Datasets:

h4iku

/

coconut_python2010

like 1

self.inputs = B.shape[-1] self.outputs = C.shape[0]

self.inputs = self.B.shape[-1] self.outputs = self.C.shape[0]

def __init__(self,*args,**kwords): """Initialize the LTI system using either: (numerator, denominator) (zeros, poles, gain) (A, B, C, D) -- state-space. """ N = len(args) if N == 2: # Numerator denominator transfer function input self.__dict__['num'], self.__dict__['den'] = normalize(*args) self.__dict__['zeros'], self.__dict__['poles'], \ self.__dict__['gain'] = tf2zpk(*args) self.__dict__['A'], self.__dict__['B'], \ self.__dict__['C'], \ self.__dict__['D'] = tf2ss(*args) self.inputs = 1 if len(self.num.shape) > 1: self.outputs = self.num.shape[0] else: self.outputs = 1 elif N == 3: # Zero-pole-gain form self.__dict__['zeros'], self.__dict__['poles'], \ self.__dict__['gain'] = args self.__dict__['num'], self.__dict__['den'] = zpk2tf(*args) self.__dict__['A'], self.__dict__['B'], \ self.__dict__['C'], \ self.__dict__['D'] = zpk2ss(*args) self.inputs = 1 if len(self.zeros.shape) > 1: self.outputs = self.zeros.shape[0] else: self.outputs = 1 elif N == 4: # State-space form self.__dict__['A'], self.__dict__['B'], \ self.__dict__['C'], \ self.__dict__['D'] = abcd_normalize(*args) self.__dict__['zeros'], self.__dict__['poles'], \ self.__dict__['gain'] = ss2zpk(*args) self.__dict__['num'], self.__dict__['den'] = ss2tf(*args) self.inputs = B.shape[-1] self.outputs = C.shape[0] else: raise ValueError, "Needs 2, 3, or 4 arguments."

3645887631251272301e527342a0676017b9dc7b /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/3645887631251272301e527342a0676017b9dc7b/ltisys.py

fact = (1-exp(-lamba_))

fact = (1-exp(-lambda_))

def _pmf(self, k, lambda_): fact = (1-exp(-lamba_)) return fact*exp(-lambda_(k))

873fd5c9f611ae7b2819c84eced96e4f1695da7c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/873fd5c9f611ae7b2819c84eced96e4f1695da7c/distributions.py

y = scipy.stats.stdev(X)

y = scipy.stats.std(X)

def check_stdX(self): y = scipy.stats.stdev(X) assert_almost_equal(y,2.738612788)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

y = scipy.stats.stdev(ZERO)

y = scipy.stats.std(ZERO)

def check_stdZERO(self): y = scipy.stats.stdev(ZERO) assert_almost_equal(y,0.0)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

y = scipy.stats.stdev(BIG)

y = scipy.stats.std(BIG)

def check_stdBIG(self): y = scipy.stats.stdev(BIG) assert_almost_equal(y,2.738612788)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

y = scipy.stats.stdev(LITTLE)

y = scipy.stats.std(LITTLE)

def check_stdLITTLE(self): y = scipy.stats.stdev(LITTLE) assert_approx_equal(y,2.738612788e-8)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

y = scipy.stats.stdev(HUGE)

y = scipy.stats.std(HUGE)

def check_stdHUGE(self): y = scipy.stats.stdev(HUGE) assert_approx_equal(y,2.738612788e12)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

y = scipy.stats.stdev(TINY)

y = scipy.stats.std(TINY)

def check_stdTINY(self): y = scipy.stats.stdev(TINY) assert_almost_equal(y,0.0)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

y = scipy.stats.stdev(ROUND)

y = scipy.stats.std(ROUND)

def check_stdROUND(self): y = scipy.stats.stdev(ROUND) assert_approx_equal(y,2.738612788)

0d3c2bdf977aa7a5fa412dd330df792dac70eeac /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0d3c2bdf977aa7a5fa412dd330df792dac70eeac/test_stats.py

import fnmatch from fnmatch

from fnmatch import fnmatch

def rmdir(dir,depth=0): import os path = os.path.abspath(dir) all_files = os.listdir(path) indent = ' ' * depth for i in all_files: if not i == 'CVS': print indent, i if os.path.isdir(os.path.join(path,i)): rmdir(os.path.join(path,i),depth+1) else: cmd = 'cd ' + path + ';rm -r ' + i + ';cvs rm ' + i + ';cd ..' print cmd os.system(cmd)

9ca5b06ad914076e0e5ac468b00c68beac815e5d /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/9ca5b06ad914076e0e5ac468b00c68beac815e5d/cvs_tools.py

19391512145,2404879675441, 370371188237525,69348874393137901, 15514534163557086905]

19391512145l,2404879675441l, 370371188237525l,69348874393137901l, 15514534163557086905l]

def check_euler(self): eu0 = euler(0) eu1 = euler(1) eu2 = euler(2) # just checking segfaults assert_almost_equal(eu0[0],1,8) assert_almost_equal(eu2[2],-1,8) eu24 = euler(24) mathworld = [1,1,5,61,1385,50521,2702765,199360981, 19391512145,2404879675441, 370371188237525,69348874393137901, 15514534163557086905] correct = zeros((25,),'d') for k in range(0,13): if (k % 2): correct[2*k] = -float(mathworld[k]) else: correct[2*k] = float(mathworld[k]) err = nan_to_num((eu24-correct)/correct) errmax = max(err) assert_almost_equal(errmax, 0.0, 14)

bf035363f49489d86bfc961375357a52d107e274 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bf035363f49489d86bfc961375357a52d107e274/test_basic.py

self.vecfunc = new.instancemethod(sgf(self._ppf_single_call), self, rv_continuous)

self.vecfunc = sgf(self._ppf_single_call) self.vecentropy = sgf(self._entropy)

def __init__(self, momtype=1, a=None, b=None, xa=-10.0, xb=10.0, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None): if badvalue is None: badvalue = nan self.badvalue = badvalue self.name = name self.a = a self.b = b if a is None: self.a = -scipy.inf if b is None: self.b = scipy.inf self.xa = xa self.xb = xb self.xtol = xtol self._size = 1 self.m = 0.0 self.moment_type = momtype self.vecfunc = new.instancemethod(sgf(self._ppf_single_call), self, rv_continuous) self.expandarr = 1 if momtype == 0: self.generic_moment = new.instancemethod(sgf(self._mom0_sc), self, rv_continuous) else: self.generic_moment = new.instancemethod(sgf(self._mom1_sc), self, rv_continuous) cdf_signature = inspect.getargspec(self._cdf.im_func) numargs1 = len(cdf_signature[0]) - 2 pdf_signature = inspect.getargspec(self._pdf.im_func) numargs2 = len(pdf_signature[0]) - 2 self.numargs = max(numargs1, numargs2)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

self.generic_moment = new.instancemethod(sgf(self._mom0_sc), self, rv_continuous)

self.generic_moment = sgf(self._mom0_sc)

def __init__(self, momtype=1, a=None, b=None, xa=-10.0, xb=10.0, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None): if badvalue is None: badvalue = nan self.badvalue = badvalue self.name = name self.a = a self.b = b if a is None: self.a = -scipy.inf if b is None: self.b = scipy.inf self.xa = xa self.xb = xb self.xtol = xtol self._size = 1 self.m = 0.0 self.moment_type = momtype self.vecfunc = new.instancemethod(sgf(self._ppf_single_call), self, rv_continuous) self.expandarr = 1 if momtype == 0: self.generic_moment = new.instancemethod(sgf(self._mom0_sc), self, rv_continuous) else: self.generic_moment = new.instancemethod(sgf(self._mom1_sc), self, rv_continuous) cdf_signature = inspect.getargspec(self._cdf.im_func) numargs1 = len(cdf_signature[0]) - 2 pdf_signature = inspect.getargspec(self._pdf.im_func) numargs2 = len(pdf_signature[0]) - 2 self.numargs = max(numargs1, numargs2)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

self.generic_moment = new.instancemethod(sgf(self._mom1_sc), self, rv_continuous)

self.generic_moment = sgf(self._mom1_sc)

def __init__(self, momtype=1, a=None, b=None, xa=-10.0, xb=10.0, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None): if badvalue is None: badvalue = nan self.badvalue = badvalue self.name = name self.a = a self.b = b if a is None: self.a = -scipy.inf if b is None: self.b = scipy.inf self.xa = xa self.xb = xb self.xtol = xtol self._size = 1 self.m = 0.0 self.moment_type = momtype self.vecfunc = new.instancemethod(sgf(self._ppf_single_call), self, rv_continuous) self.expandarr = 1 if momtype == 0: self.generic_moment = new.instancemethod(sgf(self._mom0_sc), self, rv_continuous) else: self.generic_moment = new.instancemethod(sgf(self._mom1_sc), self, rv_continuous) cdf_signature = inspect.getargspec(self._cdf.im_func) numargs1 = len(cdf_signature[0]) - 2 pdf_signature = inspect.getargspec(self._pdf.im_func) numargs2 = len(pdf_signature[0]) - 2 self.numargs = max(numargs1, numargs2)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

def _ppf_tosolve(self, x, q, *args): return apply(self.cdf, (x, )+args) - q

def _ppf_tosolve(self, x, q, *args): return apply(self.cdf, (x, )+args) - q

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

return scipy.optimize.brentq(self._ppf_tosolve, self.xa, self.xb, args=(q,)+args, xtol=self.xtol)

return scipy.optimize.brentq(self._ppf_to_solve, self.xa, self.xb, args=(q,)+args, xtol=self.xtol)

def _ppf_single_call(self, q, *args): return scipy.optimize.brentq(self._ppf_tosolve, self.xa, self.xb, args=(q,)+args, xtol=self.xtol)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

kstwobign = kstwobign_gen(a=0.0,name='kstwobign', longname='Kolmogorov-Smirnov two-sided large N statistic', extradoc=""" Kolmogorov-Smirnov two-sided large N stiatistics

kstwobign = kstwobign_gen(a=0.0,name='kstwobign', longname='Kolmogorov-Smirnov two-sided (for large N)', extradoc=""" Kolmogorov-Smirnov two-sided test for large N

def _ppf(self,q): return special.kolmogi(1.0-q)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

def _entropy(self, c): k = log(1+c) return k/2.0 - log(c/k)

def _stats(self, c, moments='mv'): k = log(1.0+c) mu = (c-k)/(c*k) mu2 = ((c+2.0)*k-2.0*c)/(2*c*k*k) g1 = None g2 = None if 's' in moments: g1 = sqrt(2)*(12*c*c-9*c*k*(c+2)+2*k*k*(c*(c+3)+3)) g1 /= sqrt(c*(c*(k-2)+2*k))*(3*c*(k-2)+6*k) if 'k' in moments: g2 = c**3*(k-3)*(k*(3*k-16)+24)+12*k*c*c*(k-4)*(k-3) \ + 6*c*k*k*(3*k-14) + 12*k**3 g2 /= 3*c*(c*(k-2)+2*k)**2 return mu, mu2, g1, g2

