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wavesynth: remove

master
Sebastien Bourdeauducq 2023-12-13 13:36:21 +08:00
parent 7a863b4f5e
commit 85850ad9e8
5 changed files with 1 additions and 494 deletions
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1
RELEASE_NOTES.rst
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@ -94,6 +94,7 @@ Accesses to the data argument should be replaced as below:
txn.put(key.encode(), pyon.encode((value, {})).encode())
new.close()
* ``artiq.wavesynth`` has been removed.
ARTIQ-7
-------

127
artiq/test/test_wavesynth.py
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@ -1,127 +0,0 @@
# Copyright (C) 2014, 2015 Robert Jordens <jordens@gmail.com>
import unittest
from artiq.wavesynth import compute_samples
class TestSynthesizer(unittest.TestCase):
program = [
[
# frame 0
{
# frame 0, segment 0, line 0
"dac_divider": 1,
"duration": 100,
"channel_data": [
{
# channel 0
"dds": {"amplitude": [0.0, 0.0, 0.01],
"phase": [0.0, 0.0, 0.0005],
"clear": False}
}
],
"trigger": True
},
{
# frame 0, segment 0, line 1
"dac_divider": 1,
"duration": 100,
"channel_data": [
{
# channel 0
"dds": {"amplitude": [49.5, 1.0, -0.01],
"phase": [0.0, 0.05, 0.0005],
"clear": False}
}
],
"trigger": False
},
],
[
# frame 1
{
# frame 1, segment 0, line 0
"dac_divider": 1,
"duration": 100,
"channel_data": [
{
# channel 0
"dds": {"amplitude": [100.0, 0.0, -0.01],
"phase": [0.0, 0.1, -0.0005],
"clear": False}
}
],
"trigger": True
},
{
# frame 1, segment 0, line 1
"dac_divider": 1,
"duration": 100,
"channel_data": [
{
# channel 0
"dds": {"amplitude": [50.5, -1.0, 0.01],
"phase": [0.0, 0.05, -0.0005],
"clear": False}
}
],
"trigger": False
}
],
[
# frame 2
{
# frame 2, segment 0, line 0
"dac_divider": 1,
"duration": 84,
"channel_data": [
{
# channel 0
"dds": {"amplitude": [100.0],
"phase": [0.0, 0.05],
"clear": False}
}
],
"trigger": True
},
{
# frame 2, segment 1, line 0
"dac_divider": 1,
"duration": 116,
"channel_data": [
{
# channel 0
"dds": {"amplitude": [100.0],
"phase": [0.0, 0.05],
"clear": True}
}
],
"trigger": True
}
]
]
def setUp(self):
self.dev = compute_samples.Synthesizer(1, self.program)
self.t = list(range(600))
def drive(self):
s = self.dev
y = []
for f in 0, 2, None, 1:
if f is not None:
s.select(f)
y += s.trigger()[0]
x = list(range(600))
return x, y
def test_run(self):
x, y = self.drive()
@unittest.skip("manual/visual test")
def test_plot(self):
from matplotlib import pyplot as plt
x, y = self.drive()
plt.plot(x, y)
plt.show()

0
artiq/wavesynth/__init__.py
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234
artiq/wavesynth/coefficients.py
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@ -1,234 +0,0 @@
# Copyright (C) 2014, 2015 Robert Jordens <jordens@gmail.com>
import numpy as np
from scipy.interpolate import splrep, splev, spalde
class UnivariateMultiSpline:
"""Multidimensional wrapper around `scipy.interpolate.sp*` functions.
`scipy.inteprolate.splprep` is limited to 12 dimensions.
"""
def __init__(self, x, y, *, x0=None, order=4, **kwargs):
self.order = order
self.x = x
self.s = []
for i, yi in enumerate(y):
if x0 is not None:
yi = self.upsample_knots(x0[i], yi, x)
self.s.append(splrep(x, yi, k=order - 1, **kwargs))
def upsample_knots(self, x0, y0, x):
return splev(x, splrep(x0, y0, k=self.order - 1))
def lev(self, x, *a, **k):
return np.array([splev(x, si) for si in self.s])
def alde(self, x):
u = np.array([spalde(x, si) for si in self.s])
if len(x) == 1:
u = u[:, None, :]
return u
def __call__(self, x, use_alde=True):
if use_alde:
u = self.alde(x)[:, :, :self.order]
s = (len(self.s), len(x), self.order)
assert u.shape == s, (u.shape, s)
return u.transpose(2, 0, 1)
else:
return np.array([self.lev(x, der=i) for i in range(self.order)])
def pad_const(x, n, axis=0):
"""Prefix and postfix the array `x` by `n` repetitions of the first and
last value along `axis`.
"""
a = np.repeat(x.take([0], axis), n, axis)
b = np.repeat(x.take([-1], axis), n, axis)
xp = np.concatenate([a, x, b], axis)
s = list(x.shape)
s[axis] += 2*n
assert xp.shape == tuple(s), (x.shape, s, xp.shape)
return xp
def build_segment(durations, coefficients, target="bias",
variable="amplitude", compress=True):
"""Build a wavesynth-style segment from homogeneous duration and
coefficient data.
:param durations: 1D sequence of line durations.
:param coefficients: 3D array with shape `(n, m, len(durations))`,
with `n` being the interpolation order + 1 and `m` the number of
channels.
:param target: The target component of the channel to affect.
:param variable: The variable within the target component.
:param compress: If `True`, skip zero high order coefficients.
"""
for dxi, yi in zip(durations, coefficients.transpose()):
cd = []
for yij in yi:
cdj = []
for yijk in reversed(yij):
if cdj or abs(yijk) or not compress:
cdj.append(float(yijk))
cdj.reverse()
if not cdj:
cdj.append(float(yij[0]))
cd.append({target: {variable: cdj}})
yield {"duration": int(dxi), "channel_data": cd}
class CoefficientSource:
def crop_x(self, start, stop, num=2):
"""Return an array of valid sample positions.
This method needs to be overloaded if this `CoefficientSource`
does not support sampling at arbitrary positions or at arbitrary
density.
:param start: First sample position.
:param stop: Last sample position.
:param num: Number of samples between `start` and `stop`.
:return: Array of sample positions. `start` and `stop` should be
returned as the first and last value in the array respectively.
"""
return np.linspace(start, stop, num)
def scale_x(self, x, scale):
# TODO: This could be moved to the the Driver/Mediator code as it is
# device-specific.
"""Scale and round sample positions.
The sample times may need to be changed and/or decimated if
incompatible with hardware requirements.
:param x: Input sample positions in data space.
:param scale: Data space position to cycles conversion scale,
in units of x-units per clock cycle.
:return: `x_sample`, the rounded sample positions and `durations`, the
integer durations of the individual samples in cycles.
"""
t = np.rint(x/scale)
x_sample = t*scale
durations = np.diff(t).astype(int)
return x_sample, durations
def __call__(self, x, **kwargs):
"""Perform sampling and return coefficients.
:param x: Sample positions.
:return: `y` the array of coefficients. `y.shape == (order, n, len(x))`
with `n` being the number of channels."""
raise NotImplementedError
def get_segment(self, start, stop, scale, *, cutoff=1e-12,
target="bias", variable="amplitude"):
"""Build wavesynth segment.
:param start: see `crop_x()`.
:param stop: see `crop_x()`.
:param scale: see `scale_x()`.
:param cutoff: coefficient cutoff towards zero to compress data.
"""
x = self.crop_x(start, stop)
x_sample, durations = self.scale_x(x, scale)
coefficients = self(x_sample)
if len(x_sample) == 1 and start == stop:
coefficients = coefficients[:1]
# rescale coefficients accordingly
coefficients *= (scale*np.sign(durations))**np.arange(
coefficients.shape[0])[:, None, None]
if cutoff:
coefficients[np.fabs(coefficients) < cutoff] = 0
return build_segment(np.fabs(durations), coefficients, target=target,
variable=variable)
def extend_segment(self, segment, *args, **kwargs):
"""Extend a wavesynth segment.
See `get_segment()` for arguments.
"""
for line in self.get_segment(*args, **kwargs):
segment.add_line(**line)
class SplineSource(CoefficientSource):
def __init__(self, x, y, order=4, pad_dx=1.):
"""
:param x: 1D sample positions.
:param y: 2D sample values.
"""
self.x = np.asanyarray(x)
assert self.x.ndim == 1
self.y = np.asanyarray(y)
assert self.y.ndim == 2
if pad_dx is not None:
a = np.arange(-order, 0)*pad_dx + self.x[0]
b = self.x[-1] + np.arange(1, order + 1)*pad_dx
self.x = np.r_[a, self.x, b]
self.y = pad_const(self.y, order, axis=1)
assert self.y.shape[1] == self.x.shape[0]
self.spline = UnivariateMultiSpline(self.x, self.y, order=order)
def crop_x(self, start, stop):
ia, ib = np.searchsorted(self.x, (start, stop))
if start > stop:
x = self.x[ia - 1:ib - 1:-1]
else:
x = self.x[ia:ib]
return np.r_[start, x, stop]
def scale_x(self, x, scale, min_duration=1, min_length=20):
"""Enforce, round, and scale x to device-dependent values.
