diff --git a/Configs/infer_1photon.yaml b/Configs/infer_1photon.yaml index 9306a7a..ceaa981 100644 --- a/Configs/infer_1photon.yaml +++ b/Configs/infer_1photon.yaml @@ -1,6 +1,6 @@ # configs/infer_1photon.yaml experiment: - name: "FlatField_2filters" # options: SiemenStarLowerLeft, SiemenStarLowerRight + name: "KnifeEdge_3filters_pos0" # options: SiemenStarLowerLeft, SiemenStarLowerRight task: "1Photon" output_base: "./InferenceResults" @@ -43,6 +43,18 @@ data: NY: 101 file_range: [0, 16] # [0, 16) roi: [0, 101, 0, 101] + KnifeEdge_1filters: + file_pattern: "2603MaxIV_Edge1Filters_12keV/1Photon_CS3_chunk{}.h5" ### 12 keV + NX: 101 + NY: 101 + file_range: [0, 16] # [0, 16) + roi: [0, 101, 0, 101] + FlatField_1filters: + file_pattern: "2603MaxIV_Flat1Filters_12keV/1Photon_CS3_chunk{}.h5" ### 12 keV + NX: 101 + NY: 101 + file_range: [0, 16] # [0, 16) + roi: [0, 101, 0, 101] Uniformity_test: file_pattern: "2603MaxIV_Flat2Filters_12keV/1Photon_CS3_chunk{}.h5" ### 12 keV NX: 101 @@ -56,10 +68,10 @@ data: num_workers: 16 model: - base_dir: "/home/xie_x1/MLXID/DeepLearning/Results/" + base_dir: "/home/xie_x1/MLXID/DeepLearning/Results/1ph/" # experiment_name: "260408_1ph_12keV_v251022_03" # name: "singlePhoton251022_12keV_Noise0.13keV_E150_aug1.pth" ### std/mean = 0.033 - experiment_name: "260512_1ph_12keV_v260511_06" + experiment_name: "260512_12keV_v260511_06" name: "singlePhoton260511_12keV_Noise0.13keV_E500_aug1.pth" ### std/mean = 0.025 inference: diff --git a/Configs/infer_2photon.yaml b/Configs/infer_2photon.yaml index 789ba23..50fa513 100644 --- a/Configs/infer_2photon.yaml +++ b/Configs/infer_2photon.yaml @@ -77,10 +77,10 @@ data: num_workers: 16 model: - base_dir: "/home/xie_x1/MLXID/DeepLearning/Results/" + base_dir: "/home/xie_x1/MLXID/DeepLearning/Results/2ph" - experiment_name: "260609_2ph_12keV_v260608_04" - name: "doublePhoton260608_12keV_Noise0.13keV_E500.pth" + experiment_name: "260610_12keV_v260610_01" + name: "doublePhoton260610_12keV_Noise0.13keV_E500.pth" inference: binning_factor: 10 diff --git a/Infer_1Photon.py b/Infer_1Photon.py index ff5f169..c4fc9a8 100644 --- a/Infer_1Photon.py +++ b/Infer_1Photon.py @@ -3,6 +3,8 @@ sys.path.append('./src') import matplotlib matplotlib.use('Agg') +from etaInterpolationFromPoints import interpolate_eta_from_points + from pathlib import Path from omegaconf import OmegaConf import torch @@ -46,11 +48,7 @@ def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) -> return corrected.mean(dim=1) def prepare_output_folder(conf): - if conf.data.normalize: - normalize_suffix = '_normalized' - else: - normalize_suffix = '' - output_base = Path(conf.experiment.output_base) / conf.experiment.name / conf.model.experiment_name / f'augX{conf.inference.num_aug_ops}{normalize_suffix}' + output_base = Path(conf.experiment.output_base) / conf.experiment.name / (f'1ph_{conf.model.experiment_name}') / (f'augX{conf.inference.num_aug_ops}') output_base.mkdir(parents=True, exist_ok=True) OmegaConf.save(conf, output_base / 'config.yaml') return output_base @@ -77,41 +75,49 @@ def run_inference(model, data_loader, conf): offset = [inputs.shape[-2] / 2., inputs.shape[-1] / 2.] offset = torch.tensor(offset).unsqueeze(0) # (1, 2) all_predictions += offset - print(f'mean x = {torch.mean(all_predictions[:, 0]):.4f}, std x = {torch.std(all_predictions[:, 0]):.4f}') - print(f'mean y = {torch.mean(all_predictions[:, 1]):.4f}, std y = {torch.std(all_predictions[:, 1]):.4f}') + print(f'[Inference]: mean x = {torch.mean(all_predictions[:, 0]):.4f}, std x = {torch.std(all_predictions[:, 0]):.4f}') + print(f'[Inference]: mean y = {torch.mean(all_predictions[:, 1]):.4f}, std y = {torch.std(all_predictions[:, 1]):.4f}') return all_predictions.numpy(), all_reference_points def accumulate_hits(predictions: np.ndarray, reference_points: np.ndarray, - binning_factor: int): - ml_super_frame = np.zeros((NY * binning_factor, NX * binning_factor)) + binning_factor: int, number_of_subframes: int = 20): + ml_super_frames = np.zeros((number_of_subframes, NY * binning_factor, NX * binning_factor)) count_frame = np.zeros((NY, NX)) subpixel_dist = np.zeros((binning_factor, binning_factor)) - # 绝对坐标 = 预测亚像素 + 参考点(像素左下角) + # absolute coordinate = predicted subpixel + reference point absolute_positions = predictions + reference_points[:, :2] - # 超分辨帧 (binning) + # super resolution frames (binning) hit_x = np.floor(absolute_positions[:, 0] * binning_factor).astype(int) hit_y = np.floor(absolute_positions[:, 1] * binning_factor).astype(int) - np.add.at(ml_super_frame, (hit_y, hit_x), 1) - # 计数帧 (按参考点像素索引) - ref_x = (reference_points[:, 0] + 1).astype(int) # 参考点是左下角，+1 得像素索引 + for i in range(number_of_subframes): + start_idx = i * len(predictions) // number_of_subframes + end_idx = min((i + 1) * len(predictions) // number_of_subframes, len(predictions)) + np.add.at(ml_super_frames[i], (hit_y[start_idx:end_idx], hit_x[start_idx:end_idx]), 1) + + # count frame (by reference point pixel index) + ref_x = (reference_points[:, 0] + 1).astype(int) # reference point is lower-left corner, +1 to get pixel index ref_y = (reference_points[:, 1] + 1).astype(int) np.add.at(count_frame, (ref_y, ref_x), 1) - # 亚像素分布 + # subpixel distribution sub_x = np.floor((absolute_positions[:, 0] % 1) * binning_factor).astype(int) sub_y = np.floor((absolute_positions[:, 1] % 1) * binning_factor).astype(int) np.add.at(subpixel_dist, (sub_y, sub_x), 1) - return ml_super_frame, count_frame, subpixel_dist + return ml_super_frames, count_frame, subpixel_dist -def save_results(ml_super_frame, count_frame, subpixel_dist, +def save_results(ml_super_frames, ml_super_frames_eta, + count_frame, subpixel_dist, subpixel_dist_eta, roi: list, binning_factor: int, output_dir: Path): x_min, x_max, y_min, y_max = roi # 1. super-resolution frame + np.save(output_dir / '1Photon_ML_superFrames.npy', ml_super_frames) + + ml_super_frame = np.sum(ml_super_frames, axis=0) plt.figure(figsize=(8, 8)) plt.imshow(ml_super_frame[y_min*binning_factor:y_max*binning_factor, x_min*binning_factor:x_max*binning_factor], origin='lower', extent=[x_min, x_max, y_min, y_max]) plt.colorbar(label='Counts') @@ -121,8 +127,22 @@ def save_results(ml_super_frame, count_frame, subpixel_dist, plt.savefig(output_dir / '1Photon_ML_superFrame.png', dpi=300, bbox_inches='tight') plt.close() np.save(output_dir / '1Photon_ML_superFrame.npy', ml_super_frame) - - # 2. count frame + + # 2. super-resolution frame with eta interpolation + np.save(output_dir / '1Photon_ML_superFrames_etaInterpolated.npy', ml_super_frames_eta) + + ml_super_frame_eta = np.sum(ml_super_frames_eta, axis=0) + plt.figure(figsize=(8, 8)) + plt.imshow(ml_super_frame_eta[y_min*binning_factor:y_max*binning_factor, x_min*binning_factor:x_max*binning_factor], origin='lower', extent=[x_min, x_max, y_min, y_max]) + plt.colorbar(label='Counts') + plt.title('ML Super-Resolution Frame with Eta Interpolation') + plt.xlabel('X (pixel)') + plt.ylabel('Y (pixel)') + plt.savefig(output_dir / '1Photon_ML_superFrame_etaInterpolated.png', dpi=300, bbox_inches='tight') + plt.close() + np.save(output_dir / '1Photon_ML_superFrame_etaInterpolated.npy', ml_super_frame_eta) + + # 3. count frame plt.figure(figsize=(8, 8)) plt.imshow(count_frame[y_min:y_max, x_min:x_max], origin='lower', extent=[x_min, x_max, y_min, y_max]) plt.colorbar(label='Counts') @@ -133,7 +153,7 @@ def save_results(ml_super_frame, count_frame, subpixel_dist, plt.close() np.save(output_dir / '1Photon_count_Frame.npy', count_frame) - # 3. sub-pixel distribution + # 4. sub-pixel distribution plt.figure(figsize=(8, 8)) plt.imshow(subpixel_dist, origin='lower', extent=[0, binning_factor, 0, binning_factor], @@ -146,9 +166,24 @@ def save_results(ml_super_frame, count_frame, subpixel_dist, plt.close() np.save(output_dir / '1Photon_subpixel_Distribution.npy', subpixel_dist) rms, mean = np.std(subpixel_dist), np.mean(subpixel_dist) - print(f"[Plotting]: Sub-pixel distribution: RMS/Mean: {rms/mean:.4f}, expected value = {1/np.sqrt(mean):.4f} for uniform distribution") + print(f"[Plotting]: Sub-pixel distribution: RMS/Mean: {rms/mean:.4f}, expected value = {1/np.sqrt(mean):.4f} for uniform distribution") + + # 5. sub-pixel distribution with eta interpolation + plt.figure(figsize=(8, 8)) + plt.imshow(subpixel_dist_eta, origin='lower', + extent=[0, binning_factor, 0, binning_factor], + cmap='viridis') + plt.colorbar(label='Counts') + plt.title('Sub-pixel Distribution with Eta Interpolation') + plt.xlabel(f'Sub-pixel X (1/{binning_factor} pixel)') + plt.ylabel(f'Sub-pixel Y (1/{binning_factor} pixel)') + plt.savefig(output_dir / '1Photon_subpixel_Distribution_etaInterpolated.png', dpi=300, bbox_inches='tight') + plt.close() + np.save(output_dir / '1Photon_subpixel_Distribution_etaInterpolated.npy', subpixel_dist_eta) + rms_eta, mean_eta = np.std(subpixel_dist_eta), np.mean(subpixel_dist_eta) + print(f"[Plotting]: Sub-pixel distribution with eta interpolation: RMS/Mean: {rms_eta/mean_eta:.4f}, expected value = {1/np.sqrt(mean_eta):.4f} for uniform distribution") - print(f"Results saved to: {output_dir}") + print(f"[Plotting]: Results saved to: {output_dir}") if __name__ == "__main__": ### output preparation @@ -168,6 +203,9 @@ if __name__ == "__main__": flag_normalize = conf.data.normalize nChunks = int(np.ceil(len(files_list) / conf.inference.chunk_size)) + list_of_predictions = [] + list_of_reference_points = [] + ml_super_frame = np.zeros((NY * BinningFactor, NX * BinningFactor)) count_frame = np.zeros((NY, NX)) subpixel_dist = np.zeros((BinningFactor, BinningFactor)) @@ -195,14 +233,30 @@ if __name__ == "__main__": pin_memory=True ) - predictions, ref_points = run_inference(model, data_loader, conf) + _predictions, _ref_points = run_inference(model, data_loader, conf) + list_of_predictions.append(_predictions) + list_of_reference_points.append(_ref_points) - chunk_super, chunk_count, chunk_subpixel = accumulate_hits( - predictions, ref_points, binning_factor=BinningFactor - ) - ml_super_frame += chunk_super - count_frame += chunk_count - subpixel_dist += chunk_subpixel - save_results(ml_super_frame, count_frame, subpixel_dist, + del dataset, data_loader + torch.cuda.empty_cache() + + predictions = np.concatenate(list_of_predictions, axis=0) + ref_points = np.concatenate(list_of_reference_points, axis=0) + + del list_of_predictions, list_of_reference_points + + ml_super_frames, count_frame, subpixel_dist = accumulate_hits( + predictions, ref_points, binning_factor=BinningFactor + ) + + print('[Main]: Applying eta interpolation to predictions...') + predictions_eta_interpolated = interpolate_eta_from_points(predictions) + ml_super_frames_eta, count_frame_eta, subpixel_dist_eta = accumulate_hits( + predictions_eta_interpolated, ref_points, binning_factor=BinningFactor + ) + + save_results(ml_super_frames, ml_super_frames_eta, + count_frame, subpixel_dist, subpixel_dist_eta, roi=roi, binning_factor=BinningFactor, - output_dir=output_dir) \ No newline at end of