diff --git a/frontend/jest.config.js b/frontend/jest.config.js new file mode 100644 index 00000000..c44ef36e --- /dev/null +++ b/frontend/jest.config.js @@ -0,0 +1,7 @@ +/** @type {import('ts-jest').JestConfigWithTsJest} **/ +export default { + testEnvironment: "node", + transform: { + "^.+.tsx?$": ["ts-jest", {}], + }, +}; diff --git a/frontend/src/components/JobView.tsx b/frontend/src/components/JobView.tsx index 12b3f57c..3b3eb7e1 100644 --- a/frontend/src/components/JobView.tsx +++ b/frontend/src/components/JobView.tsx @@ -1,4 +1,4 @@ -import { useEffect, useMemo, useState } from "react"; +import { useCallback, useEffect, useMemo, useState } from "react"; import { Alert, Button, @@ -27,6 +27,7 @@ import { deserialize } from "../utils/deserializer"; import { updateJobParams } from "../utils/updateJobParams"; import { cancelJob } from "../utils/cancelJob"; import HistogramPlot from "./jobView/HistogramPlot"; +import FitPanel from "./jobView/FitPanel"; function getPlotTitle(scheduledTime?: string, experimentName?: string): string { if (!scheduledTime) return experimentName || ""; @@ -37,9 +38,11 @@ function getPlotTitle(scheduledTime?: string, experimentName?: string): string { export const JobView = ({ jobId, onLoaded, + showFitPanel = false, }: { jobId: string | undefined; onLoaded?: () => void; + showFitPanel?: boolean; }) => { const [experimentMetadata, setExperimentMetadata] = useState(null); @@ -90,6 +93,12 @@ export const JobView = ({ experimentData.total_data_points > 0 && loadedDataPoints < experimentData.total_data_points; + const [clickedX, setClickedX] = useState(null); + const handleChartClick = useCallback((x: number) => setClickedX(x), []); + + // Reset clicked position when switching jobs + useEffect(() => setClickedX(null), [jobId]); + const [showRepetitions, setShowRepetitions] = useState(() => { const v = localStorage.getItem("showRepetitions"); return v ? JSON.parse(v) : false; @@ -206,10 +215,10 @@ export const JobView = ({ {jobId} - {experimentMetadata?.constructor_kwargs.name && ( + {experimentMetadata?.constructor_kwargs?.name && ( <> {" "} - - {experimentMetadata?.constructor_kwargs.name} ( + - {experimentMetadata?.constructor_kwargs?.name} ( {experimentMetadata?.class_name}) )} @@ -396,7 +405,7 @@ export const JobView = ({ title={win.name} subtitle={getPlotTitle( jobRunInfo?.scheduled_time, - experimentMetadata?.constructor_kwargs.name, + experimentMetadata?.constructor_kwargs?.name, )} /> )} @@ -435,19 +444,32 @@ export const JobView = ({ title={win.name} subtitle={getPlotTitle( jobRunInfo?.scheduled_time, - experimentMetadata?.constructor_kwargs.name, + experimentMetadata?.constructor_kwargs?.name, )} repetitions={jobInfo?.repetitions} showRepetitions={showRepetitions} scanParameters={jobInfo?.scan_parameters} windowSize={windowSize} yRange={{ min: yMin, max: yMax }} + fits={showFitPanel && is1D ? experimentData.fits : undefined} + onChartClick={showFitPanel && is1D ? handleChartClick : undefined} /> )} ))} + {showFitPanel && is1D && jobId && jobInfo?.status === JobStatus.PROCESSED && ( + + + + )} + diff --git a/frontend/src/components/ResultChannelPlot.tsx b/frontend/src/components/ResultChannelPlot.tsx index cbf5cd49..7b2e783c 100644 --- a/frontend/src/components/ResultChannelPlot.tsx +++ b/frontend/src/components/ResultChannelPlot.tsx @@ -1,5 +1,5 @@ import React, { useCallback, useEffect, useMemo, useState } from "react"; -import { ExperimentData } from "../types/ExperimentData"; +import { ExperimentData, FitResult } from "../types/ExperimentData"; import { ReactECharts, ReactEChartsProps } from "./ReactEcharts"; import { EChartsOption } from "echarts"; import type { ECharts } from "echarts/core"; @@ -19,6 +19,8 @@ interface ResultChannelPlotProps { scanParameters: ScanParameter[] | undefined; windowSize?: number | null; yRange?: { min: number | null; max: number | null }; + fits?: Record; + onChartClick?: (xValue: number) => void; } const formatAxisLabel = (value: string): string => { @@ -100,6 +102,8 @@ const ResultChannelPlot = ({ scanParameters = [], windowSize = null, yRange, + fits = {}, + onChartClick, }: ResultChannelPlotProps) => { const [chart, setChart] = useState(null); const notifications = useNotifications(); @@ -340,6 +344,28 @@ const ResultChannelPlot = ({ }; } + // Add fit curve overlays for 1D scans + if (scanParameters.length === 1 && fits) { + for (const [channelName, fitResult] of Object.entries(fits)) { + if (!fitResult.success || !fitResult.fit_curve) continue; + if (!channelNames.includes(channelName)) continue; + + const fitData = fitResult.fit_curve.x.map((x, i) => [ + x, + fitResult.fit_curve!.y[i], + ]); + + (chartSeries as unknown[]).push({ + name: `${channelName} fit`, + type: "line", + data: fitData, + showSymbol: false, + lineStyle: { type: "dashed", width: 2 }, + tooltip: { show: false }, + }); + } + } + return { title, textStyle: { fontFamily: "sans-serif", fontSize: 12 }, @@ -381,6 +407,8 @@ const ResultChannelPlot = ({ showRepetitions, windowSize, yRange, + fits, + channelNames, ]); const updateChart = useCallback( @@ -390,6 +418,24 @@ const ResultChannelPlot = ({ [setChart], ); + useEffect(() => { + if (!chart || !onChartClick) return; + const zr = chart.getZr(); + const handler = (params: { offsetX: number; offsetY: number }) => { + const point = [params.offsetX, params.offsetY]; + if (chart.containPixel("grid", point)) { + const dataPoint = chart.convertFromPixel("grid", point); + if (dataPoint && typeof dataPoint[0] === "number" && isFinite(dataPoint[0])) { + onChartClick(dataPoint[0]); + } + } + }; + zr.on("click", handler); + return () => { + zr.off("click", handler); + }; + }, [chart, onChartClick]); + useEffect(() => { if (!is2D || !chart) return; diff --git a/frontend/src/components/jobView/FitPanel.tsx b/frontend/src/components/jobView/FitPanel.tsx new file mode 100644 index 00000000..d5c1960c --- /dev/null +++ b/frontend/src/components/jobView/FitPanel.tsx @@ -0,0 +1,362 @@ +import { useEffect, useState } from "react"; +import { + Button, + Card, + CardContent, + FormControl, + Grid, + InputLabel, + MenuItem, + Select, + Table, + TableBody, + TableCell, + TableHead, + TableRow, + TextField, + Typography, + Accordion, + AccordionSummary, + AccordionDetails, + Alert, +} from "@mui/material"; +import { ExpandMore } from "@mui/icons-material"; +import { useNotifications } from "@toolpad/core"; +import { ExperimentData, FitResult } from "../../types/ExperimentData"; +import { ScanParameter } from "../../types/ScanParameter"; +import { runMethod } from "../../socket"; +import { + FIT_DEFAULT_UPDATE_PARAM, + FIT_PARAM_NAMES, + FIT_TYPES, +} from "../../utils/fitFunctions"; + +interface FitPanelProps { + jobId: string; + experimentData: ExperimentData; + clickedX: number | null; + scanParameters?: ScanParameter[]; +} + +export default function FitPanel({ + jobId, + experimentData, + clickedX, + scanParameters = [], +}: FitPanelProps) { + const notifications = useNotifications(); + const channelNames = Object.keys(experimentData.result_channels); + const [selectedChannel, setSelectedChannel] = useState(channelNames[0] ?? ""); + const [funcType, setFuncType] = useState("lorentzian"); + const [xMin, setXMin] = useState(""); + const [xMax, setXMax] = useState(""); + const [initOverrides, setInitOverrides] = useState>({}); + const [fitting, setFitting] = useState(false); + + const [updateParamId, setUpdateParamId] = useState(""); + const [updateValue, setUpdateValue] = useState(""); + + const channelKey = channelNames.join(","); + useEffect(() => { + if ( + channelNames.length > 0 && + (!selectedChannel || !channelNames.includes(selectedChannel)) + ) { + setSelectedChannel(channelNames[0]); + } + }, [channelKey]); + + // Reset form state when switching jobs + useEffect(() => { + setInitOverrides({}); + setXMin(""); + setXMax(""); + setUpdateParamId(""); + setUpdateValue(""); + }, [jobId]); + + const fit: FitResult | undefined = experimentData.fits[selectedChannel]; + const paramNames = FIT_PARAM_NAMES[funcType] ?? []; + + // Non-realtime scan parameters (the ones relevant for fitting) + const ordinaryScanParams = scanParameters.filter((sp) => !sp.realtime); + + // Pre-fill update parameter and value when a new fit arrives + const defaultParam = fit?.success + ? FIT_DEFAULT_UPDATE_PARAM[fit.func_type] + : undefined; + const defaultValue = + defaultParam && fit?.result && defaultParam in fit.result + ? fit.result[defaultParam] + : undefined; + + useEffect(() => { + if (!fit?.success) return; + if (ordinaryScanParams.length > 0 && !updateParamId) { + setUpdateParamId(ordinaryScanParams[0].variable_id); + } + if (defaultValue !