Added snippet tests
This commit is contained in:
PhilJ
parent
45fde86060
commit
612d07218e
26 changed files with 237 additions and 7106 deletions
1
build.sh
1
build.sh
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@ -45,3 +45,4 @@ make -C micropython/ports/unix -j${NPROC} axtls
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make -C micropython/ports/unix -j${NPROC} USER_C_MODULES="${HERE}" DEBUG=1 STRIP=: MICROPY_PY_FFI=0 MICROPY_PY_BTREE=0 CFLAGS_EXTRA=-DULAB_MAX_DIMS=$dims BUILD=build-$dims PROG=micropython-$dims
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bash test-common.sh "${dims}" "micropython/ports/unix/micropython-$dims"
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@ -51,6 +51,8 @@ def size(a, axis=None):
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return asarray(a).shape[axis]
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def nonzero(a):
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if not isinstance(a,(np.ndarray)):
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a = asarray(a)
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x = a.shape
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row = x[0]
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if len(x) == 1:
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@ -65,11 +67,12 @@ def nonzero(a):
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for i in range(0,row):
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if a[i] != 0:
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nonzero_row = numpy.append(nonzero_row,i)
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return (nonzero_row,)
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return (np.array(nonzero_row, dtype=np.int8),)
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for i in range(0,row):
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for j in range(0,column):
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if a[i,j] != 0:
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nonzero_row = numpy.append(nonzero_row,i)
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nonzero_col = numpy.append(nonzero_col,j)
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return (nonzero_row,nonzero_col)
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return (np.array(nonzero_row, dtype=np.int8), np.array(nonzero_col, dtype=np.int8))
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@ -6,6 +6,8 @@ def asarray(a, dtype=None):
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try:
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if dtype is not None:
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a = np.array([a], dtype=dtype)
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elif isinstance(a, list) or isinstance(a, tuple):
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a = np.array(a)
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else:
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a = np.array([a])
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return a
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@ -56,3 +56,4 @@ def zeros_like(a, dtype=None, order='K', subok=True, shape=None):
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res = np.full(a.shape, 0, dtype=a.dtype)
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return res
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@ -7,9 +7,9 @@ from ulab import numpy as np
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def poly(seq_of_zeros):
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seq_of_zeros = atleast_1d(seq_of_zeros)
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sh = seq_of_zeros.shape
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if len(sh) == 2 and sh[0] == sh[1] and sh[0] != 0:
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seq_of_zeros = eigvals(seq_of_zeros)
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seq_of_zeros = np.linalg.eig(seq_of_zeros)
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elif len(sh) == 1:
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dt = seq_of_zeros.dtype
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# Let object arrays slip through, e.g. for arbitrary precision
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@ -31,12 +31,12 @@ def butter(N, Wn, btype='low', analog=False, output='ba', fs=None):
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half-cycles / sample.)
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For analog filters, `Wn` is an angular frequency (e.g. rad/s).
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btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional
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btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional
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The type of filter. Default is 'lowpass'.
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analog : bool, optional
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analog : bool, optional
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When True, return an analog filter, otherwise a digital filter is
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returned.
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output : {'ba', 'zpk', 'sos'}, optional
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output : {'ba', 'zpk', 'sos'}, optional
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Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or
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second-order sections ('sos'). Default is 'ba' for backwards
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compatibility, but 'sos' should be used for general-purpose filtering.
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@ -119,7 +119,6 @@ def butter(N, Wn, btype='low', analog=False, output='ba', fs=None):
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return iirfilter(N, Wn, btype=btype, analog=analog,
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output=output, ftype='butter', fs=fs)
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def iirfilter(N, Wn, rp=None, rs=None, btype='band', analog=False,
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ftype='butter', output='ba', fs=None):
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"""
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@ -305,9 +304,9 @@ def iirfilter(N, Wn, rp=None, rs=None, btype='band', analog=False,
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raise ValueError('Must specify a single critical frequency Wn for lowpass or highpass filter')
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if btype == 'lowpass':
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z, p, k = lp2lp_zpk(z, p, k, wo=warped)
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z, p, k = lp2lp_zpk(z, p, k, wo=warped[0])
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elif btype == 'highpass':
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z, p, k = lp2hp_zpk(z, p, k, wo=warped)
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z, p, k = lp2hp_zpk(z, p, k, wo=warped[0])
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elif btype in ('bandpass', 'bandstop'):
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try:
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bw = warped[1] - warped[0]
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@ -406,13 +405,11 @@ def _to_complex(a):
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result = np.concatenate((result, t), axis=0)
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return result
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def lexsort(z):
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z = _to_tuple(z)
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return sorted(range(len(z)), key=lambda i: (z[i][0],abs(z[i][1])))
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def do_lexsort(z):
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# z = z[np.lexsort((abs(z.imag), z.real))]
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def _lexsort(z):
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z = _to_tuple(z)
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z = sorted(z,key=lambda x:(x[0], abs(x[1])))
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return _to_complex(z)
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@ -472,11 +469,7 @@ def _cplxreal(z, tol=None):
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tol = 100 * abs(7./3 - 4./3 - 1) #np.finfo((1.0 * z).dtype).eps
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# Sort by real part, magnitude of imaginary part (speed up further sorting)
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#z = z[np.lexsort((abs(z.imag), z.real))]
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#z = _to_tuple(z)
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#z = sorted(z,key=lambda x:(x[0], abs(x[1])))
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#z = _to_complex(z)
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z = do_lexsort(z)
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z = _lexsort(z)
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# Split reals from conjugate pairs
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real_indices = abs(z.imag) <= tol * abs(z)
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zr = z[real_indices].real
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@ -698,28 +691,42 @@ def zpk2sos(z, p, k, pairing='nearest'):
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raise ValueError('pairing must be one of %s, not %s'
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% (valid_pairings, pairing))
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if len(z) == len(p) == 0:
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return array([[k, 0., 0., 1., 0., 0.]])
