From a557d401d330ef4e279bbc028babe2737c1f9723 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
<laura.kuehle@uni-duesseldorf.de>
Date: Wed, 30 Jun 2021 17:13:37 +0200
Subject: [PATCH] Added explanatory comments to 'get_cells()' for the class
'ArtificialNeuralNetwork'.
---
Troubled_Cell_Detector.py | 8 ++++++--
1 file changed, 6 insertions(+), 2 deletions(-)
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index d922731..ebc71d2 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -3,6 +3,7 @@
@author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
TODO: Fix cell averages and reconstructions to create data with an x-point stencil
+TODO: Add comments to get_cells() for ArtificialNeuralNetwork -> Done
"""
import os
@@ -210,19 +211,22 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
self._model = getattr(ANN_Model, self._model)(self._model_config)
def get_cells(self, projection):
+ # Reset ghost cells to adjust for stencil length
num_ghost_cells = self._stencil_len//2
-
projection = projection[:, 1:-1]
- # projection = projection[:, :5]
projection = np.concatenate((projection[:, -num_ghost_cells:], projection, projection[:, :num_ghost_cells]),
axis=1)
+
+ # Calculate input data depending on stencil length
input_data = torch.from_numpy(np.vstack([self.calculate_cell_average_and_reconstructions(
projection[:, cell-num_ghost_cells:cell+num_ghost_cells+1])
for cell in range(num_ghost_cells, len(projection[0])-num_ghost_cells)]))
+ # Evaluate troubled cell probabilities
self._model.load_state_dict(torch.load(self._model_state))
self._model.eval()
+ # Return troubled cells
model_output = torch.round(self._model(input_data.float()))
return [cell for cell in range(len(model_output)) if model_output[cell, 0] == torch.tensor([1])]
--
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