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

def _stats(self, x, c): return burr_gen._stats(self, x, c, 1.0)

def _stats(self, c): return burr_gen._stats(self, c, 1.0) def _entropy(self, c): return 2 - log(c)

def _stats(self, x, c): return burr_gen._stats(self, x, c, 1.0)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

def _isf(self, q, dfn, dfd): return special.fdtri(dfn, dfd, q)

def _isf(self, q, dfn, dfd): return special.fdtri(dfn, dfd, q)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

return self._isf(1.0-q, dfn, dfd)

return special.fdtri(dfn, dfd, q)

def _ppf(self, q, dfn, dfd): return self._isf(1.0-q, dfn, dfd)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

self.a = where(c > 0, 0.0, -scipy.inf)

def _argcheck(self, c): c = arr(c) self.b = where(c < 0, 1.0/abs(c), scipy.inf) self.a = where(c > 0, 0.0, -scipy.inf) return where(c==0, 0, 1)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

genpareto = genpareto_gen(name='genpareto',

def _entropy(self, c): if (c > 0): return 1+c else: self.b = -1.0 / c return rv_continuous._entropy(self, c) genpareto = genpareto_gen(a=0.0,name='genpareto',

def _munp(self, n, c): k = arange(0,n+1) val = (-1.0/c)**n * sum(scipy.comb(n,k)*(-1)**k / (1.0-c*k)) return where(c*n < 1, val, scipy.inf)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

invweibull = invweibull_gen(name='invweibull',

def _entropy(self, c): return 1+_EULER + _EULER / c - log(c) invweibull = invweibull_gen(a=0,name='invweibull',

def _ppf(self, q, c): return pow(-log(q),arr(-1.0/c))

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

return 0, pi*pi/3.0, 0, 6.0/5.0

return 0, pi*pi/3.0, 0, 6.0/5.0 def _entropy(self): return 1.0

def _stats(self): return 0, pi*pi/3.0, 0, 6.0/5.0

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

def _stats(self, x):

def _stats(self):

def _stats(self, x): return 0, 0.25, 0, -1.0

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

return 0, None, 0, None

mu2 = 2*gam(lam+1.5)-lam*pow(4,-lam)*sqrt(pi)*gam(lam)*(1-2*lam) mu2 /= lam*lam*(1+2*lam)*gam(1+1.5) mu4 = 3*gam(lam)*gam(lam+0.5)*pow(2,-2*lam) / lam**3 / gam(2*lam+1.5) mu4 += 2.0/lam**4 / (1+4*lam) mu4 -= 2*sqrt(3)*gam(lam)*pow(2,-6*lam)*pow(3,3*lam) * \ gam(lam+1.0/3)*gam(lam+2.0/3) / (lam**3.0 * gam(2*lam+1.5) * \ gam(lam+0.5)) g2 = mu4 / mu2 / mu2 - 3.0 return 0, mu2, 0, g2 def _entropy(self, lam): def integ(p): return log(pow(p,lam-1)+pow(1-p,lam-1)) return scipy.integrate.quad(integ,0,1)[0]

def _stats(self, lam): return 0, None, 0, None

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

xp = extract( x<pi,x) xn = extract( x>=pi,x)

c1 = x<pi c2 = 1-c1 xp = extract( c1,x) valp = extract(c1,val) xn = extract( c2,x) valn = extract(c2,val)

def _cdf(self, x, c): output = 0.0*x val = (1.0+c)/(1.0-c) xp = extract( x<pi,x) xn = extract( x>=pi,x) if (any(xn)): xn = 2*pi - xn yn = tan(xn/2.0) on = 1.0-1.0/pi*arctan(val*yn) insert(output, x>=pi, on) if (any(xp)): yp = tan(xp/2.0) op = 1.0/pi*arctan(val*yp) insert(output, x<pi, op) return output

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

on = 1.0-1.0/pi*arctan(val*yn) insert(output, x>=pi, on)

on = 1.0-1.0/pi*arctan(valn*yn) insert(output, c2, on)

def _cdf(self, x, c): output = 0.0*x val = (1.0+c)/(1.0-c) xp = extract( x<pi,x) xn = extract( x>=pi,x) if (any(xn)): xn = 2*pi - xn yn = tan(xn/2.0) on = 1.0-1.0/pi*arctan(val*yn) insert(output, x>=pi, on) if (any(xp)): yp = tan(xp/2.0) op = 1.0/pi*arctan(val*yp) insert(output, x<pi, op) return output

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

op = 1.0/pi*arctan(val*yp) insert(output, x<pi, op) return output

op = 1.0/pi*arctan(valp*yp) insert(output, c1, op) return output def _ppf(self, q, c): val = (1.0-c)/(1.0+c) rcq = 2*arctan(val*tan(pi*q)) rcmq = 2*pi-2*arctan(val*tan(pi*(1-q))) return where(q < 1.0/2, rcq, rcmq) def _entropy(self, c): return log(2*pi*(1-c*c))

def _cdf(self, x, c): output = 0.0*x val = (1.0+c)/(1.0-c) xp = extract( x<pi,x) xn = extract( x>=pi,x) if (any(xn)): xn = 2*pi - xn yn = tan(xn/2.0) on = 1.0-1.0/pi*arctan(val*yn) insert(output, x>=pi, on) if (any(xp)): yp = tan(xp/2.0) op = 1.0/pi*arctan(val*yp) insert(output, x<pi, op) return output

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

raise ValueError

def __call__(self, *args, **kwds): raise ValueError return self.freeze(*args,**kwds)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

binom = binom_gen(a=0,name='binom',shapes="n,pr",extradoc="""

def _entropy(self, n, pr): k = r_[0:n+1] vals = self._pmf(k,n,pr) lvals = where(vals==0,0.0,log(vals)) return -sum(vals*lvals) binom = binom_gen(name='binom',shapes="n,pr",extradoc="""

def _stats(self, n, pr): q = 1.0-pr mu = n * pr var = n * pr * q g1 = (q-pr) / sqrt(n*pr*q) g2 = (1.0-6*pr*q)/(n*pr*q) return mu, var, g1, g2

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

bernoulli = bernoulli_gen(a=0,b=1,name='bernoulli',shapes="pr",extradoc="""

def _entropy(self, pr): return -pr*log(pr)-(1-pr)*log(1-pr) bernoulli = bernoulli_gen(b=1,name='bernoulli',shapes="pr",extradoc="""

def _stats(self, pr): return binom_gen._stats(self, 1, pr)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

self.a = n

def _argcheck(self, n, pr): self.a = n return (n >= 0) & (pr >= 0) & (pr <= 1)

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

poisson = poisson_gen(a=0,name="poisson", longname='A Poisson',

poisson = poisson_gen(name="poisson", longname='A Poisson',

def _stats(self, mu): var = mu g1 = 1.0/arr(sqrt(mu)) g2 = 1.0 / arr(mu) return mu, var, g1, g2

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

dlaplace = dlaplace_gen(a=1,name='dlaplace', longname='A discrete Laplacian',

def _entropy(self, a): return a / sinh(a) - log(tanh(a/2.0)) dlaplace = dlaplace_gen(a=-scipy.inf, name='dlaplace', longname='A discrete Laplacian',

def _stats(self, a): ea = exp(-a) e2a = exp(-2*a) e3a = exp(-3*a) e4a = exp(-4*a) mu2 = 2* (e2a + ea) / (1-ea)**3.0 mu4 = 2* (e4a + 11*e3a + 11*e2a + ea) / (1-ea)**5.0 return 0.0, mu2, 0.0, mu4 / mu2**2.0 - 3

e30c53e4e1f48dd6cf050ad084549d4fe72837b5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e30c53e4e1f48dd6cf050ad084549d4fe72837b5/distributions.py

A = ones((Npts,2),'d') A[:,0] = arange(1,Npts+1)*1.0/Npts

A = ones((Npts,2),dtype) A[:,0] = cast[dtype](arange(1,Npts+1)*1.0/Npts)

def detrend(data, axis=-1, type='linear', bp=0): """Remove linear trend along axis from data. If type is 'constant' then remove mean only. If bp is given, then it is a sequence of points at which to break a piecewise-linear fit to the data. """ if type not in ['linear','l','constant','c']: raise ValueError, "Trend type must be linear or constant" data = asarray(data) if type in ['constant','c']: ret = data - expand_dims(mean(data,axis),axis) return ret else: dshape = data.shape N = dshape[axis] bp = sort(unique(r_[0,bp,N])) if any(bp > N): raise ValueError, "Breakpoints must be less than length of data along given axis." Nreg = len(bp) - 1 # Restructure data so that axis is along first dimension and # all other dimensions are collapsed into second dimension rnk = len(dshape) if axis < 0: axis = axis + rnk newdims = r_[axis,0:axis,axis+1:rnk] newdata = reshape(transpose(data,tuple(newdims)),(N,prod(dshape)/N)) newdata = newdata.copy() # make sure we have a copy # Find leastsq fit and remove it for each piece for m in range(Nreg): Npts = bp[m+1] - bp[m] A = ones((Npts,2),'d') A[:,0] = arange(1,Npts+1)*1.0/Npts sl = slice(bp[m],bp[m+1]) coef,resids,rank,s = linalg.lstsq(A,newdata[sl]) newdata[sl] = newdata[sl] - dot(A,coef) # Put data back in original shape. tdshape = take(dshape,newdims) ret = reshape(newdata,tdshape) vals = range(1,rnk) olddims = vals[:axis] + [0] + vals[axis:] ret = transpose(ret,tuple(olddims)) return ret

7d4dc2a8df5b9af11d5778245e09f3923f17a4af /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/7d4dc2a8df5b9af11d5778245e09f3923f17a4af/signaltools.py

" .astype(), or set sparse.useUmfpack = False"

" .astype(), or set linsolve.useUmfpack = False"

def spsolve(A, b, permc_spec=2): if not hasattr(A, 'tocsr') and not hasattr(A, 'tocsc'): raise ValueError, "sparse matrix must be able to return CSC format--"\ "A.tocsc()--or CSR format--A.tocsr()" if not hasattr(A, 'shape'): raise ValueError, "sparse matrix must be able to return shape" \ " (rows, cols) = A.shape" M, N = A.shape if (M != N): raise ValueError, "matrix must be square" if isUmfpack and useUmfpack: mat = _toCS_umfpack( A ) if mat.dtype.char not in 'dD': raise ValueError, "convert matrix data to double, please, using"\ " .astype(), or set sparse.useUmfpack = False" family = {'d' : 'di', 'D' : 'zi'} umf = umfpack.UmfpackContext( family[mat.dtype.char] ) return umf.linsolve( umfpack.UMFPACK_A, mat, b, autoTranspose = True ) else: mat, csc = _toCS_superLU( A ) ftype, lastel, data, index0, index1 = \ mat.ftype, mat.nnz, mat.data, mat.rowind, mat.indptr gssv = eval('_superlu.' + ftype + 'gssv') print "data-ftype: %s compared to data %s" % (ftype, data.dtype.char) print "Calling _superlu.%sgssv" % ftype return gssv(N, lastel, data, index0, index1, b, csc, permc_spec)[0]

