Python scipy.ndimage.convolve1d() Examples
The following are 30
code examples of scipy.ndimage.convolve1d().
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Example #1
| Source File: test_pulse_processing.py From strax with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def test_filter_waveforms():
"""Test that filter_records gives the same output
as a simple convolution applied to the original pulse
(before splitting into records)
"""
wv = np.random.randn(300)
ir = np.random.randn(41)
ir[10] += 10 # Because it crashes for max at edges
origin = np.argmax(ir) - (len(ir)//2)
wv_after = convolve1d(wv, ir,
mode='constant',
origin=origin)
wvs = wv.reshape(3, 100)
wvs = strax.filter_waveforms(
wvs, ir,
prev_r=np.array([strax.NO_RECORD_LINK, 0, 1]),
next_r=np.array([1, 2, strax.NO_RECORD_LINK]))
wv_after_2 = np.reshape(wvs, -1)
assert np.abs(wv_after - wv_after_2).sum() < 1e-9
Example #2
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_correlate01(self):
array = numpy.array([1, 2])
weights = numpy.array([2])
expected = [2, 4]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #3
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_correlate03(self):
array = numpy.array([1])
weights = numpy.array([1, 1])
expected = [2]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #4
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate03(self):
array = numpy.array([1])
weights = numpy.array([1, 1])
expected = [2]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #5
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate01(self):
array = numpy.array([1, 2])
weights = numpy.array([2])
expected = [2, 4]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #6
| Source File: toymodel.py From ctapipe with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def get_waveform(self, charge, time, n_samples):
"""Obtain the waveform toy model.
Parameters
----------
charge : ndarray
Amount of charge in each pixel
Shape: (n_pixels)
time : ndarray
The signal time in the waveform in nanoseconds
Shape: (n_pixels)
n_samples : int
Number of samples in the waveform
Returns
-------
waveform : ndarray
Toy model waveform
Shape (n_pixels, n_samples)
"""
n_pixels = charge.size
n_upsampled_samples = n_samples * self.upsampling
readout = np.zeros((n_pixels, n_upsampled_samples))
sample = (time / self.ref_width_ns).astype(np.int)
outofrange = (sample < 0) | (sample >= n_upsampled_samples)
sample[outofrange] = 0
charge[outofrange] = 0
readout[np.arange(n_pixels), sample] = charge
convolved = convolve1d(
readout, self.ref_interp_y, mode="constant", origin=self.origin
)
sampled = (
convolved.reshape(
(n_pixels, convolved.shape[-1] // self.upsampling, self.upsampling)
).sum(-1)
* self.ref_width_ns # Waveform units: p.e.
)
return sampled
Example #7
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate25(self):
weights = numpy.array([1, 2, 1])
tcor = [[4, 8, 12], [5, 10, 15]]
tcov = [[7, 14, 21], [8, 16, 24]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='nearest', output=output, origin=1)
assert_array_almost_equal(output, tcor)
ndimage.convolve1d(array, weights, axis=0,
mode='nearest', output=output, origin=1)
assert_array_almost_equal(output, tcov)
Example #8
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate24(self):
weights = numpy.array([1, 2, 1])
tcor = [[7, 14, 21], [8, 16, 24]]
tcov = [[4, 8, 12], [5, 10, 15]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='nearest', output=output, origin=-1)
assert_array_almost_equal(output, tcor)
ndimage.convolve1d(array, weights, axis=0,
mode='nearest', output=output, origin=-1)
assert_array_almost_equal(output, tcov)
Example #9
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate23(self):
weights = numpy.array([1, 2, 1])
expected = [[5, 10, 15], [7, 14, 21]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='nearest', output=output)
assert_array_almost_equal(output, expected)
ndimage.convolve1d(array, weights, axis=0,
mode='nearest', output=output)
assert_array_almost_equal(output, expected)
Example #10
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate22(self):
weights = numpy.array([1, 2, 1])
expected = [[6, 12, 18], [6, 12, 18]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='wrap', output=output)
assert_array_almost_equal(output, expected)
ndimage.convolve1d(array, weights, axis=0,
mode='wrap', output=output)
assert_array_almost_equal(output, expected)
Example #11
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate21(self):
array = numpy.array([[1, 2, 3],
[2, 4, 6]])
expected = [[5, 10, 15], [7, 14, 21]]
weights = numpy.array([1, 2, 1])
output = ndimage.correlate1d(array, weights, axis=0)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights, axis=0)
assert_array_almost_equal(output, expected)
Example #12
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate17(self):
array = numpy.array([1, 2, 3])
tcor = [3, 5, 6]
tcov = [2, 3, 5]
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel, origin=-1)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve(array, kernel, origin=-1)
assert_array_almost_equal(tcov, output)
output = ndimage.correlate1d(array, kernel, origin=-1)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve1d(array, kernel, origin=-1)
assert_array_almost_equal(tcov, output)
Example #13
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate09(self):
array = []
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(array, output)
Example #14
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate08(self):
array = numpy.array([1, 2, 3])
tcor = [1, 2, 5]
tcov = [3, 6, 7]
weights = numpy.array([1, 2, -1])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, tcov)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, tcov)
Example #15
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate07(self):
array = numpy.array([1, 2, 3])
expected = [5, 8, 11]
weights = numpy.array([1, 2, 1])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #16
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate06(self):
array = numpy.array([1, 2, 3])
tcor = [9, 14, 17]
tcov = [7, 10, 15]
weights = numpy.array([1, 2, 3])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, tcov)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, tcov)
Example #17
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate04(self):
array = numpy.array([1, 2])
tcor = [2, 3]
tcov = [3, 4]
weights = numpy.array([1, 1])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, tcov)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, tcov)
Example #18
| Source File: utils.py From PyFNND with GNU General Public License v3.0 | 5 votes |
|
def boxcar(F, dt=0.02, avg_win=1.0):
orig_shape = F.shape
F = np.atleast_2d(F)
npix, nt = F.shape
win_len = max(1, avg_win / dt)
win = np.ones(win_len) / win_len
Fsmooth = ndimage.convolve1d(F, win, axis=1, mode='reflect')
return Fsmooth.reshape(orig_shape)
Example #19
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate02(self):
array = numpy.array([1, 2, 3])
kernel = numpy.array([1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(array, output)
Example #20
| Source File: test_savitzky_golay.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_sg_coeffs_exact():
polyorder = 4
window_length = 9
halflen = window_length // 2
x = np.linspace(0, 21, 43)
delta = x[1] - x[0]
# The data is a cubic polynomial. We'll use an order 4
# SG filter, so the filtered values should equal the input data
# (except within half window_length of the edges).
y = 0.5 * x ** 3 - x
h = savgol_coeffs(window_length, polyorder)
y0 = convolve1d(y, h)
assert_allclose(y0[halflen:-halflen], y[halflen:-halflen])
# Check the same input, but use deriv=1. dy is the exact result.
dy = 1.5 * x ** 2 - 1
h = savgol_coeffs(window_length, polyorder, deriv=1, delta=delta)
y1 = convolve1d(y, h)
assert_allclose(y1[halflen:-halflen], dy[halflen:-halflen])
# Check the same input, but use deriv=2. d2y is the exact result.
d2y = 3.0 * x
h = savgol_coeffs(window_length, polyorder, deriv=2, delta=delta)
y2 = convolve1d(y, h)
assert_allclose(y2[halflen:-halflen], d2y[halflen:-halflen])
Example #21
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate25(self):
weights = numpy.array([1, 2, 1])
tcor = [[4, 8, 12], [5, 10, 15]]
tcov = [[7, 14, 21], [8, 16, 24]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='nearest', output=output, origin=1)
assert_array_almost_equal(output, tcor)
ndimage.convolve1d(array, weights, axis=0,
mode='nearest', output=output, origin=1)
assert_array_almost_equal(output, tcov)
Example #22
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate24(self):
weights = numpy.array([1, 2, 1])
tcor = [[7, 14, 21], [8, 16, 24]]
tcov = [[4, 8, 12], [5, 10, 15]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='nearest', output=output, origin=-1)
assert_array_almost_equal(output, tcor)
ndimage.convolve1d(array, weights, axis=0,
mode='nearest', output=output, origin=-1)
assert_array_almost_equal(output, tcov)
Example #23
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate23(self):
weights = numpy.array([1, 2, 1])
expected = [[5, 10, 15], [7, 14, 21]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='nearest', output=output)
assert_array_almost_equal(output, expected)
ndimage.convolve1d(array, weights, axis=0,
mode='nearest', output=output)
assert_array_almost_equal(output, expected)
Example #24
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate22(self):
weights = numpy.array([1, 2, 1])
expected = [[6, 12, 18], [6, 12, 18]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
mode='wrap', output=output)
assert_array_almost_equal(output, expected)
ndimage.convolve1d(array, weights, axis=0,
mode='wrap', output=output)
assert_array_almost_equal(output, expected)
Example #25
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate20(self):
weights = numpy.array([1, 2, 1])
expected = [[5, 10, 15], [7, 14, 21]]
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[2, 4, 6]], type1)
for type2 in self.types:
output = numpy.zeros((2, 3), type2)
ndimage.correlate1d(array, weights, axis=0,
output=output)
assert_array_almost_equal(output, expected)
ndimage.convolve1d(array, weights, axis=0,
output=output)
assert_array_almost_equal(output, expected)
Example #26
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate17(self):
array = numpy.array([1, 2, 3])
tcor = [3, 5, 6]
tcov = [2, 3, 5]
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel, origin=-1)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve(array, kernel, origin=-1)
assert_array_almost_equal(tcov, output)
output = ndimage.correlate1d(array, kernel, origin=-1)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve1d(array, kernel, origin=-1)
assert_array_almost_equal(tcov, output)
Example #27
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate09(self):
array = []
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(array, output)
Example #28
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate08(self):
array = numpy.array([1, 2, 3])
tcor = [1, 2, 5]
tcov = [3, 6, 7]
weights = numpy.array([1, 2, -1])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, tcov)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, tcov)
Example #29
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate07(self):
array = numpy.array([1, 2, 3])
expected = [5, 8, 11]
weights = numpy.array([1, 2, 1])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #30
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate05(self):
array = numpy.array([1, 2, 3])
tcor = [2, 3, 5]
tcov = [3, 5, 6]
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(tcov, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(tcov, output)
