Extra features

src/labcore/analysis/__init__.py

@@ -1,6 +1,7 @@
 from .analysis_base import DatasetAnalysis as DatasetAnalysis
 from .fit import Fit as Fit
 from .fit import FitResult as FitResult
+from .fit import fit_and_add_to_ds
 from .hvplotting import ComplexHist as ComplexHist
 from .hvplotting import Node as Node
 from .hvplotting import ValuePlot as ValuePlot

src/labcore/analysis/analysis_base.py

@@ -5,6 +5,7 @@
 from pathlib import Path
 from types import TracebackType
 from typing import Any, Dict, List, Optional, Type, Union
+from shutil import copy2
 
 # Needed to generate hvplot from a script
 import holoviews as hv
@@ -21,6 +22,7 @@
 
 
 class AnalysisExistsError(Exception):
+    logger.info('Analysis already exists, adding another')
     pass
 
 
@@ -161,6 +163,43 @@ def add_figure(
 
     make_figure = add_figure
 
+    def archive_file(self, file_path, name: Optional[str] = None, folder: Optional[Path] = None):
+        """Copy a source file into the analysis output folder(s).
+
+        Parameters
+        ----------
+        file_path
+            Path to the file that should be archived.
+        name
+            Optional base name for the archived copy. Defaults to the source stem.
+        folder
+            Optional destination folder. Defaults to every folder managed by this analysis.
+
+        Returns
+        -------
+        list[Path]
+            Paths of the archived copies.
+        """
+        source = Path(file_path)
+        if not source.exists():
+            raise FileNotFoundError(f"source file does not exist: {source}")
+
+        archive_name = source.stem if name is None else name
+        suffix = source.suffix.lstrip(".")
+        target_folders = self.savefolders if folder is None else [Path(folder)]
+
+        archived_paths = []
+        for target_folder in target_folders:
+            if not target_folder.exists():
+                target_folder.mkdir(parents=True, exist_ok=True)
+
+            archived_path = self._new_file_path(target_folder, archive_name, suffix)
+            copy2(source, archived_path)
+            archived_paths.append(archived_path)
+
+        self.files.extend(path for path in archived_paths if path not in self.files)
+        return archived_paths
+
     def to_table(self, name: str, data: Dict[str, Any]) -> None:
         data.update(
             {

src/labcore/analysis/fitfuncs/generic.py

@@ -144,6 +144,43 @@ def guess(
 
         return dict(A=A, of=of, phi=phi, f=f, tau=tau)
 
+class ExponentialDecayCosinetoground(Fit):
+    @staticmethod
+    def model(coordinates, A, of, f, phi, tau) -> np.ndarray:
+        """$A \sin(2*\pi*(f*x + \phi/360)) \exp(-x/\tau) + of$"""
+        return A * (1+np.sin(2 * np.pi * (f * coordinates + phi/360))) * np.exp(-coordinates/tau) + of
+
+    @staticmethod
+    def guess(coordinates, data):
+        """This guess will ignore the first value because since it usually is not relaiable."""
+
+        # offset guess: The mean of the data
+        of = np.mean(data)
+
+        # amplitude guess: difference between max and min.
+        A = np.abs(np.max(data) - np.min(data)) / 2.
+        if data[0] < data[-1]:
+            A *= -1
+
+        # f guess: Maximum of the absolute value of the fourier transform.
+        fft_data = np.fft.rfft(data)[1:]
+        fft_coordinates = np.fft.rfftfreq(data.size, coordinates[1] - coordinates[0])[1:]
+
+        # note to confirm, could there be multiple peaks? I am always taking the first one here.
+        f_max_index = np.argmax(fft_data)
+        f = fft_coordinates[f_max_index]
+
+        # phi guess
+        phi = -np.angle(fft_data[f_max_index], deg=True)
+
+        # tau guess: pick the point where we reach roughly 1/e
+        one_over_e_val = of + A/3.
+        one_over_e_idx = np.argmin(np.abs(data-one_over_e_val))
+        tau = coordinates[one_over_e_idx]
+
+        return dict(A=A, of=of, phi=phi, f=f, tau=tau)
+
+
 
 class Gaussian(Fit):
     @staticmethod

src/labcore/data/__init__.py

@@ -0,0 +1,17 @@
+from .datadict import DataDict, MeshgridDataDict, dd2xr
+from .tools import split_complex
+from .datadict_storage import datadict_from_hdf5, datadict_to_hdf5, all_datadicts_from_hdf5, DDH5Writer, load_as_xr
+from .ddh5_xr import (
+    ddh5_to_xarray,
+    ddh5_to_gridded_ddh5,
+    ddh5_schema,
+    ddh5_info,
+    validate_ddh5,
+    MissingMode,
+    DDH5Schema,
+    DDH5FieldInfo,
+    GridInfo,
+    DDH5ValidationReport,
+    GridInferenceError,
+    DDH5Writer_swmr,
+)