MLXID/DeepLearning
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix plotting X/Y range bug

Browse Source
This commit is contained in:
xie_x1
2026-03-20 16:23:01 +01:00
parent 90cb2f5a47
commit c1e2117729
1 changed files with 7 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Infer_1Photon.py
+7 -7
View File
@@ -8,9 +8,9 @@ from matplotlib import pyplot as plt
import numpy as np import numpy as np
import h5py import h5py
torch.manual_seed(42) torch.manual_seed(0)
torch.cuda.manual_seed(42) torch.cuda.manual_seed(0)
np.random.seed(42) np.random.seed(0)
torch.backends.cudnn.deterministic = True torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False torch.backends.cudnn.benchmark = False
@@ -19,7 +19,7 @@ NX, NY = 400, 400
configs = {} configs = {}
configs['SiemenStarLowerLeft'] = { configs['SiemenStarLowerLeft'] = {
# 'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/SiemenStarLowerLeft/clusters_chunk{i}.h5' for i in range(1)], # 200 files, no zeroing pixels outside the cluster # 'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/SiemenStarLowerLeft/clusters_chunk{i}.h5' for i in range(1)], # 200 files, no zeroing pixels outside the cluster
'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerLeft/1Photon_CS3_chunk{i}.h5' for i in range(1)], # 160 files, no zeroing pixels outside the cluster 'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerLeft/1Photon_CS3_chunk{i}.h5' for i in range(160)], # 160 files, no zeroing pixels outside the cluster
'modelVersion': '251022', 'modelVersion': '251022',
'energy': 15, # keV 'energy': 15, # keV
'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max, 'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max,
@@ -76,7 +76,7 @@ def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) ->
if __name__ == "__main__": if __name__ == "__main__":
model = models.get_model_class(config['modelVersion'])().cuda() model = models.get_model_class(config['modelVersion'])().cuda()
modelName = f'singlePhoton{config["modelVersion"]}_{config["energy"]}keV_Noise{config["noise"]}keV_E500_aug1' modelName = f'singlePhoton{config["modelVersion"]}_{config["energy"]}keV_Noise{config["noise"]}keV_E150_aug1'
if flag_normalize: if flag_normalize:
modelName += '_normalized' modelName += '_normalized'
model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/{modelName}.pth', weights_only=True)) model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/{modelName}.pth', weights_only=True))
@@ -143,14 +143,14 @@ if __name__ == "__main__":
plt.clf() plt.clf()
mlSuperFrame = mlSuperFrame[config['roi'][2]*BinningFactor : config['roi'][3]*BinningFactor, config['roi'][0]*BinningFactor : config['roi'][1]*BinningFactor] mlSuperFrame = mlSuperFrame[config['roi'][2]*BinningFactor : config['roi'][3]*BinningFactor, config['roi'][0]*BinningFactor : config['roi'][1]*BinningFactor]
average = np.mean(mlSuperFrame) average = np.mean(mlSuperFrame)
plt.imshow(mlSuperFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed]) plt.imshow(mlSuperFrame, origin='lower', extent=[X_st, X_ed, Y_st, Y_ed])
plt.colorbar() plt.colorbar()
plt.savefig(f'{outputDir}/1Photon_ML_superFrame.png', dpi=300) plt.savefig(f'{outputDir}/1Photon_ML_superFrame.png', dpi=300)
np.save(f'{outputDir}/1Photon_ML_superFrame.npy', mlSuperFrame) np.save(f'{outputDir}/1Photon_ML_superFrame.npy', mlSuperFrame)
plt.clf() plt.clf()
countFrame = countFrame[config['roi'][2] : config['roi'][3], config['roi'][0] : config['roi'][1]] countFrame = countFrame[config['roi'][2] : config['roi'][3], config['roi'][0] : config['roi'][1]]
plt.imshow(countFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed]) plt.imshow(countFrame, origin='lower', extent=[X_st, X_ed, Y_st, Y_ed])
plt.colorbar() plt.colorbar()
plt.savefig(f'{outputDir}/1Photon_count_Frame.png', dpi=300) plt.savefig(f'{outputDir}/1Photon_count_Frame.png', dpi=300)
np.save(f'{outputDir}/1Photon_count_Frame.npy', countFrame) np.save(f'{outputDir}/1Photon_count_Frame.npy', countFrame)