store_dir: Optional[Path] = None) -> List[Tuple[str, str]]: + """List every saved configuration as (name, hash) pairs, newest first. + + Silently skips any file that isn't a valid saved configuration (e.g. a + plain CADET-Core h5 someone dropped into a configuration's subfolder by + hand). The `config_` glob already excludes a run's own raw solver-output + h5 sitting alongside it (see `run_store.run_output_path`'s `run_` prefix). + """ + store_dir = store_dir or default_store_dir() + paths = sorted(store_dir.glob("*/config_*.h5"), key=lambda p: p.stat().st_mtime, reverse=True) + entries = [] + for path in paths: + try: + name, _ = load_h5(path) + except Exception: # noqa: BLE001 + continue + entries.append((name, path.stem.removeprefix("config_"))) + return entries diff --git a/cadetgui/optimizer_runner.py b/cadetgui/optimizer_runner.py new file mode 100644 index 0000000..f00dcbc --- /dev/null +++ b/cadetgui/optimizer_runner.py @@ -0,0 +1,223 @@ +from __future__ import annotations + +import threading +from dataclasses import dataclass +from typing import Any, Callable, Mapping, Optional, Sequence + +from CADETProcess.optimization import U_NSGA3, NelderMead, OptimizationProblem + +__all__ = [ + "OptimizerKnob", + "OptimizerSpec", + "OPTIMIZERS", + "OptimizationCancelled", + "install_cancel_hook", + "OptimizerRunResult", + "run_optimization", + "RunSpec", +] + + +@dataclass(frozen=True) +class OptimizerKnob: + """One optimizer-specific numeric setting, e.g. Nelder-Mead's `maxiter`.""" + + attr: str # kwarg name passed to the optimizer's constructor + label: str # widget field label + default: int + + +@dataclass(frozen=True) +class OptimizerSpec: + """One entry in `OPTIMIZERS` -- everything needed to build and configure it.""" + + factory: Callable[..., Any] + knobs: tuple[OptimizerKnob, ...] + # Mirrors the optimizer class's own `is_population_based` (`OptimizerBase` + # attribute) -- surfaced separately so callers don't need to construct an + # instance just to read it (e.g. for a "this may take a while to cancel" + # UX note: population-based optimizers evaluate a whole population before + # the per-generation cancellation check is next reached, see + # `install_cancel_hook`). + is_population_based: bool + + +# Adding another optimizer later is one more entry here -- nothing else in +# this module, in `cadetgui.parameter_estimation`, or in a widget hardcodes +# "Nelder-Mead"/"U-NSGA-III"; every caller reads `OPTIMIZERS` generically. +# `pop_size`/`n_max_gen` default to `0`, which is falsy -- `PymooInterface` +# (CADETProcess/optimization/pymooAdapter.py) already treats an unset/falsy +# value as "size this automatically" via its own `scale_problem_size` +# heuristic, so `0` here means "let CADET-Process decide," not "population +# of zero." +OPTIMIZERS: dict[str, OptimizerSpec] = { + "Nelder-Mead": OptimizerSpec( + factory=NelderMead, + knobs=(OptimizerKnob("maxiter", "Max iterations", 1000),), + is_population_based=False, + ), + "U-NSGA-III": OptimizerSpec( + factory=U_NSGA3, + knobs=( + OptimizerKnob("pop_size", "Population size", 0), + OptimizerKnob("n_max_gen", "Max generations", 0), + ), + is_population_based=True, + ), +} + + +class OptimizationCancelled(Exception): + """Raised by the per-generation hook `install_cancel_hook` installs. + + Triggered once a caller sets the `cancel_event` passed to + `run_optimization`, mid-run. + """ + + +def install_cancel_hook(optimizer: Any, cancel_event: threading.Event) -> None: + """Make `optimizer.optimize()` raise `OptimizationCancelled` once `cancel_event` is set. + + `OptimizerBase.run_post_processing` (defined once, shared by every + adapter) is called once per generation for every optimizer -- from + inside scipy's own callback for `SciPyInterface` subclasses (`NelderMead`, + ...), and from inside CADET-Process's own generation loop for + `PymooInterface` subclasses (`U_NSGA3`, ...) -- and nothing wraps the call + to `_run` in a try/except, so an exception raised from here propagates + all the way out of `.optimize()` regardless of which optimizer is + running. Raising from an evaluator or an `OptimizationProblem.add_callback()` + callback does *not* work: CADET-Process's evaluation pipeline deliberately + catches exceptions from both and converts them into a bad-score fallback, + by design, so a single flaky simulation doesn't kill an entire + optimization. Mutating `optimizer.maxiter` after `.optimize()` has started + doesn't work either -- `SciPyInterface._run` reads it once into a plain + dict scipy owns from then on, not a live reference. + """ + original = optimizer.run_post_processing + + def _wrapped(*args: Any, **kwargs: Any) -> Any: + if cancel_event.is_set(): + raise OptimizationCancelled("Optimization cancelled by user.") + return original(*args, **kwargs) + + optimizer.run_post_processing = _wrapped + + +@dataclass(frozen=True) +class OptimizerRunResult: + """Outcome of `run_optimization` -- generic over any `OptimizationProblem`.""" + + x_best: dict[str, float] # keyed by `problem.variable_names` + objective: Optional[float] + success: bool + message: str + cancelled: bool = False + optimizer_name: str = "" + + +def run_optimization( + problem: OptimizationProblem, + optimizer_name: str, + optimizer_kwargs: Mapping[str, int], + x0: Sequence[float], + *, + cancel_event: Optional[threading.Event] = None, + on_optimizer_ready: Optional[Callable[[Any], None]] = None, +) -> OptimizerRunResult: + """Run `OPTIMIZERS[optimizer_name]` against an already-built `problem`. + + Fully generic over what built `problem` -- a hand-rolled single-reference + `OptimizationProblem` (`cadetgui.parameter_estimation.build_estimation_problem`) + and a `CADETProcess.characterization.CharacterizeXxx` instance (itself an + `OptimizationProblem` subclass) both work here unchanged, since neither + this function nor `OptimizerBase.optimize()` cares how `problem` was + constructed. + + `optimizer_kwargs` are passed straight to `OPTIMIZERS[optimizer_name] + .factory(**optimizer_kwargs)` (e.g. `{"maxiter": 1000}` for Nelder-Mead). + `x0` seeds the optimizer's starting point -- for a population-based + optimizer like U-NSGA-III it seeds one individual of the initial + population, the rest filled in by CADET-Process's own polytope sampling. + + `on_optimizer_ready`, when given, is called exactly once with the + constructed optimizer instance, right before the blocking `.optimize()` + call -- the hook a caller running this on a background thread needs to + obtain a live handle to `optimizer.results` for progress reporting. + + `cancel_event`, when given, is checked once per generation (via + `install_cancel_hook`) -- the run stops there and returns a cancelled + result instead of a normal one. + """ + optimizer = OPTIMIZERS[optimizer_name].factory(**optimizer_kwargs) + if cancel_event is not None: + install_cancel_hook(optimizer, cancel_event) + if on_optimizer_ready is not None: + on_optimizer_ready(optimizer) + + try: + results = optimizer.optimize(problem, x0=list(x0), save_results=False) + except OptimizationCancelled: + return OptimizerRunResult( + {}, None, False, "Optimization cancelled by user.", + cancelled=True, optimizer_name=optimizer_name, + ) + except Exception as exc: # noqa: BLE001 + return OptimizerRunResult({}, None, False, str(exc), optimizer_name=optimizer_name) + + # `results.x[0]` is the full, dependency-resolved point (one entry per + # `problem.variable_names`, in that order -- confirmed live: a dependent + # variable's resolved value is included, not just the independent ones a + # mid-run objective evaluation receives). `set_variables` accepts exactly + # that shape for a problem with no dependent variables (every current + # caller: cadetgui's own hand-built estimation problems, and every + # CADETProcess.characterization.CharacterizeXxx stage this module + # supports never uses `add_variable_dependency`); a problem that does + # isn't supported here. + problem.set_variables(results.x[0]) + x_best = dict(zip(problem.variable_names, (float(v) for v in results.x[0]))) + return OptimizerRunResult( + x_best, float(results.f_best[0]), True, "Optimization finished.", + optimizer_name=optimizer_name, + ) + + +@dataclass(frozen=True) +class RunSpec: + """Everything needed to drive one optimization run. + + Built by `OptimizerRunnerPanel` (widgets/composite/_optimizer_runner_panel.py)'s + caller, supplied fresh on every "Run" click via a `build_run_spec` callback. + + `preview_series(x_best)`, when given, is called during the live-progress + ticker with a candidate point's values (positionally parallel to + `problem.variable_names`) and returns the interactive live chart's series + (one per component, plus the measured reference), or `None` when the + candidate's signal isn't a time x component trace. It must never mutate + anything the worker thread is writing to (simulate a fresh deep copy). + + `render_preview(x_best, ax)` is the matplotlib fallback used whenever + `preview_series` is absent or returns `None`: it must draw onto the given + (already-created) `Axes`. The panel itself owns the figure this `Axes` + belongs to (it's laid out next to a generic objective-history panel the + panel draws on its own). + + `render_fit_table` turns a `{variable_name: fitted_value}` mapping into + an HTML results table. `accept` is called once, with the same mapping, + when the user clicks "Accept" -- it decides where each fitted value gets + written back (a `ConfigurationWidget`'s column/binding form, an + `InstrumentWidget` unit's form, ...); this module has no opinion on that. + + `on_finished`, when given, is called once after a run ends (successful, + failed, or cancelled) with the raw `OptimizerRunResult` -- the hook a + caller with its own post-fit display beyond the fit table/live-progress + panel (e.g. `ParameterEstimationWidget`'s reference-vs-simulated overlay) + uses to refresh it. + """ + + problem: OptimizationProblem + x0: list[float] + render_preview: Callable[[Sequence[float], Any], None] + render_fit_table: Callable[[Mapping[str, float]], str] + accept: Callable[[Mapping[str, float]], None] + on_finished: Optional[Callable[[OptimizerRunResult], None]] = None + preview_series: Optional[Callable[[Sequence[float]], Optional[list[dict[str, Any]]]]] = None diff --git a/cadetgui/parameter_estimation.py b/cadetgui/parameter_estimation.py new file mode 100644 index 0000000..e43d45b --- /dev/null +++ b/cadetgui/parameter_estimation.py @@ -0,0 +1,322 @@ +from __future__ import annotations + +import copy +import threading +from dataclasses import dataclass +from typing import Any, Callable, Literal, Mapping, Optional, Sequence + +import numpy as np +import numpy.typing as npt +from CADETProcess.calibration import apply_beer_lambert, normalize_area +from CADETProcess.comparison import Comparator +from CADETProcess.comparison.difference import SSE +from CADETProcess.optimization import OptimizationProblem +from CADETProcess.processModel import ComponentSystem +from CADETProcess.reference import ReferenceIO +from CADETProcess.simulator import Cadet + +from .cadetprocessadapter import FieldSpec +from .optimizer_runner import OPTIMIZERS, OptimizerKnob, OptimizerSpec, run_optimization +from .simulation import run_process + +# OptimizerKnob/OptimizerSpec/OPTIMIZERS live in `cadetgui.optimizer_runner` now +# (shared with any caller driving a `CADETProcess.characterization.CharacterizeXxx` +# problem, not just this module's own `run_estimation`) -- re-exported here so +# existing imports of `cadetgui.parameter_estimation.OPTIMIZERS` keep working. +__all__ = [ + "FittableParameter", + "EstimationResult", + "OptimizerKnob", + "OptimizerSpec", + "OPTIMIZERS", + "list_fittable_parameters", + "build_reference", + "calibrate_reference", + "build_estimation_problem", + "run_estimation", + "simulate_at", +] + +CalibrationMethod = Literal["none", "beer_lambert", "normalize_area"] + +_FITTABLE_KINDS = ("float", "float_list") + + +@dataclass(frozen=True) +class FittableParameter: + """One column/binding attribute (or one component of a per-component one) to fit.""" + + owner: Literal["column", "binding"] + name: str + label: str + component_index: Optional[int] + current_value: float + lb: float + ub: float + + +def _bounds(field: FieldSpec, current: float) -> tuple[float, float]: + lb = field.min if field.min is not None else 0.0 + ub = field.max if field.max is not None else (10.0 * current if current else 1.0) + return lb, ub + + +def list_fittable_parameters( + owner: Literal["column", "binding"], + fields: Sequence[FieldSpec], + values: Mapping[str, Any], +) -> list[FittableParameter]: + """List the fittable (float/float_list) fields, one entry per scalar/component. + + Non-numeric kinds ("bool", "text", "choice" -- e.g. `is_kinetic`) aren't + fittable and are skipped. + """ + params: list[FittableParameter] = [] + for field in fields: + if field.kind not in _FITTABLE_KINDS: + continue + if field.kind == "float": + current = float(values[field.name]) + lb, ub = _bounds(field, current) + label = field.label or field.name + params.append(FittableParameter(owner, field.name, label, None, current, lb, ub)) + continue + + for i, current in enumerate(values[field.name]): + current = float(current) + lb, ub = _bounds(field, current) + component = field.component_names[i] if field.component_names else str(i) + label = f"{field.label or field.name} — {component}" + params.append(FittableParameter(owner, field.name, label, i, current, lb, ub)) + return params + + +def build_reference( + label: str, + time_min: npt.ArrayLike, + signal: npt.ArrayLike, + *, + component_name: Optional[str] = None, +) -> ReferenceIO: + """Wrap an imported (time_min, signal) pair as a CADET-Process reference. + + Time is converted to seconds to match the simulation's own time