"""Pipeline Dither calibration from aligned, observable captures.
No probe injection, plant simulation or workflow orchestration is performed here.
See the packaged DITHER_GUIDE.md and HDC_FIXED.md for theory and model limits.
"""
from numbers import Integral
import numpy as np
__all__ = ["DitherGainTracker", "apply_dither_gain", "DitherHDCTracker"]
def _is_integer(value):
return isinstance(value, Integral) and not isinstance(value, (bool, np.bool_))
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def apply_dither_gain(coarse, backend, pn, *, gain_estimate, probe_amplitude):
"""Apply q + r/G_est - a*p using a frozen positive gain estimate.
Inputs are aligned 1D observable arrays in consistent voltage/code units.
``pn`` is the normalized known probe; amplitude is in input-referred units.
Returns ``calibrated_signal`` and ``gain_used``. The known probe must pass
through the residue gain, after the coarse decision. This function only
reconstructs the digital output; it does not inject a probe.
"""
q, r, p = [np.asarray(a, float) for a in (coarse, backend, pn)]
if (q.ndim != 1 or r.shape != q.shape or p.shape != q.shape
or not all(np.isfinite(a).all() for a in (q, r, p))):
raise ValueError("coarse, backend and pn must be matching finite 1D arrays")
if (not np.isfinite(gain_estimate) or gain_estimate <= 0
or not np.isfinite(probe_amplitude) or probe_amplitude < 0):
raise ValueError(
"gain_estimate must be positive and probe_amplitude nonnegative, both finite")
return {"calibrated_signal": q+r/gain_estimate-probe_amplitude*p,
"gain_used": float(gain_estimate)}
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class DitherGainTracker:
"""Blockwise PN-correlation gain estimate followed by exponential averaging.
The known probe ``probe_amplitude * pn`` must be injected after the coarse
decision and amplified together with the residue. Each block estimates the
gain as cov(pn, backend) / (probe_amplitude * var(pn)) and moves the active
estimate toward it by ``learning_rate``. No ideal input, true gain,
frequency or foreground label is accepted. A block is held, leaving the
estimate unchanged, on any backend rail code (selecting only non-clipped
samples can bias the correlation), on insufficient probe excitation, or
when its estimate lies outside ``gain_bounds``. Unknown injection amplitude
is inseparable from gain; statistical bias, noise and excitation limits
remain architecture-dependent.
Use :meth:`process` for each contiguous block: it corrects with the active
estimate and then updates from that block. :meth:`apply` and :meth:`update`
expose the two steps separately.
Parameters
----------
probe_amplitude : float
Known probe amplitude, in the input-referred units of ``coarse``.
initial_gain : float
Starting residue gain: the design value or a foreground estimate.
backend_bits : int
Backend resolution; ``backend_code`` must lie in [0, 2**backend_bits).
gain_bounds : tuple of float
(low, high) range containing ``initial_gain`` and the true gain. Block
estimates outside it are held, so too narrow a range stops tracking.
learning_rate : float, default 0.08
Exponential-averaging weight in (0, 1].
"""
def __init__(self, *, probe_amplitude, initial_gain, backend_bits, gain_bounds,
learning_rate=.08):
if not np.isfinite(probe_amplitude) or probe_amplitude <= 0:
raise ValueError("probe_amplitude must be positive and finite")
if not np.isfinite(learning_rate) or not 0 < learning_rate <= 1:
raise ValueError("learning_rate must be in (0, 1]")
if not _is_integer(backend_bits) or not 2 <= backend_bits <= 24:
raise ValueError("backend_bits must be an integer in [2, 24]")
if (len(gain_bounds) != 2
or not (0 < gain_bounds[0] <= initial_gain <= gain_bounds[1] < np.inf)):
raise ValueError("initial_gain must lie in finite positive gain_bounds")
self.probe_amplitude = probe_amplitude
self.initial_gain = initial_gain
self.learning_rate = learning_rate
self.backend_bits = int(backend_bits)
self.gain_bounds = gain_bounds
self.reset()
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def reset(self):
"""Restore the configured initial gain and zero accepted updates."""
self.gain_estimate = float(self.initial_gain)
self.updates = 0
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def apply(self, coarse, backend, pn):
"""Correct with the current snapshot, without updating parameters."""
return apply_dither_gain(coarse, backend, pn,
gain_estimate=self.gain_estimate,
probe_amplitude=self.probe_amplitude)
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def update(self, backend, pn, backend_code):
"""Return gain_next, observed_gain and status; a hold preserves state.
Each call is one averaging window. Changing block size changes the
estimator dynamics even for the same total number of samples.
"""
r, p, codes = np.asarray(backend, float), np.asarray(pn, float), np.asarray(backend_code)
if r.ndim != 1 or r.shape != p.shape or r.shape != codes.shape or r.size < 2:
raise ValueError(
"backend, pn and backend_code must have matching 1D shapes with >=2 samples")
if (not np.isfinite(r).all() or not np.isfinite(p).all()
or not np.isin(p, [-1., 0., 1.]).all()):
raise ValueError("Finite backend and PN in {-1, 0, 1} required")
if (not np.isfinite(codes).all() or np.any(codes != np.floor(codes))
or np.any((codes < 0) | (codes >= 2**self.backend_bits))):
raise ValueError("Invalid backend codes")
status, observed = "updated", None
centered = p-p.mean()
energy = float(centered@centered)
if np.any((codes == 0) | (codes == 2**self.backend_bits-1)):
status = "held_backend_rail"
elif energy/r.size < .25:
status = "held_probe_excitation"
else:
observed = float(centered@(r-r.mean())/(self.probe_amplitude*energy))
if not self.gain_bounds[0] <= observed <= self.gain_bounds[1]:
status = "held_gain_out_of_range"
else:
self.gain_estimate += self.learning_rate*(observed-self.gain_estimate)
self.updates += 1
return {"gain_next": self.gain_estimate, "observed_gain": observed, "status": status}
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def process(self, coarse, backend, pn, backend_code, *, learn=True):
"""Correct one block with the active gain, then learn from that block.
Returns ``calibrated_signal`` and ``gain_used`` from :meth:`apply`, plus
``gain_next``, ``observed_gain`` and ``status`` from :meth:`update`.
``learn=False`` freezes the estimate, ignores ``backend_code`` and
reports status ``"frozen"``.
"""
if not isinstance(learn, (bool, np.bool_)):
raise ValueError("learn must be boolean")
result = self.apply(coarse, backend, pn)
if learn:
result.update(self.update(backend, pn, backend_code))
else:
result.update(gain_next=self.gain_estimate, observed_gain=None, status="frozen")
return result
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class DitherHDCTracker:
"""Streaming three-PN Pipeline HDC in floating or fixed-point arithmetic.
The stage uses signed codes {-1, 0, 1}, nominal gain 2, DAC levels
``stage_code/2``, and a normalized backend range [-1, 1]. Three independent
PN signs inject ``dither_peak/3 * sum(pn_signs)`` before the amplifier.
This is the model in HDC_FIXED.md: published gain/cubic HDC plus two
extensions, a one-tap memory inverse and a two-element DAC mismatch whose
roles are swapped according to ``selection``.
Each update uses one averaging window only and replaces the active
coefficients ``controller_latency`` samples later; windows are not averaged
together, so estimator spread falls only with window length, by about half
for every +2 in ``window_log2``. On the HDC_FIXED.md model, 2^20-sample
windows gave about 51 dB SNDR and 2^24 about 56 dB, close to the limit of
the cubic inverse itself; at 2^24 the cubic coefficient error still
exceeded 10% for two of three seeds. A 2^24 window lasts 0.17 s at 100 MS/s.
``selection`` must be a balanced random sign sequence independent of the
input, such as an LFSR. With constant tags the mismatch estimate is out of
range, so only ``dac_mismatch`` is held (counted in ``held_dac_mismatch``)
while the other coefficients keep learning. A tag mean m over nonzero stage
codes biases the mismatch estimate by about -2*m*mean(abs(input)) over those
codes. For a stage without role swapping, use ``estimate_dac_mismatch=False``;
with a zero ``dac_mismatch`` coefficient, ``selection`` may then be None.
Arbitrary contiguous block partitions give the same result. ``learn=False``
freezes active coefficients, cancels a pending update and discards partial
estimation windows, but retains signal history. Call :meth:`reset` for an
independent capture. The estimator keeps the documented HDC moment
approximation; a public interface does not remove finite-record bias or
expand physical validity. Without the optional ``accel`` extra (Numba), the
per-sample loop runs in pure Python at roughly 10^5 samples per second.
Parameters
----------
fixed : bool, default False
Use the bit-accurate fixed-point datapath of HDC_FIXED.md instead of
floating arithmetic.
window_log2 : int, default 24
log2 of the samples in each estimation window, in [8, 30].
dither_peak : float, default 0.2
Peak of the summed probe in stage-input units, in (0, 1). It must match
the analog injection.
estimate_memory, estimate_dac_mismatch : bool, default True
Learn the memory or DAC-mismatch extension. When False, that
coefficient keeps its initial value.
initial_coefficients : dict, optional
Exactly ``linear``, ``cubic``, ``memory`` and ``dac_mismatch``, default
1, 0, 0, 0. These digital inverse coefficients c1, k3, h, delta are not
physical device parameters: for r = backend/2,
w = c1*r - k3*(c1*r)**3, v = w - h*v_previous and
output = stage_code/2 + v - probe + delta*selection*stage_code/2.
fraction, bits : int, default 24 and 28
Fixed-point fractional and total word bits. Validated in both modes,
but used in arithmetic only when ``fixed`` is True.
accumulator_bits : int, optional
Fixed-point correlation accumulator width, default ``bits + window_log2``.
controller_latency : int, default 512
Samples from the end of a window to the commit of its coefficients.
"""
_names = ("linear", "cubic", "memory", "dac_mismatch")
def __init__(self, *, fixed=False, window_log2=24, dither_peak=.2,
estimate_memory=True, estimate_dac_mismatch=True,
initial_coefficients=None, fraction=24, bits=28,
accumulator_bits=None, controller_latency=512):
for name, value in (("fixed", fixed), ("estimate_memory", estimate_memory),
("estimate_dac_mismatch", estimate_dac_mismatch)):
if not isinstance(value, (bool, np.bool_)):
raise ValueError(f"{name} must be boolean")
for name, value in (("fraction", fraction), ("bits", bits),
("window_log2", window_log2),
("controller_latency", controller_latency)):
if not _is_integer(value):
raise ValueError(f"{name} must be an integer")
if accumulator_bits is not None and (
not _is_integer(accumulator_bits) or not 8 <= accumulator_bits <= 62
):
raise ValueError("accumulator_bits must be an integer in [8, 62]")
self._configuration = dict(
fixed=bool(fixed), fraction=int(fraction), bits=int(bits),
window_log2=int(window_log2), accumulator_bits=(
None if accumulator_bits is None else int(accumulator_bits)),
controller_latency=int(controller_latency), dither_peak=dither_peak,
estimate_memory=bool(estimate_memory),
estimate_dac_mismatch=bool(estimate_dac_mismatch),
)
from ._dither_hdc import Tracker as _HDCTracker
# Validate numeric format and moment denominators before scaling words.
_HDCTracker(**self._configuration)
self._initial = dict(zip(self._names, (1., 0., 0., 0.)))
if initial_coefficients is not None:
if (not isinstance(initial_coefficients, dict)
or set(initial_coefficients) != set(self._names)):
raise ValueError(f"initial_coefficients must contain exactly {self._names}")
self._initial = dict(initial_coefficients)
values = np.asarray([self._initial[name] for name in self._names], dtype=float)
if values.shape != (4,) or not np.all(np.isfinite(values)) or values[0] <= 0:
raise ValueError(
"initial coefficients must be finite scalars with positive linear gain")
# Bound the recursive inverse and reject unrepresentable fixed words.
if abs(values[2]) >= 1:
raise ValueError("initial memory coefficient must have magnitude below one")
if fixed:
words = np.rint(values * (1 << int(fraction)))
if (np.any(words < -(1 << (int(bits)-1))) or np.any(words >= (1 << (int(bits)-1)))
or words[0] <= 0):
raise ValueError("initial coefficients do not fit the fixed-point format")
self.reset()
@property
def coefficients(self):
"""A detached dict of active digital correction coefficients."""
return dict(zip(self._names, map(float, self._tracker.values())))
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def reset(self):
"""Restore initial coefficients and clear all stream/estimator state."""
from ._dither_hdc import Tracker as _HDCTracker
self._tracker = _HDCTracker(**self._configuration)
values = np.asarray([self._initial[name] for name in self._names], dtype=float)
self._tracker.coefficients = (
np.rint(values*self._tracker.scale).astype(np.int64)
if self._tracker.fixed else values.copy()
)
self._previous_pn = 0
self._learning = True
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def process(self, backend, pn_signs, stage_code, selection, backend_rails, *, learn=True):
"""Correct one contiguous block and optionally learn from its moments.
Parameters
----------
backend : array_like, shape (N,)
Finite normalized backend samples in [-1, 1], not integer codes.
pn_signs : array_like, shape (N, 3)
Aligned signs {-1, +1} for the three injected PN streams.
stage_code : array_like, shape (N,)
Signed coarse decisions {-1, 0, 1}, before Dither injection.
selection : array_like, shape (N,), or None
Observable DAC role-selection tags {-1, +1}, balanced and random
(see the class notes). None is allowed only when
``estimate_dac_mismatch`` is False and ``dac_mismatch`` is zero.
backend_rails : array_like, shape (N,)
Boolean rail flags; any flag rejects its entire estimation window.
learn : bool
False freezes coefficients while advancing correction history.
Returns
-------
dict
``calibrated_signal`` in stage input units; active ``coefficients``
after the block; ``updates`` (accepted, possibly pending) and
``held_updates`` cumulative counts; ``held_dac_mismatch``, the
accepted updates that kept the previous ``dac_mismatch``;
``pending_update`` flag; and cumulative ``saturations`` for
datapath, accumulator and controller. A block may span coefficient
commits; the returned coefficients are not necessarily those used
for every sample in that block.
"""
if not isinstance(learn, (bool, np.bool_)):
raise ValueError("learn must be boolean")
r = np.asarray(backend, dtype=float)
p, q, rails = map(np.asarray, (pn_signs, stage_code, backend_rails))
if r.ndim != 1 or not r.size or not np.isfinite(r).all() or np.any(abs(r) > 1):
raise ValueError("backend must be a nonempty finite 1D array in [-1, 1]")
if p.shape != (r.size, 3) or not np.isin(p, (-1, 1)).all():
raise ValueError("pn_signs must have shape (N, 3) and values {-1, +1}")
if q.shape != r.shape or not np.isin(q, (-1, 0, 1)).all():
raise ValueError("stage_code must have shape (N,) and values {-1, 0, 1}")
if selection is None:
if self._configuration["estimate_dac_mismatch"] or self._initial["dac_mismatch"] != 0:
raise ValueError("selection is required unless estimate_dac_mismatch=False "
"and the dac_mismatch coefficient is zero")
s = np.ones(r.shape, dtype=np.int64)
else:
s = np.asarray(selection)
if s.shape != r.shape or not np.isin(s, (-1, 1)).all():
raise ValueError("selection must have shape (N,) and values {-1, +1}")
if rails.shape != r.shape or rails.dtype.kind != 'b':
raise ValueError("backend_rails must be a boolean array of shape (N,)")
p, q, s = (a.astype(np.int64) for a in (p, q, s))
tracker = self._tracker
if not learn or self._learning != learn:
tracker.pending = tracker.commit_at = None
tracker.sums[:] = 0
tracker.count = 0
tracker.window_bad = False
previous_pn = np.r_[self._previous_pn, p[:-1, 0]]
y = tracker.process(dict(r=r/2, p=p, pp=previous_pn, s=s, q=q, rails=rails), learn=learn)
self._previous_pn = int(p[-1, 0])
self._learning = bool(learn)
# The public API exposes counters; avoid retaining an unbounded event log.
tracker.events.clear()
return dict(
calibrated_signal=y, coefficients=self.coefficients,
updates=tracker.updates, held_updates=tracker.held,
held_dac_mismatch=tracker.delta_holds,
pending_update=tracker.pending is not None,
saturations=dict(datapath=tracker.saturations,
accumulator=tracker.accumulator_saturations,
controller=tracker.controller_saturations),
)
