Merge branch 'name' into peakheights

doc/api.rst

@@ -326,6 +326,27 @@ Annotated plots that describe the model and fitting process.
     plot_annotated_model
     plot_annotated_peak_search
 
+Plot Utilities & Styling
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Plot related utilies for styling and managing plots.
+
+.. currentmodule:: fooof.plts.style
+
+.. autosummary::
+    :toctree: generated/
+
+    check_style_options
+
+.. currentmodule:: fooof.plts.utils
+
+.. autosummary::
+    :toctree: generated/
+
+    check_ax
+    recursive_plot
+    save_figure
+
 Utilities
 ---------
 

specparam/core/funcs.py

@@ -32,9 +32,7 @@ def gaussian_function(xs, *params):
 
     ys = np.zeros_like(xs)
 
-    for ii in range(0, len(params), 3):
-
-        ctr, hgt, wid = params[ii:ii+3]
+    for ctr, hgt, wid in zip(*[iter(params)] * 3):
 
         ys = ys + hgt * np.exp(-(xs-ctr)**2 / (2*wid**2))
 
@@ -60,11 +58,8 @@ def expo_function(xs, *params):
         Output values for exponential function.
     """
 
-    ys = np.zeros_like(xs)
-
     offset, knee, exp = params
-
-    ys = ys + offset - np.log10(knee + xs**exp)
+    ys = offset - np.log10(knee + xs**exp)
 
     return ys
 
@@ -88,11 +83,8 @@ def expo_nk_function(xs, *params):
         Output values for exponential function, without a knee.
     """
 
-    ys = np.zeros_like(xs)
-
     offset, exp = params
-
-    ys = ys + offset - np.log10(xs**exp)
+    ys = offset - np.log10(xs**exp)
 
     return ys
 
@@ -113,11 +105,8 @@ def linear_function(xs, *params):
         Output values for linear function.
     """
 
-    ys = np.zeros_like(xs)
-
     offset, slope = params
-
-    ys = ys + offset + (xs*slope)
+    ys = offset + (xs*slope)
 
     return ys
 
@@ -138,11 +127,8 @@ def quadratic_function(xs, *params):
         Output values for quadratic function.
     """
 
-    ys = np.zeros_like(xs)
-
     offset, slope, curve = params
-
-    ys = ys + offset + (xs*slope) + ((xs**2)*curve)
+    ys = offset + (xs*slope) + ((xs**2)*curve)
 
     return ys
 

specparam/core/jacobians.py

@@ -0,0 +1,103 @@
+""""Functions for computing Jacobian matrices to be used during fitting.
+
+Notes
+-----
+These functions line up with those in `funcs`.
+The parameters in these functions are labeled {a, b, c, ...}, but follow the order in `funcs`.
+These functions are designed to be passed into `curve_fit` to provide a computed Jacobian.
+"""
+
+import numpy as np
+
+###################################################################################################
+###################################################################################################
+
+## Periodic Jacobian functions
+
+def jacobian_gauss(xs, *params):
+    """Create the Jacobian matrix for the Gaussian function.
+
+    Parameters
+    ----------
+    xs : 1d array
+        Input x-axis values.
+    *params : float
+        Parameters for the function.
+
+    Returns
+    -------
+    jacobian : 2d array
+        Jacobian matrix, with shape [len(xs), n_params].
+    """
+
+    jacobian = np.zeros((len(xs), len(params)))
+
+    for i, (a, b, c) in enumerate(zip(*[iter(params)] * 3)):
+
+        ax = -a + xs
+        ax2 = ax**2
+
+        c2 = c**2
+        c3 = c**3
+
+        exp = np.exp(-ax2 / (2 * c2))
+        exp_b = exp * b
+
+        ii = i * 3
+        jacobian[:, ii] = (exp_b * ax) / c2
+        jacobian[:, ii+1] = exp
+        jacobian[:, ii+2] = (exp_b * ax2) / c3
+
+    return jacobian
+
+
+## Aperiodic Jacobian functions
+
+def jacobian_expo(xs, *params):
+    """Create the Jacobian matrix for the exponential function.
+
+    Parameters
+    ----------
+    xs : 1d array
+        Input x-axis values.
+    *params : float
+        Parameters for the function.
+
+    Returns
+    -------
+    jacobian : 2d array
+        Jacobian matrix, with shape [len(xs), n_params].
+    """
+
+    a, b, c = params
+
+    xs_c = xs**c
+    b_xs_c = xs_c + b
+
+    jacobian = np.ones((len(xs), len(params)))
+    jacobian[:, 1] = -1 / b_xs_c
+    jacobian[:, 2] = -(xs_c * np.log10(xs)) / b_xs_c
+
+    return jacobian
+
+
+def jacobian_expo_nk(xs, *params):
+    """Create the Jacobian matrix for the exponential no-knee function.
+
+    Parameters
+    ----------
+    xs : 1d array
+        Input x-axis values.
+    *params : float
+        Parameters for the function.
+
+    Returns
+    -------
+    jacobian : 2d array
+        Jacobian matrix, with shape [len(xs), n_params].
+    """
+
+    jacobian = np.ones((len(xs), len(params)))
+    jacobian[:, 1] = -np.log10(xs)
+
+    return jacobian