grid. + `component_name`, when given, tags the reference as representing exactly + that one simulated component (see `run_estimation`'s `component_name`) -- + required for a single-channel detector signal that's only sensitive to one + molecule, the common case. Left `None`, the reference is unnamed and + `run_estimation` falls back to comparing against the summed total + concentration of every component (a blended/overlapping signal). + """ + component_system = ComponentSystem([component_name]) if component_name else None + return ReferenceIO( + label, np.asarray(time_min) * 60.0, np.asarray(signal), component_system=component_system + ) + + +def calibrate_reference( + reference: ReferenceIO, method: CalibrationMethod, **kwargs: float +) -> ReferenceIO: + """Convert a raw imported signal (e.g. UV mAU) to concentration-equivalent units. + + Thin wrapper over `CADETProcess.calibration` -- the actual conversions + (`apply_beer_lambert`, `normalize_area`) are native, not reimplemented here. + "none" (the default) returns `reference` unchanged, matching the previous + behavior of comparing the raw signal directly. + """ + if method == "none": + return reference + if method == "beer_lambert": + calibrated = apply_beer_lambert( + reference, kwargs["extinction_coefficient"], kwargs["path_length"] + ) + elif method == "normalize_area": + calibrated = normalize_area(reference, kwargs["target_area"]) + else: + raise ValueError(f"Unknown calibration method: {method!r}") + + # Neither native helper carries the input's component_system over onto the + # new ReferenceIO it builds -- reattach it, or a `component_name` passed to + # `build_reference` silently stops being respected by `run_estimation`. + return ReferenceIO( + calibrated.name, calibrated.time, calibrated.solution, calibrated.flow_rate, + component_system=reference.component_system, + ) + + +def _unit_name(process: Any, unit: Any) -> str: + return next(name for name, u in process.flow_sheet.units_dict.items() if u is unit) + + +def _parameter_path(process: Any, column: Any, param: FittableParameter) -> str: + unit_name = _unit_name(process, column) + if param.owner == "column": + return f"flow_sheet.{unit_name}.{param.name}" + return f"flow_sheet.{unit_name}.binding_model.{param.name}" + + +def _register_variables( + problem: OptimizationProblem, + working_process: Any, + working_column: Any, + params: Sequence[FittableParameter], + selected_indices: Sequence[int], +) -> None: + """Register one `add_variable` per selected parameter, in order. + + Shared between `run_estimation`'s real optimization problem and + `simulate_at`'s throwaway one -- both need the exact same variables + registered the exact same way for `problem.set_variables(values)` to + write `values` onto the right attributes. + """ + for idx in selected_indices: + param = params[idx] + kwargs: dict[str, Any] = {} + if param.component_index is not None: + kwargs["indices"] = (param.component_index,) + problem.add_variable( + name=f"var_{idx}", + parameter_path=_parameter_path(working_process, working_column, param), + lb=param.lb, + ub=param.ub, + # "auto" lets CADET-Process pick a sensible per-variable scaling + # (e.g. log for a wide-dynamic-range rate constant, linear + # otherwise) -- without it every variable is optimized in its own + # raw, wildly different units (e.g. porosity ~0.7 next to axial + # dispersion ~1e-8), which Nelder-Mead's simplex handles poorly. + transform="auto", + **kwargs, + ) + + +def simulate_at( + process: Any, + column: Any, + params: Sequence[FittableParameter], + selected_indices: Sequence[int], + values: Sequence[float], +) -> Any: + """Simulate `process` with `values` written onto the selected parameters. + + Deep-copies `process` first -- never touches the caller's own object, and + in particular never touches any other deep copy an optimization run might + be actively mutating elsewhere (e.g. `run_estimation`'s own + `working_process`, which a background optimization thread writes to on + every objective evaluation -- reading or writing it concurrently from + here would be a real race). Meant for periodic "what does the process + look like at this candidate point" previews, e.g. a live progress plot, + fully decoupled from whatever optimization run produced `values`. + """ + working_process = copy.deepcopy(process) + working_column = working_process.flow_sheet.units_dict[_unit_name(process, column)] + + problem = OptimizationProblem("simulate_at") + problem.add_evaluation_object(working_process) + _register_variables(problem, working_process, working_column, params, selected_indices) + problem.set_variables(list(values)) + + return run_process(working_process) + + +@dataclass(frozen=True) +class EstimationResult: + """Outcome of `run_estimation`.""" + + fitted: dict[int, float] + objective: Optional[float] + success: bool + message: str + # The detached, fitted copy of `process` -- safe to simulate/plot from, but + # never the caller's own process (see `run_estimation`'s docstring). + fitted_process: Optional[Any] = None + cancelled: bool = False + + +def build_estimation_problem( + process: Any, + column: Any, + params: Sequence[FittableParameter], + selected_indices: Sequence[int], + reference: ReferenceIO, + solution_path: str, + *, + component_name: Optional[str] = None, +) -> tuple[OptimizationProblem, Any]: + """Build the `OptimizationProblem` `run_estimation` fits, without running it. + + Never touches `process` -- everything is built around a deep copy. + Returns `(problem, working_process)`: `working_process` is that same deep + copy, needed by `run_estimation` afterwards to populate + `EstimationResult.fitted_process` (fitted values are only meant to reach + the real configuration through an explicit, user-approved write-back, + PRODUCT_VISION.md §16.4 -- never silently overwrite the source model). + + `component_name`, when given, restricts the comparison to that one + simulated component (`SSE(..., components=[component_name])`) -- the right + choice whenever the measured signal is specific to one molecule, which is + the common case. Left `None`, every simulated component is summed first + (`use_total_concentration=True`) before differencing, appropriate only for + a genuinely blended/overlapping signal (e.g. a non-selective detector). + """ + working_process = copy.deepcopy(process) + working_column = working_process.flow_sheet.units_dict[_unit_name(process, column)] + + if component_name is not None: + metric = SSE(reference, components=[component_name]) + else: + metric = SSE(reference, use_total_concentration=True) + comparator = Comparator() + comparator.add_difference_metric(metric, solution_path) + + problem = OptimizationProblem("parameter_estimation") + problem.add_evaluation_object(working_process) + _register_variables(problem, working_process, working_column, params, selected_indices) + + simulator = Cadet() + problem.add_evaluator(simulator) + problem.add_objective(comparator, n_objectives=comparator.n_metrics, requires=[simulator]) + + return problem, working_process + + +def run_estimation( + process: Any, + column: Any, + params: Sequence[FittableParameter], + selected_indices: Sequence[int], + reference: ReferenceIO, + solution_path: str, + *, + optimizer_name: str, + optimizer_kwargs: Mapping[str, int], + starts: Sequence[float], + component_name: Optional[str] = None, + on_optimizer_ready: Optional[Callable[[Any], None]] = None, + cancel_event: Optional[threading.Event] = None, +) -> EstimationResult: + """Fit the selected parameters against `reference`, never touching `process`. + + A thin wrapper: `build_estimation_problem` builds the problem, then + `cadetgui.optimizer_runner.run_optimization` runs it -- see both + docstrings for `optimizer_name`/`starts`/`on_optimizer_ready`/ + `cancel_event` semantics, which this function passes straight through + unchanged. + """ + if not selected_indices: + return EstimationResult({}, None, False, "No parameters selected to fit.") + + problem, working_process = build_estimation_problem( + process, column, params, selected_indices, reference, solution_path, + component_name=component_name, + ) + result = run_optimization( + problem, optimizer_name, optimizer_kwargs, list(starts), + cancel_event=cancel_event, on_optimizer_ready=on_optimizer_ready, + ) + if result.cancelled: + return EstimationResult({}, None, False, result.message, cancelled=True) + if not result.success: + return EstimationResult({}, None, False, result.message) + + fitted = {idx: result.x_best[f"var_{idx}"] for idx in selected_indices} + return EstimationResult(fitted, result.objective, True, result.message, working_process) diff --git a/cadetgui/parameter_history_store.py b/cadetgui/parameter_history_store.py new file mode 100644 index 0000000..29decaa --- /dev/null +++ b/cadetgui/parameter_history_store.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +import datetime as dt +import uuid +from dataclasses import dataclass, field +from pathlib import Path +from typing import Dict, List, Optional + +from . import __version__ as _cadetgui_version +from ._record_store import list_records, save_record + +__all__ = [ + "ParameterPushRecordState", + "default_parameter_history_store_dir", + "new_push_id", + "save_push", + "list_pushes", +] + + +@dataclass(frozen=True) +class ParameterPushRecordState: + """Persisted record of one "Push to Configuration" -- the provenance of a fit. + + Answers "which experiment(s) determined this parameter, with what + result" -- `stage`/`dataset_labels` say what produced `values`, + `objective`/`optimizer_name` say how well and with what, `config_hash` + anchors it to the configuration it was pushed into (same convention as + `run_store.RunRecordState.config_hash`). + """ + + push_id: str + stage: str + timestamp: str + values: Dict[str, float] + dataset_labels: List[str] = field(default_factory=list) + optimizer_name: Optional[str] = None + objective: Optional[float] = None + config_hash: Optional[str] = None + config_name: Optional[str] = None + cadetgui_version: str = _cadetgui_version + + +def default_parameter_history_store_dir() -> Path: + """Local directory saved parameter-push records live in, if a caller doesn't pick one.""" + store_dir = Path.home() / ".cadetgui" / "parameter_history" + store_dir.mkdir(parents=True, exist_ok=True) + return store_dir + + +def new_push_id() -> str: + """Return a fresh identifier for one push -- same shape as `run_store.new_run_id`.""" + return f"{dt.datetime.now():%Y%m%d_%H%M%S}_{uuid.uuid4().hex[:6]}" + + +def save_push( + push_id: str, + stage: str, + values: Dict[str, float], + *, + dataset_labels: Optional[List[str]] = None, + optimizer_name: Optional[str] = None, + objective: Optional[float] = None, + config_hash: Optional[str] = None, + config_name: Optional[str] = None, + store_dir: Optional[Path] = None, +) -> ParameterPushRecordState: + """Write one push's metadata.""" + store_dir = store_dir or default_parameter_history_store_dir() + state = ParameterPushRecordState( + push_id=push_id, + stage=stage, + timestamp=dt.datetime.now().isoformat(), + values=dict(values), + dataset_labels=list(dataset_labels or []), + optimizer_name=optimizer_name, + objective=objective, + config_hash=config_hash, + config_name=config_name, + ) + save_record("push", push_id, state, store_dir) + return state + + +def list_pushes(*, store_dir: Optional[Path] = None) -> List[ParameterPushRecordState]: + """List every recorded push, newest first. Silently skips unreadable manifests.""" + store_dir = store_dir or default_parameter_history_store_dir() + return list_records("push", ParameterPushRecordState, store_dir) diff --git a/cadetgui/parameters/__init__.py b/cadetgui/parameters/__init__.py new file mode 100644 index 0000000..6f604fb --- /dev/null +++ b/cadetgui/parameters/__init__.py @@ -0,0 +1,224 @@ +"""Parameter metadata: unit/description/shape/bounds/co_name/default/required-ness, +all introspected live off real CADET-Process descriptors and objects. Nothing here +is hand-typed against a separate copy of CADET-Process's own knowledge. + +CADET-Process's own `ParameterBase` accepts `unit=`/`description=` kwargs, and (as of +https://github.com/fau-advanced-separations/CADET-Process/pull/435) the column/ +binding/solver descriptors this module reads now actually set them, sourced from +CADET-Core's own interface docs. `component_dependent`/`dtype`/`min`/`max`/`co_name`/ +`default` are read live off the actual descriptor class (`Sized`/`Ranged`/`Switch`/ +`Bool`/`Integer` from `CADETProcess.dataStructure`) and off +`CADETProcess.simulator.cadetAdapter`'s own `unit_parameters_map`/ +`adsorption_parameters_map` -- the literal mapping CADET-Process uses to serialize to +CADET-Core, so `co_name` can't disagree with what's actually written. None of this can +drift from CADET-Process the way a hand-typed copy could, because it *is* +CADET-Process. + +A parameter's *name* itself is also read live, off `cls._parameters` -- CADET-Process's +own class-level list of every parameter it exposes (built by the same metaclass as +`cls._required_parameters`, so -- like that one -- its *order* is hash-seed-random +across process runs, not a stable fact; only the *set* is). This module returns names +in plain alphabetical order instead of a curated one. + +`required_parameters()` is live too, but *not* off the class-level +`cls._required_parameters` CADET-Process's metaclass builds -- that's a +superset (e.g. `Cstr._required_parameters` includes `flow_rate`, but a +constructed `Cstr(...).required_parameters` doesn't: confirmed directly, +CADET-Process does extra instance-level filtering the class attribute never +captures). Only a real, minimally-built instance is authoritative, so that's +what `_required_parameters_live` constructs. + +The one