file + output_dir=output_dir + ) \ No newline at end of file diff --git a/Infer_2Photon.py b/Infer_2Photon.py index 00d9418..e3d47eb 100644 --- a/Infer_2Photon.py +++ b/Infer_2Photon.py @@ -3,6 +3,8 @@ sys.path.append('./src') import matplotlib matplotlib.use('Agg') +from etaInterpolationFromPoints import interpolate_eta_from_points + from pathlib import Path from omegaconf import OmegaConf import torch @@ -46,11 +48,7 @@ def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) -> return corrected.mean(dim=1) def prepare_output_folder(conf): - if conf.data.normalize: - normalize_suffix = '_normalized' - else: - normalize_suffix = '' - output_base = Path(conf.experiment.output_base) / conf.experiment.name / conf.model.experiment_name / f'augX{conf.inference.num_aug_ops}{normalize_suffix}' + output_base = Path(conf.experiment.output_base) / conf.experiment.name / (f'2ph_{conf.model.experiment_name}') / (f'augX{conf.inference.num_aug_ops}') output_base.mkdir(parents=True, exist_ok=True) OmegaConf.save(conf, output_base / 'config.yaml') return output_base @@ -74,45 +72,52 @@ def run_inference(model, data_loader, conf): all_predictions = torch.cat(all_predictions, dim=0) # all_predictions = apply_inverse_transforms(all_predictions, conf.inference.num_aug_ops) all_predictions += torch.tensor([conf.data.names[conf.experiment.name].nSize/2., conf.data.names[conf.experiment.name].nSize/2.]).unsqueeze(0) # adjust back to original coordinate system - print(f'mean x = {torch.mean(all_predictions[:, 0])}, std x = {torch.std(all_predictions[:, 0])}') - print(f'mean y = {torch.mean(all_predictions[:, 1])}, std y = {torch.std(all_predictions[:, 1])}') + print(f'[Inference]: mean x = {torch.mean(all_predictions[:, 0])}, std x = {torch.std(all_predictions[:, 0])}') + print(f'[Inference]: mean y = {torch.mean(all_predictions[:, 1])}, std y = {torch.std(all_predictions[:, 1])}') referencePoints = data_loader.dataset.referencePoint ### the lower-left corner of the cluster in absolute coordinate referencePoints = np.repeat(referencePoints, 2, axis=0) ### duplicate reference points for 2-photon clusters return all_predictions.numpy(), referencePoints def accumulate_hits(predictions: np.ndarray, reference_points: np.ndarray, - binning_factor: int): - ### ret - ml_super_frame = np.zeros((NY*binning_factor, NX*binning_factor), dtype=np.int32) + binning_factor: int, number_of_subframes: int = 20): + ml_super_frames = np.zeros((number_of_subframes, NY * binning_factor, NX * binning_factor)) count_frame = np.zeros((NY, NX), dtype=np.int32) subpixel_dist = np.zeros((binning_factor, binning_factor), dtype=np.int32) ### absolute coordinate = predicted subpixel + reference point absolute_positions = predictions + reference_points - hit_x_superpixel_idx = np.floor(absolute_positions[:, 0] * binning_factor).astype(int) - hit_x_superpixel_idx = np.clip(hit_x_superpixel_idx, 0, NX*binning_factor-1) - hit_y_superpixel_idx = np.floor(absolute_positions[:, 1] * binning_factor).astype(int) - hit_y_superpixel_idx = np.clip(hit_y_superpixel_idx, 0, NY*binning_factor-1) - np.add.at(ml_super_frame, (hit_y_superpixel_idx, hit_x_superpixel_idx), 1) - hit_x_pixel_idx = np.floor(absolute_positions[:, 0]).astype(int) - hit_x_pixel_idx = np.clip(hit_x_pixel_idx, 0, NX-1) - hit_y_pixel_idx = np.floor(absolute_positions[:, 1]).astype(int) - hit_y_pixel_idx = np.clip(hit_y_pixel_idx, 0, NY-1) - np.add.at(count_frame, (hit_y_pixel_idx, hit_x_pixel_idx), 1) + # super resolution frames (binning) + hit_x = np.floor(absolute_positions[:, 0] * binning_factor).astype(int) + hit_y = np.floor(absolute_positions[:, 1] * binning_factor).astype(int) + + for i