specparam/objs/fit.py

@@ -71,6 +71,7 @@
 from specparam.core.modutils import copy_doc_func_to_method
 from specparam.core.utils import group_three, check_array_dim
 from specparam.core.funcs import gaussian_function, get_ap_func, infer_ap_func
+from specparam.core.jacobians import jacobian_gauss
 from specparam.core.errors import (FitError, NoModelError, DataError,
                                    NoDataError, InconsistentDataError)
 from specparam.core.strings import (gen_settings_str, gen_model_results_str,
@@ -191,12 +192,17 @@ def __init__(self, peak_width_limits=(0.5, 12.0), max_n_peaks=np.inf, min_peak_h
         self._gauss_overlap_thresh = 0.75
         # Parameter bounds for center frequency when fitting gaussians, in terms of +/- std dev
         self._cf_bound = 1.5
-        # The maximum number of calls to the curve fitting function
-        self._maxfev = 5000
         # The error metric to calculate, post model fitting. See `_calc_error` for options
         #   Note: this is for checking error post fitting, not an objective function for fitting
         self._error_metric = 'MAE'
 
+        ## PRIVATE CURVE_FIT SETTINGS
+        # The maximum number of calls to the curve fitting function
+        self._maxfev = 5000
+        # The tolerance setting for curve fitting (see scipy.curve_fit - ftol / xtol / gtol)
+        #   Here reduce tolerance to speed fitting. Set value to 1e-8 to match curve_fit default
+        self._tol = 0.00001
+
         ## RUN MODES
         # Set default debug mode - controls if an error is raised if model fitting is unsuccessful
         self._debug = False
@@ -400,7 +406,7 @@ def report(self, freqs=None, power_spectrum=None, freq_range=None,
             Only relevant / effective if `freqs` and `power_spectrum` passed in in this call.
         **plot_kwargs
             Keyword arguments to pass into the plot method.
-            Plot options with a name conflict be passed by pre-pending 'plot_'.
+            Plot options with a name conflict be passed by pre-pending `plot_`.
             e.g. `freqs`, `power_spectrum` and `freq_range`.
 
         Notes
@@ -944,7 +950,9 @@ def _simple_ap_fit(self, freqs, power_spectrum):
                 warnings.simplefilter("ignore")
                 aperiodic_params, _ = curve_fit(get_ap_func(self.aperiodic_mode),
                                                 freqs, power_spectrum, p0=guess,
-                                                maxfev=self._maxfev, bounds=ap_bounds)
+                                                maxfev=self._maxfev, bounds=ap_bounds,
+                                                ftol=self._tol, xtol=self._tol, gtol=self._tol,
+                                                check_finite=False)
         except RuntimeError as excp:
             error_msg = ("Model fitting failed due to not finding parameters in "
                          "the simple aperiodic component fit.")
@@ -1001,7 +1009,9 @@ def _robust_ap_fit(self, freqs, power_spectrum):
                 warnings.simplefilter("ignore")
                 aperiodic_params, _ = curve_fit(get_ap_func(self.aperiodic_mode),
                                                 freqs_ignore, spectrum_ignore, p0=popt,
-                                                maxfev=self._maxfev, bounds=ap_bounds)
+                                                maxfev=self._maxfev, bounds=ap_bounds,
+                                                ftol=self._tol, xtol=self._tol, gtol=self._tol,
+                                                check_finite=False)
         except RuntimeError as excp:
             error_msg = ("Model fitting failed due to not finding "
                          "parameters in the robust aperiodic fit.")
@@ -1147,7 +1157,9 @@ def _fit_peak_guess(self, guess):
         # Fit the peaks
         try:
             gaussian_params, _ = curve_fit(gaussian_function, self.freqs, self._spectrum_flat,
-                                           p0=guess, maxfev=self._maxfev, bounds=gaus_param_bounds)
+                                           p0=guess, maxfev=self._maxfev, bounds=gaus_param_bounds,
+                                           ftol=self._tol, xtol=self._tol, gtol=self._tol,
+                                           check_finite=False, jac=jacobian_gauss)
         except RuntimeError as excp:
             error_msg = ("Model fitting failed due to not finding "
                          "parameters in the peak component fit.")