thing live introspection can't reach: `co_group`, the CADET-Python H5 +wrapper tree path for a solver-category model (e.g. `/solver/time_integrator`) +-- CADETProcess.simulator.cadetAdapter writes it as a plain uppercase HDF5 +group, not through a cp_name<->co_name map the way column/binding parameters +are, so it isn't stored as an introspectable string anywhere in CADET-Process. +That one stays a hand-typed constant, in `_SOLVER_CO_GROUPS` below. +""" +from __future__ import annotations + +import math +from functools import lru_cache +from typing import Any + +from CADETProcess.dataStructure import Bool, Integer, Ranged, Sized, Switch +from CADETProcess.processModel import ( + ComponentSystem, + Cstr, + GeneralRateModel, + Langmuir, + Linear, + LumpedRateModelWithoutPores, + LumpedRateModelWithPores, + NoBinding, + StericMassAction, + TubularReactor, +) +from CADETProcess.simulator.cadetAdapter import ( + SolverTimeIntegratorParameters, + adsorption_parameters_map, + unit_parameters_map, +) + +__all__ = [ + "get_parameters", + "get_parameter", + "required_parameters", + "get_co_group", +] + +# The real CADET-Process class behind each (category, model name) this module +# knows about -- what live introspection actually reads. Kept in sync with +# test_every_registered_parameter_resolves_a_real_live_descriptor. +_MODEL_CLASSES: dict[tuple[str, str], type] = { + ("column", "GeneralRateModel"): GeneralRateModel, + ("column", "LumpedRateModelWithPores"): LumpedRateModelWithPores, + ("column", "LumpedRateModelWithoutPores"): LumpedRateModelWithoutPores, + ("column", "TubularReactor"): TubularReactor, + ("column", "Cstr"): Cstr, + ("binding", "NoBinding"): NoBinding, + ("binding", "Linear"): Linear, + ("binding", "Langmuir"): Langmuir, + ("binding", "StericMassAction"): StericMassAction, + ("solver", "SolverTimeIntegratorParameters"): SolverTimeIntegratorParameters, +} + +# CADET-Process's own cp_name<->co_name maps, keyed the same way as _MODEL_CLASSES' +# category. No such map exists for "solver" (it writes a plain-uppercase group, +# not one of these two conversion tables) -- co_name stays None there. +_CO_NAME_MAPS = {"column": unit_parameters_map, "binding": adsorption_parameters_map} + +# See module docstring: the one piece of metadata live introspection can't reach. +_SOLVER_CO_GROUPS: dict[str, str] = { + "SolverTimeIntegratorParameters": "/solver/time_integrator", +} + + +def get_co_group(model: str) -> str | None: + """CADET-Core H5 group path for a solver-category model, e.g. + `get_co_group("SolverTimeIntegratorParameters") == "/solver/time_integrator"`. + `None` for anything not registered. + """ + return _SOLVER_CO_GROUPS.get(model) + + +@lru_cache(maxsize=1) +def _inverse_co_name_maps() -> dict[tuple[str, str], dict[str, str]]: + """`(category, model_name) -> {cp_name: co_name}`, inverted from CADET-Process's + own parameter maps (see module docstring). + """ + out: dict[tuple[str, str], dict[str, str]] = {} + for category, param_map in _CO_NAME_MAPS.items(): + for model_name, entry in param_map.items(): + out[(category, model_name)] = { + cp_name: co_name for co_name, cp_name in entry["parameters"].items() + } + return out + + +def _descriptor(cls: type, name: str) -> Any | None: + """The real `ParameterBase` descriptor backing `name` on `cls`, or `None`. + + A few attributes (`q`, `cp`, `surface_diffusion`) are plain Python + `@property` wrappers around a differently-sized private descriptor + (`_q`/`_cp`/`_surface_diffusion`, e.g. sized by `n_bound_states` rather + than `n_comp`) -- confirmed directly against the installed CADET-Process, + not assumed. Falls back to that convention when `name` itself resolves to + a plain `property` rather than a descriptor. + """ + desc = getattr(cls, name, None) + if isinstance(desc, property): + desc = getattr(cls, f"_{name}", None) + return desc if hasattr(desc, "default") else None + + +def _live_metadata(category: str, model_name: str, name: str) -> dict[str, Any] | None: + """Full metadata for one parameter, introspected off the real CADET-Process + descriptor. `None` if `(category, model_name)` isn't registered in + `_MODEL_CLASSES` or the attribute can't be resolved to a real descriptor + (see `_descriptor`). + """ + cls = _MODEL_CLASSES.get((category, model_name)) + if cls is None: + return None + descriptor = _descriptor(cls, name) + if descriptor is None: + return None + + if isinstance(descriptor, Bool): + dtype = "bool" + elif isinstance(descriptor, Integer): + dtype = "int" + else: + dtype = "float" + + live: dict[str, Any] = { + "component_dependent": isinstance(descriptor, Sized), + "dtype": dtype, + "co_name": _inverse_co_name_maps().get((category, model_name), {}).get(name), + "unit": descriptor.unit, + "description": descriptor.description, + } + if descriptor.default is not None: + live["default"] = descriptor.default + + if isinstance(descriptor, Ranged): + if math.isfinite(descriptor.lb): + live["min"] = float(descriptor.lb) + if math.isfinite(descriptor.ub): + live["max"] = float(descriptor.ub) + elif isinstance(descriptor, Switch): + # e.g. TubularReactorBase.flow_direction = Switch(valid=[-1, 1]) -- + # not a Ranged constraint, but still a numeric range worth rendering + # as bounds. + valid = [v for v in descriptor.valid if isinstance(v, (int, float))] + if valid: + live["min"], live["max"] = float(min(valid)), float(max(valid)) + + return live + + +def get_parameters(category: str, model: str) -> dict[str, dict]: + """All known parameters for `model` in `category`, keyed by CADET-Process name, + in alphabetical order. + + Names come from `cls._parameters` (see module docstring for why only its + *set*, not its order, is trustworthy), filtered to the ones that actually + resolve to a real descriptor (see `_descriptor`). Each entry's metadata is + entirely live (see `_live_metadata`). + + Raises `KeyError` if `(category, model)` isn't registered in + `_MODEL_CLASSES`. + """ + cls = _MODEL_CLASSES[(category, model)] + names = {name for name in cls._parameters if _descriptor(cls, name) is not None} + return {name: _live_metadata(category, model, name) for name in sorted(names)} + + +def get_parameter(category: str, model: str, name: str) -> dict: + """Metadata for one parameter, e.g. `get_parameter("binding", "Langmuir", "capacity")`.""" + return get_parameters(category, model)[name] + + +@lru_cache(maxsize=None) +def _required_parameters_live(category: str, model: str) -> frozenset[str]: + """The real required-parameter set for a fresh, minimally-built `model` + instance -- see the module docstring for why this must be a real instance, + not `cls._required_parameters`. Cached: the underlying CADET-Process + objects only need constructing once per (category, model). + """ + cls = _MODEL_CLASSES.get((category, model)) + if cls is None: + return frozenset() + obj = cls() if category == "solver" else cls(ComponentSystem(1), name="x") + return frozenset(obj.required_parameters) + + +def required_parameters(category: str, model: str) -> list[str]: + """Return the subset of `get_parameters(...)` CADET-Process requires to + build `model`, in alphabetical order (CADET-Process's own order is + hash-seed-random across process runs, not a stable fact to return -- see + `ai-docs/UPSTREAM_ISSUES.md` #4). + """ + required = _required_parameters_live(category, model) + return [name for name in get_parameters(category, model) if name in required] diff --git a/cadetgui/run_store.py b/cadetgui/run_store.py new file mode 100644 index 0000000..84f2abb --- /dev/null +++ b/cadetgui/run_store.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +import datetime as dt +import uuid +from dataclasses import dataclass +from pathlib import Path +from typing import Any, List, Optional + +from . import __version__ as _cadetgui_version +from ._record_store import delete_record, list_records, load_record, save_record + +__all__ = [ + "RunRecordState", + "default_run_store_dir", + "new_run_id", + "run_output_path", + "save_run", + "load_run", + "list_runs", + "delete_run", + "load_run_results", +] + + +@dataclass(frozen=True) +class RunRecordState: + """Persisted run metadata. The raw CADET-Core output (if any) is a sibling `run_.h5`.""" + + run_id: str + label: str + timestamp: str + ok: bool + config_hash: Optional[str] = None + config_name: Optional[str] = None + error: Optional[str] = None + cadetgui_version: str = _cadetgui_version + + +def default_run_store_dir() -> Path: + """Local directory saved run records live in, if a caller doesn't pick one.""" + store_dir = Path.home() / ".cadetgui" / "runs" + store_dir.mkdir(parents=True, exist_ok=True) + return store_dir + + +def new_run_id() -> str: + """Return a fresh identifier for one run, shared by its manifest and raw-output file. + + Timestamp-first so a folder's files sort chronologically and are + recognizable at a glance rather than as opaque hex -- the trailing hex + suffix only exists to keep two runs started in the same second unique, + not for identification. + """ + return f"{dt.datetime.now():%Y%m%d_%H%M%S}_{uuid.uuid4().hex[:6]}" + + +def run_output_path(run_id: str, *, store_dir: Optional[Path] = None) -> Path: + """Where a run's raw CADET-Core output h5 belongs, whether or not it exists yet. + + Only meaningful for the file-based simulator path (`Cadet(use_dll=False)`, + the default) -- a run made via the CAPI (`use_dll=True`) never writes one. + The `run_` prefix (matching its manifest) distinguishes it at a glance + from a configuration's own `config_.h5` sitting in the same + per-configuration folder (see `configuration_store.config_dir`). + """ + store_dir = store_dir or default_run_store_dir() + return store_dir / f"run_{run_id}.h5" + + +def save_run( + run_id: str, + label: str, + *, + ok: bool, + config_hash: Optional[str] = None, + config_name: Optional[str] = None, + error: Optional[str] = None, + store_dir: Optional[Path] = None, +) -> RunRecordState: + """Write one run's metadata. + + The caller is responsible for the raw output file, if any -- it must + already sit at `run_output_path(run_id, store_dir=store_dir)` before this + is called with `ok=True`. + """ + store_dir = store_dir or default_run_store_dir() + state = RunRecordState( + run_id=run_id, + label=label, + timestamp=dt.datetime.now().isoformat(), + ok=ok, + config_hash=config_hash, + config_name=config_name, + error=error, + ) + save_record("run", run_id, state, store_dir) + return state + + +def load_run(run_id: str, *, store_dir: Optional[Path] = None) -> RunRecordState: + """Read one run's metadata by id.""" + store_dir = store_dir or default_run_store_dir() + return load_record("run", run_id, RunRecordState, store_dir) + + +def list_runs(*, store_dir: Optional[Path] = None) -> List[RunRecordState]: + """List every recorded run, newest first. Silently skips unreadable manifests.""" + store_dir = store_dir or default_run_store_dir() + return list_records("run", RunRecordState, store_dir) + + +def delete_run(run_id: str, *, store_dir: Optional[Path] = None) -> bool: + """Delete one run's manifest and raw output file; return whether anything was removed. + + Only ever touches the two files named after `run_id`. + """ + if not run_id or Path(run_id).name != run_id: + raise ValueError(f"Invalid run id {run_id!r}.") + store_dir = store_dir or default_run_store_dir() + removed = delete_record("run", run_id, store_dir) + output_path = run_output_path(run_id, store_dir=store_dir) + if output_path.exists(): + output_path.unlink() + removed = True + return removed + + +def load_run_results( + run: RunRecordState, process: Any, *, store_dir: Optional[Path] = None +) -> Any: + """Reconstruct a real SimulationResults for a persisted run from its rebuilt Process. + + `process` must already reflect the same configuration the run was made + against (e.g. via `ConfigurationWidget.import_from_store(run.config_hash)`) + -- this only re-reads the stored solver output, it never re-simulates. + """ + if not run.ok: + raise ValueError(f"Run {run.run_id!r} recorded a failure, not a result.") + output_path = run_output_path(run.run_id, store_dir=store_dir) + if not output_path.exists(): + raise FileNotFoundError(f"No stored output for run {run.run_id!r} at {output_path}.") + from CADETProcess.simulator import Cadet + + return Cadet().load_simulation_results(process, output_path) diff --git a/cadetgui/sensitivity.py b/cadetgui/sensitivity.py new file mode 100644 index 0000000..3fe391a --- /dev/null +++ b/cadetgui/sensitivity.py @@ -0,0 +1,166 @@ +from __future__ import annotations + +import copy +from dataclasses import dataclass +from typing import Any, Mapping, Sequence + +import numpy as np + +from .simulation import run_process + +__all__ = ["SensitivityReport", "perturb_parameters", "response_matrix", "analyze_sensitivity"] + + +def _get_by_path(obj: Any, path: str) -> float: + for attr in path.split("."): + obj = getattr(obj, attr) + # CADET-Process stores some scalar (non-multiplexed-per-component) + # parameters list-wrapped regardless of component count (e.g. a column's + # `axial_dispersion`) -- same convention `InstrumentWidget. + # _make_on_unit_built` already collapses. A genuinely multi-component + # list (len > 1) isn't a single scalar to perturb and is out of scope + # here -- only single-value paths are supported. + if isinstance(obj, (list, tuple)): + if len(obj) != 1: + raise ValueError(f"{path!r} is not a single scalar value: {obj!r}") + obj = obj[0] + return float(obj) + + +def _set_by_path(obj: Any, path: str, value: float) -> None: + *parents, leaf = path.split(".") + target = obj + for attr in parents: + target = getattr(target, attr) + current = getattr(target, leaf) + setattr(target, leaf, type(current)([value]) if isinstance(current, (list, tuple)) else value) + + +def perturb_parameters( + process: Any, + parameter_paths: Sequence[str], + *, + relative_step: float = 1e-3, +) -> dict[str, tuple[Any, Any, float, float]]: + """Central finite-difference perturbation of `process` around each parameter path. + + Never touches `process` -- for each path, builds two deep copies with + that one parameter nudged by `+/- relative_step` of its current value + (or by the raw `relative_step` as an absolute step when the current value + is zero, since a relative step around zero is degenerate), then simulates + both. + + Returns `{path: (result_minus, result_plus, value_minus, value_plus)}`, + consumed by `response_matrix`. + """ + results: dict[str, tuple[Any, Any, float, float]] = {} + for path in parameter_paths: + base_value = _get_by_path(process, path) + step = relative_step * base_value if base_value != 0 else relative_step + value_minus, value_plus = base_value - step, base_value + step + + process_minus = copy.deepcopy(process) + _set_by_path(process_minus, path, value_minus) + process_plus = copy.deepcopy(process) + _set_by_path(process_plus, path, value_plus) + + results[path] = ( + run_process(process_minus), run_process(process_plus), value_minus, value_plus + ) + return results + + +def response_matrix( + perturbed: Mapping[str, tuple[Any, Any, float, float]], + signal_path: tuple[str, str], +) -> np.ndarray: + """Stack each parameter's central-difference response into one `n_params x n_timepoints` matrix. + + Each row is `(solution_plus - solution_minus) / (value_plus - value_minus)` + at `signal_path` (a `(unit_name, port)` pair, the same convention + `cadetprocessadapter.classify_signal_ports` uses) -- the local sensitivity + of the simulated signal to that one parameter, in row order matching + `perturbed`'s iteration order. Every perturbed run is assumed to share the + same time grid (same process structure, only one parameter changed + between the minus/plus copies) -- not re-validated here. + """ + unit, port = signal_path + rows = [] + for result_minus, result_plus, value_minus, value_plus in perturbed.values(): + solution_minus = result_minus.solution[unit][port].solution + solution_plus = result_plus.solution[unit][port].solution + row = (np.asarray(solution_plus) - np.asarray(solution_minus)) / (value_plus - value_minus) + rows.append(row.ravel()) + return np.stack(rows) + + +@dataclass(frozen=True) +class SensitivityReport: + """Local identifiability diagnostic over a selected parameter subset. + + `cosine_similarity[i, j]` is the cosine similarity between parameter i's + and parameter j's response vectors -- close to 1.0 means an optimizer + cannot tell the two apart from this signal alone, the classic + near-degenerate-parameter-pair symptom (close trace agreement can + coexist with substantially wrong recovered values for such a pair). + `condition_number` is the response matrix's condition number: large means + the joint parameter set is poorly constrained overall, even without any + single pair looking degenerate. `flagged_pairs` lists every pair whose + similarity meets `analyze_sensitivity`'s `similarity_threshold`, most + severe first. + """ + + parameter_names: list[str] + cosine_similarity: np.ndarray + condition_number: float + flagged_pairs: list[tuple[str, str, float]] + + +def _report_from_matrix( + parameter_names: Sequence[str], matrix: np.ndarray, *, similarity_threshold: float +) -> SensitivityReport: + """Build a `SensitivityReport` from an already-computed response matrix. + + Split out from `analyze_sensitivity` so the similarity/condition-number/ + flagging arithmetic is testable against a hand-built matrix, without + running a real CADET-Process simulation. + """ + norms = np.linalg.norm(matrix, axis=1, keepdims=True) + normalized = matrix / norms + similarity = normalized @ normalized.T + condition_number = float(np.linalg.cond(matrix)) + + flagged: list[tuple[str, str, float]] = [] + for i in range(len(parameter_names)): + for j in range(i + 1, len(parameter_names)): + score = float(similarity[i, j]) + if score >= similarity_threshold: + flagged.append((parameter_names[i], parameter_names[j], score)) + flagged.sort(key=lambda item: item[2], reverse=True) + + return SensitivityReport( + parameter_names=list(parameter_names), + cosine_similarity=similarity, + condition_number=condition_number, + flagged_pairs=flagged, + ) + + +def analyze_sensitivity( + process: Any, + parameter_paths: Sequence[str], + signal_path: tuple[str, str], + *, + relative_step: float = 1e-3, + similarity_threshold: float = 0.99, +) -> SensitivityReport: + """Local forward-sensitivity/identifiability pre-check, meant to run before fitting. + + Call this before spending optimizer budget on a joint fit over + `parameter_paths` against `signal_path` -- it flags parameter pairs whose + local response, at `process`'s current operating point, is nearly + indistinguishable given that one signal. + """ + perturbed = perturb_parameters(process, parameter_paths, relative_step=relative_step) + matrix = response_matrix(perturbed, signal_path) + return _report_from_matrix(parameter_paths, matrix, similarity_threshold=similarity_threshold) diff --git a/cadetgui/simulation.py b/cadetgui/simulation.py new file mode 100644 index 0000000..163c27f --- /dev/null +++ b/cadetgui/simulation.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +from typing import Any + +try: + from CADETProcess.simulator import Cadet +except Exception: + Cadet = None # type: ignore + + +def run_process(process: Any, **kwargs: Any) -> Any: + """ + Run a CADET-Process Process with the CADETProcess simulator. + + The simulator does not need the component system explicitly. + """ + if Cadet is None: + raise RuntimeError("CADETProcess.simulator is not available") + sim = Cadet() + return sim.simulate(process, **kwargs) diff --git a/cadetgui/widgets/__init__.py b/cadetgui/widgets/__init__.py new file mode 100644 index 0000000..b4c4672 --- /dev/null +++ b/cadetgui/widgets/__init__.py @@ -0,0 +1,30 @@ +"""Composable widgets for CADET-GUI. + +This subpackage provides: +- elements: atomic anywidget input controls +- forms: FormRenderer, rendering a ModelSpec into elements +- composite: task widgets assembled from elements + forms (Configuration, + Solution, RunHistory, DataImport) + +Public API re-exports the composite widgets, the most common entry point. +Import from `cadetgui.widgets.elements`/`.forms` directly for the lower layers. +""" +from .composite import ( + ConfigurationWidget, + DataImportWidget, + ExperimentalDataset, + ParameterEstimationWidget, + RunHistoryWidget, + RunRecord, + SolutionWidget, +) + +__all__ = [ + "ConfigurationWidget", + "SolutionWidget", + "RunHistoryWidget", + "RunRecord", + "DataImportWidget", + "ExperimentalDataset", + "ParameterEstimationWidget", +] diff --git a/cadetgui/widgets/_chrome.py b/cadetgui/widgets/_chrome.py new file mode 100644 index 0000000..7601765 --- /dev/null +++ b/cadetgui/widgets/_chrome.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import base64 +from pathlib import Path + +from ._tokens import with_tokens + +_CSS = with_tokens(Path(__file__).parent / "chrome.css") +_LOGO_PNG = (Path(__file__).parent / "assets" / "cadet_logo.png").read_bytes() +_LOGO_DATA_URI = f"data:image/png;base64,{base64.b64encode(_LOGO_PNG).decode('ascii')}" + + +def style_tag() -> str: + """Shared " + + +def logo_data_uri() -> str: + """Return the CADET logo (icon + wordmark) as an inline `data:` URI PNG.""" + return _LOGO_DATA_URI diff --git a/cadetgui/widgets/_mpl_figure.py b/cadetgui/widgets/_mpl_figure.py new file mode 100644 index 0000000..cb2d21d --- /dev/null +++ b/cadetgui/widgets/_mpl_figure.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +import io +from typing import Any + +import ipywidgets as W + +__all__ = ["new_figure", "display_figure"] + + +def new_figure(**kwargs: Any) -> Any: + """Build a matplotlib Figure with its own Agg canvas -- never touches `pyplot`. + + For rendering on a background thread: `pyplot`'s figure/backend state is + global and generally not thread-safe (confirmed: a plain `plt. + subplots()` call from a non-main thread crashes under an interactive + backend like TkAgg, e.g. `RuntimeError: main thread is not in main + loop`). Building the Figure directly and attaching `FigureCanvasAgg` + sidesteps `pyplot` (and therefore the active backend) entirely -- + works the same regardless of thread or ambient backend. Must always + be paired with passing the resulting Axes into anything that would + otherwise create its own figure (e.g. `solution.plot(ax=...)`, never + bare `solution.plot()` -- the latter calls `plt.subplots()` internally). + """ + from matplotlib.backends.backend_agg import FigureCanvasAgg + from matplotlib.figure import Figure + + fig = Figure(**kwargs) + FigureCanvasAgg(fig) + return fig + + +def display_figure(output: W.Image, fig: Any, *, dpi: int = 150) -> None: + """Render `fig` into `output.value` as PNG bytes and reveal `output`. + + A direct trait assignment, not `Output`'s capture-based `display()` -- + confirmed (real Jupyter kernel, not just a sandbox) that the latter does + not reliably route from a background thread, while a plain trait update + (like `Image.value`) does, from any thread. No CSS width/max-width should + be set on `output` by the caller -- displayed size is then exactly the + PNG's own pixel dimensions, so `dpi` controls both sharpness and how big + it actually appears. + """ + buf = io.BytesIO() + fig.savefig(buf, format="png", dpi=dpi, bbox_inches="tight") + output.value = buf.getvalue() + output.layout.display = "" diff --git a/cadetgui/widgets/_series.py b/cadetgui/widgets/_series.py new file mode 100644 index 0000000..8a80687 --- /dev/null +++ b/cadetgui/widgets/_series.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +from typing import Any, Optional + +import numpy as np + +__all__ = ["decimate_minmax", "solution_series", "reference_series"] + + +def decimate_minmax( + times: np.ndarray, values: np.ndarray, max_points: int = 2000 +) -> tuple[list[float], list[float]]: + """Thin a long trace to ~`max_points`, keeping each bucket's min and max so peaks survive.""" + n = len(times) + if n <= max_points: + return times.tolist(), values.tolist() + edges = np.linspace(0, n, max(max_points // 2, 1) + 1, dtype=int) + keep = {0, n - 1} + for start, stop in zip(edges[:-1], edges[1:]): + if stop <= start: + continue + segment = values[start:stop] + keep.add(start + int(np.argmin(segment))) + keep.add(start + int(np.argmax(segment))) + idx = np.array(sorted(keep)) + return times[idx].tolist(), values[idx].tolist() + + +def solution_series(solution: Any, max_points: int = 2000) -> Optional[list[dict[str, Any]]]: + """One chart series per component (time in minutes), or None if not a time x component trace.""" + values = np.asarray(solution.solution) + names = list(solution.component_system.names) + if values.ndim != 2 or values.shape[1] != len(names): + return None + times_min = np.asarray(solution.time, dtype=float) / 60.0 + series = [] + for i, name in enumerate(names): + t, v = decimate_minmax(times_min, values[:, i], max_points) + series.append({"name": name, "times": t, "values": v}) + return series + + +def reference_series( + name: str, time_s: Any, values: Any, max_points: int = 2000 +) -> dict[str, Any]: + """Build a dashed, neutral-colored chart series for measured data (time in seconds).""" + times, vals = decimate_minmax( + np.asarray(time_s, dtype=float) / 60.0, np.asarray(values, dtype=float), max_points + ) + return {"name": name, "times": times, "values": vals, "dashed": True, "reference": True} diff --git a/cadetgui/widgets/_settings_popover.py b/cadetgui/widgets/_settings_popover.py new file mode 100644 index 0000000..80d2534 --- /dev/null +++ b/cadetgui/widgets/_settings_popover.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from typing import Any, List + +import ipywidgets as W + +__all__ = ["SettingsPopover", "toggle_box"] + + +def toggle_box(box: W.Widget) -> bool: + """Show/hide `box` (a `display: none` VBox); return whether it is now shown.""" + shown = box.layout.display == "none" + box.layout.display = "" if shown else "none" + return shown + + +class SettingsPopover: + """Gear-icon button that show/hides a settings box below a section header. + + Not a real floating popover, just a plain show/hide toggle -- matches + ARCHITECTURE.md's "no Modal element built yet" note. + """ + + def __init__(self, *, tooltip: str, children: List[Any], visible: bool = True) -> None: + self.button = W.Button( + icon="cog", tooltip=tooltip, + layout=W.Layout(width="36px", display="" if visible else "none"), + ) + self.box = W.VBox( + [W.HTML("
Settings
