Rename flax data files

flax/README.rst

@@ -5,11 +5,11 @@ This directory contains the parameters of flax CNN models trained as denoisers (
 
 Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. *IEEE Transactions on Image Processing*, 26(7):3142–3155, July 2017.
 
-Trained models are contained in files of the format ``dncnn<n><L|M|H>.npz``, where ``n`` is the number of layers in the model (currently 6 or 17), and ``L|M|H`` denotes the noise level at which the model was trained:
+Trained models are contained in files of the format ``dncnn<n><L|M|H>.mpk``, where ``n`` is the number of layers in the model (currently 6 or 17), and ``L|M|H`` denotes the noise level at which the model was trained:
 
 * ``L``: model trained for Gaussian noise with standard deviation of 0.06 for data in the range [0,1]
 * ``M``: model trained for Gaussian noise with standard deviation of 0.10 for data in the range [0,1]
 * ``H``: model trained for Gaussian noise with standard deviation of 0.20 for data in the range [0,1]
 
 
-Additional trained models for a DnCNN variant with a noise standard deviation input are contained in files of the format ``dncnn<n>N.npz``, where ``n`` is the number of layers in the model (currently 6 or 17).
+Additional trained models for a DnCNN variant with a noise standard deviation input are contained in files of the format ``dncnn<n>N.mpk``, where ``n`` is the number of layers in the model (currently 6 or 17).

notebooks/ct_astra_datagen_foam2.ipynb

@@ -55,13 +55,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-11-28 11:14:48,915\tINFO worker.py:1518 -- Started a local Ray instance.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",