review updates

neurodsp/filt/checks.py

@@ -1,8 +1,6 @@
 """Checker functions for filtering."""
 
 from warnings import warn
-import os
-import json
 
 import numpy as np
 
@@ -92,7 +90,7 @@ def check_filter_definition(pass_type, f_range):
 
 
 def check_filter_properties(filter_coefs, a_vals, fs, pass_type, f_range, transitions=(-20, -3),
-                            filt_type=None, verbose=True, save_properties=None):
+                            return_properties=False, verbose=True):
     """Check a filters properties, including pass band and transition band.
 
     Parameters
@@ -119,17 +117,18 @@ def check_filter_properties(filter_coefs, a_vals, fs, pass_type, f_range, transi
         a tuple and is assumed to be (None, f_hi) for 'lowpass', and (f_lo, None) for 'highpass'.
     transitions : tuple of (float, float), optional, default: (-20, -3)
         Cutoffs, in dB, that define the transition band.
-    filt_type : str, optional, {'FIR', 'IIR'}
-        The type of filter being applied.
+    return_properties : bool, optional, default: False
+        Returns the frequency response, pass band and transition band.
     verbose : bool, optional, default: True
-        Whether to print out filter properties.
-    save_properties : str
-        Path, including file name, to save filter properites to as a json.
+        Whether to print out transition and pass bands.
 
     Returns
     -------
     passes : bool
         Whether all the checks pass. False if one or more checks fail.
+    properties : dict
+        The frequency response, pass band and transition band.
+        Only returned if return_properties is True.
 
     Examples
     --------
@@ -145,7 +144,7 @@ def check_filter_properties(filter_coefs, a_vals, fs, pass_type, f_range, transi
 
     # Import utility functions inside function to avoid circular imports
     from neurodsp.filt.utils import (compute_frequency_response, compute_pass_band,
-                                     compute_transition_band, gen_filt_report, save_filt_report)
+                                     compute_transition_band)
 
     # Initialize variable to keep track if all checks pass
     passes = True
@@ -179,16 +178,18 @@ def check_filter_properties(filter_coefs, a_vals, fs, pass_type, f_range, transi
         warn('Transition bandwidth is  {:.1f}  Hz. This is greater than the desired'\
              'pass/stop bandwidth of  {:.1f} Hz'.format(transition_bw, pass_bw))
 
-    # Report filter properties
-    if verbose or save_properties:
-        filt_report = gen_filt_report(pass_type, filt_type, fs, f_db, db, pass_bw,
-                                      transition_bw, f_range, f_range_trans)
-
+    # Print out transition bandwidth and pass bandwidth to the user
     if verbose:
-        print('\n'.join('{} : {}'.format(key, value) for key, value in filt_report.items()))
+        print('Transition bandwidth is {:.1f} Hz.'.format(transition_bw))
+        print('Pass/stop bandwidth is {:.1f} Hz.'.format(pass_bw))
+
+    # Return the filter properties
+    if return_properties:
+
+        properties = {'f_db': f_db, 'db': db, 'pass_bw': pass_bw, 'transition_bw': transition_bw,
+                      'f_range_trans': f_range_trans}
 
-    if save_properties is not None:
-        save_filt_report(save_properties, filt_report)
+        return passes, properties
 
     return passes
 

neurodsp/filt/filter.py

@@ -10,7 +10,7 @@
 
 def filter_signal(sig, fs, pass_type, f_range, filter_type='fir', n_cycles=3, n_seconds=None,
                   remove_edges=True, butterworth_order=None, print_transitions=False,
-                  plot_properties=False, save_properties=None, return_filter=False, verbose=False):
+                  plot_properties=False, return_filter=False, save_report=None):
     """Apply a bandpass, bandstop, highpass, or lowpass filter to a neural signal.
 