== undefined) { + setUpdateValue(String(defaultValue)); + } + }, [fit?.success, defaultValue]); + + // Pre-fill x0 from click (only if user hasn't touched x0 at all) + const effectiveInit = { ...initOverrides }; + if (clickedX !== null && paramNames.includes("x0") && !("x0" in initOverrides)) { + effectiveInit["x0"] = String(clickedX); + } + + const handleFit = () => { + setFitting(true); + const xRange = + xMin !== "" && xMax !== "" ? [parseFloat(xMin), parseFloat(xMax)] : null; + + const init: Record = {}; + for (const [key, val] of Object.entries(effectiveInit)) { + if (val !== "") init[key] = parseFloat(val); + } + + runMethod( + "data.run_fit", + [], + { + job_id: Number(jobId), + result_channel: selectedChannel, + func_type: funcType, + x_range: xRange, + init: Object.keys(init).length > 0 ? init : null, + }, + () => setFitting(false), + ); + }; + + const handleDelete = () => { + runMethod("data.delete_fit", [], { + job_id: Number(jobId), + result_channel: selectedChannel, + }); + }; + + const handleUpdateParameter = () => { + if (!updateParamId || updateValue === "") return; + const displayName = + ordinaryScanParams.find((sp) => sp.variable_id === updateParamId)?.name ?? + updateParamId; + runMethod( + "parameters.update_parameter_by_id", + [updateParamId, parseFloat(updateValue)], + {}, + () => + notifications.show(`Updated ${displayName} to ${updateValue}`, { + autoHideDuration: 3000, + severity: "success", + }), + ); + }; + + return ( + + + + Curve Fitting + + + + + + Channel + + + + + + + Model + + + + + + setXMin(e.target.value)} + /> + + + setXMax(e.target.value)} + /> + + + + + }> + Initial parameter overrides + + + + {paramNames.map((name) => ( + + + setInitOverrides((prev) => ({ + ...prev, + [name]: e.target.value, + })) + } + /> + + ))} + + {clickedX !== null && paramNames.includes("x0") && ( + + Clicked x = {clickedX.toFixed(4)} (used as x0 hint if not overridden) + + )} + + + +
+ + {fit && ( + + )} +
+ + {fit && ( +
+ {!fit.success && ( + + {fit.message} + + )} + {fit.success && ( + <> + + Fit Results ({fit.func_type}) + + + + + Parameter + Value + + + + {Object.entries(fit.result).map(([key, val]) => ( + + {key} + {val.toFixed(6)} + + ))} + +
+ + + Goodness of Fit + + + + {Object.entries(fit.goodness).map(([key, val]) => ( + + {key} + {val.toFixed(6)} + + ))} + +
+ + + Update Parameter + + + + + Parameter + + + + + setUpdateValue(e.target.value)} + /> + + + + + + + )} +
+ )} + + + ); +} diff --git a/frontend/src/hooks/useExperimentData.tsx b/frontend/src/hooks/useExperimentData.tsx index 1d3b1c63..c7354a01 100644 --- a/frontend/src/hooks/useExperimentData.tsx +++ b/frontend/src/hooks/useExperimentData.tsx @@ -3,6 +3,7 @@ import { runMethod, socket } from "../socket"; import { ExperimentData, ExperimentDataPoint, + FitResult, ParameterValue, } from "../types/ExperimentData"; import { SerializedObject } from "../types/SerializedObject"; @@ -21,6 +22,7 @@ const emptyExperimentData: ExperimentData = { json_sequences: [], parameters: {}, total_data_points: 0, + fits: {}, }; /** @@ -109,9 +111,25 @@ export function useExperimentData(jobId: string | undefined) { }); }; + const fitEvent = `experiment_fit_${jobId}`; + const handleFitEvent = (data: FitResult & { deleted?: boolean }) => { + setExperimentData((prev) => { + if (data.deleted) { + const { [data.result_channel]: _removed, ...rest } = prev.fits; + void _removed; + return { ...prev, fits: rest }; + } + return { + ...prev, + fits: { ...prev.fits, [data.result_channel]: data }, + }; + }); + }; + socket.on(dataPointEvent, handleNewDataPoint); socket.on(metadataEvent, handleMetadata); socket.on(parameterValueEvent, handleValueEvent); + socket.on(fitEvent, handleFitEvent); runMethod("data.get_experiment_data_by_job_id", [], { job_id: jobId }, (ack) => { const deserialized = deserialize(ack as SerializedObject) as @@ -131,6 +149,7 @@ export function useExperimentData(jobId: string | undefined) { socket.off(dataPointEvent, handleNewDataPoint); socket.off(metadataEvent, handleMetadata); socket.off(parameterValueEvent, handleValueEvent); + socket.off(fitEvent, handleFitEvent); }; }, [jobId]); diff --git a/frontend/src/pages/data.tsx b/frontend/src/pages/data.tsx index e04b13ed..cbd71aca 100644 --- a/frontend/src/pages/data.tsx +++ b/frontend/src/pages/data.tsx @@ -107,7 +107,7 @@ export function DataPage() { {selectedJobId ? (
{layoutReady ? ( - + ) : (
Loading...
)} diff --git a/frontend/src/types/ExperimentData.ts b/frontend/src/types/ExperimentData.ts index cad018f4..a699d623 100644 --- a/frontend/src/types/ExperimentData.ts +++ b/frontend/src/types/ExperimentData.ts @@ -28,6 +28,18 @@ export interface ParameterValue { value: string | number | boolean; } +export interface FitResult { + result_channel: string; + func_type: string; + x_range: [number, number] | null; + init: Record; + result: Record; + goodness: Record; + success: boolean; + message: string; + fit_curve?: { x: number[]; y: number[] }; +} + export interface ExperimentData { plot_windows: PlotWindows; shot_channels: Record>; @@ -37,4 +49,5 @@ export interface ExperimentData { json_sequences: [number, string][]; parameters: Record; total_data_points: number; + fits: Record; } diff --git a/frontend/src/utils/__tests__/fitFunctions.test.ts b/frontend/src/utils/__tests__/fitFunctions.test.ts new file mode 100644 index 00000000..24577d52 --- /dev/null +++ b/frontend/src/utils/__tests__/fitFunctions.test.ts @@ -0,0 +1,24 @@ +import { FIT_DEFAULT_UPDATE_PARAM, FIT_PARAM_NAMES, FIT_TYPES } from "../fitFunctions"; + +describe("fit model metadata", () => { + it("every FIT_TYPE has param names defined", () => { + for (const ft of FIT_TYPES) { + expect(FIT_PARAM_NAMES[ft]).toBeDefined(); + expect(FIT_PARAM_NAMES[ft].length).toBeGreaterThan(0); + } + }); + + it("every FIT_TYPE has a default update param", () => { + for (const ft of FIT_TYPES) { + expect(FIT_DEFAULT_UPDATE_PARAM[ft]).toBeDefined(); + } + }); + + it("FIT_TYPES contains expected models", () => { + expect(FIT_TYPES).toContain("lorentzian"); + expect(FIT_TYPES).toContain("gaussian"); + expect(FIT_TYPES).toContain("poly2"); + expect(FIT_TYPES).toContain("harmonic"); + expect(FIT_TYPES).toContain("damped_harmonic"); + }); +}); diff --git a/frontend/src/utils/fitFunctions.ts b/frontend/src/utils/fitFunctions.ts new file mode 100644 index 00000000..4cf4bca5 --- /dev/null +++ b/frontend/src/utils/fitFunctions.ts @@ -0,0 +1,26 @@ +/** Names of the fit parameters for each model type. */ +export const FIT_PARAM_NAMES: Record = { + lorentzian: ["y0", "a", "x0", "gamma"], + gaussian: ["y0", "a", "x0", "sigma"], + poly2: ["a", "b", "c"], + harmonic: ["y0", "a", "omega", "phi"], + damped_harmonic: ["y0", "a", "k", "omega", "phi"], +}; + +/** Default result parameter to use for "Update Parameter" per model. */ +export const FIT_DEFAULT_UPDATE_PARAM: Record = { + lorentzian: "x0", + gaussian: "x0", + poly2: "vertex", + harmonic: "f", + damped_harmonic: "f", +}; + +/** Available fit model types. */ +export const FIT_TYPES = [ + "lorentzian", + "gaussian", + "poly2", + "harmonic", + "damped_harmonic", +] as const; diff --git a/pyproject.toml b/pyproject.toml index 2f966787..89728777 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ dependencies = [ server = [ "alembic>=1.14.0", "influxdb>=5.3.2", + "scipy>=1.14.0", "sqlalchemy>=2.0.36", "tables>=3.10.1", "h5py>=3.12.1", diff --git a/src/icon/server/api/experiment_data_controller.py b/src/icon/server/api/experiment_data_controller.py index be5287d5..f7e2f4c6 100644 --- a/src/icon/server/api/experiment_data_controller.py +++ b/src/icon/server/api/experiment_data_controller.py @@ -2,11 +2,16 @@ from dataclasses import asdict from typing import Any +import numpy as np import pydase from icon.server.data_access.repositories.experiment_data_repository import ( ExperimentDataRepository, + delete_fit_result_by_job_id, + write_fit_result_by_job_id, ) +from icon.server.fitting import run_curve_fit +from icon.server.web_server.socketio_emit_queue import emit_queue __all__ = ["ExperimentDataController"] @@ -41,3 +46,99 @@ async def get_experiment_data_by_job_id( max_transfer_bytes=max_transfer_bytes, ) return asdict(result) + + async def run_fit( + self, + job_id: int, + result_channel: str, + func_type: str, + x_range: list[float] | None = None, + init: dict[str, float] | None = None, + ) -> dict[str, Any]: + """Run a curve fit on a result channel of a finished job. + + Args: + job_id: Job identifier. + result_channel: Name of the result channel to fit. + func_type: Fit model name (e.g. "lorentzian"). + x_range: Optional [min, max] to restrict fit domain. + init: Optional initial parameter overrides. + + Returns: + Serialised FitResult dict. + """ + data = await asyncio.to_thread( + ExperimentDataRepository.get_experiment_data_by_job_id, + job_id=job_id, + ) + + # Find the first non-timestamp scan parameter for x-values + scan_param_name = next( + (p for p in data.scan_parameters if p != "timestamp"), None + ) + if scan_param_name is None: + return asdict( + run_curve_fit( + x=np.array([]), + y=np.array([]), + result_channel=result_channel, + func_type=func_type, # type: ignore[arg-type] + ) + ) + + scan_values = data.scan_parameters[scan_param_name] + channel_values = data.result_channels.get(result_channel, {}) + + # Build aligned x, y arrays sorted by index + indices = sorted(set(scan_values.keys()) & set(channel_values.keys())) + x = np.array([float(scan_values[i]) for i in indices]) + y = np.array([float(channel_values[i]) for i in indices]) + + fit_result = await asyncio.to_thread( + run_curve_fit, + x=x, + y=y, + result_channel=result_channel, + func_type=func_type, # type: ignore[arg-type] + x_range=x_range, + init=init, + ) + + if fit_result.success: + await asyncio.to_thread( + write_fit_result_by_job_id, + job_id=job_id, + fit_result=fit_result, + ) + + result_dict = asdict(fit_result) + emit_queue.put( + { + "event": f"experiment_fit_{job_id}", + "data": result_dict, + } + ) + return result_dict + + async def delete_fit( + self, + job_id: int, + result_channel: str, + ) -> None: + """Delete a fit result for a result channel. + + Args: + job_id: Job identifier. + result_channel: Name of the result channel whose fit to remove. + """ + await asyncio.to_thread( + delete_fit_result_by_job_id, + job_id=job_id, + result_channel=result_channel, + ) + emit_queue.put( + { + "event": f"experiment_fit_{job_id}", + "data": {"result_channel": result_channel, "deleted": True}, + } + ) diff --git a/src/icon/server/data_access/repositories/experiment_data_repository.py b/src/icon/server/data_access/repositories/experiment_data_repository.py index 5f3aeefb..51a77b26 100644 --- a/src/icon/server/data_access/repositories/experiment_data_repository.py +++ b/src/icon/server/data_access/repositories/experiment_data_repository.py @@ -20,6 +20,7 @@ ) from icon.server.data_access.repositories.job_repository import JobRepository from icon.server.data_access.repositories.job_run_repository import JobRunRepository +from icon.server.fitting.fit_runner import FitResult from icon.server.web_server.socketio_emit_queue import emit_queue if TYPE_CHECKING: @@ -126,6 +127,8 @@ class ExperimentData: """Mapping of parameter id to time series (tuple of timestamp str and value).""" total_data_points: int """Total number of data points in the HDF5 file (before truncation).""" + fits: dict[str, dict[str, object]] + """Fit results keyed by result channel name.""" def get_filename_by_job_id(job_id: int) -> str: @@ -611,6 +614,7 @@ def get_experiment_data_by_job_id( # noqa: C901 realtime_scan=False, parameters={}, total_data_points=0, + fits={}, ) filename = get_filename_by_job_id(job_id) @@ -746,6 +750,7 @@ def get_experiment_data_by_job_id( # noqa: C901 for entry in sequence_json_dataset ] data.parameters = extract_parameter_values(h5file) + data.fits = _read_fits_from_hdf5(h5file) return data @@ -819,3 +824,75 @@ def h5_open(path: Path, mode: str, **kwargs: Any) -> Iterator[h5py.File]: break except (OSError, FileNotFoundError): time.sleep(POLL_INTERVAL) + + +def _read_fits_from_hdf5( + h5file: h5py.File, +) -> dict[str, dict[str, object]]: + """Read all fit results from an HDF5 file.""" + if "fits" not in h5file: + return {} + + fits: dict[str, dict[str, object]] = {} + fits_group = cast("h5py.Group", h5file["fits"]) + for channel_name in fits_group: + channel_group = cast("h5py.Group", fits_group[channel_name]) + fit_data = json.loads(cast("str", channel_group.attrs["fit_result"])) + fits[channel_name] = fit_data + return fits + + +def write_fit_result_by_job_id( + *, + job_id: int, + fit_result: FitResult, +) -> None: + """Write a fit result into the HDF5 file for a job. + + Creates or overwrites the ``fits/`` group. + + Args: + job_id: Job identifier. + fit_result: The fit result to persist. + """ + filename = get_filename_by_job_id(job_id) + h5_path = Path(get_config().data.results_dir) / filename + with h5_open(h5_path, "a") as h5file: + fits_group = h5file.require_group("fits") + channel = fit_result.result_channel + if channel in fits_group: + del fits_group[channel] + grp = fits_group.create_group(channel) + grp.attrs["fit_result"] = json.dumps(asdict(fit_result)) + + +def get_fit_results_by_job_id(*, job_id: int) -> dict[str, dict[str, object]]: + """Read all fit results for a job from its HDF5 file. + + Args: + job_id: Job identifier. + + Returns: + Dict mapping result channel names to their fit result dicts. + """ + filename = get_filename_by_job_id(job_id) + h5_path = Path(get_config().data.results_dir) / filename + if not h5_path.exists(): + return {} + + with h5_open(h5_path, "r") as h5file: + return _read_fits_from_hdf5(h5file) + + +def delete_fit_result_by_job_id(*, job_id: int, result_channel: str) -> None: + """Delete a fit result for a specific channel from the HDF5 file. + + Args: + job_id: Job identifier. + result_channel: Name of the result channel whose fit to delete. + """ + filename = get_filename_by_job_id(job_id) + h5_path = Path(get_config().data.results_dir) / filename + with h5_open(h5_path, "a") as h5file: + if "fits" in h5file and result_channel in h5file["fits"]: + del h5file["fits"][result_channel] diff --git a/src/icon/server/fitting/__init__.py b/src/icon/server/fitting/__init__.py new file mode 100644 index 00000000..14a4531a --- /dev/null +++ b/src/icon/server/fitting/__init__.py @@ -0,0 +1,10 @@ +from icon.server.fitting.fit_runner import FitResult, run_curve_fit +from icon.server.fitting.models import FIT_MODELS, FitFunctionType, FitModel + +__all__ = [ + "FIT_MODELS", + "FitFunctionType", + "FitModel", + "FitResult", + "run_curve_fit", +] diff --git a/src/icon/server/fitting/auto_fit.py b/src/icon/server/fitting/auto_fit.py new file mode 100644 index 00000000..8ae66924 --- /dev/null +++ b/src/icon/server/fitting/auto_fit.py @@ -0,0 +1,142 @@ +"""Auto-fit: repeat the previous fit when a new job finishes.""" + +from __future__ import annotations + +import logging +from dataclasses import asdict +from typing import Any + +import numpy as np + +from icon.server.data_access.models.enums import JobStatus +from icon.server.data_access.repositories.experiment_data_repository import ( + ExperimentData, + ExperimentDataRepository, + get_fit_results_by_job_id, + write_fit_result_by_job_id, +) +from icon.server.data_access.repositories.job_repository import JobRepository +from