add the commits to the fit analyser
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@ -554,7 +554,7 @@ class FitAnalyser():
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return self.fitModel.guess(data=data, x=x, y=y, **kwargs)
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def guess(self, dataArray, x=None, y=None, guess_kwargs={}, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, daskKwargs=None, **kwargs):
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"""Call the guess function of the 1D fit model to guess the initial value.
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"""Call the guess function of the fit model to guess the initial value.
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:param dataArray: The data for the fit
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:type dataArray: xarray DataArray
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@ -565,7 +565,7 @@ class FitAnalyser():
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:param guess_kwargs: the keyworded arguments to send to the guess function, defaults to {}
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:type guess_kwargs: dict, optional
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:param input_core_dims: over write of the same argument in xarray.apply_ufunc, defaults to None
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:type input_core_dims: _type_, optional
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:type input_core_dims: list or array like, optional
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:param dask: over write of the same argument in xarray.apply_ufunc,, defaults to 'parallelized'
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:type dask: str, optional
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:param vectorize: over write of the same argument in xarray.apply_ufunc,, defaults to True
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@ -574,8 +574,8 @@ class FitAnalyser():
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:type keep_attrs: bool, optional
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:param daskKwargs: over write of the same argument in xarray.apply_ufunc,, defaults to None
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:type daskKwargs: dict, optional
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:return: _description_
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:rtype: _type_
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:return: The guessed initial parameters for the fit
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:rtype: xarray DataArray
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"""
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kwargs.update(
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@ -667,14 +667,60 @@ class FitAnalyser():
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)
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def _fit_1D(self, data, params, x):
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"""Call the fit function of the 1D fit model to do the fit.
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:param data: The data to fit
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:type data: 1D numpy array
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:param params: The initial paramters of the fit
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:type params: lmfit Parameters
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:param x: The data of x axis
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:type x: 1D numpy array
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:return: The result of the fit
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:rtype: lmfit FitResult
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"""
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return self.fitModel.fit(data=data, x=x, params=params, nan_policy='omit')
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def _fit_2D(self, data, params, x, y):
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"""Call the fit function of the 2D fit model to do the fit.
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:param data: The flattened data to fit
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:type data: 1D numpy array
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:param params: The flattened initial paramters of the fit
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:type params: lmfit Parameters
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:param x: The flattened data of x axis
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:type x: 1D numpy array
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:param y: The flattened data of y axis
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:type y: 1D numpy array
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:return: The result of the fit
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:rtype: lmfit FitResult
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"""
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data = data.flatten(order='F')
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return self.fitModel.fit(data=data, x=x, y=y, params=params, nan_policy='omit')
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def fit(self, dataArray, paramsArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, daskKwargs=None, **kwargs):
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"""Call the fit function of the fit model to do the fit.
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:param dataArray: The data for the fit
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:type dataArray: xarray DataArray
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:param paramsArray: The flattened initial paramters of the fit
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:type paramsArray: xarray DataArray or lmfit Parameters
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:param x: The name of x axis in data or the data of x axis, defaults to None
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:type x: str or numpy array, optional
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:param y: The name of y axis in data or the data of y axis, defaults to None
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:type y: str or numpy array, optional
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:param input_core_dims: over write of the same argument in xarray.apply_ufunc, defaults to None, defaults to None
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:type input_core_dims: list or array like, optional
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to None, defaults to 'parallelized'
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:type dask: str, optional
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:param vectorize: over write of the same argument in xarray.apply_ufunc, defaults to None, defaults to True
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:type vectorize: bool, optional
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:param keep_attrs: over write of the same argument in xarray.apply_ufunc, defaults to None, defaults to True
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:type keep_attrs: bool, optional
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:param daskKwargs: over write of the same argument in xarray.apply_ufunc, defaults to None, defaults to None
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:type daskKwargs: dict, optional
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:return: The fit result
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:rtype: xarray DataArray
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"""
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kwargs.update(
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{
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"dask": dask,
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@ -816,13 +862,56 @@ class FitAnalyser():
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**kwargs)
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def _eval_1D(self, fitResult, x):
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"""Call the eval function of the 1D fit model to calculate the curve.
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:param fitResult: The result of fit
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:type fitResult: lmfit FitResult
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:param x: The data of x axis
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:type x: 1D numpy array
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:return: The curve based on the fit result
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:rtype: 1D numpy array
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"""
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return self.fitModel.eval(x=x, **fitResult.best_values)
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def _eval_2D(self, fitResult, x, y, shape):
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"""Call the eval function of the 2D fit model to calculate the curve.
