to_dict and to_pandas added to FOOOFResults

Author: ryanhammonds

fooof/core/funcs.py

@@ -7,22 +7,30 @@
     - They are left available for easy swapping back in, if desired.
 """
 
+from inspect import isfunction
+
 import numpy as np
 
 from fooof.core.errors import InconsistentDataError
 
 ###################################################################################################
 ###################################################################################################
 
-def gaussian_function(xs, *params):
+def gaussian_function(xs, cf, pw, bw, *params):
     """Gaussian fitting function.
 
     Parameters
     ----------
     xs : 1d array
         Input x-axis values.
+    cf : float
+        The center of the gaussian.
+    pw : float
+        The height of the gaussian.
+    bw : float
+        The width of the gaussian.
     *params : float
-        Parameters that define gaussian function.
+        Additional centers, heights, and widths.
 
     Returns
     -------
@@ -32,16 +40,17 @@ def gaussian_function(xs, *params):
 
     ys = np.zeros_like(xs)
 
+    params = [cf, pw, bw, *params]
+
     for ii in range(0, len(params), 3):
 
         ctr, hgt, wid = params[ii:ii+3]
-
         ys = ys + hgt * np.exp(-(xs-ctr)**2 / (2*wid**2))
 
     return ys
 
 
-def expo_function(xs, *params):
+def expo_function(xs, offset, knee, exp):
     """Exponential fitting function, for fitting aperiodic component with a 'knee'.
 
     NOTE: this function requires linear frequency (not log).
@@ -50,26 +59,32 @@ def expo_function(xs, *params):
     ----------
     xs : 1d array
         Input x-axis values.
-    *params : float
-        Parameters (offset, knee, exp) that define Lorentzian function:
-        y = 10^offset * (1/(knee + x^exp))
+    offset : float
+        The y-intercept of the fit.
+    knee : float
+        The bend in the fit.
+    exp : float
+        The exponential slope of the fit.
 
     Returns
     -------
     ys : 1d array
         Output values for exponential function.
+
+    Notes
+    -----
+    Parameters (offset, knee, exp) that define Lorentzian function:
+    y = 10^offset * (1/(knee + x^exp))
     """
 
     ys = np.zeros_like(xs)
 
-    offset, knee, exp = params
-
     ys = ys + offset - np.log10(knee + xs**exp)
 
     return ys
 
 
-def expo_nk_function(xs, *params):
+def expo_nk_function(xs, offset, exp):
     """Exponential fitting function, for fitting aperiodic component without a 'knee'.
 
     NOTE: this function requires linear frequency (not log).
@@ -78,34 +93,40 @@ def expo_nk_function(xs, *params):
     ----------
     xs : 1d array
         Input x-axis values.
-    *params : float
-        Parameters (offset, exp) that define Lorentzian function:
-        y = 10^off * (1/(x^exp))
+    offset : float
+        The y-intercept of the fit.
+    exp : float
+        The exponential slope of the fit.
 
     Returns
     -------
     ys : 1d array
         Output values for exponential function, without a knee.
+
+    Notes
+    -----
+    Parameters (offset, exp) that define Lorentzian function:
+    y = 10^off * (1/(x^exp))
     """
 
     ys = np.zeros_like(xs)
 
-    offset, exp = params
-
     ys = ys + offset - np.log10(xs**exp)
 
     return ys
 
 
-def linear_function(xs, *params):
+def linear_function(xs, offset, slope):
     """Linear fitting function.
 
     Parameters
     ----------
     xs : 1d array
         Input x-axis values.
-    *params : float
-        Parameters that define linear function.
+    offset : float
+        The y-intercept of the fit.
+    slope : float
+        The slope of the fit.
 
     Returns
     -------
@@ -115,22 +136,24 @@ def linear_function(xs, *params):
 
     ys = np.zeros_like(xs)
 
-    offset, slope = params
-
     ys = ys + offset + (xs*slope)
 
     return ys
 
 
-def quadratic_function(xs, *params):
+def quadratic_function(xs, offset, slope, curve):
     """Quadratic fitting function.
 
     Parameters
     ----------
     xs : 1d array
         Input x-axis values.
-    *params : float
-        Parameters that define quadratic function.
+    offset : float
+        The y-intercept of the fit.
+    slope : float
+        The slope of the fit.
+    curve : float
+        The curve of the fit.
 
     Returns
     -------
@@ -140,8 +163,6 @@ def quadratic_function(xs, *params):
 
     ys = np.zeros_like(xs)
 
-    offset, slope, curve = params
-
     ys = ys + offset + (xs*slope) + ((xs**2)*curve)
 
     return ys
@@ -167,7 +188,7 @@ def get_pe_func(periodic_mode):
 
     """
 
-    if isinstance(periodic_mode, function):
+    if isfunction(periodic_mode):
         pe_func = periodic_mode
     elif periodic_mode == 'gaussian':
         pe_func = gaussian_function
@@ -196,7 +217,7 @@ def get_ap_func(aperiodic_mode):
         If the specified aperiodic mode label is not understood.
     """
 
-    if isinstance(aperiodic_mode, function):
+    if isfunction(aperiodic_mode):
         ap_func = aperiodic_mode
     elif aperiodic_mode == 'fixed':
         ap_func = expo_nk_function

fooof/data/data.py

@@ -84,8 +84,30 @@ class FOOOFResults(namedtuple('FOOOFResults', ['aperiodic_params', 'peak_params'
     -----
     This object is a data object, based on a NamedTuple, with immutable data attributes.
     """
+
     __slots__ = ()
 
+    def to_dict(self):
+
+        # Combined peak, aperiodic, and goodness of fit params
+        results_dict = OrderedDict()
+        results_dict.update(self.peak_params.to_dict())
+        results_dict.update(self.aperiodic_params.to_dict())
+        results_dict.update(OrderedDict(r_squared=self.r_squared, error=self.error))
+
+        return results_dict
+
+    def to_pandas(self):
+
+        if pd is None:
+            raise ValueError("Pandas is not installed.")
+
+        results_dict = self.to_dict()
+
+        results_df = pd.DataFrame(results_dict)
+
+        return results_df
+
 
 class SimParams(namedtuple('SimParams', ['aperiodic_params', 'periodic_params', 'nlv'])):
     """Parameters that define a simulated power spectrum.
@@ -108,29 +130,29 @@ class SimParams(namedtuple('SimParams', ['aperiodic_params', 'periodic_params',
 
 class FitParams(np.ndarray):
 
-    def __new__(cls, results, labels):
+    def __new__(cls, params, labels):
 
-        return np.asarray(results).view(cls)
+        return np.asarray(params).view(cls)
 
-    def __init__(self, results, labels):
+    def __init__(self, params, labels):
 
-        self.results = results
+        self.params = params
         self.labels = labels
 
     def to_dict(self):
 
-        results = OrderedDict((k, v) for k, v in \
-            zip(self.labels, self.results.transpose()))
+        params = OrderedDict((k, v) for k, v in \
+            zip(self.labels, self.params.transpose()))
 
-        return results
+        return params
 
     def to_pandas(self):
 
         if pd is None:
             raise ValueError("Pandas is not installed.")
 
-        results = self.to_dict()
+        params = self.to_dict()
 
-        results = pd.DataFrame(results)
+        params = pd.DataFrame(params)
 
-        return results
+        return params

fooof/objs/fit.py

@@ -56,6 +56,7 @@
 
 import warnings
 from copy import deepcopy
+from inspect import signature
 
 import numpy as np
 from numpy.linalg import LinAlgError
@@ -77,7 +78,7 @@
 from fooof.plts.style import style_spectrum_plot
 from fooof.utils.data import trim_spectrum
 from fooof.utils.params import compute_gauss_std
-from fooof.data import FOOOFResults, FOOOFSettings, FOOOFMetaData
+from fooof.data import FOOOFResults, FOOOFSettings, FOOOFMetaData, FitParams
 from fooof.sim.gen import gen_freqs, gen_aperiodic, gen_periodic, gen_model
 
 ###################################################################################################
@@ -484,6 +485,14 @@ def fit(self, freqs=None, power_spectrum=None, freq_range=None):
             # Convert gaussian definitions to peak parameters
             self.peak_params_ = self._create_peak_params(self.gaussian_params_)
 
+            # Create a flexible datatype object for ap/pe parameters
+            pe_labels = list(signature(self._pe_func).parameters.keys())[1:]
+            ap_labels = list(signature(self._ap_func).parameters.keys())[1:]
+
+            self.peak_params_ = FitParams(self.peak_params_, pe_labels)
+            self.gaussian_params_ = FitParams(self.gaussian_params_, pe_labels)
+            self.aperiodic_params_ = FitParams(self.aperiodic_params_, ap_labels)
+
             # Calculate R^2 and error of the model fit
             self._calc_r_squared()
             self._calc_error()
@@ -636,6 +645,12 @@ def get_results(self):
         return FOOOFResults(**{key.strip('_') : getattr(self, key) \
             for key in OBJ_DESC['results']})
 
+    def get_results_dict(self):
+        """Return model fit parameters and goodness of fit metrics as an ordered dictionary."""
+
+        results = self.get_results()
+
+        #pe_params =
 
     @copy_doc_func_to_method(plot_fm)
     def plot(self, plot_peaks=None, plot_aperiodic=True, plt_log=False,