     Parameters
@@ -50,12 +50,10 @@ def filter_signal(sig, fs, pass_type, f_range, filter_type='fir', n_cycles=3, n_
         If True, print out the transition and pass bandwidths.
     plot_properties : bool, optional, default: False
         If True, plot the properties of the filter, including frequency response and/or kernel.
-    save_properties : str, optional, default: None
-        Path, including file name, to save filter properites to as a json.
     return_filter : bool, optional, default: False
         If True, return the filter coefficients.
-    verbose : bool, optional, default: False
-        If True, print out detailed filter information.
+    save_report : str, optional, default: None
+        Path, including file name, to save a filter report to as a pdf.
 
     Returns
     -------
@@ -78,12 +76,12 @@ def filter_signal(sig, fs, pass_type, f_range, filter_type='fir', n_cycles=3, n_
     if filter_type.lower() == 'fir':
         return filter_signal_fir(sig, fs, pass_type, f_range, n_cycles, n_seconds,
                                  remove_edges, print_transitions, plot_properties,
-                                 save_properties, return_filter, verbose)
+                                 return_filter, save_report)
     elif filter_type.lower() == 'iir':
         _iir_checks(n_seconds, butterworth_order, remove_edges)
         return filter_signal_iir(sig, fs, pass_type, f_range, butterworth_order,
-                                 print_transitions, plot_properties, save_properties,
-                                 return_filter, verbose)
+                                 print_transitions, plot_properties, return_filter,
+                                 save_report)
     else:
         raise ValueError('Filter type not understood.')
 

neurodsp/filt/fir.py

@@ -6,16 +6,16 @@
 from neurodsp.utils import remove_nans, restore_nans
 from neurodsp.utils.decorators import multidim
 from neurodsp.plts.filt import plot_filter_properties
-from neurodsp.filt.utils import compute_frequency_response, remove_filter_edges
+from neurodsp.filt.utils import compute_frequency_response, remove_filter_edges, save_filt_report
 from neurodsp.filt.checks import (check_filter_definition, check_filter_properties,
                                   check_filter_length)
 
 ###################################################################################################
 ###################################################################################################
 
 def filter_signal_fir(sig, fs, pass_type, f_range, n_cycles=3, n_seconds=None, remove_edges=True,
-                      print_transitions=False, plot_properties=False, save_properties=None,
-                      return_filter=False, verbose=False, file_path=None, file_name=None):
+                      print_transitions=False, plot_properties=False, return_filter=False,
+                      save_report=None):
     """Apply an FIR filter to a signal.
 
     Parameters
@@ -47,12 +47,10 @@ def filter_signal_fir(sig, fs, pass_type, f_range, n_cycles=3, n_seconds=None, r
         If True, print out the transition and pass bandwidths.
     plot_properties : bool, optional, default: False
         If True, plot the properties of the filter, including frequency response and/or kernel.
-    save_properties : str
-        Path, including file name, to save filter properites to as a json.
     return_filter : bool, optional, default: False
         If True, return the filter coefficients of the FIR filter.
-    verbose : bool, optional, default: False
-        If True, print out detailed filter information.
+    save_report : str, optional, default: None
+        Path, including file name, to save a filter report to as a pdf.
 
     Returns
     -------
@@ -83,9 +81,8 @@ def filter_signal_fir(sig, fs, pass_type, f_range, n_cycles=3, n_seconds=None, r
     check_filter_length(sig.shape[-1], len(filter_coefs))
 
     # Check filter properties: compute transition bandwidth & run checks
-    check_filter_properties(filter_coefs, 1, fs, pass_type, f_range, filt_type="FIR",
-                            verbose=np.any([print_transitions, verbose]),
-                            save_properties=save_properties)
+    _, properties = check_filter_properties(filter_coefs, 1, fs, pass_type, f_range,
+                                            return_properties=True, verbose=print_transitions)
 
     # Remove any NaN on the edges of 'sig'
     sig, sig_nans = remove_nans(sig)
@@ -100,11 +97,25 @@ def filter_signal_fir(sig, fs, pass_type, f_range, n_cycles=3, n_seconds=None, r
     # Add NaN back on the edges of 'sig', if there were any at the beginning
     sig_filt = restore_nans(sig_filt, sig_nans)
 
