From 5b9cf41b8756a5f7f7ba454618a2bb169b359ba8 Mon Sep 17 00:00:00 2001
From: perceptualrobots <rupert@perceptualrobots.com>
Date: Mon, 14 Oct 2024 19:42:08 +0100
Subject: [PATCH] add arc visualisations

---
 nbs/14_helpers.ipynb      | 138 +++++++++++++++++++++++++++++++++++---
 nbs/18_applications.ipynb |  31 +++++----
 2 files changed, 145 insertions(+), 24 deletions(-)

diff --git a/nbs/14_helpers.ipynb b/nbs/14_helpers.ipynb
index 83ecbd80..8fe97fd4 100644
--- a/nbs/14_helpers.ipynb
+++ b/nbs/14_helpers.ipynb
@@ -643,8 +643,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Execution time of challenges load: 0.0658 seconds\n",
-      "Execution time of solutions load: 0.0070 seconds\n",
+      "Execution time of challenges load: 0.1150 seconds\n",
+      "Execution time of solutions load: 0.0110 seconds\n",
       "['007bbfb7', '00d62c1b', '017c7c7b', '025d127b', '045e512c', '0520fde7', '05269061', '05f2a901', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0a938d79', '0b148d64', '0ca9ddb6', '0d3d703e', '0dfd9992', '0e206a2e', '10fcaaa3', '11852cab', '1190e5a7', '137eaa0f', '150deff5', '178fcbfb', '1a07d186', '1b2d62fb', '1b60fb0c', '1bfc4729', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f85a75f', '1f876c06', '1fad071e', '2013d3e2', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '234bbc79', '23581191', '239be575', '23b5c85d', '253bf280', '25d487eb', '25d8a9c8', '25ff71a9', '264363fd', '272f95fa', '27a28665', '28bf18c6', '28e73c20', '29623171', '29c11459', '29ec7d0e', '2bcee788', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '31aa019c', '321b1fc6', '32597951', '3345333e', '3428a4f5', '3618c87e', '3631a71a', '363442ee', '36d67576', '36fdfd69', '3906de3d', '39a8645d', '39e1d7f9', '3aa6fb7a', '3ac3eb23', '3af2c5a8', '3bd67248', '3bdb4ada', '3befdf3e', '3c9b0459', '3de23699', '3e980e27', '3eda0437', '3f7978a0', '40853293', '4093f84a', '41e4d17e', '4258a5f9', '4290ef0e', '42a50994', '4347f46a', '444801d8', '445eab21', '447fd412', '44d8ac46', '44f52bb0', '4522001f', '4612dd53', '46442a0e', '469497ad', '46f33fce', '47c1f68c', '484b58aa', '48d8fb45', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c4377d9', '4c5c2cf0', '50846271', '508bd3b6', '50cb2852', '5117e062', '5168d44c', '539a4f51', '53b68214', '543a7ed5', '54d82841', '54d9e175', '5521c0d9', '5582e5ca', '5614dbcf', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5bd6f4ac', '5c0a986e', '5c2c9af4', '5daaa586', '60b61512', '6150a2bd', '623ea044', '62c24649', '63613498', '6430c8c4', '6455b5f5', '662c240a', '67385a82', '673ef223', '6773b310', '67a3c6ac', '67a423a3', '67e8384a', '681b3aeb', '6855a6e4', '68b16354', '694f12f3', '6a1e5592', '6aa20dc0', '6b9890af', '6c434453', '6cdd2623', '6cf79266', '6d0160f0', '6d0aefbc', '6d58a25d', '6d75e8bb', '6e02f1e3', '6e19193c', '6e82a1ae', '6ecd11f4', '6f8cd79b', '6fa7a44f', '72322fa7', '72ca375d', '73251a56', '7447852a', '7468f01a', '746b3537', '74dd1130', '75b8110e', '760b3cac', '776ffc46', '77fdfe62', '780d0b14', '7837ac64', '794b24be', '7b6016b9', '7b7f7511', '7c008303', '7ddcd7ec', '7df24a62', '7e0986d6', '7f4411dc', '7fe24cdd', '80af3007', '810b9b61', '82819916', '83302e8f', '834ec97d', '8403a5d5', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '88a62173', '890034e9', '8a004b2b', '8be77c9e', '8d5021e8', '8d510a79', '8e1813be', '8e5a5113', '8eb1be9a', '8efcae92', '8f2ea7aa', '90c28cc7', '90f3ed37', '913fb3ed', '91413438', '91714a58', '9172f3a0', '928ad970', '93b581b8', '941d9a10', '94f9d214', '952a094c', '9565186b', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '995c5fa3', '99b1bc43', '99fa7670', '9aec4887', '9af7a82c', '9d9215db', '9dfd6313', '9ecd008a', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61ba2ce', 'a61f2674', 'a64e4611', 'a65b410d', 'a68b268e', 'a699fb00', 'a740d043', 'a78176bb', 'a79310a0', 'a85d4709', 'a87f7484', 'a8c38be5', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'aba27056', 'ac0a08a4', 'ae3edfdc', 'ae4f1146', 'aedd82e4', 'af902bf9', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b230c067', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b60334d2', 'b6afb2da', 'b7249182', 'b775ac94', 'b782dc8a', 'b8825c91', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bb43febb', 'bbc9ae5d', 'bc1d5164', 'bd4472b8', 'bda2d7a6', 'bdad9b1f', 'be94b721', 'beb8660c', 'c0f76784', 'c1d99e64', 'c3e719e8', 'c3f564a4', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'c9e6f938', 'c9f8e694', 'caa06a1f', 'cbded52d', 'cce03e0d', 'cdecee7f', 'ce22a75a', 'ce4f8723', 'ce602527', 'ce9e57f2', 'cf98881b', 'd037b0a7', 'd06dbe63', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd13f3404', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd406998b', 'd43fd935', 'd4469b4b', 'd4a91cb9', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd90796e8', 