"), *children], + layout=W.Layout(display="none"), + ) + self.box.add_class("cadetgui-section") + self.box.add_class("cadetgui-settings-box") + self.button.on_click(self._on_click) + + def _on_click(self, _btn: Any) -> None: + toggle_box(self.box) + + @property + def visible(self) -> bool: + return self.button.layout.display != "none" + + @visible.setter + def visible(self, value: bool) -> None: + self.button.layout.display = "" if value else "none" + if not value: + self.box.layout.display = "none" diff --git a/cadetgui/widgets/_sidebar_shell.py b/cadetgui/widgets/_sidebar_shell.py new file mode 100644 index 0000000..a0b83a2 --- /dev/null +++ b/cadetgui/widgets/_sidebar_shell.py @@ -0,0 +1,266 @@ +from __future__ import annotations + +import itertools +from typing import ( + Callable, + Dict, + List, + Mapping, + Optional, + Sequence, + Set, + Tuple, + TypeVar, +) + +import ipywidgets as W + +__all__ = ["SidebarShell", "validate_steps", "resolve_step", "collect_panes"] + +_T = TypeVar("_T") + + +class SidebarShell: + """A left-sidebar nav that shows exactly one pane at a time. + + Generic over its panes: takes `(label, widget)` pairs and does only the + nav/show-hide mechanics (single `ToggleButtons`, `layout.display` + toggling -- state on a hidden pane survives switching away from it, same + as `_settings_popover.toggle_box`). Wiring between panes + (`bind_to_config`, `add_listener`, ...) is the caller's job before + constructing this -- `WorkbenchWidget` is the reference example. + + `groups` maps a group label to the pane labels nested under it. A group + is one collapsible entry in the nav, placed where its first child sits; + expanded, its children follow, indented and shown without a shared + `": "` prefix. Without `groups`, `nav.options` is just the pane + labels; with them it is `(display, key)` pairs and `nav.value` is the + pane label, or `None` while the selected pane sits in a collapsed group. + """ + + _instances = itertools.count() + + def __init__( + self, + panes: Sequence[Tuple[str, W.Widget]], + groups: Optional[Mapping[str, Sequence[str]]] = None, + ) -> None: + if not panes: + raise ValueError("SidebarShell needs at least one pane") + labels = [label for label, _ in panes] + if len(set(labels)) != len(labels): + raise ValueError(f"Pane labels must be unique, got {labels!r}") + + self._order: List[str] = labels + self.panes: Dict[str, W.Widget] = dict(panes) + self._groups: Dict[str, List[str]] = {} + self._group_of: Dict[str, str] = {} + for group, children in (groups or {}).items(): + if group in self.panes: + raise ValueError(f"Group label {group!r} clashes with a pane label") + present = [label for label in labels if label in children] + for child in present: + if child in self._group_of: + raise ValueError(f"Pane {child!r} is in more than one group") + self._group_of[child] = group + if present: + self._groups[group] = present + for widget in self.panes.values(): + widget.layout.display = "none" + + self._warnings: Dict[str, str] = {} + self._expanded: Set[str] = set() + self._current: str = self._order[0] + self._entries: List[Tuple[str, str]] = [] + self._syncing = False + + self._nav_class = f"cadetgui-sidebar-nav-{next(self._instances)}" + self.nav = W.ToggleButtons(options=self._order) + self.nav.add_class("cadetgui-sidebar-nav") + self.nav.add_class(self._nav_class) + self.nav.observe(self._on_nav_change, names="index") + self._nav_style = W.HTML() + + content = W.VBox([self._nav_style, *self.panes.values()]) + content.add_class("cadetgui-workbench-content") + + self.body = W.HBox([self.nav, content]) + self.body.add_class("cadetgui-workbench-body") + + self.show(self._order[0]) + + def show(self, label: str) -> None: + """Switch to the pane registered under `label`, expanding its group.""" + if label not in self.panes: + raise KeyError(label) + self._current = label + if label in self._group_of: + self._expanded.add(self._group_of[label]) + for name, widget in self.panes.items(): + widget.layout.display = "" if name == label else "none" + self._sync_nav() + + def set_warning(self, label: str, message: Optional[str]) -> None: + """Mark a step with a warning icon and hover text, or clear it with `None`. + + Purely informational -- the step stays selectable. A collapsed group + shows the marker for any warned child. + """ + if label not in self.panes: + raise KeyError(label) + if message: + self._warnings[label] = message + else: + self._warnings.pop(label, None) + self._sync_nav() + + def _visible_entries(self) -> List[Tuple[str, str]]: + entries: List[Tuple[str, str]] = [] + seen: Set[str] = set() + for label in self._order: + group = self._group_of.get(label) + if group is None: + entries.append((label, label)) + elif group not in seen: + seen.add(group) + chevron = "\u25be" if group in self._expanded else "\u25b8" + entries.append((f"{chevron} {group}", group)) + if group in self._expanded: + for child in self._groups[group]: + entries.append((self._child_label(group, child), child)) + return entries + + @staticmethod + def _child_label(group: str, child: str) -> str: + prefix = f"{group}: " + return child[len(prefix):] if child.startswith(prefix) else child + + def _warning_for(self, key: str) -> str: + if key in self._groups: + if key in self._expanded: + return "" + return "\n".join( + f"{child}: {self._warnings[child]}" + for child in self._groups[key] + if child in self._warnings + ) + return self._warnings.get(key, "") + + def _sync_nav(self) -> None: + entries = self._visible_entries() + keys = [key for _, key in entries] + collapsed_group = self._group_of.get(self._current) + if collapsed_group is not None and collapsed_group in self._expanded: + collapsed_group = None + value = self._current if self._current in keys else None + + self._syncing = True + try: + if entries != self._entries: + self._entries = entries + if self._groups: + self.nav.options = entries + else: + self.nav.options = keys + if self.nav.value != value: + self.nav.value = value + warnings = [self._warning_for(key) for key in keys] + self.nav.icons = tuple("exclamation-triangle" if w else "" for w in warnings) + self.nav.tooltips = tuple(warnings) + finally: + self._syncing = False + self._nav_style.value = self._style_for(entries, collapsed_group) + + def _style_for(self, entries: List[Tuple[str, str]], active_group: Optional[str]) -> str: + rules = [] + for position, (_, key) in enumerate(entries, start=1): + target = f".{self._nav_class} .widget-toggle-button:nth-child({position})" + if key in self._group_of: + rules.append(f"{target}{{padding-left:calc(var(--cg-space-3) + 16px)}}") + elif key == active_group: + rules.append( + f"{target}{{background:color-mix(in srgb, var(--cg-focus) 12%, transparent);" + "box-shadow:inset 3px 0 0 var(--cg-focus);color:var(--cg-focus);" + "font-weight:600}" + ) + return f"" if rules else "" + + def _on_nav_change(self, change: dict) -> None: + if self._syncing or change.get("name") != "index": + return + key = self.nav.value + if key is None: + return + if key in self._groups: + if key in self._expanded: + self._expanded.discard(key) + else: + self._expanded.add(key) + self._sync_nav() + return + self.show(key) + + def display(self) -> None: + """Render this widget in a Jupyter cell.""" + from IPython.display import display as _display + + _display(self.body) + + +def validate_steps(steps: Sequence[str], known: Sequence[str]) -> None: + """Raise ValueError if `steps` contains anything outside `known`. + + For validating an `include=(...)` argument against the fixed set of + steps a shell actually knows how to build -- see `resolve_step` for the + per-step half of the same pattern. + """ + unknown = set(steps) - set(known) + if unknown: + raise ValueError( + f"Unknown step(s) {sorted(unknown)!r}, expected a subset of {tuple(known)!r}" + ) + + +def resolve_step( + label: str, + given: Optional[_T], + *, + steps: Sequence[str], + factory: Callable[[], _T], +) -> Optional[_T]: + """Resolve one optional, possibly-excluded pane's widget. + + The recurring shape behind any `WorkbenchWidget`-style "build me a + sidebar over these steps, let the caller inject their own widget for + any of them" composer: + + - `given` (an explicit pre-built widget, or None) always wins. + - Otherwise, build a fresh default via `factory()` -- but only if + `label` is actually in `steps`; an excluded step resolves to `None` + rather than being built and then hidden, so skipping a step costs + nothing (no widget constructed, nothing to wire listeners to). + - Passing `given` for a `label` not in `steps` is a contradiction (you + supplied a widget for a step you also said to skip) and raises, + rather than silently building it anyway or silently dropping it. + + A shell with three optional steps calls this once per step: + + self.configuration = resolve_step( + "Process Configuration", configuration, steps=steps, factory=ConfigurationWidget + ) + """ + if given is not None and label not in steps: + raise ValueError(f"{label!r} widget was given but not in include={tuple(steps)!r}") + if given is not None: + return given + return factory() if label in steps else None + + +def collect_panes(order: Sequence[str], slots: Mapping[str, Optional[_T]]) -> List[Tuple[str, _T]]: + """Build `SidebarShell`'s pane list from a `{label: widget-or-None}` map. + + Keeps `order`'s ordering; drops any label whose slot is `None` -- the + "excluded via `include`" result `resolve_step` returns -- since + `SidebarShell` requires every pane it's given to be a real widget. + """ + return [(label, slots[label]) for label in order if slots.get(label) is not None] diff --git a/cadetgui/widgets/_status.py b/cadetgui/widgets/_status.py new file mode 100644 index 0000000..535a0f0 --- /dev/null +++ b/cadetgui/widgets/_status.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +import html +from typing import Literal + +__all__ = ["StatusKind", "status_html", "chip_html"] + +StatusKind = Literal["ok", "warn", "error", "info", "running"] + + +def status_html(kind: StatusKind, text: str) -> str: + """HTML for a status-line message; `text` is escaped, styling lives in chrome.css.""" + body = html.escape(text, quote=False) + if kind == "running": + return f"{body}" + return f"{body}" + + +def chip_html(kind: StatusKind, label: str) -> str: + """HTML for a short pill label (e.g. a run's ok/failed state).""" + body = html.escape(label, quote=False) + return f"{body}" diff --git a/cadetgui/widgets/_tokens.py b/cadetgui/widgets/_tokens.py new file mode 100644 index 0000000..2c6b606 --- /dev/null +++ b/cadetgui/widgets/_tokens.py @@ -0,0 +1,12 @@ +from __future__ import annotations + +from pathlib import Path + +__all__ = ["with_tokens"] + +_TOKENS = (Path(__file__).parent / "tokens.css").read_text() + + +def with_tokens(path: Path) -> str: + """CSS text of `path` preceded by the shared design tokens (see tokens.css).""" + return f"{_TOKENS}\n{path.read_text()}" diff --git a/cadetgui/widgets/assets/cadet_logo.png b/cadetgui/widgets/assets/cadet_logo.png new file mode 100644 index 0000000..f56f4fe Binary files /dev/null and b/cadetgui/widgets/assets/cadet_logo.png differ diff --git a/cadetgui/widgets/chrome.css b/cadetgui/widgets/chrome.css new file mode 100644 index 0000000..b57a9d7 --- /dev/null +++ b/cadetgui/widgets/chrome.css @@ -0,0 +1,545 @@ +/* Shared chrome for composite/form widgets (FormRenderer, ConfigurationWidget, + SolutionWidget) — these compose plain ipywidgets containers, not anywidget + elements, so this gets injected via a \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
run_idshear_leveltube_typepilot_conditionvolume_mLrpmspin_time_minflow_Lphyield_percentclarity_NTUpellet_volume_mLsupernatant_OD600endotoxin_UA_per_mLsolids_remaining_fracsigma_lab_m2flow_m3_per_sQ_over_sigma_m_per_sdelta_OD600_vs_pilotendotoxin_reduction_vs_feeddelta_solids_vs_pilot
\n", + "" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [run_id, shear_level, tube_type, pilot_condition, volume_mL, rpm, spin_time_min, flow_Lph, yield_percent, clarity_NTU, pellet_volume_mL, supernatant_OD600, endotoxin_UA_per_mL, solids_remaining_frac, sigma_lab_m2, flow_m3_per_s, Q_over_sigma_m_per_s, delta_OD600_vs_pilot, endotoxin_reduction_vs_feed, delta_solids_vs_pilot]\n", + "Index: []" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# This DataFrame will hold all runs (example data and your own entries)\n", + "runs = pd.DataFrame(\n", + " columns=[\n", + " \"run_id\",\n", + " \"shear_level\",\n", + " \"tube_type\",\n", + " \"pilot_condition\",\n", + " \"volume_mL\",\n", + " \"rpm\",\n", + " \"spin_time_min\",\n", + " \"flow_Lph\",\n", + " \"yield_percent\",\n", + " \"clarity_NTU\",\n", + " \"pellet_volume_mL\",\n", + " \"supernatant_OD600\",\n", + " \"endotoxin_UA_per_mL\",\n", + " \"solids_remaining_frac\",\n", + " \"sigma_lab_m2\",\n", + " \"flow_m3_per_s\",\n", + " \"Q_over_sigma_m_per_s\",\n", + " \"delta_OD600_vs_pilot\",\n", + " \"endotoxin_reduction_vs_feed\",\n", + " \"delta_solids_vs_pilot\"\n", + " ]\n", + ")\n", + "\n", + "runs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration for shear levels and tube types\n", + "\n", + "You can edit the tables in this cell to match your experiment.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shear levels:\n" + ] + }, + { + "data": { + "text/html": [ + "
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shear_levelkompas_rpmdefault_flow_Lphdescription
0Well SpunNaN100.0No additional shear (reference condition)
1Low6000.0100.0Low shear, equivalent to hydrohermetic centrifuge
2Medium7500.0100.0Medium shear condition
3High9000.0100.0High shear, non hydrohermetic equivalent
\n", + "
" + ], + "text/plain": [ + " shear_level kompas_rpm default_flow_Lph \\\n", + "0 Well Spun NaN 100.0 \n", + "1 Low 6000.0 100.0 \n", + "2 Medium 7500.0 100.0 \n", + "3 High 9000.0 100.0 \n", + "\n", + " description \n", + "0 No additional shear (reference condition) \n", + "1 Low shear, equivalent to hydrohermetic centrifuge \n", + "2 Medium shear condition \n", + "3 High shear, non hydrohermetic equivalent " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tube types:\n" + ] + }, + { + "data": { + "text/html": [ + "
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tube_typemax_volume_mLmin_volume_mLdescription
02 mL Eppendorf2.01.0Standard 2 mL microcentrifuge tube
11.5 mL Eppendorf1.50.5Standard 1.5 mL microcentrifuge tube
\n", + "
