Go to the source code of this file.
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| | run_cnn_1class.parser = argparse.ArgumentParser(description='Run CNN over a full 2D projection.') |
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| | run_cnn_1class.help |
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| | run_cnn_1class.default |
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| | run_cnn_1class.args = parser.parse_args() |
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| | run_cnn_1class.PATCH_SIZE_W = int(args.rows) |
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| | run_cnn_1class.PATCH_SIZE_D = int(args.cols) |
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| bool | run_cnn_1class.crop_event = False |
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| | run_cnn_1class.rootModule = args.module |
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| | run_cnn_1class.rootFile = TFile(args.input) |
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| list | run_cnn_1class.keys = [rootModule+'/'+k.GetName()[:-4] for k in rootFile.Get(rootModule).GetListOfKeys() if '_raw' in k.GetName()] |
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| | run_cnn_1class.evname = keys[int(args.event)] |
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| | run_cnn_1class.raw |
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| | run_cnn_1class.deposit |
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| | run_cnn_1class.pdg |
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| | run_cnn_1class.tracks |
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| | run_cnn_1class.showers |
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| | run_cnn_1class.full2d = int(args.full) |
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| | run_cnn_1class.total_patches |
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| | run_cnn_1class.inputs = np.zeros((total_patches, PATCH_SIZE_W, PATCH_SIZE_D), dtype=np.float32) |
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| int | run_cnn_1class.cnt_ind = 0 |
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| | run_cnn_1class.model_name = args.net |
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| | run_cnn_1class.m = load_model(model_name) |
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| | run_cnn_1class.loss |
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| | run_cnn_1class.optimizer |
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| | run_cnn_1class.pred = m.predict(inputs.reshape(inputs.shape[0], 1, PATCH_SIZE_W, PATCH_SIZE_D)) |
| |
| | run_cnn_1class.outputs = np.zeros((raw.shape[0], raw.shape[1]), dtype=np.float32) |
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| | run_cnn_1class.fig |
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| | run_cnn_1class.ax |
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| | run_cnn_1class.figsize |
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| | run_cnn_1class.cs = ax[0,0].pcolor(np.transpose(pdg & 0xFF), cmap='gist_ncar') |
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1.8.11
