def __init__(self, sigma = 0.2, _lambda = np.exp(-15), Nadd = 150, output = 0, maxIter = np.inf,
epsilon = 0.001, delta_k = 1, tempInt = 0.95, epsilon_back = 0.001, flyComputeK = 0,
deselect = 0, CV = None, lambdas = np.r_[[np.exp(-12)], np.exp(np.arange(-10,-3))],
sigmas = np.sqrt(1. / (2. * 2.**np.arange(-5, 3)))):
self.sigma = sigma
self._lambda = _lambda
self.Nadd = Nadd
self.output = output
self.maxIter = maxIter
self.epsilon = epsilon
self.delta_k = delta_k
self.tempInt = tempInt
self.epsilon_back = epsilon_back
self.flyComputeK = flyComputeK
self.deselect = deselect
self.CV = CV
self.lambdas = lambdas
self.sigmas = sigmas
this_dir, this_filename = os.path.split(__file__)
src_dir = os.path.join(this_dir, "ivmSoftware4.3/src")
mlab.addpath(src_dir)
def load_matlab():
"""
Imports and starts matlab bridge if not started.
"""
global mlab
global MatlabError
from mlabwrap import mlab
from mlabraw import error as MatlabError
abspath = os.path.abspath(__file__)
features = os.path.join(os.path.dirname(os.path.dirname(abspath)),
"features")
toolbox = os.path.join(features, "matlab-chroma-toolbox")
mlab.addpath(toolbox)
import os
import sys
import numpy as np
from mlabwrap import mlab
import scikits.audiolab as AUDIOLAB
# makes sure we have the right matlab files
# save their absolute path
_code_dir = os.path.dirname(__file__)
_find_landmarks_path = os.path.join(os.path.abspath(_code_dir), "find_landmarks.m")
if not os.path.exists(_find_landmarks_path):
print "can't find find_lanmarks.m, not same place as get_landmarks?"
print "get_landmarks.py dir:", _code_dir
raise ImportError
mlab.addpath(_code_dir)
def wavread(path):
"""
Wrapper around scikits functions
Returns: wavdata, sample rate, encoding type
See pyaudiolab or scikits.audiolab for more information
"""
return AUDIOLAB.wavread(path)
def find_landmarks_from_wav(wavpath):
"""
utility function, open wav, calls find_landmarks
"""
import scipy as sp
import scipy.io
import plca
from string import lower
import csv
logging.basicConfig(level=logging.INFO,
format='%(levelname)s %(name)s %(asctime)s '
'%(filename)s:%(lineno)d %(message)s')
logger = logging.getLogger('segmenter')
try:
from mlabwrap import mlab
mlab.addpath('coversongs')
except:
logger.warning('Unable to import mlab module. Feature extraction '
'and evaluation will not work.')
def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
if lower(wavfilename[-4:]) == '.csv':
logger.info('CSV filename reading preprocessed features from %s',
wavfilename)
csvr = csv.reader(open(wavfilename, 'rb'), delimiter=',')
feats = np.array([[float(x) for x in row] for row in csvr])
"""
import os
import sys
import tempfile
import numpy as np
# MATLAB wrapper, we remove the dperecation warnings
import warnings
warnings.filterwarnings('ignore',category=DeprecationWarning)
from mlabwrap import mlab
warnings.filterwarnings('default',category=DeprecationWarning)
# kalman toolbox path
kal_toolbox_path = '/home/thierry/Columbia/Imputation/PythonSrc/KalmanAll'
mlab.warning('off','all') # hack, remove warnings
# some functions are redefined but who cares!
mlab.addpath(kal_toolbox_path)
mlab.addpath(os.path.join(kal_toolbox_path,'Kalman'))
mlab.addpath(os.path.join(kal_toolbox_path,'KPMstats'))
mlab.addpath(os.path.join(kal_toolbox_path,'KPMtools'))
mlab.warning('on','all')
def learn_kalman(data,A,C,Q,R,initx,initV,niter,diagQ=1,diagR=1):
"""
Main function, take initial parameters and train a Kalman filter.
We assume a model:
x(t+1) = A*x(t) + w(t), w ~ N(0, Q), x(0) ~ N(init_x, init_V)
y(t) = C*x(t) + v(t), v ~ N(0, R)
INPUT
data - one sequence, one observation per col
A - DxD matrix, D dimension of hidden state
# --------------------------------------------------------------------------- #
# example.py
# Tarik Tosun, 2012-07-19
# Description:
# Demonstrates what you can do with python-retargeter.
# --------------------------------------------------------------------------- #
import numpy as np
import scipy as sp
from mlabwrap import mlab
# when mlabwrap is imported, it starts an instance of matlab which runs in the
# background. Nearly any matlab function may be called as: 'mlab.[function]'
mlab.addpath(mlab.genpath('../minimal'))