icon.server.fitting.fit_runner import run_curve_fit +from icon.server.web_server.socketio_emit_queue import emit_queue + +logger = logging.getLogger(__name__) + + +def try_auto_fit(job_id: int, experiment_source_id: int) -> None: + """Auto-fit the new job using the fit config from the previous job. + + Looks up the most recent previous PROCESSED job for the same experiment + source that has a stored fit, then runs the same model on the new data + with fresh initial guesses. Failures are logged but never raised. + + Args: + job_id: The newly completed job. + experiment_source_id: The experiment source to find previous jobs. + """ + try: + _auto_fit(job_id, experiment_source_id) + except Exception: + logger.exception("Auto-fit failed for job %d", job_id) + + +def _auto_fit(job_id: int, experiment_source_id: int) -> None: + previous_job_id = _find_previous_job_with_fit( + experiment_source_id, exclude_job_id=job_id + ) + if previous_job_id is None: + return + + previous_fits = get_fit_results_by_job_id(job_id=previous_job_id) + if not previous_fits: + return + + data = ExperimentDataRepository.get_experiment_data_by_job_id( + job_id=job_id, + max_transfer_bytes=2**62, + ) + + scan_param_name = next((p for p in data.scan_parameters if p != "timestamp"), None) + if scan_param_name is None: + return + + for channel_name, fit_data in previous_fits.items(): + if fit_data.get("success") and fit_data.get("func_type"): + _fit_channel(job_id, data, scan_param_name, channel_name, fit_data) + + +def _fit_channel( + job_id: int, + data: ExperimentData, + scan_param_name: str, + channel_name: str, + fit_data: dict[str, Any], +) -> None: + """Run a single auto-fit for one channel and persist the result.""" + channel_values = data.result_channels.get(channel_name, {}) + if not channel_values: + return + + scan_values = data.scan_parameters[scan_param_name] + indices = sorted(set(scan_values.keys()) & set(channel_values.keys())) + x = np.array([float(scan_values[i]) for i in indices]) + y = np.array([float(channel_values[i]) for i in indices]) + + func_type = fit_data["func_type"] + fit_result = run_curve_fit( + x=x, + y=y, + result_channel=channel_name, + func_type=func_type, # type: ignore[arg-type] + ) + + if fit_result.success: + write_fit_result_by_job_id(job_id=job_id, fit_result=fit_result) + emit_queue.put( + { + "event": f"experiment_fit_{job_id}", + "data": asdict(fit_result), + } + ) + logger.info( + "Auto-fit %s on job %d channel '%s' succeeded", + func_type, + job_id, + channel_name, + ) + else: + logger.warning( + "Auto-fit %s on job %d channel '%s' failed: %s", + func_type, + job_id, + channel_name, + fit_result.message, + ) + + +_MAX_PREVIOUS_JOBS_TO_CHECK = 10 + + +def _find_previous_job_with_fit( + experiment_source_id: int, + exclude_job_id: int, +) -> int | None: + """Find the most recent PROCESSED job with a stored fit. + + Only checks the last few jobs to avoid scanning the entire history. + """ + rows = JobRepository.get_job_by_experiment_source_and_status( + experiment_source_id=experiment_source_id, + status=JobStatus.PROCESSED, + ) + # Rows are ordered by (priority asc, created asc), so iterate in reverse + checked = 0 + for (job,) in reversed(rows): + if job.id == exclude_job_id: + continue + fits = get_fit_results_by_job_id(job_id=job.id) + if fits: + return job.id + checked += 1 + if checked >= _MAX_PREVIOUS_JOBS_TO_CHECK: + break + return None diff --git a/src/icon/server/fitting/fit_runner.py b/src/icon/server/fitting/fit_runner.py new file mode 100644 index 00000000..2ff16f8d --- /dev/null +++ b/src/icon/server/fitting/fit_runner.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import logging +from dataclasses import dataclass + +import numpy as np +import numpy.typing as npt +from scipy.optimize import curve_fit # type: ignore[import-untyped] + +from icon.server.fitting.models import FIT_MODELS, FitFunctionType + +logger = logging.getLogger(__name__) + +_EXPECTED_RANGE_LEN = 2 +_MAX_FIT_EVALS = 10000 + + +_FIT_CURVE_POINTS = 200 + + +@dataclass +class FitResult: + """Result of a curve fit operation.""" + + result_channel: str + func_type: str + x_range: list[float] | None + init: dict[str, float] + result: dict[str, float] + goodness: dict[str, float] + success: bool + message: str + fit_curve: dict[str, list[float]] | None = None + + +def _filter_valid( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], +) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: + mask = np.isfinite(x) & np.isfinite(y) + return x[mask], y[mask] + + +def _compute_goodness( + y: npt.NDArray[np.float64], + y_fit: npt.NDArray[np.float64], + n_params: int, +) -> dict[str, float]: + n = len(y) + ss_res = float(np.sum((y - y_fit) ** 2)) + ss_tot = float(np.sum((y - np.mean(y)) ** 2)) + + r2 = 1.0 - ss_res / ss_tot if ss_tot > 0 else 0.0 + + dof = n - n_params + chi2_red = ss_res / dof if dof > 0 else float("inf") + + log_term = float(n * np.log(ss_res / n)) if ss_res > 0 and n > 0 else 0.0 + aic = log_term + 2.0 * n_params + bic = log_term + n_params * float(np.log(n)) if n > 0 else 0.0 + + return {"r2": r2, "chi2_red": chi2_red, "aic": aic, "bic": bic} + + +def _make_error( + result_channel: str, + func_type: str, + x_range: list[float] | None, + init: dict[str, float], + message: str, +) -> FitResult: + return FitResult( + result_channel=result_channel, + func_type=func_type, + x_range=x_range, + init=init, + result={}, + goodness={}, + success=False, + message=message, + ) + + +def _apply_range( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], + x_range: list[float] | None, +) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: + if x_range is not None and len(x_range) == _EXPECTED_RANGE_LEN: + mask = (x >= x_range[0]) & (x <= x_range[1]) + return x[mask], y[mask] + return x, y + + +def run_curve_fit( # noqa: C901 + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], + result_channel: str, + func_type: FitFunctionType, + x_range: list[float] | None = None, + init: dict[str, float] | None = None, +) -> FitResult: + """Run a curve fit on the given data.""" + if func_type not in FIT_MODELS: + return _make_error( + result_channel, + func_type, + x_range, + init or {}, + f"Unknown fit function: {func_type}", + ) + + model = FIT_MODELS[func_type] + x, y = _apply_range(x, y, x_range) + x, y = _filter_valid(x, y) + + min_points = len(model.param_names) + 1 + if len(x) < min_points: + return _make_error( + result_channel, + func_type, + x_range, + init or {}, + f"Insufficient data points: {len(x)} (need at least {min_points})", + ) + + # Build initial guess: merge user overrides on top of auto-guess + p0 = list(model.guess(x, y)) + if init: + for i, name in enumerate(model.param_names): + if name in init: + p0[i] = init[name] + + init_dict = dict(zip(model.param_names, p0, strict=True)) + + try: + popt, _ = curve_fit(model.func, x, y, p0=p0, maxfev=_MAX_FIT_EVALS) + except (RuntimeError, ValueError) as exc: + return _make_error( + result_channel, + func_type, + x_range, + init_dict, + str(exc), + ) + + result_dict = dict(zip(model.param_names, (float(v) for v in popt), strict=True)) + + if model.derived_params: + result_dict.update(model.derived_params(result_dict)) + + y_fit = model.func(x, *popt) + goodness = _compute_goodness(y, y_fit, len(model.param_names)) + + # Generate smooth fit curve for plotting + x_min, x_max = float(x.min()), float(x.max()) + step = (x_max - x_min) / (_FIT_CURVE_POINTS - 1) + curve_x = [x_min + i * step for i in range(_FIT_CURVE_POINTS)] + curve_y = model.func(np.array(curve_x), *popt).tolist() + fit_curve = {"x": curve_x, "y": curve_y} + + return FitResult( + result_channel=result_channel, + func_type=func_type, + x_range=x_range, + init=init_dict, + result=result_dict, + goodness=goodness, + success=True, + message="Fit converged", + fit_curve=fit_curve, + ) diff --git a/src/icon/server/fitting/models.py b/src/icon/server/fitting/models.py new file mode 100644 index 00000000..7ee8f257 --- /dev/null +++ b/src/icon/server/fitting/models.py @@ -0,0 +1,233 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Literal + +import numpy as np +import numpy.typing as npt + +if TYPE_CHECKING: + from collections.abc import Callable + +FitFunctionType = Literal[ + "gaussian", + "lorentzian", + "poly2", + "harmonic", + "damped_harmonic", +] + +_MIN_FWHM_POINTS = 2 + + +@dataclass(frozen=True) +class FitModel: + """Definition of a curve-fitting model.""" + + func: Callable[..., npt.NDArray[np.float64]] + param_names: list[str] + default_update_param: str + guess: Callable[[npt.NDArray[np.float64], npt.NDArray[np.float64]], list[float]] + derived_params: Callable[[dict[str, float]], dict[str, float]] | None = None + + +def _lorentzian( + x: npt.NDArray[np.float64], + y0: float, + a: float, + x0: float, + gamma: float, +) -> npt.NDArray[np.float64]: + return np.asarray(y0 + a / (1.0 + ((x - x0) / gamma) ** 2)) + + +def _lorentzian_guess( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], +) -> list[float]: + n = len(y) + n10 = max(1, n // 10) + sorted_y = np.sort(y) + baseline = float(np.median(np.concatenate([sorted_y[:n10], sorted_y[-n10:]]))) + + peak_idx = int(np.argmax(np.abs(y - baseline))) + a = float(y[peak_idx] - baseline) + x0 = float(x[peak_idx]) + + above = np.where(np.abs(y - baseline) >= np.abs(a) / 2.0)[0] + if len(above) >= _MIN_FWHM_POINTS: + gamma = float(abs(x[above[-1]] - x[above[0]])) / 2.0 + else: + gamma = float(abs(x[-1] - x[0])) / 4.0 + + gamma = max(gamma, 1e-12) + return [baseline, a, x0, gamma] + + +def _gaussian( + x: npt.NDArray[np.float64], + y0: float, + a: float, + x0: float, + sigma: float, +) -> npt.NDArray[np.float64]: + return np.asarray(y0 + a * np.exp(-((x - x0) ** 2) / (2.0 * sigma**2))) + + +def _gaussian_guess( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], +) -> list[float]: + n = len(y) + n20 = max(1, n // 5) + baseline = float(np.mean(np.sort(y)[:n20])) + + peak_idx = int(np.argmax(np.abs(y - baseline))) + a = float(y[peak_idx] - baseline) + x0 = float(x[peak_idx]) + + half_max = np.abs(a) / 2.0 + above = np.where(np.abs(y - baseline) >= half_max)[0] + if len(above) >= _MIN_FWHM_POINTS: + fwhm = float(abs(x[above[-1]] - x[above[0]])) + sigma = fwhm / (2.0 * np.sqrt(2.0 * np.log(2.0))) + else: + sigma = float(abs(x[-1] - x[0])) / 10.0 + + sigma = max(sigma, 1e-12) + return [baseline, a, x0, sigma] + + +def _poly2( + x: npt.NDArray[np.float64], + a: float, + b: float, + c: float, +) -> npt.NDArray[np.float64]: + return np.asarray(a * x**2 + b * x + c) + + +def _poly2_guess( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], +) -> list[float]: + coeffs = np.polyfit(x, y, 2) + return coeffs.tolist() + + +def _harmonic( + x: npt.NDArray[np.float64], + y0: float, + a: float, + omega: float, + phi: float, +) -> npt.NDArray[np.float64]: + return np.asarray(y0 + a * np.cos(omega * x + phi)) + + +_MIN_FFT_POINTS = 3 + + +def _harmonic_guess( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], +) -> list[float]: + y0 = float(np.mean(y)) + a = float(np.max(y) - np.min(y)) / 2.0 + n = len(y) + if n >= _MIN_FFT_POINTS: + dx = float(np.mean(np.diff(x))) + fft_vals = np.abs(np.fft.rfft(y - y0)) + fft_vals[0] = 0 + if len(fft_vals) > 1: + peak_freq_idx = int(np.argmax(fft_vals)) + freq = peak_freq_idx / (n * dx) if dx > 0 else 1.0 + omega = 2.0 * np.pi * freq + else: + omega = 1.0 + else: + omega = 1.0 + phi = 0.0 + return [y0, a, omega, phi] + + +def _damped_harmonic( + x: npt.NDArray[np.float64], + y0: float, + a: float, + k: float, + omega: float, + phi: float, +) -> npt.NDArray[np.float64]: + return np.asarray(y0 + np.exp(k * x) * a * np.cos(omega * x + phi)) + + +def _damped_harmonic_guess( + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], +) -> list[float]: + hg = _harmonic_guess(x, y) + return [hg[0], hg[1], 0.0, hg[2], hg[3]] + + +def _poly2_derived(result: dict[str, float]) -> dict[str, float]: + a = result.get("a", 0) + if a != 0: + return {"vertex": -result["b"] / (2.0 * a)} + return {} + + +def _harmonic_derived(result: dict[str, float]) -> dict[str, float]: + if "omega" in result: + return {"f": result["omega"] / (2.0 * np.pi)} + return {} + + +# Adding a new fit model +# ---------------------- +# 1. Define _my_model(x, p1, p2, ...) -> NDArray (the model function). +# 2. Define _my_model_guess(x, y) -> list[float] (initial parameter estimator). +# 3. (Optional) Define _my_model_derived(result) -> dict if the model has +# useful derived quantities (e.g. vertex from quadratic coefficients). +# 4. Add an entry to FIT_MODELS below. The param_names order must match the +# function signature. default_update_param is the fitted value pre-filled +# in the "Update Parameter" UI (can be a derived param name). +# 5. Add the model name to FIT_TYPES, FIT_PARAM_NAMES, and +# FIT_DEFAULT_UPDATE_PARAM in frontend/src/utils/fitFunctions.ts. +# 6. Add tests in tests/server/fitting/. + +FIT_MODELS: dict[str, FitModel] = { + "lorentzian": FitModel( + func=_lorentzian, + param_names=["y0", "a", "x0", "gamma"], + default_update_param="x0", + guess=_lorentzian_guess, + ), + "gaussian": FitModel( + func=_gaussian, + param_names=["y0", "a", "x0", "sigma"], + default_update_param="x0", + guess=_gaussian_guess, + ), + "poly2": FitModel( + func=_poly2, + param_names=["a", "b", "c"], + default_update_param="vertex", + guess=_poly2_guess, + derived_params=_poly2_derived, + ), + "harmonic": FitModel( + func=_harmonic, + param_names=["y0", "a", "omega", "phi"], + default_update_param="f", + guess=_harmonic_guess, + derived_params=_harmonic_derived, + ), + "damped_harmonic": FitModel( + func=_damped_harmonic, + param_names=["y0", "a", "k", "omega", "phi"], + default_update_param="f", + guess=_damped_harmonic_guess, + derived_params=_harmonic_derived, + ), +} diff --git a/src/icon/server/frontend/assets/index-Ciiw6jzV.js b/src/icon/server/frontend/assets/index-Ciiw6jzV.js deleted file mode 100644 index f1b5e5a7..00000000 --- a/src/icon/server/frontend/assets/index-Ciiw6jzV.js +++ /dev/null @@ -1,45 +0,0 @@ -import{r as db,a as hb,b as y,c as rt,j as d,u as Mr,S as mb,d as pb,B as vb,A as Sv,e as yb,f as $t,I as xt,g as gb,h as Jc,T as Ev,i as bb,C as wv,k as Cv,l as il,m as xb,n as $p,o as bt,s as Uf,p as be,q as gn,t as _v,v as Wn,P as Sb,D as Or,w as vf,x as Eb,L as wb,y as Cb,z as wr,G as _b,E as Bf,F as Tb,H as Cr,J as _r,K as Tr,M as Fc,N as Tv,O as jv,Q as So,R as rl,U as Dv,V as Rn,W as sl,X as ol,Y as ea,Z as jb,_ as Rv,$ as Db,a0 as ti,a1 as ni,a2 as yn,a3 as vr,a4 as ft,a5 as Rb,a6 as Ab,a7 as yf,a8 as gf,a9 as bf,aa as Mb,ab as Av,ac as Ob,ad as Nb,ae as zb,af as Lb,ag as Ub,ah as Da}from"./mui-bAivgoRt.js";import{u as Bb,i as Hb,a as Vb,b as kb,c as qb,d as Yb,e as Gb,f as Xb,g as Qb,h as Pb,j as Zb,k as Kb,l as $b}from"./echarts-D1BCANXe.js";import"./zrender-Co-hdh87.js";(function(){const 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rc(e,t){return(e.lanes&t)!==0?!0:(e=e.dependencies,!!(e!==null&&cs(e)))}function d1(e,t,n){switch(t.tag){case 3:hl(t,t.stateNode.containerInfo),sa(t,ht,e.memoizedState.cache),Di();break;case 27:case 5:st(t);break;case 4:hl(t,t.stateNode.containerInfo);break;case 10:sa(t,t.type,t.memoizedProps.value);break;case 13:var i=t.memoizedState;if(i!==null)return i.dehydrated!==null?(da(t),t.flags|=128,null):(n&t.child.childLanes)!==0?_m(e,t,n):(da(t),e=Zn(e,t,n),e!==null?e.sibling:null);da(t);break;case 19:var o=(e.flags&128)!==0;if(i=(n&t.childLanes)!==0,i||(Ai(e,t,n,!1),i=(n&t.childLanes)!==0),o){if(i)return jm(e,t,n);t.flags|=128}if(o=t.memoizedState,o!==null&&(o.rendering=null,o.tail=null,o.lastEffect=null),ce(mt,mt.current),i)break;return null;case 22:case 23:return t.lanes=0,xm(e,t,n);case 24:sa(t,ht,e.memoizedState.cache)}return Zn(e,t,n)}function Dm(e,t,n){if(e!==null)if(e.memoizedProps!==t.pendingProps)vt=!0;else{if(!rc(e,n)&&(t.flags&128)===0)return 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o=t.type._context,i=t.pendingProps.children,Ia(t),o=Tt(o),i=i(o),t.flags|=1,Et(e,t,i,n),t.child;case 14:return gm(e,t,t.type,t.pendingProps,n);case 15:return bm(e,t,t.type,t.pendingProps,n);case 19:return jm(e,t,n);case 31:return i=t.pendingProps,n=t.mode,i={mode:i.mode,children:i.children},e===null?