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return np.array([[k, 0., 0., 1., 0., 0.]])
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# ensure we have the same number of poles and zeros, and make copies
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if len(z) != len(p):
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p = np.concatenate((p, np.zeros(max(len(z) - len(p), 0), dtype=np.complex)))
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z = np.concatenate((z, np.zeros(max(len(p) - len(z), 0), dtype=np.complex)))
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if max(len(z) - len(p),0) > 0:
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p = np.concatenate((p, np.zeros((max(len(z) - len(p), 0)),dtype=np.complex)))
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if max(len(p) - len(z),0) > 0:
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z = np.concatenate((z, np.zeros((max(len(p) - len(z), 0)), dtype=np.complex)))
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n_sections = (max(len(p), len(z)) + 1) // 2
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sos = np.zeros((n_sections, 6))
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if len(p) % 2 == 1 and pairing == 'nearest':
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p = np.concatenate((p, [0.]))
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z = np.concatenate((z, [0.]))
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p = np.concatenate((p, np.array([0.],dtype=np.complex)))
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z = np.concatenate((z, np.array([0.],dtype=np.complex)))
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assert len(p) == len(z)
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z = np.array(z, dtype=np.complex)
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# Ensure we have complex conjugate pairs
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# (note that _cplxreal only gives us one element of each complex pair):
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cplx, real = _cplxreal(p)
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p = cplx
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cplx, real = _cplxreal(z)
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z = real
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cplx, rel = _cplxreal(p)
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if len(rel) > 0 and len(cplx) > 0:
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p = np.concatenate((cplx, np.array(rel, dtype=np.complex)))
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else:
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p = cplx
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cplx, rel = _cplxreal(z)
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if len(rel) > 0 and len(cplx) > 0:
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z = np.concatenate((cplx, np.array(rel, dtype=np.complex)))
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else:
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z = rel
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p_sos = np.zeros((n_sections, 2))
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z_sos = zeros_like(p_sos)
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@ -730,7 +737,7 @@ def zpk2sos(z, p, k, pairing='nearest'):
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p1 = p[p1_idx]
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p = delete(p, p1_idx)
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# Pair that pole with a zero
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if isreal(p1) and np.sum(isreal(p)) == 0:
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if isreal(p1) and np.sum([isreal(p)]) == 0:
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# Special case to set a first-order section
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z1_idx = _nearest_real_complex_idx(z, p1, 'real')
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z1 = z[z1_idx]
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@ -766,9 +773,12 @@ def zpk2sos(z, p, k, pairing='nearest'):
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assert isreal(p2)
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else: # real pole, real zero
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# pick the next "worst" pole to use
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idx = np.nonzero(np.isreal(p))[0]
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idx = nonzero(isreal(p))[0]
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assert len(idx) > 0
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p2_idx = idx[np.argmin(np.abs(abs(p[idx]) - 1))]
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a = abs(abs(p[idx[0]]) - 1)
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a = np.array([a])
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p2_idx = idx[np.argmin(a) - 1]
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p2 = p[p2_idx]
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# find a real zero to match the added pole
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assert isreal(p2)
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@ -797,7 +807,6 @@ def zpk2sos(z, p, k, pairing='nearest'):
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sos[si] = np.concatenate(x)
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return sos
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def lp2bp_zpk(z, p, k, wo=1.0, bw=1.0):
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r"""
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Transform a lowpass filter prototype to a bandpass filter.
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@ -855,7 +864,6 @@ def lp2bp_zpk(z, p, k, wo=1.0, bw=1.0):
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degree = _relative_degree(z, p)
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# Scale poles and zeros to desired bandwidth
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# z_lp = z * bw/2
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z_lp = []
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for x in z:
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z_lp.append(x * bw/2)
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@ -869,48 +877,23 @@ def lp2bp_zpk(z, p, k, wo=1.0, bw=1.0):
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# Square root needs to produce complex result, not NaN
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# z_lp = z_lp.astype(complex)
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# p_lp = p_lp.astype(complex)
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# Duplicate poles and zeros and shift from baseband to +wo and -wo
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z_bp = []
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for x in z_lp:
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z_bp.append(x + np.sqrt(x**2 - wo**2))
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for x in z_lp:
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z_bp.append(x - np.sqrt(x**2 - wo**2))
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z_bp = np.array(z_bp, dtype=np.complex)
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# z_bp = np.concatenate((z_lp + np.sqrt(z_lp**2 - wo**2),
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# z_lp - np.sqrt(z_lp**2 - wo**2)))
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p_bp = []
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for x in p_lp:
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p_bp.append(x + np.sqrt(x**2 - wo**2))
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for x in p_lp:
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p_bp.append(x - np.sqrt(x**2 - wo**2))
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p_bp = np.array(p_bp, dtype=np.complex)
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# p_bp = np.concatenate((p_lp + np.sqrt(p_lp**2 - wo**2),
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# p_lp - np.sqrt(p_lp**2 - wo**2)))
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if len(z_lp) > 0:
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z_bp = np.concatenate((z_lp + np.sqrt(z_lp*z_lp - wo*wo, dtype=np.complex),
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z_lp - np.sqrt(z_lp*z_lp - wo*wo, dtype=np.complex)))
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z_bp = append(z_bp, np.zeros(degree),dtype=np.complex)
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else:
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z_bp = np.zeros(degree, dtype=np.complex)
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p_bp = np.concatenate((p_lp + np.sqrt(p_lp*p_lp - wo*wo, dtype=np.complex),
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p_lp - np.sqrt(p_lp*p_lp - wo*wo, dtype=np.complex)))
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# Move degree zeros to origin, leaving degree zeros at infinity for BPF
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t = []
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for x in z_bp:
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t.append(x)
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for x in np.zeros(degree):
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t.append(x)
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z_bp = np.array(t, dtype=np.complex)
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# Cancel out gain change from frequency scaling
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k_bp = k * bw**degree
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@ -965,8 +948,8 @@ def lp2bs_zpk(z, p, k, wo=1.0, bw=1.0):
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.. versionadded:: 1.1.0
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"""
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z = np.atleast_1d(z)
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p = np.atleast_1d(p)
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z = atleast_1d(z)
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p = atleast_1d(p)
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wo = float(wo)
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bw = float(bw)
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@ -976,22 +959,29 @@ def lp2bs_zpk(z, p, k, wo=1.0, bw=1.0):
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z_hp = (bw/2) / z
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p_hp = (bw/2) / p
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# Square root needs to produce complex result, not NaN
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z_hp = z_hp.astype(complex)
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p_hp = p_hp.astype(complex)
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if z_hp == float('inf'):
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z_hp = []
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# Duplicate poles and zeros and shift from baseband to +wo and -wo
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z_bs = concatenate((z_hp + sqrt(z_hp**2 - wo**2),
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z_hp - sqrt(z_hp**2 - wo**2)))
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p_bs = concatenate((p_hp + sqrt(p_hp**2 - wo**2),
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p_hp - sqrt(p_hp**2 - wo**2)))
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# Square root needs to produce complex result, not NaN
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z_hp = np.array(z_hp, dtype=np.complex)
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p_hp = np.array(p_hp, dtype=np.complex)
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if len(z_hp) > 0:
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# Duplicate poles and zeros and shift from baseband to +wo and -wo
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z_bs = np.concatenate((z_hp + np.sqrt(z_hp*z_hp - wo*wo, dtype=np.complex),
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z_hp - np.sqrt(z_hp*z_hp - wo*wo, dtype=np.complex)))
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else:
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z_bs = np.array([], dtype=np.complex)
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p_bs = np.concatenate((p_hp + np.sqrt(p_hp*p_hp - wo*wo, dtype=np.complex),
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p_hp - np.sqrt(p_hp*p_hp - wo*wo, dtype=np.complex)))
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# Move any zeros that were at infinity to the center of the stopband
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z_bs = append(z_bs, full(degree, +1j*wo))
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z_bs = append(z_bs, full(degree, -1j*wo))
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z_bs = append(z_bs, np.full(degree, 1j*wo, dtype=np.complex))
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z_bs = append(z_bs, np.full(degree, -1j*wo, dtype=np.complex))
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# Cancel out gain change caused by inversion
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k_bs = k * real(prod(-z) / prod(-p))
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k_bs = k * (prod(-z) / prod(-p)).real
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return z_bs, p_bs, k_bs
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@ -1056,6 +1046,7 @@ def bilinear_zpk(z, p, k, fs):
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>>> plt.ylabel('Magnitude [dB]')
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>>> plt.grid()
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"""
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z = atleast_1d(z)
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p = atleast_1d(p)
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@ -1070,7 +1061,8 @@ def bilinear_zpk(z, p, k, fs):
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# Any zeros that were at infinity get moved to the Nyquist frequency
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a = -np.ones(degree) + 0j
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z_z = append(z_z, a)
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if len(a) > 0:
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z_z = append(z_z, a)
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# Compensate for gain change
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k_z = k * (prod(fs2 - z) / prod(fs2 - p)).real
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@ -1215,8 +1207,9 @@ def lp2hp_zpk(z, p, k, wo=1.0):
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.. versionadded:: 1.1.0
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"""
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z = np.atleast_1d(z)
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p = np.atleast_1d(p)
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z = atleast_1d(z)
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p = atleast_1d(p)
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wo = float(wo)
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degree = _relative_degree(z, p)
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@ -1226,11 +1219,14 @@ def lp2hp_zpk(z, p, k, wo=1.0):
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z_hp = wo / z
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p_hp = wo / p
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if z_hp == float('inf'):
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z_hp = []
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# If lowpass had zeros at infinity, inverting moves them to origin.
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z_hp = append(z_hp, zeros(degree))
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z_hp = append(z_hp, np.zeros(degree))
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# Cancel out gain change caused by inversion
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k_hp = k * real(prod(-z) / prod(-p))
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k_hp = k * (prod(-z) / prod(-p)).real
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return z_hp, p_hp, k_hp
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@ -1430,7 +1426,6 @@ def buttap(N):
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k = 1
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return z, p, k
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def butter_bandpass(lowcut, highcut, fs, order=5):
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nyq = 0.5 * fs
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low = lowcut / nyq
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@ -1438,7 +1433,6 @@ def butter_bandpass(lowcut, highcut, fs, order=5):
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sos = butter(order, [low, high], analog=False, btype='band', output='sos')
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return sos
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def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
|
||||
sos = butter_bandpass(lowcut, highcut, fs, order=order)
|
||||
y = spy.sosfilter(sos, data)
|
||||
|
|
@ -1452,7 +1446,6 @@ def fft(x):
|
|||
odd = fft(x[1::2])
|
||||
return [even[m] + math.e**(-2j*math.pi*m/n)*odd[m] for m in range(n//2)] + [even[m] - math.e**(-2j*math.pi*m/n)*odd[m] for m in range(n//2)]
|
||||
|
||||
|
||||
filter_dict = {'butter': [buttap, buttord],
|
||||
'butterworth': [buttap, buttord]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -4,3 +4,7 @@
|
|||
5000
|
||||
1
|
||||
2.0
|
||||
(array([0, 1, 2], dtype=int8),)
|
||||
(array([1, 1, 1, 2, 2, 2], dtype=int8), array([0, 1, 2, 0, 1, 2], dtype=int8))
|
||||
(array([1], dtype=int8),)
|
||||
(array([3], dtype=int8),)
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ import sys
|
|||
sys.path.append('.')
|
||||
|
||||
from snippets import numpy
|
||||
from ulab import numpy as np
|
||||
np.set_printoptions(threshold=100)
|
||||
|
||||
print (numpy.asarray([1]))
|
||||
print (numpy.asarray([1.0, 2.0, 3j]))
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
array([1.0], dtype=float64)
|
||||
array([1.0+0.0j, 2.0+0.0j, 0.0+3.0j], dtype=complex)
|
||||
array([(4.0+0.0j, 3.0+0.0j, 1.0+0.0j, 2.0-2.0j, 2.0+2.0j, 2.0-1.0j, 2.0+1.0j, 2.0-1.0j, 2.0+1.0j, 1.0+1.0j, 1.0-1.0j], dtype=complex)
|
||||
array([4.0+0.0j, 3.0+0.0j, 1.0+0.0j, 2.0-2.0j, 2.0+2.0j, 2.0-1.0j, 2.0+1.0j, 2.0-1.0j, 2.0+1.0j, 1.0+1.0j, 1.0-1.0j], dtype=complex)
|
||||
|
|
|
|||
|
|
@ -1 +1,4 @@
|
|||
array([[0, 0, 0],[0, 0, 0]])
|
||||
array([[0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0]], dtype=float64)
|
||||
array([[0.0+0.0j, 0.0+0.0j, 0.0+0.0j],
|
||||
[0.0+0.0j, 0.0+0.0j, 0.0+0.0j]], dtype=complex)
|
||||
|
|
|
|||
15
snippets/tests/numpy/core/shape_base.py
Normal file
15
snippets/tests/numpy/core/shape_base.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
import math
|
||||
import sys
|
||||
sys.path.append('.')
|
||||
|
||||
from ulab import numpy as np
|
||||
from snippets import numpy
|
||||
|
||||
print(numpy.atleast_1d(1.0))
|
||||
|
||||
x = np.arange(9.0).reshape((3,3))
|
||||
|
||||
print(numpy.atleast_1d(x))
|
||||
print(numpy.atleast_1d(x) is x)
|
||||
|
||||
print(numpy.atleast_1d(1, [3, 4]))
|
||||
6
snippets/tests/numpy/core/shape_base.py.exp
Normal file
6
snippets/tests/numpy/core/shape_base.py.exp
Normal file
|
|
@ -0,0 +1,6 @@
|
|||
array([1.0], dtype=float64)
|
||||
array([[0.0, 1.0, 2.0],
|
||||
[3.0, 4.0, 5.0],
|
||||
[6.0, 7.0, 8.0]], dtype=float64)
|
||||
True
|
||||
[array([1.0], dtype=float64), array([3.0, 4.0], dtype=float64)]
|
||||
|
|
@ -4,3 +4,8 @@ sys.path.append('.')
|
|||
|
||||
from snippets import numpy
|
||||
from ulab import numpy as np
|
||||
|
||||
print(numpy.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]]))
|
||||
|
||||
print(numpy.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0))
|
||||
|
||||
|
|
|
|||
4
snippets/tests/numpy/lib/function_base.py.exp
Normal file
4
snippets/tests/numpy/lib/function_base.py.exp
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype=float64)
|
||||
array([[1.0, 2.0, 3.0],
|
||||
[4.0, 5.0, 6.0],
|
||||
[7.0, 8.0, 9.0]], dtype=float64)
|
||||
16
snippets/tests/numpy/lib/polynomial.py
Normal file
16
snippets/tests/numpy/lib/polynomial.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
import math
|
||||
import sys
|
||||
sys.path.append('.')
|
||||
|
||||
from snippets import numpy
|
||||
from ulab import numpy as np
|
||||
|
||||
|
||||
print(numpy.poly((0, 0, 0)))
|
||||
|
||||
print(numpy.poly((-1./2, 0, 1./2)))
|
||||
|
||||
print(numpy.poly((0.847, 0, 0.9883)))
|
||||
|
||||
#P = np.array([[0, 1./3], [-1./2, 0]])
|
||||
#print(numpy.poly(P))
|
||||
3
snippets/tests/numpy/lib/polynomial.py.exp
Normal file
3
snippets/tests/numpy/lib/polynomial.py.exp
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
array([1.0, 0.0, 0.0, 0.0], dtype=float64)
|
||||
array([1.0, 0.0, -0.25, 0.0], dtype=float64)
|
||||
array([1.0, -1.8353, 0.8370901, 0.0], dtype=float64)
|
||||
|
|
@ -5,5 +5,13 @@ sys.path.append('.')
|
|||
from snippets import numpy
|
||||
from ulab import numpy as np
|
||||
|
||||
a = np.array([1, 2j, 3, 4j], dtype=np.complex)
|
||||
print (numpy.isreal(a))
|
||||
a = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j], dtype=np.complex)
|
||||
print(numpy.isreal(a))
|
||||
|
||||
|
||||
a = np.array([1+2j, 2+1j], dtype=np.complex)
|
||||
print(numpy.isreal(a))
|
||||
|
||||
|
||||
print(numpy.isreal(1))
|
||||
print(numpy.isreal(1j))
|
||||
4
snippets/tests/numpy/lib/type_check.py.exp
Normal file
4
snippets/tests/numpy/lib/type_check.py.exp
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
[False, True, True, True, True, False]
|
||||
[False, False]
|
||||
True
|
||||
False
|
||||
|
|
@ -21,10 +21,39 @@ lowerCutoffFrequency_Hz = centerFrequency_Hz/math.sqrt(2)
|
|||
upperCutoffFrequenc_Hz = centerFrequency_Hz*math.sqrt(2)
|
||||
wn = np.array([ lowerCutoffFrequency_Hz, upperCutoffFrequenc_Hz])/nyquistRate
|
||||
|
||||
#print('butter: ', scipy.butter(N=4, Wn=wn, btype='bandpass', analog=False, output='ba'))
|
||||
#print('butter: ', scipy.butter(N=4, Wn=wn, btype='bandpass', analog=False, output='zpk'))
|
||||
print('butter: ', scipy.butter(N=4, Wn=wn, btype='bandpass', analog=False, output='sos'))
|
||||
#print('butter: ', scipy.butter(N=4, Wn=wn, btype='bandstop', analog=False, output='ba'))
|
||||
#print('butter: ', scipy.butter(N=4, Wn=wn, btype='lowpass', analog=False, output='ba'))
|
||||
#print('butter: ', scipy.butter(N=4, Wn=wn, btype='highpass', analog=False, output='ba'))
|
||||
z = []
|
||||
p = np.array([-0.1564344650402309+0.9876883405951379j, -0.4539904997395468+0.8910065241883679j,
|
||||
-0.7071067811865476+0.7071067811865475j, -0.8910065241883679+0.4539904997395467j, -0.9876883405951379+0.1564344650402309j,
|
||||
-0.9876883405951379-0.1564344650402309j, -0.8910065241883679-0.4539904997395467j, -0.7071067811865476-0.7071067811865475j,
|
||||
-0.4539904997395468-0.8910065241883679j, -0.1564344650402309-0.9876883405951379j], dtype=np.complex)
|
||||
k = 1
|
||||
wo = 0.1886352115099219
|
||||
|
||||
print(scipy.lp2hp_zpk(z,p,k,wo))
|
||||
|
||||
z = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float)
|
||||
p = np.array([-0.02950904840030544-0.1863127990340476j, -0.08563859394186457-0.1680752041469931j,
|
||||
-0.1333852372292245-0.1333852372292244j, -0.1680752041469931-0.08563859394186453j, -0.1863127990340476-0.02950904840030543j,
|
||||
-0.1863127990340476+0.02950904840030543j, -0.1680752041469931+0.08563859394186453j, -0.1333852372292245+0.1333852372292244j,
|
||||
-0.08563859394186457+0.1680752041469931j, -0.02950904840030544+0.1863127990340476j], dtype=np.complex)
|
||||
k = 1.0
|
||||
fs = 2.0
|
||||
|
||||
print(scipy.bilinear_zpk(z,p,k,fs))
|
||||
|
||||
z = np.array([], dtype=np.float)
|
||||
p = np.array([-0.3826834323650898+0.9238795325112868j,
|
||||
-0.9238795325112868+0.3826834323650898j, -0.9238795325112868-0.3826834323650898j,
|
||||
-0.3826834323650898-0.9238795325112868j], dtype=np.complex)
|
||||
k = 1
|
||||
wo = 0.03141673402115484
|
||||
bw = 0.02221601345771878
|
||||
print(scipy.lp2bs_zpk(z, p, k, wo=wo, bw=bw))
|
||||
|
||||
print(scipy.butter(N=4, Wn=wn, btype='bandpass', analog=False, output='ba'))
|
||||
print(scipy.butter(N=4, Wn=wn, btype='bandpass', analog=False, output='zpk'))
|
||||
print(scipy.butter(N=4, Wn=wn, btype='bandpass', analog=False, output='sos'))
|
||||
print(scipy.butter(N=4, Wn=wn, btype='bandstop', analog=False, output='ba'))
|
||||
print(scipy.butter(10, 15, 'lp', fs=1000, output='sos'))
|
||||
print(scipy.butter(10, 15, 'hp', fs=1000, output='sos'))
|
||||
|
||||
|
|
|
|||
|
|
@ -1 +1,21 @@
|
|||
butter: (array([9.375938694653579e-10, 0.0, -3.750375477861432e-09, 0.0, 5.625563216792147e-09, 0.0, -3.750375477861432e-09, 0.0, 9.375938694653579e-10], dtype=float64), array([1.0, -7.969992171296993, 27.79137077985833, -55.37836320535709, 68.97098091968704, -54.97798122059838, 27.39096409669159, -7.798371663621388, 0.9713924646368845], dtype=float64))
|
||||
(array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=float64), array([-0.02950904840030542-0.1863127990340476j, -0.08563859394186453-0.1680752041469931j, -0.1333852372292245-0.1333852372292245j, -0.1680752041469931-0.08563859394186455j, -0.1863127990340476-0.02950904840030542j, -0.1863127990340476+0.02950904840030542j, -0.1680752041469931+0.08563859394186455j, -0.1333852372292245+0.1333852372292245j, -0.08563859394186453+0.1680752041469931j, -0.02950904840030542+0.1863127990340476j], dtype=complex), 1.0)
|
||||
(array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=float64), array([0.9811181553845975-0.09160115148355542j, 0.9547700993580936-0.08041542979283999j, 0.9334461398558574-0.06239272587315813j, 0.918541250895572-0.03941895649644052j, 0.9108945995807357-0.01346977255018339j, 0.9108945995807357+0.01346977255018339j, 0.918541250895572+0.03941895649644052j, 0.9334461398558574+0.06239272587315813j, 0.9547700993580936+0.08041542979283999j, 0.9811181553845975+0.09160115148355542j], dtype=complex), 0.73979400584056)
|
||||
(array([0.0+0.03141673402115484j, 0.0+0.03141673402115484j, 0.0+0.03141673402115484j, 0.0+0.03141673402115484j, 0.0-0.03141673402115484j, 0.0-0.03141673402115484j, 0.0-0.03141673402115484j, 0.0-0.03141673402115484j], dtype=complex), array([-0.002920960939069395+0.02254040782120927j, -0.008809831448862637+0.02578034941498454j, -0.008809831448862637-0.02578034941498454j, -0.002920960939069395-0.02254040782120927j, -0.005580739344399452-0.04306532794879095j, -0.01171508867871904-0.03428204969845339j, -0.01171508867871904+0.03428204969845339j, -0.005580739344399452+0.04306532794879095j], dtype=complex), 1.0)
|
||||
(array([9.375938694653579e-10, 0.0, -3.750375477861432e-09, 0.0, 5.625563216792147e-09, 0.0, -3.750375477861432e-09, 0.0, 9.375938694653579e-10], dtype=float64), array([1.0, -7.969992171296993, 27.79137077985833, -55.37836320535709, 68.97098091968704, -54.97798122059838, 27.39096409669159, -7.798371663621388, 0.9713924646368845], dtype=float64))
|
||||
(array([1.0+0.0j, 1.0+0.0j, 1.0+0.0j, 1.0+0.0j, -1.0+0.0j, -1.0+0.0j, -1.0+0.0j, -1.0+0.0j], dtype=complex), array([0.9984772174419884-0.01125340518638924j, 0.9955222362276896-0.01283305087503436j, 0.9955222362276896+0.01283305087503436j, 0.9984772174419884+0.01125340518638924j, 0.9969826844852829+0.02147022362342683j, 0.9940139474935357+0.01703981557421542j, 0.9940139474935357-0.01703981557421542j, 0.9969826844852829-0.02147022362342683j], dtype=complex), 9.375938694653579e-10)
|
||||
array([[9.375938694653579e-10, 1.875187738930716e-09, 9.375938694653579e-10, 1.0, -1.988027894987071, 0.9883540831264847],
|
||||
[1.0, 2.0, 1.0, 1.0, -1.991044472455379, 0.9912292100185409],
|
||||
[1.0, -2.0, 1.0, 1.0, -1.993965368970566, 0.9944354436659212],
|
||||
[1.0, -2.0, 1.0, 1.0, -1.996954434883977, 0.9970833928789848]], dtype=float64)
|
||||
(array([0.9855924434759455, -7.883766817056333, 27.59075239283292, -55.17858731338059, 68.97201858825612, -55.17858731338059, 27.59075239283291, -7.883766817056331, 0.9855924434759455], dtype=float64), array([1.0, -7.969992171296993, 27.79137077985833, -55.37836320535709, 68.97098091968704, -54.97798122059838, 27.39096409669159, -7.798371663621388, 0.9713924646368845], dtype=float64))
|
||||
array([[1.609898589131747e-13, 3.219797178263494e-13, 1.609898589131747e-13, 1.0, 0.0, 0.0],
|
||||
[1.0, 2.0, 1.0, 1.0, -1.821789199161471, 0.8299104063179026],
|
||||
[1.0, 2.0, 1.0, 1.0, -1.837082501791144, 0.8452718837280704],
|
||||
[1.0, 2.0, 1.0, 1.0, -1.866892279711715, 0.8752145482536838],
|
||||
[1.0, 2.0, 1.0, 1.0, -1.909540198716187, 0.918052583977031],
|
||||
[1.0, -1.0, 0.0, 1.0, -1.962236310769195, 0.9709836057783884]], dtype=float64)
|
||||
array([[0.73979400584056, -1.47958801168112, 0.73979400584056, 1.0, -1.821789199161471, 0.8299104063179026],
|
||||
[1.0, -2.0, 1.0, 1.0, -1.837082501791144, 0.8452718837280704],
|
||||
[1.0, -2.0, 1.0, 1.0, -1.866892279711715, 0.8752145482536838],
|
||||
[1.0, -2.0, 1.0, 1.0, -1.909540198716187, 0.918052583977031],
|
||||
[1.0, -2.0, 1.0, 1.0, -1.962236310769195, 0.9709836057783884]], dtype=float64)
|
||||
|
|
|
|||
0
test-common.sh
Normal file → Executable file
0
test-common.sh
Normal file → Executable file
18
test-snippets.sh
Executable file
18
test-snippets.sh
Executable file
|
|
@ -0,0 +1,18 @@
|
|||
#!/bin/sh
|
||||
set -e
|
||||
micropython="$1"
|
||||
for level1 in numpy scipy;
|
||||
do
|
||||
for level2 in core lib signal; do
|
||||
rm -f *.exp
|
||||
if ! env MICROPY_MICROPYTHON="$micropython" ./run-tests -d snippets/tests/"$level1"/"$level2"; then
|
||||
for exp in *.exp; do
|
||||
testbase=$(basename $exp .exp);
|
||||
echo -e "\nFAILURE $testbase";
|
||||
diff -u $testbase.exp $testbase.out;
|
||||
done
|
||||
exit 1
|
||||
fi
|
||||
done
|
||||
done
|
||||
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
import math
|
||||
|
||||
from ulab import numpy
|
||||
from ulab import numpy as np
|
||||
from ulab import scipy as spy
|
||||
|
||||
def to_tuple(a):
|
||||
result = []
|
||||
for x in a:
|
||||
result.append([x.real, x.imag])
|
||||
return result
|
||||
|
||||
def to_complex(a):
|
||||
result = np.array([], dtype=np.complex)
|
||||
for x in a:
|
||||
t = np.array([complex(x[0] + x[1] * 1j)], dtype=np.complex)
|
||||
result = np.concatenate((result, t), axis=0)
|
||||
return result
|
||||
|
||||
roots = np.array([0.99847722-0.01125341j, 0.99552224-0.01283305j, 0.99552224+0.01283305j, 0.99847722+0.01125341j,
|
||||
0.99698268+0.02147022j, 0.9940139500000001+0.01703982j, 0.9940139500000001-0.01703982j, 0.99698268-0.02147022j], dtype=np.complex)
|
||||
|
||||
print(roots)
|
||||
r = to_tuple(roots)
|
||||
s = sorted(r,key=lambda x:(x[0], x[1]))
|
||||
f = to_complex(s)
|
||||
|
||||
print(f)
|
||||
#print()
|
||||
#print(sorted(r,key=lambda x:(x[0], x[1])))
|
||||
|
||||
#print(np.sort_complex(np.array([-0.9800000000000001+1.0j, 0.9800000000000001-1.0j, 0.99+1.0j, 0.99-1.0j], dtype=np.complex)))
|
||||
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
|
|
@ -1,70 +0,0 @@
|
|||
import math
|
||||
|
||||
from ulab import numpy
|
||||
from ulab import numpy as np
|
||||
from ulab import scipy as spy
|
||||
|
||||
def asarray(a, dtype=None):
|
||||
if isinstance(a,(np.ndarray)):
|
||||
return a
|
||||
return a
|
||||
|
||||
def atleast_1d(*arys):
|
||||
res = []
|
||||
for ary in arys:
|
||||
ary = asarray(ary)
|
||||
if not isinstance(ary,(np.ndarray)):
|
||||
ary = np.array([ary])
|
||||
result = ary.reshape((1,))
|
||||
else:
|
||||
result = ary
|
||||
res.append(result)
|
||||
if len(res) == 1:
|
||||
return res[0]
|
||||
else:
|
||||
return res
|
||||
|
||||
def poly(seq_of_zeros):
|
||||
seq_of_zeros = atleast_1d(seq_of_zeros)
|
||||
sh = seq_of_zeros.shape
|
||||
|
||||
if len(sh) == 2 and sh[0] == sh[1] and sh[0] != 0:
|
||||
seq_of_zeros = eigvals(seq_of_zeros)
|
||||
elif len(sh) == 1:
|
||||
dt = seq_of_zeros.dtype
|
||||
# Let object arrays slip through, e.g. for arbitrary precision
|
||||
if dt != object:
|
||||
seq_of_zeros = seq_of_zeros #seq_of_zeros.astype(mintypecode(dt.char))
|
||||
else:
|
||||
raise ValueError("input must be 1d or non-empty square 2d array.")
|
||||
|
||||
if len(seq_of_zeros) == 0:
|
||||
return 1.0
|
||||
dt = seq_of_zeros.dtype
|
||||
print(dt)
|
||||
a = np.ones((1,), dtype=dt)
|
||||
print(a)
|
||||
print(seq_of_zeros)
|
||||
for k in range(len(seq_of_zeros)):
|
||||
a = np.convolve(a, np.array([1, -seq_of_zeros[k]], dtype=dt))
|
||||
print(a)
|
||||
#if issubclass(a.dtype.type, NX.complexfloating):
|
||||
# if complex roots are all complex conjugates, the roots are real.
|
||||
roots = asarray(seq_of_zeros, complex)
|
||||
print('roots',roots)
|
||||
p = np.sort_complex(roots)
|
||||
print(p)
|
||||
q = np.conjugate(roots)
|
||||
|
||||
s = np.sort_complex(q)
|
||||
print(s)
|
||||
#if NX.all(NX.sort(roots) == NX.sort(roots.conjugate())):
|
||||
a = a.real.copy()
|
||||
|
||||
return a
|
||||
|
||||
|
||||
p = np.array([-0.3826834323650898+0.9238795325112868j, -0.9238795325112868+0.3826834323650898j,
|
||||
-0.9238795325112868-0.3826834323650898j, -0.3826834323650898-0.9238795325112868j], dtype=np.complex)
|
||||
|
||||
print(poly(p))
|
||||
Loading…
Reference in a new issue