Performance optimization
Use lists as intermediate data structure to avoid lots of numpy array allocations
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@ -169,23 +169,26 @@ def merge_h5_scans(scan_into, scan_from):
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scan_motor = scan_into["scan_motor"] # the same as scan_from["scan_motor"]
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pos_all = scan_into["init_scan"][scan_motor]
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val_all = scan_into["init_scan"]["counts"]
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err_all = scan_into["init_scan"]["counts_err"] ** 2
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pos_all = [scan_into["init_scan"][scan_motor]]
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val_all = [scan_into["init_scan"]["counts"]]
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err_all = [scan_into["init_scan"]["counts_err"] ** 2]
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for scan in scan_into["merged_scans"]:
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pos_all = np.append(pos_all, scan[scan_motor])
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val_all = np.concatenate((val_all, scan["counts"]))
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err_all = np.concatenate((err_all, scan["counts_err"] ** 2))
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pos_all.append(scan[scan_motor])
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val_all.append(scan["counts"])
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err_all.append(scan["counts_err"] ** 2)
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pos_all = np.concatenate(pos_all)
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val_all = np.concatenate(val_all)
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err_all = np.concatenate(err_all)
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sort_index = np.argsort(pos_all)
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pos_all = pos_all[sort_index]
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val_all = val_all[sort_index]
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err_all = err_all[sort_index]
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pos_tmp = pos_all[:1]
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val_tmp = val_all[:1]
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err_tmp = err_all[:1]
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num_tmp = np.array([1])
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pos_tmp = [pos_all[0]]
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val_tmp = [val_all[:1]]
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err_tmp = [err_all[:1]]
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num_tmp = [1]
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for pos, val, err in zip(pos_all[1:], val_all[1:], err_all[1:]):
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if pos - pos_tmp[-1] < MOTOR_POS_PRECISION:
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# the repeated motor position
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@ -194,10 +197,14 @@ def merge_h5_scans(scan_into, scan_from):
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num_tmp[-1] += 1
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else:
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# a new motor position
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pos_tmp = np.append(pos_tmp, pos)
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val_tmp = np.concatenate((val_tmp, val[None, :]))
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err_tmp = np.concatenate((err_tmp, err[None, :]))
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num_tmp = np.append(num_tmp, 1)
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pos_tmp.append(pos)
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val_tmp.append(val[None, :])
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err_tmp.append(err[None, :])
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num_tmp.append(1)
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pos_tmp = np.array(pos_tmp)
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val_tmp = np.concatenate(val_tmp)
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err_tmp = np.concatenate(err_tmp)
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num_tmp = np.array(num_tmp)
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scan_into[scan_motor] = pos_tmp
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scan_into["counts"] = val_tmp / num_tmp[:, None, None]
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