(n=As(i,n),n.ref=t.ref,t.child=n,n.return=t,t=n):(n=kn(e.child,i),n.ref=t.ref,t.child=n,n.return=t,t=n),t;case 22:return xm(e,t,n);case 24:return Ia(t),i=Tt(ht),e===null?(o=Tu(),o===null&&(o=Pe,c=Cu(),o.pooledCache=c,c.refCount++,c!==null&&(o.pooledCacheLanes|=n),o=c),t.memoizedState={parent:i,cache:o},Du(t),sa(t,ht,o)):((e.lanes&n)!==0&&(Ru(e,t),Bi(t,null,null,n),Ui()),o=e.memoizedState,c=t.memoizedState,o.parent!==i?(o={parent:i,cache:i},t.memoizedState=o,t.lanes===0&&(t.memoizedState=t.updateQueue.baseState=o),sa(t,ht,i)):(i=c.cache,sa(t,ht,i),i!==o.cache&&wu(t,[ht],n,!0))),Et(e,t,t.pendingProps.children,n),t.child;case 29:throw t.pendingProps}throw Error(s(156,t.tag))}function Kn(e){e.flags|=4}function Rm(e,t){if(t.type!=="stylesheet"||(t.state.loading&4)!==0)e.flags&=-16777217;else if(e.flags|=16777216,!Bp(t)){if(t=an.current,t!==null&&((ze&4194048)===ze?Cn!==null:(ze&62914560)!==ze&&(ze&536870912)===0||t!==Cn))throw zi=ju,dh;e.flags|=8192}}function Ms(e,t){t!==null&&(e.flags|=4),e.flags&16384&&(t=e.tag!==22?Qr():536870912,e.lanes|=t,Yl|=t)}function Xi(e,t){if(!He)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var i=null;n!==null;)n.alternate!==null&&(i=n),n=n.sibling;i===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:i.sibling=null}}function Ie(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,i=0;if(t)for(var o=e.child;o!==null;)n|=o.lanes|o.childLanes,i|=o.subtreeFlags&65011712,i|=o.flags&65011712,o.return=e,o=o.sibling;else for(o=e.child;o!==null;)n|=o.lanes|o.childLanes,i|=o.subtreeFlags,i|=o.flags,o.return=e,o=o.sibling;return e.subtreeFlags|=i,e.childLanes=n,t}function h1(e,t,n){var i=t.pendingProps;switch(bu(t),t.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Ie(t),null;case 1:return Ie(t),null;case 3:return n=t.stateNode,i=null,e!==null&&(i=e.memoizedState.cache),t.memoizedState.cache!==i&&(t.flags|=2048),Xn(ht),na(),n.pendingContext&&(n.context=n.pendingContext,n.pendingContext=null),(e===null||e.child===null)&&(ji(t)?Kn(t):e===null||e.memoizedState.isDehydrated&&(t.flags&256)===0||(t.flags|=1024,sh())),Ie(t),null;case 26:return n=t.memoizedState,e===null?(Kn(t),n!==null?(Ie(t),Rm(t,n)):(Ie(t),t.flags&=-16777217)):n?n!==e.memoizedState?(Kn(t),Ie(t),Rm(t,n)):(Ie(t),t.flags&=-16777217):(e.memoizedProps!==i&&Kn(t),Ie(t),t.flags&=-16777217),null;case 27:cn(t),n=Mt.current;var o=t.type;if(e!==null&&t.stateNode!=null)e.memoizedProps!==i&&Kn(t);else{if(!i){if(t.stateNode===null)throw Error(s(166));return Ie(t),null}e=Ue.current,ji(t)?ih(t):(e=Rp(o,i,n),t.stateNode=e,Kn(t))}return Ie(t),null;case 5:if(cn(t),n=t.type,e!==null&&t.stateNode!=null)e.memoizedProps!==i&&Kn(t);else{if(!i){if(t.stateNode===null)throw Error(s(166));return Ie(t),null}if(e=Ue.current,ji(t))ih(t);else{switch(o=Xs(Mt.current),e){case 1:e=o.createElementNS("http://www.w3.org/2000/svg",n);break;case 2:e=o.createElementNS("http://www.w3.org/1998/Math/MathML",n);break;default:switch(n){case"svg":e=o.createElementNS("http://www.w3.org/2000/svg",n);break;case"math":e=o.createElementNS("http://www.w3.org/1998/Math/MathML",n);break;case"script":e=o.createElement("div"),e.innerHTML=" + diff --git a/src/icon/server/pre_processing/worker.py b/src/icon/server/pre_processing/worker.py index d0fddb43..f93a38c2 100644 --- a/src/icon/server/pre_processing/worker.py +++ b/src/icon/server/pre_processing/worker.py @@ -33,6 +33,7 @@ from icon.server.data_access.repositories.parameters_repository import ( ParametersRepository, ) +from icon.server.fitting.auto_fit import try_auto_fit from icon.server.hardware_processing.task import HardwareProcessingTask if TYPE_CHECKING: @@ -214,6 +215,11 @@ def run(self) -> None: run_id=pre_processing_task.job_run.id, status=JobRunStatus.DONE, ) + + try_auto_fit( + job_id=pre_processing_task.job.id, + experiment_source_id=pre_processing_task.job.experiment_source_id, + ) except Exception as e: logger.exception( "JobRun with id '%s' failed", pre_processing_task.job_run.id diff --git a/tests/generate_test_hdf5.py b/tests/generate_test_hdf5.py new file mode 100644 index 00000000..fa265f4d --- /dev/null +++ b/tests/generate_test_hdf5.py @@ -0,0 +1,491 @@ +"""Generate synthetic HDF5 files visible to the running ICON server. + +Creates HDF5 files in the configured results directory and inserts the +matching SQLite records (experiment_source, job, scan_parameter, job_run) +so the server can find the data by job ID. + +Usage:: + + uv run python tests/generate_test_hdf5.py + +The script prints the job ID for each created entry so you can navigate to +it in the UI at http://localhost:8004. + +Requirements: +- The ICON database must already exist (run the server at least once so + Alembic has applied all migrations). +- The results directory must be writable. +""" + +from __future__ import annotations + +import json +import logging +import sys +from datetime import datetime, timedelta, timezone + +import h5py # type: ignore +import numpy as np +import sqlalchemy +import sqlalchemy.orm + +from icon.config.config import get_config +from icon.server.data_access.db_context.sqlite import engine +from icon.server.data_access.models.enums import JobRunStatus, JobStatus +from icon.server.data_access.models.sqlite.experiment_source import ExperimentSource +from icon.server.data_access.models.sqlite.job import Job +from icon.server.data_access.models.sqlite.job_run import JobRun +from icon.server.data_access.models.sqlite.scan_parameter import ScanParameter + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Shared experiment source — reused across all generated jobs +# --------------------------------------------------------------------------- + +_EXPERIMENT_ID = "test.SyntheticExperiment (SyntheticExperiment)" + + +def _get_or_create_experiment_source( + session: sqlalchemy.orm.Session, +) -> ExperimentSource: + existing = session.execute( + sqlalchemy.select(ExperimentSource).where( + ExperimentSource.experiment_id == _EXPERIMENT_ID + ) + ).scalar_one_or_none() + if existing: + return existing + source = ExperimentSource(experiment_id=_EXPERIMENT_ID) + session.add(source) + session.flush() # populate source.id without committing + return source + + +def _insert_job_and_run( + session: sqlalchemy.orm.Session, + source: ExperimentSource, + scan_values: list[float], + variable_id: str, + scheduled_time: datetime, +) -> tuple[Job, JobRun]: + """Insert a Job, ScanParameter, and JobRun; return both ORM objects.""" + job = Job( + experiment_source_id=source.id, + status=JobStatus.PROCESSED, + repetitions=1, + number_of_shots=1, + ) + session.add(job) + session.flush() # populate job.id + + scan_param = ScanParameter( + job_id=job.id, + name=variable_id, + variable_id=variable_id, + scan_values=scan_values, + ) + session.add(scan_param) + + run = JobRun( + job_id=job.id, + scheduled_time=scheduled_time, + status=JobRunStatus.DONE, + ) + session.add(run) + session.flush() + + return job, run + + +def _hdf5_path(scheduled_time: datetime) -> str: + """Return the HDF5 file path for a given scheduled time. + + Must match exactly how ``get_filename_by_job_id`` builds the name: + ``f"{scheduled_time}.h5"`` + """ + results_dir = get_config().data.results_dir + return f"{results_dir}/{scheduled_time}.h5" + + +def _write_hdf5( + filepath: str, + job_id: int, + x: np.ndarray, + y: np.ndarray, + variable_id: str, + result_channel: str, +) -> None: + """Write a minimal ICON-compatible HDF5 file.""" + n = len(x) + with h5py.File(filepath, "w") as h5: + h5.attrs["number_of_data_points"] = n + h5.attrs["number_of_shots"] = 1 + h5.attrs["experiment_id"] = _EXPERIMENT_ID + h5.attrs["job_id"] = job_id + h5.attrs["repetitions"] = 1 + h5.attrs["realtime_scan"] = False + + scan_dtype = [("timestamp", "S26"), (variable_id, np.float64)] + scan_ds = h5.create_dataset( + "scan_parameters", + shape=(n, 1), + dtype=scan_dtype, + compression="gzip", + ) + for