def compress_shape(decompressed_shape):
"""
Returns a run-length encoded compressed shape.
Parameters
----------
decompressed_shape : ndarray
Decompressed shape.
Returns
-------
compressed_shape : Holder
A Holder object containing the shape of the compressed shape ndarray and the compressed shape ndarray itself.
"""
if decompressed_shape.shape[0] != 1:
raise ValueError("input should be of shape (1,x)")
if not isinstance(decompressed_shape, np.ndarray):
raise TypeError("input should be of type numpy.ndarray")
data = np.array([decompressed_shape[0][0]])
data = np.concatenate((data, np.diff(decompressed_shape[0])))
mask_changes = np.array([1])
diff_as_ones = (abs(np.diff(data)) > 1e-8).astype(int)
mask_changes = np.concatenate((mask_changes, diff_as_ones))
vals = data[np.nonzero(mask_changes)].astype(float)
k = np.array(np.nonzero(np.append(mask_changes, 1)))
k = k.reshape((1, k.shape[1]))
n = np.diff(k)[0]
n_extra = (n - 2).astype(float)
vals2 = np.copy(vals)
vals2[np.where(n_extra < 0)] = np.NAN
n_extra[np.where(n_extra < 0)] = np.NAN
v = np.array([vals, vals2, n_extra])
v = np.concatenate(np.hsplit(v, v.shape[1]))
finite_vals = np.isfinite(v)
v = v[finite_vals]
v_abs = abs(v)
smallest_indices = np.where(v_abs < 1e-10)
v[smallest_indices] = 0
compressed_shape = Holder()
compressed_shape.num_samples = decompressed_shape.shape[1]
compressed_shape.data = v.reshape([1, v.shape[0]])
return compressed_shape
def makeadc(kwargs):
"""
Makes a Holder object for an ADC Event.
Parameters
----------
kwargs : dict
Key value mappings of ADC Event parameters_params and values.
Returns
-------
adc : Holder
ADC Event.
"""
num_samples = kwargs.get("num_samples", 0)
system = kwargs.get("system", Opts())
dwell = kwargs.get("dwell", 0)
duration = kwargs.get("duration", 0)
delay = kwargs.get("delay", 0)
freq_offset = kwargs.get("freq_offset", 0)
phase_offset = kwargs.get("phase_offset", 0)
adc = Holder()
adc.type = 'adc'
adc.num_samples = num_samples
adc.dwell = dwell
adc.delay = delay
adc.freq_offset = freq_offset
adc.phase_offset = phase_offset
adc.dead_time = system.adc_dead_time
if (dwell == 0 and duration == 0) or (dwell > 0 and duration > 0):
raise ValueError("Either dwell or duration must be defined, not both")
if duration > 0:
# getcontext().prec = 4
adc.dwell = float(Decimal(duration) / Decimal(num_samples))
adc.duration = dwell * num_samples if dwell > 0 else 0
return adc
def get_block(self, block_index):
"""
Returns Block at position specified by block_index.
Parameters
----------
block_index : int
Index of Block to be retrieved.
Returns
-------
block : dict
Block at position specified by block_index.
"""
block = {}
event_ind = self.block_events[block_index]
if event_ind[0] > 0:
delay = Holder()
delay.type = 'delay'
delay.delay = self.delay_library.data[event_ind[0]]
block['delay'] = delay
elif event_ind[1] > 0:
rf = Holder()
rf.type = 'rf'
lib_data = self.rf_library.data[event_ind[1]]
amplitude, mag_shape, phase_shape = lib_data[0], lib_data[1], lib_data[
2]
shape_data = self.shape_library.data[mag_shape]
compressed = Holder()
compressed.num_samples = shape_data[0][0]
compressed.data = shape_data[0][1:]
compressed.data = compressed.data.reshape(
(1, compressed.data.shape[0]))
mag = decompress_shape(compressed)
shape_data = self.shape_library.data[phase_shape]
compressed.num_samples = shape_data[0][0]
compressed.data = shape_data[0][1:]
compressed.data = compressed.data.reshape(
(1, compressed.data.shape[0]))
phase = decompress_shape(compressed)
rf.signal = 1j * 2 * np.pi * phase
rf.signal = amplitude * mag * np.exp(rf.signal)
rf.t = [(x * self.rf_raster_time)
for x in range(1,
max(mag.shape) + 1)]
rf.t = np.reshape(rf.t, (1, len(rf.t)))
rf.freq_offset = lib_data[3]
rf.phase_offset = lib_data[4]
if max(lib_data.shape) < 6:
lib_data = np.append(lib_data, 0)
rf.dead_time = lib_data[5]
block['rf'] = rf
grad_channels = ['gx', 'gy', 'gz']
for i in range(1, len(grad_channels) + 1):
if event_ind[2 + (i - 1)] > 0:
grad, compressed = Holder(), Holder()
type = self.grad_library.type[event_ind[2 + (i - 1)]]
lib_data = self.grad_library.data[event_ind[2 + (i - 1)]]
grad.type = 'trap' if type == 't' else 'grad'
grad.channel = grad_channels[i - 1][1]
if grad.type == 'grad':
amplitude = lib_data[0]
shape_id = lib_data[1]
shape_data = self.shape_library.data[shape_id]
compressed.num_samples = shape_data[0][0]
compressed.data = np.array([shape_data[0][1:]])
g = decompress_shape(compressed)
grad.waveform = amplitude * g
grad.t = np.array([[
x * self.grad_raster_time for x in range(1, g.size + 1)
]])
else:
grad.amplitude, grad.rise_time, grad.flat_time, grad.fall_time = [
lib_data[x] for x in range(4)
]
grad.area = grad.amplitude * (
grad.flat_time + grad.rise_time / 2 + grad.fall_time / 2)
grad.flat_area = grad.amplitude * grad.flat_time
block[grad_channels[i - 1]] = grad
if event_ind[5] > 0:
lib_data = self.adc_library.data[event_ind[5]]
if max(lib_data.shape) < 6:
lib_data = np.append(lib_data, 0)
adc = Holder()
adc.num_samples, adc.dwell, adc.delay, adc.freq_offset, adc.phase_offset, adc.dead_time = [
lib_data[x] for x in range(6)
]
adc.type = 'adc'
block['adc'] = adc
return block