'd9f24cd1', 'd9fac9be', 'dae9d2b5', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc0a314f', 'dc1df850', 'dc433765', 'ddf7fa4f', 'de1cd16c', 'ded97339', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e3497940', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e76a88a6', 'e8593010', 'e8dc4411', 'e9614598', 'e98196ab', 'e9afcf9a', 'ea32f347', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'ed36ccf7', 'ef135b50', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f25ffba3', 'f2829549', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8a8fe49', 'f8b3ba0a', 'f8c80d96', 'f8ff0b80', 'f9012d9b', 'fafffa47', 'fcb5c309', 'fcc82909', 'feca6190', 'ff28f65a', 'ff805c23']\n",
       "400\n",
       "get_keys_with_equal_size_input_output\n",
@@ -885,8 +885,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Execution time of __init__: 0.0000 seconds\n",
-      "Execution time of __init__: 0.0009 seconds\n",
+      "Execution time of __init__: 0.0050 seconds\n",
+      "Execution time of __init__: 0.0040 seconds\n",
       "{'test': [{'input': [[2]]}], 'train': [{'input': [[3]], 'output': [[4]]}, {'input': [[7]], 'output': [[8]]}, {'input': [[1]], 'output': [[2]]}]}\n",
       "[[3]]\n",
       "3\n"
@@ -1019,8 +1019,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Execution time of reload_data: 0.1108 seconds\n",
-      "Execution time of reload_data: 0.0040 seconds\n",
+      "Execution time of reload_data: 0.1050 seconds\n",
+      "Execution time of reload_data: 0.0130 seconds\n",
       "{'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}\n",
       "[[7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 7, 0, 7, 7, 0, 0, 0, 0]]\n",
       "5\n"
@@ -1318,8 +1318,130 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'test': [{'input': [[0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 2, 8, 8, 2, 0, 0, 0], [0, 8, 8, 2, 8, 8, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}]}\n",
+      "[[0, 0, 8, 8, 0, 2, 2, 2, 0, 0, 0], [0, 0, 8, 8, 0, 0, 2, 0, 0, 0, 0], [0, 2, 2, 2, 8, 8, 2, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 2, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
+      "#15, 150deff5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#| gui\n",
+    "code = '150deff5'\n",
+    "task = data_mgr.get_data_for_code(code)\n",
+    "print(task)\n",
+    "task_solution = data_mgr.get_solutions_for_code(code)\n",
+    "print(task_solution)\n",
+    "plot_task(task,  task_solution, 15, code)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'test': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 7, 1, 7, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 2, 0, 0], [0, 7, 1, 7, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 7, 1, 7, 0], [0, 2, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 7, 1, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0]]}]}\n",
+      "[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 7, 1, 7, 0], [0, 0, 2, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4]]\n",
+      "#15, 0ca9ddb6\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1200x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#| gui\n",
+    "code = '0ca9ddb6'\n",
+    "task = data_mgr.get_data_for_code(code)\n",
+    "print(task)\n",
+    "task_solution = data_mgr.get_solutions_for_code(code)\n",
+    "print(task_solution)\n",
+    "plot_task(task,  task_solution, 15, code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'test': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 0, 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+      "#15, 00d62c1b\n"
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",
+      "text/plain": [
+       "<Figure size 1800x600 with 13 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#| gui\n",
+    "code = '00d62c1b'\n",
+    "task = data_mgr.get_data_for_code(code)\n",
+    "print(task)\n",
+    "task_solution = data_mgr.get_solutions_for_code(code)\n",
+    "print(task_solution)\n",
+    "plot_task(task,  task_solution, 15, code)"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/nbs/18_applications.ipynb b/nbs/18_applications.ipynb
index 20a8c249..174dd7a2 100644
--- a/nbs/18_applications.ipynb
+++ b/nbs/18_applications.ipynb
@@ -79,18 +79,21 @@
     "\n",
     "power_control result = 1172.9\n",
     "\n",
+    "\n",
     "controller trained on steady dataset applied to variable dataset\n",
     "\n",
     "run_wind_turbine('variable', \"testfiles\\\\\", \"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties\")\n",
     "\n",
     "power_control result = 739.6\n",
     "\n",
+    "\n",
     "controller trained on variable dataset applied to variable dataset\n",
     "\n",
     "run_wind_turbine('variable', \"testfiles\\\\\", \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\")\n",
     "\n",
     "power_control result = 741.7\n",
     "\n",
+    "\n",
     "controller trained on variable dataset applied to steady dataset\n",
     "\n",
     "run_wind_turbine('steady', \"testfiles\\\\\", \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\")\n",
@@ -107,7 +110,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2024-10-12 12:05:40.863096 25060 Start\n",
+      "2024-10-12 16:23:04.245655 26796 Start\n",
       "Score=-1172.912 power=1172.912\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 6.907099999999999, 'angle covered_trad_baseline_logs': 27.30000000000004, 'yaw count_trad_baseline_logs': 5, 'time_yawing_trad_baseline_logs': 0.43004300430043}\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 6.5231, 'angle covered_trad_baseline_simu': 53.46000000000012, 'yaw count_trad_baseline_simu': 15, 'time_yawing_trad_baseline_simu': 2.0502050205020503}\n",
@@ -116,9 +119,9 @@
       "average_yaw_error_decrease_simu=5.85\n",
       "energy_gain = 0.38\n",
       "net_energy_gain = 0.35\n",
-      "2024-10-12 12:05:52.778136 25060 End\n",
-      "Elapsed time: 11.92\n",
-      "2024-10-12 12:05:52.778136 25060 Start\n",
+      "2024-10-12 16:23:16.296789 26796 End\n",
+      "Elapsed time: 12.05\n",
+      "2024-10-12 16:23:16.296789 26796 Start\n",
       "Score=-739.604 power=739.604\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 7.5581999999999985, 'angle covered_trad_baseline_logs': 97.31000000000012, 'yaw count_trad_baseline_logs': 14, 'time_yawing_trad_baseline_logs': 1.5801580158015802}\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 7.0147549, 'angle covered_trad_baseline_simu': 112.13400000000004, 'yaw count_trad_baseline_simu': 27, 'time_yawing_trad_baseline_simu': 4.160416041604161}\n",
@@ -127,9 +130,9 @@
       "average_yaw_error_decrease_simu=13.89\n",
       "energy_gain = 0.49\n",
       "net_energy_gain = 0.42\n",
-      "2024-10-12 12:06:04.737131 25060 End\n",
-      "Elapsed time: 11.96\n",
-      "2024-10-12 12:06:04.737131 25060 Start\n",
+      "2024-10-12 16:23:28.205166 26796 End\n",
+      "Elapsed time: 11.91\n",
+      "2024-10-12 16:23:28.205166 26796 Start\n",
       "Score=-741.680 power=741.680\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 7.5581999999999985, 'angle covered_trad_baseline_logs': 97.31000000000012, 'yaw count_trad_baseline_logs': 14, 'time_yawing_trad_baseline_logs': 1.5801580158015802}\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 7.0147549, 'angle covered_trad_baseline_simu': 112.13400000000004, 'yaw count_trad_baseline_simu': 27, 'time_yawing_trad_baseline_simu': 4.160416041604161}\n",
@@ -138,9 +141,9 @@
       "average_yaw_error_decrease_simu=20.73\n",
       "energy_gain = 0.77\n",
       "net_energy_gain = 0.57\n",
-      "2024-10-12 12:06:16.844109 25060 End\n",
-      "Elapsed time: 12.11\n",
-      "2024-10-12 12:06:16.844109 25060 Start\n",
+      "2024-10-12 16:23:40.128764 26796 End\n",
+      "Elapsed time: 11.92\n",
+      "2024-10-12 16:23:40.128764 26796 Start\n",
       "Score=-1171.489 power=1171.489\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 6.907099999999999, 'angle covered_trad_baseline_logs': 27.30000000000004, 'yaw count_trad_baseline_logs': 5, 'time_yawing_trad_baseline_logs': 0.43004300430043}\n",
       "{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 6.5231, 'angle covered_trad_baseline_simu': 53.46000000000012, 'yaw count_trad_baseline_simu': 15, 'time_yawing_trad_baseline_simu': 2.0502050205020503}\n",
@@ -149,21 +152,17 @@
       "average_yaw_error_decrease_simu=3.89\n",
       "energy_gain = 0.26\n",
       "net_energy_gain = 0.07\n",
-      "2024-10-12 12:06:28.924149 25060 End\n",
-      "Elapsed time: 12.08\n"
+      "2024-10-12 16:23:52.161731 26796 End\n",
+      "Elapsed time: 12.03\n"
      ]
     }
    ],
    "source": [
     "#| gui\n",
     "\n",
-    "# controller trained on steady dataset applied to steady dataset\n",
     "# run_wind_turbine('steady', \"testfiles\\\\\", \"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties\")\n",
-    "# controller trained on steady dataset applied to variable dataset\n",
     "# run_wind_turbine('variable', \"testfiles\\\\\", \"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties\")\n",
-    "# controller trained on variable dataset applied to variable dataset\n",
     "# run_wind_turbine('variable', \"testfiles\\\\\", \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\")\n",
-    "# controller trained on variable dataset applied to steady dataset\n",
     "# run_wind_turbine('steady', \"testfiles\\\\\", \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\")\n",
     "\n",
     "\n"