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https://github.com/nqrduck/nqrduck-spectrometer.git
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Merge branch 'feature-fitting' into development
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jupfi
commit
169244a9eb
1 changed files with 235 additions and 45 deletions
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@ -1,7 +1,8 @@
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"""Class for handling measurement data."""
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"""This module defines the measurement data structure and the fit class for measurement data."""
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import logging
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import numpy as np
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from scipy.optimize import curve_fit
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from nqrduck.helpers.signalprocessing import SignalProcessing as sp
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from nqrduck.helpers.functions import Function
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@ -28,8 +29,8 @@ class Measurement:
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target_frequency (float): Target frequency of the measurement.
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frequency_shift (float): Frequency shift of the measurement.
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IF_frequency (float): Intermediate frequency of the measurement.
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xf (np.array): Frequency axis for the x axis of the measurement data.
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yf (np.array): Frequency axis for the y axis of the measurement data.
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fdx (np.array): Frequency axis for the x axis of the measurement data.
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fdy (np.array): Frequency axis for the y axis of the measurement data.
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"""
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def __init__(
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@ -46,69 +47,89 @@ class Measurement:
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self.tdx = tdx
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self.tdy = tdy
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self.target_frequency = target_frequency
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self.fdx, self.fdy = sp.fft(tdx, tdy, frequency_shift)
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self.frequency_shift = frequency_shift
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self.IF_frequency = IF_frequency
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self.fdx, self.fdy = sp.fft(tdx, tdy, frequency_shift)
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self.fits = []
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def apodization(self, function: Function):
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def apodization(self, function: Function) -> "Measurement":
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"""Applies apodization to the measurement data.
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Args:
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function (Function): Apodization function.
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returns:
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Measurement : The apodized measurement.
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Returns:
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Measurement: The apodized measurement.
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"""
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# Get the y data weights from the function
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duration = (self.tdx[-1] - self.tdx[0]) * 1e-6
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resolution = duration / len(self.tdx)
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logger.debug("Resolution: %s", resolution)
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y_weight = function.get_pulse_amplitude(duration, resolution)
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tdy_measurement = self.tdy * y_weight
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tdy_apodized = self.tdy * y_weight
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apodized_measurement = Measurement(
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self.name,
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self.tdx,
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tdy_measurement,
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tdy_apodized,
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target_frequency=self.target_frequency,
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IF_frequency=self.IF_frequency,
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)
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return apodized_measurement
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# Data saving and loading
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def add_fit(self, fit: "Fit") -> None:
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"""Adds a fit to the measurement.
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def to_json(self):
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"""Converts the measurement to a json-compatible format.
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Args:
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fit (Fit): The fit to add.
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"""
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self.fits.append(fit)
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def delete_fit(self, fit: "Fit") -> None:
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"""Deletes a fit from the measurement.
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Args:
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fit (Fit): The fit to delete.
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"""
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self.fits.remove(fit)
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def edit_fit_name(self, fit: "Fit", name: str) -> None:
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"""Edits the name of a fit.
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Args:
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fit (Fit): The fit to edit.
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name (str): The new name.
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"""
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logger.debug(f"Editing fit name to {name}.")
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fit.name = name
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def to_json(self) -> dict:
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"""Converts the measurement to a JSON-compatible format.
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Returns:
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dict : The measurement in json-compatible format.
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dict: The measurement in JSON-compatible format.
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"""
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return {
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"name": self.name,
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"tdx": self.tdx.tolist(),
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"tdy": [
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[x.real, x.imag] for x in self.tdy
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], # Convert complex numbers to list
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"tdy": [[x.real, x.imag] for x in self.tdy],
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"target_frequency": self.target_frequency,
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"IF_frequency": self.IF_frequency,
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"fits": [fit.to_json() for fit in self.fits],
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}
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@classmethod
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def from_json(cls, json: dict):
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"""Converts the json format to a measurement.
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def from_json(cls, json: dict) -> "Measurement":
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"""Converts the JSON format to a measurement.
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Args:
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json (dict) : The measurement in json-compatible format.
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json (dict): The measurement in JSON-compatible format.
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Returns:
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Measurement : The measurement.
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Measurement: The measurement.
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"""
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tdy = np.array([complex(y[0], y[1]) for y in json["tdy"]])
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return cls(
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measurement = cls(
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json["name"],
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np.array(json["tdx"]),
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tdy,
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@ -116,58 +137,227 @@ class Measurement:
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IF_frequency=json["IF_frequency"],
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)
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# Measurement data
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for fit_json in json["fits"]:
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measurement.add_fit(Fit.from_json(fit_json, measurement))
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return measurement
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# Properties for encapsulation
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@property
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def name(self):
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def name(self) -> str:
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"""Name of the measurement."""
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return self._name
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@name.setter
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def name(self, value):
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def name(self, value: str) -> None:
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self._name = value
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@property
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def tdx(self):
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"""Time axis for the x axis of the measurement data."""
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def tdx(self) -> np.array:
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"""Time domain data for the measurement (x)."""
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return self._tdx
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@tdx.setter
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def tdx(self, value):
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def tdx(self, value: np.array) -> None:
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self._tdx = value
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@property
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def tdy(self):
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"""Time axis for the y axis of the measurement data."""
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def tdy(self) -> np.array:
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"""Time domain data for the measurement (y)."""
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return self._tdy
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@tdy.setter
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def tdy(self, value):
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def tdy(self, value: np.array) -> None:
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self._tdy = value
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@property
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def fdx(self):
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"""Frequency axis for the x axis of the measurement data."""