# ----------------------------------------------------------- #
# Creating Kinematic Chain Models:
# ----------------------------------------------------------- #
# pr2larm(length_total) returns a simulated PR2 left arm of specified total
# length. (pr2rarm returns a simulated PR2 right arm.)
# Degrees of freedom:
# [shoulder_yaw, shoulder_tilt, shoulder_roll, elbow_flex]
L = 300
pr2 = mlab.pr2rarm(L)
# human24(lengths) returns a 2-link, 4-dof chain similar to a human arm. the
# lengths vector specifies link lengths.
# Degrees of freedom:
# [shoulder_roll, shoulder_yaw, shoulder_pitch, elbow_pitch]
from mlabwrap import mlab as matlab
# give an interface to the mlab system through cobra.matlab
cobra.matlab = matlab
matlab.__doc__ = """
This is an mlabwrap connection to MATLAB which can be used to call
MATLAB functions. For example, if model is a python model, the following
can be used to optimize the model in MATLAB:
> matlab_model = cobra.mlab.cobra_model_object_to_cobra_matlab_struct(model)
> result = cobra.matlab.optimizeCbModel(matlab_model)
Any MATLAB function can be called this way"""
# add path with module's python scripts to the MATLAB path
mlab_path = os.path.join(cobra.__path__[0], 'mlab', 'matlab_scripts')
matlab.addpath(mlab_path)
_possible_cobra_locations = ["~/MATLAB/cobra", "~/cobra",
"~/Documents/MATLAB/cobra", "~/Documents/opencobra/matlab/cobra"]
def init_matlab_toolbox(matlab_cobra_path=None, discover_functions=True):
"""initialize the matlab cobra toolbox, and load its functions
into mlab's namespace (very useful for ipython tab completion)
matlab_cobra_path: the path to the directory containing the MATLAB
cobra installation. Using the default None will attempt to find the
toolbox in the MATLAB path
discover_functions: Whether mlabwrap should autodiscover all cobra toolbox
functions in matlab. This is convenient for tab completion, but may take
some time."""
except ImportError:
print 'Unable to import mlab module. Attempting to install...'
os.system('cd %s; python setup.py build' % MLABWRAPDIR)
basedir = '%s/build/' % MLABWRAPDIR
sys.path.extend([os.path.join(basedir, x) for x in os.listdir(basedir)
if x.startswith('lib')])
from mlabwrap import mlab
try:
mlab.sin(1)
except:
# Re-initialize the broken connection to the Matlab engine.
import mlabraw
mlab._session = mlabraw.open()
mlab.addpath(os.path.join(CURRDIR, 'coversongs'))
def extract_features(track, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features.
Uses Dan Ellis' chrombeatftrs Matlab function (via the mlabwrap
module, which is included with this feature extractor).
See http://labrosa.ee.columbia.edu/projects/coversongs
for more details.
Parameters
----------
track : gordon Track instance
fctr : float
import os
import sys
import numpy as np
import scipy as sp
import scipy.io
import plca
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("segmenter")
try:
from mlabwrap import mlab
mlab.addpath("coversongs")
except:
logger.warning("Unable to import mlab module. Feature extraction " "and evaluation will not work.")
def extract_features(wavfilename, fctr=400, fsd=1.0, type=1):
"""Computes beat-synchronous chroma features from the given wave file
Calls Dan Ellis' chrombeatftrs Matlab function.
"""
x, fs = mlab.wavread(wavfilename, nout=2)
feats, beats = mlab.chrombeatftrs(x, fs, fctr, fsd, type, nout=2)
return feats, beats.flatten()
def segment_song(
Also, can use codebook encoding. T. Bertin-Mahieux (2010) Columbia University [email protected] """ import os import sys import glob import numpy as np import scipy.io as sio rondir = 'ronwsiplca' from ronwsiplca import segmenter as SEGMENTER from mlabwrap import mlab mlab.addpath(os.path.abspath(rondir)) mlab.addpath(os.path.abspath('.')) # beatles directories on my machines (TBM) _enfeats_dir = os.path.expanduser('~/Columbia/InfiniteListener/beatles_enbeatfeats') _audio_dir = os.path.expanduser('~/Columbia/InfiniteListener/beatles_audio') _seglab_dir = os.path.expanduser('~/Columbia/InfiniteListener/beatles_seglab') def get_all_files(basedir,pattern='*.wav') : """ From a root directory, go through all subdirectories and find all files that fit the pattern. Return them in a list. """ allfiles = [] for root, dirs, files in os.walk(basedir):
from mlabwrap import mlab
import pycuda.autoinit
import pycuda.driver as drv
import numpy
from pycuda.compiler import SourceModule
mlab.addpath('../FasihSarStuff/', nout=0)
# Params
clight = 299792458.0
block_width = 16
block_height = 16
# Matlab data loading
data = mlab.helper3DSAR()
#data = mlab.helperMTI()
data = mlab.rangeCompress(data)
mdouble = mlab.double;
do = lambda x: float(mdouble(x)[0,0])
nint = numpy.int32
nfloat = numpy.float32
rp = mdouble(data.upsampled_range_profiles)
im = numpy.zeros_like(data.im_final).astype(numpy.complex64)
[Nimg_height, Nimg_width] = im.shape
delta_pixel_x = numpy.diff(data.x_vec)[0,0]
delta_pixel_y = numpy.diff(data.y_vec)[0,0]
c__4_deltaF = clight / (4.0 * do(data.deltaF))