54b08fd3512073d5332812d8dbddb47a9afdfa84 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/54b08fd3512073d5332812d8dbddb47a9afdfa84/linsolve.py

eps = scipy.limits.epsilon('d')

eps = scipy_base.limits.double_epsilon

def _stats(self): return 0.5, 1.0/12, 0, -1.2

92a160e16cdc545e467f99467b874950769185a0 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/92a160e16cdc545e467f99467b874950769185a0/distributions.py

def configuration(parent_package='', parent_path=None): local_path = get_path(__name__) config = Configuration('delaunay', parent_package, parent_path)

def configuration(parent_package='', top_path=None): config = Configuration('delaunay', parent_package, top_path)

def configuration(parent_package='', parent_path=None): local_path = get_path(__name__) config = Configuration('delaunay', parent_package, parent_path) config.add_extension("_delaunay", sources=["_delaunay.cpp", "VoronoiDiagramGenerator.cpp", "delaunay_utils.cpp", "natneighbors.cpp"], include_dirs=[local_path], ) return config

66c1a31ae024475e908a3af24e480b5a83f769cf /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/66c1a31ae024475e908a3af24e480b5a83f769cf/setup.py

include_dirs=[local_path],

include_dirs=['.'],

def configuration(parent_package='', parent_path=None): local_path = get_path(__name__) config = Configuration('delaunay', parent_package, parent_path) config.add_extension("_delaunay", sources=["_delaunay.cpp", "VoronoiDiagramGenerator.cpp", "delaunay_utils.cpp", "natneighbors.cpp"], include_dirs=[local_path], ) return config

66c1a31ae024475e908a3af24e480b5a83f769cf /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/66c1a31ae024475e908a3af24e480b5a83f769cf/setup.py

if type(M) == type('d'):

if type(M) == type('d'):

def tri(N, M=None, k=0, typecode=None): """ returns a N-by-M matrix where all the diagonals starting from lower left corner up to the k-th are all ones. """ if M is None: M = N if type(M) == type('d'): typecode = M M = N m = greater_equal(subtract.outer(arange(N), arange(M)),-k) if typecode is None: return m else: return m.astype(typecode)

53ae39e65068d51a5b7fc37529d2b031a755ed97 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/53ae39e65068d51a5b7fc37529d2b031a755ed97/basic.py

svsp = m.spacesaver()

svsp = getattr(m,'spacesaver',lambda:0)()

def tril(m, k=0): """ returns the elements on and below the k-th diagonal of m. k=0 is the main diagonal, k > 0 is above and k < 0 is below the main diagonal. """ svsp = m.spacesaver() m = asarray(m,savespace=1) out = tri(m.shape[0], m.shape[1], k=k, typecode=m.typecode())*m out.savespace(svsp) return out

53ae39e65068d51a5b7fc37529d2b031a755ed97 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/53ae39e65068d51a5b7fc37529d2b031a755ed97/basic.py

svsp = m.spacesaver()

svsp = getattr(m,'spacesaver',lambda:0)()

def triu(m, k=0): """ returns the elements on and above the k-th diagonal of m. k=0 is the main diagonal, k > 0 is above and k < 0 is below the main diagonal. """ svsp = m.spacesaver() m = asarray(m,savespace=1) out = (1-tri(m.shape[0], m.shape[1], k-1, m.typecode()))*m out.savespace(svsp) return out

53ae39e65068d51a5b7fc37529d2b031a755ed97 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/53ae39e65068d51a5b7fc37529d2b031a755ed97/basic.py

func -- a Python function or method to integrate.

func -- a Python function or method to integrate (must accept vector inputs)

def fixed_quad(func,a,b,args=(),n=5): """Compute a definite integral using fixed-order Gaussian quadrature. Description: Integrate func from a to b using Gaussian quadrature of order n. Inputs: func -- a Python function or method to integrate. a -- lower limit of integration b -- upper limit of integration args -- extra arguments to pass to function. n -- order of quadrature integration. Outputs: (val, None) val -- Gaussian quadrature approximation to the integral. """ [x,w] = p_roots(n) ainf, binf = map(scipy.isinf,(a,b)) if ainf or binf: raise ValueError, "Gaussian quadrature is only available for finite limits." y = (b-a)*(x+1)/2.0 + a return (b-a)/2.0*sum(w*func(y,*args)), None

a6b1098d53872ca1a4cb2f81a6740b8c20e9efbd /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/a6b1098d53872ca1a4cb2f81a6740b8c20e9efbd/quadrature.py

newval = fixed_quad(func,a,b,args,n)[0]

newval = fixed_quad(vec_func,a,b,(func,)+args,n)[0]

def quadrature(func,a,b,args=(),tol=1.49e-8,maxiter=50): """Compute a definite integral using fixed-tolerance Gaussian quadrature. Description: Integrate func from a to b using Gaussian quadrature with absolute tolerance tol. Inputs: func -- a Python function or method to integrate. a -- lower limit of integration. b -- upper limit of integration. args -- extra arguments to pass to function. tol -- iteration stops when error between last two iterates is less than tolerance. maxiter -- maximum number of iterations. Outputs: (val, err) val -- Gaussian quadrature approximation (within tolerance) to integral. err -- Difference between last two estimates of the integral. """ err = 100.0 val = err n = 1 while (err > tol) and (n < maxiter): newval = fixed_quad(func,a,b,args,n)[0] err = abs(newval-val) val = newval n = n + 1 if (n==maxiter): print "maxiter (%d) exceeded. Latest difference = %e" % (n,err) else: print "Took %d points." % n return val, err

a6b1098d53872ca1a4cb2f81a6740b8c20e9efbd /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/a6b1098d53872ca1a4cb2f81a6740b8c20e9efbd/quadrature.py

from scipy_distutils.misc_util import fortran_library_item, dot_join,\ SourceGenerator, get_path, default_config_dict, get_build_temp from scipy_distutils.system_info import get_info,dict_append,\ AtlasNotFoundError,LapackNotFoundError,BlasNotFoundError,\ LapackSrcNotFoundError,BlasSrcNotFoundError,NotFoundError

from scipy_distutils.misc_util import dot_join, get_path, default_config_dict from scipy_distutils.system_info import get_info, dict_append, NotFoundError

def configuration(parent_package='',parent_path=None): from scipy_distutils.core import Extension from scipy_distutils.misc_util import fortran_library_item, dot_join,\ SourceGenerator, get_path, default_config_dict, get_build_temp from scipy_distutils.system_info import get_info,dict_append,\ AtlasNotFoundError,LapackNotFoundError,BlasNotFoundError,\ LapackSrcNotFoundError,BlasSrcNotFoundError,NotFoundError package = 'linalg' from interface_gen import generate_interface config = default_config_dict(package,parent_package) local_path = get_path(__name__,parent_path) def local_join(*paths): return os.path.join(*((local_path,)+paths)) abs_local_path = os.path.abspath(local_path) no_atlas = 0 lapack_opt = get_info('lapack_opt') if not lapack_opt: raise NotFoundError,'no lapack/blas resources found' atlas_version = ([v[3:-3] for k,v in lapack_opt.get('define_macros',[]) \ if k=='ATLAS_INFO']+[None])[0] if atlas_version: print 'ATLAS version',atlas_version target_dir = '' skip_names = {'clapack':[],'flapack':[],'cblas':[],'fblas':[]} if skip_single_routines: target_dir = 'dbl' skip_names['clapack'].extend(\ 'sgesv cgesv sgetrf cgetrf sgetrs cgetrs sgetri cgetri'\ ' sposv cposv spotrf cpotrf spotrs cpotrs spotri cpotri'\ ' slauum clauum strtri ctrtri'.split()) skip_names['flapack'].extend(skip_names['clapack']) skip_names['flapack'].extend(\ 'sgesdd cgesdd sgelss cgelss sgeqrf cgeqrf sgeev cgeev'\ ' sgegv cgegv ssyev cheev slaswp claswp sgees cgees' ' sggev cggev'.split()) skip_names['cblas'].extend('saxpy caxpy'.split()) skip_names['fblas'].extend(skip_names['cblas']) skip_names['fblas'].extend(\ 'srotg crotg srotmg srot csrot srotm sswap cswap sscal cscal'\ ' csscal scopy ccopy sdot cdotu cdotc snrm2 scnrm2 sasum scasum'\ ' isamax icamax sgemv cgemv chemv ssymv strmv ctrmv'\ ' sgemm cgemm'.split()) if using_lapack_blas: target_dir = join(target_dir,'blas') skip_names['fblas'].extend(\ 'drotmg srotmg drotm srotm'.split()) if atlas_version=='3.2.1_pre3.3.6': target_dir = join(target_dir,'atlas321') skip_names['clapack'].extend(\ 'sgetri dgetri cgetri zgetri spotri dpotri cpotri zpotri'\ ' slauum dlauum clauum zlauum strtri dtrtri ctrtri ztrtri'.split()) elif atlas_version>'3.4.0' and atlas_version<='3.5.12': skip_names['clapack'].extend('cpotrf zpotrf'.split()) def generate_pyf(extension, build_dir): name = extension.name.split('.')[-1] target = join(build_dir,target_dir,name+'.pyf') if name[0]=='c' and atlas_version is None and newer(__file__,target): f = open(target,'w') f.write('python module '+name+'\n') f.write('usercode void empty_module(void) {}\n') f.write('interface\n') f.write('subroutine empty_module()\n') f.write('intent(c) empty_module\n') f.write('end subroutine empty_module\n') f.write('end interface\nend python module'+name+'\n') f.close() return target if newer_group(extension.depends,target): generate_interface(name, extension.depends[0], target, skip_names[name]) return target # fblas: ext_args = {'name': dot_join(parent_package,package,'fblas'), 'sources': [generate_pyf, local_join('src','fblaswrap.f')], 'depends': map(local_join,['generic_fblas.pyf', 'generic_fblas1.pyf', 'generic_fblas2.pyf', 'generic_fblas3.pyf']) } dict_append(ext_args,**lapack_opt) ext = Extension(**ext_args) config['ext_modules'].append(ext) # cblas: ext_args = {'name': dot_join(parent_package,package,'cblas'), 'sources': [generate_pyf], 'depends': map(local_join,['generic_cblas.pyf', 'generic_cblas1.pyf']) } dict_append(ext_args,**lapack_opt) ext = Extension(**ext_args) config['ext_modules'].append(ext) # flapack: ext_args = {'name': dot_join(parent_package,package,'flapack'), 'sources': [generate_pyf], 'depends': map(local_join,['generic_flapack.pyf', 'flapack_user_routines.pyf']) } dict_append(ext_args,**lapack_opt) ext = Extension(**ext_args) config['ext_modules'].append(ext) # clapack: ext_args = {'name': dot_join(parent_package,package,'clapack'), 'sources': [generate_pyf], 'depends': map(local_join,['generic_clapack.pyf']) } dict_append(ext_args,**lapack_opt) ext = Extension(**ext_args) config['ext_modules'].append(ext) # _flinalg: ext_args = {'name':dot_join(parent_package,package,'_flinalg'), 'sources':[local_join('src','det.f'), local_join('src','lu.f')] } dict_append(ext_args,**lapack_opt) config['ext_modules'].append(Extension(**ext_args)) # calc_lwork: ext_args = {'name':dot_join(parent_package,package,'calc_lwork'), 'sources':[local_join('src','calc_lwork.f')], } dict_append(ext_args,**lapack_opt) config['ext_modules'].append(Extension(**ext_args)) # atlas_version: ext_args = {'name':dot_join(parent_package,package,'atlas_version'), 'sources':[os.path.join(local_path,'atlas_version.c')]} dict_append(ext_args,**lapack_opt) ext = Extension(**ext_args) config['ext_modules'].append(ext) return config

d22345c4009069cb0d5194f7bcc58445d8409832 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d22345c4009069cb0d5194f7bcc58445d8409832/setup_linalg.py

