Python numpy.left_shift() Examples
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Example #1
Source File: led.py From audio-reactive-led-strip with MIT License | 6 votes |
def _update_pi(): """Writes new LED values to the Raspberry Pi's LED strip Raspberry Pi uses the rpi_ws281x to control the LED strip directly. This function updates the LED strip with new values. """ global pixels, _prev_pixels # Truncate values and cast to integer pixels = np.clip(pixels, 0, 255).astype(int) # Optional gamma correction p = _gamma[pixels] if config.SOFTWARE_GAMMA_CORRECTION else np.copy(pixels) # Encode 24-bit LED values in 32 bit integers r = np.left_shift(p[0][:].astype(int), 8) g = np.left_shift(p[1][:].astype(int), 16) b = p[2][:].astype(int) rgb = np.bitwise_or(np.bitwise_or(r, g), b) # Update the pixels for i in range(config.N_PIXELS): # Ignore pixels if they haven't changed (saves bandwidth) if np.array_equal(p[:, i], _prev_pixels[:, i]): continue #strip._led_data[i] = rgb[i] strip._led_data[i] = int(rgb[i]) _prev_pixels = np.copy(p) strip.show()
Example #2
Source File: LCD_1in44.py From Piano-LED-Visualizer with MIT License | 6 votes |
def LCD_ShowImage(self,Image,Xstart,Ystart): if (Image == None): return imwidth, imheight = Image.size if imwidth != self.width or imheight != self.height: raise ValueError('Image must be same dimensions as display \ ({0}x{1}).' .format(self.width, self.height)) img = np.asarray(Image) pix = np.zeros((self.width,self.height,2), dtype = np.uint8) pix[...,[0]] = np.add(np.bitwise_and(img[...,[0]],0xF8),np.right_shift(img[...,[1]],5)) pix[...,[1]] = np.add(np.bitwise_and(np.left_shift(img[...,[1]],3),0xE0),np.right_shift(img[...,[2]],3)) pix = pix.flatten().tolist() self.LCD_SetWindows(0, 0, self.width , self.height) GPIO.output(LCD_Config.LCD_DC_PIN, GPIO.HIGH) for i in range(0,len(pix),4096): LCD_Config.SPI_Write_Byte(pix[i:i+4096])
Example #3
Source File: test_ufunc.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #4
Source File: led.py From Systematic-LEDs with MIT License | 6 votes |
def _update_pi(): """Writes new LED values to the Raspberry Pi's LED strip Raspberry Pi uses the rpi_ws281x to control the LED strip directly. This function updates the LED strip with new values. """ global pixels, _prev_pixels # Truncate values and cast to integer pixels = np.clip(pixels, 0, 255).astype(int) # Optional gamma correction p = _gamma[pixels] if config.settings["configuration"]["SOFTWARE_GAMMA_CORRECTION"] else np.copy(pixels) # Encode 24-bit LED values in 32 bit integers r = np.left_shift(p[0][:].astype(int), 8) g = np.left_shift(p[1][:].astype(int), 16) b = p[2][:].astype(int) rgb = np.bitwise_or(np.bitwise_or(r, g), b) # Update the pixels for i in range(config.settings["configuration"]["N_PIXELS"]): # Ignore pixels if they haven't changed (saves bandwidth) if np.array_equal(p[:, i], _prev_pixels[:, i]): continue strip._led_data[i] = rgb[i] _prev_pixels = np.copy(p) strip.show()
Example #5
Source File: devices.py From Systematic-LEDs with MIT License | 6 votes |
def show(self, pixels): """Writes new LED values to the Raspberry Pi's LED strip Raspberry Pi uses the rpi_ws281x to control the LED strip directly. This function updates the LED strip with new values. """ # Truncate values and cast to integer n_pixels = pixels.shape[1] pixels = pixels.clip(0, 255).astype(int) # Optional gamma correction pixels = _GAMMA_TABLE[pixels] # Encode 24-bit LED values in 32 bit integers r = np.left_shift(pixels[0][:].astype(int), 8) g = np.left_shift(pixels[1][:].astype(int), 16) b = pixels[2][:].astype(int) rgb = np.bitwise_or(np.bitwise_or(r, g), b) # Update the pixels for i in range(n_pixels): self.strip.setPixelColor(i, neopixel.Color(rgb[i])) self.strip.show()
Example #6
Source File: test_ufunc.py From coffeegrindsize with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a)
Example #7
Source File: output.py From music_led_strip_control with MIT License | 6 votes |
def show(self, output_array): import _rpi_ws281x as ws # pylint: disable=import-error # Typecast the array to int output_array = output_array.clip(0, 255).astype(int) # sort the colors. grb g = np.left_shift(output_array[1][:].astype(int), 16) # pylint: disable=assignment-from-no-return r = np.left_shift(output_array[0][:].astype(int), 8) # pylint: disable=assignment-from-no-return b = output_array[2][:].astype(int) rgb = np.bitwise_or(np.bitwise_or(r, g), b).astype(int) # You can only use ws2811_leds_set with the custom version. #ws.ws2811_leds_set(self.channel, rgb) for i in range(self._led_count): ws.ws2811_led_set(self.channel, i, rgb[i].item()) resp = ws.ws2811_render(self._leds) if resp != ws.WS2811_SUCCESS: message = ws.ws2811_get_return_t_str(resp) raise RuntimeError('ws2811_render failed with code {0} ({1})'.format(resp, message))
Example #8
Source File: test_ufunc.py From mxnet-lambda with Apache License 2.0 | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #9
Source File: test_ufunc.py From pySINDy with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #10
Source File: test_ufunc.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a)
Example #11
Source File: test_ufunc.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #12
Source File: test_op_level4.py From incubator-tvm with Apache License 2.0 | 6 votes |
def test_binary_int_broadcast_1(): for op, ref in [(relay.right_shift, np.right_shift), (relay.left_shift, np.left_shift)]: x = relay.var("x", relay.TensorType((10, 4), "int32")) y = relay.var("y", relay.TensorType((5, 10, 1), "int32")) z = op(x, y) zz = run_infer_type(z) assert zz.checked_type == relay.TensorType((5, 10, 4), "int32") if ref is not None: x_shape = (10, 4) y_shape = (5, 10, 1) t1 = relay.TensorType(x_shape, 'int32') t2 = relay.TensorType(y_shape, 'int32') x_data = np.random.randint(1, 10000, size=(x_shape)).astype(t1.dtype) y_data = np.random.randint(1, 31, size=(y_shape)).astype(t2.dtype) func = relay.Function([x, y], z) ref_res = ref(x_data, y_data) for target, ctx in ctx_list(): intrp = relay.create_executor("graph", ctx=ctx, target=target) op_res = intrp.evaluate(func)(x_data, y_data) tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
Example #13
Source File: test_ufunc.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a)
Example #14
Source File: test_ufunc.py From vnpy_crypto with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #15
Source File: test_ufunc.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #16
Source File: test_ufunc.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #17
Source File: test_ufunc.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #18
Source File: test_ufunc.py From keras-lambda with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #19
Source File: display.py From push2-python with MIT License | 5 votes |
def rgb565_to_bgr565(rgb565_frame): r_filter = int('1111100000000000', 2) g_filter = int('0000011111100000', 2) b_filter = int('0000000000011111', 2) frame_r_filtered = numpy.bitwise_and(rgb565_frame, r_filter) frame_r_shifted = numpy.right_shift(frame_r_filtered, 11) # Shift bits so R compoenent goes to the right frame_g_filtered = numpy.bitwise_and(rgb565_frame, g_filter) frame_g_shifted = frame_g_filtered # No need to shift green, it stays in the same position frame_b_filtered = numpy.bitwise_and(rgb565_frame, b_filter) frame_b_shifted = numpy.left_shift(frame_b_filtered, 11) # Shift bits so B compoenent goes to the left return frame_r_shifted + frame_g_shifted + frame_b_shifted # Combine all channels # Non-vectorized function for converting from rgb to bgr565
Example #20
Source File: readDatFiles.py From tierpsy-tracker with MIT License | 5 votes |
