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"""Transient simulation related data processing methods."""
from __future__ import annotations
from typing import Sequence, List, Tuple, Any, Iterable, Optional, Callable, Union
from enum import Flag, auto
from itertools import islice
import numpy as np
from scipy.interpolate import interp1d
from bag.util.search import BinaryIterator
[docs]class EdgeType(Flag):
@property
[docs] def opposite(self) -> EdgeType:
if self is EdgeType.RISE:
return EdgeType.FALL
if self is EdgeType.FALL:
return EdgeType.RISE
if self is EdgeType.CROSS:
return EdgeType.CROSS
raise ValueError(f'Unknown edge type: {self.name}')
[docs]def interp1d_no_nan(tvec: np.ndarray, yvec: np.ndarray
) -> Callable[[Union[float, np.ndarray]], np.ndarray]:
tsize = len(tvec)
if np.isnan(tvec[-1]):
bin_iter = BinaryIterator(1, tsize + 1)
while bin_iter.has_next():
delta = bin_iter.get_next()
if np.isnan(tvec[tsize - delta]):
bin_iter.save()
bin_iter.up()
else:
bin_iter.down()
tsize -= bin_iter.get_last_save()
return interp1d(tvec[:tsize], yvec[:tsize], assume_sorted=True, copy=False)
[docs]def bits_to_pwl_iter(values: Sequence[Any]) -> Iterable[Tuple[float, float, float, Any]]:
"""Convert discrete samples to PWL waveform.
This method yields coefficients to td, tbit and trf, so user can generate symbolic PWL
waveform files. Note that td must be positive.
Parameters
----------
values : List[float]
list of values for each bit.
Yields
------
td_scale : float
coefficient for td
tbit_scale : float
coefficient for tbit
trf : float
coefficient for trf
val : Any
the value
"""
cur_info = [1, 0, 0, values[0]]
yield tuple(cur_info)
cur_info[1] += 1
cur_info[2] -= 0.5
for ycur in islice(values, 1, None):
if ycur != cur_info[3]:
yield tuple(cur_info)
cur_info[3] = ycur
cur_info[2] += 1
yield tuple(cur_info)
cur_info[1] += 1
cur_info[2] -= 1
else:
cur_info[1] += 1
# output last point
yield tuple(cur_info)
[docs]def get_first_crossings(tvec: np.ndarray, yvec: np.ndarray, threshold: Union[float, np.ndarray],
start: Union[float, np.ndarray] = 0,
stop: Union[float, np.ndarray] = float('inf'),
etype: EdgeType = EdgeType.CROSS, rtol: float = 1e-8, atol: float = 1e-22,
shape: Optional[Tuple[int, ...]] = None) -> np.ndarray:
"""Find the first time where waveform crosses a given threshold.
tvec and yvec can be multi-dimensional, in which case the waveforms are stored in the
last axis. The returned numpy array will have the same shape as yvec with the last
axis removed. If the waveform never crosses the threshold, positive infinity will be
returned.
"""
swp_shape = yvec.shape[:-1]
if shape is None:
shape = yvec.shape[:-1]
try:
th_vec = np.broadcast_to(np.asarray(threshold), swp_shape)
start = np.broadcast_to(np.asarray(start), swp_shape)
stop = np.broadcast_to(np.asarray(stop), swp_shape)
except ValueError as err:
raise ValueError('Failed to make threshold/start/stop the same shape as data. '
'Make sure they are either scalar or has the same sweep shape.') from err
t_shape = tvec.shape
nlast = t_shape[len(t_shape) - 1]
yvec = yvec.reshape(-1, nlast)
tvec = tvec.reshape(-1, nlast)
th_vec = th_vec.flatten()
t0_vec = start.flatten()
t1_vec = stop.flatten()
n_swp = th_vec.size
ans = np.empty(n_swp)
num_tvec = tvec.shape[0]
for idx in range(n_swp):
cur_thres = th_vec[idx]
cur_t0 = t0_vec[idx]
cur_t1 = t1_vec[idx]
ans[idx] = _get_first_crossings_time_1d(tvec[idx % num_tvec, :], yvec[idx, :], cur_thres,
cur_t0, cur_t1, etype, rtol, atol)
return ans.reshape(shape)
[docs]def _get_first_crossings_time_1d(tvec: np.ndarray, yvec: np.ndarray, threshold: float,
start: float, stop: float, etype: EdgeType, rtol: float,
atol: float) -> float:
# eliminate NaN from time vector in cases where simulation time is different between runs.
mask = ~np.isnan(tvec)
tvec = tvec[mask]
yvec = yvec[mask]
sidx = np.searchsorted(tvec, start)
eidx = np.searchsorted(tvec, stop)
if eidx < tvec.size and np.isclose(stop, tvec[eidx], rtol=rtol, atol=atol):
eidx += 1
# quantize waveform values, then detect edge.
dvec = np.diff((yvec[sidx:eidx] >= threshold).astype(int))
if dvec.size == 0:
return float('nan')
ans = float('inf')
if EdgeType.RISE in etype:
sel_mask = np.maximum(dvec, 0)
arg = sel_mask.argmax()
if arg != 0 or sel_mask[0] != 0:
# has edge
ans = _get_first_crossings_helper(tvec, yvec, threshold, sidx, arg)
if EdgeType.FALL in etype:
sel_mask = np.minimum(dvec, 0)
arg = sel_mask.argmin()
if arg == 0 and sel_mask[0] == 0:
# no edge
return ans
return min(ans, _get_first_crossings_helper(tvec, yvec, threshold, sidx, arg))
return ans
[docs]def _get_first_crossings_helper(tvec: np.ndarray, yvec: np.ndarray,
threshold: float, idx0: int, arg: int) -> float:
arg += idx0
t0 = tvec[arg]
y0 = yvec[arg]
t1 = tvec[arg + 1]
y1 = yvec[arg + 1]
with np.errstate(divide='ignore', invalid='ignore'):
ans = t0 + (threshold - y0) * (t1 - t0) / (y1 - y0)
return ans if t0 <= ans <= t1 else np.inf