'generic_fblas3.pyf'])

'generic_fblas3.pyf', 'interface_gen.py'])

def generate_pyf(extension, build_dir): name = extension.name.split('.')[-1] target = join(build_dir,target_dir,name+'.pyf') if name[0]=='c' and atlas_version is None and newer(__file__,target): f = open(target,'w') f.write('python module '+name+'\n') f.write('usercode void empty_module(void) {}\n') f.write('interface\n') f.write('subroutine empty_module()\n') f.write('intent(c) empty_module\n') f.write('end subroutine empty_module\n') f.write('end interface\nend python module'+name+'\n') f.close() return target if newer_group(extension.depends,target): generate_interface(name, extension.depends[0], target, skip_names[name]) return target

d22345c4009069cb0d5194f7bcc58445d8409832 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d22345c4009069cb0d5194f7bcc58445d8409832/setup_linalg.py

'generic_cblas1.pyf'])

'generic_cblas1.pyf', 'interface_gen.py'])

def generate_pyf(extension, build_dir): name = extension.name.split('.')[-1] target = join(build_dir,target_dir,name+'.pyf') if name[0]=='c' and atlas_version is None and newer(__file__,target): f = open(target,'w') f.write('python module '+name+'\n') f.write('usercode void empty_module(void) {}\n') f.write('interface\n') f.write('subroutine empty_module()\n') f.write('intent(c) empty_module\n') f.write('end subroutine empty_module\n') f.write('end interface\nend python module'+name+'\n') f.close() return target if newer_group(extension.depends,target): generate_interface(name, extension.depends[0], target, skip_names[name]) return target

d22345c4009069cb0d5194f7bcc58445d8409832 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d22345c4009069cb0d5194f7bcc58445d8409832/setup_linalg.py

'flapack_user_routines.pyf'])

'flapack_user_routines.pyf', 'interface_gen.py'])

def generate_pyf(extension, build_dir): name = extension.name.split('.')[-1] target = join(build_dir,target_dir,name+'.pyf') if name[0]=='c' and atlas_version is None and newer(__file__,target): f = open(target,'w') f.write('python module '+name+'\n') f.write('usercode void empty_module(void) {}\n') f.write('interface\n') f.write('subroutine empty_module()\n') f.write('intent(c) empty_module\n') f.write('end subroutine empty_module\n') f.write('end interface\nend python module'+name+'\n') f.close() return target if newer_group(extension.depends,target): generate_interface(name, extension.depends[0], target, skip_names[name]) return target

d22345c4009069cb0d5194f7bcc58445d8409832 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d22345c4009069cb0d5194f7bcc58445d8409832/setup_linalg.py

'depends': map(local_join,['generic_clapack.pyf'])

'depends': map(local_join,['generic_clapack.pyf', 'interface_gen.py'])

def generate_pyf(extension, build_dir): name = extension.name.split('.')[-1] target = join(build_dir,target_dir,name+'.pyf') if name[0]=='c' and atlas_version is None and newer(__file__,target): f = open(target,'w') f.write('python module '+name+'\n') f.write('usercode void empty_module(void) {}\n') f.write('interface\n') f.write('subroutine empty_module()\n') f.write('intent(c) empty_module\n') f.write('end subroutine empty_module\n') f.write('end interface\nend python module'+name+'\n') f.close() return target if newer_group(extension.depends,target): generate_interface(name, extension.depends[0], target, skip_names[name]) return target

d22345c4009069cb0d5194f7bcc58445d8409832 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d22345c4009069cb0d5194f7bcc58445d8409832/setup_linalg.py

elif mtype in ['f','float','float32','real*4']:

elif mtype in ['f','float','float32','real*4', 'real']:

def getsize_type(mtype): if mtype in ['b','uchar','byte','unsigned char','integer*1', 'int8']: mtype = 'b' elif mtype in ['c', 'char','char*1']: mtype = 'c' elif mtype in ['1','schar', 'signed char']: mtype = '1' elif mtype in ['s','short','int16','integer*2']: mtype = 's' elif mtype in ['i','int']: mtype = 'i' elif mtype in ['l','long','int32','integer*4']: mtype = 'l' elif mtype in ['f','float','float32','real*4']: mtype = 'f' elif mtype in ['d','double','float64','real*8']: mtype = 'd' elif mtype in ['F','complex float','complex*8','complex64']: mtype = 'F' elif mtype in ['D','complex*16','complex128','complex','complex double']: mtype = 'D' else: raise TypeError, 'Bad datatype -- ' + mtype argout = (array(0,mtype).itemsize(),mtype) return argout

eaa41670893901ba398c3eed51f9ad161dd61865 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/eaa41670893901ba398c3eed51f9ad161dd61865/mio.py

elif mtype in ['d','double','float64','real*8']:

elif mtype in ['d','double','float64','real*8', 'double precision']:

def getsize_type(mtype): if mtype in ['b','uchar','byte','unsigned char','integer*1', 'int8']: mtype = 'b' elif mtype in ['c', 'char','char*1']: mtype = 'c' elif mtype in ['1','schar', 'signed char']: mtype = '1' elif mtype in ['s','short','int16','integer*2']: mtype = 's' elif mtype in ['i','int']: mtype = 'i' elif mtype in ['l','long','int32','integer*4']: mtype = 'l' elif mtype in ['f','float','float32','real*4']: mtype = 'f' elif mtype in ['d','double','float64','real*8']: mtype = 'd' elif mtype in ['F','complex float','complex*8','complex64']: mtype = 'F' elif mtype in ['D','complex*16','complex128','complex','complex double']: mtype = 'D' else: raise TypeError, 'Bad datatype -- ' + mtype argout = (array(0,mtype).itemsize(),mtype) return argout

eaa41670893901ba398c3eed51f9ad161dd61865 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/eaa41670893901ba398c3eed51f9ad161dd61865/mio.py

sz,mtype = getsize_type(args[0])

if len(args) > 0: sz,mtype = getsize_type(args[0]) else: sz,mtype = getsize_type(fmt.typecode())

def fort_write(self,fmt,*args): """Write a Fortran binary record.

eaa41670893901ba398c3eed51f9ad161dd61865 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/eaa41670893901ba398c3eed51f9ad161dd61865/mio.py

name = ''.join(asarray(_get_element(fid)[0]).astype('c'))

name = asarray(_get_element(fid)[0]).tostring()

def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) tupdims = tuple(dims[::-1]) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused =_get_element(fid) try: result = result + _unit_imag[imag.typecode()] * imag except KeyError: result = result + 1j*imag result = squeeze(transpose(reshape(result,tupdims))) elif dclass == mxCELL_CLASS: length = product(dims) result = zeros(length, PyObject) for i in range(length): sa, unused = _get_element(fid) result[i]= sa result = squeeze(transpose(reshape(result,tupdims))) if rank(result)==0: result = result.toscalar() elif dclass == mxSTRUCT_CLASS: length = product(dims) result = zeros(length, PyObject) namelength = _get_element(fid)[0] # get field names names = _get_element(fid)[0] splitnames = [names[i:i+namelength] for i in \ xrange(0,len(names),namelength)] fieldnames = [''.join(asarray(x).astype('c')).strip('\x00') for x in splitnames] for i in range(length): result[i] = mat_struct() for element in fieldnames: val,unused = _get_element(fid) result[i].__dict__[element] = val result = squeeze(transpose(reshape(result,tupdims))) if rank(result)==0: result = result.toscalar() # object is like a structure with but with a class name elif dclass == mxOBJECT_CLASS: class_name = ''.join(asarray(_get_element(fid)[0]).astype('c')) length = product(dims) result = zeros(length, PyObject) namelength = _get_element(fid)[0] # get field names names = _get_element(fid)[0] splitnames = [names[i:i+namelength] for i in \ xrange(0,len(names),namelength)] fieldnames = [''.join(asarray(x).astype('c')).strip('\x00') for x in splitnames] for i in range(length): result[i] = mat_obj() result[i]._classname = class_name for element in fieldnames: val,unused = _get_element(fid) result[i].__dict__[element] = val result = squeeze(transpose(reshape(result,tupdims))) if rank(result)==0: result = result.toscalar() elif dclass == mxSPARSE_CLASS: rowind, unused = _get_element(fid) colind, unused = _get_element(fid) res, unused = _get_element(fid) if cmplx: imag, unused = _get_element(fid) try: res = res + _unit_imag[imag.typecode()] * imag except KeyError: res = res + 1j*imag if have_sparse: spmat = scipy.sparse.csc_matrix(res, (rowind[:len(res)], colind), M=dims[0],N=dims[1]) result = spmat else: result = (dims, rowind, colind, res) return result, name

0ba0d91003a4097ee4fb2114712c033b1877b8f1 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/0ba0d91003a4097ee4fb2114712c033b1877b8f1/mio.py

except AtrributeError:

except AttributeError:

def getnzmax(self): try: nzmax = self.nzmax except AttributeError: try: nzmax = self.nnz except AtrributeError: nzmax = 0 return nzmax

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

tcode = s.typecode func = getattr(sparsetools,tcode+'transp')

func = getattr(sparsetools,s.ftype+'transp')

def __init__(self,s,ij=None,M=None,N=None,nzmax=100,typecode=Float,copy=0): spmatrix.__init__(self, 'csc') if isinstance(s,spmatrix): if isinstance(s, csc_matrix): # do nothing but copy information self.shape = s.shape if copy: self.data = s.data.copy() self.rowind = s.rowind.copy() self.indptr = s.indptr.copy() else: self.data = s.data self.rowind = s.rowind self.indptr = s.indptr elif isinstance(s, csr_matrix): self.shape = s.shape tcode = s.typecode func = getattr(sparsetools,tcode+'transp') self.data, self.rowind, self.indptr = \ func(s.data, s.colind, s.indptr) else: temp = s.tocsc() self.data = temp.data self.rowind = temp.rowind self.indptr = temp.indptr self.shape = temp.shape elif isinstance(s,type(3)): M=s N=ij self.data = zeros((nzmax,),typecode) self.rowind = zeros((nzmax,),'i') self.indptr = zeros((N+1,),'i') self.shape = (M,N) elif (isinstance(s,ArrayType) or \ isinstance(s,type([]))): s = asarray(s) if (rank(s) == 2): # converting from a full array M, N = s.shape s = asarray(s) if s.typecode() not in 'fdFD': s = s*1.0 typecode = s.typecode() func = getattr(sparsetools,_transtabl[typecode]+'fulltocsc') ierr = irow = jcol = 0 nnz = sum(ravel(s != 0.0)) a = zeros((nnz,),typecode) rowa = zeros((nnz,),'i') ptra = zeros((N+1,),'i') while 1: a, rowa, ptra, irow, jcol, ierr = \ func(s, a, rowa, ptra, irow, jcol, ierr) if (ierr == 0): break nnz = nnz + ALLOCSIZE a = resize1d(a, nnz) rowa = resize1d(rowa, nnz)