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def fdx(self) -> np.array:
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"""Frequency domain data for the measurement (x)."""
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return self._fdx
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@fdx.setter
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def fdx(self, value):
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def fdx(self, value: np.array) -> None:
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self._fdx = value
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@property
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def fdy(self):
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"""Frequency axis for the y axis of the measurement data."""
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def fdy(self) -> np.array:
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"""Frequency domain data for the measurement (y)."""
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return self._fdy
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@fdy.setter
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def fdy(self, value):
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def fdy(self, value: np.array) -> None:
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self._fdy = value
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# Pulse parameters
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@property
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def target_frequency(self):
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def target_frequency(self) -> float:
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"""Target frequency of the measurement."""
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return self._target_frequency
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@target_frequency.setter
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def target_frequency(self, value):
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def target_frequency(self, value: float) -> None:
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self._target_frequency = value
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@property
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def fits(self) -> list:
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"""Fits of the measurement."""
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return self._fits
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@fits.setter
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def fits(self, value: list) -> None:
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self._fits = value
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class Fit:
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"""The fit class for measurement data. A fit can be performed on either the frequency or time domain data.
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A measurement can have multiple fits.
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"""
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subclasses = []
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def __init_subclass__(cls, **kwargs) -> None:
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"""Adds the subclass to the list of subclasses."""
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super().__init_subclass__(**kwargs)
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cls.subclasses.append(cls)
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def __init__(self, name: str, domain: str, measurement: Measurement) -> None:
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"""Initializes the fit."""
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self.name = name
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self.domain = domain
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self.measurement = measurement
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self.fit()
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def fit(self) -> None:
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"""Fits the measurement data and sets the fit parameters and covariance."""
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if self.domain == "time":
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x = self.measurement.tdx
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y = self.measurement.tdy
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elif self.domain == "frequency":
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x = self.measurement.fdx
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y = self.measurement.fdy
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else:
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raise ValueError("Domain not recognized.")
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initial_guess = self.initial_guess()
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self.parameters, self.covariance = curve_fit(
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self.fit_function, x, abs(y), p0=initial_guess
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)
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self.x = x
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self.y = self.fit_function(x, *self.parameters)
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def fit_function(self, x: np.array, *parameters) -> np.array:
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"""The fit function.
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Args:
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x (np.array): The x data.
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*parameters: The fit parameters.
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Returns:
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np.array: The y data.
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"""
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raise NotImplementedError
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def initial_guess(self) -> list:
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"""Initial guess for the fit.
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Returns:
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list: The initial guess.
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"""
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raise NotImplementedError
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def to_json(self) -> dict:
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"""Converts the fit to a JSON-compatible format.
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Returns:
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dict: The fit in JSON-compatible format.
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"""
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return {
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"name": self.name,
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"class": self.__class__.__name__,
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}
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@classmethod
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def from_json(cls, data: dict, measurement: Measurement) -> "Fit":
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"""Converts the JSON format to a fit.
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Args:
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data (dict): The fit in JSON-compatible format.
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measurement (Measurement): The measurement.
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Returns:
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Fit: The fit.
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"""
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for subclass in cls.subclasses:
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if subclass.__name__ == data["class"]:
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return subclass(name=data["name"], measurement=measurement)
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raise ValueError(f"Subclass {data['class']} not found.")
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@property
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def x(self) -> np.array:
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"""The x data of the fit."""
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return self._x
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@x.setter
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def x(self, value: np.array) -> None:
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self._x = value
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@property
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def y(self) -> np.array:
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"""The y data of the fit."""
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return self._y
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@y.setter
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def y(self, value: np.array) -> None:
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self._y = value
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class T2StarFit(Fit):
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"""T2* fit for measurement data."""
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def __init__(self, measurement: Measurement, name: str = "T2*") -> None:
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"""Initializes the T2* fit."""
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super().__init__(name, "time", measurement)
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def fit(self) -> None:
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"""Fits the measurement data and sets the fit parameters and covariance."""
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super().fit()
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self.parameters = {
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"S0": self.parameters[0],
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"T2Star": self.parameters[1],
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"covariance": self.covariance,
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}
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def fit_function(self, t: np.array, S0: float, T2Star: float) -> np.array:
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"""The T2* fit function used for curve fitting."""
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return S0 * np.exp(-t / T2Star)
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def initial_guess(self) -> list:
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"""Initial guess for the T2* fit."""
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return [1, 1]
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class LorentzianFit(Fit):
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"""Lorentzian fit for measurement data."""
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def __init__(self, measurement: Measurement, name: str = "Lorentzian") -> None:
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"""Initializes the Lorentzian fit."""
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super().__init__(name, "frequency", measurement)
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def fit(self) -> None:
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"""Fits the measurement data and sets the fit parameters and covariance."""
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super().fit()
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self.parameters = {
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"S0": self.parameters[0],
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"T2Star": self.parameters[1],
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"covariance": self.covariance,
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}
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logger.debug("Lorentzian fit parameters: %s", self.parameters)
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def fit_function(self, f: np.array, S0: float, T2Star: float) -> np.array:
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"""The Lorentzian fit function used for curve fitting."""
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return S0 / (1 + (2 * np.pi * f * T2Star) ** 2)
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def initial_guess(self) -> list:
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"""Initial guess for the Lorentzian fit."""
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return [1, 1]
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