def __init__(self, dirName): self.fid = dirName if not os.path.exists(self.fid): print('Error: Directory (%s) does not exist.' % self.fid) exit() self.files = glob.glob(os.path.join(self.fid, '*.dat')) # TODO: figure out how to really do this. This file order works half of the time # get the order of the frames from the file name. file_num_str = [os.path.split(x)[1].partition('spool')[ 0] for x in self.files] # first we assume that the filename contains the frame number 00001, # 00002, 00003 self.dat_order = sorted([int(x) for x in file_num_str]) # check in the indexes in the file order are really continuous. The # ordered index should go 1, 2, 3, 4 is_continous = all(np.diff(self.dat_order) == 1) if not is_continous: # the file name can contain the image number as an inverted string, # e.g. 6100000 -> 0000016 self.dat_order = sorted([int(x[::-1]) for x in file_num_str]) # check again in the indexes in the file order are really # continuous. This will throw and error if it is not the case assert all(np.diff(self.dat_order) == 1) # It seems that the last 40 bytes of each file are the header (it # contains zeros and the size of the image 2080*2156) bin_dat = np.fromfile(self.files[0], np.uint8) header = bin_dat[-40:].astype(np.uint16) header = np.left_shift(header[1::2], 8) + header[0::2] im_size = header[14:16] self.height = im_size[1] self.width = im_size[0] self.dtype = np.uint16 self.num_frames = len(self.dat_order) # initialize pointer for frames self.curr_frame = -1
Example #21
Source File: column.py From Carnets with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _wrapx(input, output, repeat): """ Wrap the X format column Boolean array into an ``UInt8`` array. Parameters ---------- input input Boolean array of shape (`s`, `repeat`) output output ``Uint8`` array of shape (`s`, `nbytes`) repeat number of bits """ output[...] = 0 # reset the output nbytes = ((repeat - 1) // 8) + 1 unused = nbytes * 8 - repeat for i in range(nbytes): _min = i * 8 _max = min((i + 1) * 8, repeat) for j in range(_min, _max): if j != _min: np.left_shift(output[..., i], 1, output[..., i]) np.add(output[..., i], input[..., j], output[..., i]) # shift the unused bits np.left_shift(output[..., i], unused, output[..., i])
Example #22
Source File: test_target_codegen_llvm.py From incubator-tvm with Apache License 2.0 | 5 votes |
def np_bf162np_float(arr): ''' Convert a numpy array of bf16 (uint16) to a numpy array of float''' u32 = np.left_shift(arr.astype('uint32'), 16) return u32.view('<f4')
Example #23
Source File: test_topi_broadcast.py From incubator-tvm with Apache License 2.0 | 5 votes |
def test_shift(): # explicit specify the output type verify_broadcast_binary_ele( (2, 1, 2), None, topi.right_shift, np.right_shift, dtype="int32", rhs_min=0, rhs_max=32) verify_broadcast_binary_ele( (1, 2, 2), (2,), topi.left_shift, np.left_shift, dtype="int32", rhs_min=0, rhs_max=32) verify_broadcast_binary_ele( (1, 2, 2), (2,), topi.left_shift, np.left_shift, dtype="int8", rhs_min=0, rhs_max=32)
Example #24
Source File: seqc.py From qupulse with MIT License | 5 votes |
def to_csv_compatible_table(self): """The integer values in that file should be 18-bit unsigned integers with the two least significant bits being the markers. The values are mapped to 0 => -FS, 262143 => +FS, with FS equal to the full scale. >>> np.savetxt(waveform_dir, binary_waveform.to_csv_compatible_table(), fmt='%u') """ table = np.zeros((len(self), 2), dtype=np.uint32) table[:, 0] = self.ch1 table[:, 1] = self.ch2 np.left_shift(table, 2, out=table) table[:, 0] += self.markers_ch1 table[:, 1] += self.markers_ch2 return table
Example #25
Source File: readDatFiles.py From tierpsy-tracker with MIT License | 5 votes |
def read(self): self.curr_frame += 1 if self.curr_frame < self.num_frames: fname = self.files[self.dat_order[self.curr_frame]] # is this indexing correct, or do we need to shift down by one? bin_dat = np.fromfile(fname, np.uint8) # every 3 bytes will correspond two pixel levels. D1 = bin_dat[:-40:3] D2 = bin_dat[1:-40:3] D3 = bin_dat[2:-40:3] # the image format is mono 12 packed (see web) # the first and third bytes represent the higher bits of the pixel intensity # while the second byte is divided into the lower bits. D1s = np.left_shift(D1.astype(np.uint16), 4) + \ np.bitwise_and(D2, 15) D3s = np.left_shift(D3.astype(np.uint16), 4) + \ np.right_shift(D2, 4) # the pixels seemed to be organized in this order image_decoded = np.zeros((self.height, self.width), np.uint16) image_decoded[::-1, -2::-2] = D3s.reshape((self.height, -1)) image_decoded[::-1, ::-2] = D1s.reshape((self.height, -1)) return (1, image_decoded) else: return (0, [], [], [])