+    # Unpack filter properties
+    if plot_properties or save_report is not None:
+        f_db = properties['f_db']
+        db = properties['db']
+
+    if save_report is not None:
+        pass_bw = properties['pass_bw']
+        transition_bw = properties['transition_bw']
+        f_range_trans = properties['f_range_trans']
+
     # Plot filter properties, if specified
     if plot_properties:
-        f_db, db = compute_frequency_response(filter_coefs, 1, fs)
         plot_filter_properties(f_db, db, fs, filter_coefs)
 
+    # Save a pdf filter report containing plots and parameters
+    if save_report is not None:
+        save_filt_report(save_report, pass_type, 'IIR', fs, f_db, db, pass_bw, transition_bw,
+                         f_range, f_range_trans, len(f_db)-1, filter_coefs=filter_coefs)
+
     if return_filter:
         return sig_filt, filter_coefs
     else:

neurodsp/filt/iir.py

@@ -1,19 +1,17 @@
 """Filter signals with IIR filters."""
 
-import numpy as np
 from scipy.signal import butter, sosfiltfilt
 
 from neurodsp.utils import remove_nans, restore_nans
-from neurodsp.filt.utils import compute_nyquist, compute_frequency_response
+from neurodsp.filt.utils import compute_nyquist, compute_frequency_response, save_filt_report
 from neurodsp.filt.checks import check_filter_definition, check_filter_properties
 from neurodsp.plts.filt import plot_frequency_response
 
 ###################################################################################################
 ###################################################################################################
 
 def filter_signal_iir(sig, fs, pass_type, f_range, butterworth_order, print_transitions=False,
-                      plot_properties=False, save_properties=None, return_filter=False,
-                      verbose=False):
+                      plot_properties=False, return_filter=False, save_report=None):
     """Apply an IIR filter to a signal.
 
     Parameters
@@ -41,12 +39,10 @@ def filter_signal_iir(sig, fs, pass_type, f_range, butterworth_order, print_tran
         If True, print out the transition and pass bandwidths.
     plot_properties : bool, optional, default: False
         If True, plot the properties of the filter, including frequency response and/or kernel.
-    save_properties : str
-        Path, including file name, to save filter properites to as a json.
     return_filter : bool, optional, default: False
         If True, return the second order series coefficients of the IIR filter.
-    verbose : bool, optional, default: False
-        If True, print out detailed filter information.
+    save_report : str, optional, default: None
+        Path, including file name, to save a filter report to as a pdf.
 
     Returns
     -------
@@ -71,9 +67,8 @@ def filter_signal_iir(sig, fs, pass_type, f_range, butterworth_order, print_tran
     sos = design_iir_filter(fs, pass_type, f_range, butterworth_order)
 
     # Check filter properties: compute transition bandwidth & run checks
-    check_filter_properties(sos, None, fs, pass_type, f_range, filt_type="IIR",
-                            verbose=np.any([print_transitions, verbose]),
-                            save_properties=save_properties)
+    _, properties = check_filter_properties(sos, None, fs, pass_type, f_range,
+                                            return_properties=True, verbose=print_transitions)
 
     # Remove any NaN on the edges of 'sig'
     sig, sig_nans = remove_nans(sig)
@@ -84,11 +79,26 @@ def filter_signal_iir(sig, fs, pass_type, f_range, butterworth_order, print_tran
     # Add NaN back on the edges of 'sig', if there were any at the beginning
     sig_filt = restore_nans(sig_filt, sig_nans)
 
+    # Unpack filter properties
+    if plot_properties or save_report is not None:
+        f_db = properties['f_db']
+        db = properties['db']
+
+    if save_report is not None:
+        pass_bw = properties['pass_bw']
+        transition_bw = properties['transition_bw']
+        f_range_trans = properties['f_range_trans']
+
     # Plot frequency response, if desired
     if plot_properties:
-        f_db, db = compute_frequency_response(sos, None, fs)
         plot_frequency_response(f_db, db)
 
+    # Save a pdf filter report containing plots and parameters
+    if save_report is not None:
+
+        save_filt_report(save_report, pass_type, 'IIR', fs, f_db, db, pass_bw,
+                         transition_bw, f_range, f_range_trans, butterworth_order)
+
     if return_filter:
         return sig_filt, sos
     else: