Initial commit

snippets/numpy/__init__.py

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+
+from . import core
+from .core import *
+from . import lib
+from .lib import *

snippets/numpy/core/__init__.py

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+
+from .multiarray import *
+from .numeric import *
+from .fromnumeric import *
+from .shape_base import *

snippets/numpy/core/fromnumeric.py

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+
+from .overrides import set_module
+from .multiarray import asarray
+
+@set_module('numpy')
+def prod(arr):
+    result = 1
+    for x in arr:
+        result = result * x
+    return result
+
+def size(a, axis=None):
+    """
+    Return the number of elements along a given axis.
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    axis : int, optional
+        Axis along which the elements are counted.  By default, give
+        the total number of elements.
+    Returns
+    -------
+    element_count : int
+        Number of elements along the specified axis.
+    See Also
+    --------
+    shape : dimensions of array
+    ndarray.shape : dimensions of array
+    ndarray.size : number of elements in array
+    Examples
+    --------
+    >>> a = np.array([[1,2,3],[4,5,6]])
+    >>> np.size(a)
+    6
+    >>> np.size(a,1)
+    3
+    >>> np.size(a,0)
+    2
+    """
+    if axis is None:
+        try:
+            return a.size
+        except AttributeError:
+            return asarray(a).size
+    else:
+        try:
+            return a.shape[axis]
+        except AttributeError:
+            return asarray(a).shape[axis]

snippets/numpy/core/multiarray.py

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+from ulab import numpy as np
+
+def asarray(a, dtype=None):
+    if isinstance(a,(np.ndarray)):
+        return a
+    try:
+        a = np.array(a)
+        return a
+    except Exception as e:
+        if "can't convert complex to float" in e.args:
+          try:
+             a = np.array(a, dtype=np.complex)
+             return a
+          except:
+              pass
+        raise ValueError('Could not cast %s to array' % (a))
+
+

snippets/numpy/core/numeric.py

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+
+from .multiarray import asarray
+

snippets/numpy/core/overrides.py

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+
+import sys
+
+def set_module(module):
+    """Decorator for overriding __module__ on a function or class.
+    Example usage::
+        @set_module('numpy')
+        def example():
+            pass
+        assert example.__module__ == 'numpy'
+    """
+    def decorator(func):
+        if module is not None:
+            sys.modules[func.__globals__['__name__']] = module
+        return func
+    return decorator

snippets/numpy/core/shape_base.py

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+from ulab import numpy as np
+from .multiarray import asarray
+
+def atleast_1d(*arys):
+    res = []
+    for ary in arys:
+        ary = asarray(ary)
+        if not isinstance(ary,(np.ndarray)):
+            result = ary.reshape((1,))
+        else:
+            result = ary
+        res.append(result)
+    if len(res) == 1:
+        return res[0]
+    else:
+        return res

snippets/numpy/lib/__init__.py

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+
+from .function_base import *
+from .polynomial import *

snippets/numpy/lib/function_base.py

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+from ulab import numpy as np
+from ..core.multiarray import asarray
+
+def append(arr, values, axis=None):
+    arr = asarray(arr)
+    if axis is None:
+        if len(arr.shape) != 1:
+            arr.flatten()
+        values.flatten()
+        axis = len(arr.shape)-1
+    return np.concatenate((arr, values), axis=axis)

snippets/numpy/lib/polynomial.py

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+
+from ..core import (atleast_1d, asarray)
+from ..core.overrides import set_module
+from ulab import numpy as np
+
+@set_module('numpy')
+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
+    a = np.ones((1,), dtype=dt)
+
+    for k in range(len(seq_of_zeros)):
+        a = np.convolve(a, np.array([1, -seq_of_zeros[k]], dtype=dt))
+
+    if a.dtype == np.complex:
+        # if complex roots are all complex conjugates, the roots are real.
+        roots = asarray(seq_of_zeros, complex)
+        p = np.sort_complex(roots)
+        c = np.real(p) - np.imag(p) * 1j
+        q = np.sort_complex(c)
+        if np.all(p == q):
+            a = a.real.copy()
+    return a

snippets/scipy/__init__.py

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+
+from . import signal
+from .signal import *

snippets/scipy/signal/__init__.py

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+
+from .filter_design import *

snippets/tests/numpy/core/fromnumeric.py

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+import math
+import sys
+sys.path.append('.')
+
+from snippets import numpy
+from ulab import numpy as np
+
+a = np.array([[1,2,3],[4,5,6]])
+print(numpy.size(a))
+print(numpy.size(a,1))
+print(numpy.size(a,0))
+
+print(numpy.prod([1, 10, 100, 5]))
+print(numpy.prod([]))
+print(numpy.prod([1.,2.]))
+
+

snippets/tests/numpy/core/fromnumeric.py.exp

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+6
+3
+2
+5000
+1
+2.0

snippets/tests/numpy/core/multiarray.py

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+import math
+import sys
+sys.path.append('.')
+
+from snippets import numpy
+
+print (numpy.asarray([1]))
+print (numpy.asarray([1.0, 2.0, 3j]))
+print (numpy.asarray([4, 3, 1, (2-2j), (2+2j), (2-1j), (2+1j), (2-1j), (2+1j), (1+1j), (1-1j)]))

snippets/tests/numpy/core/multiarray.py.exp

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+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)

snippets/tests/numpy/lib/function_base.py

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+from ulab import numpy as np
+from ..core.multiarray import asarray
+
+def append(arr, values, axis=None):
+    arr = asarray(arr)
+    if axis is None:
+        if len(arr.shape) != 1:
+            arr.flatten()
+        values.flatten()
+        axis = len(arr.shape)-1
+    return np.concatenate((arr, values), axis=axis)

snippets/tests/scipy/signal/filter_design.py

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+import math
+import sys
+sys.path.append('.')
+
+from snippets import scipy
+from ulab import numpy as np
+
+a = [4, 3, 1, 2-2j, 2+2j, 2-1j, 2+1j, 2-1j, 2+1j, 1+1j, 1-1j]
+print('_cplxreal: ', scipy.cplxreal(a))
+
+nyquistRate = 48000 * 2
+centerFrequency_Hz = 480.0
+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'))
+

snippets/tests/scipy/signal/filter_design.py.exp

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+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))