in range(number_of_subframes): + start_idx = i * len(predictions) // number_of_subframes + end_idx = min((i + 1) * len(predictions) // number_of_subframes, len(predictions)) + np.add.at(ml_super_frames[i], (hit_y[start_idx:end_idx], hit_x[start_idx:end_idx]), 1) + + # count frame (by reference point pixel index) + ref_x = (reference_points[:, 0] + 1).astype(int) # reference point is lower-left corner, +1 to get pixel index + ref_y = (reference_points[:, 1] + 1).astype(int) + np.add.at(count_frame, (ref_y, ref_x), 1) + + # subpixel distribution + sub_x = np.floor((absolute_positions[:, 0] % 1) * binning_factor).astype(int) + sub_y = np.floor((absolute_positions[:, 1] % 1) * binning_factor).astype(int) + np.add.at(subpixel_dist, (sub_y, sub_x), 1) + + return ml_super_frames, count_frame, subpixel_dist - subpixel_x_idx = np.floor((absolute_positions[:, 0] % 1) * binning_factor).astype(int) - subpixel_y_idx = np.floor((absolute_positions[:, 1] % 1) * binning_factor).astype(int) - np.add.at(subpixel_dist, (subpixel_y_idx, subpixel_x_idx), 1) - - return ml_super_frame, count_frame, subpixel_dist - -def save_results(ml_super_frame, count_frame, subpixel_dist, +def save_results(ml_super_frames, ml_super_frames_eta, + count_frame, subpixel_dist, subpixel_dist_eta, roi: list, binning_factor: int, output_dir: Path): x_st, x_ed, y_st, y_ed = roi # 1. super-resolution frame + np.save(output_dir / '2Photon_ML_superFrames.npy', ml_super_frames) + + ml_super_frame = np.sum(ml_super_frames, axis=0) plt.figure(figsize=(8, 8)) plt.imshow(ml_super_frame[y_st*binning_factor:y_ed*binning_factor, x_st*binning_factor:x_ed*binning_factor], origin='lower', extent=[x_st, x_ed, y_st, y_ed]) plt.colorbar(label='Counts') @@ -123,7 +128,21 @@ def save_results(ml_super_frame, count_frame, subpixel_dist, plt.clf() np.save(output_dir / '2Photon_ML_superFrame.npy', ml_super_frame) - # 2. count frame + # 2. super-resolution frame with eta interpolation + np.save(output_dir / '2Photon_ML_superFrames_etaInterpolated.npy', ml_super_frames_eta) + + ml_super_frame_eta = np.sum(ml_super_frames_eta, axis=0) + plt.figure(figsize=(8, 8)) + plt.imshow(ml_super_frame_eta[y_st*binning_factor:y_ed*binning_factor, x_st*binning_factor:x_ed*binning_factor], origin='lower', extent=[x_st, x_ed, y_st, y_ed]) + plt.colorbar(label='Counts') + plt.title('ML Super-Resolution Frame with Eta Interpolation') + plt.xlabel('X (pixel)') + plt.ylabel('Y (pixel)') + plt.savefig(output_dir / '2Photon_ML_superFrame_etaInterpolated.png', dpi=300, bbox_inches='tight') + plt.clf() + np.save(output_dir / '2Photon_ML_superFrame_etaInterpolated.npy', ml_super_frame_eta) + + # 3. count frame plt.imshow(count_frame[y_st:y_ed, x_st:x_ed], origin='lower', extent=[x_st, x_ed, y_st, y_ed]) plt.colorbar(label='Counts') plt.title('Photon Count Frame') @@ -133,7 +152,7 @@ def save_results(ml_super_frame, count_frame, subpixel_dist, plt.clf() np.save(output_dir / '2Photon_count_Frame.npy', count_frame) - # 3. subpixel distribution + # 4. subpixel distribution plt.imshow(subpixel_dist, origin='lower', extent=[0, 1, 0, 1]) plt.colorbar(label='Counts') plt.title('Subpixel Distribution') @@ -145,7 +164,19 @@ def save_results(ml_super_frame, count_frame, subpixel_dist, std, mean = np.std(subpixel_dist), np.mean(subpixel_dist) print(f"[Plotting]: Sub-pixel distribution: RMS/Mean: {std/mean:.4f}, expected value = {1/np.sqrt(mean):.4f} for uniform distribution") - print(f"Results saved to: {output_dir}") + # 5. subpixel distribution with eta interpolation + plt.imshow(subpixel_dist_eta, origin='lower', extent=[0, 1, 0, 1]) + plt.colorbar(label='Counts') + plt.title('Subpixel Distribution