Due to minimum duration and/or minimum segment length constraints
this method may drop samples from `x_sample` to comply.
:param min_duration: Minimum duration of a line.
:param min_length: Minimum segment length to space triggers.
"""
# We want to only sample a spline at t_knot + epsilon
# where the highest order derivative has just jumped
# and is valid at least up to the next knot after t_knot.
#
# To ensure that we are on the correct side of a knot:
# * only ever increase t when rounding (for increasing t)
# * or only ever decrease it (for decreasing t)
t = x/scale
inc = np.diff(t) >= 0
inc = np.r_[inc, inc[-1]]
t = np.where(inc, np.ceil(t), np.floor(t))
dt = np.diff(t.astype(int))
valid = np.absolute(dt) >= min_duration
if not np.any(valid):
valid[0] = True
dt[0] = max(dt[0], min_length)
dt = dt[valid]
x_sample = t[:-1][valid]*scale
return x_sample, dt
def __call__(self, x):
return self.spline(x)
def discrete_compensate(c):
"""Compensate spline coefficients for discrete accumulators
Given continuous-time b-spline coefficients, this function
compensates for the effect of discrete time steps in the
target devices.
The compensation is performed in-place.
"""
l = len(c)
if l > 2:
c[1] += c[2]/2.
if l > 3:
c[1] += c[3]/6.
c[2] += c[3]
if l > 4:
raise ValueError("only third-order splines supported")

133
artiq/wavesynth/compute_samples.py
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@ -1,133 +0,0 @@
# Copyright (C) 2014, 2015 M-Labs Limited
# Copyright (C) 2014, 2015 Robert Jordens <jordens@gmail.com>
from copy import copy
from math import cos, pi
from artiq.wavesynth.coefficients import discrete_compensate
class Spline:
def __init__(self):
self.c = [0.0]
def set_coefficients(self, c):
if not c:
c = [0.]
self.c = copy(c)
discrete_compensate(self.c)
def next(self):
r = self.c[0]
for i in range(len(self.c) - 1):
self.c[i] += self.c[i + 1]
return r
class SplinePhase:
def __init__(self):
self.c = [0.0]
self.c0 = 0.0
def set_coefficients(self, c):
if not c:
c = [0.]
self.c0 = c[0]
c1p = c[1:]
discrete_compensate(c1p)
self.c[1:] = c1p
def clear(self):
self.c[0] = 0.0
def next(self):
r = self.c[0]
for i in range(len(self.c) - 1):
self.c[i] += self.c[i + 1]
self.c[i] %= 1.0
return r + self.c0
class DDS:
def __init__(self):
self.amplitude = Spline()
self.phase = SplinePhase()
def next(self):
return self.amplitude.next()*cos(2*pi*self.phase.next())
class Channel:
def __init__(self):
self.bias = Spline()
self.dds = DDS()
self.v = 0.
self.silence = False
def next(self):
v = self.bias.next() + self.dds.next()
if not self.silence:
self.v = v
return self.v
def set_silence(self, s):
self.silence = s
class TriggerError(Exception):
pass
class Synthesizer:
def __init__(self, nchannels, program):
self.channels = [Channel() for _ in range(nchannels)]
self.program = program
# line_iter is None: "wait for segment selection" state
# otherwise: iterator on the current position in the frame
self.line_iter = None
def select(self, selection):
if self.line_iter is not None:
raise TriggerError("a frame is already selected")
self.line_iter = iter(self.program[selection])
self.line = next(self.line_iter)
def trigger(self):
if self.line_iter is None:
raise TriggerError("no frame selected")
line = self.line
if not line.get("trigger", False):
raise TriggerError("segment is not triggered")
r = [[] for _ in self.channels]
while True:
for channel, channel_data in zip(self.channels,
line["channel_data"]):
channel.set_silence(channel_data.get("silence", False))
if "bias" in channel_data:
channel.bias.set_coefficients(
channel_data["bias"]["amplitude"])
if "dds" in channel_data:
channel.dds.amplitude.set_coefficients(
channel_data["dds"]["amplitude"])
if "phase" in channel_data["dds"]:
channel.dds.phase.set_coefficients(
channel_data["dds"]["phase"])
if channel_data["dds"].get("clear", False):
channel.dds.phase.clear()
if line.get("dac_divider", 1) != 1:
raise NotImplementedError
for channel, rc in zip(self.channels, r):
for i in range(line["duration"]):
rc.append(channel.next())
try:
self.line = line = next(self.line_iter)
if line.get("trigger", False):
return r
except StopIteration:
self.line_iter = None
return r