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:param fitResult: The result of fit
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:type fitResult: lmfit FitResult
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:param x: The data of x axis
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:type x: 1D numpy array
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:param y: The flattened data of y axis
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:type y: 1D numpy array
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:param shape: The desired shape for output
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:type shape: list, tuple or array like
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:return: The curve based on the fit result
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:rtype: 2D numpy array
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"""
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res = self.fitModel.eval(x=x, y=y, **fitResult.best_values)
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return res.reshape(shape, order='F')
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def eval(self, fitResultArray, x=None, y=None, output_core_dims=None, prefix="", dask='parallelized', vectorize=True, daskKwargs=None, **kwargs):
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"""_summary_
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:param fitResultArray: The result of fit
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:type fitResultArray: xarray DataArray
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:param x: The name of x axis in data or the data of x axis, defaults to None
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:type x: str or numpy array, optional
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:param y: The name of y axis in data or the data of y axis, defaults to None
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:type y: str or numpy array, optional
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:param output_core_dims: over write of the same argument in xarray.apply_ufunc, defaults to None
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:type output_core_dims: list, optional
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:param prefix: prefix str of the fit model, defaults to ""
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:type prefix: str, optional
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:param vectorize: over write of the same argument in xarray.apply_ufunc, defaults to True
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:type vectorize: bool, optional
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:param daskKwargs: over write of the same argument in xarray.apply_ufunc, defaults to None
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:type daskKwargs: dict, optional
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:return: The curve based on the fit result
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:rtype: xarray
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"""
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kwargs.update(
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{
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@ -896,9 +985,27 @@ class FitAnalyser():
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return res.assign_coords({prefix+'x':np.array(x), prefix+'y':np.array(y)})
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def _get_fit_value_single(self, fitResult, key):
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"""get value of one single parameter from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param key: The name of the parameter
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:type key: str
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:return: The vaule of the parameter
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:rtype: float
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"""
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return fitResult.params[key].value
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def _get_fit_value(self, fitResult, params):
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"""get value of parameters from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param params: A list of the name of paramerters to read
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:type params: list or array like
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:return: The vaule of the parameter
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:rtype: 1D numpy array
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"""
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func = np.vectorize(self._get_fit_value_single)
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res = tuple(
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func(fitResult, key)
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@ -908,6 +1015,15 @@ class FitAnalyser():
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return res
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def get_fit_value(self, fitResult, dask='parallelized', **kwargs):
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"""get value of parameters from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:return: The vaule of the parameter
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:rtype: xarray DataArray
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"""
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firstIndex = {
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key: fitResult[key][0]
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for key in fitResult.dims
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@ -938,9 +1054,27 @@ class FitAnalyser():
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return value
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def _get_fit_std_single(self, fitResult, key):
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"""get standard deviation of one single parameter from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param key: The name of the parameter
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:type key: str
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:return: The vaule of the parameter
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:rtype: float
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"""
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return fitResult.params[key].stderr
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def _get_fit_std(self, fitResult, params):
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"""get standard deviation of parameters from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param params: A list of the name of paramerters to read
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:type params: list or array like
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:return: The vaule of the parameter
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:rtype: 1D numpy array
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"""
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func = np.vectorize(self._get_fit_std_single)
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res = tuple(
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func(fitResult, key)
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@ -950,6 +1084,15 @@ class FitAnalyser():
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return res
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def get_fit_std(self, fitResult, dask='parallelized', **kwargs):
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"""get standard deviation of parameters from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:return: The vaule of the parameter
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:rtype: xarray DataArray
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"""
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firstIndex = {
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key: fitResult[key][0]
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for key in fitResult.dims
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@ -980,6 +1123,15 @@ class FitAnalyser():
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return value
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def _get_fit_full_result_single(self, fitResult, key):
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"""get the full result of one single parameter from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param key: The name of the parameter
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:type key: str
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:return: The vaule of the parameter
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:rtype: float
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"""
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if not fitResult.params[key].value is None:
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value = fitResult.params[key].value
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@ -994,6 +1146,15 @@ class FitAnalyser():
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return ufloat(value, std)
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def _get_fit_full_result(self, fitResult, params):
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"""get the full result of parameters from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param params: A list of the name of paramerters to read
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:type params: list or array like
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:return: The vaule of the parameter
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:rtype: 1D numpy array
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"""
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func = np.vectorize(self._get_fit_full_result_single)
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res = tuple(
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func(fitResult, key)
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@ -1003,6 +1164,15 @@ class FitAnalyser():
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return res
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def get_fit_full_result(self, fitResult, dask='parallelized', **kwargs):
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"""get the full result of parameters from fit result
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:param fitResult: The result of the fit
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:type fitResult: lmfit FitResult
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:return: The vaule of the parameter
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:rtype: xarray DataArray
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"""
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firstIndex = {
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key: fitResult[key][0]
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for key in fitResult.dims
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