removed old binning code

lasp/tools/bin_narrow.py

@@ -1,86 +0,0 @@
-#!/usr/bin/python
-"""
-Bin narrow band power in octave/third octave band data
-"""
-from lasp.filter.bandpass_fir import (OctaveBankDesigner, 
-                                      ThirdOctaveBankDesigner)
-import numpy as np
-import warnings
-
-
-def binPower(freq, narrow_power, band=3, start_band=-16, stop_band=13):
-    """
-    Apply binning to narrow band frequency domain power results
-    
-    Args:
-        freq: Array of frequency indices
-        narrow_power: narrow-band power values in units of [W] or [Pa^2]
-        band: 1, or 3
-    
-    Returns:
-        ( ['25', '31.5', '40', '50', ... ],
-          [float(power_25), float(power_31p5), ...]) putting NAN values where
-           inaccurate.
-    """
-
-    if band == 3:
-        designer = ThirdOctaveBankDesigner()
-    elif band == 1:
-        designer = OctaveBankDesigner()
-    else:
-        raise ValueError("Parameter 'Band' should be either '1', or '3'")
-
-    freq = np.copy(freq)
-    narrow_power = np.copy(narrow_power)
-
-    
-    # Exact midband, lower and upper frequency limit of each band
-    fm = [designer.fm(x) for x in range(start_band, stop_band+1)]
-    fl = [designer.fl(x) for x in range(start_band, stop_band+1)]
-    fu = [designer.fu(x) for x in range(start_band, stop_band+1)]
-    fex = [designer.nominal_txt(x) for x in range(start_band, stop_band+1)]
-#    print(fl)
-    binned_power = np.zeros(len(fm), dtype=float)
-    ## Start: linear interpolation between bins while Parseval is conserved
-
-    # current frequency resolution
-    df_old = freq[1]-freq[0]
-    
-    # preferred new frequency resolution
-    df_new = .1
-
-    # ratio of resolutions
-
-    ratio = int(df_old/df_new)
-    # calculate new frequency bins
-    freq_new = np.linspace(freq[0],freq[-1],(len(freq)-1)*ratio+1)         
-    
-    # calculate the new bin data
-    interp_power = np.interp(freq_new, freq, narrow_power)/ratio
-    
-    # adapt first and last bin values so that Parseval still holds
-    interp_power[0] = binned_power[0]*(1.+1./ratio)/2.
-    interp_power[-1] = binned_power[-1]*(1.+1./ratio)/2.    
-
-    # check if Parseval still holds        
-    # print(np.sum(y, axis=0))
-    # print(np.sum(y_new, axis=0))
-    
-    ## Stop: linear interpolation between bins while Parseval is conserved        
-    binned_power = np.zeros(len(fm), dtype=float)
-    for k in range(len(fm)):
-#        print(k)
-        # find the bins which are in the corresponding band        
-        bins = (fl[k] <= freq_new) & (freq_new < fu[k])
-#        print(bins)
-        # sum the output values of these bins to obtain the band value
-        binned_power[k] = np.sum(interp_power[bins], axis=0)
-        # if no frequency bin falls in a certain band, skip previous bands 
-#        if not any(bins):
-#            binned_power[0:k+1] = np.nan
-
-    # check if data is valid
-    if(np.isnan(binned_power).all()):
-        warnings.warn('Invalid frequency array, we cannot bin these values')
-
-    return fm, fex, binned_power