" + ], + "text/plain": [ + " tube_type max_volume_mL min_volume_mL \\\n", + "0 2 mL Eppendorf 2.0 1.0 \n", + "1 1.5 mL Eppendorf 1.5 0.5 \n", + "\n", + " description \n", + "0 Standard 2 mL microcentrifuge tube \n", + "1 Standard 1.5 mL microcentrifuge tube " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Shear configuration\n", + "SHEAR_CONFIG = pd.DataFrame([\n", + " {\n", + " \"shear_level\": \"Well Spun\",\n", + " \"kompas_rpm\": None,\n", + " \"default_flow_Lph\": 100.0,\n", + " \"description\": \"No additional shear (reference condition)\"\n", + " },\n", + " {\n", + " \"shear_level\": \"Low\",\n", + " \"kompas_rpm\": 6000,\n", + " \"default_flow_Lph\": 100.0,\n", + " \"description\": \"Low shear, equivalent to hydrohermetic centrifuge\"\n", + " },\n", + " {\n", + " \"shear_level\": \"Medium\",\n", + " \"kompas_rpm\": 7500,\n", + " \"default_flow_Lph\": 100.0,\n", + " \"description\": \"Medium shear condition\"\n", + " },\n", + " {\n", + " \"shear_level\": \"High\",\n", + " \"kompas_rpm\": 9000,\n", + " \"default_flow_Lph\": 100.0,\n", + " \"description\": \"High shear, non hydrohermetic equivalent\"\n", + " }\n", + "])\n", + "\n", + "# Tube configuration\n", + "TUBE_CONFIG = pd.DataFrame([\n", + " {\n", + " \"tube_type\": \"2 mL Eppendorf\",\n", + " \"max_volume_mL\": 2.0,\n", + " \"min_volume_mL\": 1.0,\n", + " \"description\": \"Standard 2 mL microcentrifuge tube\"\n", + " },\n", + " {\n", + " \"tube_type\": \"1.5 mL Eppendorf\",\n", + " \"max_volume_mL\": 1.5,\n", + " \"min_volume_mL\": 0.5,\n", + " \"description\": \"Standard 1.5 mL microcentrifuge tube\"\n", + " }\n", + "])\n", + "\n", + "print(\"Shear levels:\")\n", + "display(SHEAR_CONFIG)\n", + "\n", + "print(\"Tube types:\")\n", + "display(TUBE_CONFIG)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pilot reference values\n", + "\n", + "Enter pilot scale reference values here.\n", + "\n", + "Defaults below are set to a mock example similar to your Sanofi trial:\n", + "- Feed OD600 = 8.1\n", + "- Pilot clarification fraction = 0.878 (about 88 percent)\n", + "- Feed endotoxin = 3 260 000 UA/mL\n", + "\n", + "Below that you can define pilot condition specific data such as solids remaining or pilot supernatant OD600 per condition.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "182cf455f696407c9244dd69a12dc3c0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(, \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pilot_conditionsolids_remaining_fracpilot_supernatant_OD600pilot_pellet_wet_mass_g
0Q10.161.30.35
1Q20.141.10.37
2Q30.120.90.40
\n", + "" + ], + "text/plain": [ + " pilot_condition solids_remaining_frac pilot_supernatant_OD600 \\\n", + "0 Q1 0.16 1.3 \n", + "1 Q2 0.14 1.1 \n", + "2 Q3 0.12 0.9 \n", + "\n", + " pilot_pellet_wet_mass_g \n", + "0 0.35 \n", + "1 0.37 \n", + "2 0.40 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "from cadetgui.widgets.elements import FloatField, ChoiceField, TextField\n", + "\n", + "# Global pilot inputs (edit as needed)\n", + "pilot_feed_OD600_widget = FloatField(label=\"Feed OD600\", value=8.1, min=0.0)\n", + "\n", + "pilot_percent_clar_widget = FloatField(\n", + " label=\"Pilot clarification fraction\", value=0.878, min=0.0, max=1.0\n", + ")\n", + "\n", + "pilot_feed_endotoxin_widget = FloatField(\n", + " label=\"Feed endotoxin\", value=3260000.0, units=\"UA/mL\", min=0.0\n", + ")\n", + "\n", + "pilot_box = widgets.VBox([\n", + " pilot_feed_OD600_widget,\n", + " pilot_percent_clar_widget,\n", + " pilot_feed_endotoxin_widget\n", + "])\n", + "\n", + "display(pilot_box)\n", + "\n", + "# Pilot condition specific data (edit this table to match your pilot runs)\n", + "# Example mock values:\n", + "# Q1: slightly worse than global pilot\n", + "# Q2: similar\n", + "# Q3: slightly better clarification\n", + "\n", + "PILOT_CONDITIONS = pd.DataFrame([\n", + " {\n", + " \"pilot_condition\": \"Q1\",\n", + " \"solids_remaining_frac\": 0.16,\n", + " \"pilot_supernatant_OD600\": 1.3,\n", + " \"pilot_pellet_wet_mass_g\": 0.35\n", + " },\n", + " {\n", + " \"pilot_condition\": \"Q2\",\n", + " \"solids_remaining_frac\": 0.14,\n", + " \"pilot_supernatant_OD600\": 1.1,\n", + " \"pilot_pellet_wet_mass_g\": 0.37\n", + " },\n", + " {\n", + " \"pilot_condition\": \"Q3\",\n", + " \"solids_remaining_frac\": 0.12,\n", + " \"pilot_supernatant_OD600\": 0.9,\n", + " \"pilot_pellet_wet_mass_g\": 0.40\n", + " }\n", + "])\n", + "\n", + "print(\"Pilot condition table (edit values in this cell if needed):\")\n", + "PILOT_CONDITIONS\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Centrifuge parameters and Sigma helper\n", + "\n", + "You can adjust these values to match your bench top centrifuge and rotor.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Physical constant\n", + "g = 9.81 # m s^-2\n", + "\n", + "# Bench top centrifuge configuration (adjust as needed)\n", + "CENTRIFUGE_CONFIG = {\n", + " \"name\": \"Eppendorf 5424R\",\n", + " \"rotor\": \"F45-24-11\",\n", + " \"r0\": 0.040,\n", + " \"L_tube\": 0.040,\n", + " \"theta_deg\": 45.0,\n", + " \"h_gel\": 0.011,\n", + " \"t_acc\": 15.0,\n", + " \"t_dec\": 16.0\n", + "}\n", + "\n", + "def fractional_time(total_time_s, acc_s, dec_s):\n", + " f_acc = acc_s / total_time_s\n", + " f_dec = dec_s / total_time_s\n", + " return f_acc, f_dec\n", + "\n", + "def r_top_and_bottom(r0, L_tube, h_gel, theta_deg):\n", + " \"\"\"Simplified geometry based on tube length and agar height.\n", + "\n", + " You can replace this with expressions from your exact Eq. 4 and 5.\n", + " \"\"\"\n", + " theta = np.deg2rad(theta_deg)\n", + " r_top = r0 + (L_tube - h_gel) * np.sin(theta)\n", + " r_bottom = r0 + L_tube * np.sin(theta)\n", + " return r_top, r_bottom\n", + "\n", + "def sigma_lab(V_m3, rpm, spin_time_s, config=CENTRIFUGE_CONFIG, correction_factor=1.0):\n", + " \"\"\"Compute an approximate equivalent settling area Sigma of the lab centrifuge.\n", + "\n", + " You can refine this function once you decide on the exact Eq. 3 you want to use.\n", + " \"\"\"\n", + " omega = 2 * np.pi * rpm / 60.0\n", + " f_acc, f_dec = fractional_time(spin_time_s, config[\"t_acc\"], config[\"t_dec\"])\n", + " r_top, r_bottom = r_top_and_bottom(config[\"r0\"], config[\"L_tube\"], config[\"h_gel\"], config[\"theta_deg\"])\n", + "\n", + " sigma = (\n", + " omega**2\n", + " * V_m3\n", + " * (1.0 - f_acc - f_dec)\n", + " * (r_bottom**2 - r_top**2)\n", + " / (2.0 * g * np.log(r_bottom / r_top))\n", + " )\n", + "\n", + " return correction_factor * sigma\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mock USD example trial\n", + "\n", + "This cell creates a small mock USD data set with a few runs at different shear levels and Q values, linked to pilot conditions Q1 to Q3.\n", + "\n", + "Run it once to populate `runs` with example data. You can still add more runs via the widgets afterward.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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run_idshear_leveltube_typepilot_conditionvolume_mLrpmspin_time_minflow_Lphyield_percentclarity_NTUpellet_volume_mLsupernatant_OD600endotoxin_UA_per_mLsolids_remaining_fracsigma_lab_m2flow_m3_per_sQ_over_sigma_m_per_sdelta_OD600_vs_pilotendotoxin_reduction_vs_feeddelta_solids_vs_pilot
0mock1Well Spun2 mL EppendorfQ12.060005.0100.096.020.00.321.40900000.00.180.0002990.0000280.0929330.41180.7239260.02
1mock2Low2 mL EppendorfQ22.060005.0100.094.018.00.341.10750000.00.150.0002990.0000280.0929330.11180.7699390.01
2mock3High2 mL EppendorfQ32.090005.0100.090.016.00.360.85650000.00.130.0006730.0000280.041303-0.13820.8006130.01
3mock1_repWell Spun2 mL EppendorfQ12.060005.0100.095.021.00.311.50920000.00.190.0002990.0000280.0929330.51180.7177910.03
4mock2_repLow2 mL EppendorfQ22.060005.0100.093.019.00.351.20780000.00.160.0002990.0000280.0929330.21180.7607360.02
5mock3_repHigh2 mL EppendorfQ32.090005.0100.089.015.00.370.80640000.00.120.0006730.0000280.041303-0.18820.8036810.00
\n", + "
" + ], + "text/plain": [ + " run_id shear_level tube_type pilot_condition volume_mL rpm \\\n", + "0 mock1 Well Spun 2 mL Eppendorf Q1 2.0 6000 \n", + "1 mock2 Low 2 mL Eppendorf Q2 2.0 6000 \n", + "2 mock3 High 2 mL Eppendorf Q3 2.0 9000 \n", + "3 mock1_rep Well Spun 2 mL Eppendorf Q1 2.0 6000 \n", + "4 mock2_rep Low 2 mL Eppendorf Q2 2.0 6000 \n", + "5 mock3_rep High 2 mL Eppendorf Q3 2.0 9000 \n", + "\n", + " spin_time_min flow_Lph yield_percent clarity_NTU pellet_volume_mL \\\n", + "0 5.0 100.0 96.0 20.0 0.32 \n", + "1 5.0 100.0 94.0 18.0 0.34 \n", + "2 5.0 100.0 90.0 16.0 0.36 \n", + "3 5.0 100.0 95.0 21.0 0.31 \n", + "4 5.0 100.0 93.0 19.0 0.35 \n", + "5 5.0 100.0 89.0 15.0 0.37 \n", + "\n", + " supernatant_OD600 endotoxin_UA_per_mL solids_remaining_frac \\\n", + "0 1.40 900000.0 0.18 \n", + "1 1.10 750000.0 0.15 \n", + "2 0.85 650000.0 0.13 \n", + "3 1.50 920000.0 0.19 \n", + "4 1.20 780000.0 0.16 \n", + "5 0.80 640000.0 0.12 \n", + "\n", + " sigma_lab_m2 flow_m3_per_s Q_over_sigma_m_per_s delta_OD600_vs_pilot \\\n", + "0 0.000299 0.000028 0.092933 0.4118 \n", + "1 0.000299 0.000028 0.092933 0.1118 \n", + "2 0.000673 0.000028 0.041303 -0.1382 \n", + "3 0.000299 0.000028 0.092933 0.5118 \n", + "4 0.000299 0.000028 0.092933 0.2118 \n", + "5 0.000673 0.000028 0.041303 -0.1882 \n", + "\n", + " endotoxin_reduction_vs_feed delta_solids_vs_pilot \n", + "0 0.723926 0.02 \n", + "1 0.769939 0.01 \n", + "2 0.800613 0.01 \n", + "3 0.717791 0.03 \n", + "4 0.760736 0.02 \n", + "5 0.803681 0.00 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def create_mock_example_runs():\n", + " \"\"\"Create a small mock USD trial and compute all derived quantities.\"\"\"\n", + " example_rows = []\n", + "\n", + " # Read pilot global values\n", + " feed_OD600 = pilot_feed_OD600_widget.value\n", + " pilot_clar = pilot_percent_clar_widget.value\n", + " feed_endotoxin = pilot_feed_endotoxin_widget.value\n", + "\n", + " # Helper to compute derived metrics for one run\n", + " def one_run(run_id, shear_level, tube_type, pilot_condition,\n", + " volume_mL, rpm, spin_time_min, flow_Lph,\n", + " yield_percent, clarity_NTU, pellet_volume_mL,\n", + " supernatant_OD600, endotoxin_UA_per_mL,\n", + " solids_remaining_frac):\n", + "\n", + " V_m3 = volume_mL * 1e-6\n", + " t_s = spin_time_min * 60.0\n", + " sigma_val = sigma_lab(V_m3, rpm, t_s)\n", + "\n", + " if flow_Lph is not None:\n", + " flow_m3_per_s = flow_Lph / 1000.0 / 3600.0\n", + " Q_over_sigma = flow_m3_per_s / sigma_val if sigma_val not in [0, np.nan] else np.nan\n", + " else:\n", + " flow_m3_per_s = np.nan\n", + " Q_over_sigma = np.nan\n", + "\n", + " # Global pilot OD600 comparison\n", + " if (\n", + " supernatant_OD600 is not None\n", + " and pd.notna(feed_OD600)\n", + " and pd.notna(pilot_clar)\n", + " ):\n", + " pilot_supernatant_OD600_global = feed_OD600 * (1.0 - pilot_clar)\n", + " delta_OD600_vs_pilot = supernatant_OD600 - pilot_supernatant_OD600_global\n", + " else:\n", + " delta_OD600_vs_pilot = np.nan\n", + "\n", + " # Endotoxin reduction vs feed\n", + " if (\n", + " endotoxin_UA_per_mL is not None\n", + " and pd.notna(feed_endotoxin)\n", + " and feed_endotoxin != 0\n", + " ):\n", + " endotoxin_reduction_vs_feed = 1.0 - endotoxin_UA_per_mL / feed_endotoxin\n", + " else:\n", + " endotoxin_reduction_vs_feed = np.nan\n", + "\n", + " # Pilot condition specific solids comparison\n", + " if pilot_condition is not None and pilot_condition != \"\":\n", + " pilot_row = PILOT_CONDITIONS.set_index(\"pilot_condition\").loc[pilot_condition]\n", + " pilot_solids_remaining_frac = pilot_row[\"solids_remaining_frac\"]\n", + " else:\n", + " pilot_solids_remaining_frac = np.nan\n", + "\n", + " if solids_remaining_frac is not None and pd.notna(pilot_solids_remaining_frac):\n", + " delta_solids_vs_pilot = solids_remaining_frac - pilot_solids_remaining_frac\n", + " else:\n", + " delta_solids_vs_pilot = np.nan\n", + "\n", + " return {\n", + " \"run_id\": run_id,\n", + " \"shear_level\": shear_level,\n", + " \"tube_type\": tube_type,\n", + " \"pilot_condition\": pilot_condition,\n", + " \"volume_mL\": volume_mL,\n", + " \"rpm\": rpm,\n", + " \"spin_time_min\": spin_time_min,\n", + " \"flow_Lph\": flow_Lph,\n", + " \"yield_percent\": yield_percent,\n", + " \"clarity_NTU\": clarity_NTU,\n", + " \"pellet_volume_mL\": pellet_volume_mL,\n", + " \"supernatant_OD600\": supernatant_OD600,\n", + " \"endotoxin_UA_per_mL\": endotoxin_UA_per_mL,\n", + " \"solids_remaining_frac\": solids_remaining_frac,\n", + " \"sigma_lab_m2\": sigma_val,\n", + " \"flow_m3_per_s\": flow_m3_per_s,\n", + " \"Q_over_sigma_m_per_s\": Q_over_sigma,\n", + " \"delta_OD600_vs_pilot\": delta_OD600_vs_pilot,\n", + " \"endotoxin_reduction_vs_feed\": endotoxin_reduction_vs_feed,\n", + " \"delta_solids_vs_pilot\": delta_solids_vs_pilot\n", + " }\n", + "\n", + " # Example runs (2 mL, 5 min, 100 L/h), three shear levels and three pilot conditions\n", + " example_rows.append(one_run(\n", + " run_id=\"mock1\",\n", + " shear_level=\"Well Spun\",\n", + " tube_type=\"2 mL Eppendorf\",\n", + " pilot_condition=\"Q1\",\n", + " volume_mL=2.0,\n", + " rpm=6000,\n", + " spin_time_min=5.0,\n", + " flow_Lph=100.0,\n", + " yield_percent=96.0,\n", + " clarity_NTU=20.0,\n", + " pellet_volume_mL=0.32,\n", + " supernatant_OD600=1.4,\n", + " endotoxin_UA_per_mL=900000.0,\n", + " solids_remaining_frac=0.18\n", + " ))\n", + "\n", + " example_rows.append(one_run(\n", + " run_id=\"mock2\",\n", + " shear_level=\"Low\",\n", + " tube_type=\"2 mL Eppendorf\",\n", + " pilot_condition=\"Q2\",\n", + " volume_mL=2.0,\n", + " rpm=6000,\n", + " spin_time_min=5.0,\n", + " flow_Lph=100.0,\n", + " yield_percent=94.0,\n", + " clarity_NTU=18.0,\n", + " pellet_volume_mL=0.34,\n", + " supernatant_OD600=1.1,\n", + " endotoxin_UA_per_mL=750000.0,\n", + " solids_remaining_frac=0.15\n", + " ))\n", + "\n", + " example_rows.append(one_run(\n", + " run_id=\"mock3\",\n", + " shear_level=\"High\",\n", + " tube_type=\"2 mL Eppendorf\",\n", + " pilot_condition=\"Q3\",\n", + " volume_mL=2.0,\n", + " rpm=9000,\n", + " spin_time_min=5.0,\n", + " flow_Lph=100.0,\n", + " yield_percent=90.0,\n", + " clarity_NTU=16.0,\n", + " pellet_volume_mL=0.36,\n", + " supernatant_OD600=0.85,\n", + " endotoxin_UA_per_mL=650000.0,\n", + " solids_remaining_frac=0.13\n", + " ))\n", + "\n", + " # Second replicate set to have some variation\n", + " example_rows.append(one_run(\n", + " run_id=\"mock1_rep\",\n", + " shear_level=\"Well Spun\",\n", + " tube_type=\"2 mL Eppendorf\",\n", + " pilot_condition=\"Q1\",\n", + " volume_mL=2.0,\n", + " rpm=6000,\n", + " spin_time_min=5.0,\n", + " flow_Lph=100.0,\n", + " yield_percent=95.0,\n", + " clarity_NTU=21.0,\n", + " pellet_volume_mL=0.31,\n", + " supernatant_OD600=1.5,\n", + " endotoxin_UA_per_mL=920000.0,\n", + " solids_remaining_frac=0.19\n", + " ))\n", + "\n", + " example_rows.append(one_run(\n", + " run_id=\"mock2_rep\",\n", + " shear_level=\"Low\",\n", + " tube_type=\"2 mL Eppendorf\",\n", + " pilot_condition=\"Q2\",\n", + " volume_mL=2.0,\n", + " rpm=6000,\n", + " spin_time_min=5.0,\n", + " flow_Lph=100.0,\n", + " yield_percent=93.0,\n", + " clarity_NTU=19.0,\n", + " pellet_volume_mL=0.35,\n", + " supernatant_OD600=1.2,\n", + " endotoxin_UA_per_mL=780000.0,\n", + " solids_remaining_frac=0.16\n", + " ))\n", + "\n", + " example_rows.append(one_run(\n", + " run_id=\"mock3_rep\",\n", + " shear_level=\"High\",\n", + " tube_type=\"2 mL Eppendorf\",\n", + " pilot_condition=\"Q3\",\n", + " volume_mL=2.0,\n", + " rpm=9000,\n", + " spin_time_min=5.0,\n", + " flow_Lph=100.0,\n", + " yield_percent=89.0,\n", + " clarity_NTU=15.0,\n", + " pellet_volume_mL=0.37,\n", + " supernatant_OD600=0.80,\n", + " endotoxin_UA_per_mL=640000.0,\n", + " solids_remaining_frac=0.12\n", + " ))\n", + "\n", + " df = pd.DataFrame(example_rows)\n", + " return df\n", + "\n", + "# Create example data and store it in `runs`\n", + "runs = create_mock_example_runs()\n", + "runs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Widget based input for additional runs\n", + "\n", + "Use the widgets to enter further USD runs and click \"Add run\".\n", + "\n", + "These runs will be appended to the existing example data in `runs`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92e1db6df28e4d878481149b62b8da3a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(VBox(children=(HTML(value='1. Run information'), max_v:\n", + " volume_float.value = max_v\n", + " if volume_float.value < min_v:\n", + " volume_float.value = min_v\n", + "\n", + "# ChoiceField's real synced trait is selected_index, not value -- see\n", + "# ai-docs/ARCHITECTURE.md's note on this exact gotcha.\n", + "tube_dropdown.observe(update_volume_bounds, names=tube_dropdown._value_trait_name)\n", + "\n", + "def on_add_button_clicked(b):\n", + " global runs\n", + " with output_last_run:\n", + " clear_output()\n", + "\n", + " run_id = run_id_text.value.strip() or \"user_run\"\n", + " shear_level = shear_dropdown.value\n", + " tube_type = tube_dropdown.value\n", + " pilot_condition = pilot_condition_dropdown.value or None\n", + "\n", + " volume_mL = float(volume_float.value)\n", + " rpm = float(rpm_float.value)\n", + " spin_time_min = float(spin_time_float.value)\n", + " flow_Lph = float(flow_float.value) if not np.isnan(flow_float.value) else None\n", + "\n", + " # Optional measured outputs -- 0 means \"not measured\" (see field setup above)\n", + " yield_percent = yield_float.value or None\n", + " clarity_NTU = clarity_float.value or None\n", + " pellet_volume_mL = pellet_float.value or None\n", + " supernatant_OD600 = supernatant_OD600_float.value or None\n", + " endotoxin_run = endotoxin_run_float.value or None\n", + " solids_remaining = solids_remaining_float.value or None\n", + "\n", + " # Derived quantities\n", + " V_m3 = volume_mL * 1e-6\n", + " t_s = spin_time_min * 60.0\n", + " sigma_val = sigma_lab(V_m3, rpm, t_s)\n", + "\n", + " if flow_Lph is not None:\n", + " flow_m3_per_s = flow_Lph / 1000.0 / 3600.0\n", + " Q_over_sigma = flow_m3_per_s / sigma_val if sigma_val not in [0, np.nan] else np.nan\n", + " else:\n", + " flow_m3_per_s = np.nan\n", + " Q_over_sigma = np.nan\n", + "\n", + " feed_OD600 = pilot_feed_OD600_widget.value\n", + " pilot_clar = pilot_percent_clar_widget.value\n", + " feed_endotoxin = pilot_feed_endotoxin_widget.value\n", + "\n", + " if (\n", + " supernatant_OD600 is not None\n", + " and pd.notna(feed_OD600)\n", + " and pd.notna(pilot_clar)\n", + " ):\n", + " pilot_supernatant_OD600_global = feed_OD600 * (1.0 - pilot_clar)\n", + " delta_OD600_vs_pilot = supernatant_OD600 - pilot_supernatant_OD600_global\n", + " else:\n", + " delta_OD600_vs_pilot = np.nan\n", + "\n", + " if (\n", + " endotoxin_run is not None\n", + " and pd.notna(feed_endotoxin)\n", + " and feed_endotoxin != 0\n", + " ):\n", + " endotoxin_reduction_vs_feed = 1.0 - endotoxin_run / feed_endotoxin\n", + " else:\n", + " endotoxin_reduction_vs_feed = np.nan\n", + "\n", + " if pilot_condition is not None and pilot_condition != \"\":\n", + " pilot_row = PILOT_CONDITIONS.set_index(\"pilot_condition\").loc[pilot_condition]\n", + " pilot_solids_remaining_frac = pilot_row[\"solids_remaining_frac\"]\n", + " else:\n", + " pilot_solids_remaining_frac = np.nan\n", + "\n", + " if solids_remaining is not None and pd.notna(pilot_solids_remaining_frac):\n", + " delta_solids_vs_pilot = solids_remaining - pilot_solids_remaining_frac\n", + " else:\n", + " delta_solids_vs_pilot = np.nan\n", + "\n", + " row = {\n", + " \"run_id\": run_id,\n", + " \"shear_level\": shear_level,\n", + " \"tube_type\": tube_type,\n", + " \"pilot_condition\": pilot_condition,\n", + " \"volume_mL\": volume_mL,\n", + " \"rpm\": rpm,\n", + " \"spin_time_min\": spin_time_min,\n", + " \"flow_Lph\": flow_Lph,\n", + " \"yield_percent\": yield_percent,\n", + " \"clarity_NTU\": clarity_NTU,\n", + " \"pellet_volume_mL\": pellet_volume_mL,\n", + " \"supernatant_OD600\": supernatant_OD600,\n", + " \"endotoxin_UA_per_mL\": endotoxin_run,\n", + " \"solids_remaining_frac\": solids_remaining,\n", + " \"sigma_lab_m2\": sigma_val,\n", + " \"flow_m3_per_s\": flow_m3_per_s,\n", + " \"Q_over_sigma_m_per_s\": Q_over_sigma,\n", + " \"delta_OD600_vs_pilot\": delta_OD600_vs_pilot,\n", + " \"endotoxin_reduction_vs_feed\": endotoxin_reduction_vs_feed,\n", + " \"delta_solids_vs_pilot\": delta_solids_vs_pilot\n", + " }\n", + "\n", + " runs = pd.concat([runs, pd.DataFrame([row])], ignore_index=True)\n", + "\n", + " print(\"Run added:\")\n", + " display(pd.DataFrame([row]))\n", + "\n", + "add_button.on_click(on_add_button_clicked)\n", + "\n", + "# Layout with clear separation of inputs and outputs\n", + "form_left = widgets.VBox([\n", + " widgets.HTML(\"1. Run information\"),\n", + " run_id_text,\n", + " shear_dropdown,\n", + " tube_dropdown,\n", + " pilot_condition_dropdown,\n", + " widgets.HTML(\"2. Centrifugation settings\"),\n", + " volume_float,\n", + " rpm_float,\n", + " spin_time_float,\n", + " flow_float\n", + "])\n", + "\n", + "form_right = widgets.VBox([\n", + " widgets.HTML(\"3. Measured results (optional)\"),\n", + " supernatant_OD600_float,\n", + " solids_remaining_float,\n", + " endotoxin_run_float,\n", + " pellet_float,\n", + " yield_float,\n", + " clarity_float,\n", + " add_button\n", + "])\n", + "\n", + "form = widgets.HBox([form_left, form_right])\n", + "\n", + "display(form)\n", + "display(output_last_run)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View all runs (example plus any additional user runs)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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run_idshear_leveltube_typepilot_conditionvolume_mLrpmspin_time_minflow_Lphyield_percentclarity_NTUpellet_volume_mLsupernatant_OD600endotoxin_UA_per_mLsolids_remaining_fracsigma_lab_m2flow_m3_per_sQ_over_sigma_m_per_sdelta_OD600_vs_pilotendotoxin_reduction_vs_feeddelta_solids_vs_pilot
0mock1Well Spun2 mL EppendorfQ12.060005.0100.096.020.00.321.40900000.00.180.0002990.0000280.0929330.41180.7239260.02
1mock2Low2 mL EppendorfQ22.060005.0100.094.018.00.341.10750000.00.150.0002990.0000280.0929330.11180.7699390.01
2mock3High2 mL EppendorfQ32.090005.0100.090.016.00.360.85650000.00.130.0006730.0000280.041303-0.13820.8006130.01
3mock1_repWell Spun2 mL EppendorfQ12.060005.0100.095.021.00.311.50920000.00.190.0002990.0000280.0929330.51180.7177910.03
4mock2_repLow2 mL EppendorfQ22.060005.0100.093.019.00.351.20780000.00.160.0002990.0000280.0929330.21180.7607360.02
5mock3_repHigh2 mL EppendorfQ32.090005.0100.089.015.00.370.80640000.00.120.0006730.0000280.041303-0.18820.8036810.00
\n", + "
" + ], + "text/plain": [ + " run_id shear_level tube_type pilot_condition volume_mL rpm \\\n", + "0 mock1 Well Spun 2 mL Eppendorf Q1 2.0 6000 \n", + "1 mock2 Low 2 mL Eppendorf Q2 2.0 6000 \n", + "2 mock3 High 2 mL Eppendorf Q3 2.0 9000 \n", + "3 mock1_rep Well Spun 2 mL Eppendorf Q1 2.0 6000 \n", + "4 mock2_rep Low 2 mL Eppendorf Q2 2.0 6000 \n", + "5 mock3_rep High 2 mL Eppendorf Q3 2.0 9000 \n", + "\n", + " spin_time_min flow_Lph yield_percent clarity_NTU pellet_volume_mL \\\n", + "0 5.0 100.0 96.0 20.0 0.32 \n", + "1 5.0 100.0 94.0 18.0 0.34 \n", + "2 5.0 100.0 90.0 16.0 0.36 \n", + "3 5.0 100.0 95.0 21.0 0.31 \n", + "4 5.0 100.0 93.0 19.0 0.35 \n", + "5 5.0 100.0 89.0 15.0 0.37 \n", + "\n", + " supernatant_OD600 endotoxin_UA_per_mL solids_remaining_frac \\\n", + "0 1.40 900000.0 0.18 \n", + "1 1.10 750000.0 0.15 \n", + "2 0.85 650000.0 0.13 \n", + "3 1.50 920000.0 0.19 \n", + "4 1.20 780000.0 0.16 \n", + "5 0.80 640000.0 0.12 \n", + "\n", + " sigma_lab_m2 flow_m3_per_s Q_over_sigma_m_per_s delta_OD600_vs_pilot \\\n", + "0 0.000299 0.000028 0.092933 0.4118 \n", + "1 0.000299 0.000028 0.092933 0.1118 \n", + "2 0.000673 0.000028 0.041303 -0.1382 \n", + "3 0.000299 0.000028 0.092933 0.5118 \n", + "4 0.000299 0.000028 0.092933 0.2118 \n", + "5 0.000673 0.000028 0.041303 -0.1882 \n", + "\n", + " endotoxin_reduction_vs_feed delta_solids_vs_pilot \n", + "0 0.723926 0.02 \n", + "1 0.769939 0.01 \n", + "2 0.800613 0.01 \n", + "3 0.717791 0.03 \n", + "4 0.760736 0.02 \n", + "5 0.803681 0.00 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "runs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary tables and averages\n", + "\n", + "This cell computes averages per shear level and tube type.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = [\n", + " \"yield_percent\",\n", + " \"clarity_NTU\",\n", + " \"pellet_volume_mL\",\n", + " \"Q_over_sigma_m_per_s\",\n", + " \"supernatant_OD600\",\n", + " \"delta_OD600_vs_pilot\",\n", + " \"endotoxin_UA_per_mL\",\n", + " \"endotoxin_reduction_vs_feed\",\n", + " \"solids_remaining_frac\",\n", + " \"delta_solids_vs_pilot\"\n", + "]\n", + "\n", + "group_cols = [\"shear_level\", \"tube_type\"]\n", + "\n", + "if not runs.empty:\n", + " summary = (\n", + " runs\n", + " .groupby(group_cols)[metrics]\n", + " .agg([\"mean\", \"std\", \"count\"])\n", + " .sort_index()\n", + " )\n", + " summary\n", + "else:\n", + " print(\"No runs recorded yet.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary per shear level only\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = [\n", + " \"yield_percent\",\n", + " \"clarity_NTU\",\n", + " \"pellet_volume_mL\",\n", + " \"Q_over_sigma_m_per_s\",\n", + " \"supernatant_OD600\",\n", + " \"delta_OD600_vs_pilot\",\n", + " \"endotoxin_UA_per_mL\",\n", + " \"endotoxin_reduction_vs_feed\",\n", + " \"solids_remaining_frac\",\n", + " \"delta_solids_vs_pilot\"\n", + "]\n", + "\n", + "if not runs.empty:\n", + " summary_shear = (\n", + " runs\n", + " .groupby(\"shear_level\")[metrics]\n", + " .agg([\"mean\", \"std\", \"count\"])\n", + " .sort_index()\n", + " )\n", + " summary_shear\n", + "else:\n", + " print(\"No runs recorded yet.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots: USD vs pilot\n", + "\n", + "The following cells create a few meaningful plots for this workflow, as interactive Plotly figures (hover over a point/bar to inspect its exact value)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "mock1" + ], + [ + "mock1_rep" + ] + ], + "hovertemplate": "Shear level=Well Spun
Q/Sigma (m/s)=%{x}
Solids remaining fraction=%{y}
run_id=%{customdata[0]}", + "legendgroup": "Well Spun", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "Well Spun", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "MJyFYW3Ktz8wnIVhbcq3Pw==", + "dtype": "f8" + }, + "xaxis": "x", + "y": { + "bdata": "CtejcD0Kxz9SuB6F61HIPw==", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "customdata": [ + [ + "mock2" + ], + [ + "mock2_rep" + ] + ], + "hovertemplate": "Shear level=Low
Q/Sigma (m/s)=%{x}
Solids remaining fraction=%{y}
run_id=%{customdata[0]}", + "legendgroup": "Low", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Low", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": { + "bdata": "MJyFYW3Ktz8wnIVhbcq3Pw==", + "dtype": "f8" + }, + "xaxis": "x", + "y": { + "bdata": "MzMzMzMzwz97FK5H4XrEPw==", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "customdata": [ + [ + "mock3" + ], + [ + "mock3_rep" + ] + ], + "hovertemplate": "Shear level=High
Q/Sigma (m/s)=%{x}
Solids remaining fraction=%{y}