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

func(s.data, s.colind, s.indptr)

func(s.shape[1], s.data, s.colind, s.indptr)

def __init__(self,s,ij=None,M=None,N=None,nzmax=100,typecode=Float,copy=0): spmatrix.__init__(self, 'csc') if isinstance(s,spmatrix): if isinstance(s, csc_matrix): # do nothing but copy information self.shape = s.shape if copy: self.data = s.data.copy() self.rowind = s.rowind.copy() self.indptr = s.indptr.copy() else: self.data = s.data self.rowind = s.rowind self.indptr = s.indptr elif isinstance(s, csr_matrix): self.shape = s.shape tcode = s.typecode func = getattr(sparsetools,tcode+'transp') self.data, self.rowind, self.indptr = \ func(s.data, s.colind, s.indptr) else: temp = s.tocsc() self.data = temp.data self.rowind = temp.rowind self.indptr = temp.indptr self.shape = temp.shape elif isinstance(s,type(3)): M=s N=ij self.data = zeros((nzmax,),typecode) self.rowind = zeros((nzmax,),'i') self.indptr = zeros((N+1,),'i') self.shape = (M,N) elif (isinstance(s,ArrayType) or \ isinstance(s,type([]))): s = asarray(s) if (rank(s) == 2): # converting from a full array M, N = s.shape s = asarray(s) if s.typecode() not in 'fdFD': s = s*1.0 typecode = s.typecode() func = getattr(sparsetools,_transtabl[typecode]+'fulltocsc') ierr = irow = jcol = 0 nnz = sum(ravel(s != 0.0)) a = zeros((nnz,),typecode) rowa = zeros((nnz,),'i') ptra = zeros((N+1,),'i') while 1: a, rowa, ptra, irow, jcol, ierr = \ func(s, a, rowa, ptra, irow, jcol, ierr) if (ierr == 0): break nnz = nnz + ALLOCSIZE a = resize1d(a, nnz) rowa = resize1d(rowa, nnz)

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

data1, data2 = _convert_data(self.data[:nnz1], other.data[:nnz2], typecode)

data1, data2 = _convert_data(self.data[:nnz1], ocs.data[:nnz2], typecode)

def __add__(self, other): ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." typecode = _coerce_rules[(self.typecode,other.typecode)] nnz1, nnz2 = self.nnz, other.nnz data1, data2 = _convert_data(self.data[:nnz1], other.data[:nnz2], typecode) func = getattr(sparsetools,_transtabl[typecode]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,other.rowind[:nnz2],other.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix(c,(rowc,ptrc),M=M,N=N)

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,other.rowind[:nnz2],other.indptr)

c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,ocs.rowind[:nnz2],ocs.indptr)

def __add__(self, other): ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." typecode = _coerce_rules[(self.typecode,other.typecode)] nnz1, nnz2 = self.nnz, other.nnz data1, data2 = _convert_data(self.data[:nnz1], other.data[:nnz2], typecode) func = getattr(sparsetools,_transtabl[typecode]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,other.rowind[:nnz2],other.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix(c,(rowc,ptrc),M=M,N=N)

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

tcode = s.typecode func = getattr(sparsetools,tcode+'transp')

func = getattr(sparsetools,s.ftype+'transp')

def __init__(self,s,ij=None,M=None,N=None,nzmax=100,typecode=Float,copy=0): spmatrix.__init__(self, 'csr') if isinstance(s,spmatrix): if isinstance(s, csr_matrix): # do nothing but copy information self.shape = s.shape if copy: self.data = s.data.copy() self.colind = s.colind.copy() self.indptr = s.indptr.copy() else: self.data = s.data self.colind = s.colind self.indptr = s.indptr elif isinstance(s, csc_matrix): self.shape = s.shape tcode = s.typecode func = getattr(sparsetools,tcode+'transp') self.data, self.colind, self.indptr = \ func(s.data, s.rowind, s.indptr) else: try: temp = s.tocsr() except AttributeError: temp = csr_matrix(s.tocsc()) self.data = temp.data self.rowind = temp.rowind self.indptr = temp.indptr self.shape = temp.shape elif isinstance(s,type(3)): M=s N=ij self.data = zeros((nzmax,),typecode) self.colind = zeros((nzmax,),'i') self.indptr = zeros((N+1,),'i') self.shape = (M,N) elif (isinstance(s,ArrayType) or \ isinstance(s,type([]))): s = asarray(s) if (rank(s) == 2): # converting from a full array ocsc = csc_matrix(transpose(s)) self.shape = ocsc.shape[1], ocsc.shape[0] self.colind = ocsc.rowind self.indptr = ocsc.indptr self.data = ocsc.data elif isinstance(ij, ArrayType) and (rank(ij) == 2) and (shape(ij) == (len(s),2)): ijnew = ij.copy() ijnew[:,0] = ij[:,1] ijnew[:,1] = ij[:,0] temp = coo_matrix(s,ijnew,M=M,N=N,nzmax=nzmax, typecode=typecode) temp = temp.tocsc() self.data = temp.data self.colind = temp.colind self.indptr = temp.indptr self.shape = temp.shape elif isinstance(ij, types.TupleType) and (len(ij)==2): self.data = asarray(s) self.colind = ij[0] self.indptr = ij[1] if N is None: N = max(self.colind) if M is None: M = len(self.indptr) - 1 self.shape = (M,N) else: raise ValueError, "Unrecognized form for csr_matrix constructor." else: raise ValueError, "Unrecognized form for csr_matrix constructor."

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

func(s.data, s.rowind, s.indptr)

func(s.shape[1], s.data, s.rowind, s.indptr)

def __init__(self,s,ij=None,M=None,N=None,nzmax=100,typecode=Float,copy=0): spmatrix.__init__(self, 'csr') if isinstance(s,spmatrix): if isinstance(s, csr_matrix): # do nothing but copy information self.shape = s.shape if copy: self.data = s.data.copy() self.colind = s.colind.copy() self.indptr = s.indptr.copy() else: self.data = s.data self.colind = s.colind self.indptr = s.indptr elif isinstance(s, csc_matrix): self.shape = s.shape tcode = s.typecode func = getattr(sparsetools,tcode+'transp') self.data, self.colind, self.indptr = \ func(s.data, s.rowind, s.indptr) else: try: temp = s.tocsr() except AttributeError: temp = csr_matrix(s.tocsc()) self.data = temp.data self.rowind = temp.rowind self.indptr = temp.indptr self.shape = temp.shape elif isinstance(s,type(3)): M=s N=ij self.data = zeros((nzmax,),typecode) self.colind = zeros((nzmax,),'i') self.indptr = zeros((N+1,),'i') self.shape = (M,N) elif (isinstance(s,ArrayType) or \ isinstance(s,type([]))): s = asarray(s) if (rank(s) == 2): # converting from a full array ocsc = csc_matrix(transpose(s)) self.shape = ocsc.shape[1], ocsc.shape[0] self.colind = ocsc.rowind self.indptr = ocsc.indptr self.data = ocsc.data elif isinstance(ij, ArrayType) and (rank(ij) == 2) and (shape(ij) == (len(s),2)): ijnew = ij.copy() ijnew[:,0] = ij[:,1] ijnew[:,1] = ij[:,0] temp = coo_matrix(s,ijnew,M=M,N=N,nzmax=nzmax, typecode=typecode) temp = temp.tocsc() self.data = temp.data self.colind = temp.colind self.indptr = temp.indptr self.shape = temp.shape elif isinstance(ij, types.TupleType) and (len(ij)==2): self.data = asarray(s) self.colind = ij[0] self.indptr = ij[1] if N is None: N = max(self.colind) if M is None: M = len(self.indptr) - 1 self.shape = (M,N) else: raise ValueError, "Unrecognized form for csr_matrix constructor." else: raise ValueError, "Unrecognized form for csr_matrix constructor."

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

if (len(self.data) != len(self.colind)):

if (len(self.data) != nzmax):

def _check(self): M,N = self.shape if (rank(self.data) != 1) or (rank(self.colind) != 1) or \ (rank(self.indptr) != 1): raise ValueError, "Data, colind, and indptr arrays "\ "should be rank 1." if (len(self.data) != len(self.colind)): raise ValueError, "Data and row list should have same length" if (len(self.indptr) != M+1): raise ValueError, "Index pointer should be of length #rows + 1" if (nzmax < nnz): raise ValueError, "Nzmax must not be less than nnz." if (nnz>0) and (max(self.colind[:nnz]) >= M): raise ValueError, "Column-values must be < N." if (self.indptr[-1] > len(self.colind)): raise ValueError, \ "Last value of index list should be less than "\ "the size of data list" self.nnz = self.indptr[-1] self.nzmax = len(self.colind) self.typecode = self.data.typecode() if self.typecode not in 'fdFD': self.typecode = 'd' self.data = self.data.astype('d') self.ftype = _transtabl[self.typecode]

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

if (nzmax < nnz): raise ValueError, "Nzmax must not be less than nnz."

def _check(self): M,N = self.shape if (rank(self.data) != 1) or (rank(self.colind) != 1) or \ (rank(self.indptr) != 1): raise ValueError, "Data, colind, and indptr arrays "\ "should be rank 1." if (len(self.data) != len(self.colind)): raise ValueError, "Data and row list should have same length" if (len(self.indptr) != M+1): raise ValueError, "Index pointer should be of length #rows + 1" if (nzmax < nnz): raise ValueError, "Nzmax must not be less than nnz." if (nnz>0) and (max(self.colind[:nnz]) >= M): raise ValueError, "Column-values must be < N." if (self.indptr[-1] > len(self.colind)): raise ValueError, \ "Last value of index list should be less than "\ "the size of data list" self.nnz = self.indptr[-1] self.nzmax = len(self.colind) self.typecode = self.data.typecode() if self.typecode not in 'fdFD': self.typecode = 'd' self.data = self.data.astype('d') self.ftype = _transtabl[self.typecode]