with Eta Interpolation') + plt.xlabel('Subpixel X') + plt.ylabel('Subpixel Y') + plt.savefig(output_dir / '2Photon_subpixel_Distribution_etaInterpolated.png', dpi=300, bbox_inches='tight') + plt.close() + np.save(output_dir / '2Photon_subpixel_Distribution_etaInterpolated.npy', subpixel_dist_eta) + std_eta, mean_eta = np.std(subpixel_dist_eta), np.mean(subpixel_dist_eta) + print(f"[Plotting]: Sub-pixel distribution with eta interpolation: RMS/Mean: {std_eta/mean_eta:.4f}, expected value = {1/np.sqrt(mean_eta):.4f} for uniform distribution") + + print(f"[Plotting]: Results saved to: {output_dir}") if __name__ == "__main__": ### output folder preparation @@ -165,6 +196,9 @@ if __name__ == "__main__": flag_normalize = conf.data.normalize nChunks = np.ceil(len(files_list) / conf.inference.chunk_size).astype(int) + list_of_predictions = [] + list_of_reference_points = [] + ml_super_frame = np.zeros((NY*BinningFactor, NX*BinningFactor), dtype=np.int32) count_frame = np.zeros((NY, NX), dtype=np.int32) subpixel_dist = np.zeros((BinningFactor, BinningFactor), dtype=np.int32) @@ -191,8 +225,29 @@ if __name__ == "__main__": ) predictions, reference_points = run_inference(model, dataLoader, conf) - ml_super_frame_chunk, count_frame_chunk, subpixel_dist_chunk = accumulate_hits(predictions, reference_points, BinningFactor) - ml_super_frame += ml_super_frame_chunk - count_frame += count_frame_chunk - subpixel_dist += subpixel_dist_chunk - save_results(ml_super_frame, count_frame, subpixel_dist, roi, BinningFactor, output_dir) + list_of_predictions.append(predictions) + list_of_reference_points.append(reference_points) + + del dataset, dataLoader + torch.cuda.empty_cache() + + predictions = np.concatenate(list_of_predictions, axis=0) + ref_points = np.concatenate(list_of_reference_points, axis=0) + + del list_of_predictions, list_of_reference_points + + ml_super_frames, count_frame, subpixel_dist = accumulate_hits( + predictions, ref_points, binning_factor=BinningFactor + ) + + print('[Main]: Applying eta interpolation to predictions...') + predictions_eta_interpolated = interpolate_eta_from_points(predictions) + ml_super_frames_eta, count_frame_eta, subpixel_dist_eta = accumulate_hits( + predictions_eta_interpolated, ref_points, binning_factor=BinningFactor + ) + + save_results(ml_super_frames, ml_super_frames_eta, + count_frame, subpixel_dist, subpixel_dist_eta, + roi=roi, binning_factor=BinningFactor, + output_dir=output_dir + ) diff --git a/src/etaInterpolationFromPoints.py b/src/etaInterpolationFromPoints.py new file mode 100644 index 0000000..084856c --- /dev/null +++ b/src/etaInterpolationFromPoints.py @@ -0,0 +1,27 @@ +import sys +sys.path.append('/home/xie_x1/MLXID/EtaInterpolation/') +from EtaInterpolationFunctions import build_xy_lut_Rosenblatt, bilinear_xy_lookup + +import numpy as np + +NEtaBins = 201 + +def interpolate_eta_from_points(points): + points_integer = np.int32(points) + points_fractional = points - points_integer + points_fractional_x = points_fractional[:, 0] + points_fractional_y = points_fractional[:, 1] + + print('[EtaInterpolation]: Building eta histogram from points...') + eta_hist2D = np.histogram2d( + points_fractional_x, points_fractional_y, + bins = NEtaBins, range=[[0, 1], [0, 1]] + )[0] ### shape (201, 201), X, Y, according build_xy + + U_tab, V_tab = build_xy_lut_Rosenblatt(eta_hist2D) + + print('[EtaInterpolation]: Performing bilinear interpolation...') + x_frac, y_frac = bilinear_xy_lookup(points_fractional_x, points_fractional_y, U_tab, V_tab) + + corrected_points = points_integer + np.stack([x_frac, y_frac], axis=-1) + return corrected_points \ No newline at end of file