diff --git a/Python/waveform.py b/Python/waveform.py
index 1c93946284ec1d979faaa4887b4783f28a2f20fc..e7167d8cc7002d5e7ea62f64724baf9d57ab3cd3 100644
--- a/Python/waveform.py
+++ b/Python/waveform.py
@@ -136,14 +136,51 @@ def create_path_table(wfm: Waveform) -> any:
# return path_table.astype(int), np.sum(path_table.diagonal().T, axis=0, dtype=int)
+def stack_left(i_start, i_end, offset, stack_size=0):
+ # calculate first index where the reduced path algorithm is applied
+ # threshold = 0.01
+ # cutoff = np.ceil(np.log(threshold) / np.log(1-load_p))
+ # cutoff = int(cutoff)
+ # print(cutoff)
+ if stack_size == 0:
+ stack_size = np.floor((i_end - i_start) / 2)
+ stack_last = int(stack_size + i_start) - 1
+ dist_mod = (i_end - i_start - stack_size) / (i_end - i_start) # max_distance ratio
+ dist_add = offset
+
+ # get a list of moves to pre-generate
+ moves = []
+ max_dist = 0
+ for i in range(i_start, i_end):
+ moves.append([])
+ j_max = i if i < stack_last else stack_last
+ dist = np.ceil((i - i_start) * dist_mod + dist_add)
+ j_min = int(i - dist) if i - dist >= i_start else i_start
+ for j in range(j_min, j_max + 1):
+ moves[i - i_start].append(j) # add all paths between j_min and j_max
+ if max_dist < abs(j-i):
+ max_dist = abs(j-i)
+ return moves, max_dist
+
+
+def stack_right(i_start, i_end, offset, stack_size=0):
+ moves, max_dist = stack_left(i_start, i_end, offset=offset, stack_size=stack_size)
+ moves.reverse()
+ for i in range(len(moves)):
+ moves[i].reverse()
+ for j in range(len(moves[i])):
+ moves[i][j] = i_end - 1 - moves[i][j] + i_start
+ return moves, max_dist
+
+
def create_path_table_reduced(
- wfm: Waveform, target_idx, max_dist=np.inf, save_path=None
+ wfm: Waveform, target_idx, dist_offset=np.inf, save_path=None, partition=False
) -> 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 dist_offset: maximum move distance in indices
:param wfm: waveform object already initialized with basic parameters.
:return: dictionary containing rearrange paths
"""
@@ -153,19 +190,38 @@ def create_path_table_reduced(
a = wfm.amplitude
omega_interp = interp1d(w, a, kind='cubic')
- # obtain all move combinations:
- n = len(wfm.omega) # total number of tweezers
+ nt = len(wfm.omega) # total number of tweezers
moves = []
+ target = np.zeros(nt)
+ target[target_idx] = 1
dw_max = 0 # longest move, this sets the size of path_table
- for i in range(n):
- moves.append([])
- for j in target_idx:
- if i < j and True: # only allow uni-direction moves
- continue
- if abs(i - j) <= max_dist:
- moves[i].append(j)
- dw = abs(wfm.omega[j] - wfm.omega[i])
- if dw_max < dw: dw_max = dw
+
+ if not partition:
+ # obtain all move combinations, target based, non-partitioned:
+ for i in range(nt):
+ moves.append([])
+ for j in target_idx:
+ if i < j and True: # only allow uni-direction moves
+ continue
+ if abs(i - j) <= dist_offset:
+ moves[i].append(j)
+ dw = abs(wfm.omega[j] - wfm.omega[i])
+ if dw_max < dw: dw_max = dw
+ if partition:
+ offset = dist_offset
+ divide_idx = int(np.floor(np.median(target_idx)))
+ left_size = np.sum(target[:divide_idx], dtype=int)
+ right_size = np.sum(target[divide_idx:], dtype=int)
+ moves_l, dw_max_l = stack_right(0, divide_idx, offset, left_size) # stack left side to right
+ moves_r, dw_max_r = stack_left(divide_idx, nt, offset, right_size)
+ # print("stack size, half size, middle:", len(t_idx), left_size, right_size)
+ moves_l.extend(moves_r)
+ moves = moves_l
+ dw_max = dw_max_l if dw_max_l > dw_max_r else dw_max_r
+ dw_max = abs(wfm.omega[dw_max] - wfm.omega[0])
+
+ print(dw_max / 2 / np.pi)
+
# setup basic variables
twopi = 2 * np.pi
vmax = KILO(20) * MEGA(1) # convert units, 20 kHz/us -> 20e3 * 1e6 Hz/s
@@ -271,13 +327,13 @@ def create_path_table_reduced(
def create_path_table_reduced_gpu(
- wfm: Waveform, target_idx, max_dist=np.inf, save_path=None
+ wfm: Waveform, target_idx, dist_offset=np.inf, save_path=None, partition=False
) -> 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 dist_offset: maximum move distance in indices
:param wfm: waveform object already initialized with basic parameters.
:return: dictionary containing rearrange paths
"""
@@ -289,19 +345,37 @@ def create_path_table_reduced_gpu(
a = wfm.amplitude
omega_interp = interp1d(w, a, kind='cubic')
- # obtain all move combinations:
- n = len(wfm.omega) # total number of tweezers
+ nt = len(wfm.omega) # total number of tweezers
moves = []
+ target = np.zeros(nt)
+ target[target_idx] = 1
dw_max = 0 # longest move, this sets the size of path_table
- for i in range(n):
- moves.append([])
- for j in target_idx:
- if i < j and True: # only allow uni-direction moves
- continue
- if abs(i - j) <= max_dist:
- moves[i].append(j)
- dw = abs(wfm.omega[j] - wfm.omega[i])
- if dw_max < dw: dw_max = dw
+
+ if not partition:
+ # obtain all move combinations, target based, non-partitioned:
+ for i in range(nt):
+ moves.append([])
+ for j in target_idx:
+ if i < j and True: # only allow uni-direction moves
+ continue
+ if abs(i - j) <= dist_offset:
+ moves[i].append(j)
+ dw = abs(wfm.omega[j] - wfm.omega[i])
+ if dw_max < dw: dw_max = dw
+ if partition:
+ offset = dist_offset
+ divide_idx = int(np.floor(np.median(target_idx)))
+ left_size = np.sum(target[:divide_idx], dtype=int)
+ right_size = np.sum(target[divide_idx:], dtype=int)
+ moves_l, dw_max_l = stack_right(0, divide_idx, offset, left_size) # stack left side to right
+ moves_r, dw_max_r = stack_left(divide_idx, nt, offset, right_size)
+ # print("stack size, half size, middle:", len(t_idx), left_size, right_size)
+ moves_l.extend(moves_r)
+ moves = moves_l
+ dw_max = dw_max_l if dw_max_l > dw_max_r else dw_max_r
+ dw_max = abs(wfm.omega[dw_max] - wfm.omega[0])
+
+ print("max dw:", dw_max / 2 / np.pi)
# setup basic variables
twopi = 2 * np.pi