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

if (self.indptr[-1] > len(self.colind)):

if (nnz > nzmax):

def _check(self): M,N = self.shape if (rank(self.data) != 1) or (rank(self.colind) != 1) or \ (rank(self.indptr) != 1): raise ValueError, "Data, colind, and indptr arrays "\ "should be rank 1." if (len(self.data) != len(self.colind)): raise ValueError, "Data and row list should have same length" if (len(self.indptr) != M+1): raise ValueError, "Index pointer should be of length #rows + 1" if (nzmax < nnz): raise ValueError, "Nzmax must not be less than nnz." if (nnz>0) and (max(self.colind[:nnz]) >= M): raise ValueError, "Column-values must be < N." if (self.indptr[-1] > len(self.colind)): raise ValueError, \ "Last value of index list should be less than "\ "the size of data list" self.nnz = self.indptr[-1] self.nzmax = len(self.colind) self.typecode = self.data.typecode() if self.typecode not in 'fdFD': self.typecode = 'd' self.data = self.data.astype('d') self.ftype = _transtabl[self.typecode]

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

self.nnz = self.indptr[-1] self.nzmax = len(self.colind)

def _check(self): M,N = self.shape if (rank(self.data) != 1) or (rank(self.colind) != 1) or \ (rank(self.indptr) != 1): raise ValueError, "Data, colind, and indptr arrays "\ "should be rank 1." if (len(self.data) != len(self.colind)): raise ValueError, "Data and row list should have same length" if (len(self.indptr) != M+1): raise ValueError, "Index pointer should be of length #rows + 1" if (nzmax < nnz): raise ValueError, "Nzmax must not be less than nnz." if (nnz>0) and (max(self.colind[:nnz]) >= M): raise ValueError, "Column-values must be < N." if (self.indptr[-1] > len(self.colind)): raise ValueError, \ "Last value of index list should be less than "\ "the size of data list" self.nnz = self.indptr[-1] self.nzmax = len(self.colind) self.typecode = self.data.typecode() if self.typecode not in 'fdFD': self.typecode = 'd' self.data = self.data.astype('d') self.ftype = _transtabl[self.typecode]

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

new = csr_matrix(N,M,nzmax=0,typecode=self.typecode)

new = csc_matrix(N,M,nzmax=0,typecode=self.typecode)

def transp(self, copy=0): M,N = self.shape new = csr_matrix(N,M,nzmax=0,typecode=self.typecode) if copy: new.data = self.data.copy() new.colind = self.rowind.copy() new.indptr = self.indptr.copy() else: new.data = self.data new.colind = self.rowind new.indptr = self.indptr new._check() return new

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

new.colind = self.rowind.copy()

new.rowind = self.colind.copy()

def transp(self, copy=0): M,N = self.shape new = csr_matrix(N,M,nzmax=0,typecode=self.typecode) if copy: new.data = self.data.copy() new.colind = self.rowind.copy() new.indptr = self.indptr.copy() else: new.data = self.data new.colind = self.rowind new.indptr = self.indptr new._check() return new

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

new.colind = self.rowind

new.rowind = self.colind

def transp(self, copy=0): M,N = self.shape new = csr_matrix(N,M,nzmax=0,typecode=self.typecode) if copy: new.data = self.data.copy() new.colind = self.rowind.copy() new.indptr = self.indptr.copy() else: new.data = self.data new.colind = self.rowind new.indptr = self.indptr new._check() return new

1b70bf704019519bf06c10fab13fe3a67941a824 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1b70bf704019519bf06c10fab13fe3a67941a824/Sparse.py

dict['__header__'] = fid.fid.read(124).strip(' \t\n\000')

dict['__header__'] = fid.raw_read(124).strip(' \t\n\000')

def _parse_header(fid, dict): correct_endian = (ord('M')<<8) + ord('I') # if this number is read no BS fid.seek(126) # skip to endian detector endian_test = fid.read(1,'int16') if (endian_test == correct_endian): openstr = 'n' else: # must byteswap if LittleEndian: openstr = 'b' else: openstr = 'l' fid.setformat(openstr) # change byte-order if necessary fid.rewind() dict['__header__'] = fid.fid.read(124).strip(' \t\n\000') vers = fid.read(1,'int16') dict['__version__'] = '%d.%d' % (vers >> 8, vers & 255) fid.seek(2,1) # move to start of data return

90ccb96fe397f4e8738bfa79a3ee767f6fb8478a /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/90ccb96fe397f4e8738bfa79a3ee767f6fb8478a/mio.py

test = fid.fid.read(1)

test = fid.raw_read(1)

def _get_element(fid): test = fid.fid.read(1) if len(test) == 0: # nothing left raise EOFError else: fid.rewind(1) # get the data tag raw_tag = fid.read(1,'u') # check for compressed numbytes = raw_tag >> 16 if numbytes > 0: # compressed format if numbytes > 4: raise IOError, "Problem with MAT file: " \ "too many bytes in compressed format." dtype = raw_tag & 65535 el = fid.read(numbytes,miDataTypes[dtype][2],c_is_b=1) fid.seek(4-numbytes,1) # skip padding return el, None # otherwise parse tag dtype = raw_tag numbytes = fid.read(1,'u') if dtype != miMATRIX: # basic data type try: outarr = fid.read(numbytes,miDataTypes[dtype][2],c_is_b=1) except KeyError: raise ValueError, "Unknown data type" mod8 = numbytes%8 if mod8: # skip past padding skip = 8-mod8 fid.seek(skip,1) return outarr, None # handle miMatrix type el, name = _parse_mimatrix(fid,numbytes) return el, name

90ccb96fe397f4e8738bfa79a3ee767f6fb8478a /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/90ccb96fe397f4e8738bfa79a3ee767f6fb8478a/mio.py

if typecode is None: return out else: return out.astype(typecode)

if typecode is not None: out = out.astype(typecode) if not isinstance(out, ndarray): out = asarray(out) return out

def valarray(shape,value=nan,typecode=None): """Return an array of all value. """ out = reshape(repeat([value],product(shape)),shape) if typecode is None: return out else: return out.astype(typecode)

6d573b0feabf10e65e8328a6ab536d15eb52ddd2 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/6d573b0feabf10e65e8328a6ab536d15eb52ddd2/distributions.py

if not _active.window_is_alive():

if not _active.proxy_object_alive:

def validate_active(): global _active if _active is None: figure() try: if not _active.window_is_alive(): _active = None figure() except: pass

735f2504c6f1ea394e7101567ae128a9190c9318 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/735f2504c6f1ea394e7101567ae128a9190c9318/interface.py

"""

""" fs =float(fs)

def bilinear(b,a,fs=1.0): """Return a digital filter from an analog filter using the bilinear transform. The bilinear transform substitutes (z-1) / (z+1) for s """ a,b = map(r1array,(a,b)) D = len(a) - 1 N = len(b) - 1 artype = Num.Float M = max([N,D]) Np = M Dp = M bprime = Num.zeros(Np+1,artype) aprime = Num.zeros(Dp+1,artype) for j in range(Np+1): val = 0.0 for i in range(N+1): for k in range(i+1): for l in range(M-i+1): if k+l == j: val += comb(i,k)*comb(M-i,l)*b[N-i]*pow(2*fs,i)*(-1)**k bprime[j] = val for j in range(Dp+1): val = 0.0 for i in range(D+1): for k in range(i+1): for l in range(M-i+1): if k+l == j: val += comb(i,k)*comb(M-i,l)*a[D-i]*pow(2*fs,i)*(-1)**k aprime[j] = val return normalize(bprime, aprime)

d966da2111cf06c8e7a648614352f33ae55646c7 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d966da2111cf06c8e7a648614352f33ae55646c7/filter_design.py

fs = 2

fs = 2.0

def iirfilter(N, Wn, rp=None, rs=None, btype='band', analog=0, ftype='butter', output='ba'): """IIR digital and analog filter design given order and critical points. Description: Design an Nth order lowpass digital or analog filter and return the filter coefficients in (B,A) (numerator, denominator) or (Z,P,K) form. Inputs: N -- the order of the filter. Wn -- a scalar or length-2 sequence giving the critical frequencies. rp, rs -- For chebyshev and elliptic filters provides the maximum ripple in the passband and the minimum attenuation in the stop band. btype -- the type of filter (lowpass, highpass, bandpass, or bandstop). analog -- non-zero to return an analog filter, otherwise a digital filter is returned. ftype -- the type of IIR filter (Butterworth, Cauer (Elliptic), Bessel, Chebyshev1, Chebyshev2) output -- 'ba' for (b,a) output, 'zpk' for (z,p,k) output. SEE ALSO butterord, cheb1ord, cheb2ord, ellipord """ ftype, btype, output = map(string.lower, (ftype, btype, output)) Wn = Num.asarray(Wn) try: btype = band_dict[btype] except KeyError: raise ValueError, "%s is an invalid bandtype for filter." % btype try: typefunc = filter_dict[ftype][0] except KeyError: raise ValueError, "%s is not a valid basic iir filter." % ftype if output not in ['ba', 'zpk']: raise ValueError, "%s is not a valid output form." % output #pre-warp frequencies for digital filter design if not analog: fs = 2 warped = 2*fs*tan(pi*Wn/fs) else: warped = Wn # convert to low-pass prototype if btype in ['lowpass', 'highpass']: wo = warped else: bw = warped[1] - warped[0] wo = sqrt(warped[0]*warped[1]) # Get analog lowpass prototype if typefunc in [buttap, besselap]: z, p, k = typefunc(N) elif typefunc == cheb1ap: if rp is None: raise ValueError, "passband ripple (rp) must be provided to design a Chebyshev I filter." z, p, k = typefunc(N, rp) elif typefunc == cheb2ap: if rs is None: raise ValueError, "stopband atteunatuion (rs) must be provided to design an Chebyshev II filter." z, p, k = typefunc(N, rs) else: # Elliptic filters if rs is None or rp is None: raise ValueErrro, "Both rp and rs must be provided to design an elliptic filter." z, p, k = typefunc(N, rp, rs) b, a = zpk2tf(z,p,k) # transform to lowpass, bandpass, highpass, or bandstop if btype == 'lowpass': b, a = lp2lp(b,a,wo=wo) elif btype == 'highpass': b, a = lp2hp(b,a,wo=wo) elif btype == 'bandpass': b, a = lp2bp(b,a,wo=wo,bw=bw) else: # 'bandstop' b, a = lp2bs(b,a,wo=wo,bw=bw) # Find discrete equivalent if necessary if not analog: b, a = bilinear(b, a, fs=fs) # Transform to proper out type (pole-zero, state-space, numer-denom) if output == 'zpk': return tf2zpk(b,a) else: return b,a

d966da2111cf06c8e7a648614352f33ae55646c7 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d966da2111cf06c8e7a648614352f33ae55646c7/filter_design.py

for k, col in enumerate(j):

for k, col in enumerate(seq):

def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or self.shape[1], i.step or 1) elif operator.isSequenceType(i): seq = i else: raise IndexError, "invalid index" try: if not len(x) == len(seq): raise ValueError, "number of elements in source must be" \ " same as number of elements in destimation" except TypeError: # Either x or seq is not a sequence. Note that a sparse matrix # is also not a sequence under this definition. # Currently we don't support setting to/from non-sequence types. # This could be enhanced, though, to allow a scalar source, # and/or a sparse vector. raise TypeError, "unsupported type for lil_matrix.__setitem__" else: # Sequence: call __setitem__ recursively, once for each row for i in xrange(len(seq)): self[seq[i], index[1]] = x[i] return