Example #26
Source File: payload.py From baseband with GNU General Public License v3.0 | 5 votes |
def decode_4bit(words): """Decode 4-bit data. For a given int8 byte containing bits 76543210, the first sample is in 3210, the second in 7654, and both are interpreted as signed 4-bit integers. """ # left_shift(byte[:,np.newaxis], shift40): [3210xxxx, 76543210] split = np.left_shift(words[:, np.newaxis], shift40).ravel() # right_shift(..., 4): [33333210, 77777654] # so least significant bits go first. split >>= 4 return split.astype(np.float32)
Example #27
Source File: photometric.py From mmcv with Apache License 2.0 | 5 votes |
def posterize(img, bits): """Posterize an image (reduce the number of bits for each color channel) Args: img (ndarray): Image to be posterized. bits (int): Number of bits (1 to 8) to use for posterizing. Returns: ndarray: The posterized image. """ shift = 8 - bits img = np.left_shift(np.right_shift(img, shift), shift) return img
Example #28
Source File: test_topi_broadcast.py From training_results_v0.6 with Apache License 2.0 | 5 votes |
def test_shift(): # explicit specify the output type verify_broadcast_binary_ele( (2, 1, 2), None, topi.right_shift, np.right_shift, dtype="int32", rhs_min=0, rhs_max=32) verify_broadcast_binary_ele( (1, 2, 2), (2,), topi.left_shift, np.left_shift, dtype="int32", rhs_min=0, rhs_max=32) verify_broadcast_binary_ele( (1, 2, 2), (2,), topi.left_shift, np.left_shift, dtype="int8", rhs_min=0, rhs_max=32)
Example #29
Source File: spcreader.py From FRETBursts with GNU General Public License v2.0 | 5 votes |
def load_spc(fname): """Load data from Becker&Hickl SPC files. Returns: 3 numpy arrays: timestamps, detector, nanotime """ spc_dtype = np.dtype([('field0', '<u2'), ('b', '<u1'), ('c', '<u1'), ('a', '<u2')]) data = np.fromfile(fname, dtype=spc_dtype) nanotime = 4095 - np.bitwise_and(data['field0'], 0x0FFF) detector = data['c'] # Build the macrotime (timestamps) using in-place operation for efficiency timestamps = data['b'].astype('int64') np.left_shift(timestamps, 16, out=timestamps) timestamps += data['a'] # extract the 13-th bit from data['field0'] overflow = np.bitwise_and(np.right_shift(data['field0'], 13), 1) overflow = np.cumsum(overflow, dtype='int64') # Add the overflow bits timestamps += np.left_shift(overflow, 24) return timestamps, detector, nanotime
Example #30
Source File: sunrgbd_dataset.py From DepthAwareCNN with MIT License | 5 votes |
def __getitem__(self, index): #self.paths['images'][index] img = np.asarray(Image.open(self.paths_dict['images'][index]))#.astype(np.uint8) HHA = np.asarray(Image.open(self.paths_dict['HHAs'][index]))[:,:,::-1] seg = np.asarray(Image.open(self.paths_dict['segs'][index])).astype(np.uint8)-1 depth = np.asarray(Image.open(self.paths_dict['depths'][index])).astype(np.uint16) depth = np.bitwise_or(np.right_shift(depth,3),np.left_shift(depth,16-3)) depth = depth.astype(np.float32)/120. # 1/5 * depth assert (img.shape[0]==HHA.shape[0]==seg.shape[0]==depth.shape[0]) assert (img.shape[1]==HHA.shape[1]==seg.shape[1]==depth.shape[1]) params = get_params_sunrgbd(self.opt, seg.shape, test=True) depth_tensor_tranformed = transform(depth, params, normalize=False,istrain=self.opt.isTrain) seg_tensor_tranformed = transform(seg, params, normalize=False,method='nearest',istrain=self.opt.isTrain) # HHA_tensor_tranformed = transform(HHA, params,istrain=self.opt.isTrain) if self.opt.inputmode == 'bgr-mean': img_tensor_tranformed = transform(img, params, normalize=False, istrain=self.opt.isTrain, option=1) HHA_tensor_tranformed = transform(HHA, params, normalize=False, istrain=self.opt.isTrain, option=2) else: img_tensor_tranformed = transform(img, params, istrain=self.opt.isTrain, option=1) HHA_tensor_tranformed = transform(HHA, params, istrain=self.opt.isTrain, option=2) return {'image':img_tensor_tranformed, 'depth':depth_tensor_tranformed, 'seg': seg_tensor_tranformed, 'HHA': HHA_tensor_tranformed, 'imgpath': self.paths_dict['segs'][index]}