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Commit 8ae2dd7a authored by xiyehu2's avatar xiyehu2
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single moving signal generation

parent d45bc07f
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from typing import Dict, Tuple, Any
import numpy as np
from scipy.interpolate import interp1d
from AWG import *
......@@ -136,10 +138,11 @@ def create_path_table(wfm: Waveform) -> any:
def create_path_table_reduced(
wfm: Waveform, target_idx, max_dist=np.inf
wfm: Waveform, target_idx, max_dist=np.inf, save_path=None
) -> Tuple[Dict[Tuple[int, int], np.ndarray], np.ndarray]:
"""
create a dim-3 look up table where the table[i,j] contains a sine wave to move tweezer i to tweezer j
:param save_path: file saving path
:param target_idx: indices of target pattern
:param max_dist: maximum move distance in indices
:param wfm: waveform object already initialized with basic parameters.
......@@ -179,7 +182,8 @@ def create_path_table_reduced(
path_table = {} # lookup table to store all moves
static_sig = np.zeros(sample_len) # for fast real-time waveform generation purposes
t = np.arange(sample_len) / wfm.sample_rate # time series
# iterate! I think this part can be vectorized as well... but unnecessary.
# iterate!
for i in range(n):
omega_i = wfm.omega[i]
for j in moves[i]: # j is the target position, i is starting position
......@@ -259,6 +263,10 @@ def create_path_table_reduced(
path_table[key] -= path_table[(key[1], key[1])] # for fast real-time generation
# path_table[key] = path_table[key].astype(np.int16)
# save stuff if prompted
if save_path is not None:
np.savez(save_path, table=path_table, static_sig=static_sig, wfm=wfm, target=target_idx)
return path_table, static_sig.astype(np.int16)
......@@ -313,18 +321,23 @@ def create_moving_array(path_table: np.ndarray, paths: np.ndarray) -> np.ndarray
def create_moving_array_reduced(
sig: np.ndarray,
path_table: Dict,
paths: np.ndarray,
off: np.ndarray
sig: np.ndarray,
filled_idx: np.ndarray,
target_idx: np.ndarray,
# paths: np.ndarray,
# off: np.ndarray
):
"""
create a rearranging signal that moves tweezers as specified by paths.
:param sig: initially a static-array-generating waveform.
:param path_table: lookup table returned from create_path_table_reduced().
:param filled_idx: see get_rearrange_paths for detail.
:param target_idx: see get_rearrange_paths for detail.
:param paths: 2d array with moving trajectories, [:,0] stores start pos, [:,1] stores end pos.
:param off: 1d array with tweezer indices that need to be set to 0.
"""
paths, off = get_rearrange_paths(filled_idx, target_idx)
for i, j in paths:
if i == j:
continue
......@@ -335,3 +348,30 @@ def create_moving_array_reduced(
for i in off:
sig -= path_table[(i, i)]
pass
def create_moving_signal_single(omega_i, omega_f, sample_rate, signal_time):
min_len = 2 * sample_rate / (10e3)
sample_len = sample_rate * signal_time
sample_len += min_len - sample_len % min_len
sample_len = int(sample_len)
t = np.arange(sample_len) / sample_rate
t_tot = sample_len / sample_rate
a = 4 * np.abs(omega_f - omega_i) / (t_tot ** 2)
end = sample_len
half = int(end / 2) + 1
t1 = t[:half]
t2 = t[half:end] - t_tot / 2
amps = 2**12
signal = np.zeros(sample_len)
signal[:half] = omega_i * t1 - a / 6 * t1 ** 3 # t<=T/2
# ph = wfm.phi[i] + omega_i * t_tot / 2 + a / 6 * (t_tot / 2) ** 3
signal[half:end] = signal[half - 1] + \
(omega_i - a / 2 * (t_tot / 2) ** 2) * t2 - \
a / 2 * t_tot / 2 * t2 ** 2 + \
a / 6 * t2 ** 3 # t>=T/2
signal[end:] = signal[end - 1] + omega_f * (t[end:] - t[end - 1])
path = (amps * np.sin(signal)).astype(np.int16)
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