| /cts/apps/CameraITS/pymodules/its/ |
| dng.py | 15 import numpy
16 import numpy.linalg
46 CM: The 3x3 ColorMatrix for the specified illuminant, as a numpy array
47 FM: The 3x3 ForwardMatrix for the specified illuminant, as a numpy array
55 W = numpy.array([
62 HH = numpy.array([
73 H_D65 = numpy.array([
77 H_A = numpy.array([
89 G = numpy.array([[gains[0],0,0], [0,gains[1],0], [0,0,gains[3]]])
92 S = numpy.array([ccm[0:3], ccm[3:6], ccm[6:9]]
[all...] |
| image.py | 21 import numpy
28 DEFAULT_YUV_TO_RGB_CCM = numpy.matrix([
33 DEFAULT_YUV_OFFSETS = numpy.array([0, 128, 128])
35 DEFAULT_GAMMA_LUT = numpy.array(
39 DEFAULT_INVGAMMA_LUT = numpy.array(
101 img = numpy.ndarray(shape=(2*h*w*4,), dtype='<f', buffer=cap["data"])
136 img: A raw-10 image, as a uint8 numpy array.
139 Image as a uint16 numpy array, with all row padding stripped.
146 msbs = numpy.delete(img, numpy.s_[4::5], 1
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| math_utils_test.py | 21 import numpy
40 numpy.random.seed(10)
43 transition = numpy.random.normal(size=[4, 4]).astype(numpy.float32)
44 addition = numpy.random.normal(size=[4, 4]).astype(numpy.float32)
47 transition_power = numpy.identity(4)
48 running_sum = numpy.zeros([4, 4], dtype=numpy.float32)
51 current_contribution = numpy.dot(numpy.dot(transition_power, addition)
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| /cts/suite/audio_quality/test_description/conf/ |
| check_conf.py | 18 import numpy as np
20 from numpy import *
|
| /external/walt/pywalt/pywalt/ |
| minimization.py | 23 import numpy
30 laser_data = numpy.loadtxt(fname_laser)
40 p = numpy.polyfit(x, y, 1, full=True)
52 shifts = numpy.arange(min_shift, max_shift, step)
56 side = ((numpy.arange(len(tl)) + 1) / 2) % 2
62 yl = numpy.interp(tl + shift, ty, y)
63 xl = numpy.interp(tl + shift, tx, x)
69 best_shift0 = shifts[numpy.argmin(residuals0)]
70 best_shift1 = shifts[numpy.argmin(residuals1)]
90 if numpy.std(x)*2 < numpy.std(y)
[all...] |
| screen_stats.py | 1 import numpy
6 sensor_data = numpy.loadtxt(sensor_file_name)
7 blinker_data = numpy.loadtxt(blinker_file_name)
42 (numpy.median(dt_even), numpy.std(dt_even)))
44 (numpy.median(dt_odd), numpy.std(dt_odd)))
|
| /external/tensorflow/tensorflow/contrib/periodic_resample/python/kernel_tests/ |
| periodic_resample_op_test.py | 21 import numpy
34 input_tensor = numpy.arange(12).reshape((3, 4))
35 desired_shape = numpy.array([6, None])
45 input_tensor = numpy.arange(12).reshape((3, 4))
46 desired_shape = numpy.array([5, None])
56 input_tensor = numpy.arange(2 * 2 * 4).reshape((2, 2, 4))
57 desired_shape = numpy.array([4, 4, None])
58 output_tensor = numpy.array([[[0], [2], [4], [6]], [[1], [3], [5], [7]],
74 input_tensor = numpy.arange(2 * 2 * 2 * 8).reshape((2, 2, 2, 8))
75 desired_shape = numpy.array([4, 4, 4, None]
[all...] |
| /test/vts/script/ |
| download-pypi-packages.sh | 33 pip download -d $VTS_PYPI_PATH -r $ANDROID_BUILD_TOP/test/vts/script/pip_requirements.txt --no-binary protobuf,grpcio,numpy,Pillow,scipy
40 pip download -d $VTS_PYPI_PATH matplotlib --no-binary matplotlib,numpy
|
| /external/autotest/client/cros/cellular/mbim_compliance/ |
| mbim_data_channel.py | 6 import numpy
68 numpy.set_printoptions(formatter={'int':lambda x: hex(int(x))},
72 written, ntb_length, numpy.array(ntb))
98 numpy.set_printoptions(formatter={'int':lambda x: hex(int(x))},
101 ntb_length, numpy.array(ntb))
|
| /cts/apps/CameraITS/tests/scene0/ |
| test_gyro_bias.py | 25 import numpy
51 times = numpy.array([(e["time"] - gyro_events[0]["time"])/1000000000.0
53 xs = numpy.array([e["x"] for e in gyro_events])
54 ys = numpy.array([e["y"] for e in gyro_events])
55 zs = numpy.array([e["z"] for e in gyro_events])
74 assert(numpy.var(samples) < VAR_THRESH)
|
| /external/webrtc/tools/cpu/ |
| cpu_mon.py | 15 import numpy
32 (self.label, numpy.average(self.samples),
33 numpy.median(self.samples),
34 numpy.min(self.samples), numpy.max(self.samples)))
37 return numpy.max(self.samples)
|
| /cts/apps/CameraITS/tests/inprog/ |
| test_black_level.py | 22 import numpy
69 its.image.write_image(numpy.absolute(uimg - 0.5) * 2,
75 yhist,_ = numpy.histogram(yimg*255, 256, (0,256))
76 ymodes.append(numpy.argmax(yhist))
77 uhist,_ = numpy.histogram(uimg*255, 256, (0,256))
78 umodes.append(numpy.argmax(uhist))
79 vhist,_ = numpy.histogram(vimg*255, 256, (0,256))
80 vmodes.append(numpy.argmax(vhist))
|
| test_param_black_level_lock.py | 22 import numpy
58 hist,_ = numpy.histogram(yimg*255, 256, (0,256))
59 modes.append(numpy.argmax(hist))
|
| /cts/apps/CameraITS/tests/scene5/ |
| test_lens_shading_and_color_uniformity.py | 20 import numpy
41 SPB_CT_LIST = numpy.arange(spb_r, 1/2., spb_r*2)
85 img_legend_ls = numpy.copy(img_rgb)
86 img_legend_ufmt = numpy.copy(img_rgb)
98 center_y = numpy.mean(img_y[top:bottom, left:right])
99 center_r_g = numpy.mean(r_g[top:bottom, left:right])
100 center_b_g = numpy.mean(b_g[top:bottom, left:right])
123 num_sample = int(numpy.asscalar((1-spb_ct*2)/spb_r/2 + 1))
124 ct_cord_x = numpy.concatenate(
125 (numpy.arange(spb_ct, 1-spb_ct+spb_r, spb_r*2)
[all...] |
| /cts/apps/CameraITS/tests/inprog/scene2/ |
| test_dng_tags.py | 19 import numpy
51 print "HAL reported gains:\n", numpy.array(gains)
52 print "HAL reported ccm:\n", numpy.array(ccm).reshape(3,3)
53 print "HAL reported cal:\n", numpy.array(cal).reshape(3,3)
69 cm_ref = numpy.array(its.objects.rational_to_float(
71 fm_ref = numpy.array(its.objects.rational_to_float(
73 asn_ref = numpy.array(its.objects.rational_to_float(
|
| /external/tensorflow/tensorflow/contrib/py2tf/utils/ |
| tensor_list_test.py | 36 self.assertEqual(l.count().numpy(), 1)
38 self.assertEqual(l.count().numpy(), 2)
40 self.assertEqual(l.count().numpy(), 1)
42 self.assertEqual(l.count().numpy(), 0)
43 self.assertEqual(a.numpy(), a2.numpy())
51 self.assertEqual(l[0].numpy(), a.numpy())
53 self.assertEqual(l[0].numpy(), b.numpy())
[all...] |
| type_check_test.py | 21 import numpy
39 self.assertFalse(type_check.is_tensor(numpy.eye(3)))
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/ |
| mnist.py | 23 import numpy
36 dt = numpy.dtype(numpy.uint32).newbyteorder('>')
37 return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
41 """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
47 data: A 4D uint8 numpy array [index, y, x, depth].
63 data = numpy.frombuffer(buf, dtype=numpy.uint8)
71 index_offset = numpy.arange(num_labels) * num_classes
72 labels_one_hot = numpy.zeros((num_labels, num_classes)
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/examples/ |
| multivariate.py | 28 import numpy
72 numpy.random.seed(1) # Make the example a bit more deterministic
81 next_sample = numpy.random.multivariate_normal(
83 mean=numpy.squeeze(current_prediction["mean"], axis=[0, 1]),
84 cov=numpy.squeeze(current_prediction["covariance"], axis=[0, 1]))
100 all_observations = numpy.squeeze(numpy.concatenate(values, axis=1), axis=0)
101 all_times = numpy.squeeze(numpy.concatenate(times, axis=1), axis=0)
|
| /external/autotest/client/cros/audio/ |
| audio_analysis.py | 8 import numpy
35 @returns: A numpy array containing normalized signal. The normalized signal
39 signal = numpy.array(signal)
74 signal_rms = numpy.linalg.norm(signal) / numpy.sqrt(len(signal))
87 y_conv_w = signal * numpy.hanning(len(signal))
93 y_f = 2.0 / length * numpy.fft.rfft(y_conv_w)
96 abs_y_f = numpy.abs(y_f)
124 @returns: A numpy array containing frequency corresponding to
125 numpy.fft.rfft result at each index
[all...] |
| audio_analysis_unittest.py | 3 import numpy
14 numpy.random.seed(0)
75 array = numpy.random.uniform(0, 1, 1000000)
93 noise = numpy.random.standard_normal(samples) * 0.005
94 x = numpy.linspace(0.0, (samples - 1) * 1.0 / rate, samples)
95 y = (coeff_1 * numpy.sin(freq_1 * 2.0 * numpy.pi * x) +
96 coeff_2 * numpy.sin(freq_2 * 2.0 * numpy.pi * x)) + noise
135 noise = numpy.random.standard_normal(samples) * noise_amplitud
[all...] |
| /tools/test/connectivity/acts/framework/acts/test_utils/audio_analysis_lib/ |
| audio_analysis.py | 19 import numpy
62 A numpy array containing normalized signal. The normalized signal has
66 signal = numpy.array(signal)
106 signal_rms = numpy.linalg.norm(signal) / numpy.sqrt(len(signal))
119 y_conv_w = signal * numpy.hanning(len(signal))
125 y_f = 2.0 / length * numpy.fft.rfft(y_conv_w)
128 abs_y_f = numpy.abs(y_f)
159 A numpy array containing frequency corresponding to numpy.fft.rff
[all...] |
| /tools/test/connectivity/acts/framework/tests/ |
| audio_analysis_unittest.py | 18 import numpy
29 numpy.random.seed(0)
93 array = numpy.random.uniform(0, 1, 1000000)
110 noise = numpy.random.standard_normal(int(samples)) * 0.005
111 x = numpy.linspace(0.0, (samples - 1) * 1.0 / rate, samples)
112 y = (coeff_1 * numpy.sin(freq_1 * 2.0 * numpy.pi * x) + coeff_2 *
113 numpy.sin(freq_2 * 2.0 * numpy.pi * x)) + noise
151 noise = numpy.random.standard_normal(int(samples)) * noise_amplitud
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/ |
| datasets_test.py | 21 import numpy as np
41 got.append(t.numpy())
46 self.assertEqual(0, iterator.get_next().numpy())
47 self.assertEqual(1, iterator.get_next().numpy())
48 self.assertEqual(2, iterator.get_next().numpy())
49 self.assertEqual(3, iterator.get_next().numpy())
57 got = [x.numpy() for x in it1]
60 got = [x.numpy() for x in it2]
71 got = (x[0].numpy(), (x[1][0].numpy(), x[1][1].numpy())
[all...] |
| saver_test.py | 56 self.assertEqual(v1.read_value().numpy(), 2.0)
59 self.assertEqual(v1.read_value().numpy(), 1.0)
100 self.assertEqual(v1.read_value().numpy(), 2.0)
101 self.assertEqual(v2.read_value().numpy(), 2.0)
104 self.assertEqual(v1.read_value().numpy(), 1.0)
105 self.assertEqual(v1.read_value().numpy(), 1.0)
116 self.assertEqual(v3.read_value().numpy(), 1.0)
117 self.assertEqual(v4.read_value().numpy(), 1.0)
135 self.assertEqual(ret.numpy(), 1.0)
164 2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
[all...] |
