beautified ipynb outupt, removed stderr cells

bayesian-code.ipynb

@@ -42,11 +42,6 @@
       "Downloading training data (300MB), this can take a few minutes the first time...\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -405,11 +400,6 @@
     "outputId": "13c2ada4-79bf-42f7-f2f9-4355c0173fb0"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
     {
      "name": "stdout",
      "output_type": "stream",

diffphys-code-ns.ipynb

@@ -116,7 +116,7 @@
     {
      "data": {
       "text/plain": [
-       "(inflow_locᵇ=4, xˢ=32, yˢ=40)"
+       "(inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40)"
       ]
      },
      "execution_count": 2,
@@ -253,7 +253,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Velocity dimensions: (inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y)\n"
+      "Velocity dimensions: (inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40, vector\u1d9c=x,y)\n"
      ]
     }
    ],
@@ -295,20 +295,12 @@
     "outputId": "93dae39b-1b31-429a-cf81-f615e23bbf7d"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/thuerey/miniconda3/envs/tf/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3427: SyntaxWarning: Specifying wrt by position is deprecated in phi.math.funcitonal_gradient() and phi.math.jacobian(). Please pass a list or comma-separated string of parameter names.\n",
-      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Some gradient info: StaggeredGrid[(inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
+      "Some gradient info: StaggeredGrid[(inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40, vector\u1d9c=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
-      "\u001b[92m(xˢ=31, yˢ=40)\u001b[0m \u001b[94m2.61e-08 ± 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
+      "\u001b[92m(x\u02e2=31, y\u02e2=40)\u001b[0m \u001b[94m2.61e-08 \u00b1 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
      ]
     }
    ],

diffphys-code-sol.ipynb

@@ -158,7 +158,7 @@
     "id": "RY1F4kdWPLNG"
    },
    "source": [
-    "Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we set the random seed - obviously, 42 is the ultimate choice here 🙂"
+    "Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we set the random seed - obviously, 42 is the ultimate choice here \ud83d\ude42"
    ]
   },
   {
@@ -622,21 +622,6 @@
     "outputId": "9e95aa79-6124-4e30-baf1-28bc37801b0f"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-10-594cd5e5046a>:10: FutureWarning: Domain (phi.physics._boundaries) is deprecated and will be removed in a future release.\n",
-      "Please create grids directly, replacing the domain with a dict, e.g.\n",
-      "    domain = dict(x=64, y=128, bounds=Box(x=1, y=1))\n",
-      "    grid = CenteredGrid(0, **domain)\n",
-      "  from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
-      "<ipython-input-10-594cd5e5046a>:17: DeprecationWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
-      "  domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n",
-      "<ipython-input-10-594cd5e5046a>:17: FutureWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
-      "  domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -864,7 +849,6 @@
       "epoch 003/005, batch 001/002, step 0401/0496: loss=\u001b[94m4.7433133\u001b[0m\n",
       "epoch 004/005, batch 001/002, step 0401/0496: loss=\u001b[94m4.522671\u001b[0m\n",
       "epoch 005/005, batch 001/002, step 0401/0496: loss=\u001b[94m2.0179803\u001b[0m\n",
-      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
       "Training done, saved NN\n"
      ]
     }
@@ -997,7 +981,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reynolds numbers in test data set: \u001b[94m(120000.000, 480000.000, 1920000.000, 7680000.000)\u001b[0m along \u001b[92mbatchᵇ\u001b[0m\n"
+      "Reynolds numbers in test data set: \u001b[94m(120000.000, 480000.000, 1920000.000, 7680000.000)\u001b[0m along \u001b[92mbatch\u1d47\u001b[0m\n"
      ]
     }
    ],

json-cleanup-for-pdf.py

@@ -21,11 +21,15 @@ fileList = [
 	]
 
 #fileList = [ "physgrad-code.ipynb"] # debug, only 1 file
+#fileList = [ "t1.ipynb" ] # debug
 
 
 # main
 
 for fnOut in fileList:
+	if not os.path.isfile(fnOut):
+		print("Error: "+fnOut+" not found!"); exit(1)
+
 	# create backups
 	fn0 = fnOut[:-5] + "bak"
 	fn = fn0 + "0"; cnt = 0
@@ -47,6 +51,7 @@ for fnOut in fileList:
 	#print(d.keys()) #print(d["cells"][0].keys())
 
 	# remove TF / pytorch warnings, build list of regular expressions to search for
+	# double check, redundant with removing stderr cells (cf delE)
 	res = []
 	res.append( re.compile(r"WARNING:tensorflow:") )
 	res.append( re.compile(r"UserWarning:") )
@@ -90,16 +95,26 @@ for fnOut in fileList:
 					deletes = deletes+1
 					#print( d[t][i]["source"][j] +"\n >>> \n" +d2 )
 
+			delE = [] # collect whole entries (sections) to delete
+
 			#print(len( d[t][i]["outputs"] ))
 			for j in range(len( d[t][i]["outputs"] )):
 				#print(type( d[t][i]["outputs"][j] ))
 				#print( d[t][i]["outputs"][j].keys() )
 
+				# search for error stderr cells
+				if d[t][i]["outputs"][j]["output_type"]=="stream":
+					#print("output j name: "+ format( d[t][i]["outputs"][j]["name"] ) )
+					#print("output j: "+ format( d[t][i]["outputs"][j] ) )
+					if d[t][i]["outputs"][j]["name"]=="stderr":
+						print("stderr found! len text "+ format(len( d[t][i]["outputs"][j]["text"]) ) +", removing entry "+format(j) )
+						delE.append(j) # remove the whole stderr entry
+
 				# images
 				if d[t][i]["outputs"][j]["output_type"]=="stream":
-					#print("len "+  len( d[t][i]["outputs"][j]["text"] ) )
+					#print("len "+  format(len( d[t][i]["outputs"][j]["text"] )) )
 
-					dell = [] # collect entries to delete
+					dell = [] # collect lines to delete
 					for k in range(  len( d[t][i]["outputs"][j]["text"] )  ):
 						#print(" tout "+   d[t][i]["outputs"][j]["text"][k] ) # debug , print all lines
 						nums = []; all_good = True
@@ -117,9 +132,18 @@ for fnOut in fileList:
 
 					for dl in dell:
 						d[t][i]["outputs"][j]["text"].remove(dl)
-
 				#print("len after "+format( len( d[t][i]["outputs"][j]["text"] )) + " A") # debug
 
+			# afterwards (potentially remove whole entries)
+			# if len(delE)>0:
+			# 	print("len bef "+format( len( d[t][i]["outputs"] )) + " A " + format(delE)) # debug
+			for de in delE:
+				#print(type(d[t][i]["outputs"])); print(de)
+				d[t][i]["outputs"].pop(de) # remove array element
+				deletes+=1
+			# if len(delE)>0:
+			# 	print("len after "+format( len( d[t][i]["outputs"] )) + " A") # debug
+
 	if deletes==0:
 		print("Warning: Nothing found in "+fn+"!")
 		if not os.path.isfile(fnOut):

physgrad-code.ipynb

@@ -363,11 +363,6 @@
     "outputId": "4d938ed4-484b-46df-d0ca-d2268cb97dd0"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -413,11 +408,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
     {
      "name": "stdout",
      "output_type": "stream",

reinflearn-code.ipynb

@@ -75,13 +75,7 @@
     "id": "UI1_mMnNQXrN",
     "outputId": "73046435-964d-4d69-adae-ec7e519ede10"
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    }
-   ],
    "source": [
     "import sys; sys.path.append('PDE-Control/src'); sys.path.append('PDE-Control-RL/src')\n",
     "import time, csv, os, shutil\n",
@@ -144,13 +138,6 @@
     "outputId": "bf634970-0b1d-4202-ef7b-6b6043af3f84"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Cause: [Errno 2] No such file or directory: '<ipython-input-4-a61f572da032>'\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -355,14 +342,6 @@
       "Storing agent and hyperparameters to disk...\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  \"See the documentation of nn.Upsample for details.\".format(mode)\n",
-      "  return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -408,13 +387,6 @@
     "outputId": "00a91082-04c5-48ab-f7be-4debf0f5586a"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  \"See the documentation of nn.Upsample for details.\".format(mode)\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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truncated
@@ -484,14 +456,6 @@
      "text": [
       "Could not load resample cuda libraries: CUDA binaries not found at /usr/local/lib/python3.7/dist-packages/phi/tf/cuda/build/resample.so. Run \"python setup.py cuda\" to compile them\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "$ python setup.py tf_cuda\n",
-      "before reinstalling phiflow.\n"
-     ]
     }
    ],
    "source": [
@@ -532,11 +496,6 @@
       "Partition length 32 sequence (from 0 to 32) at frame 16\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -769,13 +728,6 @@
     "outputId": "2f22a1da-669e-44b2-cfd7-8d3962bfe208"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  \"See the documentation of nn.Upsample for details.\".format(mode)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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 truncated
@@ -874,15 +826,7 @@
     "id": "EP8S7UC5_vQD",
     "outputId": "2d447412-4b6d-4043-ad47-7d5c437fe03e"
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  \"See the documentation of nn.Upsample for details.\".format(mode)\n"
-     ]
-    }
-   ],
    "source": [
     "rl_frames, gt_frames, unc_frames = rl_trainer.infer_test_set_frames()\n",
     "\n",
@@ -980,13 +924,6 @@
       "Sanity check - maximum deviation from target state: 0.000000\n",
       "Sanity check - maximum deviation from target state: 0.000011\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  \"See the documentation of nn.Upsample for details.\".format(mode)\n"
-     ]
     }
    ],
    "source": [

supervised-airfoils.ipynb

@@ -78,24 +78,16 @@
    },
    "outputs": [
     {
-          "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Torch version 1.11.0+cu113\n",
       "Downloading training data (300MB), this can take a few minutes the first time...\n"
      ]
     },
     {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-            "  InsecureRequestWarning)\n"
-          ]
-        },
-        {
-          "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Loaded data, 320 training, 80 validation samples\n",
       "Size of the inputs array: (320, 3, 128, 128)\n"
@@ -167,16 +159,16 @@
    },
    "outputs": [
     {
-          "output_type": "display_data",
      "data": {
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1152x360 with 2 Axes>"
-            ],
+      ]
-            "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "light"
-          }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -227,8 +219,8 @@
    },
    "outputs": [
     {
-          "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Training & validation batches: 32 , 8\n"
      ]
@@ -400,8 +392,8 @@
    },
    "outputs": [
     {
-          "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Trainable params: 147363   -> crucial! always keep in view... \n"
      ]
@@ -462,8 +454,8 @@
    },
    "outputs": [
     {
-          "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Training from scratch\n",
       "Epoch: 0, L1 train: 0.29219, L1 vali: 0.23295\n",
@@ -552,16 +544,16 @@
    },
    "outputs": [
     {
-          "output_type": "display_data",
      "data": {
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
-            ],
+      ]
-            "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "light"
-          }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -610,16 +602,16 @@
    },
    "outputs": [
     {
-          "output_type": "display_data",
      "data": {
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1152x360 with 2 Axes>"
-            ],
+      ]
-            "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "light"
-          }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -671,8 +663,8 @@
    },
    "outputs": [
     {
-          "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Downloading test data, this should be fast...\n",
       "Loaded 10/10 test samples\n",
@@ -682,40 +674,40 @@
      ]
     },
     {
-          "output_type": "display_data",
      "data": {
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1152x360 with 2 Axes>"
-            ],
+      ]
-            "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "light"
-          }
+     },
+     "output_type": "display_data"
     },
     {
-          "output_type": "display_data",
      "data": {
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1152x360 with 2 Axes>"
-            ],
+      ]
-            "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "light"
-          }
+     },
+     "output_type": "display_data"
     },
     {
-          "output_type": "display_data",
      "data": {
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1152x360 with 2 Axes>"
-            ],
+      ]
-            "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "light"
-          }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [