Change to using distutils.

Currently only builds the simplsndobj module

1 files changed, 0 insertions, 86 deletions

diff --git a/lp.py b/lp.py
deleted file mode 100644
index 740ca97..0000000
--- a/lp.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) 2009 John Glover, National University of Ireland, Maynooth
-# 
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation; either version 2 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of 
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
-
-import simpl
-import numpy as np
-
-def burg(signal, order):
-    """Using the burg method, calculate order coefficients.
-    Returns a numpy array."""
-    coefs = np.array([1.0])
-    # initialise f and b - the forward and backwards predictors
-    f = np.zeros(len(signal))
-    b = np.zeros(len(signal))
-    for i in range(len(signal)):
-        f[i] = b[i] = signal[i]
-    # burg algorithm
-    for k in range(order):
-        # fk is f without the first element
-        fk = f[1:]
-        # bk is b without the last element
-        bk = b[0:b.size-1]
-        # calculate mu
-        if sum((fk*fk)+(bk*bk)):
-            # check for division by zero
-            mu = -2.0 * sum(fk*bk) / sum((fk*fk)+(bk*bk))
-        else:
-            mu = 0.0
-        # update coefs
-        # coefs[::-1] just reverses the coefs array
-        coefs = np.hstack((coefs,0)) + (mu * np.hstack((0, coefs[::-1])))
-        # update f and b
-        f = fk + (mu*bk)
-        b = bk + (mu*fk)
-    return coefs[1:]
-
-def predict(signal, coefs, num_predictions):
-    """Using Linear Prediction, return the estimated next num_predictions
-    values of signal, using the given coefficients.
-    Returns a numpy array."""
-    predictions = np.zeros(num_predictions)
-    past_samples = np.zeros(len(coefs))
-    for i in range(len(coefs)):
-        past_samples[i] = signal[-1-i]
-    sample_pos = 0
-    for i in range(num_predictions):
-        # each sample in past_samples is multiplied by a coefficient
-        # results are summed
-        for j in range(len(coefs)):
-            predictions[i] -= coefs[j] * past_samples[(j+sample_pos) % len(coefs)]
-        sample_pos -= 1
-        if sample_pos < 0:
-            sample_pos = len(coefs) - 1
-        past_samples[sample_pos] = predictions[i] 
-    return predictions
-
-
-class LPPartialTracking(simpl.PartialTracking):
-    "Partial tracking, based on the McAulay and Quatieri (MQ) algorithm"    
-    def __init__(self):
-        simpl.PartialTracking.__init__(self)
-        self._matching_interval = 100  # peak matching interval, in Hz
-
-        
-    def update_partials(self, frame, frame_number):
-        """Streamable (real-time) LP peak-tracking.
-        """
-        frame_partials = []
-                
-        for peak in frame:
-            pass
-
-        return frame_partials
-