e65ce38e1e8d829ba1ac01ffbb53bfcb3612e539 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e65ce38e1e8d829ba1ac01ffbb53bfcb3612e539/sparse.py

a, axis = asarray(a, axis)

a, axis = _chk_asarray(a, axis)

def tmax(a,upperlimit,axis=0,inclusive=True): """Returns the maximum value of a, along axis, including only values greater than (or equal to, if inclusive is True) upperlimit. If the limit is set to None, a limit larger than the max value in the array is used. """ a, axis = asarray(a, axis) if inclusive: upperfcn = less else: upperfcn = less_equal if upperlimit is None: upperlimit = maximum.reduce(ravel(a))+1 smallest = minimum.reduce(ravel(a)) ta = where(upperfcn(a,upperlimit),a,smallest) return maximum.reduce(ta,axis)

c19d2dfabdea18d5dde3aba39e8afa02db11f2ba /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c19d2dfabdea18d5dde3aba39e8afa02db11f2ba/stats.py

assert_array_almost_equal(sort(roots(a3)),sort([-3,-2]),11)

assert_array_almost_equal(sort(roots(a3)),sort([-3,-2]),7)

def check_basic(self): a1 = [1,-4,4] a2 = [4,-16,16] a3 = [1,5,6] assert_array_almost_equal(roots(a1),[2,2],11) assert_array_almost_equal(roots(a2),[2,2],11) assert_array_almost_equal(sort(roots(a3)),sort([-3,-2]),11)

b2b10d79f63de2efc55775a51b3dd7e86cdd5753 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/b2b10d79f63de2efc55775a51b3dd7e86cdd5753/test_basic1a.py

assert_array_almost_equal(sort(roots(poly(a))),sort(a),11)

assert_array_almost_equal(sort(roots(poly(a))),sort(a),5)

def check_inverse(self): a = rand(5) assert_array_almost_equal(sort(roots(poly(a))),sort(a),11)

b2b10d79f63de2efc55775a51b3dd7e86cdd5753 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/b2b10d79f63de2efc55775a51b3dd7e86cdd5753/test_basic1a.py

assert_array_equal(trapz(y,x,1),val)

assert_array_equal(trapz(y,x,axis=1),val)

def check_nd(self): x = sort(20*rand(10,20,30)) y = x**2 + 2*x + 1 dx = x[:,1:,:] - x[:,:-1,:] val = add.reduce(dx*(y[:,1:,:] + y[:,:-1,:])/2.0,1) assert_array_equal(trapz(y,x,1),val)

e2ebf654010bc5dfa0fb21e92b34042f89f6ae4f /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/e2ebf654010bc5dfa0fb21e92b34042f89f6ae4f/test_basic.py

"""blackman(M) returns the M-point Blackman window.

"""The M-point Blackman window.

def blackman(M): """blackman(M) returns the M-point Blackman window. """ n = arange(0,M) return 0.42-0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1))

bba05349a34e29337f122cc88babd78041933c34 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bba05349a34e29337f122cc88babd78041933c34/signaltools.py

"""bartlett(M) returns the M-point Bartlett window.

"""The M-point Bartlett window.

def bartlett(M): """bartlett(M) returns the M-point Bartlett window. """ n = arange(0,M) return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))

bba05349a34e29337f122cc88babd78041933c34 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bba05349a34e29337f122cc88babd78041933c34/signaltools.py

"""hanning(M) returns the M-point Hanning window.

"""The M-point Hanning window.

def hanning(M): """hanning(M) returns the M-point Hanning window. """ n = arange(0,M) return 0.5-0.5*cos(2.0*pi*n/(M-1))

bba05349a34e29337f122cc88babd78041933c34 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bba05349a34e29337f122cc88babd78041933c34/signaltools.py

"""hamming(M) returns the M-point Hamming window.

"""The M-point Hamming window.

def hamming(M): """hamming(M) returns the M-point Hamming window. """ n = arange(0,M) return 0.54-0.46*cos(2.0*pi*n/(M-1))

bba05349a34e29337f122cc88babd78041933c34 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bba05349a34e29337f122cc88babd78041933c34/signaltools.py

"""kaiser(M, beta) returns a Kaiser window of length M with shape parameter beta. It depends on the cephes module for the modified bessel function i0.

"""Returns a Kaiser window of length M with shape parameter beta.

def kaiser(M,beta): """kaiser(M, beta) returns a Kaiser window of length M with shape parameter beta. It depends on the cephes module for the modified bessel function i0. """ n = arange(0,M) alpha = (M-1)/2.0 return special.i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/special.i0(beta)

bba05349a34e29337f122cc88babd78041933c34 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bba05349a34e29337f122cc88babd78041933c34/signaltools.py

'csscal scopy ccopy sdot cdotu cdotc snrm2 scnrm2 sasum scasum'\ 'samax camax sgemv cgemv chemv ssymv strmv ctrmv sgemm cgemm'.split())

' csscal scopy ccopy sdot cdotu cdotc snrm2 scnrm2 sasum scasum'\ ' isamax icamax sgemv cgemv chemv ssymv strmv ctrmv'\ ' sgemm cgemm'.split())

def configuration(parent_package=''): package = 'linalg' from interface_gen import generate_interface config = default_config_dict(package,parent_package) local_path = get_path(__name__) atlas_info = get_info('atlas') #atlas_info = {} # uncomment if ATLAS is available but want to use # Fortran LAPACK/BLAS; useful for testing f_libs = [] blas_info,lapack_info = {},{} if not atlas_info: warnings.warn(AtlasNotFoundError.__doc__) blas_info = get_info('blas') #blas_info = {} # test building BLAS from sources. if not blas_info: warnings.warn(BlasNotFoundError.__doc__) blas_src_info = get_info('blas_src') if not blas_src_info: raise BlasSrcNotFoundError,BlasSrcNotFoundError.__doc__ dict_append(blas_info,libraries=['blas_src']) f_libs.append(fortran_library_item(\ 'blas_src',blas_src_info['sources'], )) lapack_info = get_info('lapack') #lapack_info = {} # test building LAPACK from sources. if not lapack_info: warnings.warn(LapackNotFoundError.__doc__) lapack_src_info = get_info('lapack_src') if not lapack_src_info: raise LapackSrcNotFoundError,LapackSrcNotFoundError.__doc__ dict_append(lapack_info,libraries=['lapack_src']) f_libs.append(fortran_library_item(\ 'lapack_src',lapack_src_info['sources'], )) mod_sources = {} if atlas_info or blas_info: mod_sources['fblas'] = ['generic_fblas.pyf', 'generic_fblas1.pyf', 'generic_fblas2.pyf', 'generic_fblas3.pyf', os.path.join('src','fblaswrap.f'), ] if atlas_info or lapack_info: mod_sources['flapack'] = ['generic_flapack.pyf'] if atlas_info: mod_sources['cblas'] = ['generic_cblas.pyf', 'generic_cblas1.pyf'] mod_sources['clapack'] = ['generic_clapack.pyf'] else: dict_append(atlas_info,**lapack_info) dict_append(atlas_info,**blas_info) skip_names = {'clapack':[],'flapack':[],'cblas':[],'fblas':[]} if skip_single_routines: skip_names['clapack'].extend(\ 'sgesv cgesv sgetrf cgetrf sgetrs cgetrs sgetri cgetri'\ ' sposv cposv spotrf cpotrf spotrs cpotrs spotri cpotri'\ ' slauum clauum strtri ctrtri'.split()) skip_names['flapack'].extend(skip_names['clapack']) skip_names['flapack'].extend(\ 'sgesdd cgesdd sgelss cgelss sgeqrf cgeqrf sgeev cgeev'\ ' sgegv cgegv ssyev cheev slaswp claswp sgees cgees' ' sggev cggev'.split()) skip_names['cblas'].extend('saxpy caxpy'.split()) skip_names['fblas'].extend(skip_names['cblas']) skip_names['fblas'].extend(\ 'srotg crotg srotmg srot csrot srotm sswap cswap sscal cscal'\ 'csscal scopy ccopy sdot cdotu cdotc snrm2 scnrm2 sasum scasum'\ 'samax camax sgemv cgemv chemv ssymv strmv ctrmv sgemm cgemm'.split()) if atlas_version_pre_3_3: skip_names['clapack'].extend(\ 'sgetri dgetri cgetri zgetri spotri dpotri cpotri zpotri'\ ' slauum dlauum clauum zlauum strtri dtrtri ctrtri ztrtri'.split()) for mod_name,sources in mod_sources.items(): sources = [os.path.join(local_path,s) for s in sources] mod_file = os.path.join(local_path,mod_name+'.pyf') if dep_util.newer_group(sources,mod_file): generate_interface(mod_name,sources[0],mod_file, skip_names.get(mod_name,[])) sources = filter(lambda s:s[-4:]!='.pyf',sources) ext_args = {'name':dot_join(parent_package,package,mod_name), 'sources':[mod_file]+sources} dict_append(ext_args,**atlas_info) ext = Extension(**ext_args) ext.need_fcompiler_opts = 1 config['ext_modules'].append(ext) flinalg = [] for f in ['det.f','lu.f', #'wrappers.c','inv.f', ]: flinalg.append(os.path.join(local_path,'src',f)) ext_args = {'name':dot_join(parent_package,package,'_flinalg'), 'sources':flinalg} dict_append(ext_args,**atlas_info) config['ext_modules'].append(Extension(**ext_args)) ext_args = {'name':dot_join(parent_package,package,'calc_lwork'), 'sources':[os.path.join(local_path,'src','calc_lwork.f')], } dict_append(ext_args,**atlas_info) config['ext_modules'].append(Extension(**ext_args)) config['fortran_libraries'].extend(f_libs) return config

3e67051532356b3eb25dd0b7e3f04fb49a2e24c9 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/3e67051532356b3eb25dd0b7e3f04fb49a2e24c9/setup_linalg.py

entires = rows*cols

entries = rows*cols

def mmwrite(target,a,comment='',field=None,precision=None): """ Writes the sparse or dense matrix A to a Matrix Market formatted file. Inputs: target - Matrix Market filename (extension .mtx) or open file object a - sparse or full matrix comment - comments to be prepended to the Matrix Market file field - 'real' | 'complex' | 'pattern' | 'integer' precision - Number of digits to display for real or complex values. """ close_it = 0 if type(target) is type(''): if target[-4:] != '.mtx': target = target + '.mtx' target = open(target,'w') close_it = 1 if type(a) in [ListType,ArrayType,TupleType] or hasattr(a,'__array__'): rep = 'array' a = asarray(a) if len(a.shape) != 2: raise ValueError, 'expected matrix' rows,cols = a.shape entires = rows*cols typecode = a.typecode() if field is not None: if field=='integer': a = a.astype('i') elif field=='real': if typecode not in 'fd': a = a.astype('d') elif field=='complex': if typecode not in 'FD': a = a.astype('D') elif field=='pattern': pass else: raise ValueError,'unknown field '+field typecode = a.typecode() else: rep = 'coordinate' from scipy.sparse import spmatrix if not isinstance(a,spmatrix): raise ValueError,'unknown matrix type ' + `type(a)` rows,cols = a.shape entries = a.getnnz() typecode = a.gettypecode() if precision is None: if typecode in 'fF': precision = 8 else: precision = 16 if field is None: if typecode in 'li': field = 'integer' elif typecode in 'df': field = 'real' elif typecode in 'DF': field = 'complex' else: raise TypeError,'unexpected typecode '+typecode if rep == 'array': symm = _get_symmetry(a) else: symm = 'general' target.write('%%%%MatrixMarket matrix %s %s %s\n' % (rep,field,symm)) for line in comment.split('\n'): target.write('%%%s\n' % (line)) if field in ['real','integer']: if field=='real': format = '%%.%ie\n' % precision else: format = '%i\n' elif field=='complex': format = '%%.%ie %%.%ie\n' % (precision,precision) if rep == 'array': target.write('%i %i\n' % (rows,cols)) if field in ['real','integer']: if symm=='general': for j in range(cols): for i in range(rows): target.write(format % a[i,j]) else: for j in range(cols): for i in range(j,rows): target.write(format % a[i,j]) elif field=='complex': if symm=='general': for j in range(cols): for i in range(rows): aij = a[i,j] target.write(format % (real(aij),imag(aij))) else: for j in range(cols): for i in range(j,rows): aij = a[i,j] target.write(format % (real(aij),imag(aij))) elif field=='pattern': raise ValueError,'Pattern type inconsisted with dense matrix' else: raise TypeError,'Unknown matrix type '+`field` else: format = '%i %i ' + format target.write('%i %i %i\n' % (rows,cols,entires)) assert symm=='general',`symm` if field in ['real','integer']: for i in range(entries): target.write(format % (a.rowcol(i)+(a.getdata(i),))) elif field=='complex': for i in range(entries): value = a.getdata(i) target.write(format % ((a.rowcol(i))+(real(value),imag(value)))) elif field=='pattern': raise NotImplementedError,`field` else: raise TypeError,'Unknown matrix type '+`field` if close_it: target.close() else: target.flush() return

f2b7ea21f0c7e99b91ff347f26b32220be816767 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/f2b7ea21f0c7e99b91ff347f26b32220be816767/mmio.py

target.write('%i %i %i\n' % (rows,cols,entires))

target.write('%i %i %i\n' % (rows,cols,entries))

def mmwrite(target,a,comment='',field=None,precision=None): """ Writes the sparse or dense matrix A to a Matrix Market formatted file. Inputs: target - Matrix Market filename (extension .mtx) or open file object a - sparse or full matrix comment - comments to be prepended to the Matrix Market file field - 'real' | 'complex' | 'pattern' | 'integer' precision - Number of digits to display for real or complex values. """ close_it = 0 if type(target) is type(''): if target[-4:] != '.mtx': target = target + '.mtx' target = open(target,'w') close_it = 1 if type(a) in [ListType,ArrayType,TupleType] or hasattr(a,'__array__'): rep = 'array' a = asarray(a) if len(a.shape) != 2: raise ValueError, 'expected matrix' rows,cols = a.shape entires = rows*cols typecode = a.typecode() if field is not None: if field=='integer': a = a.astype('i') elif field=='real': if typecode not in 'fd': a = a.astype('d') elif field=='complex': if typecode not in 'FD': a = a.astype('D') elif field=='pattern': pass else: raise ValueError,'unknown field '+field typecode = a.typecode() else: rep = 'coordinate' from scipy.sparse import spmatrix if not isinstance(a,spmatrix): raise ValueError,'unknown matrix type ' + `type(a)` rows,cols = a.shape entries = a.getnnz() typecode = a.gettypecode() if precision is None: if typecode in 'fF': precision = 8 else: precision = 16 if field is None: if typecode in 'li': field = 'integer' elif typecode in 'df': field = 'real' elif typecode in 'DF': field = 'complex' else: raise TypeError,'unexpected typecode '+typecode if rep == 'array': symm = _get_symmetry(a) else: symm = 'general' target.write('%%%%MatrixMarket matrix %s %s %s\n' % (rep,field,symm)) for line in comment.split('\n'): target.write('%%%s\n' % (line)) if field in ['real','integer']: if field=='real': format = '%%.%ie\n' % precision else: format = '%i\n' elif field=='complex': format = '%%.%ie %%.%ie\n' % (precision,precision) if rep == 'array': target.write('%i %i\n' % (rows,cols)) if field in ['real','integer']: if symm=='general': for j in range(cols): for i in range(rows): target.write(format % a[i,j]) else: for j in range(cols): for i in range(j,rows): target.write(format % a[i,j]) elif field=='complex': if symm=='general': for j in range(cols): for i in range(rows): aij = a[i,j] target.write(format % (real(aij),imag(aij))) else: for j in range(cols): for i in range(j,rows): aij = a[i,j] target.write(format % (real(aij),imag(aij))) elif field=='pattern': raise ValueError,'Pattern type inconsisted with dense matrix' else: raise TypeError,'Unknown matrix type '+`field` else: format = '%i %i ' + format target.write('%i %i %i\n' % (rows,cols,entires)) assert symm=='general',`symm` if field in ['real','integer']: for i in range(entries): target.write(format % (a.rowcol(i)+(a.getdata(i),))) elif field=='complex': for i in range(entries): value = a.getdata(i) target.write(format % ((a.rowcol(i))+(real(value),imag(value)))) elif field=='pattern': raise NotImplementedError,`field` else: raise TypeError,'Unknown matrix type '+`field` if close_it: target.close() else: target.flush() return

f2b7ea21f0c7e99b91ff347f26b32220be816767 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/f2b7ea21f0c7e99b91ff347f26b32220be816767/mmio.py

new = zeros((newlen,), arr.dtype.char)

new = zeros((newlen,), arr.dtype)

def resize1d(arr, newlen): old = len(arr) new = zeros((newlen,), arr.dtype.char) new[:old] = arr return new

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

(self.shape + (self.dtype.char, self.getnnz(), self.nzmax, \

(self.shape + (self.dtype.type, self.getnnz(), self.nzmax, \

def __repr__(self): format = self.getformat() return "<%dx%d sparse matrix of type '%s'\n\twith %d stored "\ "elements (space for %d)\n\tin %s format>" % \ (self.shape + (self.dtype.char, self.getnnz(), self.nzmax, \ _formats[format][1]))

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

csc.dtype.char = csc.data.dtype.char

csc.dtype = csc.data.dtype

def _real(self): csc = self.tocsc() csc.data = real(csc.data) csc.dtype.char = csc.data.dtype.char csc.ftype = _transtabl[csc.dtype.char] return csc

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

csc.dtype.char = csc.data.dtype.char

csc.dtype = csc.data.dtype

def _imag(self): csc = self.tocsc() csc.data = imag(csc.data) csc.dtype.char = csc.data.dtype.char csc.ftype = _transtabl[csc.dtype.char] return csc

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

self.dtype.char = self.data.dtype.char

self.dtype = self.data.dtype

def _check(self): M, N = self.shape nnz = self.indptr[-1] nzmax = len(self.rowind)

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

self.dtype.char = self.data.dtype.char

self.dtype = self.data.dtype

def _check(self): M, N = self.shape nnz = self.indptr[-1] nzmax = len(self.rowind)

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

new.dtype.char = new.data.dtype.char

new.dtype = new.data.dtype

def __mul__(self, other): """ Scalar, vector, or matrix multiplication """ if isscalar(other) or (isdense(other) and rank(other)==0): new = self.copy() new.data *= other new.dtype.char = new.data.dtype.char new.ftype = _transtabl[new.dtype.char] return new else: return self.dot(other) #else: # return TypeError, "unknown type for sparse matrix multiplication"

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

new.dtype.char = new.data.dtype.char

new.dtype = new.data.dtype

def __rmul__(self, other): # other * self if isscalar(other) or (isdense(other) and rank(other)==0): new = self.copy() new.data = other * new.data new.dtype.char = new.data.dtype.char new.ftype = _transtabl[new.dtype.char] return new else: other = asarray(other) return self.transpose().dot(other.transpose()).transpose()

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

new.dtype.char = new.data.dtype.char

new.dtype = new.data.dtype

def __pow__(self, other): """ Element-by-element power (unless other is a scalar, in which case return the matrix power.) """ if isscalar(other) or (isdense(other) and rank(other)==0): new = self.copy() new.data = new.data ** other new.dtype.char = new.data.dtype.char new.ftype = _transtabl[new.dtype.char] return new else: ocs = other.tocsc() if (ocs.shape != self.shape): raise ValueError, "inconsistent shapes" dtypechar = _coerce_rules[(self.dtype.char, ocs.dtype.char)] nnz1, nnz2 = self.nnz, ocs.nnz data1, data2 = _convert_data(self.data[:nnz1], ocs.data[:nnz2], dtypechar) func = getattr(sparsetools, _transtabl[dtypechar]+'cscmul') c, rowc, ptrc, ierr = func(data1, self.rowind[:nnz1], self.indptr, data2, ocs.rowind[:nnz2], ocs.indptr) if ierr: raise ValueError, "ran out of space (but shouldn't have happened)" M, N = self.shape return csc_matrix((c, rowc, ptrc), dims=(M, N))

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

new = csr_matrix((N, M), nzmax=self.nzmax, dtype=self.dtype.char)

new = csr_matrix((N, M), nzmax=self.nzmax, dtype=self.dtype)

def transpose(self, copy=False): M, N = self.shape new = csr_matrix((N, M), nzmax=self.nzmax, dtype=self.dtype.char) if copy: new.data = self.data.copy() new.colind = self.rowind.copy() new.indptr = self.indptr.copy() else: new.data = self.data new.colind = self.rowind new.indptr = self.indptr new._check() return new

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

new = csc_matrix(self.shape, nzmax=self.nzmax, dtype=self.dtype.char)

new = csc_matrix(self.shape, nzmax=self.nzmax, dtype=self.dtype)

def conj(self, copy=False): new = csc_matrix(self.shape, nzmax=self.nzmax, dtype=self.dtype.char) if copy: new.data = self.data.conj().copy() new.rowind = self.rowind.conj().copy() new.indptr = self.indptr.conj().copy() else: new.data = self.data.conj() new.rowind = self.rowind.conj() new.indptr = self.indptr.conj() new._check() return new

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py

new = csc_matrix(self.shape, nzmax=self.nzmax, dtype=self.dtype.char)

new = csc_matrix(self.shape, nzmax=self.nzmax, dtype=self.dtype)

def copy(self): new = csc_matrix(self.shape, nzmax=self.nzmax, dtype=self.dtype.char) new.data = self.data.copy() new.rowind = self.rowind.copy() new.indptr = self.indptr.copy() new._check() return new

755141cd47edbf647cfcd8d09fd47ede3320459c /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/755141cd47edbf647cfcd8d09fd47ede3320459c/sparse.py