specparam/plts/aperiodic.py

@@ -33,7 +33,7 @@ def plot_aperiodic_params(aps, colors=None, labels=None, ax=None, **plot_kwargs)
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['params']))
@@ -83,7 +83,7 @@ def plot_aperiodic_fits(aps, freq_range, control_offset=False,
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['params']))

specparam/plts/error.py

@@ -33,7 +33,7 @@ def plot_spectral_error(freqs, error, shade=None, log_freqs=False, ax=None, **pl
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['spectral']))

specparam/plts/group.py

@@ -28,7 +28,7 @@ def plot_group(group, **plot_kwargs):
     group : SpectralGroupModel
         Object containing results from fitting a group of power spectra.
     **plot_kwargs
-        Keyword arguments to apply to the plot.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
 
     Raises
     ------
@@ -72,7 +72,7 @@ def plot_group_aperiodic(group, ax=None, **plot_kwargs):
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     if group.aperiodic_mode == 'knee':
@@ -97,7 +97,7 @@ def plot_group_goodness(group, ax=None, **plot_kwargs):
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     plot_scatter_2(group.get_params('error'), 'Error',
@@ -117,7 +117,7 @@ def plot_group_peak_frequencies(group, ax=None, **plot_kwargs):
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     plot_hist(group.get_params('peak_params', 0)[:, 0], 'Center Frequency',

specparam/plts/model.py

@@ -56,7 +56,7 @@ def plot_model(model, plot_peaks=None, plot_aperiodic=True, freqs=None, power_sp
     data_kwargs, model_kwargs, aperiodic_kwargs, peak_kwargs : None or dict, optional
         Keyword arguments to pass into the plot call for each plot element.
     **plot_kwargs
-        Keyword arguments to apply to the plot.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
 
     Notes
     -----
@@ -163,7 +163,7 @@ def _add_peaks_shade(model, plt_log, ax, **plot_kwargs):
     ax : matplotlib.Axes
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``fill_between``.
+        Keyword arguments to pass into ``fill_between``.
     """
 
     defaults = {'color' : PLT_COLORS['periodic'], 'alpha' : 0.25}

specparam/plts/periodic.py

@@ -35,7 +35,7 @@ def plot_peak_params(peaks, freq_range=None, colors=None, labels=None, ax=None,
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the ``style_plot``.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['params']))
@@ -86,7 +86,7 @@ def plot_peak_fits(peaks, freq_range=None, colors=None, labels=None, ax=None, **
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
-        Keyword arguments to pass into the plot call.
+        Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
     ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['params']))

specparam/plts/settings.py

@@ -28,7 +28,8 @@
                'linestyle' : ['ls', 'linestyle']}
 
 # Plot style arguments are those that can be defined on an axis object
-AXIS_STYLE_ARGS = ['title', 'xlabel', 'ylabel', 'xlim', 'ylim']
+AXIS_STYLE_ARGS = ['title', 'xlabel', 'ylabel', 'xlim', 'ylim',
+                   'xticks', 'yticks', 'xticklabels', 'yticklabels']
 
 # Line style arguments are those that can be defined on a line object
 LINE_STYLE_ARGS = ['alpha', 'lw', 'linewidth', 'ls', 'linestyle',
@@ -40,8 +41,13 @@
 # Custom style arguments are those that are custom-handled by the plot style function
 CUSTOM_STYLE_ARGS = ['title_fontsize', 'label_size', 'tick_labelsize',
                      'legend_size', 'legend_loc']
-STYLERS = ['axis_styler', 'line_styler', 'custom_styler']
-STYLE_ARGS = AXIS_STYLE_ARGS + LINE_STYLE_ARGS + CUSTOM_STYLE_ARGS + STYLERS
+
+# Define list of available style functions - these can also be replaced by arguments
+STYLERS = ['axis_styler', 'line_styler', 'collection_styler', 'custom_styler']
+
+# Collect the full set of possible style related input keyword arguments
+STYLE_ARGS = \
+    AXIS_STYLE_ARGS + LINE_STYLE_ARGS + COLLECTION_STYLE_ARGS + CUSTOM_STYLE_ARGS + STYLERS
 
 ## Define default values for plot aesthetics
 # These are all custom style arguments