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+ }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "USD supernatant OD600 by shear level" + }, + "xaxis": { + "title": { + "text": "Shear level" + } + }, + "yaxis": { + "title": { + "text": "Supernatant OD600" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "if runs.empty:\n", + " print(\"No data in 'runs' to plot. Run the mock example cell or add runs via the widgets first.\")\n", + "else:\n", + " feed_OD600 = pilot_feed_OD600_widget.value\n", + " pilot_clar = pilot_percent_clar_widget.value\n", + " pilot_sup_OD = (\n", + " feed_OD600 * (1.0 - pilot_clar) if pd.notna(feed_OD600) and pd.notna(pilot_clar) else np.nan\n", + " )\n", + "\n", + " od_summary = runs.groupby(\"shear_level\")[\"supernatant_OD600\"].agg([\"mean\", \"std\"]).dropna(subset=[\"mean\"])\n", + "\n", + " if od_summary.empty:\n", + " print(\"No supernatant OD600 values available for plotting.\")\n", + " else:\n", + " fig = go.Figure(go.Bar(\n", + " x=od_summary.index,\n", + " y=od_summary[\"mean\"],\n", + " error_y=dict(type=\"data\", array=od_summary[\"std\"].fillna(0)),\n", + " ))\n", + " if not np.isnan(pilot_sup_OD):\n", + " fig.add_hline(y=pilot_sup_OD, line_dash=\"dash\", annotation_text=\"pilot\")\n", + " fig.update_layout(\n", + " title=\"USD supernatant OD600 by shear level\",\n", + " xaxis_title=\"Shear level\",\n", + " yaxis_title=\"Supernatant OD600\",\n", + " )\n", + " fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "error_y": { + "array": { + "bdata": "F3eGDs7EYT+jsskVNad6Pxd3hg7OxHE/", + "dtype": "f8" + }, + "type": "data" + }, + "type": "bar", + "x": [ + "High", + "Low", + "Well Spun" + ], + "y": { + "bdata": "Bd/mrzCr6T9rFHHopH3oP5y/wqxGEec/", + "dtype": "f8" + } + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + 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"landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Endotoxin reduction by shear level" + }, + "xaxis": { + "title": { + "text": "Shear level" + } + }, + "yaxis": { + "range": [ + 0, + 1 + ], + "title": { + "text": "Mean endotoxin reduction vs feed" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "if runs.empty:\n", + " print(\"No data in 'runs' to plot.\")\n", + "else:\n", + " et_summary = runs.groupby(\"shear_level\")[\"endotoxin_reduction_vs_feed\"].agg([\"mean\", \"std\"]).dropna(subset=[\"mean\"])\n", + "\n", + " if et_summary.empty:\n", + " print(\"No endotoxin reduction values available for plotting.\")\n", + " else:\n", + " fig = go.Figure(go.Bar(\n", + " x=et_summary.index,\n", + " y=et_summary[\"mean\"],\n", + " error_y=dict(type=\"data\", array=et_summary[\"std\"].fillna(0)),\n", + " ))\n", + " fig.update_layout(\n", + " title=\"Endotoxin reduction by shear level\",\n", + " xaxis_title=\"Shear level\",\n", + " yaxis_title=\"Mean endotoxin reduction vs feed\",\n", + " yaxis_range=[0, 1],\n", + " )\n", + " fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + 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"" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + 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\"solids_remaining_frac\"]].rename(\n", + " columns={\"solids_remaining_frac\": \"pilot_solids_frac\"}\n", + " )\n", + " df_merge = runs.merge(pilot_solids, on=\"pilot_condition\", how=\"left\")\n", + "\n", + " df_plot = df_merge[\n", + " [\"run_id\", \"Q_over_sigma_m_per_s\", \"solids_remaining_frac\", \"shear_level\", \"pilot_condition\", \"pilot_solids_frac\"]\n", + " ].dropna(subset=[\"Q_over_sigma_m_per_s\", \"solids_remaining_frac\"])\n", + "\n", + " if df_plot.empty:\n", + " print(\"No complete Q/Sigma and solids data to plot.\")\n", + " else:\n", + " fig = px.scatter(\n", + " df_plot,\n", + " x=\"Q_over_sigma_m_per_s\",\n", + " y=\"solids_remaining_frac\",\n", + " color=\"shear_level\",\n", + " hover_data=[\"run_id\"],\n", + " labels={\n", + " \"Q_over_sigma_m_per_s\": \"Q/Sigma (m/s)\",\n", + " \"solids_remaining_frac\": \"Solids remaining fraction\",\n", + " \"shear_level\": \"Shear level\",\n", + " },\n", + " )\n", + " # Pilot solids as its own hoverable horizontal-line trace per pilot condition\n", + " for cond, sub in df_plot.groupby(\"pilot_condition\"):\n", + " if pd.isna(cond) or cond == \"\":\n", + " continue\n", + " pilot_val = sub[\"pilot_solids_frac\"].iloc[0]\n", + " q_min = sub[\"Q_over_sigma_m_per_s\"].min()\n", + " q_max = sub[\"Q_over_sigma_m_per_s\"].max()\n", + " fig.add_trace(go.Scatter(\n", + " x=[q_min, q_max],\n", + " y=[pilot_val, pilot_val],\n", + " mode=\"lines\",\n", + " name=f\"{cond} pilot\",\n", + " line=dict(dash=\"dash\"),\n", + " ))\n", + "\n", + " fig.update_layout(title=\"Q/Sigma vs solids remaining\\nUSD predictions and pilot levels\")\n", + " fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e948d93", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 2: USP bioreactor CFD compartmentalisation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2770988d-781a-431d-b3f5-746165904cd7", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "import sys\n", + "!{sys.executable} -m pip install cbsm\n", + "!{sys.executable} -m pip install pandas\n", + "!{sys.executable} -m pip install jupyterlab-spreadsheet-editor" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6afb78f0-2b72-4737-add7-7cbe317b92bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/IBT/lanzrath/CADET/CADET-GUI/examples/inno4vac\n" + ] + } + ], + "source": [ + "import os\n", + "from cbsm.compartmentalisation import CM\n", + "import pandas as pd\n", + "import os\n", + "print(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8ab74d9f-6a56-4ff8-8405-0cfafdd76a51", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(os.getcwd()+\"/\"+\"/2_A310_60_BM_160rpm_FlowStats_003.csv\")\n", + "df.to_excel(os.getcwd()+\"/\"+\"/2_A310_60_BM_160rpm_FlowStats_003.xlsx\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "092f96eb-8d5d-4a0b-95f8-125d8fd790c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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X [ m ]Y [ m ]Z [ m ]Area [ m^2 ]Gas.Volume Fraction.Trnavgkla [ s^-1 ]Liquid.Velocity.Trnavg Axial [ m s^-1 ]Liquid.Velocity.Trnavg Radial [ m s^-1 ]Ostar [ kg m^-3 ]Radius [ m ]
00.0087280.000366-0.0087280.0027510.0162780.0072430.0026160.0058920.0200830.015292
10.0043020.000392-0.0043020.0032600.0162670.0072410.0026190.0084960.0200850.017024
20.0807510.003482-0.0807510.0044770.0162310.0078170.0002310.0494520.0200950.114443
3-0.0817090.0036340.0817090.0063540.0158690.007640-0.0048470.1257700.0201270.144917
40.1283520.005612-0.1283520.0019250.0161900.008139-0.0012090.0815420.0201030.183175
.................................
205531.0697385.205605-1.0697380.0012630.1656310.0780180.1426360.0592920.0180811.512878
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20556-0.1376135.2064470.1376130.0005690.1816880.0944800.1219100.0030360.0180250.195211
20557-0.7922125.2064570.7922120.0056090.1902370.0949820.0085170.5498730.0180991.122126
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", 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", 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"bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "CFD flow speed" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "X [m]" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "scaleanchor": "x", + "scaleratio": 1, + "title": { + "text": "Y [m]" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "# X/Y and per-point flow speed from the first two + two velocity-component columns\n", + "x = df.iloc[:, 0]\n", + "y = df.iloc[:, 1]\n", + "speed = np.hypot(df.iloc[:, 6], df.iloc[:, 7])\n", + "\n", + "# A per-point hoverable scatter trades the continuous gouraud-shaded mesh for\n", + "# real point-level inspection (exact speed under the cursor).\n", + "fig = px.scatter(\n", + " x=x, y=y, color=speed,\n", + " color_continuous_scale=\"Viridis\",\n", + " labels={\"x\": \"X [m]\", \"y\": \"Y [m]\", \"color\": \"Speed [m/s]\"},\n", + " title=\"CFD flow speed\",\n", + ")\n", + "fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filename = \"/2_A310_60_BM_160rpm_FlowStats_003.xlsx\"\n", + "# Read CFD data\n", + "data = pd.read_excel(os.getcwd()+\"/\"+filename)\n", + "CFD_data = data.values\n", + "# Extract data\n", + "# start at 5 to avoid the header\n", + "X = CFD_data[1:, 9].tolist()\n", + "Y = CFD_data[1:, 1].tolist()\n", + "Vx = CFD_data[1:, 7].tolist()\n", + "Vy = CFD_data[1:, 6].tolist()\n", + "\n", + "(grid_info, volume, img) = CM(X, Y, Vx, Vy)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba67318d-9b0c-45a4-b0dc-7b7cb929e345", + "metadata": {}, + "outputs": [], + "source": [ + "from cbsm import splitting as splitting\n", + "import numpy as np\n", + "\n", + "\n", + "center_info = np.array([[1.0, 1.0, 1.0], [1.5, 1.5, 1.0], [3.0, 3.0, 1.0]])\n", + "delta_x = 1.0\n", + "delta_y = 1.0\n", + "\n", + "split_result = splitting.split(center_info, delta_x, delta_y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45f1e9b8-0139-4fbe-a9a1-e472becdd92a", + "metadata": {}, + "outputs": [], + "source": [ + "split_result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37a2bb51-f2c0-438f-92b6-7a22af5dd090", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cadet_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/webapp/README.md b/examples/webapp/README.md new file mode 100644 index 0000000..00dcd33 --- /dev/null +++ b/examples/webapp/README.md @@ -0,0 +1,56 @@ +# CADET-GUI as a website + +`app.ipynb` renders `ConfigurationWidget`, `SolutionWidget` (with its built-in +run history), and `ParameterEstimationWidget` — the same widgets used in +`examples/configuration_and_solution.ipynb` — composed into three tabs +(**Configuration** / **Solution** / **Parameter Estimation**), served via +[Voilà](https://voila.readthedocs.io/). Comparison against measured data lives +under **Parameter Estimation**, not alongside Solution — that's a deliberate +separation, not a demotion: the Solution tab shows only the raw simulation; +importing a dataset, calibrating it, and overlaying it against a simulated +signal is its own concern, with its own "Preview" step. Try uploading one of +the three example datasets in `examples/data/` (see `examples/EXAMPLE_DATA.md` +for exactly what configuration each was generated from, and what to check in +the parameter checklist) and clicking "Preview" to see it overlaid — +switching tabs doesn't lose anything; a plot rendered on one tab is still +there when you switch back. + +## Run it + +```bash +pip install voila # one-time, not a cadetgui runtime dependency +voila examples/webapp/app.ipynb +``` + +Then open the URL Voilà prints (defaults to `http://localhost:8866`). What you +get is a plain web page — no notebook UI, no code, no cell boundaries, just a +header and three tabs. Anyone visiting the URL in a browser sees a website; the +`.ipynb` file is just this app's source format (convenient because it's also +directly editable/runnable in JupyterLab), not something end users interact +with. + +## Why this proves anything + +The widgets need a live Python backend regardless of deployment target — running +an actual CADET-Process/CADET-Core simulation can't happen client-side in a +browser. Voilà gives us that backend (one Jupyter kernel per visitor session) for +free, using the exact same widget-sync protocol our anywidget elements already +speak for Jupyter. So "the same widget code, unmodified, works in a notebook and +on a website" isn't a promise — `app.ipynb` and +`examples/configuration_and_solution.ipynb` import and use `ConfigurationWidget`/ +`SolutionWidget` identically; only the composition/layout code differs. + +## If you get `ModuleNotFoundError: No module named 'cadetgui'` + +Voilà runs the notebook's cell(s) in a Jupyter **kernel**, which may not be the +same Python environment you're running `voila` from. Register a kernel for the +environment `cadetgui` is installed in (once): + +```bash +python -m ipykernel install --user --name --display-name "Python 3 ()" +``` + +Then make sure the notebook's kernel metadata (`Kernel > Change Kernel` in +JupyterLab, or edit `metadata.kernelspec` in the `.ipynb` JSON) points at that +kernel name. Both notebooks in `examples/` are currently pinned to a kernel named +`cadet_dev` — rename to match your own environment if it's called something else. diff --git a/examples/webapp/app.ipynb b/examples/webapp/app.ipynb new file mode 100644 index 0000000..733bccb --- /dev/null +++ b/examples/webapp/app.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a9463514", + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as W\n", + "from cadetgui.widgets.composite import (\n", + " ConfigurationWidget,\n", + " InstrumentWidget,\n", + " ParameterEstimationWidget,\n", + " SolutionWidget,\n", + ")\n", + "from IPython.display import display\n", + "\n", + "PAGE_CSS = \"\"\"\n", + "\n", + "\"\"\"\n", + "\n", + "header = W.HTML(\n", + " \"
\"\n", + " \"

CADET-GUI

\"\n", + " \"

Configure a standard chromatography process, run it, and compare against \"\n", + " \"measured data \u2014 powered by CADET-Process.

\"\n", + " \"
\"\n", + ")\n", + "\n", + "instrument = InstrumentWidget()\n", + "config = ConfigurationWidget(instrument=instrument)\n", + "solution = SolutionWidget()\n", + "solution.bind_to_config(config)\n", + "\n", + "parameter_estimation = ParameterEstimationWidget()\n", + "parameter_estimation.bind_to_config(config)\n", + "\n", + "\n", + "def tab_page(widget_root: W.Widget) -> W.VBox:\n", + " \"\"\"Wrap a widget root in the shared tab-page padding class.\"\"\"\n", + " page = W.VBox([widget_root])\n", + " page.add_class(\"cadetgui-tab-page\")\n", + " return page\n", + "\n", + "\n", + "tabs = W.Tab(\n", + " children=[\n", + " tab_page(instrument.root),\n", + " tab_page(config.root),\n", + " tab_page(solution.root),\n", + " tab_page(parameter_estimation.root),\n", + " ]\n", + ")\n", + "tabs.set_title(0, \"Instrument\")\n", + "tabs.set_title(1, \"Configuration\")\n", + "tabs.set_title(2, \"Solution\")\n", + "tabs.set_title(3, \"Parameter Estimation\")\n", + "\n", + "app = W.VBox([W.HTML(PAGE_CSS), header, tabs])\n", + "app.add_class(\"cadetgui-app\")\n", + "\n", + "display(app)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (cadet_dev)", + "language": "python", + "name": "cadet_dev" + }, + "language_info": { + "name": "python", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/workbench.ipynb b/examples/workbench.ipynb new file mode 100644 index 0000000..1a0939f --- /dev/null +++ b/examples/workbench.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "workbench-cell", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/IBT/lanzrath/CADET/CADET-Process/CADETProcess/modelBuilder/batchElutionBuilder.py:62: UserWarning: Renaming column to `column` for consistency\n", + " warnings.warn(\"Renaming column to `column` for consistency\")\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b391b307a3104130bb50b3d85e515b9c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='