i in range(n): + scan_ds[i] = (b"2025-01-01T00:00:00.000000", x[i]) + + result_dtype = [(result_channel, np.float64)] + result_ds = h5.create_dataset( + "result_channels", + shape=(n,), + dtype=result_dtype, + compression="gzip", + ) + result_ds[result_channel] = y + result_ds.attrs["Plot window metadata"] = json.dumps( + [ + { + "name": "Results", + "index": 0, + "type": "readout", + "channel_names": [result_channel], + }, + ] + ) + + shot_group = h5.create_group("shot_channels") + shot_group.attrs["Plot window metadata"] = json.dumps([]) + vector_group = h5.create_group("vector_channels") + vector_group.attrs["Plot window metadata"] = json.dumps([]) + h5.create_group("parameters") + + +def _create_job( + x: np.ndarray, + y: np.ndarray, + variable_id: str, + result_channel: str, + scheduled_time: datetime, + label: str, +) -> int: + """Create SQLite records + HDF5 file; return the job ID.""" + with sqlalchemy.orm.Session(engine) as session: + source = _get_or_create_experiment_source(session) + job, _run = _insert_job_and_run( + session, + source, + scan_values=x.tolist(), + variable_id=variable_id, + scheduled_time=scheduled_time, + ) + session.commit() + job_id = job.id + + filepath = _hdf5_path(scheduled_time) + _write_hdf5(filepath, job_id, x, y, variable_id, result_channel) + logger.info("[%s] job_id=%s file=%s", label, job_id, filepath) + return job_id + + +# --------------------------------------------------------------------------- +# Curve generators — one per scenario +# --------------------------------------------------------------------------- + +# Use a fixed base time and offset each scenario by a minute so filenames +# don't collide even if the script is re-run on the same second. +_BASE_TIME = datetime(2025, 6, 1, 12, 0, 0, tzinfo=timezone.utc) + + +def generate_clean_lorentzian() -> int: + """Clean Lorentzian peak (y0=1, A=5, x0=150 MHz, gamma=5 MHz).""" + x = np.linspace(100, 200, 100) + y = 1.0 + 5.0 / (1 + ((x - 150.0) / 5.0) ** 2) + y += np.random.default_rng(42).normal(0, 0.05, len(x)) + return _create_job( + x, + y, + variable_id="frequency", + result_channel="counts", + scheduled_time=_BASE_TIME, + label="lorentzian_clean", + ) + + +def generate_noisy_lorentzian() -> int: + """Noisy Lorentzian peak (same params, noise=1.0).""" + x = np.linspace(100, 200, 100) + y = 1.0 + 5.0 / (1 + ((x - 150.0) / 5.0) ** 2) + y += np.random.default_rng(42).normal(0, 1.0, len(x)) + return _create_job( + x, + y, + variable_id="frequency", + result_channel="counts", + scheduled_time=_BASE_TIME + timedelta(minutes=1), + label="lorentzian_noisy", + ) + + +def generate_w_shape() -> int: + """W-shaped curve: two Lorentzian dips at 130 and 170 MHz.""" + x = np.linspace(100, 200, 200) + y = ( + 10.0 + - 5.0 / (1 + ((x - 130.0) / 3.0) ** 2) + - 5.0 / (1 + ((x - 170.0) / 3.0) ** 2) + ) + y += np.random.default_rng(42).normal(0, 0.1, len(x)) + return _create_job( + x, + y, + variable_id="frequency", + result_channel="counts", + scheduled_time=_BASE_TIME + timedelta(minutes=2), + label="w_shape", + ) + + +def generate_gaussian() -> int: + """Gaussian peak (y0=2, A=3, x0=5, sigma=0.8).""" + x = np.linspace(0, 10, 100) + y = 2.0 + 3.0 * np.exp(-((x - 5.0) ** 2) / (2 * 0.8**2)) + y += np.random.default_rng(42).normal(0, 0.05, len(x)) + return _create_job( + x, + y, + variable_id="detuning", + result_channel="fluorescence", + scheduled_time=_BASE_TIME + timedelta(minutes=3), + label="gaussian", + ) + + +def generate_poly2() -> int: + """Quadratic curve (a=2, b=-3, c=1).""" + x = np.linspace(-5, 5, 50) + y = 2.0 * x**2 - 3.0 * x + 1.0 + y += np.random.default_rng(42).normal(0, 0.5, len(x)) + return _create_job( + x, + y, + variable_id="voltage", + result_channel="counts", + scheduled_time=_BASE_TIME + timedelta(minutes=4), + label="poly2", + ) + + +def generate_harmonic() -> int: + """Harmonic oscillation (y0=1, A=2, omega=4, phi=0.5).""" + x = np.linspace(0, 10, 200) + y = 1.0 + 2.0 * np.cos(4.0 * x + 0.5) + y += np.random.default_rng(42).normal(0, 0.1, len(x)) + return _create_job( + x, + y, + variable_id="time", + result_channel="population", + scheduled_time=_BASE_TIME + timedelta(minutes=5), + label="harmonic", + ) + + +def generate_damped_harmonic() -> int: + """Damped harmonic (y0=1, A=2, k=-0.3, omega=4, phi=0.5).""" + x = np.linspace(0, 10, 200) + y = 1.0 + np.exp(-0.3 * x) * 2.0 * np.cos(4.0 * x + 0.5) + y += np.random.default_rng(42).normal(0, 0.05, len(x)) + return _create_job( + x, + y, + variable_id="time", + result_channel="population", + scheduled_time=_BASE_TIME + timedelta(minutes=6), + label="damped_harmonic", + ) + + +def _insert_job_and_run_2d( + session: sqlalchemy.orm.Session, + source: ExperimentSource, + scan_values_x: list[float], + variable_id_x: str, + scan_values_y: list[float], + variable_id_y: str, + scheduled_time: datetime, +) -> tuple[Job, JobRun]: + """Insert a Job with two ScanParameters and a JobRun; return both ORM objects.""" + job = Job( + experiment_source_id=source.id, + status=JobStatus.PROCESSED, + repetitions=1, + number_of_shots=1, + ) + session.add(job) + session.flush() + + scan_param_x = ScanParameter( + job_id=job.id, + name=variable_id_x, + variable_id=variable_id_x, + scan_values=scan_values_x, + ) + scan_param_y = ScanParameter( + job_id=job.id, + name=variable_id_y, + variable_id=variable_id_y, + scan_values=scan_values_y, + ) + session.add(scan_param_x) + session.add(scan_param_y) + + run = JobRun( + job_id=job.id, + scheduled_time=scheduled_time, + status=JobRunStatus.DONE, + ) + session.add(run) + session.flush() + + return job, run + + +def _write_hdf5_2d( + filepath: str, + job_id: int, + x_flat: np.ndarray, + y_flat: np.ndarray, + z: np.ndarray, + variable_id_x: str, + variable_id_y: str, + result_channel: str, +) -> None: + """Write a minimal ICON-compatible HDF5 file for a 2D scan.""" + n = len(z) + with h5py.File(filepath, "w") as h5: + h5.attrs["number_of_data_points"] = n + h5.attrs["number_of_shots"] = 1 + h5.attrs["experiment_id"] = _EXPERIMENT_ID + h5.attrs["job_id"] = job_id + h5.attrs["repetitions"] = 1 + h5.attrs["realtime_scan"] = False + + scan_dtype = [ + ("timestamp", "S26"), + (variable_id_x, np.float64), + (variable_id_y, np.float64), + ] + scan_ds = h5.create_dataset( + "scan_parameters", + shape=(n, 1), + dtype=scan_dtype, + compression="gzip", + ) + for i in range(n): + scan_ds[i] = (b"2025-01-01T00:00:00.000000", x_flat[i], y_flat[i]) + + result_dtype = [(result_channel, np.float64)] + result_ds = h5.create_dataset( + "result_channels", + shape=(n,), + dtype=result_dtype, + compression="gzip", + ) + result_ds[result_channel] = z + result_ds.attrs["Plot window metadata"] = json.dumps( + [ + { + "name": "Results", + "index": 0, + "type": "readout", + "channel_names": [result_channel], + }, + ] + ) + + shot_group = h5.create_group("shot_channels") + shot_group.attrs["Plot window metadata"] = json.dumps([]) + vector_group = h5.create_group("vector_channels") + vector_group.attrs["Plot window metadata"] = json.dumps([]) + h5.create_group("parameters") + + +def generate_2d_gaussian() -> int: + """2D Gaussian peak over frequency x amplitude grid. + + z = A * exp(-((x-x0)^2/(2*sx^2) + (y-y0)^2/(2*sy^2))) + offset + with A=5, x0=150, y0=5, sx=15, sy=2, offset=1. + """ + variable_id_x = "frequency" + variable_id_y = "amplitude" + result_channel = "counts" + scheduled_time = _BASE_TIME + timedelta(minutes=7) + + nx, ny = 20, 15 + x_vals = np.linspace(100, 200, nx) + y_vals = np.linspace(0, 10, ny) + + # Outer loop over x, inner loop over y + x_grid, y_grid = np.meshgrid(x_vals, y_vals, indexing="ij") + x_flat = x_grid.ravel() + y_flat = y_grid.ravel() + + # 2D Gaussian + amp, x0, y0, sx, sy, offset = 5.0, 150.0, 5.0, 15.0, 2.0, 1.0 + z = ( + amp + * np.exp(-((x_flat - x0) ** 2 / (2 * sx**2) + (y_flat - y0) ** 2 / (2 * sy**2))) + + offset + ) + z += np.random.default_rng(42).normal(0, 0.1, len(z)) + + with sqlalchemy.orm.Session(engine) as session: + source = _get_or_create_experiment_source(session) + job, _run = _insert_job_and_run_2d( + session, + source, + scan_values_x=x_vals.tolist(), + variable_id_x=variable_id_x, + scan_values_y=y_vals.tolist(), + variable_id_y=variable_id_y, + scheduled_time=scheduled_time, + ) + session.commit() + job_id = job.id + + filepath = _hdf5_path(scheduled_time) + _write_hdf5_2d( + filepath, + job_id, + x_flat, + y_flat, + z, + variable_id_x, + variable_id_y, + result_channel, + ) + logger.info("[2d_gaussian] job_id=%s file=%s", job_id, filepath) + return job_id + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO, stream=sys.stdout, format="%(message)s") + + logger.info("Results dir : %s", get_config().data.results_dir) + logger.info("Database : %s", get_config().databases.sqlite.file) + logger.info("") + + generate_clean_lorentzian() + generate_noisy_lorentzian() + generate_w_shape() + generate_gaussian() + generate_poly2() + generate_harmonic() + generate_damped_harmonic() + generate_2d_gaussian() + + logger.info( + "\nDone. Open http://localhost:8004 and go to the Data page to see the jobs." + ) diff --git a/tests/server/fitting/__init__.py b/tests/server/fitting/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/server/fitting/test_fit_runner.py b/tests/server/fitting/test_fit_runner.py new file mode 100644 index 00000000..88319798 --- /dev/null +++ b/tests/server/fitting/test_fit_runner.py @@ -0,0 +1,114 @@ +import numpy as np +import pytest + +from icon.server.fitting.fit_runner import FitResult, run_curve_fit + +# Minimum R^2 for a fit on clean synthetic data to be considered good. +MIN_GOOD_R2 = 0.99 + + +def _synthetic_lorentzian( + n: int = 100, + noise: float = 0.0, +) -> tuple[np.ndarray, np.ndarray, dict[str, float]]: + """Generate synthetic Lorentzian data.""" + true_params = {"y0": 1.0, "a": 5.0, "x0": 5.0, "gamma": 0.5} + x = np.linspace(0, 10, n) + y = true_params["y0"] + true_params["a"] / ( + 1.0 + ((x - true_params["x0"]) / true_params["gamma"]) ** 2 + ) + if noise > 0: + rng = np.random.default_rng(42) + y = y + rng.normal(0, noise, n) + return x, y, true_params + + +class TestRunCurveFit: + def test_lorentzian_clean(self) -> None: + x, y, true = _synthetic_lorentzian(noise=0.01) + result = run_curve_fit(x, y, "ch1", "lorentzian") + assert result.success + assert result.result["x0"] == pytest.approx(true["x0"], abs=0.1) + assert result.result["a"] == pytest.approx(true["a"], abs=0.5) + assert result.goodness["r2"] > MIN_GOOD_R2 + + def test_lorentzian_noisy(self) -> None: + x, y, true = _synthetic_lorentzian(noise=0.5) + result = run_curve_fit(x, y, "ch1", "lorentzian") + assert result.success + assert result.result["x0"] == pytest.approx(true["x0"], abs=1.0) + + def test_unknown_func_type(self) -> None: + x = np.linspace(0, 10, 50) + y = np.ones(50) + result = run_curve_fit(x, y, "ch1", "nonexistent") # type: ignore[arg-type] + assert not result.success + assert "Unknown" in result.message + + def test_insufficient_points(self) -> None: + x = np.array([1.0, 2.0]) + y = np.array([1.0, 2.0]) + result = run_curve_fit(x, y, "ch1", "lorentzian") + assert not result.success + assert "Insufficient" in result.message + + def test_nan_data_filtered(self) -> None: + x, y, _ = _synthetic_lorentzian(n=100, noise=0.01) + y[10] = np.nan + y[20] = np.inf + x[30] = np.nan + result = run_curve_fit(x, y, "ch1", "lorentzian") + assert result.success + + def test_x_range_filter(self) -> None: + x, y, true = _synthetic_lorentzian(n=200, noise=0.01) + result = run_curve_fit(x, y, "ch1", "lorentzian", x_range=[3.0, 7.0]) + assert result.success + assert result.result["x0"] == pytest.approx(true["x0"], abs=0.2) + + def test_init_override(self) -> None: + x, y, true = _synthetic_lorentzian(noise=0.01) + result = run_curve_fit(x, y, "ch1", "lorentzian", init={"x0": 4.9}) + assert result.success + assert result.result["x0"] == pytest.approx(true["x0"], abs=0.2) + + def test_w_shape_with_init(self) -> None: + """Two Lorentzian dips; init selects correct peak.""" + x = np.linspace(0, 20, 200) + y = ( + 10.0 + - 5.0 / (1 + ((x - 5.0) / 0.5) ** 2) + - 5.0 / (1 + ((x - 15.0) / 0.5) ** 2) + ) + # Guide to the second peak + result = run_curve_fit(x, y, "ch1", "lorentzian", init={"x0": 15.0}) + assert result.success + assert result.result["x0"] == pytest.approx(15.0, abs=1.0) + + def test_gaussian_fit(self) -> None: + x = np.linspace(-5, 5, 100) + y = 2.0 + 3.0 * np.exp(-((x - 1.0) ** 2) / (2.0 * 0.5**2)) + rng = np.random.default_rng(42) + y += rng.normal(0, 0.05, len(y)) + result = run_curve_fit(x, y, "ch1", "gaussian") + assert result.success + assert result.result["x0"] == pytest.approx(1.0, abs=0.3) + + def test_poly2_fit(self) -> None: + x = np.linspace(-5, 5, 50) + y = 2.0 * x**2 - 3.0 * x + 1.0 + result = run_curve_fit(x, y, "ch1", "poly2") + assert result.success + assert result.result["a"] == pytest.approx(2.0, abs=0.1) + assert "vertex" in result.result + + def test_fit_result_fields(self) -> None: + x, y, _ = _synthetic_lorentzian(noise=0.01) + result = run_curve_fit(x, y, "ch1", "lorentzian") + assert isinstance(result, FitResult) + assert result.func_type == "lorentzian" + assert result.result_channel == "ch1" + assert "r2" in result.goodness + assert "chi2_red" in result.goodness + assert "aic" in result.goodness + assert "bic" in result.goodness diff --git a/tests/server/fitting/test_models.py b/tests/server/fitting/test_models.py new file mode 100644 index 00000000..feecc930 --- /dev/null +++ b/tests/server/fitting/test_models.py @@ -0,0 +1,83 @@ +import numpy as np +import pytest + +from icon.server.fitting.models import FIT_MODELS + + +class TestLorentzianFunction: + def test_peak_at_x0(self) -> None: + model = FIT_MODELS["lorentzian"] + x = np.array([5.0]) + result = model.func(x, 0.0, 10.0, 5.0, 1.0) + assert result[0] == pytest.approx(10.0) + + def test_baseline(self) -> None: + model = FIT_MODELS["lorentzian"] + x = np.array([1e6]) + result = model.func(x, 3.0, 10.0, 0.0, 1.0) + assert result[0] == pytest.approx(3.0, abs=0.01) + + def test_symmetry(self) -> None: + model = FIT_MODELS["lorentzian"] + x0 = 5.0 + left = model.func(np.array([x0 - 2.0]), 0.0, 1.0, x0, 1.0) + right = model.func(np.array([x0 + 2.0]), 0.0, 1.0, x0, 1.0) + assert left[0] == pytest.approx(right[0]) + + +class TestLorentzianGuess: + def test_reasonable_peak_guess(self) -> None: + model = FIT_MODELS["lorentzian"] + x = np.linspace(0, 10, 100) + y0, a, x0, gamma = 1.0, 5.0, 5.0, 0.5 + y = y0 + a / (1.0 + ((x - x0) / gamma) ** 2) + guess = model.guess(x, y) + # x0 guess should be near the peak + assert abs(guess[2] - x0) < 1.0 + + def test_dip_guess(self) -> None: + model = FIT_MODELS["lorentzian"] + x = np.linspace(0, 10, 100) + y = 10.0 - 5.0 / (1.0 + ((x - 5.0) / 0.5) ** 2) + guess = model.guess(x, y) + # A should be negative for a dip + assert guess[1] < 0 + + +class TestGaussianFunction: + def test_peak_at_x0(self) -> None: + model = FIT_MODELS["gaussian"] + x = np.array([3.0]) + result = model.func(x, 0.0, 5.0, 3.0, 1.0) + assert result[0] == pytest.approx(5.0) + + +class TestPoly2Function: + def test_known_values(self) -> None: + model = FIT_MODELS["poly2"] + x = np.array([0.0, 1.0, 2.0]) + result = model.func(x, 1.0, 2.0, 3.0) + np.testing.assert_array_almost_equal(result, [3.0, 6.0, 11.0]) + + def test_guess(self) -> None: + model = FIT_MODELS["poly2"] + x = np.linspace(-5, 5, 50) + y = 2.0 * x**2 - 3.0 * x + 1.0 + guess = model.guess(x, y) + assert guess[0] == pytest.approx(2.0, abs=0.1) + + +class TestHarmonicFunction: + def test_at_zero_phase(self) -> None: + model = FIT_MODELS["harmonic"] + x = np.array([0.0]) + result = model.func(x, 1.0, 2.0, np.pi, 0.0) + assert result[0] == pytest.approx(3.0) + + +class TestDampedHarmonicFunction: + def test_no_damping_matches_harmonic(self) -> None: + x = np.linspace(0, 10, 50) + harmonic = FIT_MODELS["harmonic"].func(x, 1.0, 2.0, 3.0, 0.5) + damped = FIT_MODELS["damped_harmonic"].func(x, 1.0, 2.0, 0.0, 3.0, 0.5) + 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